$\beta$-calibration of Language Model Confidence Scores for Generative QA\nTo use generative question-and-answering (QA) systems for decision-making and in any critical application, these systems need to provide well-calibrated confidence scores that reflect the correctness of their answers. Existing calibration methods aim to ensure that the confidence score is *on average* indicative of the likelihood that the answer is correct.  We argue, however, that this standard (average-case) notion of c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30480<END_URL>

$\forall$uto$\exists$$\lor\!\land$L: Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks\nThis paper presents $\forall$uto$\exists$$\lor\\!\land$L, a novel benchmark for scaling Large Language Model (LLM) assessment in formal tasks with clear notions of correctness, such as truth maintenance in translation and logical reasoning.  $\forall$uto$\exists$$\lor\\!\land$L is the first benchmarking paradigm that offers several key advantages necessary for scaling objective evaluatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28665<END_URL>

$\gamma-$MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models\nDespite the significant progress in multimodal large language models (MLLMs), their high computational cost remains a barrier to real-world deployment. Inspired by the mixture of depths (MoDs) in natural language processing, we aim to address this limitation from the perspective of ``activated tokens''. Our key insight is that if most tokens are redundant for the layer computation, then can be skipped direc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28266<END_URL>

$InterLCM$: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration\nDiffusion priors have been used for blind face restoration (BFR) by fine-tuning diffusion models (DMs) on restoration datasets to recover low-quality images. However, the naive application of DMs presents several key limitations. (i) The diffusion prior has inferior semantic consistency (e.g., ID, structure and color.),  increasing the difficulty of optimizing the BFR model;(i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28192<END_URL>

$k$NN Attention Demystified: A Theoretical Exploration for Scalable Transformers\nDespite their power, Transformers \citep{vaswani2017attention} face challenges with long sequences due to the quadratic complexity of self-attention. To address this limitation, methods like k-Nearest-Neighbor ($k$NN) attention have been introduced \citep{roy2021efficient}, enabling each token to attend to only its $k$ closest tokens. While $k$NN attention has shown empirical success in making Transformers more eff<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31032<END_URL>

$\mathbb{X}$-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs\nLearning good representations involves capturing the diverse ways in which data samples relate. Contrastive loss—an objective matching related samples—underlies methods from self-supervised to multimodal learning. Contrastive losses, however, can be viewed more broadly as modifying a similarity graph to indicate how samples should relate in the embedding space.  This view reveals a shortcoming in <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29076<END_URL>

$\phi$-Update: A Class of Policy Update Methods with Policy Convergence Guarantee\nInspired by the similar update pattern of softmax natural policy gradient and Hadamard policy gradient, we propose to study a general policy update rule called $\phi$-update, where $\phi$ refers to a scaling function on advantage functions. Under very mild conditions on $\phi$, the global asymptotic state value convergence of $\phi$-update is firstly established. Then we show that the policy produced by $\phi$-upd<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28864<END_URL>

$q$-exponential family for policy optimization\nPolicy optimization methods benefit from a simple and tractable policy parametrization, usually the Gaussian for continuous action spaces. In this paper, we consider a broader policy family  that remains tractable: the $q$-exponential family. This family of policies is flexible, allowing the specification of both heavy-tailed policies ($q>1$) and light-tailed policies ($q<1$). This paper examines the interplay between $q$-exponential policies for s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29786<END_URL>

$R^2$-Guard: Robust Reasoning Enabled LLM Guardrail via Knowledge-Enhanced Logical Reasoning\nAs large language models (LLMs) become increasingly prevalent across various applications, it is critical to establish safety guardrails to moderate input/output content of LLMs and ensure compliance with safety policies. Existing guardrail models, such as OpenAI Mod and LlamaGuard, treat various safety categories (e.g., self-harm, self-harm/instructions) independently and fail to explicitly capture the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30497<END_URL>

$\sigma$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples\nEvaluating the adversarial robustness of deep networks to gradient-based attacks is challenging.While most attacks consider $\ell_2$- and $\ell_\infty$-norm constraints to craft input perturbations, only a few investigate sparse $\ell_1$- and $\ell_0$-norm attacks.In particular, $\ell_0$-norm attacks remain the least studied due to the inherent complexity of optimizing over a non-convex and non-differentiable const<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30114<END_URL>

{$\tau$}-bench: A Benchmark for \underline{T}ool-\underline{A}gent-\underline{U}ser Interaction in Real-World Domains\nExisting benchmarks for language agents do not set them up to interact with human users or follow domain-specific rules, both of which are vital to safe and realistic deployment. We propose $\tau$-bench, a benchmark with two domains (retail and airline) emulating dynamic conversations between a user (simulated by language models) and a customer service agent provided with domain<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28170<END_URL>

$\text{I}^2\text{AM}$: Interpreting Image-to-Image Latent Diffusion Models via Bi-Attribution Maps\nLarge-scale diffusion models have made significant advances in image generation, particularly through cross-attention mechanisms. While cross-attention has been well-studied in text-to-image tasks, their interpretability in image-to-image (I2I) diffusion models remains underexplored. This paper introduces Image-to-Image Attribution Maps $(\textbf{I}^2\textbf{AM})$, a method that enhances the inter<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29121<END_URL>

$\text{Transformer}^2$: Self-adaptive LLMs\nSelf-adaptive large language models (LLMs) aim to solve the challenges posed by traditional fine-tuning methods, which are often computationally intensive and static in their ability to handle diverse tasks. We introduce $\text{Transformer}^2$, a novel self-adaptation framework that adapts LLMs for unseen tasks in real-time by selectively adjusting only the singular components of their weight matrices. During inference, $\text{Transformer}^2$ employs a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28974<END_URL>

$\texttt{BirdSet}$: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics\nDeep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domains. While AudioSet is a pivotal step to bridge this gap as a universal-domain dataset, its restricted accessibility and limited range of evaluation use cases challenge its role as the sole resource. Therefore, we introduce $\textt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28986<END_URL>

2nd Workshop on Navigating and Addressing Data Problems for Foundation Models (DPFM)\nFoundation models (FMs) have become central to modern machine learning, with data playing a crucial role in their development and sparking increased attention to data-related challenges such as curation and attribution. Adapting traditional data-centric methods to FMs is challenging due to the scale of both data and model architectures, necessitating interdisciplinary collaboration and community efforts. Buildi<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23969<END_URL>

3D-AffordanceLLM: Harnessing Large Language Models for Open-Vocabulary Affordance Detection in 3D Worlds\n3D Affordance detection is a challenging problem with broad applications on various robotic tasks. Existing methods typically formulate the detection paradigm as a label-based semantic segmentation task.This paradigm relies on predefined labels and lacks the ability to comprehend complex natural language, resulting in limited generalization in open-world scene.To address these limitations, w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30275<END_URL>

3DGS-Drag: Dragging Gaussians for Intuitive Point-Based 3D Editing\nThe transformative potential of 3D content creation has been progressively unlocked through advancements in generative models. Recently, intuitive drag editing with geometric changes has attracted significant attention in 2D editing yet remains challenging for 3D scenes. In this paper, we introduce 3DGS-Drag, a point-based 3D editing framework that provides efficient, intuitive drag manipulation of real 3D scenes. Our approach b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30830<END_URL>

3DIS: Depth-Driven Decoupled Instance Synthesis for Text-to-Image Generation\nThe increasing demand for controllable outputs in text-to-image generation has spurred advancements in multi-instance generation (MIG), allowing users to define both instance layouts and attributes. However, unlike image-conditional generation methods such as ControlNet, MIG techniques have not been widely adopted in state-of-the-art models like SD2 and SDXL, primarily due to the challenge of building robust renderers <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29926<END_URL>

3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian Splatting\nScene image editing is crucial for entertainment, photography, and advertising design. Existing methods solely focus on either 2D individual object or 3D global scene editing. This results in a lack of a unified approach to effectively control and manipulate scenes at the 3D level with different levels of granularity. In this work, we propose 3DitScene, a novel and unified scene editing framework leveraging languag<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28702<END_URL>

3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery\nStructure-based drug discovery, encompassing the tasks of protein-ligand docking and pocket-aware 3D drug design, represents a core challenge in drug discovery. However, no existing work can deal with both tasks to effectively leverage the duality between them, and current methods for each task are hindered by challenges in modeling 3D information and the limitations of available data. To address these issues, we propose 3<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29642<END_URL>

3D-Properties: Identifying Challenges in DPO and Charting a Path Forward\nAligning large language models (LLMs) with human preferences has recently garnered significant attention, with Proximal Policy Optimization (PPO) being a canonical yet computationally expensive method, and Direct Preference Optimization (DPO) offering a simpler and more efficient alternative. While prior studies have explored the trade-offs between PPO and DPO, DPO remains underutilized in state-of-the-art production-level<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30704<END_URL>

3D-SPATIAL MULTIMODAL MEMORY\nWe present 3D Spatial MultiModal Memory (M3), a multimodal memory system designed to retain information about medium-sized static scenes through video sources for visual perception. By integrating 3D Gaussian Splatting techniques with foundation models, M3 builds a multimodal memory capable of rendering feature representations across granularities, encompassing a wide range of knowledge. In our exploration, we identify two key challenges in previous works on feature<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29300<END_URL>

3D StreetUnveiler with Semantic-aware 2DGS - a simple baseline\nUnveiling an empty street from crowded observations captured by in-car cameras is crucial for autonomous driving. However, removing all temporarily static objects, such as stopped vehicles and standing pedestrians, presents a significant challenge. Unlike object-centric 3D inpainting, which relies on thorough observation in a small scene, street scene cases involve long trajectories that differ from previous 3D inpainting tasks. The<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30301<END_URL>

3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation\nThis paper aims to manipulate multi-entity 3D motions in video generation. Previous methods on controllable video generation primarily leverage 2D control signals to manipulate object motions and have achieved remarkable synthesis results. However, 2D control signals are inherently limited in expressing the 3D nature of object motions. To overcome this problem, we introduce 3DTrajMaster, a robust controller that r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30249<END_URL>

3D Vision-Language Gaussian Splatting\nRecent advancements in 3D reconstruction methods and vision-language models have propelled the development of multi-modal 3D scene understanding, which has vital applications in robotics, autonomous driving, and virtual/augmented reality. However, current multi-modal scene understanding approaches have naively embedded semantic representations into 3D reconstruction methods without striking a balance between visual and language modalities, which leads to un<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29604<END_URL>

3rd ICLR Workshop on Machine Learning for Remote Sensing\nMachine learning for remote sensing (ML4RS) has emerged as a critical and exciting area of research, with the potential to address some of the most pressing global challenges, including climate change, food security, disaster management, and conservation. Remote sensing data, collected from diverse instruments capturing the Earth across various spatial, temporal, and spectral dimensions, offers unique research opportunities and challenges<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23993<END_URL>

4K4DGen: Panoramic 4D Generation at 4K Resolution\nThe blooming of virtual reality and augmented reality (VR/AR) technologies has driven an increasing demand for the creation of high-quality, immersive, and dynamic environments. However, existing generative techniques either focus solely on dynamic objects or perform outpainting from a single perspective image, failing to meet the requirements of VR/AR applications that need free-viewpoint, 360$^{\circ}$ virtual views where users can move in all<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28230<END_URL>

6DGS: Enhanced Direction-Aware Gaussian Splatting for Volumetric Rendering\nNovel view synthesis has advanced significantly with the development of neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS). However, achieving high quality without compromising real-time rendering remains challenging, particularly for physically-based ray tracing with view-dependent effects. Recently, N-dimensional Gaussians (N-DG) introduced a 6D spatial-angular representation to better incorporate view-depe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28126<END_URL>

6D Object Pose Tracking in Internet Videos for Robotic Manipulation\nWe seek to extract a temporally consistent 6D pose trajectory of a manipulated  object from an Internet instructional video. This is a challenging set-up for current 6D pose estimation methods due to uncontrolled capturing conditions, fine-grained dynamic object motions, and the fact that the exact mesh of the manipulated object is not known. To address these challenges, we present the following contributions. First, we develop<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31220<END_URL>

7th Robot Learning Workshop: Towards Robots with Human-Level Abilities\nThe year 2024 has seen an explosion of interest in humanoid robots. In the 7th Robot Learning workshop, to be held at ICLR-2025, we will look beyond the humanoid embodiment and ask: how far are we from robots with human-level abilities? What do we need to improve about embodied learning, decision-making, perception, and data collection to train generally physically capable robots to robustly perform a wide range of activitie<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23997<END_URL>

A3D: Does Diffusion Dream about 3D Alignment?\nWe tackle the problem of text-driven 3D generation from a geometry alignment perspective.Given a set of text prompts, we aim to generate a collection of objects with semantically corresponding parts aligned across them.Recent methods based on Score Distillation have succeeded in distilling the knowledge from 2D diffusion models to high-quality representations of the 3D objects.These methods handle multiple text queries separately, and therefore the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29688<END_URL>

A-Bench: Are LMMs Masters at Evaluating AI-generated Images?\nHow to accurately and efficiently assess AI-generated images (AIGIs) remains a critical challenge for generative models. Given the high costs and extensive time commitments required for user studies, many researchers have turned towards employing large multi-modal models (LMMs) as AIGI evaluators, the precision and validity of which are still questionable. Furthermore, traditional benchmarks often utilize mostly natural-captured conte<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30994<END_URL>

A Benchmark for Semantic Sensitive Information in LLMs Outputs\nLarge language models (LLMs) can output sensitive information, which has emerged as a novel safety concern. Previous works focus on structured sensitive information (e.g. personal identifiable information). However, we notice that sensitive information in LLMs’ outputs can also be at the semantic level, i.e. semantic sensitive information (SemSI). Particularly, simple natural questions can let state-of-the-art (SOTA) LLMs output Sem<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28322<END_URL>

A Causal Lens for Learning Long-term Fair Policies\nFairness-aware learning studies the development of algorithms that avoid discriminatory decision outcomes despite biased training data. While most studies have concentrated on immediate bias in static contexts, this paper highlights the importance of investigating long-term fairness in dynamic decision-making systems while simultaneously considering instantaneous fairness requirements. In the context of reinforcement learning, we propose a gene<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28199<END_URL>

ACC-Debate: An Actor-Critic Approach to Multi-Agent Debate\nLarge language models (LLMs) have demonstrated a remarkable ability to serve as general-purpose tools on various language-based tasks.   Recent works have demonstrated that the efficacy of such models can be improved through iterative dialog between multiple models, frequently referred to as multi-agent debate (MAD).  While debate shows promise as a means of improving model efficacy, most works in this area treat debate as an emergent b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28401<END_URL>

Accelerated Over-Relaxation Heavy-Ball Method: Achieving Global Accelerated Convergence with Broad Generalization\nThe heavy-ball momentum method accelerates gradient descent with a momentum term but lacks accelerated convergence for general smooth strongly convex problems. This work introduces the Accelerated Over-Relaxation Heavy-Ball (AOR-HB) method, the first variant with provable global and accelerated convergence for such problems. AOR-HB closes a long-standing theoretical gap, extends to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29597<END_URL>

Accelerated training through iterative gradient propagation along the residual path\nDespite being the cornerstone of deep learning, backpropagation is criticized for its inherent sequentiality, which can limit the scalability of very deep models.Such models faced convergence issues due to vanishing gradient, later resolved using residual connections. Variants of these are now widely used in modern architectures.However, the computational cost of backpropagation remains a major burden, accountin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30119<END_URL>

Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport\nThis paper proposes a new 3D molecule generation framework, called GOAT, for fast and effective 3D molecule generation based on the flow-matching optimal transport objective. Specifically, we formulate a geometric transport formula for measuring the cost of mapping multi-modal features (e.g., continuous atom coordinates and categorical atom types) between a base distribution and a target data distribution. Our formula i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29423<END_URL>

Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding\nThe current large auto-regressive models can generate high-quality, high-resolution images, but these models require hundreds or even thousands of steps of next-token prediction during inference, resulting in substantial time consumption. In existing studies, Jacobi decoding, an iterative parallel decoding algorithm, has been used to accelerate the auto-regressive generation and can be executed <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29988<END_URL>

Accelerating Diffusion Transformers with Token-wise Feature Caching\nDiffusion transformers have shown significant effectiveness in both image and video synthesis at the expense of huge computation costs. To address this problem, feature caching methods have been introduced to accelerate diffusion transformers by caching the features in previous timesteps and reusing them in the following timesteps. However, previous caching methods ignore that different tokens exhibit different sensitivities to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27718<END_URL>

Accelerating Goal-Conditioned Reinforcement Learning Algorithms and Research\nSelf-supervision has the potential to transform reinforcement learning (RL), paralleling the breakthroughs it has enabled in other areas of machine learning. While self-supervised learning in other domains aims to find patterns in a fixed dataset, self-supervised goal-conditioned reinforcement learning (GCRL) agents discover *new* behaviors by learning from the goals achieved during unstructured interaction with the en<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31000<END_URL>

Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection\nLarge language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility by incorporating external contexts. However, the input length grows linearly in the number of retrieved documents, causing a dramatic increase in latency. In this paper, we propose a novel paradigm named Sparse RAG, which seeks to cut computation costs through sparsity. Speciﬁcally, Sparse RAG encodes ret<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30237<END_URL>

Accelerating neural network training: An analysis of the AlgoPerf competition\nThe goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by improving the underlying training algorithms. In the external tuning ruleset, submissions must provide workload-agnostic hyperparameter search spaces, while in the self-tuning ruleset they must be completely hyperparameter-free. In both rulesets, submissions are compared on time-to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30490<END_URL>

Accelerating Neural ODEs: A Variational Formulation-based Approach\nNeural Ordinary Differential Equations (Neural ODEs or NODEs) excel at modeling continuous dynamical systems from observational data, especially when the data is irregularly sampled. However, existing training methods predominantly rely on numerical ODE solvers, which are time-consuming and prone to accumulating numerical errors over time due to autoregression. In this work, we propose the VF-NODE, a novel approach based on the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28017<END_URL>

Accelerating Task Generalisation with Multi-Level Hierarchical Options\nCreating reinforcement learning agents that generalise effectively to new tasks is a key challenge in AI research. This paper introduces Fracture Cluster Options (FraCOs), a multi-level hierarchical reinforcement learning method that achieves state-of-the-art performance on difficult generalisation tasks. FraCOs identifies patterns in agent behaviour and forms options based on the expected future usefulness of those patterns<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30040<END_URL>

Accelerating Training with Neuron Interaction and Nowcasting Networks\nNeural network training can be accelerated when a learnable update rule is used in lieu of classic adaptive optimizers (e.g. Adam). However, learnable update rules can be costly and unstable to train and use. Recently, Jang et al. (2023) proposed a simpler approach to accelerate training based on weight nowcaster networks (WNNs). In their approach, Adam is used for most of the optimization steps and periodically, only every f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29046<END_URL>

Accurate and Scalable Graph Neural Networks via Message Invariance\nMessage passing-based graph neural networks (GNNs) have achieved great success in many real-world applications. For a sampled mini-batch of target nodes, the message passing process is divided into two parts: message passing between nodes within the batch (MP-IB) and message passing from nodes outside the batch to those within it (MP-OB). However, MP-OB recursively relies on higher-order out-of-batch neighbors, leading to an exp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29451<END_URL>

ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer\nDiffusion models have emerged as a powerful generative technology and have been found to be applicable in various scenarios. Most existing foundational diffusion models are primarily designed for text-guided visual generation and do not support multi-modal conditions, which are essential for many visual editing tasks. This limitation prevents these foundational diffusion models from serving as a unified model in <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30543<END_URL>

ACES: Automatic Cohort Extraction System for Event-Stream Datasets\nReproducibility remains a significant challenge in machine learning (ML) for healthcare. Datasets, model pipelines, and even task/cohort definitions are often private in this field, leading to a significant barrier in sharing, iterating, and understanding ML results on electronic health record (EHR) datasets. This paper addresses a significant part of this problem by introducing the Automatic Cohort Extraction System (ACES) for <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29780<END_URL>

Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization\nFederated Learning (FL) offers a promising framework for collaborative and privacy-preserving machine learning across distributed data sources. However, the substantial communication costs associated with FL pose a significant challenge to its efficiency. Specifically, in each communication round, the communication costs scale linearly with the model's dimension, which presents a formidable obstacle, espe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28334<END_URL>

A CLIP-Powered Framework for Robust and Generalizable Data Selection\nLarge-scale datasets have been pivotal to the advancements of deep learning models in recent years, but training on such large datasets invariably incurs substantial storage and computational overhead.  Meanwhile, real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance. Data selection has shown promise in identifying the most representative samples fro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30679<END_URL>

A Closer Look at Machine Unlearning for Large Language Models\nLarge language models (LLMs) may memorize sensitive or copyrighted content, raising privacy and legal concerns. Due to the high cost of retraining from scratch, researchers attempt to employ machine unlearning to remove specific content from LLMs while preserving the overall performance. In this paper, we discuss several issues in machine unlearning for LLMs and provide our insights on possible approaches. To address the issue of ina<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29714<END_URL>

A Coefficient Makes SVRG Effective\nStochastic Variance Reduced Gradient (SVRG), introduced by Johnson & Zhang (2013), is a theoretically compelling optimization method. However, as Defazio & Bottou (2019) highlight, its effectiveness in deep learning is yet to be proven. In this work, we demonstrate the potential of SVRG in optimizing real-world neural networks. Our empirical analysis finds that, for deeper neural networks, the strength of the variance reduction term in SVRG should be smaller a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28009<END_URL>

A Computational Framework for Modeling Emergence of Color Vision in the Human Brain\nIt is a mystery how the brain decodes color vision purely from the optic nerve signals it receives, with a core inferential challenge being how it disentangles internal perception with the correct color dimensionality from the unknown encoding properties of the eye. In this paper, we introduce a computational framework for modeling this emergence of human color vision by simulating both the eye and the cortex. E<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28836<END_URL>

A Conditional Independence Test in the Presence of Discretization\nTesting conditional independence (CI) has many important applications, such as Bayesian network learning and causal discovery. Although several approaches have been developed for learning CI structures for observed variables, those existing methods generally fail to work when the variables of interest can not be directly observed and only discretized values of those variables are available. For example, if $X_1$, $\tilde{X}_2$ an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28799<END_URL>

Action abstractions for amortized sampling\nAs trajectories sampled by policies used by reinforcement learning (RL) and generative flow networks (GFlowNets) grow longer, credit assignment and exploration become more challenging, and the long planning horizon hinders mode discovery and generalization.The challenge is particularly pronounced in entropy-seeking RL methods, such as generative flow networks, where the agent must learn to sample from a structured distribution and discover multiple hig<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28667<END_URL>

ActionReasoningBench: Reasoning about Actions with and without Ramification Constraints\nReasoning about Actions and Change (RAC) has historically played a pivotal role in solving foundational AI problems, such as the frame problem. It has driven advancements in AI fields, such as non-monotonic and commonsense reasoning. RAC remains crucial for AI systems that operate in dynamic environments, engage in interactive scenarios, or rely on commonsense reasoning. Despite substantial advances made by <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29872<END_URL>

Action Sequence Augmentation for Action Anticipation\nAction anticipation models require an understanding of temporal action patterns and dependencies to predict future actions from previous events. The key challenges arise from the vast number of possible action sequences, given the flexibility in action ordering and the interleaving of multiple goals. Since only a subset of such action sequences are present in action anticipation datasets, there is an inherent ordering bias in them. Another ch<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28900<END_URL>

Actions Speak Louder Than Words: Rate-Reward Trade-off in Markov Decision Processes\nThe impact of communication on decision-making systems has been extensively studied under the assumption of dedicated communication channels. We instead consider communicating through actions, where the message is embedded into the actions of an agent which interacts with the environment in a Markov decision process (MDP) framework. We conceptualize the MDP environment as a finite-state channel (FSC), where the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29211<END_URL>

Activation Gradient based Poisoned Sample Detection Against Backdoor Attacks\nThis work studies the task of poisoned sample detection for defending against data poisoning based backdoor attacks. Its core challenge is finding a generalizable and discriminative metric to distinguish between clean and various types of poisoned samples (e.g., various triggers, various poisoning ratios). Inspired by a common phenomenon in backdoor attacks that the backdoored model tend to map significantly different <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29418<END_URL>

Active Learning for Continual Learning: Keeping the Past Alive in the Present\n*Continual learning (CL)* enables deep neural networks to adapt to ever-changing data distributions. In practice, there may be scenarios where annotation is costly, leading to *active continual learning (ACL)*, which performs *active learning (AL)* for the CL scenarios when reducing the labeling cost by selecting the most informative subset is preferable. However, conventional AL strategies are not suitable for ACL, a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28450<END_URL>

Active Learning for Neural PDE Solvers\nSolving partial differential equations (PDEs) is a fundamental problem in engineering and science.  While neural PDE solvers can be more efficient than established numerical solvers, they often require large amounts of training data that is costly to obtain. Active Learning (AL) could help surrogate models reach the same accuracy with smaller training sets by querying classical solvers with more informative initial conditions and PDE parameters. While AL i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27803<END_URL>

ACTIVE: Offline Reinforcement Learning via Adaptive Imitation and In-sample $V$-Ensemble\nOffline reinforcement learning (RL) aims to learn from static datasets and thus faces the challenge of value estimation errors for out-of-distribution actions. The in-sample learning scheme addresses this issue by performing implicit TD backups that does not query the values of unseen actions. However, pre-existing in-sample value learning and policy extraction methods suffer from over-regularization, limit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28239<END_URL>

Active Task Disambiguation with LLMs\nDespite the impressive performance of large language models (LLMs) across various benchmarks, their ability to address ambiguously specified problems—frequent in real-world interactions—remains underexplored. To address this gap, we introduce a formal definition of task ambiguity and frame the problem of task disambiguation through the lens of Bayesian Experimental Design. By posing clarifying questions, LLM agents can acquire additional task specifications,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30123<END_URL>

ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning\nReinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentiallyunsafe, interactions with their environments to learn effectively. These limitationsconfine RL agents to simulated environments, hindering their ability to learndirectly in real-world settings. In this work, we present ActSafe, a novelmodel-based RL algorithm for saf<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29177<END_URL>

A Curious Case of the Missing Measure: Better Scores and Worse Generation\nOur field has a secret: nobody fully trusts audio evaluation measures. As neural audio generation nears perceptual fidelity, these measures fail to detect subtle differences that human listeners readily identify, often contradicting each other when comparing state-of-the-art models. The gap between human perception and automatic measures means we have increasingly sophisticated models while losing our ability to understan<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31366<END_URL>

AdaFisher: Adaptive Second Order Optimization via Fisher Information\nFirst-order optimization methods are currently the mainstream in training deep neural networks (DNNs). Optimizers like Adam incorporate limited curvature information by employing the diagonal matrix preconditioning of the stochastic gradient during the training. Despite their widespread, second-order optimization algorithms exhibit superior convergence properties compared to their first-order counterparts e.g. Adam and SGD. Ho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28275<END_URL>

AdaGrad under Anisotropic Smoothness:  A Fine-Grained Analysis\nAdaptive gradient methods have been widely adopted in training large-scale deep neural networks, especially large foundation models. Despite the huge success in practice, their theoretical advantages over classical gradient methods with uniform step sizes across all coordinates (e.g. SGD) have not been fully understood, especially in the large batch-size setting commonly used in practice. This is because the only theoretical result <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31022<END_URL>

AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation\nIn the image acquisition process, various forms of degradation, including noise, blur, haze, and rain, are frequently introduced. These degradations typically arise from the inherent limitations of cameras or unfavorable ambient conditions. To recover clean images from their degraded versions, numerous specialized restoration methods have been developed, each targeting a specific type of degradation. Recently, all-<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29955<END_URL>

Ada-K Routing: Boosting the Efficiency of MoE-based LLMs\nIn the era of Large Language Models (LLMs), Mixture-of-Experts (MoE) architectures offer a promising approach to managing computational costs while scaling up model parameters. Conventional MoE-based LLMs typically employ static Top-K routing, which activates a fixed and equal number of experts for each token regardless of their significance within the context. In this paper, we propose a novel Ada-K routing strategy that dynamically adju<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30715<END_URL>

ADAM: An Embodied Causal Agent in Open-World Environments\nIn open-world environments like Minecraft, existing agents face challenges in continuously learning structured knowledge, particularly causality. These challenges stem from the opacity inherent in black-box models and an excessive reliance on prior knowledge during training, which impair their interpretability and generalization capability. To this end, we introduce ADAM, An emboDied causal Agent in Minecraft, that can autonomously navig<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29794<END_URL>

AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning\nArticulated object manipulation is a critical capability for robots to perform various tasks in real-world scenarios. Composed of multiple parts connected by joints, articulated objects are endowed with diverse functional mechanisms through complex relative motions. For example, a safe consists of a door, a handle, and a lock, where the door can only be opened when the latch is unlocked. The internal structure, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29969<END_URL>

Adam Exploits $\ell_\infty$-geometry of Loss Landscape via Coordinate-wise Adaptivity\nAdam outperforms SGD when training language models. Yet such benefits are not well-understood theoretically --  previous convergence analysis for Adam and SGD mainly focuses on the number of steps $T$ and is already minimax-optimal in non-convex cases, which are both $O(T^{-1/4})$. In this work, we argue that the better dependence on the loss smoothness is the key advantage of Adam over SGD. More specifically,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29751<END_URL>

Adam-mini: Use Fewer Learning Rates To Gain More\nWe propose Adam-mini, an optimizer that achieves on-par or better performance than AdamW with $50$% less memory footprint. Adam-mini reduces memory by cutting down the learning rate resources in Adam (i.e., $1/\sqrt{v}$). By delving into the Hessian structure of neural nets, we find Adam’s $v$ might not function at its full potential as effectively as we expected. We find that $\geq 99.9$% of these learning rates in $v$ could be harmlessly remove<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28710<END_URL>

ADAM Optimization with Adaptive Batch Selection\nAdam is a widely used optimizer in neural network training due to its adaptive learning rate. However, because different data samples influence model updates to varying degrees, treating them equally can lead to inefficient convergence. To address this, a prior work proposed adapting the sampling distribution using a bandit framework to select samples adaptively. While promising, both the original Adam and its bandit-based variant suffer from flaw<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30565<END_URL>

ADAPT: Attentive Self-Distillation and Dual-Decoder Prediction Fusion for Continual Panoptic Segmentation\nPanoptic segmentation, which unifies semantic and instance segmentation into a single task, has witnessed considerable success on predefined tasks. However, traditional methods tend to struggle with catastrophic forgetting and poor generalization when learning from a continuous stream of new tasks. Continual learning, emerged to tackle these challenges, has garnered increasing attention in <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30235<END_URL>

Adapters for Altering LLM Vocabularies: What Languages Benefit the Most?\nVocabulary adaptation, which integrates new vocabulary into pre-trained language models (LMs), enables expansion to new languages and mitigates token over-fragmentation. However, existing approaches are limited by their reliance on heuristic or external embeddings. We propose VocADT, a novel method for vocabulary adaptation using adapter modules that are trained to learn the optimal linear combination of existing embedding<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30024<END_URL>

Adapting Multi-modal Large Language Model to Concept Drift From Pre-training Onwards\nMulti-modal Large Language Models (MLLMs) frequently face challenges from concept drift when dealing with real-world streaming data, wherein distributions change unpredictably. This mainly includes gradual drift due to long-tailed data and sudden drift from Out-Of-Distribution (OOD) data, both of which have increasingly drawn the attention of the research community. While these issues have been extensively stud<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29128<END_URL>

Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning\nDeep Reinforcement Learning (RL) is well known for being highly sensitive to hyperparameters, requiring practitioners substantial efforts to optimize them for the problem at hand. This also limits the applicability of RL in real-world scenarios. In recent years, the field of automated Reinforcement Learning (AutoRL) has grown in popularity by trying to address this issue. However, these approaches typically hinge <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28508<END_URL>

Adaptive backtracking for fast optimization\nBacktracking line search is foundational in numerical optimization. The basic idea is to adjust the step size of an algorithm by a {\em constant} factor until some chosen criterion (e.g. Armijo, Goldstein, Descent Lemma) is satisfied. We propose a new way for adjusting step sizes, replacing the constant factor used in regular backtracking with one that takes into account the degree to which the chosen criterion is violated, without additional computat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29575<END_URL>

Adaptive Batch Size for Privately Finding Second-Order Stationary Points\nThere is a gap between finding a first-order stationary point (FOSP) and a second-order stationary point (SOSP) under differential privacy constraints, and it remains unclear whether privately finding an SOSP is more challenging than finding an FOSP. Specifically, Ganesh et al. (2023) demonstrated that an $\alpha$-SOSP can be found with $\alpha=\Tilde{O}(\frac{1}{n^{1/3}}+(\frac{\sqrt{d}}{n\epsilon})^{3/7})$, where $n$ is <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28679<END_URL>

Adaptive Camera Sensor for Vision Models\nDomain shift remains a persistent challenge in deep-learning-based computer vision, often requiring extensive model modifications or large labeled datasets to address. Inspired by human visual perception, which adjusts input quality through corrective lenses rather than over-training the brain, we propose Lens, a novel camera sensor control method that enhances model performance by capturing high-quality images from the model's perspective, rather than r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30215<END_URL>

Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts\nAdvancements in foundation models (FMs) have led to a paradigm shift in machinelearning. The rich, expressive feature representations from these pre-trained, large-scale FMs are leveraged for multiple downstream tasks, usually via lightweightfine-tuning of a shallow fully-connected network following the representation.However, the non-interpretable, black-box nature of this prediction pipeline can bea challenge, especia<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30736<END_URL>

Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws\nThe composition of pretraining data is a key determinant of foundation models' performance, but there is no standard guideline for allocating a limited computational budget across different data sources. Most current approaches either rely on extensive experiments with smaller models or dynamic data adjustments that also require proxy models, both of which significantly increase the workflow complexity and computational over<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29145<END_URL>

Adaptive Energy Alignment for Accelerating Test-Time Adaptation\nIn response to the increasing demand for tackling out-of-domain (OOD) scenarios, test-time adaptation (TTA) has garnered significant research attention in recent years. To adapt a source pre-trained model to target samples without getting access to their labels, existing approaches have typically employed entropy minimization (EM) loss as a primary objective function. In this paper, we propose an adaptive energy alignment (AEA) sol<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28139<END_URL>

Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise\nDespite the vast empirical evidence supporting the efficacy of adaptive optimization methods in deep learning, their theoretical understanding is far from complete. This work introduces novel SDEs for commonly used adaptive optimizers: SignSGD, RMSprop(W), and Adam(W). These SDEs offer a quantitatively accurate description of these optimizers and help illuminate an intricate relationship between adaptivity, gra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27812<END_URL>

Adaptive Pruning of Pretrained Transformer via Differential Inclusions\nLarge transformers have demonstrated remarkable success, making it necessary to compress these models to reduce inference costs while preserving their performance. Current compression algorithms prune transformers at fixed compression ratios, requiring a unique pruning process for each ratio, which results in high computational costs. In contrast, we propose pruning of pretrained transformers at any desired ratio within a si<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29380<END_URL>

Adaptive Rank Allocation: Speeding Up Modern Transformers with RaNA Adapters\nLarge Language Models (LLMs) are computationally intensive, particularly during inference. Neuron-adaptive techniques, which selectively activate neurons in Multi-Layer Perceptron (MLP) layers, offer some speedups but suffer from limitations in modern Transformers. These include reliance on sparse activations, incompatibility with attention layers, and the use of costly neuron masking techniques. To address these issue<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27996<END_URL>

Adaptive Rentention & Correction for Continual Learning\nContinual learning, also known as lifelong learning or incremental learning, refers to the process by which a model learns from a stream of incoming data over time. A common problem in continual learning is the classification layer’s bias towards the most recent task. Traditionally, methods have relied on incorporating data from past tasks during training to mitigate this issue. However, the recent shift in continual learning to memory-fre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30680<END_URL>

Adaptive Shrinkage Estimation for Personalized Deep Kernel Regression in Modeling Brain Trajectories\nLongitudinal biomedical studies track individuals over time to capture dynamics in brain development, disease progression, and treatment effects. Estimating  trajectories of brain measurements in such studies is challenging due to biological variability and inconsistencies in measurement protocols (e.g. MRI scanner variations and upgrades). Herein, we introduce a novel personalized Deep Kernel R<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28284<END_URL>

Adaptive teachers for amortized samplers\nAmortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnormalized density where exact sampling is intractable. When sampling is modeled as a sequential decision-making process, reinforcement learning (RL) methods, such as generative flow networks, can be used to train the sampling policy. Off-policy RL training facilitates the discovery of diverse, high-reward candidates, but<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30561<END_URL>

Adaptive Transformer Programs: Bridging the Gap Between Performance and Interpretability in Transformers\nBalancing high performance with interpretability in increasingly powerful Transformer-based models remains a challenge. While mechanistic interpretability aims to specify neural network computations in explicit, pseudocode-like formats, existing methods often involve laborious manual analysis or struggle to fully elucidate learned internal algorithms. Recent efforts to build intrinsically in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29383<END_URL>

AdaRankGrad: Adaptive Gradient Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning\nTraining and fine-tuning large language models (LLMs) come with challenges related to memory and computational requirements due to the increasing size of the model weights and the optimizer states. To tackle these challenges, various techniques have been developed, such as low-rank adaptation (LoRA), which involves introducing a parallel trainable low-rank matrix to the fixed pre-trained weights a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29966<END_URL>

AdaRC: Mitigating Graph Structure Shifts during Test-Time\nPowerful as they are, graph neural networks (GNNs) are known to be vulnerable to distribution shifts. Recently, test-time adaptation (TTA) has attracted attention due to its ability to adapt a pre-trained model to a target domain, without re-accessing the source domain. However, existing TTA algorithms are primarily designed for attribute shifts in vision tasks, where samples are independent. These methods perform poorly on graph data th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30388<END_URL>

AdaWM: Adaptive World Model based Planning for Autonomous Driving\nWorld model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a   planning policy. To speed up the learning process, the pretrain-finetune paradigm is often used, where online RL is initialized by a pretrained model and a policy learned offline. However, naively performing such initialization in RL may result in dramatic performa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29891<END_URL>

Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models\nFine-tuning language models (LMs) with the standard Adam optimizer often demands excessive memory, limiting accessibility. The ``in-place'' version of Stochastic Gradient Descent (IP-SGD) and Memory-Efficient Zeroth-order Optimizer (MeZO) have been proposed as solutions to improve memory efficiency. However, IP-SGD still requires a decent amount of memory, and MeZO suffers<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29681<END_URL>

Adding Conditional Control to Diffusion Models with Reinforcement Learning\nDiffusion models are powerful generative models that allow for precise control over the characteristics of the generated samples. While these diffusion models trained on large datasets have achieved success, there is often a need to introduce additional controls in downstream fine-tuning processes, treating these powerful models as pre-trained diffusion models. This work presents a novel method based on reinforcement lea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28089<END_URL>

Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models\nAdding Object into images based on text instructions is a challenging task in semantic image editing, requiring a balance between preserving the original scene and seamlessly integrating the new object in a fitting location. Despite extensive efforts, existing models often struggle with this balance, particularly with finding a natural location for adding an object in complex scenes. We introduce Add-it, a trainin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29207<END_URL>

Addressing Label Shift in Distributed Learning via Entropy Regularization\nWe address the challenge of minimizing *true risk* in multi-node distributed learning. These systems are frequently exposed to both inter-node and intra-node *label shifts*, which present a critical obstacle to effectively optimizing model performance while ensuring that data remains confined to each node.To tackle this, we propose the Versatile Robust Label Shift (VRLS) method, which enhances the maximum likelihood estim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28558<END_URL>

A Decade's Battle on Dataset Bias: Are We There Yet?\nWe revisit the ``dataset classification'' experiment suggested by Torralba & Efros (2011) a decade ago, in the new era with large-scale, diverse, and hopefully less biased datasets as well as more capable neural network architectures. Surprisingly, we observe that modern neural networks can achieve excellent accuracy in classifying which dataset an image is from: e.g., we report 84.7% accuracy on held-out validation data for the three-way cla<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29591<END_URL>

A Deep Generative Learning Approach for Two-stage Adaptive Robust Optimization\nTwo-stage adaptive robust optimization (ARO) is a powerful approach for planning under uncertainty, balancing first-stage decisions with recourse decisions made after uncertainty is realized. To account for uncertainty, modelers typically define a simple uncertainty set over which potential outcomes are considered. However, classical methods for defining these sets unintentionally capture a wide range of unrealistic <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30520<END_URL>

A deep inverse-mapping model for a flapping robotic wing\nIn systems control, the dynamics of a system are governed by modulating its inputs to achieve a desired outcome. For example, to control the thrust of a quad-copter propeller the controller modulates its rotation rate, relying on a straightforward mapping between the input rotation rate and the resulting thrust. This mapping can be inverted to determine the rotation rate needed to generate a desired thrust. However, in complex systems, su<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31165<END_URL>

ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning\nPrompt Tuning (PT) enables the adaptation of Pre-trained Large Language Models (PLMs) to downstream tasks by optimizing a small amount of soft virtual tokens, which are prepended to the input token embeddings. Recently, Decomposed Prompt Tuning (DePT) has demonstrated superior adaptation capabilities by decomposing the soft prompt into a shorter soft prompt and a pair of low-rank matrices. The product of the pair of lo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28848<END_URL>

A Differentiable Metric for Discovering Groups and Unitary Representations\nDiscovering group structures within data is a significant challenge with broad implications across various scientific domains. The main hurdle stems from the non-differentiable nature of group axioms, hindering their seamless integration into deep learning frameworks. To address this, we introduce a novel differentiable approach that leverages the representation theory of finite groups. Our method employs a unique neural<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29498<END_URL>

A Differentiable Rank-Based Objective for Better Feature Learning\nIn this paper, we leverage existing statistical methods to better understand feature learning from data. We tackle this by modifying the model-free variable selection method, Feature Ordering by Conditional Independence (FOCI), which is introduced in Azadkia & Chatterjee (2021). While FOCI is based on a non-parametric coefficient of conditional dependence, we introduce its parametric, differentiable approximation. With this appro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30039<END_URL>

ADIFF: Explaining audio difference using natural language\nUnderstanding and explaining differences between audio recordings is crucial for fields like audio forensics, quality assessment, and audio generation. This involves identifying and describing audio events, acoustic scenes, signal characteristics, and their emotional impact on listeners. This paper stands out as the first work to comprehensively study the task of explaining audio differences and then propose benchmark, baselines for the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28536<END_URL>

A Distributional Approach to Uncertainty-Aware Preference Alignment Using Offline Demonstrations\nDesigning reward functions in Reinforcement Learning (RL) often demands significant task-specific expertise. Offline preference-based Reinforcement Learning (PbRL) provides an effective alternative to address the complexity of reward design by learning policies from offline datasets that contain human preferences between trajectory pairs. Existing offline PbRL studies typically model a reward functi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29655<END_URL>

Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control\nDynamical generative models that produce samples through an iterative process, such as Flow Matching and denoising diffusion models, have seen widespread use, but there have not been many theoretically-sound methods for improving these models with reward fine-tuning. In this work, we cast reward fine-tuning as stochastic optimal control (SOC). Critically, we prove that a very specific *<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27782<END_URL>

ADMM for Nonconvex Optimization under Minimal Continuity Assumption\nThis paper introduces a novel approach to solving multi-block nonconvex composite optimization problems through a proximal linearized Alternating Direction Method of Multipliers (ADMM). This method incorporates an Increasing Penalization and Decreasing Smoothing (IPDS) strategy. Distinguishing itself from existing ADMM-style algorithms, our approach (denoted IPDS-ADMM) imposes a less stringent condition, specifically requiring <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30287<END_URL>

ADMM for Structured Fractional Minimization\nWe consider a class of structured fractional minimization problems, where the numerator includes a differentiable function, a simple nonconvex nonsmooth function, a concave nonsmooth function, and a convex nonsmooth function composed with a linear operator, while the denominator is a continuous function that is either weakly convex or has a weakly convex square root. These problems are widespread and span numerous essential applications in machine lea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30451<END_URL>

Advances in Financial AI: Opportunities, Innovations, and Responsible AI\nThe financial industry is experiencing a paradigm shift propelled by rapid advancements in artificial intelligence. From algorithmic trading and fraud detection to personalized banking and investment strategies, AI technologies are redefining financial services. Our workshop aims to convene researchers, industry professionals, and policymakers to explore the latest developments, discuss challenges, and chart a course for r<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23978<END_URL>

Advancing Graph Generation through Beta Diffusion\nDiffusion models have excelled in generating natural images and are now being adapted to a variety of data types, including graphs. However, conventional models often rely on Gaussian or categorical diffusion processes, which can struggle to accommodate the mixed discrete and continuous components characteristic of graph data. Graphs typically feature discrete structures and continuous node attributes that often exhibit rich statistical patterns<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27807<END_URL>

Advancing LLM Reasoning Generalists with Preference Trees\nWe introduce EURUS, a suite of large language models (LLMs) optimized for reasoning. Finetuned from Mistral-7B, Llama-3-8B, and Mixtral-8x22B, EURUS models achieve state-of-the-art results among open-source models on a diverse set of benchmarks covering mathematics, code generation, and logical reasoning problems. Notably, EURUX-8X22B outperforms GPT-3.5 Turbo in reasoning through a comprehensive benchmarking across 12 test sets covering<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31127<END_URL>

Advancing Mathematical Reasoning in Language Models: The Impact of Problem-Solving Data, Data Synthesis Methods, and Training Stages\nAdvancements in large language models (LLMs) have significantly expanded their capabilities across various domains. However, mathematical reasoning remains a challenging area, prompting the development of math-specific LLMs such as LLEMMA, DeepSeekMath, and Qwen2-Math, among others. These models typically follow a two-stage training paradigm: pre-training with mat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30252<END_URL>

Advancing Out-of-Distribution Detection via Local Neuroplasticity\nIn the domain of machine learning, the assumption that training and test data share the same distribution is often violated in real-world scenarios, requiring effective out-of-distribution (OOD) detection. This paper presents a novel OOD detection method that leverages the unique local neuroplasticity property of Kolmogorov-Arnold Networks (KANs). Unlike traditional multilayer perceptrons, KANs exhibit local plasticity, allowing <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31215<END_URL>

Advancing Prompt-Based Methods for Replay-Independent General Continual Learning\nGeneral continual learning (GCL) is a broad concept to describe real-world continual learning (CL) problems, which are often characterized by online data streams without distinct transitions between tasks, i.e., blurry task boundaries. These requirements result in poor initial performance, limited generalizability, and severe catastrophic forgetting, heavily impacting the effectiveness of mainstream GCL models trai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29430<END_URL>

Advantage Alignment Algorithms\nArtificially intelligent agents are increasingly being integrated into human decision-making: from large language model (LLM) assistants to autonomous vehicles. These systems often optimize their individual objective, leading to conflicts, particularly in general-sum games where naive reinforcement learning agents empirically converge to Pareto-suboptimal Nash equilibria. To address this issue, opponent shaping has emerged as a paradigm for finding socially benefi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29701<END_URL>

Advantage-Guided Distillation for Preference Alignment in Small Language Models\nAlignment techniques such as RLHF enable LLMs to generate outputs that align with human preferences and play an essential role in their effectiveness. However, their impact often diminishes when applied to smaller language models, likely due to the limited capacity of these models. Instead of directly applying existing alignment techniques to smaller models, we propose to utilize a well-aligned teacher LLM to guide <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27759<END_URL>

Advantages, Risks and Insights from Comparing In-Context Learning Models with Typical Meta-Learners\nWe investigate in-context learning (ICL) models from the perspective of learning to learn. Unlike existing studies that focus on identifying the specific learning algorithms that ICL models learn, we compare ICL models with typical meta-learners to understand their superior performance. We theoretically prove the expressiveness of ICL models as learning algorithms and examine their learnability a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28701<END_URL>

Adversarial Attacks on Data Attribution\nData attribution aims to quantify the contribution of individual training data points to the outputs of an AI model, which has been used to measure the value of training data and compensate data providers. Given the impact on financial decisions and compensation mechanisms, a critical question arises concerning the adversarial robustness of data attribution methods. However, there has been little to no systematic research addressing this issue. In this wo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28363<END_URL>

Adversarial Diffusion Bridge Model for Reliable Adversarial Purification\nRecently Diffusion-based Purification (DiffPure) has been recognized as an effective defense method against adversarial examples. However, we find DiffPure which directly employs the original pre-trained diffusion models for adversarial purification, to be suboptimal. This is due to an inherent trade-off between noise purification performance and data recovery quality. Additionally, the reliability of existing evaluations <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28839<END_URL>

Adversarial Generative Flow Network for Solving Vehicle Routing Problems\nRecent research into solving vehicle routing problems (VRPs) has gained significant traction, particularly through the application of deep (reinforcement) learning for end-to-end solution construction. However, many current construction-based neural solvers predominantly utilize Transformer architectures, which can face scalability challenges and struggle to produce diverse solutions. To address these limitations, we intro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28069<END_URL>

Adversarial Latent Feature Augmentation for Fairness\nAchieving fairness in machine learning remains a critical challenge, especially due to the opaque effects of data augmentation on input spaces within nonlinear neural networks. Nevertheless, current approaches that emphasize augmenting latent features, rather than input spaces, offer limited insights into their ability to detect and mitigate bias. In response, we introduce the concept of the "unfair region" in the latent space, a subspace tha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29055<END_URL>

Adversarially Robust Anomaly Detection through Spurious Negative Pair Mitigation\nDespite significant progress in Anomaly Detection (AD), the robustness of existing detection methods against adversarial attacks remains a challenge, compromising their reliability in critical real-world applications such as autonomous driving. This issue primarily arises from the AD setup, which assumes that training data is limited to a group of unlabeled normal samples, making the detectors vulnerable to adversa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28075<END_URL>

Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings\nDespite significant advancements in out-of-distribution (OOD) detection, existing methods still struggle to maintain robustness against adversarial attacks, compromising their reliability in critical real-world applications. Previous studies have attempted to address this challenge by exposing detectors to auxiliary OOD datasets alongside adversarial training. However, the increased data complexity inherent <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30255<END_URL>

Adversarial Machine Unlearning\nThis paper focuses on the challenge of machine unlearning, aiming to remove the influence of specific training data on machine learning models. Traditionally, the development of unlearning algorithms runs parallel with that of membership inference attacks (MIA), a type of privacy threat to determine whether a data instance was used for training. However, the two strands are intimately connected: one can view machine unlearning through the lens of MIA success with <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28088<END_URL>

Adversarial Mixup Unlearning\nMachine unlearning is a critical area of research aimed at safeguarding data privacy by enabling the removal of sensitive information from machine learning models. One unique challenge in this field is catastrophic unlearning, where erasing specific data from a well-trained model unintentionally removes essential knowledge, causing the model to deviate significantly from a retrained one. To address this, we introduce a novel approach that regularizes the unlearning <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30273<END_URL>

Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI\nArtists are increasingly concerned about advancements in image generation models that can closely replicate their unique artistic styles.In response, several protection tools against style mimicry have been developed that incorporate small adversarial perturbations into artworks published online. In this work, we evaluate the effectiveness of popular protections---with millions of downloads---and show they only provide<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27708<END_URL>

Adversarial Policy Optimization for Preference-based Reinforcement Learning\nIn this paper, we study offline preference-based reinforcement learning (PbRL), where learning is based on pre-collected preference feedback over pairs of trajectories. While offline PbRL has demonstrated remarkable empirical success, existing theoretical approaches face challenges in ensuring conservatism under uncertainty, requiring computationally intractable confidence set constructions. We address this limitation b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30939<END_URL>

Adversarial Score Identity Distillation: Rapidly Surpassing the Teacher in One Step\nScore Identity Distillation (SiD) is a data-free method that has achieved state-of-the-art performance in image generation by leveraging only a pretrained diffusion model, without requiring any training data. However, the ultimate performance of SiD is constrained by the accuracy with which the pretrained model captures the true data scores at different stages of the diffusion process. In this paper, we introduc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28519<END_URL>

Adversarial Search Engine Optimization for Large Language Models\nLarge Language Models (LLMs) are increasingly used in applications where the model selects from competing third-party content, such as in LLM-powered search engines or chatbot plugins. In this paper, we introduce *Preference Manipulation Attacks*, a new class of attacks that manipulate an LLM’s selections to favor the attacker. We demonstrate that carefully crafted website content or plugin documentations can trick an LLM to promo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28746<END_URL>

Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data\nAdversarial training is a widely-applied approach to training deep neural networks to be robust against adversarial perturbation. However, although adversarial training has achieved empirical success in practice, it still remains unclear why adversarial examples exist and how adversarial training methods improve model robustness. In this paper, we provide a theoretical un<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28674<END_URL>

Adversarial Training for Defense Against Label Poisoning Attacks\nAs machine learning models advance in complexity and increasingly depend on large volumes of publicly sourced data, such as the human-annotated labels used in training large language models, they become more vulnerable to label poisoning attacks. These attacks, in which adversaries subtly alter the labels within a training dataset, can severely degrade model performance, posing significant risks in critical applications. In this p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29458<END_URL>

Adversaries With Incentives:  A Strategic Alternative to Adversarial Robustness\nAdversarial training aims to defend against *adversaries*: malicious opponents whose sole aim is to harm predictive performance in any way possible. This presents a rather harsh perspective, which we assert results in unnecessarily conservative training. As an alternative, we propose to model opponents as simply pursuing their own goals—rather than working directly against the classifier. Employing tools from strate<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29487<END_URL>

AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption\nThe outstanding capability of diffusion models in generating high-quality images poses significant threats when misused by adversaries. In particular, we assume malicious adversaries exploiting diffusion models for inpainting tasks, such as replacing a specific region with a celebrity. While existing methods for protecting images from manipulation in diffusion-based generative models have primarily foc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28482<END_URL>

AdvWave: Stealthy Adversarial Jailbreak Attack against Large Audio-Language Models\nRecent advancements in large audio-language models (LALMs) have enabled speech-based user interactions, significantly enhancing user experience and accelerating the deployment of LALMs in real-world applications. However, ensuring the safety of LALMs is crucial to prevent risky outputs that may raise societal concerns or violate AI regulations. Despite the importance of this issue, research on jailbreaking LALMs <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31267<END_URL>

Affine Steerable Equivariant Layer for Canonicalization of Neural Networks\nIn the field of equivariant networks, achieving affine equivariance, particularly for general group representations, has long been a challenge.In this paper, we propose the steerable EquivarLayer, a generalization of InvarLayer (Li et al., 2024), by building on the concept of equivariants beyond invariants.The steerable EquivarLayer supports affine equivariance with arbitrary input and output representations, marking the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30933<END_URL>

AFlow: Automating Agentic Workflow Generation\nLarge language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed instructions and operational sequences. However, constructing these workflows requires significant human effort, limiting scalability and generalizability. Recent research has sought to automate the generation and optimization of these workflows, but existing methods still <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27691<END_URL>

A Formal Framework for Understanding Length Generalization in Transformers\nA major challenge for transformers is generalizing to sequences longer than those observed during training. While previous works have empirically shown that transformers can either succeed or fail at length generalization depending on the task, theoretical understanding of this phenomenon remains limited. In this work, we introduce a rigorous theoretical framework to analyze length generalization in causal transformers w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29490<END_URL>

A General Framework for Producing Interpretable Semantic Text Embeddings\nSemantic text embedding is essential to many tasks in Natural Language Processing (NLP). While black-box models are capable of generating high-quality embeddings, their lack of interpretability limits their use in tasks that demand transparency. Recent approaches have improved interpretability by leveraging domain-expert-crafted or LLM-generated questions, but these methods rely heavily on expert input or well-prompt desig<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31167<END_URL>

A Generalist Hanabi Agent\nTraditional multi-agent reinforcement learning (MARL) systems can develop cooperative strategies through repeated interactions. However, these systems are unable to perform well on any other setting than the one they have been trained on, and struggle to successfully cooperate with unfamiliar collaborators. This is particularly visible in the Hanabi benchmark, a popular 2-to-5 player cooperative card-game which requires complex reasoning and precise assistance to other<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28311<END_URL>

A Generic Framework for Conformal Fairness\nConformal Prediction (CP) is a popular method for uncertainty quantification with machine learning models. While the method provides probabilistic guarantees regarding the coverage of the true label, these guarantees are agnostic to the presence of sensitive attributes within the dataset. In this work, we formalize \textit{Conformal Fairness}, a notion of fairness using conformal predictors, and provide a theoretically well-founded algorithm and associ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27770<END_URL>

AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents\nAutonomy via agents based on large language models (LLMs) that can carry out personalized yet standardized tasks presents a significant opportunity to drive human efficiency. There is an emerging need and interest in automating web tasks  (e.g., booking a hotel for a given date within a budget). Being a practical use case itself, the web agent also serves as an important proof-of-concept example for various agent grounding scenar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28353<END_URL>

Agent-Oriented Planning in Multi-Agent Systems\nThrough the collaboration of multiple agents possessing diverse expertise and tools, multi-agent systems achieve impressive progress in solving real-world problems. Given the user queries, the meta-agents, serving as the brain within these systems, are required to decompose the queries into multiple sub-tasks that can be allocated to suitable agents capable of solving them, so-called agent-oriented planning. In this study, we identify three critica<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30386<END_URL>

AgentQuest: Benchmarking LLM and VLM Agents on Long-Horizon Interactive Tasks\nLarge Language Models (LLMs) and Vision Language Models (VLMs) possess extensive knowledge and exhibit promising reasoning abilities, however, they still struggle to perform well in complex, dynamic environments. Real-world tasks require handling intricate interactions, advanced spatial reasoning, long-term planning, and continuous exploration of new strategies—areas in which we lack effective methodologies for compre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28856<END_URL>

AgentRefine: Enhancing Agent Generalization through Refinement Tuning\nLarge Language Model (LLM) based agents have proved their ability to perform complex tasks like humans. However, there is still a large gap between open-sourced LLMs and commercial models like the GPT series. In this paper, we focus on improving the agent generalization capabilities of LLMs via instruction tuning. We first observe that the existing agent training corpus exhibits satisfactory results on held-in evaluation sets<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30355<END_URL>

Agent S: An Open Agentic Framework that Uses Computers Like a Human\nWe present Agent S, an open agentic framework that enables autonomous interaction with computers through Graphical User Interface (GUI), aimed at transforming human-computer interaction by automating complex, multi-step tasks. Agent S addresses three key challenges in automating computer tasks: acquiring domain-specific knowledge, planning over long task horizons, and handling dynamic, non-uniform interfaces. To this end, Agent<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28525<END_URL>

Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents\nAlthough LLM-based agents, powered by Large Language Models (LLMs), can use external tools and memory mechanisms to solve complex real-world tasks, they may also introduce critical security vulnerabilities. However, the existing literature does not comprehensively evaluate attacks and defenses against LLM-based agents. To address this, we introduce Agent Security Bench (ASB), a comprehensive framew<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29432<END_URL>

Agent Skill Acquisition for Large Language Models via CycleQD\nTraining large language models to acquire specific skills remains a challenging endeavor. Conventional training approaches often struggle with data distribution imbalances and inadequacies in objective functions that do not align well with task-specific performance. To address these challenges, we introduce CycleQD, a novel approach that leverages the Quality Diversity framework through a cyclic adaptation of the algorithm, along wit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30026<END_URL>

Agents' Room:  Narrative Generation through Multi-step Collaboration\nWriting compelling fiction is a multifaceted process combining elements such as crafting a plot, developing interesting characters, and using evocative language. While large language models (LLMs) show promise for story writing, they currently rely heavily on intricate prompting, which limits their use. We propose Agents' Room, a generation framework inspired by narrative theory, that decomposes narrative writing into subtasks<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30214<END_URL>

AgentStudio: A Toolkit for Building General Virtual Agents\nGeneral virtual agents need to handle multimodal observations, master complex action spaces, and self-improve in dynamic, open-domain environments. However, existing environments are often domain-specific and require complex setups, which limits agent development and evaluation in real-world settings. As a result, current evaluations lack in-depth analyses that decompose fundamental agent capabilities. We introduce AgentStudio, a trinit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29133<END_URL>

Agent-to-Sim: Learning Interactive Behavior Model from Casual Longitudinal Videos\nWe present Agent-to-Sim (ATS), a framework for learning interactive behavior models of 3D agents in a 3D environment from casually-captured videos. Different from prior works that rely on marker-based tracking and multiview cameras, ATS learns natural behaviors of animal and human agents in a non-invasive way, directly from monocular video collections. Modeling 3D behavior of an agent requires persistent 3D tracki<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27750<END_URL>

AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials\nGraphical User Interface (GUI) agents hold great potential for automating complex tasks across diverse digital environments, from web applications to desktop software. However, the development of such agents is hindered by the lack of high-quality, multi-step trajectory data required for effective training. Existing approaches rely on expensive and labor-intensive human annotation, making them unsustainable at scale. To<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30416<END_URL>

A Geometric Framework for Understanding Memorization in Generative Models\nAs deep generative models have progressed, recent work has shown them to be capable of memorizing and reproducing training datapoints when deployed. These findings call into question the usability of generative models, especially in light of the legal and privacy risks brought about by memorization. To better understand this phenomenon, we propose the *manifold memorization hypothesis* (MMH), a geometric framework which l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29158<END_URL>

A Graph Enhanced Symbolic Discovery Framework For Efficient Circuit Synthesis\nThe efficiency of Circuit Synthesis (CS) has become one of the key bottlenecks in chip design. To prompt efficient CS, previous studies propose using a key scoring function to predict and prune a large number of ineffective nodes of the CS heuristics. However, the existing scoring functions struggle to balance inference efficiency, interpretability, andgeneralization performance, which severely hinders their applicati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30414<END_URL>

Agree to Disagree: Demystifying Homogeneous Deep Ensembles through Distributional Equivalence\nDeep ensembles improve the performance of the models by taking the average predictions of a group of ensemble members. However, the origin of these capabilities remains a mystery and deep ensembles are used as a reliable “black box” to improve the performance. Existing studies typically attribute such improvement to Jensen gaps of the deep ensemble method, where the loss of the mean does not exceed the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29301<END_URL>

AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation\nRobotic manipulation in open-world settings requires not only task execution but also the ability to detect and learn from failures. While recent advances in vision-language models (VLMs) and large language models (LLMs) have improved robots' spatial reasoning and problem-solving abilities, they still struggle with failure recognition, limiting their real-world applicability. We introduce AHA, an open<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30106<END_URL>

A Hypothesis on Black Swan in Unchanging Environments\nBlack swan events are statistically rare occurrences that carry extremely high risks. A typical view of defining black swan events is heavily assumed to originate from an unpredictable time-varying environments; however, the community lacks a comprehensive definition of black swan events. To this end, this paper challenges that the standard view is incomplete and claims that high-risk, statistically rare events can also occur in unchanging e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30807<END_URL>

AI2TALE: An Innovative Information Theory-based Approach for Learning to Localize Phishing Attacks\nPhishing attacks remain a significant challenge for detection, explanation, and defense, despite over a decade of research on both technical and non-technical solutions. AI-based phishing detection methods are among the most effective approaches for defeating phishing attacks, providing predictions on the vulnerability label (i.e., phishing or benign) of data. However, they often lack intrinsic ex<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31048<END_URL>

AI4MAT-ICLR-2025: ICLR 2025 Workshop on AI for Accelerated Materials Design\nWe propose a full-day, medium-sized workshop at ICLR 2025 titled “AI for Accelerated Materials Design” (AI4Mat-ICLR-2025). This workshop will serve as a venue for researchers at the intersection of AI and materials science to address pressing scientific challenges using AI-driven techniques. AI is starting to revolutionize materials science and engineering, driving major global research initiatives from academic and gov<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23992<END_URL>

AI as Humanity’s Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text\nCreativity has long been considered one of the most difficult aspect of human intelligence for AI to mimic. However, the rise of Large Language Models (LLMs), like ChatGPT, has raised questions about whether AI can match or even surpasshuman creativity. We present CREATIVITY INDEX as the first step to quantify the linguistic creativity of a text by reconstru<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28676<END_URL>

AI for Nucleic Acids (AI4NA)\nIn recent years, the AI community has made significant strides in protein research, particularly since the breakthrough of AlphaFold2, which has led to advancements in structural biology and drug discovery. The success achieved on proteins gives hope to achieve comparable success on nucleic acids, RNA and DNA. The proposed workshop aims to highlight the unique challenges and possibilities of applying AI to nucleic acids. While advances in RNA structure prediction an<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23998<END_URL>

AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements\nDespite over a decade of legislative efforts to address modern slavery in the supply chains of large corporations, the effectiveness of government oversight remains hampered by the challenge of scrutinizing thousands of statements annually. While Large Language Models (LLMs) can be considered a well established solution for the automatic analysis and summarization of documents, recognizing concrete mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27711<END_URL>

Aioli: A Unified Optimization Framework for Language Model Data Mixing\nLanguage model performance depends on identifying the optimal mixture of data groups to train on (e.g., law, code, math). Prior work has proposed a diverse set of methods to efficiently learn mixture proportions, ranging from fitting regression models over training runs to dynamically updating proportions throughout training. Surprisingly, we find that no existing method consistently outperforms a simple stratified sampling <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28121<END_URL>

AIR-BENCH 2024: A Safety Benchmark based on Regulation and Policies Specified Risk Categories\nFoundation models (FMs) provide societal benefits but also amplify risks. Governments, companies, and researchers have proposed regulatory frameworks, acceptable use policies, and safety benchmarks in response. However, existing public benchmarks often define safety categories based on previous literature, intuitions, or common sense, leading to disjointed sets of categories for risks specified in rece<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29470<END_URL>

Air Quality Prediction with Physics-Informed Dual Neural ODEs in Open Systems\nAir pollution significantly threatens human health and ecosystems, necessitating effective air quality prediction to inform public policy. Traditional approaches are generally categorized into physics-based and data-driven models. Physics-based models usually struggle with high computational demands and closed-system assumptions, while data-driven models may overlook essential physical dynamics, confusing the capturin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28589<END_URL>

AI Sandbagging: Language Models can Strategically Underperform on Evaluations\nTrustworthy capability evaluations are crucial for ensuring the safety of AI systems, and are becoming a key component of AI regulation. However, the developers of an AI system, or the AI system itself, may have incentives for evaluations to understate the AI's actual capability. These conflicting interests lead to the problem of *sandbagging* – which we define as *strategic underperformance on an evaluation*. In this<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30824<END_URL>

A Large-scale Dataset and Benchmark for Commuting Origin-Destination Flow Generation\nCommuting Origin-Destination~(OD) flows are critical inputs for urban planning and transportation, providing crucial information about the population residing in one region and working in another within an interested area. Due to the high cost of data collection, researchers have developed physical and computational models to generate commuting OD flows using readily available urban attributes, such as sociodem<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29354<END_URL>

A Large-scale Training Paradigm for Graph Generative Models\nLarge Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse from numerous domains. This large-scale training paradigm on diverse well-curated data enhances the creativity and diversity of the generated content. However, all previous graph-generative models (e.g., GraphRNN, MDVAE, MoFlow, GDSS, and DiGress) have been tr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29077<END_URL>

ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition\nBias in machine learning models can lead to unfair decision making, and while it has been well-studied in the image and text domains, it remains underexplored in action recognition. Action recognition models often suffer from background bias (i.e., inferring actions based on background cues) and foreground bias (i.e., relying on subject appearance), which can be detrimental to real-life applications such as autonomous<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30700<END_URL>

Alchemy: Amplifying Theorem-Proving Capability Through Symbolic Mutation\nFormal proofs are challenging to write even for experienced experts. Recent progress in Neural Theorem Proving (NTP) shows promise in expediting this process. However, the formal corpora available on the Internet are limited compared to the general text, posing a significant data scarcity challenge for NTP. To address this issue, this work proposes Alchemy, a general framework for data synthesis that constructs formal theo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30827<END_URL>

Algorithmic Phases of In-Context Learning\nIn-Context Learning (ICL) has significantly expanded the general-purpose nature of large language models, allowing them to adapt to novel tasks using merely the inputted context. This has motivated a series of papers that analyze tractable synthetic domains and postulate precise mechanisms that may underlie ICL. However, the use of relatively distinct setups that often lack a sequence modeling nature to them makes it unclear how general the reported ins<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29296<END_URL>

Algorithmic Stability Based Generalization Bounds for Adversarial Training\nIn this paper, we present a novel stability analysis of adversarial training and prove generalization upper bounds in terms of an expansiveness property of adversarial perturbations used during training and used for evaluation. These expansiveness parameters appear not only govern the vanishing rate of the generalization error but also govern its scaling constant. Our proof techniques do not rely on artificial assumption<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31151<END_URL>

Aligned Better, Listen Better For Audio-Visual Large Language Models\nAudio is essential for multimodal video understanding. On the one hand, video inherently contains audio and audio supplies complementary information to the visual modality. Besides, video large language models (Video-LLMs) can encounter many audio-centric settings. However, existing Video-LLMs and Audio-Visual Large Language Models (AV-LLMs) exhibit deficiencies in exploiting audio information, leading to weak understanding an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31200<END_URL>

Aligned LLMs Are Not Aligned Browser Agents\nDespite significant efforts spent by large language model (LLM) developers toalign model outputs towards safety and helpfulness, there remains an open ques-tion if this safety alignment, typically enforced in chats, generalize to non-chatand agentic use cases? Unlike chatbots, agents equipped with general-purposetools, such as web browsers and mobile devices, can directly influence the realworld, making it even more crucial to ensure the safety of LLM<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29856<END_URL>

Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception\nWith success in image generation, generative diffusion models are increasingly adopted for discriminative scenarios because generating pixels is a unified and natural perception interface. Although directly re-purposing their generative denoising process has established promising progress in specialist (e.g., depth estimation) and generalist models, the inherent gaps between a generative proce<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28201<END_URL>

Aligning Human Motion Generation with Human Perceptions\nHuman motion generation is a critical task with a wide spectrum of applications. Achieving high realism in generated motions requires naturalness, smoothness, and plausibility. However, current evaluation metrics often rely on simple heuristics or distribution distances and do not align well with human perceptions. In this work, we propose a data-driven approach to bridge this gap by introducing a large-scale human perceptual evaluation da<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29694<END_URL>

Aligning Language Models with Demonstrated Feedback\nLanguage models are aligned to emulate the collective voice of many, resulting in outputs that align with no one in particular. Steering LLMs away from generic output is possible through supervised finetuning or RLHF, but requires prohibitively large datasets for new ad-hoc tasks. We argue that it is instead possible to align an LLM to a specific setting by leveraging a very small number ($<10$) of demonstrations as feedback. Our method, Demon<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31183<END_URL>

Aligning Visual Contrastive learning models via Preference Optimization\nContrastive learning models have demonstrated impressive abilities to capture semantic similarities by aligning representations in the embedding space. However, their performance can be limited by the quality of the training data and its inherent biases. While Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) have been applied to generative models to align them with human preferences<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27829<END_URL>

Alignment without Over-optimization: Training-Free Solution for Diffusion Models\nDiffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Addressing these limitations, we propose a training-free sampling method based on Sequential Monte Carlo (SMC) to samp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27896<END_URL>

A Little Goes a Long Way: Efficient Long Context Training and Inference with Partial Contexts\nTraining and serving long-context large language models (LLMs) incurs substantial overhead. To address this, two critical steps are often required: a pretrained LLM typically undergoes a separate stage for context length extension by training on long-context data, followed by architectural modifications to reduce the overhead of KV cache during serving. This paper argues that integrating length extensi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29510<END_URL>

ALLaM: Large Language Models for Arabic and English\nIn this work, we present ALLaM: Arabic Large Language Model, a series of large language models to support the ecosystem of Arabic Language Technologies (ALT). ALLaM is carefully trained, considering the values of language alignment and transferability of knowledge at scale. The models are based on an autoregressive decoder-only architecture and are pretrained on a mixture of Arabic and English texts. We illustrate how the second-language acqui<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29915<END_URL>

Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits\nWe study the optimal batch-regret tradeoff for batch linear contextual bandits. For this problem, we design batch learning algorithms and prove that they achieve the optimal regret bounds (up to logarithmic factors) for any batch number $M$, number of actions $K$, time horizon $T$, and dimension $d$. Therefore, we establish the \emph{full-parameter-range} (almost) optimal batch-regret tradeoff for the batch linear contextu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28187<END_URL>

AlphaEdit: Null-Space Constrained Model Editing for Language Models\nLarge language models (LLMs)  often exhibit hallucinations, producing incorrect or outdated knowledge. Hence, model editing methods have emerged to enable targeted knowledge updates. To achieve this, a prevailing paradigm  is the locating-then-editing approach, which first locates influential parameters and then edits them by introducing a perturbation. While effective, current studies have demonstrated that this perturbation i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30202<END_URL>

Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data\nWe provide a framework for solving inverse problems with diffusion models learned from linearly corrupted data. Firstly, we extend the Ambient Diffusion framework to enable training directly from measurements corrupted in the Fourier domain. Subsequently, we train diffusion models for MRI with access only to Fourier subsampled multi-coil measurements at acceleration factors R$=2, 4, 6,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28241<END_URL>

A Meta-Learning Approach to Bayesian Causal Discovery\nDiscovering a unique causal structure is difficult due to both inherent identifiability issues, and the consequences of finite data.As such, uncertainty over causal structures, such as those obtained from a Bayesian posterior, are often necessary for downstream tasks.Finding an accurate approximation to this posterior is challenging, due to the large number of possible causal graphs, as well as the difficulty in the subproblem of finding pos<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28913<END_URL>

Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series\nMany real-world datasets, such as healthcare, climate, and economics, are often collected as irregular time series, which poses challenges for accurate modeling. In this paper, we propose the Amortized Control of continuous State Space Model (ACSSM) for continuous dynamical modeling of time series for irregular and discrete observations. We first present a multi-marginal Doob's $h$-transform to construct a c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30730<END_URL>

Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs\nHow to align large language models (LLMs) with user preferences from a static general dataset has been frequently studied. However, user preferences are usually personalized, changing, and diverse. This leads to the problem that the actual user preferences often do not coincide with those trained by the model developers in the practical use of LLMs. Since we cannot collect enough data and retrain for every deman<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28890<END_URL>

A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules\nTraining large models is both resource-intensive and time-consuming, making it crucial to understand the quantitative relationship between model performance and hyperparameters. In this paper, we derive an empirical law that predicts pretraining loss for large language models for every intermediate training step across various learning rate schedules, including constant, cosine, and step decay schedules. Our proposed law<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30030<END_URL>

A Multiscale Frequency Domain Causal Framework for Enhanced Pathological Analysis\nMultiple Instance Learning (MIL) in digital pathology Whole Slide Image (WSI) analysis has shown significant progress. However, due to data bias and unobservable confounders, this paradigm still faces challenges in terms of performance and interpretability. Existing MIL methods might identify patches that do not have true diagnostic significance, leading to false correlations, and experience difficulties in integr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30857<END_URL>

ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning\nIn the recent years, Physics Informed Neural Networks (PINNs) have received strong interest as a method to solve PDE driven systems, in particular for data assimilation purpose. This method is still in its infancy, with many shortcomings and failures that remain not properly understood.In this paper we propose a natural gradient approach to PINNs which contributes to speed-up and improve the accuracy of the train<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28381<END_URL>

AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies\nThe massive and large-scale design of foundational semiconductor integrated circuits (ICs) is crucial to sustaining the advancement of many emerging and future technologies, such as generative AI, 5G/6G, and quantum computing.Excitingly, recent studies have shown the great capabilities of foundational models in expediting the design of digital ICs.Yet, applying generative AI techniques to accelerate the design<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28646<END_URL>

Analysing The Spectral Biases in Generative Models\nDiffusion and GAN models have demonstrated remarkable success in synthesizing high-quality images propelling them into various real-life applications across different domains. However, it has been observed that they exhibit spectral biases that impact their ability to generate certain frequencies and makes it pretty straightforward to distinguish real images from fake ones. In this blog we analyze these models and attempt to explain the reason <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31344<END_URL>

Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching: With Insights into Other Permutation Search Methods\nRecently, Ainsworth et al. (2023) showed that using weight matching (WM) to minimize the $L_2$ distance in a permutation search of model parameters effectively identifies permutations that satisfy linear mode connectivity (LMC), where the loss along a linear path between two independently trained models with different seeds remains nearly constant. This paper analyzes <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28512<END_URL>

Analytic DAG Constraints for Differentiable DAG Learning\nRecovering underlying Directed Acyclic Graph (DAG) structures from observational data presents a formidable challenge due to the combinatorial nature of the DAG-constrained optimization problem. Recently, researchers have identified gradient vanishing as one of the primary obstacles in differentiable DAG learning and have proposed several DAG constraints to mitigate this issue. By developing the necessary theory to establish a connection <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28370<END_URL>

Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models\nMulti-modal large language models (MLLMs) have shown remarkable abilities in various visual understanding tasks. However, MLLMs still struggle with fine-grained visual recognition (FGVR), which aims to identify subordinate-level categories from images. This can negatively impact more advanced capabilities of MLLMs, such as object-centric visual question answering and reasoning. In our study<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28323<END_URL>

Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectra\nNeural scaling laws describe how the performance of deep neural networks scales with key factors such as training data size, model complexity, and training time, often following power-law behaviors over multiple orders of magnitude. Despite their empirical observation, the theoretical understanding of these scaling laws remains limited. In this work, we employ techniques from statistical mechanics to analyze one-pas<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27863<END_URL>

Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks\nAdvances in machine learning research drive progress in real-world applications. To ensure this progress, it is important to understand the potential pitfalls on the way from a novel method's success on academic benchmarks to its practical deployment. In this work, we analyze existing tabular benchmarks and find two common characteristics of tabular data in typical industrial applications that are underrepresented in th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30020<END_URL>

An Asynchronous Bundle Method for Distributed Learning Problems\nWe propose a novel asynchronous bundle method to solve distributed learning problems. Compared to existing asynchronous methods, our algorithm computes the next iterate based on a more accurate approximation of the objective function and does not require any prior  information about the maximal information delay in the system. This makes the proposed method fast and easy to tune. We prove that the algorithm converges in both determ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30025<END_URL>

An Auditing Test to Detect Behavioral Shift in Language Models\nAs language models (LMs) approach human-level performance, a comprehensive understanding of their behavior becomes crucial. This includes evaluating capabilities, biases, task performance, and alignment with societal values. Extensive initial evaluations, including red teaming and diverse benchmarking, can establish a model’s behavioral profile. However, subsequent fine-tuning or deployment modifications may alter these behaviors in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28786<END_URL>

AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents\nAutonomous agents that execute human tasks by controlling computers can enhance human productivity and application accessibility. However, progress in this field will be driven by realistic and reproducible benchmarks. We present AndroidWorld, a fully functional Android environment that provides reward signals for 116 programmatic tasks across 20 real-world Android apps. Unlike existing interactive environments, which provid<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28677<END_URL>

An Effective Manifold-based Optimization Method for Distributionally Robust Classification\nHow to promote the robustness of existing deep learning models is a  challenging problem for many practical classification tasks. Recently, Distributionally Robust Optimization (DRO) methods have shown promising potential to tackle this problem. These methods aim to construct reliable models by minimizing the worst-case risk within a local region (called ''uncertainty set'') around the empirical data dist<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28383<END_URL>

An Effective Theory of Bias Amplification\nMachine learning models may capture and amplify biases present in data, leading to disparate test performance across social groups. To better understand, evaluate, and mitigate these possible biases, a deeper theoretical understanding of how model design choices and data distribution properties could contribute to bias is needed. In this work, we contribute a precise analytical theory in the context of ridge regression, both with and without random proj<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29400<END_URL>

An Efficient Framework for Crediting Data Contributors of Diffusion Models\nAs diffusion models are deployed in real-world settings and their performance driven by training data, appraising the contribution of data contributors is crucial to creating incentives for sharing quality data and to implementing policies for data compensation. Depending on the use case, model performance corresponds to various global properties of the distribution learned by a diffusion model (e.g., overall aesthetic q<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30712<END_URL>

An Empirical Analysis of Uncertainty in Large Language Model Evaluations\nAs LLM-as-a-Judge emerges as a new paradigm for assessing large language models (LLMs), concerns have been raised regarding the alignment, bias, and stability of LLM evaluators. While substantial work has focused on alignment and bias, little research has concentrated on the stability of LLM evaluators. In this paper, we conduct extensive experiments involving 9 widely used LLM evaluators across 2 different evaluation sett<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30131<END_URL>

An Engorgio Prompt Makes Large Language Model Babble on\nAuto-regressive large language models (LLMs) have yielded impressive performance in many real-world tasks. However, the new paradigm of these LLMs also exposes novel threats. In this paper, we explore their vulnerability to inference cost attacks, where a malicious user crafts Engorgio prompts to intentionally increase the computation cost and latency of the inference process. We design Engorgio, a novel methodology, to efficiently generat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28485<END_URL>

An Evolved Universal Transformer Memory\nPrior methods propose to offset the escalating costs of modern foundation models by dropping specific parts of their contexts with hand-designed rules, while attempting to preserve their original performance. We overcome this trade-off with Neural Attention Memory Models (NAMMs), introducing a learned network for memory management that improves both the performance and efficiency of transformers. We evolve NAMMs atop pre-trained transformers to provide di<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28152<END_URL>

A new framework for evaluating model out-of-distribution generalisation for the biochemical domain\nQuantifying model generalization to out-of-distribution data has been a longstanding challenge in machine learning. Addressing this issue is crucial for leveraging machine learning in scientific discovery, where models must generalize to new molecules or materials. Current methods typically split data into train and test sets using various criteria — temporal, sequence identity, scaffold, or rando<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28260<END_URL>

A New Perspective on Shampoo's Preconditioner\nShampoo, a second-order optimization algorithm that uses a Kronecker product preconditioner, has recently received increasing attention from the machine learning community. Despite the increasing popularity of Shampoo, the theoretical foundations of its effectiveness are not well understood. The preconditioner used by Shampoo can be viewed as either an approximation of the Gauss--Newton component of the Hessian or the covariance matrix of the gradie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29069<END_URL>

An Exploration with Entropy Constrained 3D Gaussians for 2D Video Compression\n3D Gaussian Splatting (3DGS) has witnessed its rapid development in novel view synthesis, which attains high quality reconstruction and real-time rendering. At the same time, there is still a gap before implicit neural representation (INR)  can become a practical compressor due to the lack of stream decoding and real-time frame reconstruction on consumer-grade hardware. It remains a question whether the fast rendering<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30096<END_URL>

An Expressive Quantum-Driven Graph Learning Approach with Application to Mixed-integer Linear Programming\nGraph-structure data is ubiquitous, and graph learning models have recently been further extended to address complex problems like mixed-integer linear programming (MILP). However, recent literature has shown that classic graph neural networks (GNNs) suffer fundamental limitations in learning MILP graph representation, i.e., GNNs may map two different MILP graphs to the same representation.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30170<END_URL>

An Illustrated Guide to Automatic Sparse Differentiation\nIn numerous applications of machine learning, Hessians and Jacobians exhibit sparsity, a property that can be leveraged to vastly accelerate their computation. While the usage of automatic differentiation in machine learning is ubiquitous, automatic sparse differentiation (ASD) remains largely unknown. This post introduces ASD, explaining its key components and their roles in the computation of both sparse Jacobians and Hessians. We concl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31324<END_URL>

An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels\nThis work does not introduce a new method. Instead, we present an interesting finding that questions the necessity of the inductive bias of locality in modern computer vision architectures. Concretely, we find that vanilla Transformers can operate by directly treating each individual pixel as a token and achieve highly performant results. This is substantially different from the popular design in Vision Trans<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28029<END_URL>

Animate-X: Universal Character Image Animation with Enhanced Motion Representation\nCharacter image animation, which generates high-quality videos from a reference image and target pose sequence, has seen significant progress in recent years. However, most existing methods only apply to human figures, which usually do not generalize well on anthropomorphic characters commonly used in industries like gaming and entertainment. Our in-depth analysis suggests to attribute this limitation to their in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31209<END_URL>

Animate Your Thoughts: Reconstruction of Dynamic Natural Vision from Human Brain Activity\nReconstructing human dynamic vision from brain activity is a challenging task with great scientific significance.  Although prior video reconstruction methods have made substantial progress, they still suffer from several limitations, including: (1) difficulty in simultaneously reconciling semantic (e.g. categorical descriptions), structure (e.g. size and color), and consistent motion information (e.g. ord<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30544<END_URL>

An Information Criterion for Controlled Disentanglement of Multimodal Data\nMultimodal representation learning seeks to relate and decompose information inherent in multiple modalities. By disentangling modality-specific information from information that is shared across modalities, we can improve interpretability and robustness and enable downstream tasks such as the generation of counterfactual outcomes. Separating the two types of information is challenging since they are often deeply entangl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31055<END_URL>

An Intelligent Agentic System for Complex Image Restoration Problems\nReal-world image restoration (IR) is inherently complex and often requires combining multiple specialized models to address diverse degradations. Inspired by human problem-solving, we propose AgenticIR, an agentic system that mimics the human approach to image processing by following five key stages: Perception, Scheduling, Execution, Reflection, and Rescheduling. AgenticIR leverages large language models (LLMs) and vision-lan<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31076<END_URL>

An Investigation of Conformal Isometry Hypothesis for Grid Cells\nThis paper investigates the conformal isometry hypothesis as a potential explanation for hexagonal periodic patterns in grid cell response maps. The hypothesis posits that grid cell activity forms a high-dimensional vector in neural space, encoding the agent’s position in 2D physical space. As the agent moves, this vector rotates within a 2D manifold in the neural space, driven by a recurrent neural network. The conformal hypothes<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29290<END_URL>

AniSDF: Fused-Granularity Neural Surfaces with Anisotropic Encoding for High-Fidelity 3D Reconstruction\nNeural radiance fields have recently revolutionized novel-view synthesis and achieved high-fidelity renderings. However, these methods sacrifice the geometry for the rendering quality, limiting their further applications including relighting and deformation. How to synthesize photo-realistic rendering while reconstructing accurate geometry remains an unsolved problem. In this work, we present<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27938<END_URL>

AnoLLM: Large Language Models for Tabular Anomaly Detection\nWe introduce AnoLLM, a novel framework that leverages large language models (LLMs) for unsupervised tabular anomaly detection.  By converting tabular data into a standardized text format, we further adapt a pre-trained LLM with this serialized data, and assign anomaly scores based on the negative log likelihood generated by the LLM. Unlike traditional methods that can require extensive feature engineering, and often lose textual inform<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30820<END_URL>

A Non-Contrastive Learning Framework for Sequential Recommendation with Preference-Preserving Profile Generation\nContrastive Learning (CL) proves to be effective for learning generalizable user representations in Sequential Recommendation (SR), but it suffers from high computational costs due to its reliance on negative samples. To overcome this limitation, we propose the first Non-Contrastive Learning (NCL) framework for SR, which eliminates computational overhead of identifying and generating<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30042<END_URL>

An Online Learning Theory of Trading-Volume Maximization\nWe explore brokerage between traders in an online learning framework.At any round $t$, two traders meet to exchange an asset, provided the exchange is mutually beneficial.The broker proposes a trading price, and each trader tries to sell their asset or buy the asset from the other party, depending on whether the price is higher or lower than their private valuations.A trade happens if one trader is willing to sell and the other is willing<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29793<END_URL>

Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions\nMultiple-choice question answering (MCQA) is a key competence of performant transformer language models that is tested by mainstream benchmarks. However, recent evidence shows that models can have quite a range of performance, particularly when the task format is diversified slightly (such as by shuffling answer choice order). In this work we ask: how do successful models perform formatted MCQA? We employ vocabulary p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30890<END_URL>

Anti-Exposure Bias in Diffusion Models via Prompt Learning\nDiffusion models (DMs) have achieved record-breaking performance in image generation tasks.Nevertheless, in practice, the training-sampling discrepancy, caused by score estimation error and discretization error, limits the modeling ability of DMs, a phenomenon known as exposure bias.To alleviate such exposure bias and further improve the generative performance, we put forward a prompt learning framework built upon a lightweight prompt p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29914<END_URL>

An undetectable watermark for generative image models\nWe present the first undetectable watermarking scheme for generative image models._Undetectability_ ensures that no efficient adversary can distinguish between watermarked and un-watermarked images, even after making many adaptive queries.In particular, an undetectable watermark does not degrade image quality under any efficiently computable metric.Our scheme works by selecting the initial latents of a diffusion model using a pseudorandom er<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28619<END_URL>

AnyPrefer: An Automatic Framework for Preference Data Synthesis\nHigh-quality preference data is essential for aligning foundation models with human values through preference learning. However, manual annotation of such data is often time-consuming and costly. Recent methods adopt a self-rewarding approach, where the target model generates and annotates its own preference data, but this can lead to inaccuracies due to the reward model sharing weights with the target model, amplifying inherent bi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29342<END_URL>

Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning\nModel-based methods in reinforcement learning offer a promising approach to enhance data efficiency by facilitating policy exploration within a dynamics model. However, accurately predicting sequential steps in the dynamics model remains a challenge due to the bootstrapping prediction, which attributes the next state to the prediction of the current state. This leads to accumulated errors during mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30099<END_URL>

APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding\nMany modern language model applications, such as RAG and in-context learning, require the efficient combination of multiple external contexts to generate a response. Directly incorporating these contexts sequentially presents two challenges: (i) re-encoding each combined selection of contexts for every request creates a significant computational burden. (ii) concatenating selected contexts into a single sequence <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27725<END_URL>

A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language\nIncrease in data, size, or compute can lead to sudden learning of specific capabilities by a neural network---a phenomenon often called "emergence". Beyond scientific understanding, establishing the causal factors underlying such emergent capabilities is crucial to enable risk regulation frameworks for AI. In this work, we seek inspiration from study of emergent properties in other fields and propose a phenome<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31234<END_URL>

A Periodic Bayesian Flow for Material Generation\nGenerative modeling of crystal data distribution is an important yet challenging task due to the unique periodic physical symmetry of crystals. Diffusion-based methods have shown early promise in modeling crystal distribution. More recently, Bayesian Flow Networks were introduced to aggregate noisy latent variables, resulting in a variance-reduced parameter space that has been shown to be advantageous for modeling Euclidean data distributions wit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29962<END_URL>

API Pack: A Massive Multi-Programming Language Dataset for API Call Generation\nWe introduce API Pack, a massive multi-programming language dataset containing more than one million instruction-API calls that seeks to improve the API call generation capabilities of large language models. Our evaluation highlights three key findings: First, fine-tuning on API Pack enables open-source models to outperform both GPT-3.5 and GPT-4 in generating code for completely new API calls. We demonstrate this by<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28893<END_URL>

A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergence\nPolicy gradient methods have become a staple of any single-agent reinforcement learning toolbox, due to their combination of desirable properties: iterate convergence, efficient use of stochastic trajectory feedback, and theoretically-sound avoidance of importance sampling corrections. In multi-agent imperfect-information settings (extensive-form games), however, it is still unknown whether the same <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29215<END_URL>

Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming\nLeveraging machine learning (ML) to predict an initial solution for mixed-integer linear programming (MILP) has gained considerable popularity in recent years. These methods predict a solution and fix a subset of variables to reduce the problem dimension. Then, they solve the reduced problem to obtain the final solutions. However, directly fixing variable values can lead to low-qualit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28474<END_URL>

Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding\nNeural compression has brought tremendous progress in designing lossy compressors with good rate-distortion (RD) performance at low complexity. Thus far, neural compression design involves transforming the source to a latent vector, which is then rounded to integers and entropy coded. While this approach has been shown to be optimal on a few specific sources, we show that it can be highly sub-optimal on synth<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29503<END_URL>

Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence\nUncertainty quantification is an important prerequisite for the deployment of deep learning models in safety-critical areas. Yet, this hinges on the uncertainty estimates being useful to the extent the predictive prediction intervals are well-calibrated and sharp. In the absence of inherent uncertainty estimates (e.g. pretrained models), popular approaches that operate post-hoc include Laplace’s me<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27902<END_URL>

Approximation algorithms for combinatorial optimization with predictions\nWe initiate a systematic study of utilizing predictions to improve over approximation guarantees of classic algorithms, without increasing the running time. We propose a generic method for a wide class of optimization problems that ask to select a feasible subset of input items of minimal (or maximal) total weight. This gives simple (near-)linear-time algorithms for, e.g., Vertex Cover, Steiner Tree, Minimum Weight Perfect<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30643<END_URL>

A primer on analytical learning dynamics of nonlinear neural networks\nThe learning dynamics of neural networks—in particular, how parameters change over time during training—describe how data, architecture, and algorithm interact in time to produce a trained neural network model. Characterizing these dynamics, in general, remains an open problem in machine learning, but, handily, restricting the setting allows careful empirical studies and even analytical results. In this blog post, we review a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31338<END_URL>

A Probabilistic Perspective on Unlearning and Alignment for Large Language Models\nComprehensive evaluation of Large Language Models (LLMs) is an open research problem. Existing evaluations rely on deterministic point estimates generated via greedy decoding. However, we find that deterministic evaluations fail to capture the whole output distribution of a model, yielding inaccurate estimations of model capabilities. This is particularly problematic in critical contexts such as unlearning and ali<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30975<END_URL>

A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning\nQuantum-centric supercomputing presents a compelling framework for large-scale hybrid quantum-classical tasks. Although quantum machine learning (QML) offers theoretical benefits in various applications, challenges such as large-size data encoding in the input stage and the reliance on quantum resources in the inference stage limit its practicality for tasks like fine-tuning large language models (LLMs). Quantum pa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29122<END_URL>

ARB-LLM: Alternating Refined Binarizations for Large Language Models\nLarge Language Models (LLMs) have greatly pushed forward advancements in natural language processing, yet their high memory and computational demands hinder practical deployment. Binarization, as an effective compression technique, can shrink model weights to just 1 bit, significantly reducing the high demands on computation and memory. However, current binarization methods struggle to narrow the distribution gap between binar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29217<END_URL>

Are Large Vision Language Models Good Game Players?\nLarge Vision Language Models (LVLMs) have demonstrated remarkable abilities in understanding and reasoning about both visual and textual information. However, existing evaluation methods for LVLMs, primarily based on benchmarks like Visual Question Answering and image captioning, often fail to capture the full scope of LVLMs' capabilities. These benchmarks are limited by issues such as inadequate assessment of detailed visual perception, data <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29074<END_URL>

Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?\nHumans exhibit remarkable compositional reasoning by integrating knowledge from various sources. For example, if someone learns ( B = f(A) ) from one source and ( C = g(B) ) from another, they can deduce the value of ( C ) by reasoning ( C=g(B)=g(f(A)) ) even without encountering ( ABC ) together, showcasing the generalization ability of human intelligence.  In this paper, we introduce a synthetic learning task,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31196<END_URL>

A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics\nBy incorporating physical consistency as inductive bias, deep neural networks display increased generalization capabilities and data efficiency in learning nonlinear dynamic models. However, the complexity of these models generally increases with the system dimensionality, requiring larger datasets, more complex deep networks, and significant computational effort.We propose a novel geometric network architecture to learn phys<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29638<END_URL>

Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count\nTransformers often struggle with *length generalization*, meaning they fail to generalize to sequences longer than those encountered during training. While arithmetic tasks are commonly used to study length generalization, certain tasks are considered notoriously difficult, e.g., multi-operand addition (requiring generalization over both the number of operands and their lengths) and multiplication (requiring generali<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28933<END_URL>

Arithmetic Without Algorithms: Language Models Solve Math with a Bag of Heuristics\nDo large language models (LLMs) solve reasoning tasks by learning robust generalizable algorithms, or do they memorize training data? To investigate this question, we use arithmetic reasoning as a representative task. Using causal analysis, we identify a subset of the model (a circuit) that explains most of the model's behavior for basic arithmetic logic and examine its functionality. By zooming in on the level o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29843<END_URL>

ARLON: Boosting Diffusion Transformers with Autoregressive Models for Long Video Generation\nText-to-video (T2V) models have recently undergone rapid and substantial advancements. Nevertheless, due to limitations in data and computational resources, achieving efficient generation of long videos with rich motion dynamics remains a significant challenge. To generate high-quality, dynamic, and temporally consistent long videos, this paper presents ARLON,  a novel framework that boosts diffusion Tra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30742<END_URL>

A Robust Method to Discover Causal or Anticausal Relation\nUnderstanding whether the data generative process follows causal or anticausal relations is important for many applications. Existing causal discovery methods struggle with high-dimensional perceptual data such as images. Moreover, they require well-labeled data, which may not be feasible due to measurement error. In this paper, we propose a robust method to detect whether the data generative process is causal or anticausal. To determine<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29717<END_URL>

Articulate-Anything:  Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model\nInteractive 3D simulated objects are crucial in AR/VR, animations, and robotics, driving immersive experiences and advanced automation.However, creating these articulated objects requires extensive human effort and expertise, limiting their broader applications. To overcome this challenge, we present Articulate-Anything, a system that automates the articulation of diverse, complex objects from<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28149<END_URL>

ARTIFICIAL KURAMOTO OSCILLATORY NEURONS\nIt has long been known in both neuroscience and AI that ``binding'' between neurons leads to a form of competitive learning where representations are compressed in order to represent more abstract concepts in deeper layers of the network. More recently, it was also hypothesized that dynamic (spatiotemporal) representations play an important role in both neuroscience and AI. Building on these ideas, we introduce Artificial Kuramoto Oscillatory Neurons (*AK<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28387<END_URL>

A Sanity Check for AI-generated Image Detection\nWith the rapid development of generative models, discerning AI-generated content has evoked increasing attention from both industry and academia. In this paper, we conduct a sanity check on "whether the task of AI-generated image detection has been solved". To start with, we present Chameleon dataset, consisting AI-generated images that are genuinely challenging for human perception. To quantify the generalization of existing methods, we evaluate <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29835<END_URL>

A Second-Order Perspective on Model Compositionality and Incremental Learning\nThe fine-tuning of deep pre-trained models has revealed compositional properties, with multiple specialized modules that can be arbitrarily composed into a single, multi-task model. However, identifying the conditions that promote compositionality remains an open issue, with recent efforts concentrating mainly on linearized networks. We conduct a theoretical study that attempts to demystify compositionality in standar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29821<END_URL>

A Simple Approach to Unifying Diffusion-based Conditional Generation\nRecent progress in image generation has sparked research into controlling these models through condition signals, with various methods addressing specific challenges in conditional generation. Instead of proposing another specialized technique, we introduce a simple, unified framework to handle diverse conditional generation tasks involving a specific image-condition correlation. By learning a joint distribution over a correla<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28070<END_URL>

A Simple Baseline for Multivariate Time Series Forecasting\nThe versatility of large language models has led to intensive ongoing work focused on adaptations to other modalities. This can involve moderate modifications of an existing model, piggybacking on the language model's capabilities to train multimodal models or even starting with pre-trained checkpoints and attaching specialized adapters to recast a new modality (e.g., time-series) as ``language''. This latter approach, prominent in a gr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28373<END_URL>

A Simple Diffusion Transformer on Unified Video, 3D, and Game Field Generation\nThe probabilistic field models the distribution of continuous functions defined over metric spaces. While these models hold great potential for unifying data generation across various modalities, including images, videos, and 3D geometry, they still struggle with long-context generation beyond simple examples. This limitation can be attributed to their MLP architecture, which lacks sufficient inductive bias to captur<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27874<END_URL>

A Simple Framework for Open-Vocabulary Zero-Shot Segmentation\nZero-shot classification capabilities naturally arise in models trained within a vision-language contrastive framework. Despite their classification prowess, these models struggle in dense tasks like zero-shot open-vocabulary segmentation. This deficiency is often attributed to the absence of localization cues in captions and the intertwined nature of the learning process, which encompasses both image/text representation learning and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29668<END_URL>

A Simple yet Effective $\Delta\Delta G$ Predictor is An Unsupervised Antibody Optimizer and Explainer\nThe proteins that exist today have been optimized over billions of years of natural evolution, during which nature creates random mutations and selects them. The discovery of functionally promising mutations is challenged by the limited evolutionary accessible regions, i.e., only a small region on the fitness landscape is beneficial. There have been numerous priors used to constrain protein evo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30140<END_URL>

A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals\nIn this paper, we present empirical evidence of skills and directed exploration emerging from a simple RL algorithm long before any successful trials are observed. For example, in a manipulation task, the agent is given a single observation of the goal state (see Fig. 1) and learns skills, first for moving its end-effector, then for pushing the block, and finally for pic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27798<END_URL>

A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery\nReal-world data often violates the equal-variance assumption (homoscedasticity), making it essential to account for heteroscedastic noise in causal discovery. In this work, we explore heteroscedastic symmetric noise models (HSNMs), where the effect $Y$ is modeled as $Y = f(X) + \sigma(X)N$, with $X$ as the cause and $N$ as independent noise following a symmetric distribution. We introduce a novel criterion for i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27676<END_URL>

A Soft and Fast Pattern Matcher for Billion-Scale Corpus Searches\nResearchers and practitioners in natural language processing and computational linguistics frequently observe and analyze the real language usage in large-scale corpora.For that purpose, they often employ off-the-shelf pattern-matching tools, such as grep, and keyword-in-context concordancers, which is widely used in corpus linguistics for gathering examples.Nonetheless, these existing techniques rely on surface-level string matc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29709<END_URL>

A Solvable Attention for Neural Scaling Laws\nTransformers and many other deep learning models are empirically shown to predictably enhance their performance as a power law in training time, model size, or the number of training data points, which is termed as the neural scaling law. This paper studies this intriguing phenomenon particularly for the transformer architecture in theoretical setups. Specifically, we propose a framework for self-attention, the underpinning block of transformer, to l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27834<END_URL>

A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation\nThis work tackles the information loss bottleneck of vector-quantization (VQ) autoregressive image generation by introducing a novel model architecture called the 2-Dimensional Autoregression (DnD) Transformer. The DnD-Transformer predicts more codes for an image by introducing a new autoregression direction, \textit{model depth}, along with the sequence length direction.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27817<END_URL>

AssembleFlow: Rigid Flow Matching with Inertial Frames for Molecular Assembly\nMolecular assembly, where a cluster of rigid molecules aggregated into strongly correlated forms, is fundamental to determining the properties of materials. However, traditional numerical methods for simulating this process are computationally expensive, and existing generative models on material generation overlook the rigidity inherent in molecular structures, leading to unwanted distortions and invalid internal str<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28629<END_URL>

As Simple as Fine-tuning: LLM Alignment via Bidirectional Negative Feedback Loss\nDirect Preference Optimization (DPO) has emerged as a more computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) with Proximal Policy Optimization (PPO), eliminating the need for reward models and online sampling. Despite these benefits, DPO and its variants remain sensitive to hyper-parameters and prone to instability, particularly on mathematical datasets. We argue that these <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28849<END_URL>

Associative memory and dead neurons\nIn ``Large Associative Memory Problem in Neurobiology and Machine Learning,'' Dmitry Krotov and John Hopfield introduced a general technique for the systematic construction of neural ordinary differential equations with non-increasing energy or Lyapunov function. We study this energy function and identify that it is vulnerable to the problem of dead neurons. Each point in the state space where the neuron dies is contained in a non-compact region with constant<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28453<END_URL>

A Statistical Approach for Controlled Training Data Detection\nDetecting training data for large language models (LLMs) is receiving growing attention, especially in applications requiring high reliability. While numerous efforts have been made to address this issue, they typically focus on accuracy without ensuring controllable results.To fill this gap, we propose **K**nockoff Inference-based **T**raining data **D**etector (KTD), a novel method that achieves rigorous false discovery rate (FDR) <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29318<END_URL>

A Statistical Framework for Ranking LLM-based Chatbots\nLarge language models (LLMs) have transformed natural language processing, with frameworks like Chatbot Arena providing pioneering platforms for evaluating these models. By facilitating millions of pairwise comparisons based on human judgments, Chatbot Arena has become a cornerstone in LLM evaluation, offering rich datasets for ranking models in open-ended conversational tasks. Building upon this foundation, we propose a statistical framewo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28215<END_URL>

A Stochastic Approach to the Subset Selection Problem via Mirror Descent\nThe subset selection problem is fundamental in machine learning and other fields of computer science.We introduce a stochastic formulation for the minimum cost subset selection problem in a black box setting, in which only the subset metric value is available.Subsequently, we can handle two-stage schemes, with an outer subset-selection component and an inner subset cost evaluation component. We propose formulating the subs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30952<END_URL>

ASTrA: Adversarial Self-supervised Training with Adaptive-Attacks\nExisting self-supervised adversarial training (self-AT) methods rely on handcraftedadversarial attack strategies for PGD attacks, which fail to adapt tothe evolving learning dynamics of the model and do not account for instance specificcharacteristics of images. This results in sub-optimal adversarial robustnessand limits the alignment between clean and adversarial data distributions.To address this, we propose ASTrA (Adversarial<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29209<END_URL>

AstroCompress: A benchmark dataset for multi-purpose compression of astronomical data\nThe site conditions that make astronomical observatories in space and on the ground so desirable---cold and dark---demand a physical remoteness that leads to limited data transmission capabilities. Such transmission limitations directly bottleneck the amount of data acquired and in an era of costly modern observatories, any improvements in lossless data compression has the potential scale to billions of dollar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28585<END_URL>

Asymmetric Factorized Bilinear Operation for Vision Transformer\nAs a core component of Transformer-like deep architectures, a feed-forward network (FFN) for channel mixing is responsible for learning features of each token. Recent works show channel mixing can be enhanced by increasing computational burden or can be slimmed at the sacrifice of performance. Although some efforts have been made, existing works are still struggling to solve the paradox of performance and complexity trade-offs. In <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29941<END_URL>

Asymptotic Analysis of Two-Layer Neural Networks after One Gradient Step under Gaussian Mixtures Data with Structure\nIn this work, we study the training and generalization performance of two-layer neural networks (NNs) after one gradient descent step under structured data modeled by Gaussian mixtures. While previous research has extensively analyzed this model under isotropic data assumption, such simplifications overlook the complexities inherent in real-world datasets. Our work addresses this<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28057<END_URL>

Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis\nTo improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a global model through collaboration among $N$ agents using policy gradient (PG) updates. To address the challenge of lagged policies in asynchronous settings, we design a delay-adaptive lookahead technique \<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30959<END_URL>

A Theoretical Analysis of Self-Supervised Learning for Vision Transformers\nSelf-supervised learning has become a cornerstone in computer vision, primarily divided into reconstruction-based methods like masked autoencoders (MAE) and discriminative methods such as contrastive learning (CL).  Recent empirical observations reveal that MAE and CL capture different types of representations: CL tends to focus on global patterns, while MAE adeptly captures  **both global and subtle local** information <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30614<END_URL>

A Theoretical Framework for Partially-Observed Reward States in RLHF\nThe growing deployment of reinforcement learning from human feedback (RLHF) calls for a deeper theoretical investigation of its underlying models. The prevalent models of RLHF do not account for neuroscience-backed, partially-observed "internal states'' that can affect human feedback, nor do they accommodate intermediate feedback during an interaction. Both of these can be instrumental in speeding up learning and improving ali<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29804<END_URL>

A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules\nGraph neural networks (GNNs) -- learn graph representations by exploiting graph's sparsity, connectivity, and symmetries -- have become indispensable for learning geometric data like molecules. However, the most used graphs (e.g., radial cutoff graphs) in molecular modeling lack theoretical guarantees for achieving connectivity and sparsity simultaneously, which are essential for the performance and scalab<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29832<END_URL>

A Theoretical Perspective: When and How Self-consuming Training Loops Generalize\nHigh-quality data is essential for training large generative models, yet the vast reservoir of real data available online has become nearly depleted. Consequently, models increasingly generate their own data for further training, forming Self-consuming Training Loops (STLs). However, the empirical results have been strikingly inconsistent: some models degrade or even collapse, while others successfully avoid these <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29338<END_URL>

A Theory for Token-Level Harmonization in Retrieval-Augmented Generation\nRetrieval-augmented generation (RAG) utilizes retrieved texts to enhance large language models (LLMs). Studies show that while RAG provides valuable external information (benefit), it may also mislead LLMs (detriment) with noisy or incorrect retrieved texts. Although many existing methods attempt to preserve benefit and avoid detriment, they lack a theoretical explanation for RAG. The benefit and detriment in the next toke<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28042<END_URL>

A Theory of Initialisation's Impact on Specialisation\nPrior work has demonstrated a consistent tendency in neural networks engaged in continual learning tasks, wherein intermediate task similarity results in the highest levels of catastrophic interference. This phenomenon is attributed to the network's tendency to reuse learned features across tasks. However, this explanation heavily relies on the premise that neuron specialisation occurs, i.e. the emergence of localised representations. Our in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29650<END_URL>

A Tight Convergence Analysis of Inexact Stochastic Proximal Point Algorithm for Stochastic Composite Optimization Problems\nThe \textbf{i}nexact \textbf{s}tochastic \textbf{p}roximal \textbf{p}oint \textbf{a}lgorithm (isPPA) is popular for solving stochastic composite optimization problems with many applications in machine learning. While the convergence theory of the (inexact) PPA has been well established, the known convergence guarantees of isPPA require restrictive assumptions. In this paper<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28431<END_URL>

Atlas Gaussians Diffusion for 3D Generation\nUsing the latent diffusion model has proven effective in developing novel 3D generation techniques. To harness the latent diffusion model, a key challenge is designing a high-fidelity and efficient representation that links the latent space and the 3D space. In this paper, we introduce Atlas Gaussians, a novel representation for feed-forward native 3D generation. Atlas Gaussians represent a shape as the union of local patches, and each patch can decod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30246<END_URL>

Atomas: Hierarchical Adaptive Alignment on Molecule-Text for Unified Molecule Understanding and Generation\nMolecule-and-text cross-modal representation learning has emerged as a promising direction for enhancing the quality of molecular representation, thereby improving performance in various scientific fields. However, most approaches employ a global alignment approach to learn the knowledge from different modalities that may fail to capture fine-grained information, such as molecule-and-text <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28439<END_URL>

AtomSurf: Surface Representation for Learning on Protein Structures\nWhile there has been significant progress in evaluating and comparing different representations for learning on protein data, the role of surface-based learning approaches remains not well-understood. In particular, there is a lack of direct and fair benchmark comparison between the best available surface-based learning methods against alternative representations such as graphs. Moreover, the few existing surface-based approach<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30633<END_URL>

A Training-Free Sub-quadratic Cost Transformer Model Serving Framework with Hierarchically Pruned Attention\nIn modern large language models (LLMs), increasing the context length is crucial for improving comprehension and coherence in long-context, multi-modal, and retrieval-augmented language generation. While many recent transformer models attempt to extend their context length over a million tokens, they remain impractical due to the quadratic time and space complexities.Although recent works<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29752<END_URL>

A Transfer Attack to Image Watermarks\nWatermark has been widely deployed by industry to detect AI-generated images. The robustness of such watermark-based detector against evasion attacks in the white-box and black-box settings is well understood in the literature. However, the robustness in the no-box setting is much less understood. In this work, we propose a new transfer evasion attack to image watermark in the no-box setting. Our transfer attack adds a perturbation to a watermarked image to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29465<END_URL>

A transfer learning framework for weak to strong generalization\nModern large language model (LLM) alignment techniques rely on human feedback, but it is unclear whether the techniques fundamentally limit the capabilities of aligned LLMs. In particular, it is unclear whether it is possible to align (stronger) LLMs with superhuman capabilities with (weaker) human feedback *without degrading their capabilities*. This is an instance of the weak-to-strong generalization problem: using weaker (less c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29741<END_URL>

A Truncated Newton Method for Optimal Transport\nDeveloping a contemporary optimal transport (OT) solver requires navigating trade-offs among several critical requirements: GPU parallelization, scalability to high-dimensional problems, theoretical convergence guarantees, empirical performance in terms of precision versus runtime, and numerical stability in practice. With these challenges in mind, we introduce a specialized truncated Newton algorithm for entropic regularized OT. In addition to pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28814<END_URL>

Attention as a Hypernetwork\nTransformers can under some circumstances generalize to novel problem instances whose constituent parts might have been encountered during training but whose compositions have not.What mechanisms underlie this ability for compositional generalization?By reformulating multi-head attention as a hypernetwork, we reveal that a composable, low-dimensional latent code specifies key-query specific operations.We find empirically that this latent code is predictive of the sub<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29433<END_URL>

Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers\nInformation retrieval (IR) systems have played a vital role in modern digital life and have cemented their continued usefulness in this new era of generative AI via retrieval-augmented generation. With strong language processing capabilities and remarkable versatility, large language models (LLMs) have become popular choices for zero-shot re-ranking in IR systems. So far, LLM-based re-ranking methods rely on strong generat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27696<END_URL>

Attention layers provably solve single-location regression\nAttention-based models, such as Transformer, excel across various tasks but lack a comprehensive theoretical understanding, especially regarding token-wise sparsity and internal linear representations. To address this gap, we introduce the single-location regression task, where only one token in a sequence determines the output, and its position is a latent random variable, retrievable via a linear projection of the input. To solve this<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30454<END_URL>

Attention with Markov: A Curious Case of Single-layer Transformers\nAttention-based transformers have achieved tremendous success across a variety of disciplines including natural languages. To deepen our understanding of their sequential modeling capabilities, there is a growing interest in using Markov input processes to study them. A key finding is that when trained on first-order Markov chains, transformers with two or more layers consistently develop an induction head mechanism to estimate <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29578<END_URL>

AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution\nThe influence of contextual input on the behavior of large language models (LLMs) has prompted the development of context attribution methods that aim to quantify each context span's effect on an LLM's generations. The leave-one-out (LOO) error, which measures the change in the likelihood of the LLM's response when a given span of the context is removed, provides a principled way to perform context attribu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30669<END_URL>

Attribute-based Visual Reprogramming for Image Classification with CLIP\n*Visual reprogramming* (VR) reuses pre-trained vision models for downstream image classification tasks by adding trainable noise patterns to inputs. When applied to vision-language models (e.g., CLIP), existing VR approaches follow the same pipeline used in vision models (e.g., ResNet, ViT), where ground-truth class labels are inserted into fixed text templates to guide the optimization of VR patterns. This label-based appr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28650<END_URL>

Attributing Culture-Conditioned Generations to Pretraining Corpora\nIn open-ended generative tasks such as narrative writing or dialog interaction, large language models are known to manifest culture biases, showing inadequate knowledge and producing templated generations on less prevalent cultures. Previous works suggest that such biased generations are due to the uneven representation of each culture in pretraining corpora of the language models. In thiswork, we study how pretraining data lead<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29287<END_URL>

Audio Large Language Models Can Be Descriptive Speech Quality Evaluators\nAn ideal multimodal agent should be aware of the quality of its input modalities. Recent advances have enabled large language models (LLMs) to incorporate auditory systems for handling various speech-related tasks. However, most audio LLMs remain unaware of the quality of the speech they process. This limitation arises because speech quality evaluation is typically excluded from multi-task training due to the lack of suita<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29492<END_URL>

AugKD: Ingenious Augmentations Empower Knowledge Distillation for Image Super-Resolution\nKnowledge distillation (KD) compresses deep neural networks by transferring task-related knowledge from cumbersome pre-trained teacher models to more compact student models. However, vanilla KD for image super-resolution (SR) networks only yields limited improvements due to the inherent nature of SR tasks, where the outputs of teacher models are noisy approximations of high-quality label images. In this wor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30646<END_URL>

A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations\nIn subsurface imaging, learning the mapping from velocity maps to seismic waveforms (forward problem) and waveforms to velocity (inverse problem) is important for several applications. While traditional techniques for solving forward and inverse problems are computationally prohibitive, there is a growing interest to leverage recent advances in deep learning to learn the mapping between ve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27736<END_URL>

A Unified Theory of Quantum Neural Network Loss Landscapes\nClassical neural networks with random initialization famously behave as Gaussian processes in the limit of many neurons, which allows one to completely characterize their training and generalization behavior. No such general understanding exists for quantum neural networks (QNNs), which—outside of certain special cases—are known to not behave as Gaussian processes when randomly initialized. We here prove that QNNs and their first two de<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28843<END_URL>

A Unifying Framework for Representation Learning\nAs the field of unsupervised learning grows, there has been a proliferation of different loss functions to solve different classes of problems. We find that a large collection of modern loss functions can be generalized by a single equation rooted in information theory. In particular, we introduce I-Con, a framework that shows that several broad classes of machine learning methods are precisely minimizing an integrated KL divergence between two m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29352<END_URL>

AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark\nVideo detailed captioning is a key task which aims to generate comprehensive and coherent textual descriptions of video content, benefiting both video understanding and generation. In this paper, we propose AuroraCap, a video captioner based on a large multimodal model. We follow the simplest architecture design without additional parameters for temporal modeling. To address the overhead caused by lengthy video seque<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28051<END_URL>

AutoBencher: Towards Declarative Benchmark Construction\nWe present AutoBencher, a declarative framework for automatic benchmark construction, and use it to scalably discover novel insights and vulnerabilities of existing language models. Concretely, given a few desiderata of benchmarks (e.g., question difficulty, topic salience), we operationalize each desideratum and cast benchmark creation as an optimization problem. Specifically, we experiment with two settings with different optimization ob<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27700<END_URL>

Autocorrelation Matters: Understanding the Role of Initialization Schemes for State Space Models\nCurrent methods for initializing state space model (SSM) parameters primarily rely on the HiPPO framework \citep{gu2023how}, which is based on online function approximation with the SSM kernel basis. However, the HiPPO framework does not explicitly account for the effects of the temporal structures of input sequences on the optimization of SSMs.In this paper, we take a further step to investigate th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28120<END_URL>

AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs\nJailbreak attacks serve as essential red-teaming tools, proactively assessing whether LLMs can behave responsibly and safely in adversarial environments. Despite diverse strategies (e.g., cipher, low-resource language, persuasions, and so on) that have been proposed and shown success, these strategies are still manually designed, limiting their scope and effectiveness as a red-teaming tool. In this paper, we propose<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29096<END_URL>

Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation\nWhile retrieval augmented generation (RAG) has been shown to enhance factuality of large language model (LLM) outputs, LLMs still suffer from hallucination, generating incorrect or irrelevant information. One common detection strategy involves prompting the LLM again to assess whether its response is grounded in the retrieved evidence, but this approach is costly. Alternatively, lightwei<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27875<END_URL>

AutoG: Towards automatic graph construction from tabular data\nRecent years have witnessed significant advancements in graph machine learning, with its applications spanning numerous domains. However, the focus has predominantly been on developing powerful models, often overlooking a crucial initial step: constructing suitable graphs from common data formats, such as tabular data.This construction process is fundamental to applying graph-based models, yet it remains largely understudied and lack<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28741<END_URL>

Automated Design of Agentic Systems\nResearchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thought, Self-Reflection, Toolformer). However, the history of machine learning teaches us that hand-designed solutions are eventually replaced by learned solutions. We describe a newly forming research area, Automated Design of Agentic Systems (ADAS), which aims to automatically create<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28073<END_URL>

Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models\nFine-tuning text-to-image diffusion models with human feedback is an effective method for aligning model behavior with human intentions. However, this alignment process often suffers from slow convergence due to the large size and noise present in human feedback datasets. In this work, we propose FiFA, a novel automated data filtering algorithm designed to enhance the fine-tuning of diffusion models using hum<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30748<END_URL>

Automated Proof Generation for Rust Code via Self-Evolution\nEnsuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pressing need for automation. The primary obstacle lies in the severe lack of data — there is much less proof than code for LLMs to train upon. In this paper, we introduce SAFE, a framework thatovercomes the lack of human-written proof to ena<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31144<END_URL>

Automatic Curriculum Expert Iteration for Reliable LLM Reasoning\nHallucinations (i.e., generating plausible but inaccurate content) and laziness (i.e. excessive refusals or defaulting to "I don't know") persist as major challenges in LLM reasoning. Current efforts to reduce hallucinations primarily focus on factual errors in knowledge-grounded tasks, often neglecting hallucinations related to faulty reasoning. Meanwhile, some approaches render LLMs overly conservative, limiting their problem-so<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31053<END_URL>

Autonomous agents from automatic reward modeling and planning\nLarge language models (LLMs) have demonstrated remarkable capabilities across a range of text-generation tasks. However, LLMs still struggle with problems requiring multi-step decision-making and environmental feedback, such as online shopping, scientific reasoning, and mathematical problem-solving. Unlike pure text data, collecting large-scale decision-making data is challenging. Moreover, many powerful LLMs are only accessible thro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27820<END_URL>

Autoregressive Pretraining with Mamba in Vision\nThe vision community has started to build with the recently developed state space model, Mamba, as the new backbone for a range of tasks. This paper shows that Mamba's visual capability can be significantly enhanced through autoregressive pretraining, a direction not previously explored. Efficiency-wise, the autoregressive nature can well capitalize on the Mamba's unidirectional recurrent structure, enabling faster overall training speed and reduc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29755<END_URL>

Autoregressive Transformers are Zero-Shot Video Imitators\nPeople interact with the real-world largely dependent on visual signal, which are ubiquitous and illustrate detailed demonstrations. In this paper, we explore utilizing visual signals as a new interface for models to interact with the environment. Specifically, we choose videos as a representative visual signal. And by training autoregressive Transformers on video datasets in a self-supervised objective, we find that the model emerges a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27826<END_URL>

Autoregressive Video Generation without Vector Quantization\nGenerating a video causally in an autoregressive manner is considered a promising path toward infinite video generation in a flexible context. Prior autoregressive approaches typically rely on vector quantization to convert a video into a discrete-valued space, which could raise challenges in efficiency when modeling long videos. In this work, we propose a novel approach that enables autoregressive video generation without vector quant<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30117<END_URL>

AutoUAD: Hyper-parameter Optimization for Unsupervised Anomaly Detection\nUnsupervised anomaly detection (UAD) has important applications in diverse fields such as manufacturing industry and medical diagnosis. In the past decades, although numerous insightful and effective UAD methods have been proposed, it remains a huge challenge to tune the hyper-parameters of each method and select the most appropriate method among many candidates for a specific dataset, due to the absence of labeled anomali<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30384<END_URL>

AvatarGO: Zero-shot 4D Human-Object Interaction Generation and Animation\nRecent advancements in diffusion models have led to significant improvements in the generation and animation of 4D full-body human-object interactions (HOI). Nevertheless, existing methods primarily focus on SMPL-based motion generation, which is limited by the scarcity of realistic large-scale interaction data. This constraint affects their ability to create everyday HOI scenes. This paper addresses this challenge using a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29508<END_URL>

AVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models\nFollowing the success of Large Language Models (LLMs), expanding their boundaries to new modalities represents a significant paradigm shift in multimodal understanding. Human perception is inherently multimodal, relying not only on text but also on auditory and visual cues for a complete understanding of the world. In recognition of this fact, audio-visual LLMs have recently emerged. Despite promising develop<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28638<END_URL>

A Visual Dive into Conditional Flow Matching\nConditional flow matching was introduced by three simultaneous papers at ICLR 2023, through different approaches (conditional matching, rectifying flows and stochastic interpolants). In this blog post, we provide self-contained explanations and visualizations to understand standard flow techniques (Part 1) and conditional flow matching (Part 2). In addition we provide insights to grab new intuition on conditional flow matching (Part 3) .<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31356<END_URL>

A Watermark for Order-Agnostic Language Models\nStatistical watermarking techniques are well-established for sequentially decoded language models (LMs). However, these techniques cannot be directly applied to order-agnostic LMs, as the tokens in order-agnostic LMs are not generated sequentially. In this work, we introduce PATTERN-MARK, a pattern-based watermarking framework specifically designed for order-agnostic LMs. We develop aMarkov-chain-based watermark generator that produces watermark ke<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29861<END_URL>

BaB-ND: Long-Horizon Motion Planning with Branch-and-Bound and Neural Dynamics\nNeural-network-based dynamics models learned from observational data have shown strong predictive capabilities for scene dynamics in robotic manipulation tasks. However, their inherent non-linearity presents significant challenges for effective planning. Current planning methods, often dependent on extensive sampling or local gradient descent, struggle with long-horizon motion planning tasks involving complex contact<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30104<END_URL>

Backdooring Vision-Language Models with Out-Of-Distribution Data\nThe emergence of Vision-Language Models (VLMs) represents a significant advancement in integrating computer vision with Large Language Models (LLMs) to generate detailed text descriptions from visual inputs. Despite their growing importance, the security of VLMs, particularly against backdoor attacks, is under explored. Moreover, prior works often assume attackers have access to the original training data, which is often unrealist<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28044<END_URL>

Backtracking Improves Generation Safety\nText generation has a fundamental limitation almost by definition: there is no taking back tokens that have been generated, even when they are clearly problematic.In the context of language model safety, when a partial unsafe generation is produced, language models by their nature tend to happily keep on generating similarly unsafe additional text.This is in fact how safety alignment of frontier models gets circumvented in the wild, despite great efforts <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30547<END_URL>

BadJudge: Backdoor Vulnerabilities of LLM-As-A-Judge\nThis paper exposes the backdoor threat in automatic evaluation with LLM-as-a-Judge. We propose a novel threat model, where the adversary assumes control of both the candidate model and evaluator model. The victims are the benign users who are unfairly rated by the backdoored evaluator in favor of the adversary. A trivial single token backdoor poisoning 1% of the evaluator training data triples the score of the adversary, compared with the adv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28942<END_URL>

Bad-PFL: Exploiting Backdoor Attacks against Personalized Federated Learning\nData heterogeneity and backdoor attacks rank among the most significant challenges facing federated learning (FL). For data heterogeneity, personalized federated learning (PFL) enables each client to maintain a private personalized model to cater to client-specific knowledge. Meanwhile, vanilla FL has proven vulnerable to backdoor attacks. However, recent advancements in PFL community have demonstrated a potential immu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30844<END_URL>

Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations\nVariational Autoencoders (VAEs) are a powerful framework for learning latent representations of reduced dimensionality, while Neural ODEs excel in learning transient system dynamics. This work combines the strengths of both to generate fast surrogate models with adjustable complexity reacting on time-varying inputs signals. By leveraging the VAE’s dimensionality reduction using a non-hierarchical prior, ou<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28422<END_URL>

Balanced Ranking with Relative Centrality: A multi-core periphery perspective\nRanking of vertices in a graph for different objectives is one of the most fundamental tasks in computer science. It is known that traditional ranking algorithms can generate unbalanced ranking when the graph has underlying communities, resulting in loss of information, polarised opinions, and reduced diversity (Celis, Straszak \& Vishnoi [ICALP 2018]).In this paper, we focus on *unsupervised ranking* on graphs and ob<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31168<END_URL>

Balancing Act: Diversity and Consistency in Large Language Model Ensembles\nEnsembling strategies for Large Language Models (LLMs) have demonstrated significant potential in improving performance across various tasks by combining the strengths of individual models. However, identifying the most effective ensembling method remains an open challenge, as neither maximizing output consistency through self-consistency decoding nor enhancing model diversity via frameworks like "Mixture of Agents" has <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30442<END_URL>

Balancing Bias in Two-sided Markets for Fair Stable Matchings\nThe Balanced Stable Marriage (BSM) problem aims to find a stable matching in a two-sided market that minimizes the maximum dissatisfaction among two sides. The classical Deferred Acceptance algorithm merely produces an unfair stable marriage, providing optimal partners for one side while partially assigning pessimal partners to the other. Solving BSM is NP-hard, thwarting attempts to resolve the problem exactly. As the instance size <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28228<END_URL>

BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping\nIntrinsic motivation and reward shaping guide reinforcement learning (RL) agentsby adding pseudo-rewards, which can lead to useful emergent behaviors. However, they can also exhibit unanticipated side effects – leading to reward hackingor fixation with noisy TVs. Here we provide a theoretical model which anticipates these behaviors, and provides broad criteria under which their effects canbe bounded. We c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28031<END_URL>

Bandit Learning in Matching Markets with Indifference\nA rich line of recent works studies how participants in matching markets learn their unknown preferences through iterative interactions with each other. Two sides of participants in the market can be respectively formulated as players and arms in the bandit problem. To ensure market stability, the objective is to minimize the stable regret of each player. Though existing works provide significant theoretical upper bounds for players' stable <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30837<END_URL>

BANGS: Game-theoretic Node Selection for Graph Self-Training\nGraph self-training is a semi-supervised learning method that iteratively selects a set of unlabeled data to retrain the underlying graph neural network (GNN) model and improve its prediction performance. While selecting highly confident nodes has proven effective for self-training, this pseudo-labeling strategy ignores the combinatorial dependencies between nodes and suffers from a local view of the distribution.To overcome these iss<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28782<END_URL>

Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression\nLarge Language Models (LLMs) have achieved remarkable breakthroughs. However, the huge number of parameters in LLMs require significant amount of memory storage in inference, which prevents their practical deployment in many applications. To reduce memory storage of LLMs, singular value decomposition (SVD) provides a promising solution to approximate weight matrices for compressing LLMs. In this paper, we take a s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28800<END_URL>

Bayesian Analysis of Combinatorial Gaussian Process Bandits\nWe consider the combinatorial volatile Gaussian process (GP) semi-bandit problem. Each round, an agent is provided a set of available base arms and must select a subset of them to maximize the long-term cumulative reward. We study the Bayesian setting and provide novel Bayesian cumulative regret bounds for three GP-based algorithms: GP-UCB, GP-BayesUCB and GP-TS. Our bounds extend previous results for GP-UCB and GP-TS to the \emph{infi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30976<END_URL>

BAYESIAN EXPERIMENTAL DESIGN VIA CONTRASTIVE DIFFUSIONS\nBayesian Optimal Experimental Design (BOED) is a powerful tool to reduce the cost of running a sequence of experiments.When based on the Expected Information Gain (EIG), design optimization corresponds to the maximization of some intractable expected  *contrast* between prior and posterior distributions.Scaling this maximization to high dimensional and complex settings has been an issue due to BOED inherent computational complexity.In this<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28775<END_URL>

Bayesian Image Regression with Soft-thresholded Conditional Autoregressive Prior\nIn the analysis of brain functional MRI (fMRI) data using regression models, Bayesian methods are highly valued for their flexibility and ability to quantify uncertainty. However, these methods face computational challenges in high-dimensional settings typical of brain imaging, and the often pre-specified correlation structures may not accurately capture the true spatial relationships within the brain. To address t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28171<END_URL>

Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences\nTo build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or by learning from large antibody databases to predict only typical antibodies. Unfortunately, the space of typical antibodies is enormous to search, and experiments often fail to find suitable antibodies on a budget. Here we introdu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30425<END_URL>

Bayesian Optimization via Continual Variational Last Layer Training\nGaussian Processes (GPs) are widely seen as the state-of-the-art surrogate models for Bayesian optimization (BO) due to their ability to model uncertainty and their performance on tasks where correlations are easily captured (such as those defined by Euclidean metrics) and their ability to be efficiently updated online. However, the performance of GPs depends on the choice of kernel, and kernel selection for complex correlation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31188<END_URL>

Bayesian Regularization of Latent Representation\nThe effectiveness of statistical and machine learning methods often depends on how well data features are characterized. Developing informative and interpretable latent representations with controlled complexity is essential for visualizing data structure and for facilitating efficient model building through dimensionality reduction. Latent variable models, such as Gaussian Process Latent Variable Models (GP-LVM), have become popular for learning<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29414<END_URL>

Bayesian Treatment of the Spectrum of the Empirical Kernel in (Sub)Linear-Width Neural Networks\nWe study Bayesian neural networks (BNNs) in the theoretical limits of infinitely increasing number of training examples, network width and input space dimension. Our findings establish new bridges between kernel-theoretic approaches and techniques derived from statistical mechanics through the correspondence between Mercer's eigenvalues and limiting spectral distributions of covariance matrices studi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29844<END_URL>

Bayesian WeakS-to-Strong from Text Classification to Generation\nAdvances in large language models raise the question of how alignment techniques will adapt as models become increasingly complex and humans will only be able to supervise them weakly. Weak-to-Strong mimics such a scenario where weak model supervision attempts to harness the full capabilities of a much stronger model. This work extends Weak-to-Strong to WeakS-to-Strong by exploring an ensemble of weak models which simulate the vari<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28306<END_URL>

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts\nEarly Exit (EE) techniques have emerged as a means to reduce inference latency in Deep Neural Networks (DNNs). The latency improvement and accuracy in these techniques crucially depend on the criteria used to make exit decisions. We propose a new decision criterion BEEM where exit classifiers are treated as experts and aggregate their confidence scores. The confidence scores are aggregated only if neighbouring<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30371<END_URL>

Behavioral Entropy-Guided Dataset Generation for Offline Reinforcement Learning\nEntropy-based objectives are widely used to perform state space exploration in reinforcement learning (RL) and dataset generation for offline RL. Behavioral entropy (BE), a rigorous generalization of classical entropies that incorporates cognitive and perceptual biases of agents, was recently proposed for discrete settings and shown to be a promising metric for robotic exploration problems. In this work, we propose <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29971<END_URL>

Be More Diverse than the Most Diverse: Online Selection of Diverse Mixtures of Generative Models\nThe availability of multiple training algorithms and architectures for generative models requires a selection mechanism to form a single model over a group of well-trained generation models. The selection task is commonly addressed by identifying the model that maximizes an evaluation score based on the diversity and quality of the generated data. However, such a best-model identification approach o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31154<END_URL>

Benchmarking Agentic Workflow Generation\nLarge Language Models (LLMs), with their remarkable task-handling capabilities, have catalyzed significant achievements in tackling reasoning and planning tasks, wherein decomposing complex problems into executable workflows is a crucial step in this process. Existing workflow evaluation frameworks either focus solely on holistic performance or suffer from limitations such as restricted scenario coverage, simplistic workflow structures, and lax evaluatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27882<END_URL>

Benchmarking LLMs' Judgments with No Gold Standard\nWe introduce the GEM (Generative Estimator for Mutual Information), an evaluation metric for assessing language generation by large language models (LLMs), particularly in generating informative judgments, without the need for a gold standard reference. GEM broadens the scenarios where we can benchmark LLM generation performance-from traditional ones, like machine translation and summarization, where gold standard references are readily availab<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27990<END_URL>

Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent\nMultimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the “hallucination” issue inherent in multimodal large language models (MLLMs). Although promising, existing heuristic mRAGs typically predefined fixed retrieval processes, which causes two issues: (1) Non-adaptive Retrieval Queries. (2) Overloaded Retrieval Queries. However, these flaws cannot be <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29392<END_URL>

Benchmarking Predictive Coding Networks -- Made Simple\nIn this work, we tackle the problems of efficiency and scalability for predictive coding networks (PCNs) in machine learning. To do so, we  propose a library that focuses on performance and simplicity, and use it to implement a large set of standard benchmarks for the community to use for their experiments. As most works in the field propose their own tasks and architectures, do not compare one against each other, and focus on small-scale t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28118<END_URL>

Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset\nMachine unlearning has emerged as an effective strategy for forgetting specific information in the training data. However, with the increasing integration of visual data, privacy concerns in Vision Language Models (VLMs) remain underexplored. To address this, we introduce Facial Identity Unlearning Benchmark (FIUBench), a novel VLM unlearning benchmark designed to robustly evaluate the effectiveness of unlearni<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31229<END_URL>

Benign Overfitting in Out-of-Distribution Generalization of Linear Models\nBenign overfitting refers to the phenomenon where a over-parameterized model fits the training data perfectly, including noise in the data, but still generalizes well to the unseen test data. While prior work provide a solid theoretical understanding of this phenomenon under the in-distribution setup, modern machine learning often operates in a more challenging Out-of-Distribution (OOD) regime, where the target (test) dis<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30869<END_URL>

BenTo: Benchmark Reduction with In-Context Transferability\nEvaluating large language models (LLMs) is costly: it requires the generation and examination of LLM outputs on a large-scale benchmark of various tasks. This paper investigates how to efficiently reduce the tasks used to benchmark LLMs without affecting the evaluation quality. Our study reveals that task transferability and relevance provide critical information to identify the most representative subset of tasks via optimizing a facil<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28565<END_URL>

Better autoregressive regression with LLMs\nLarge language models (LLMs) have proven successful on many machine learning  tasks,including those that do not involve language generation. In specific, LLMs have been shown to be effective in solving regression, where the targets are real-numbers.One common approach is to fine tune the LLM based on the log-perplexity loss and use  autoregressive sampling at the inference time. Another approach relies on adding a predictive head and finetuning it with<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27796<END_URL>

Better Instruction-Following Through Minimum Bayes Risk\nGeneral-purpose LLM judges capable of human-level evaluation provide not only a scalable and accurate way of evaluating instruction-following LLMs but also new avenues for supervising and improving their performance. One promising way of leveraging LLM judges for supervision is through Minimum Bayes Risk (MBR) decoding, which uses a reference-based evaluator to select a high-quality output from amongst a set of candidate outputs. In the fi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30792<END_URL>

Better than Your Teacher: LLM Agents that learn from Privileged AI Feedback\nWhile large language models (LLMs) show impressive decision-making abilities, current methods lack a mechanism for automatic self-improvement from errors during task execution. We propose LEAP, an iterative fine-tuning framework that continually improves LLM agents using feedback from AI expert teachers. Our key insight is to equip the expert teachers with a privileged state -- information available during training but <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28093<END_URL>

Beware of Calibration Data for Pruning Large Language Models\nAs large language models (LLMs) are widely applied across various fields, modelcompression has become increasingly crucial for reducing costs and improvinginference efficiency. Post-training pruning is a promising method that does notrequire resource-intensive iterative training and only needs a small amount ofcalibration data to assess the importance of parameters. Recent research has enhanced post-training pruning from different asp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27801<END_URL>

Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning\nAutoregressive language models, despite their impressive capabilities, struggle with complex reasoning and long-term planning tasks. We introduce discrete diffusion models as a novel solution to these challenges. Through the lens of subgoal imbalance, we demonstrate how diffusion models effectively learn difficult subgoals that elude autoregressive approaches. We propose Multi-granularity Diffusion Modeling (MDM), whic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29876<END_URL>

Beyond Auto-Regression: Fast LLMs via Self-Distillation Through Time\nAutoregressive (AR) Large Language Models (LLMs) have demonstrated significant success across numerous tasks. However, the AR modeling paradigm presents certain limitations; for instance, contemporary autoregressive LLMs are trained to generate one token at a time, which can result in noticeable latency. Recent advancements have indicated that search and repeated sampling can enhance performance in various applications, such a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27972<END_URL>

Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing\nIn numerous applications of geometric deep learning, the studied systems exhibit spatial symmetries and it is desirable to enforce these. For the symmetry of global rotations and reflections, this means that the model should be equivariant with respect to the transformations that form the group of $\mathrm O(d)$.While many approaches for equivariant message passing require specialized architectures, including no<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27926<END_URL>

Beyond Circuit Connections: A Non-Message Passing Graph Transformer Approach for Quantum Error Mitigation\nDespite the progress in quantum computing, one major bottleneck against the practical utility is its susceptibility to noise, which frequently occurs in current quantum systems. Existing quantum error mitigation (QEM) methods either lack generality to noise and circuit types or fail to capture the global dependencies of entire systems in addition to circuit structure. In this work, we first<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29291<END_URL>

Beyond Content Relevance: Evaluating Instruction Following in Retrieval Models\nLarge language models (LLMs) have been widely adopted for training embedding and ranking models, with recent advancements significantly improving the performance of Information Retrieval systems. However, while instruction-following is a core capability of LLMs, their ability to handle detailed user instructions has not been thoroughly investigated in search models. This study evaluates the instruction-following capa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29803<END_URL>

Beyond correlation: The impact of human uncertainty in measuring the effectiveness of automatic evaluation and LLM-as-a-judge\nThe effectiveness of automatic evaluation of generative models is typically measured by comparing it to human evaluation using correlation metrics. However, metrics like Krippendorff's $\alpha$ and Randolph's $\kappa$, originally designed to measure the reliability of human labeling, make  assumptions about human behavior and the labeling process. In this paper, we show <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30419<END_URL>

Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration\nlearning where the goal is to learn a policy that mimics the expert's behavior. In practice, it is often challenging to learn the expert policy from a limited number of demonstrations accurately due to the complexity of the state space. Moreover, it is essential to explore the environment and collect data to achieve beyond-expert performance. To overcome these challenges, we propose a nov<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30314<END_URL>

Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality\nThe Fréchet Video Distance (FVD) is a widely adopted metric for evaluating video generation distribution quality. However, its effectiveness relies on critical assumptions. Our analysis reveals three significant limitations: (1) the non-Gaussianity of the Inflated 3D Convnet (I3D) feature space; (2) the insensitivity of I3D features to temporal distortions; (3) the impractical sample sizes required for reliable estimation. The<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29066<END_URL>

Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?\nExisting benchmarks like NLGraph and GraphQA evaluate LLMs on graphs by focusing mainly on pairwise relationships, overlooking the high-order correlations found in real-world data. Hypergraphs, which can model complex beyond-pairwise relationships, offer a more robust framework but are still underexplored in the context of LLMs. To address this gap, we introduce LLM4Hypergraph, the first comprehensive benchmark comprising 21,500 p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31158<END_URL>

Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness\nDeep learning models often suffer from a lack of interpretability due to polysemanticity, where individual neurons are activated by multiple unrelated semantics, resulting in unclear attributions of model behavior. Recent advances in monosemanticity, where neurons correspond to consistent and distinct semantics, have significantly improved interpretability but are commonly believed to compromise accuracy. In this <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28835<END_URL>

Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix\nLarge Language Models (LLMs) have shown immense potential in enhancing various aspects of our daily lives, from conversational AI to search and AI assistants. However, their growing capabilities come at the cost of extremely large model sizes, making deployment on edge devices challenging due to memory and computational constraints. This paper introduces a novel approach to LLM weight pruning that directly optimizes for<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28105<END_URL>

Beyond Mere Token Analysis: A Hypergraph Metric Space Framework for Defending Against Socially Engineered LLM Attacks\nRecent jailbreak attempts on Large Language Models (LLMs) have shifted from algorithm-focused to human-like social engineering attacks, with persuasion-based techniques emerging as a particularly effective subset. These attacks evolve rapidly, demonstrate high creativity, and boast superior attack success rates. To combat such threats, we propose a promising approach to enhancin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28172<END_URL>

Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification\nLarge Language Models (LLM) are increasingly trained on data generated by other LLMs, either because generated text and images become part of the pre-training corpus, or because synthetized data is used as a replacement for expensive human-annotation. This raises concerns about *model collapse*, a drop in model performance when their training sets include generated data. Considering that it is easier for both humans a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29933<END_URL>

Beyond Random Augmentations: Pretraining with Hard Views\nSelf-Supervised Learning (SSL) methods typically rely on random image augmentations, or views, to make models invariant to different transformations. We hypothesize that the efficacy of pretraining pipelines based on conventional random view sampling can be enhanced by explicitly selecting views that benefit the learning progress. A simple yet effective approach is to select hard views that yield a higher loss. In this paper, we propose H<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30639<END_URL>

Beyond Random Masking: When Dropout meets Graph Convolutional Networks\nGraph Convolutional Networks (GCNs) have emerged as powerful tools for learning on graph-structured data, yet the behavior of dropout in these models remains poorly understood. This paper presents a comprehensive theoretical analysis of dropout in GCNs, revealing its unique interactions with graph structure. We demonstrate that dropout in GCNs creates dimension-specific stochastic sub-graphs, leading to a form of structural <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29723<END_URL>

Beyond Sequence: Impact of Geometric Context for RNA Property Prediction\nAccurate prediction of RNA properties, such as stability and interactions, is crucial for advancing our understanding of biological processes and developing RNA-based therapeutics. RNA structures can be represented as 1D sequences, 2D topological graphs, or 3D all-atom models, each offering different insights into its function. Existing works predominantly focus on 1D sequence-based models, which overlook the geometric con<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30672<END_URL>

Beyond Single Concept Vector: Modeling Concept Subspace in LLMs with Gaussian Distribution\nProbing learned concepts in large language models (LLMs) is crucial for understanding how semantic knowledge is encoded internally. Training linear classifiers on probing tasks is a principle approach to denote the vector of a certain concept in the representation space. However, the single vector identified for a concept varies with both data and training, making it less robust and weakening its effectiv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30487<END_URL>

Beyond single neurons: population response geometry in digital twins of mouse visual cortex\nHierarchical visual processing  is essential for cognitive functions like object recognition and spatial localization. Traditional studies of the neural basis of these computations have focused on single-neuron activity, but recent advances in large-scale neural recordings emphasize the growing need to understand computations at the population level. Digital twins-computational models trained on neural d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28582<END_URL>

Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks\nGenerative Flow Networks (GFlowNets) are a novel class of generative models designed to sample from unnormalized distributions and have found applications in various important tasks, attracting great research interest in their training algorithms. In general, GFlowNets are trained by fitting the forward flow to the backward flow on sampled training objects. Prior work focused on the choice of training <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31016<END_URL>

Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension ability\nLarge language models (LLMs) have shown remarkable capability in natural language tasks, yet debate persists on whether they truly comprehend deep structure (i.e., core semantics) or merely rely on surface structure (e.g., presentation format). Prior studies observe that LLMs' performance declines when intervening on surface structure, arguing their success relies on surface structure recognition. However, surface stru<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28795<END_URL>

Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints\nSynthetic tabular data generation has traditionally been a challenging problem due to the high complexity of the underlying distributions that characterise this type of data. Despite recent advances in deep generative models (DGMs), existing methods often fail to produce realistic datapoints that are well-aligned with available background knowledge.In this paper, we address this limi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28159<END_URL>

Beyond Worst-Case Dimensionality Reduction for Sparse Vectors\nWe study beyond worst-case dimensionality reduction for $s$-sparse vectors (vectors with at most $s$ non-zero coordinates). Our work is divided into two parts, each focusing on a different facet of beyond worst-case analysis:\noindent (a)  We first consider average-case guarantees for embedding $s$-sparse vectors. Here, a well-known folklore upper bound based on the birthday-paradox states: For any collection $X$ of $s$-sparse vector<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28947<END_URL>

Bias Mitigation in Graph Diffusion Models\nMost existing graph generative diffusion models suffer from significant exposure bias during graph sampling. We observe that the forward diffusion’s maximum perturbation distribution in most models deviates from the standard normal distribution, while reverse sampling consistently starts from a standard normal distribution. This mismatch results in a reverse starting bias, which, together with the exposure bias, degrades generation quality. The exposure<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30512<END_URL>

Bidirectional Decoding: Improving Action Chunking via Closed-Loop Resampling\nPredicting and executing a sequence of actions without intermediate replanning, known as action chunking, is increasingly used in robot learning from human demonstrations. Yet, its reported effects on the learned policy are inconsistent: some studies find it crucial for achieving strong results, while others observe decreased performance. In this paper, we first dissect how action chunking impacts the divergence betwee<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28245<END_URL>

Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models\nFine-tuning large language models (LLMs)  on human preferences, typically through reinforcement learning from human feedback (RLHF), has proven successful in enhancing their capabilities.  However, ensuring the safety of LLMs during fine-tuning remains a critical concern, and mitigating the potential conflicts in  safety and helpfulness  is costly in RLHF.  To address this issue, we propose a supervised learni<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30263<END_URL>

BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions\nTask automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks range from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or sta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29245<END_URL>

BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks\nMultimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29129<END_URL>

BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities\nWe introduce BiGR, a novel conditional image generation model using compact binary latent codes for generative training, focusing on enhancing both generative and representation capabilities. BiGR is the first conditional generative model that unifies generation and discrimination within the same framework. BiGR features a binary tokenizer, a masking modeling mechanism, and a binary transco<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31194<END_URL>

Bilinear MLPs enable weight-based mechanistic interpretability\nA mechanistic understanding of how MLPs do computation in deep neural networks remains elusive. Current interpretability work can extract features from hidden activations over an input dataset but generally cannot explain how MLP weights construct features. One challenge is that element-wise nonlinearities introduce higher-order interactions and make it difficult to trace computations through the MLP layer. In this paper, we analyze<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28827<END_URL>

BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models\nWith the advancement of diffusion models (DMs) and the substantially increased computational requirements, quantization emerges as a practical solution to obtain compact and efficient low-bit DMs. However, the highly discrete representation leads to severe accuracy degradation, hindering the quantization of diffusion models to ultra-low bit-widths. This paper proposes a novel weight binarization approach for DMs, namely Binar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29258<END_URL>

Binary Losses for Density Ratio Estimation\nEstimating the ratio of two probability densities from finitely many observations of the densities, is a central problem in machine learning and statistics. A large class of methods constructs estimators from binary classifiers which distinguish observations from the two densities. However, the error of these constructions depends on the choice of the binary loss function, raising the question of which loss function to choose based on desired error pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30970<END_URL>

BingoGuard: LLM Content Moderation Tools with Risk Levels\nMalicious content generated by large language models (LLMs) can pose varying degrees of harm. Although existing LLM-based moderators can detect harmful content, they struggle to assess risk levels and may miss lower-risk outputs. Accurate risk assessment allows platforms with different safety thresholds to tailor content filtering and rejection. In this paper, we introduce per-topic severity rubrics for 11 harmful topics and build BingoG<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30227<END_URL>

BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments\nAgents based on large language models have shown great potential in accelerating scientific discovery by leveraging their rich background knowledge and reasoning capabilities. In this paper, we introduce BioDiscoveryAgent, an agent that designs new experiments, reasons about their outcomes, and efficiently navigates the hypothesis space to reach desired solutions. We demonstrate our agent on the problem of designing g<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30240<END_URL>

Biologically Plausible Brain Graph Transformer\nState-of-the-art brain graph analysis methods fail to fully encode the small-world architecture of brain graphs (accompanied by the presence of hubs and functional modules), and therefore lack biological plausibility to some extent. This limitation hinders their ability to accurately represent the brain's structural and functional properties, thereby restricting the effectiveness of machine learning models in tasks such as brain disorder detection.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28197<END_URL>

Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences\nLanguage models for biological and chemical sequences enable crucial applications such as drug discovery, protein engineering, and precision medicine. Currently, these language models are predominantly based on Transformer architectures. While Transformers have yielded impressive results, their quadratic runtime dependency on sequence length complicates their use for long genomic sequence<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30154<END_URL>

BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models\nPredictive models often need to work with incomplete information in real-world tasks. Consequently, they must provide reliable probability or confidence estimation, especially in large-scale decision making and planning tasks. Current large language models (LLM) are insufficient for such accurate estimations, but they can generate relevant factors that may affect the probabilities, produce coarse-grained probabilities wh<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28889<END_URL>

BISIMULATION METRIC FOR MODEL PREDICTIVE CONTROL\nModel-based reinforcement learning (MBRL) has shown promise for improving sample efficiency and decision-making in complex environments. However, existing methods face challenges in training stability, robustness to noise, and computational efficiency. In this paper, we propose Bisimulation Metric for Model Predictive Control (BS-MPC), a novel approach that incorporates bisimulation metric loss in its objective function to directly optimize the e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30370<END_URL>

BitStack: Fine-Grained Size Control for Compressed Large Language Models in Variable Memory Environments\nLarge language models (LLMs) have revolutionized numerous applications, yet their deployment remains challenged by memory constraints on local devices. While scaling laws have enhanced LLM capabilities, the primary bottleneck has shifted from $\textit{capability}$ to $\textit{availability}$, emphasizing the need for efficient memory management. Traditional compression methods, such as quanti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28531<END_URL>

Black-Box Detection of Language Model Watermarks\nWatermarking has emerged as a promising way to detect LLM-generated text, by augmenting LLM generations with later detectable signals. Recent work has proposed multiple families of watermarking schemes, several of which focus on preserving the LLM distribution. This distribution-preservation property is motivated by the fact that it is a tractable proxy for retaining LLM capabilities, as well as the inherently implied undetectability of the water<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30423<END_URL>

Black Sheep in the Herd: Playing with Spuriously Correlated Attributes for Vision-Language Recognition\nFew-shot adaptation for Vision-Language Models (VLMs) presents a dilemma: balancing in-distribution accuracy with out-of-distribution generalization. Recent research has utilized low-level concepts such as visual attributes to enhance generalization. However, this study reveals that VLMs overly rely on a small subset of attributes on decision-making, which co-occur with the category but are no<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28838<END_URL>

BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation\nModeling the nonlinear dynamics of neuronal populations represents a key pursuit in computational neuroscience. Recent research has increasingly focused on jointly modeling neural activity and behavior to unravel their interconnections. Despite significant efforts, these approaches often necessitate either intricate model designs or oversimplified assumptions. Given the frequent absence of perfectly<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28643<END_URL>

BlendRL: A Framework for Merging Symbolic and Neural Policy Learning\nHumans can leverage both symbolic reasoning and intuitive responses. In contrast, reinforcement learning policies are typically encoded in either opaque systems like neural networks or symbolic systems that rely on predefined symbols and rules. This disjointed approach severely limits the agents’ capabilities, as they often lack either the flexible low-level reaction characteristic of neural agents or the interpretable reasoni<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30913<END_URL>

Block-Attention for Efficient RAG\nWe introduce Block-Attention, an attention mechanism designed to address the increased inference latency and cost in Retrieval-Augmented Generation (RAG) scenarios. Traditional approaches often encode the entire context.Instead, Block-Attention divides retrieved documents into discrete blocks, with each block independently calculating key-value (KV) states except for the final block.In RAG scenarios, by defining each passage as a block, Block-Attention enables <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30791<END_URL>

Block Verification Accelerates Speculative Decoding\nSpeculative decoding is an  effective method for lossless acceleration of large language models during inference. It uses a fast model to draft a block of tokens which are then verified in parallel by the target model, and provides a guarantee that the output is distributed identically to a sample from the target model. In prior works, draft verification is performed independently token-by-token. Surprisingly, we show that this approach is not<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28852<END_URL>

BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks\nDespite their superb multimodal capabilities, Vision-Language Models (VLMs) have been shown to be vulnerable to jailbreak attacks, which are inference-time attacks that induce the model to output harmful responses with tricky prompts. It is thus essential to defend VLMs against potential jailbreaks for their trustworthy deployment in real-world applications. In this work, we focus on black-box defense for V<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27811<END_URL>

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL\nBayesian optimization (BO) offers an efficient pipeline for optimizing black-box functions with the help of a Gaussian process prior and an acquisition function (AF). Recently, in the context of single-objective BO, learning-based AFs witnessed promising empirical results given its favorable non-myopic nature. Despite this, the direct extension of these approaches to multi-objective Bayesian optimization (MOB<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29456<END_URL>

Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\nPredicting the change in binding free energy ($\Delta \Delta G$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design.Due to the scarcity of experimental $\Delta\Delta G$ data, existing methods focus on pre-training, while alignment receives less attention.In this work, we propose the Boltzmann Alignment technique to transfer knowled<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28490<END_URL>

Boltzmann priors for Implicit Transfer Operators\nAccurate prediction of thermodynamic properties is essential in drug discovery and materials science. Molecular dynamics (MD) simulations provide a principled approach to this task, yet they typically rely on prohibitively long sequential simulations. Implicit Transfer Operator (ITO) Learning offers a promising approach to address this limitation by enabling stable simulation with time-steps orders of magnitude larger than MD. However, to train I<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28298<END_URL>

Boltzmann Semantic Score: A Semantic Metric for Evaluating Large Vision Models Using Large Language Models\nDo Large Vision Models (LVMs) extract medically and semantically relevant features similar to those identified by human experts? Currently, only biased, qualitative approaches with limited, small-scale expert evaluations are available to answer this question. In this study, we propose the Boltzmann Semantic Score (BSS), a novel method inspired by state space modeling, to evaluate the encod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30657<END_URL>

BOND: Aligning LLMs with Best-of-N Distillation\nReinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models.Yet, a surprisingly simple and strong inference-time strategy is Best-of-N sampling that selects the best generation among N candidates.In this paper, we propose Best-of-N Distillation (BOND), a novel RLHF algorithm that seeks to emulate Best-of-N but without its significant computational overhead at inference time. Spe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31233<END_URL>

BoneMet: An Open Large-Scale Multi-Modal Murine Dataset for Breast Tumor Bone Metastasis Diagnosis and Prognosis\nBreast tumor bone metastasis (BTBM) affects women’s health globally, calling for the development of effective solutions for its diagnosis and prognosis. While the deep learning has exhibited impressive capacities across various healthcare domains, their applicability to managing BTBM diseases is consistently hindered by the lack of an open, large-scale, deep learning-ready dataset. A<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29268<END_URL>

Bonsai: Gradient-free Graph Distillation for Node Classification\nGraph distillation has emerged as a promising avenue to enable scalable training of GNNs by compressing the training dataset while preserving essential graph characteristics. Our study uncovers significant shortcomings in current graph distillation techniques. First, the majority of the algorithms paradoxically require training on the full dataset to perform distillation. Second, due to their gradient-emulating approach, these met<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30924<END_URL>

Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation\nHarmful fine-tuning attack \citep{qi2023fine} poses serious safety concerns for Large language models' fine-tuning-as-a-service. While existing defenses have been proposed to mitigate the issue, their performances are still far away from satisfactory, and the root cause of the problem has not been fully recovered. To this end, we in this paper show that \textit{harmful perturbation} over the mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28050<END_URL>

Boosting Latent Diffusion with Perceptual Objectives\nLatent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the latent space of an autoencoder (AE) and produce images by mapping the generated latents into RGB image space using the AE decoder. While this approach allows for efficient model training and sampling, it induces a disconnect between the training of the diffusion model and the decoder, resulting in a loss of de<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27753<END_URL>

Boosting Methods for Interval-censored Data with Regression and Classification\nBoosting has garnered significant interest across both machine learning and statistical communities. Traditional boosting algorithms, designed for fully observed random samples, often struggle with real-world problems, particularly with interval-censored data. This type of data is common in survival analysis and time-to-event studies where exact event times are unobserved but fall within known intervals. Effective ha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30432<END_URL>

Boosting Multiple Views for pretrained-based Continual Learning\nRecent research has shown that Random Projection (RP) can effectively improve the performance of pre-trained models in Continual learning (CL). The authors hypothesized that using RP to map features onto a higher-dimensional space can make them more linearly separable. In this work, we theoretically analyze the role of RP and present its benefits for improving the model’s generalization abilityin each task and facilitating CL overa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30627<END_URL>

Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems\nNeural Combinatorial Optimization (NCO) methods have exhibited promising performance in solving Vehicle Routing Problems (VRPs). However, most NCO methods rely on the conventional self-attention mechanism that induces excessive computational complexity, thereby struggling to contend with large-scale VRPs and hindering their practical applicability. In this paper, we propose a lightweight cross-attention mechanis<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29530<END_URL>

Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games\nThis paper presents a payoff perturbation technique, introducing a strong convexity to players' payoff functions in games. This technique is specifically designed for first-order methods to achieve last-iterate convergence in games where the gradient of the payoff functions is monotone in the strategy profile space, potentially containing additive noise. Although perturbation is known to facilitate the convergence of learn<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30088<END_URL>

Boosting Ray Search Procedure of Hard-label Attacks with Transfer-based Priors\nOne of the most practical and challenging types of black-box adversarial attacks is the hard-label attack, where only top-1 predicted labels are available. One effective approach is to search for the optimal ray direction from the benign image that minimizes the $\ell_p$ norm distance to the adversarial region. The unique advantage of this approach is that it transforms the hard-label attack into a continuous optimiz<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28063<END_URL>

Boosting the visual interpretability of CLIP via adversarial fine-tuning\nCLIP has achieved great success in visual representation learning and is becoming an important plug-in component for many large multi-modal models like LLaVA and DALL-E. However, the lack of interpretability caused by the intricate image encoder architecture and training process restrict its wider use in high-stake decision making applications. In this work, we propose an unsupervised adversarial fine-tuning (AFT) with nor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28566<END_URL>

Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation\nAlthough larger datasets are crucial for training large deep models, the rapid growth of dataset size has brought a significant challenge in terms of considerable training costs, which even results in prohibitive computational expenses. Dataset Distillation becomes a popular technique recently to reduce the dataset size via learning a highly compact set of representative exemplars, where <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31137<END_URL>

Bootstrapped Energy Based Models: What are they good for?\nTraining a generative model with energy or unnormalized density functions is considered an important problem for physical systems such as molecules. This provides a path to train generative models to sample from the much desired Boltzmann distribution in situations of data scarcity. As of late, several generative frameworks have been proposed to target this problem. However, as we show in the following blog post, these methods have not b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31333<END_URL>

Bootstrapped Model Predictive Control\nModel Predictive Control (MPC) has been demonstrated to be effective in continuous control tasks. When a world model and a value function are available, planning a sequence of actions ahead of time leads to a better policy. Existing methods typically obtain the value function and the corresponding policy in a model-free manner. However, we find that such an approach struggles with complex tasks, resulting in poor policy learning and inaccurate value estimat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28718<END_URL>

Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel\nCreating high-quality data for training robust language-instructed agents is a long-lasting challenge in embodied AI. In this paper, we introduce a Self-Refining Data Flywheel (SRDF) that generates high-quality and large-scale navigational instruction-trajectory pairs by iteratively refining the data pool through the collaboration between two models, the instruction generator and the navigator, without any human-<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29824<END_URL>

Bootstrapping Language Models with DPO Implicit Rewards\nHuman alignment in large language models (LLMs) is an active area of research. A recent groundbreaking work, direct preference optimization (DPO), has greatly simplified the process from past work in reinforcement learning from human feedback (RLHF) by bypassing the reward learning stage in RLHF. DPO, after training, provides an implicit reward model. In this work, we make a novel observation that this implicit reward model can by itself b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28967<END_URL>

Both Ears Wide Open: Towards Language-Driven Spatial Audio Generation\nRecently, diffusion models have achieved great success in mono-channel audio generation.However, when it comes to stereo audio generation, the soundscapes often have a complex scene of multiple objects and directions.Controlling stereo audio with spatial contexts remains challenging due to high data costs and unstable generative models. To the best of our knowledge, this work represents the first attempt to address these issu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28250<END_URL>

Bounds on $L_p$ Errors in Density Ratio Estimation via $f$-Divergence Loss Functions\nDensity ratio estimation (DRE) is a fundamental machine learning technique for identifying relationships between two probability distributions. $f$-divergence loss functions, derived from variational representations of $f$-divergence, are commonly employed in DRE to achieve state-of-the-art results. This study presents a novel perspective on DRE using $f$-divergence loss functions by deriving the upper and lowe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28015<END_URL>

BP-Modified Local Loss for Efficient Training of Deep Neural Networks\nThe training of large models is memory-constrained, one direction to relieve    this is training using local loss, like GIM, LoCo, and Forward-Forward    algorithms. However, the local loss methods often face the issue of slow or    non-convergence. In this paper, we propose a novel BP-modified local loss    method that uses the true Backward Propagation (BP) gradient to modify the    local loss gradient to improve the perfor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29913<END_URL>

BRAID: Input-driven Nonlinear Dynamical Modeling of Neural-Behavioral Data\nNeural populations exhibit complex recurrent structures that drive behavior, while continuously receiving and integrating external inputs from sensory stimuli, upstream regions, and neurostimulation. However, neural populations are often modeled as autonomous dynamical systems, with little consideration given to the influence of external inputs that shape the population activity and behavioral outcomes. Here, we introduc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31051<END_URL>

BrainACTIV: Identifying visuo-semantic properties driving cortical selectivity using diffusion-based image manipulation\nThe human brain efficiently represents visual inputs through specialized neural populations that selectively respond to specific categories. Advancements in generative modeling have enabled data-driven discovery of neural selectivity using brain-optimized image synthesis. However, current methods independently generate one sample at a time, without enforcing structural constra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30527<END_URL>

Brain Bandit: A Biologically Grounded Neural Network for Efficient Control of Exploration\nHow to balance between exploration and exploitation in an uncertain environment is a central challenge in reinforcement learning. In contrast, humans and animals have demonstrated superior exploration efficiency in novel conditions. To understand how the brain’s neural network controls exploration under uncertainty, we analyzed the dynamical systems model of a biological neural network that controls explor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29647<END_URL>

Brain-inspired -Convolution benefits large kernels and aligns better with visual cortex\nConvolutional Neural Networks (CNNs) have profoundly influenced the field of computer vision, drawing significant inspiration from the visual processing mechanisms inherent in the brain. Despite sharing fundamental structural and representational similarities with the biological visual system, differences in local connectivity patterns within CNNs open up an interesting area to explore. In this work, we expl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31259<END_URL>

Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers\nWe introduce BrainSAIL (Semantic Attribution and Image Localization), a method for linking neural selectivity with spatially distributed semantic visual concepts in natural scenes. BrainSAIL leverages recent advances in large-scale artificial neural networks, using them to provide insights into the functional topology of the brain. To overcome the challenge presented by the co-o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27735<END_URL>

BrainOOD: Out-of-distribution Generalizable Brain Network Analysis\nIn neuroscience, identifying distinct patterns linked to neurological disorders, such as Alzheimer’s and Autism, is critical for early diagnosis and effective intervention. Graph Neural Networks (GNNs) have shown promising in analyzing brain networks, but there are two major challenges in using GNNs: (1) distribution shifts in multi-site brain network data, leading to poor Out-of-Distribution (OOD) generalization, and (2) limite<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31047<END_URL>

BrainUICL: An Unsupervised Individual Continual Learning Framework for EEG Applications\nElectroencephalography (EEG) is a non-invasive brain-computer interface technology used for recording brain electrical activity. It plays an important role in human life and has been widely uesd in real life, including sleep staging, emotion recognition, and motor imagery. However, existing EEG-related models cannot be well applied in practice, especially in clinical settings, where new patients with individ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30870<END_URL>

Breach By A Thousand Leaks: Unsafe Information Leakage in 'Safe' AI Responses\nVulnerability of Frontier language models to misuse and jailbreaks has prompted the development of safety measures like filters and alignment training in an effort to ensure safety through robustness to adversarially crafted prompts. We assert that robustness is fundamentally insufficient for ensuring safety goals, and current defenses and evaluation methods fail to account for risks of dual-intent queries and their c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30768<END_URL>

Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator\nDataset distillation has emerged as a technique aiming to condense informative features from large, natural datasets into a compact and synthetic form. While recent advancements have refined this technique, its performance is bottlenecked by the prevailing class-specific synthesis paradigm. Under this paradigm, synthetic data is optimized exclusively for a pre-assigned one-hot label, creating an implicit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29331<END_URL>

Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization\nExtracting a small subset of crucial rationales from the full input is a key problem in explainability research. The most widely used fundamental criterion for rationale extraction is the maximum mutual information (MMI) criterion. In this paper, we first demonstrate that MMI suffers from diminishing marginal returns. Once part of the rationale has been identified, finding the remaining portions contributes o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29357<END_URL>

Breaking Mental Set to Improve Reasoning through Diverse Multi-Agent Debate\nLarge Language Models (LLMs) have seen significant progress but continue to struggle with persistent reasoning mistakes. Previous methods of self-reflection have been proven limited due to the models’ inherent fixed thinking patterns. While Multi-Agent Debate (MAD) attempts to mitigate this by incorporating multiple agents, it often employs the same reasoning methods, even though assigning different personas to models. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28079<END_URL>

Breaking Neural Network Scaling Laws with Modularity\nModular neural networks outperform nonmodular neural networks on tasks ranging from visual question answering to robotics. These performance improvements are thought to be due to modular networks' superior ability to model the compositional and combinatorial structure of real-world problems. However, a theoretical explanation of how modularity improves generalizability, and how to leverage task modularity while training networks remains elusi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30948<END_URL>

Breaking the $\log(1/\Delta_2)$ Barrier: Better Batched Best Arm Identification with Adaptive Grids\nWe investigate the problem of batched best arm identification in multi-armed bandits, where we want to find the best arm from a set of $n$ arms while minimizing both the number of samples and batches. We introduce an algorithm that achieves near-optimal sample complexity and features an instance-sensitive batch complexity, which breaks the $\log(1/\Delta_2)$ barrier. The main contribution of our <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29082<END_URL>

Breaking the Reclustering Barrier in Centroid-based Deep Clustering\nThis work investigates an important phenomenon in centroid-based deep clustering(DC) algorithms: Performance quickly saturates after a period of rapid early gains. Practitioners commonly address early saturation with periodic reclustering, which we demonstrate to be insufficient to address performance plateaus. We call this phenomenon the “reclustering barrier” and empirically show when the reclustering barrier occurs, what its<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28226<END_URL>

Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization\nDirect preference optimization (DPO), a widely adopted offline preference optimization algorithm, aims to align large language models (LLMs) with human-desired behaviors using pairwise preference data. However, the generation of the winning response and the losing response within pairwise data are typically isolated, leading to weak correlations between them as well as suboptimal alignment performance. To add<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28762<END_URL>

Bridging Compressed Image Latents and Multimodal Large Language Models\nThis paper presents the first-ever study of adapting compressed image latents to suit the needs of downstream vision tasks that adopt Multimodal Large Language Models (MLLMs). MLLMs have extended the success of large language models to modalities (e.g. images) beyond text, but their billion scale hinders deployment on resource-constrained end devices. While cloud-hosted MLLMs could be available, transmitting raw, uncompresse<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30277<END_URL>

Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding\nLarge language models (LLMs) have shown remarkable capabilities in natural language processing; however, they still face difficulties when tasked with understanding lengthy contexts and executing effective question answering. These challenges often arise due to the complexity and ambiguity present in longer texts. To enhance the performance of LLMs in such scenarios, we introduce the Long Question Corefer<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29052<END_URL>

Bridging General and Personalized Federated Learning through Selective Model Integration\nPersonalized federated learning (PFL) achieves high performance by assuming clients only meet test data locally, which does not meet many generic federated learning (GFL) scenarios. In this work, we theoretically show that PMs can be used to enhance GFL with a new learning problem named Selective FL (SFL), which involves optimizing PFL and model selection. However, storing and selecting whole models require<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30591<END_URL>

Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach\nAs online video content rapidly grows, the task of text-video retrieval (TVR) becomes increasingly important. A key challenge in TVR is the information asymmetry between video and text: videos are inherently richer in information, while their textual descriptions often capture only fragments of this complexity. This paper introduces a novel, data-centric framework to bridge this gap by enriching textual representati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29517<END_URL>

Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation\nGroup max-min fairness (MMF) is commonly used in fairness-aware recommender systems (RS) as an optimization objective, as it aims to protect marginalized item groups and ensures a fair competition platform. However, our theoretical analysis indicates that integrating MMF constraint violates the assumption of sample independence during optimization, causing the loss function to deviate from linear additivity. Such nonl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31203<END_URL>

Bridging the Data Provenance Gap Across Text, Speech, and Video\nProgress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established datasets beyond text. In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities---popular text, speech, and video datasets---from their detailed sourcing trends and use restrictions to their geographical and linguistic rep<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30303<END_URL>

Bridging the Gap Between $f$-divergences and Bayes Hilbert Spaces\nWe introduce a novel framework that generalizes $f$-divergences by incorporating locally non-convex divergence-generating functions.Using this extension, we define a new class of pseudo $f$-divergences, encompassing a wider range of distances between distributions that traditional $f$-divergences cannot capture.Among these, we focus on a particular pseudo divergence obtained by considering the induced metric of Bayes Hilbert spac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28483<END_URL>

Bridging the Gap between Database Search and \emph{De Novo} Peptide Sequencing with SearchNovo\nAccurate protein identification from mass spectrometry (MS) data is fundamental to unraveling the complex roles of proteins in biological systems, with peptide sequencing being a pivotal step in this process. The two main paradigms for peptide sequencing are database search, which matches experimental spectra with peptide sequences from databases, and \emph{de novo} sequencing, which infers peptide se<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29582<END_URL>

Bridging the Gap between Variational Inference and Stochastic Gradient MCMC in Function Space\nTraditional parameter-space posterior inference for Bayesian neural networks faces several challenges, such as the difficulty in specifying meaningful prior, the potential pathologies in deep models and the intractability for multi-modal posterior. To address these issues, functional variational inference (fVI) and functional Markov Chain Monte Carlo (fMCMC) are two recently emerged Bayesian inference <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29112<END_URL>

BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval\nExisting retrieval benchmarks primarily consist of information-seeking queries (e.g., aggregated questions from search engines) where keyword or semantic-based retrieval is usually sufficient. However, many complex real-world queries require in-depth reasoning to identify relevant documents that go beyond surface form matching. For example, finding documentation for a coding question requires understanding the logic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27702<END_URL>

Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder\nWhile pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides reducing the training and rendering complexity, applying inverse graphics in the latent space enables a valuable interoperability with other latent-based 2D methods.The major challenge is that inverse graphics cannot be directly applied to such image latent<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29995<END_URL>

Broadening Target Distributions for Accelerated Diffusion Models via a Novel Analysis Approach\nAccelerated diffusion models hold the potential to significantly enhance the efficiency of standard diffusion processes. Theoretically, these models have been shown to achieve faster convergence rates than the standard $\mathcal O(1/\epsilon^2)$ rate of vanilla diffusion models, where $\epsilon$ denotes the target accuracy. However, current theoretical studies have established the acceleration advanta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28182<END_URL>

Broaden your SCOPE! Efficient Conversation Planning for LLMs with Semantic Space\nLarge language models (LLMs) are used in chatbots or AI assistants to hold conversations with a human user. In such applications, the quality (e.g., user engagement, safety) of a conversation is important and can only be exactly known at the end of the conversation. To maximize its expected quality, conversation planning reasons about the stochastic transitions within a conversation to select the optimal LLM respon<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31066<END_URL>

B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners\nIn the absence of extensive human-annotated data for complex reasoning tasks, self-improvement -- where models are trained on their own outputs -- has emerged as a primary method for enhancing performance. Recently, the approach to self-improvement has shifted toward a more dynamic, online fashion through iterative training processes. However, the critical factors underlying the mechanism of these self-improv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29776<END_URL>

BTBS-LNS: Binarized-Tightening, Branch and Search on Learning LNS Policies for MIP\nLearning to solve large-scale Mixed Integer Program (MIP) problems is an emerging research topic, and policy learning-based Large Neighborhood Search (LNS) has been a popular paradigm. However, the explored space of LNS policy is often limited even in the training phase, making the learned policy sometimes wrongly fix some potentially important variables early in the search, leading to local optimum in some cases<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28102<END_URL>

Budgeted Online Continual Learning by Adaptive Layer Freezing and Frequency-based Sampling\nThe majority of online continual learning (CL) advocates single-epoch training and imposes restrictions on the size of replay memory.However, single-epoch training would incur a different amount of computations per CL algorithm, and the additional storage cost to store logit or model in addition to replay memory is largely ignored in calculating the storage budget.Arguing different computational and stora<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28988<END_URL>

Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation\nWe propose a diffusion-based approach for Text-to-Image (T2I) generation with interactive 3D layout control.Layout control has been widely studied to alleviate the shortcomings of T2I diffusion models in understanding objects' placement and relationships from text descriptions.Nevertheless, existing approaches for layout control are limited to 2D layouts, require the user to provide a static layout beforehand, and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28806<END_URL>

Building Blocks of Differentially Private Training\nIn this blog, we introduce the building blocks of training a neural network in a differentially private way.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31339<END_URL>

Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting\nBuilding interactable replicas of articulated objects is a key challenge in computer vision. Existing methods often fail to effectively integrate information across different object states, limiting the accuracy of part-mesh reconstruction and part dynamics modeling, particularly for complex multi-part articulated objects. We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and effici<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28664<END_URL>

Building Math Agents with Multi-Turn Iterative Preference Learning\nRecent studies have shown that large language models' (LLMs) mathematical problem-solving capabilities can be enhanced by integrating external tools, such as code interpreters, and employing multi-turn Chain-of-Thought (CoT) reasoning. While current methods focus on synthetic data generation and Supervised Fine-Tuning (SFT), this paper studies the complementary direct preference learning approach to further improve model perform<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29347<END_URL>

Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences\nHumans excel at learning abstract patterns across different sequences, filtering out irrelevant details, and transferring these generalized concepts to new sequences.In contrast, many sequence learningmodels lack the ability to abstract, which leads to memoryinefficiency and poor transfer. We introduce a non-parametric hierarchical variable learning model (HVM) that learns chunks from sequences and abstracts co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27794<END_URL>

Bundle Neural Network for message diffusion on graphs\nThe dominant paradigm for learning on graphs is message passing. Despite being a strong inductive bias, the local message passing mechanism faces challenges such as over-smoothing, over-squashing, and limited expressivity. To address these issues, we introduce Bundle Neural Networks (BuNNs), a novel graph neural network architecture that operates via *message diffusion* on *flat vector bundles* — geometrically inspired structures that assign<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28114<END_URL>

Cached Multi-Lora Composition for Multi-Concept Image Generation\nLow-Rank Adaptation (LoRA) has emerged as a widely adopted technique in text-to-image models, enabling precise rendering of multiple distinct elements, such as characters and styles, in multi-concept image generation. However, current approaches face significant challenges when composing these LoRAs for multi-concept image generation, particularly as the number of LoRAs increases, resulting in diminished generated image quality. I<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30998<END_URL>

C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets\nConformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers. To optimize the classifier for maximum predictive efficiency, Conformal Training rectifies the training objective with a regularization that minimizes the average prediction set size at a specific error rate. However, the regularization term inevitably deteriorate<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30775<END_URL>

Cafe-Talk: Generating 3D Talking Face Animation with Multimodal Coarse- and Fine-grained Control\nSpeech-driven 3D talking face method should offer both accurate lip synchronization and controllable expressions. Previous methods solely adopt discrete emotion labels to globally control expressions throughout sequences while limiting flexible fine-grained facial control within the spatiotemporal domain. We propose a diffusion-transformer-based 3D talking face generation model, Cafe-Talk, which sim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29627<END_URL>

CAKE: Cascading and Adaptive KV Cache Eviction with Layer Preferences\nLarge language models (LLMs)' proficiency in handling long sequences boosts KV caching demand. Recent efforts to evict KV cache have alleviated the burden for inference, but they often fail to allocate resources rationally across layers with different attention patterns. In this paper, we introduce Cascading and Adaptive KV cache Eviction (CAKE), a method that significantly improves LLM inference efficiency by optimizing KV c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30406<END_URL>

Calibrating Expressions of Certainty\nWe present a novel approach to calibrating linguistic expressions of certainty, e.g., "Maybe" and "Likely". Unlike prior work that assigns a single score to each certainty phrase, we model uncertainty as distributions over the simplex to capture their semantics more accurately. To accommodate this new representation of certainty, we generalize existing measures of miscalibration and introduce a novel post-hoc calibration method. Leveraging these tools, we an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28989<END_URL>

Calibrating LLMs with Information-Theoretic Evidential Deep Learning\nFine-tuned large language models (LLMs) often exhibit overconfidence, particularly when trained on small datasets, resulting in poor calibration and inaccurate uncertainty estimates. Evidential Deep Learning (EDL), an uncertainty-aware approach, enables uncertainty estimation in a single forward pass, making it a promising method for calibrating fine-tuned LLMs. However, despite its computational efficiency, EDL is prone to ov<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29255<END_URL>

CameraCtrl: Enabling Camera Control for Text-to-Video Generation\nControllability plays a crucial role in video generation, as it allows users to create and edit content more precisely. Existing models, however, lack control of camera pose that serves as a cinematic language to express deeper narrative nuances. To alleviate this issue, we introduce CameraCtrl, enabling accurate camera pose control for video diffusion models. Our approach explores effective camera trajectory parameterization alon<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29236<END_URL>

CAMEx: Curvature-aware Merging of Experts\nExisting methods for merging experts during model training and fine-tuning predominantly rely on Euclidean geometry, which assumes a flat parameter space. This assumption can limit the model's generalization ability, especially during the pre-training phase, where the parameter manifold might exhibit more complex curvature. Curvature-aware merging methods typically require additional information and computational resources to approximate the Fisher Info<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28408<END_URL>

Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning\nSelf-supervised learning has the potential of lifting several of the key challenges in reinforcement learning today, such as exploration, representation learning, and reward design. Recent work (METRA) has effectively argued that moving away from mutual information and instead optimizing a certain Wasserstein distance is important for good performance. In this paper, we argue that the benefits seen in that paper can <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27762<END_URL>

Can Generative AI Solve Your In-Context Learning Problem?  A Martingale Perspective\nThis work is about estimating when a conditional generative model (CGM) can solve an in-context learning (ICL) problem. An in-context learning (ICL) problem comprises a CGM, a dataset, and a prediction task. The CGM could be a multi-modal foundation model; the dataset, a collection of patient histories, test results, and recorded diagnoses; and the prediction task to communicate a diagnosis to a new patient. A B<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29101<END_URL>

Can In-context Learning Really Generalize to Out-of-distribution Tasks?\nIn this work, we explore the mechanism of in-context learning (ICL) on out-of-distribution (OOD) tasks that were not encountered during training. To achieve this, we conduct synthetic experiments where the objective is to learn OOD mathematical functions through ICL using a GPT-2 model. We reveal that Transformers may struggle to learn OOD task functions through ICL. Specifically, ICL performance resembles implementing a fu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30175<END_URL>

Can Knowledge Editing Really Correct Hallucinations?\nLarge Language Models (LLMs) suffer from hallucinations, referring to the non-factual information in generated content, despite their superior capacities across tasks. Meanwhile, knowledge editing has been developed as a new popular paradigm to correct the erroneous factual knowledge encoded in LLMs with the advantage of avoiding retraining from scratch. However, one common issue of existing evaluation datasets for knowledge editing is that t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28744<END_URL>

Can Large Language Models Understand Symbolic Graphics Programs?\nAgainst the backdrop of enthusiasm for large language models (LLMs), there is an urgent need to scientifically assess their capabilities and shortcomings. This is nontrivial in part because it is difficult to find tasks which the models have not encountered during training.Utilizing symbolic graphics programs, we propose a domain well-suited to test multiple spatial-semantic reasoning skills of LLMs. Popular in computer graphics, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29250<END_URL>

Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers\nRecent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29961<END_URL>

Can LLM Simulations Truly Reflect Humanity? A Deep Dive\nSimulation powered by Large Language Models (LLMs) has become a promising method for exploring complex human social behaviors. However, the application of LLMs in simulations presents significant challenges, particularly regarding their capacity to accurately replicate the complexities of human behaviors and societal dynamics, as evidenced by recent studies highlighting discrepancies between simulated and real-world interactions. This blog<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31340<END_URL>

Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?\nExtremely low-resource (XLR) languages lack substantial corpora for training NLP models, motivating the use of all available resources such as dictionaries and grammar books. Machine Translation from One Book (Tanzer et al., 2024) suggests prompting long-context LLMs with one grammar book enables English–Kalamang translation, an unseen XLR language—a noteworthy case of linguistic knowledge helping an NLP task. We <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29173<END_URL>

Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?\nLarge Language Models (LLMs) show impressive results in numerous practical applications, but they lack essential safety features that are common in other areas of computer science, particularly an explicit separation of instructions and data. This makes them vulnerable to manipulations such as indirect prompt injections and generally unsuitable for safety-critical tasks. Surprisingly, there is currently no established d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30777<END_URL>

Can LLMs Solve Long Math Word Problems Better?\nMath Word Problems (MWPs) play a vital role in assessing the capabilities of Large Language Models (LLMs), yet current research primarily focuses on questions with concise contexts. The impact of longer contexts on mathematical reasoning remains under-explored. This study pioneers the investigation of Context Length Generalizability (CoLeG), which refers to the ability of LLMs to solve MWPs with extended narratives. We introduce Extended Grade-Scho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30529<END_URL>

Can LLMs Understand Time Series Anomalies?\nLarge Language Models (LLMs) have gained popularity in time series forecasting, but their potential for anomaly detection remains largely unexplored. Our study investigates whether LLMs can understand and detect anomalies in time series data, focusing on zero-shot and few-shot scenarios. Inspired by conjectures about LLMs' behavior from time series forecasting research, we formulate key hypotheses about LLMs' capabilities in time series anomaly detecti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30008<END_URL>

Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model\nIn this work, we tackle the following question: Can neural networks trained with gradient-based methods achieve the optimal statistical-computational tradeoff in learning Gaussian single-index models? Prior research has shown that any polynomial-time algorithm under the statistical query (SQ) framework requires $\Omega(d^{s^\star/2}\lor d)$ samples, where $s^\star$ is the generative exponen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28668<END_URL>

Can One Modality Model Synergize Training of Other Modality Models?\nLearning with multiple modalities has recently demonstrated significant gains in many domains by maximizing the shared information across modalities. However, the current approaches strongly rely on high-quality paired datasets, which allow co-training from the paired labels from different modalities. In this context, we raise a pivotal question: Can a model with one modality synergize the training of other models with the diff<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30961<END_URL>

Can Reinforcement Learning Solve Asymmetric Combinatorial-Continuous Zero-Sum Games?\nThere have been extensive studies on learning in zero-sum games, focusing on the analysis of the existence and algorithmic convergence of Nash equilibrium (NE). Existing studies mainly focus on symmetric games where the strategy spaces of the players are of the same type and size. For the few studies that do consider asymmetric games, they are mostly restricted to matrix games. In this paper, we define and stud<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30815<END_URL>

Can Textual Gradient Work in Federated Learning?\nRecent studies highlight the promise of LLM-based prompt optimization, especially with TextGrad, which automates ``differentiation'' via texts and backpropagates textual feedback provided by LLMs. This approach facilitates training in various real-world applications that do not support numerical gradient propagation or loss calculation.  It opens new avenues for optimization in decentralized, resource-constrained environments, suggesting that use<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30485<END_URL>

Can Transformers Do Enumerative Geometry?\nWe introduce a Transformer-based approach to computational enumerative geometry, specifically targeting the computation of $\psi$-class intersection numbers on the moduli space of curves. Traditional methods for calculating these numbers suffer from factorial computational complexity, making them impractical to use. By reformulating the problem as a continuous optimization task, we compute intersection numbers across a wide value range from $10^{-45}$ t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31007<END_URL>

Can Video LLMs Refuse to Answer? Alignment for Answerability in Video Large Language Models\nIn the broader context of deep learning, Multimodal Large Language Models have achieved significant breakthroughs by leveraging powerful Large Language Models as a backbone to align different modalities into the language space. A prime exemplification is the development of Video Large Language Models (Video-LLMs). While numerous advancements have been proposed to enhance the video understanding capabilit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29769<END_URL>

Can Watermarked LLMs be Identified by Users via Crafted Prompts?\nText watermarking for Large Language Models (LLMs) has made significant progress in detecting LLM outputs and preventing misuse. Current watermarking techniques offer high detectability, minimal impact on text quality, and robustness to text editing.     However, current researches lack investigation into the imperceptibility of watermarking techniques in LLM services.    This is crucial as LLM providers may not want to disclose t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27962<END_URL>

Can Watermarks be Used to Detect LLM IP Infringement For Free?\nThe powerful capabilities of LLMs stem from their rich training data and high-quality labeled datasets, making the training of strong LLMs a resource-intensive process, which elevates the importance of IP protection for such LLMs. Compared to gathering high-quality labeled data, directly sampling outputs from these fully trained LLMs as training data presents a more cost-effective approach. This practice—where a suspect model is fin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30061<END_URL>

Can We Ignore Labels in Out of Distribution Detection?\nOut-of-distribution (OOD) detection methods have recently become more prominent, serving as a core element in safety-critical autonomous systems. One major purpose of OOD detection is to reject invalid inputs that could lead to unpredictable errors and compromise safety. Due to the cost of labeled data, recent works have investigated the feasibility of self-supervised learning (SSL) OOD detection, unlabled OOD detection, and zero shot OOD d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28870<END_URL>

Can we talk models into seeing the world differently?\nUnlike traditional vision-only models, vision language models (VLMs) offer an intuitive way to access visual content through language prompting by combining a large language model (LLM) with a vision encoder. However, both the LLM and the vision encoder come with their own set of biases, cue preferences, and shortcuts, which have been rigorously studied in uni-modal models. A timely question is how such (potentially misaligned) biases and cu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28696<END_URL>

Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-Based Decision-Making Systems\nLarge Language Models (LLMs) have shown significant promise in real-world decision-making tasks for embodied artificial intelligence, especially when fine-tuned to leverage their inherent common sense and reasoning abilities while being tailored to specific applications. However, this fine-tuning process introduces considerable safety and security vulnerabilities, especially in safety-cri<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29631<END_URL>

Capability Localization: Capabilities Can be Localized rather than Individual Knowledge\nLarge scale language models have achieved superior performance in tasks related to natural language processing, however, it is still unclear how model parameters affect performance improvement. Previous studies assumed that individual knowledge is stored in local parameters, and the storage form of individual knowledge is dispersed parameters, parameter layers, or parameter chains, which are not unified. We <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28895<END_URL>

CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation\nConventional 2D pose estimation models are constrained by their design to specific object categories. This limits their applicability to predefined objects. To overcome these limitations, category-agnostic pose estimation (CAPE) emerged as a solution. CAPE aims to facilitate keypoint localization for diverse object categories using a unified model, which can generalize from minimal annotated support images.Recent CAPE works<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28113<END_URL>

CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperation\nIn this work, we address the cooperation problem among large language model (LLM) based embodied agents, where agents must cooperate to achieve a common goal. Previous methods often execute actions extemporaneously and incoherently, without long-term  strategic and cooperative planning, leading to redundant steps, failures, and even serious repercussions in complex tasks like search-and-rescue missions where disc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30059<END_URL>

Captured by Captions: On Memorization and its Mitigation in CLIP Models\nMulti-modal models, such as CLIP, have demonstrated strong performance in aligning visual and textual representations, excelling in tasks like image retrieval and zero-shot classification. Despite this success, the mechanisms by which these models utilize training data, particularly the role of memorization, remain unclear. In uni-modal models, both supervised and self-supervised, memorization has been shown to be essential<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30943<END_URL>

Capturing the Temporal Dependence of Training Data Influence\nTraditional data influence estimation methods, like influence function, assume that learning algorithms are permutation-invariant with respect to training data. However, modern training paradigms—especially for foundation models using stochastic algorithms and non-convergent, multi-stage curricula—are sensitive to data ordering, thus violating this assumption. This mismatch renders influence functions inadequate for answering some cri<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27988<END_URL>

CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling\nTerrestrial carbon fluxes provide vital information about our biosphere's health and its capacity to absorb anthropogenic CO$_2$ emissions. The importance of predicting carbon fluxes has led to the emerging field of data-driven carbon flux modelling (DDCFM), which uses statistical techniques to predict carbon fluxes from biophysical data. However, the field lacks a standardized dataset to promote comparisons between models<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28534<END_URL>

CARTS: Advancing Neural Theorem Proving with Diversified Tactic Calibration and Bias-Resistant Tree Search\nRecent advancements in neural theorem proving integrate large language models with tree search algorithms like Monte Carlo Tree Search (MCTS), where the language model suggests tactics and the tree search finds the complete proof path. However, many tactics proposed by the language model converge to semantically or strategically similar, reducing diversity and increasing search costs by ex<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29413<END_URL>

CAT-3DGS: A Context-Adaptive Triplane Approach to Rate-Distortion-Optimized 3DGS Compression\n3D Gaussian Splatting (3DGS) has recently emerged as a promising 3D representation. Much research has been focused on reducing its storage requirements and memory footprint. However, the needs to compress and transmit the 3DGS representation to the remote side are overlooked. This new application calls for rate-distortion-optimized 3DGS compression. How to quantize and entropy encode sparse Gaussian pri<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28486<END_URL>

Catastrophic Failure of LLM Unlearning via Quantization\nLarge language models (LLMs) have shown remarkable proficiency in generating text, benefiting from extensive training on vast textual corpora. However, LLMs may also acquire unwanted behaviors from the diverse and sensitive nature of their training data, which can include copyrighted and private content. Machine unlearning has been introduced as a viable solution to remove the influence of such problematic content without the need for cost<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28528<END_URL>

CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching\nAnomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning normal patterns in the frequency domain to detect diverse abnormal subsequences, achieve promising results, while still falling short on capturing fine-grained frequency characteristics and channel correlations. To contend with the limitations, we<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28489<END_URL>

CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models\nVirtual try-on methods based on diffusion models achieve realistic effects but often require additional encoding modules, a large number of training parameters, and complex preprocessing, which increases the burden on training and inference. In this work, we re-evaluate the necessity of additional modules and analyze how to improve training efficiency and reduce redundant steps in the inference process. Based on the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28612<END_URL>

Cauchy-Schwarz Regularizers\nWe introduce a novel class of regularization functions, called Cauchy–Schwarz~(CS) regularizers, which can be designed to induce a wide range of properties in solution vectors of optimization problems. To demonstrate the versatility of CS regularizers, we derive concrete regularization functions that promote discrete-valued vectors, eigenvectors of a given matrix, and orthogonal matrices. The resulting CS regularizers are simple, differentiable, and can be free of sp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30046<END_URL>

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning\nCausal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models. This leads to the inability to rely on and verify state-of-the-art DNN-based systems, especially in high-stakes scenarios. For this reason, circumventing causal opacity in DNNs represents a key open challenge at the intersection of deep learning, interpretability, and causality. This<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28500<END_URL>

Causal Discovery via Bayesian Optimization\nExisting score-based methods for directed acyclic graph (DAG) learning from observational data struggle to recover the causal graph accurately and sample-efficiently. To overcome this, in this study, we propose DrBO (DAG recovery via Bayesian Optimization)—a novel DAG learning framework leveraging Bayesian optimization (BO) to find high-scoring DAGs. We show that, by sophisticatedly choosing the promising DAGs to explore, we can find higher-scoring one<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30746<END_URL>

Causal Effect Estimation with Mixed Latent Confounders and Post-treatment Variables\nCausal inference from observational data has attracted considerable attention among researchers. One main obstacle to drawing valid causal conclusions is handling of confounders. As the direct measurement of confounders may not always be feasible, recent methods seek to address the confounding bias via proxy variables, i.e., variables postulated to be causally related to unobserved confounders. However, the sele<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28242<END_URL>

Causal Graphical Models for Vision-Language Compositional Understanding\nRecent work has empirically shown that Vision-Language Models (VLMs) struggleto fully understand the compositional properties of the human language, usuallymodeling an image caption as a “bag of words”. As a result, they performpoorly on compositional tasks, which require a deeper understanding of the differententities of a sentence (subject, verb, etc.) jointly with their mutual relationshipsin order to be solved. In this <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28753<END_URL>

Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference\nNetworked interference, also known as the peer effect in social science and spillover effect in economics, has drawn increasing interest across various domains. This phenomenon arises when a unit’s treatment and outcome are influenced by the actions of its peers, posing significant challenges to causal inference, particularly in treatment assignment and effect estimation in real applications, due to the violatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28858<END_URL>

Causal Identification for Complex Functional Longitudinal Studies\nReal-time monitoring in modern medical research introduces functional longitudinal data, characterized by continuous-time measurements of outcomes, treatments, and confounders. This complexity leads to uncountably infinite treatment-confounder feedbacks, which traditional causal inference methodologies cannot handle. Inspired by the coarsened data framework, we adopt stochastic process theory, measure theory, and net convergence <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30722<END_URL>

Causal Information Prioritization for Efficient Reinforcement Learning\nCurrent Reinforcement Learning (RL) methods often suffer from sample-inefficiency, resulting from blind exploration strategies that neglect causal relationships among states, actions, and rewards. Although recent causal approaches aim to address this problem, they lack grounded modeling of reward-guided causal understanding of states and actions for goal-orientation, thus impairing learning efficiency. To tackle this issue, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28418<END_URL>

Causally Motivated Sycophancy Mitigation for Large Language Models\nIncorporating user preferences into large language models (LLMs) can enhance the personalization and reliability of model outputs and facilitate the application of LLMs to real-world scenarios. However, leveraging user preferences can be a double-edged sword. Recent studies have found that improper utilization can incur sycophancy, where LLMs prioritize alignment with user preferences over the correctness of their outputs. To ad<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27727<END_URL>

Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference\nLarge Language Models (LLMs) have recently been used as experts to infer causal graphs, often by repeatedly applying a pairwise prompt that asks about the causal relationship of each variable pair. However, such experts, including human domain experts, cannot distinguish between direct and indirect effects given a pairwise prompt. Therefore, instead of the graph, we propose that causal order be used as a more stable outpu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30671<END_URL>

Causal Representation Learning from Multimodal Biological Observations\nPrevalent in biological applications (e.g., human phenotype measurements), multimodal datasets can provide valuable insights into the underlying biological mechanisms. However, current machine learning models designed to analyze such datasets still lack interpretability and theoretical guarantees, which are essential to biological applications. Recent advances in causal representation learning have shown promise in uncoverin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28747<END_URL>

CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series\nCausal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it.Despite this, in-the-wild evaluation is still lacking, as works frequently rely on synthetic data evaluation and sparse real-world examples under critical theoretical assumptions. Real-world causal structures, however, are often complex, evolving over tim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27823<END_URL>

CAX: Cellular Automata Accelerated in JAX\nCellular automata have become a cornerstone for investigating emergence and self-organization across diverse scientific disciplines, spanning neuroscience, artificial life, and theoretical physics. However, the absence of a hardware-accelerated cellular automata library limits the exploration of new research directions, hinders collaboration, and impedes reproducibility. In this work, we introduce CAX (Cellular Automata Accelerated in JAX), a high-perfo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28380<END_URL>

CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph\nStructure-based drug design (SBDD) aims to generate potential drugs that can bind to a target protein and is greatly expedited by the aid of AI techniques in generative models. However, a lack of systematic understanding persists due to the diverse settings, complex implementation, difficult reproducibility, and task singularity. Firstly, the absence of standardization can lead to unfair comparisons and inconclusive insights.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28468<END_URL>

CBMA: Improving Conformal Prediction through Bayesian Model Averaging\nConformal prediction has emerged as a popular technique for facilitating valid predictive inference across a spectrum of machine learning models, under minimal assumption of exchangeability. Recently, Hoff (2023) showed that full conformal Bayes provides the most efficient prediction sets (smallest by expected volume) among all prediction sets that are valid at the  $(1 - \alpha)$ level if the model is correctly specified. Ho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30578<END_URL>

CBQ: Cross-Block Quantization for Large Language Models\nPost-training quantization (PTQ) has played a pivotal role in compressing large language models (LLMs) at ultra-low costs. Although current PTQ methods have achieved promising results by addressing outliers and employing layer- or block-wise loss optimization techniques, they still suffer from significant performance degradation at ultra-low bits precision. To dissect this issue, we conducted an in-depth analysis of quantization errors spe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28924<END_URL>

CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding\nElectroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding methods rely on supervised learning, limited by specific tasks and datasets, hindering model performance and generalizability. With the success of large language models, there is a growing body of studies focusing on EEG foundation models. However, these <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29880<END_URL>

C-CLIP: Multimodal Continual Learning for Vision-Language Model\nMultimodal pre-trained models like CLIP need large image-text pairs for training but often struggle with domain-specific tasks. Since retraining with specialized and historical data incurs significant memory and time costs, it is important to continually learn new domains in the open world while preserving original performance. However, current continual learning research mainly focuses on single-modal scenarios, and the evaluation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28116<END_URL>

CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models\nAs Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks, concerns regarding the potential negative societal impacts of LLM-generated content have also arisen. To evaluate the biases exhibited by LLMs, researchers have recently proposed a variety of datasets. However, existing bias evaluation efforts often focus on only a particular type of bias and employ inc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30167<END_URL>

Centrality-guided Pre-training for Graph\nSelf-supervised learning (SSL) has shown great potential in learning generalizable representations for graph-structured data. However, existing SSL-based graph pre-training methods largely focus on improving graph representations by learning the structure information based on disturbing or reconstructing graphs, which ignores an important issue: the importance of different nodes in the graph structure may vary.To fill this gap, we propose a Centrality-gu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29321<END_URL>

CertainlyUncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness\nThe ability to acknowledge the inevitable uncertainty in their knowledge and reasoning is a prerequisite for AI systems to be truly truthful and reliable. In this paper, we present a taxonomy of uncertainty specific to vision-language AI systems, distinguishing between epistemic uncertainty (arising from a lack of information) and aleatoric uncertainty (due to inherent unpredictability), and further expl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29051<END_URL>

Certified Robustness Under Bounded Levenshtein Distance\nText classifiers suffer from small perturbations, that if chosen adversarially, can dramatically change the output of the model. Verification methods can provide robustness certificates against such adversarial perturbations, by computing a sound lower bound on the robust accuracy. Nevertheless, existing verification methods incur in prohibitive costs and cannot practically handle Levenshtein distance constraints. We propose the first meth<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29038<END_URL>

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks\nThe widespread deployment of pre-trained language models (PLMs) has exposed them to textual backdoor attacks, particularly those planted during the pre-training stage. These attacks pose significant risks to high-reliability applications, as they can stealthily affect multiple downstream tasks. While certifying robustness against such threats is crucial, existing defenses struggl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29472<END_URL>

CFD: Learning Generalized Molecular Representation via Concept-Enhanced Feedback Disentanglement\nTo accelerate biochemical research, e.g., drug and protein discovery, molecular representation learning (MRL) has attracted much attention. However, most existing methods follow the closed-set assumption that training and testing data share identical distribution, which limits their generalization abilities in out-of-distribution (OOD) cases. In this paper, we explore designing a new disentangled me<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30491<END_URL>

CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models\nClassifier-free guidance (CFG) is a fundamental tool in modern diffusion models for text-guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality outputs, frequently result in issues like mode collapse. Contrary to the widespread belief that these are inherent limitations of d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30421<END_URL>

CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding\nMost existing video understanding benchmarks for multimodal large language models (MLLMs) focus only on short videos. The limited number of benchmarks for long video understanding often rely solely on multiple-choice questions (MCQs). However, because of the inherent limitation of MCQ-based evaluation and the increasing reasoning ability of MLLMs, models can give the current answer purely by combining short video <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28509<END_URL>

Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models\nWe present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA). Compared to the literature, CoA overcomes two major challenges of current QA applications: (i) unfaithful hallucination that is inconsistent with real-time or domain facts and (ii) weak reasoning performance over compositional information. Our key contribution is a novel reasoning-retrieval mechanis<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31221<END_URL>

Chain-of-Focus Prompting: Leveraging Sequential Visual Cues to Prompt Large Autoregressive Vision Models\nIn-context learning (ICL) has revolutionized natural language processing by enabling models to adapt to diverse tasks with only a few illustrative examples. However, the exploration of ICL within the field of computer vision remains limited. Inspired by Chain-of-Thought (CoT) prompting in the language domain, we propose Chain-of-Focus (CoF) Prompting, which enhances vision models by enabling<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28393<END_URL>

Chain-of-region: Visual Language Models Need  Details for Diagram Analysis\nVisual Language Models (VLMs) like GPT-4V have broadened the scope of LLM applications, yet they face significant challenges in accurately processing visual details, particularly in scientific diagrams. This paper explores the necessity of meticulous visual detail collection and region decomposition for enhancing the performance of VLMs in scientific diagram analysis. We propose a novel approach that combines traditional<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29954<END_URL>

Chain-of-Thought Provably Enables Learning the (Otherwise) Unlearnable\nModern language models have demonstrated remarkable reasoning capabilities by using chain-of-thought (CoT). One hypothesis about the inner workings of CoT is that it breaks down originally complex tasks into smaller subtasks that are more amenable to learning. We formalize this notion by showing possibility and impossibility results of learning from in-context demonstrations with and without CoT. In particular, with CoT, we <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29898<END_URL>

CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators\nWe introduce CHAMP, a novel method for learning sequence-to-sequence, multi-hypothesis 3D human poses from 2D keypoints by leveraging a conditional distribution with a diffusion model. To predict a single output 3D pose sequence, we generate and aggregate multiple 3D pose hypotheses. For better aggregation results, we develop a method to score these hypotheses during training, effectively integrating conformal prediction into the le<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28587<END_URL>

Charting the Design Space of Neural Graph Representations for Subgraph Matching\nSubgraph matching is vital in knowledge graph (KG) question answering, molecule design, scene graph, code and circuit search, etc.Neural methods have shown promising results for subgraph matching.Our study of recent systems suggests refactoring them into a unified design space for graph matching networks.Existing methods occupy only a few isolated patches in this space, which remains largely uncharted.We undertake t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30929<END_URL>

ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation\nWe introduce a new benchmark, ChartMimic, aimed at assessing the visually-grounded code generation capabilities of large multimodal models (LMMs). ChartMimic utilizes information-intensive visual charts and textual instructions as inputs, requiring LMMs to generate the corresponding code for chart rendering.ChartMimic includes $4,800$ human-curated (figure, instruction, code) triplets, which represent the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28138<END_URL>

ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding\nAutomatic chart understanding is crucial for content comprehension and document parsing. Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding through domain-specific alignment and fine-tuning. However, current MLLMs still struggle to provide faithful data and reliable analysis only based on charts. To address it, we propose ChartMoE, which employs the Mixture of E<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28378<END_URL>

CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL\nIn addressing the challenges of improving large language model (LLM) performance for Text-to-SQL tasks, we propose a new framework, CHASE-SQL, that is comprised of innovative strategies that leverage judiciously-designed test-time compute in multi-agent modeling to enhance candidate generation and selection. Our approach leverages LLMs’ intrinsic knowledge to generate diverse and high-quality SQL candida<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30489<END_URL>

ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities\nIn this work, we introduce ChatQA 2, an Llama 3.0-based model with a 128K context window, designed to bridge the gap between open-source LLMs and leading proprietary models (e.g., GPT-4-Turbo) in long-context understanding and retrieval-augmented generation (RAG) capabilities. These two capabilities are essential for LLMs to process large volumes of information that cannot fit into a single prompt and are comple<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29053<END_URL>

CheapNet: Cross-attention on Hierarchical representations for Efficient protein-ligand binding Affinity Prediction\nAccurately predicting protein-ligand binding affinity is a critical challenge in drug discovery, crucial for understanding drug efficacy. While existing models typically rely on atom-level interactions, they often fail to capture the complex, higher-order interactions, resulting in noise and computational inefficiency. Transitioning to modeling these interactions at the cluster lev<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30656<END_URL>

Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates\nAutomatic LLM benchmarks, such as AlpacaEval 2.0, Arena-Hard-Auto, and MT-Bench, have become popular for evaluating language models due to their cost-effectiveness and scalability compared to human evaluation. Achieving high win rates on these benchmarks can significantly boost the promotional impact of newly released language models. This promotional benefit may motivate tricks, such as manipulating model output length or st<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28083<END_URL>

ChemAgent: Self-updating Memories in Large Language Models Improves Chemical Reasoning\nChemical reasoning usually involves complex, multi-step processes that demand precise calculations, where even minor errors can lead to cascading failures. Furthermore, large language models (LLMs) encounter difficulties handling domain-specific formulas, executing reasoning steps accurately, and integrating code ef- effectively when tackling chemical reasoning tasks. To address these challenges, we present C<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28557<END_URL>

Chemistry-Inspired Diffusion with Non-Differentiable Guidance\nRecent advances in diffusion models have shown remarkable potential in the conditional generation of novel molecules. These models can be guided in two ways: (i) explicitly, through additional features representing the condition, or (ii) implicitly, using a property predictor. However, training property predictors in conditional diffusion models requires an abundance of labeled data and is inherently challenging in real-world applica<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31002<END_URL>

CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs\nMultimodal Large Language Models (MLLMs) still struggle with hallucinations despite their impressive capabilities. Recent studies have attempted to mitigate this by applying Direct Preference Optimization (DPO) to multimodal scenarios using preference pairs from text-based responses. However, our analysis of representation distributions reveals that multimodal DPO struggles to align image and text representations <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30804<END_URL>

ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains\nLarge language models (LLMs) have brought significant changes to many aspects of our lives.However, assessing and ensuring their chronological knowledge remains challenging.Existing approaches fall short in addressing the temporal adaptability of knowledge, often relying on a fixed time-point view. To overcome this, we introduce ChroKnowBench, a benchmark dataset designed to evaluate chronologically accumula<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27828<END_URL>

Chunk-Distilled Language Modeling\nWe introduce Chunk-Distilled Language Modeling (CD-LM), an approach to text generation that addresses two challenges in current large language models (LLMs): the inefficiency of token-level generation, and the difficulty of adapting to new data and knowledge. Our method combines deep network-based LLMs with a straightforward retrieval module, which allows the generation of multi-token text chunks at a single decoding step. Our retrieval framework enables flexib<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28391<END_URL>

CipherPrune:  Efficient and Scalable Private Transformer Inference\nPrivate Transformer inference using cryptographic protocols offers promising solutions for privacy-preserving machine learning; however, it still faces significant runtime overhead (efficiency issues) and challenges in handling long-token inputs (scalability issues). We observe that the Transformer's operational complexity scales quadratically with the number of input tokens, making it essential to reduce the input token length.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28463<END_URL>

CircuitFusion: Multimodal Circuit Representation Learning for Agile Chip Design\nThe rapid advancements of AI rely on the support of integrated circuits (ICs). However, the growing complexity of digital ICs makes the traditional IC design process costly and time-consuming. In recent years, AI-assisted IC design methods have demonstrated great potential, but most methods are task-specific or focus solely on the circuit structure in graph format, overlooking other circuit modalities with rich func<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28186<END_URL>

Circuit Representation Learning with Masked Gate Modeling and Verilog-AIG Alignment\nUnderstanding the structure and function of circuits is crucial for electronic design automation (EDA). Circuits can be formulated as And-Inverter graphs (AIGs), enabling efficient implementation of representation learning through graph neural networks (GNNs).Masked modeling paradigms have been proven effective in graph representation learning.However, masking augmentation to original circuits will destroy their<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29473<END_URL>

Circuit Transformer: A Transformer That Preserves Logical Equivalence\nImplementing Boolean functions with circuits consisting of logic gates is fundamental in digital computer design. However, the implemented circuit must be exactly equivalent, which hinders generative neural approaches on this task due to their occasionally wrong predictions. In this study, we introduce a generative neural model, the “Circuit Transformer”, which eliminates such wrong predictions and produces logic circuits str<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28560<END_URL>

CirT: Global Subseasonal-to-Seasonal Forecasting with Geometry-inspired Transformer\nAccurate Subseasonal-to-Seasonal (S2S) climate forecasting is pivotal for decision-making including agriculture planning and disaster preparedness but is known to be challenging due to its chaotic nature. Although recent data-driven models have shown promising results, their performance is limited by inadequate consideration of geometric inductive biases. Usually, they treat the spherical weather data as planar <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29244<END_URL>

CityAnchor: City-scale 3D Visual Grounding with Multi-modality LLMs\nIn this paper, we present a 3D visual grounding method called CityAnchor for localizing an urban object in a city-scale point cloud. Recent developments in multiview reconstruction enable us to reconstruct city-scale point clouds but how to conduct visual grounding on such a large-scale urban point cloud remains an open problem. Previous 3D visual grounding system mainly concentrates on localizing an object in an image or a sma<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30802<END_URL>

CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes\nRecently, 3D Gaussian Splatting (3DGS) has revolutionized radiance field reconstruction, manifesting efficient and high-fidelity novel view synthesis. However, accurately representing surfaces, especially in large and complex scenarios, remains a significant challenge due to the unstructured nature of 3DGS. In this paper, we present CityGaussianV2, a novel approach for large-scale scene reconstruction tha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29187<END_URL>

ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance\nRecent text-to-image customization works have proven successful in generating images of given concepts by fine-tuning diffusion models on a few examples. However, tuning-based methods inherently tend to overfit the concepts, resulting in failure to create the concept under multiple conditions (*e.g.*, headphone is missing when generating "a <sks>`dog wearing a headphone"). Interestingly, we notice that the base mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28697<END_URL>

Class Distribution-induced Attention Map for Open-vocabulary Semantic Segmentations\nOpen-vocabulary semantic segmentation is a challenging task that assigns seen or unseen class labels to individual pixels. While recent works with vision-language models (VLMs) have shown promising results in zero-shot semantic segmentation, they still struggle to accurately localize class-related objects. In this work, we argue that CLIP-based prior works yield patch-wise noisy class predictions while having hi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30516<END_URL>

Classic but Everlasting: Traditional Gradient-Based Algorithms Converges Fast Even in Time-Varying Multi-Player Games\nLast-iterate convergence behaviours of well-known algorithms are intensively investigated in various games, such as two-player bilinear zero-sum games.However, most known last-iterate convergence properties rely on strict settings where the underlying games must have time-invariant payoffs.Besides, the limited known attempts on the games with time-varying payoffs are in two-play<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28077<END_URL>

Classifier-Agnostic Zero-Shot Natural Language Explanations\nNatural Language Explanations (NLEs) interpret the decision-making process of a given model through textual sentences. Current NLEs suffer from a severe limitation; they are unfaithful to the model’s actual reasoning process, as a separate textual decoder is explicitly trained to generate those explanations using annotated datasets for a specific task, leading them to reflect what annotators desire. In this work, we take the first step<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29324<END_URL>

ClawMachine: Learning to Fetch Visual Tokens for Referential Comprehension\nAligning vision and language concepts at a finer level remains an essential topic of multimodal large language models (MLLMs), particularly for tasks such as referring and grounding. Existing methods, such as proxy encoding and geometry encoding, incorporate additional syntax to encode spatial information, imposing extra burdens when communicating between language with vision modules. In this study, we propose ClawMachin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29545<END_URL>

CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained Models\nThe emergence of the foundation model era has sparked immense research interest in utilizing pre-trained representations for continual learning~(CL), yielding a series of strong CL methods with outstanding performance on standard evaluation benchmarks. Nonetheless, there are growing concerns regarding potential data contamination within the massive pre-training datasets. Furthermore, the static nature of standard<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29639<END_URL>

CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale\nMeasuring biodiversity is crucial for understanding ecosystem health. While prior works have developed machine learning models for taxonomic classification of photographic images and DNA separately, in this work, we introduce a multi-modal approach combining both, using CLIP-style contrastive learning to align images, barcode DNA, and text-based representations of taxonomic labels in a unified embedding space. This allows <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29009<END_URL>

ClimaQA: An Automated Evaluation Framework for Climate Foundation Models\nThe use of Large Language Models (LLMs) in climate science has recently gained significant attention. However, a critical issue remains: the lack of a comprehensive evaluation framework capable of assessing the quality and scientific validity of model outputs. To address this issue, we develop *ClimaGen* (Climate QA Generator), an adaptive learning framework that generates question-answer pairs from graduate textbooks with<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28801<END_URL>

CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification\nIn this paper, we aim to build an adversarially robust zero-shot image classifier that can accurately and efficiently classify unseen examples while defending against unforeseen adversarial attacks, addressing critical challenges in real-world safety-sensitive scenarios. To achieve this, we focus on two key challenges: zero-shot classification and defense against unforeseen attacks. We ground our wo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29543<END_URL>

Clique Number Estimation via Differentiable Functions of Adjacency Matrix Permutations\nEstimating the clique number in a graph is central to various applications, e.g., community detection, graph retrieval, etc. Existing estimators often rely on non-differentiable combinatorial components. Here, we propose a full differentiable estimator for clique number estimation, which can be trained from distant supervision of clique numbers, rather than demonstrating actual cliques.Our key insight is a fo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30469<END_URL>

CL-MFAP: A contrastive learning-based multimodal foundation model for antibiotic property prediction\nDue to the rise in antimicrobial resistance, identifying novel compounds with antibiotic potential is crucial for combatting this global health issue. However, traditional drug development methods are costly and inefficient. Recognizing the pressing need for more effective solutions, researchers have turned to machine learning techniques to streamline the prediction and development of novel anti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28842<END_URL>

CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control\nMotion diffusion models and Reinforcement Learning (RL) based control for physics-based simulations have complementary strengths for human motion generation. The former is capable of generating a wide variety of motions, adhering to intuitive control such as text, while the latter offers physically plausible motion and direct interaction with the environment. In this work, we present a method that combines<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28291<END_URL>

Closed-Form Merging of Parameter-Efficient Modules for Federated Continual Learning\nModel merging has emerged as a crucial technique in Deep Learning, enabling the integration of multiple models into a unified system while preserving performance and scalability. In this respect, the compositional properties of low-rank adaptation techniques (e.g., LoRA) have proven beneficial, as simple averaging LoRA modules yields a single model that mostly integrates the capabilities of all individual module<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29651<END_URL>

Closed-loop Diffusion Control of Complex Physical Systems\nThe control problems of complex physical systems have broad applications in science and engineering.  Previous studies have shown that generative control methods based on diffusion models offer significant advantages for solving these problems. However, existing generative control approaches face challenges in both performance and efficiency when extended to the closed-loop setting, which is essential for effective control. In this paper<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29738<END_URL>

Co$^{\mathbf{3}}$Gesture: Towards Coherent Concurrent Co-speech 3D Gesture Generation with Interactive Diffusion\nGenerating gestures from human speech has gained tremendous progress in animating virtual avatars. While the existing methods enable synthesizing gestures cooperated by people self-talking, they overlook the practicality of concurrent gesture modeling with two-person interactive conversations. Moreover, the lack of high-quality datasets with concurrent co-speech gestures also limits <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29405<END_URL>

COAT: Compressing Optimizer states and Activations for Memory-Efficient FP8 Training\nFP8 training has emerged as a promising method for improving training efficiency. Existing frameworks accelerate training by applying FP8 computation to linear layers while leaving optimizer states and activations in higher precision, which fails to fully optimize memory usage. This paper introduces COAT (**C**ompressing **O**ptimizer States and **A**ctivations for FP8 **T**raining), a novel FP8 training framew<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29297<END_URL>

Cocoon: Robust Multi-Modal Perception with Uncertainty-Aware Sensor Fusion\nAn important paradigm in 3D object detection is the use of multiple modalities to enhance accuracy in both normal and challenging conditions, particularly for long-tail scenarios. To address this, recent studies have explored two directions of adaptive approaches: MoE-based adaptive fusion, which struggles with uncertainties arising from distinct object configurations, and late fusion for output-level adaptive fusion, wh<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30466<END_URL>

CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities of CodeLLMs\nRecent advancements in Code Large Language Models (CodeLLMs) have predominantly focused on open-ended code generation tasks, often neglecting the critical aspect of code understanding and comprehension. To bridge this gap, we present CodeMMLU, a comprehensive multiple-choice question-answer benchmark designed to evaluate the depth of software and code understanding in LLMs. CodeMMLU includes nearly 20,000<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30509<END_URL>

CofCA: A STEP-WISE Counterfactual Multi-hop QA benchmark\nWhile Large Language Models (LLMs) excel in question-answering (QA) tasks, their real reasoning abilities on multiple evidence retrieval and integration on Multi-hop QA tasks remain less explored. Firstly, LLMs sometimes generate answers that rely on internal memory rather than retrieving evidence and reasoning in the given context, which brings concerns about the evaluation quality of real reasoning abilities. Although previous counterfa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28269<END_URL>

COFlowNet: Conservative Constraints on Flows Enable High-Quality Candidate Generation\nGenerative flow networks (GFlowNet) have been considered as powerful tools for generating candidates with desired properties. Given that evaluating the property of candidates can be complex and time-consuming, existing GFlowNets train proxy models for efficient online evaluation. However, the performance of proxy models is heavily dependent on the amount of data and is of considerable uncertainty. Therefore, i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28047<END_URL>

CogCoM: A Visual Language Model with Chain-of-Manipulations Reasoning\nVision-Language Models (VLMs) have shown broad effectiveness due to extensive training that aligns visual inputs with corresponding language responses. However, this conclusive alignment training causes models to overlook essential visual reasoning, leading to failures in handling detailed visual tasks and producing unfaithful responses. Drawing inspiration from human cognition in solving visual problems (e.g., marking, zoom <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30334<END_URL>

CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer\nWe present CogVideoX, a large-scale text-to-video generation model based on diffusion transformer, which can generate 10-second continuous videos aligned with text prompt, with a frame rate of 16 fps and resolution of 768 $\times$ 1360 pixels. Previous video generation models often had limited movement and short durations, and is difficult to generate videos with coherent narratives based on text. We propose several designs to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29998<END_URL>

Collab: Controlled Decoding using Mixture of Agents for LLM Alignment\nAlignment of Large Language models (LLMs) is crucial for safe and trustworthy deployment in applications. Reinforcement learning from human feedback (RLHF) has emerged as an effective technique to align LLMs to human preferences, and broader utilities, but it requires updating billions of model parameters which is computationally expensive. Controlled Decoding, by contrast, provides a mechanism for aligning a model at inferen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30799<END_URL>

CollabEdit: Towards Non-destructive Collaborative Knowledge Editing\nCollaborative learning of large language models (LLMs) has emerged as anew paradigm for utilizing private data from different parties to guaranteeefﬁciency and privacy. Meanwhile, Knowledge Editing (KE) for LLMs has alsogarnered increased attention due to its ability to manipulate the behaviors ofLLMs explicitly, yet leaves the collaborative KE case—in which knowledgeedits of multiple parties are aggregated in a privacy-preserv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31140<END_URL>

Collaborative Discrete-Continuous Black-Box Prompt Learning for Language Models\nLarge Scale Pre-Trained Language Models (PTMs) have demonstrated unprecedented capabilities across diverse natural language processing tasks. Adapting such models to downstream tasks is computationally intensive  and time-consuming, particularly in black-box scenarios common in Language-Model-as-a-Service (LMaaS) environments, where model parameters and gradients are inaccessible. Recently, black-box prompt learning<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28111<END_URL>

Collapsed Language Models Promote Fairness\nTo mitigate societal biases implicitly encoded in recent successful pretrained language models, a diverse array of approaches have been proposed to encourage model fairness, focusing on prompting, data augmentation, regularized fine-tuning, and more. Despite the development, it is nontrivial to reach a principled understanding of fairness and an effective algorithm that can consistently debias language models. In this work, by rigorous evaluations of N<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28548<END_URL>

CoLoRA: A Competitive Learning Approach for Enhancing LoRA\nWe propose a Competitive Low-Rank Adaptation (CoLoRA) framework to address the limitations of the LoRA method, which either lacks capacity with a single rank-$r$ LoRA or risks inefficiency and overfitting with a larger rank-$Kr$ LoRA, where $K$ is an integer larger than 1. The proposed CoLoRA method initializes $K$ distinct LoRA components, each with rank $r$, and allows them to compete during training. This competition drives each LoRA<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28642<END_URL>

ColPali: Efficient Document Retrieval with Vision Language Models\nDocuments are visually rich structures that convey information through text, but also figures, page layouts, tables, or even fonts. Since modern retrieval systems mainly rely on the textual information they extract from document pages to index documents -often through lengthy and brittle processes-, they struggle to exploit key visual cues efficiently. This limits their capabilities in many practical document retrieval applicatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28336<END_URL>

ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization\nOffline reinforcement learning (RL) has garnered significant attention for its ability to learn effective policies from pre-collected datasets without the need for further environmental interactions. While promising results have been demonstrated in single-agent settings, offline multi-agent reinforcement learning (MARL) presents additional challenges due to the large joint state-a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30931<END_URL>

Combatting Dimensional Collapse in LLM Pre-Training Data via Submodular File Selection\nSelecting high-quality pre-training data for large language models (LLMs) is crucial for enhancing their overall performance under limited computation budget, improving both training and sample efficiency. Recent advancements in file selection primarily rely on using an existing or trained proxy model to assess the similarity of samples to a target domain, such as high quality sources BookCorpus and Wikipedia<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28897<END_URL>

Combining Induction and Transduction for Abstract Reasoning\nWhen learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test outputs, e.g. using a neural network? We study this question on ARC by training neural models for \emph{induction} (inferring latent functions) and \emph{transduction} (directly predicting the test output for a given test input). We train on synthetically ge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29457<END_URL>

Combining Text-based and Drag-based Editing for Precise and Flexible Image Editing\nPrecise and flexible image editing remains a fundamental challenge in computer vision. Based on the modified areas, most editing methods can be divided into two main types: global editing and local editing. In this paper, we choose the two most common editing approaches (\ie text-based editing and drag-based editing) and analyze their drawbacks. Specifically, text-based methods often fail to describe the desired <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31150<END_URL>

COMBO: Compositional World Models for Embodied Multi-Agent Cooperation\nIn this paper, we investigate the problem of embodied multi-agent cooperation, where decentralized agents must cooperate given only egocentric views of the world. To effectively plan in this setting, in contrast to learning world dynamics in a single-agent scenario, we must simulate world dynamics conditioned on an arbitrary number of agents' actions given only partial egocentric visual observations of the world. To address <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29260<END_URL>

COME: Test-time Adaption by Conservatively Minimizing Entropy\nMachine learning models must continuously self-adjust themselves for novel data distribution in the open world. As the predominant principle, entropy minimization (EM) has been proven to be a simple yet effective cornerstone in existing test-time adaption (TTA) methods. While unfortunately its fatal limitation (i.e., overconfidence) tends to result in model collapse. For this issue, we propose to conservatively minimize the entropy (<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30977<END_URL>

Commit0: Library Generation from Scratch\nWith the goal of supporting research into AI that exceeds typical expert software development ability, we introduce Commit0, a benchmark that challenges AI agents to write libraries from scratch. Agents are provided with a specification document outlining the library's API as well as a suite of interactive unit tests, with the goal of producing an implementation of this API accordingly. The implementation is validated through running these unit tests. As<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29935<END_URL>

Common Pitfalls of Margin-based Preference Optimization in Language Model Alignment\nReinforcement Learning from Human Feedback (RLHF) has become the predominant approach for aligning language models (LMs) to be more helpful and less harmful. At its core, RLHF uses a margin-based loss for preference optimization, which specifies the ideal LM behavior only in terms of the difference between preferred and dispreferred responses. This under-specification of ideal behavior for each response individu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29259<END_URL>

CO-MOT: Boosting End-to-end Transformer-based Multi-Object Tracking via Coopetition Label Assignment and Shadow Sets\nExisting end-to-end Multi-Object Tracking (e2e-MOT) methods have not surpassed non-end-to-end tracking-by-detection methods. One potential reason is its label assignment strategy during training that consistently binds the tracked objects with tracking queries and then assigns the few newborns to detection queries. With one-to-one bipartite matching, such an assignment will yield<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31235<END_URL>

CoMotion: Concurrent Multi-person 3D Motion\nWe introduce an approach for tracking detailed 3D poses of multiple people from a single monocular camera stream. Our system maintains temporally coherent predictions in crowded scenes filled with difficult poses and occlusions. Rather than detect poses and associate them to current tracks, our model directly updates all tracked poses simultaneously given a new input image. We train on numerous single-image and video datasets with both 2D and 3D annot<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28254<END_URL>

Comparing noisy neural population dynamics using optimal transport distances\nBiological and artificial neural systems form high-dimensional neural representations that underpin their computational capabilities. Consequently, methods for quantifying geometric similarity in neural representations have become a popular tool for identifying computational principles that are potentially shared across neural systems. These methods generally assume that neural responses are deterministic and static. H<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29054<END_URL>

Comparing Targeting Strategies for Maximizing Social Welfare with Limited Resources\nMachine learning is increasingly used to select which individuals receive limited-resource interventions in domains such as human services, education, development, and more. However, it is often not apparent what the right quantity is for models to predict. In particular, policymakers rarely have access to data from a randomized controlled trial (RCT) that would enable accurate estimates of treatment effects -- <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31240<END_URL>

ComPC: Completing a 3D Point Cloud with 2D Diffusion Priors\n3D point clouds directly collected from objects through sensors are often incomplete due to self-occlusion. Conventional methods for completing these partial point clouds rely on manually organized training sets and are usually limited to object categories seen during training. In this work, we propose a test-time framework for completing partial point clouds across unseen categories without any requirement for training. Leveraging poi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29579<END_URL>

Competing Large Language Models in Multi-Agent Gaming Environments\nDecision-making is a complex process requiring diverse abilities, making it an excellent framework for evaluating Large Language Models (LLMs). Researchers have examined LLMs' decision-making through the lens of Game Theory. However, existing evaluation mainly focus on two-player scenarios where an LLM competes against another. Additionally, previous benchmarks suffer from test set leakage due to their static design. We introduc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30468<END_URL>

Competitive Fair Scheduling with Predictions\nBeyond the worst-case analysis of algorithms, the recent learning-augmented framework considers that an algorithm can leverage possibly imperfect predictions about the unknown variables to make decisions to have guarantees tied to the prediction quality. We consider online non-clairvoyant scheduling to minimize the max-stretch under the learning-augmented framework, where the scheduler has access to job size predictions. We present a family of algori<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28648<END_URL>

Complementary Label Learning with Positive Label Guessing and Negative Label Enhancement\nComplementary label learning (CLL) is a weakly supervised learning paradigm that constructs a multi-class classifier only with complementary labels, specifying classes that the instance does not belong to. We reformulate CLL as an inverse problem that infers the full label information from the output space information. To be specific, we propose to split the inverse problem into two subtasks: positive label<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29999<END_URL>

Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness\nRecent results in non-convex stochastic optimization demonstrate the convergence of popular adaptive algorithms (e.g., AdaGrad) under the $(L_0, L_1)$-smoothness condition, but the rate of convergence is a higher-order polynomial in terms of problem parameters like the smoothness constants. The complexity guaranteed by such algorithms to find an $\epsilon$-stationary point may<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29203<END_URL>

Composable Interventions for Language Models\nTest-time interventions for language models can enhance factual accuracy, mitigate harmful outputs, and improve model efficiency without costly retraining.But despite a flood of new methods, different types of interventions are largely developing independently.In practice, multiple interventions must be applied sequentially to the same model, yet we lack standardized ways to study how interventions interact.We fill this gap by introducing composable <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28014<END_URL>

Composing Unbalanced Flows for Flexible Docking and Relaxation\nDiffusion models have emerged as a successful approach for molecular docking, but they often cannot model protein flexibility or generate nonphysical poses. We argue that both these challenges can be tackled by framing the problem as a transport between distributions. Still, existing paradigms lack the flexibility to define effective maps between such complex distributions. To address this limitation we propose Unbalanced Flow Match<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28828<END_URL>

Compositional 4D Dynamic Scenes Understanding with Physics Priors for Video Question Answering\nFor vision-language models (VLMs), understanding the dynamic properties of objects and their interactions in 3D scenes from videos is crucial for effective reasoning about high-level temporal and action semantics. Although humans are adept at understanding these properties by constructing 3D and temporal (4D) representations of the world, current video understanding models struggle to extract these dy<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30879<END_URL>

Compositional Entailment Learning for Hyperbolic Vision-Language Models\nImage-text representation learning forms a cornerstone in vision-language models, where pairs of images and textual descriptions are contrastively aligned in a shared embedding space. Since visual and textual concepts are naturally hierarchical, recent work has shown that hyperbolic space can serve as a high-potential manifold to learn vision-language representation with strong downstream performance. In this work, for the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31060<END_URL>

Compositional simulation-based inference for time series\nAmortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this strategy avoids the need for tractable likelihoods, it often requires a large number of simulations and has been challenging to scale to time series data. Scientific simulators frequently emulate real-world dynamics through thousands of single-state transitions over time. We propose an SBI approach that can <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27993<END_URL>

Compressing Vision Foundation Models at ImageNet-level Costs\nVision foundation models are renowned for the generalization ability due to massive training data. Nevertheless, they demand tremendous training resources, and the training data is often inaccessible, e.g., CLIP, DINOv2, posing great challenges to developing derivatives that could facilitate the research. In this work, we offer a very simple and general solution, named Proteus, to distill foundation models into smaller equivalents on <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30012<END_URL>

Computational Explorations of Total Variation Distance\nWe investigate some previously unexplored (or under-explored) computational aspects of total variation (TV) distance.First, we give a simple deterministic polynomial-time algorithm for checking equivalence between mixtures of product distributions, over arbitrary alphabets.This corresponds to a special case, whereby the TV distance between the two distributions is zero.Second, we prove that unless $\mathsf{NP} \subseteq \mathsf{RP}$ it is i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27775<END_URL>

Computational Limits of Low-Rank Adaptation for Transformer Models\nWe study the computational limits of Low-Rank Adaptation (LoRA) for finetuning transformer-based models using fine-grained complexity theory.Our key observation is that the existence of low-rank decompositions within the gradient computation of LoRA adaptation leads to possible algorithmic speedup.This allows us to (i) identify a phase transition behavior of efficiency assuming the Strong Exponential Time Hypothesis (SETH), and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29984<END_URL>

Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics\nWe study computationally and statistically efficient Reinforcement Learning algorithms for the *linear Bellman Complete* setting, a setting that uses linear function approximation to capture value functions and unifies existing models like linear Markov Decision Processes (MDP) and Linear Quadratic Regulators (LQR).  While it is known from the prior works that this setting is statistically tractable, it re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28728<END_URL>

Compute-Constrained Data Selection\nData selection can reduce the amount of training data needed to finetune LLMs; however, the efficacy of data selection scales directly with its compute. Motivated by the practical challenge of compute-constrained finetuning, we consider the setting in which both the cost of selecting data and training are budgeted for. We first formalize the problem of data selection with a cost-aware utility function, and model the data selection problem as trading off initia<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31001<END_URL>

Computing Circuits Optimization via Model-Based Circuit Genetic Evolution\nOptimizing computing circuits such as multipliers and adders is a fundamental challenge in modern integrated circuit design. Recent efforts propose formulating this optimization problem as a reinforcement learning (RL) proxy task, offering a promising approach to search high-speed and area-efficient circuit design solutions. However, we show that the RL-based formulation (proxy task) converges to a local optimal design so<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30053<END_URL>

CoMRes: Semi-Supervised Time Series Forecasting Utilizing Consensus Promotion of Multi-Resolution\nLong-term time series forecasting poses significant challenges due to the complex dynamics and temporal variations, particularly when dealing with unseen patterns and data scarcity. Traditional supervised learning approaches, which rely on cleaned and labeled data, struggle to capture these unseen characteristics, limiting their effectiveness in real-world applications. In this study, we propose a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29110<END_URL>

Concept Bottleneck Language Models For Protein Design\nWe introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our architecture offers three key benefits: i) Control: We can intervene on concept values to precisely control the properties of generated proteins, achieving a 3$\times$ larger change in desired concept values compared to baselines. ii) Interpretability: A linear mappin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29243<END_URL>

Concept Bottleneck Large Language Models\nWe introduce the Concept Bottleneck Large Language Model (CB-LLM), a pioneering approach to creating inherently interpretable Large Language Models (LLMs). Unlike traditional black-box LLMs that rely on post-hoc interpretation methods with limited neuron function insights, CB-LLM sets a new standard with its built-in interpretability, scalability, and ability to provide clear, accurate explanations. We investigate two essential tasks in the NLP domain: t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29660<END_URL>

Concept Pinpoint Eraser for Text-to-image Diffusion Models via Residual Attention Gate\nRemarkable progress in text-to-image diffusion models has brought a major concern about potentially generating images on inappropriate or trademarked concepts. Concept erasing has been investigated with the goals of deleting target concepts in diffusion models while preserving other concepts with minimal distortion. To achieve these goals, recent concept erasing methods usually fine-tune the cross-attention l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29221<END_URL>

ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning\nWhile large-scale text-to-image diffusion models have demonstrated impressive image-generation capabilities, there are significant concerns about their potential misuse for generating unsafe content, violating copyright, and perpetuating societal biases. Recently, the text-to-image generation community has begun addressing these concerns by editing or unlearning undesired concepts from pre-trained models. However, thes<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28581<END_URL>

Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing\nModel editing methods modify specific behaviors of Large Language Models by altering a small, targeted set of network weights and require very little data and compute. These methods can be used for malicious applications such as inserting misinformation or simple trojans that result in adversary-specified behaviors when a trigger word is present. While previous editing methods have focused on relatively constrained scen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29632<END_URL>

Concise, Efficient, and Faithful Sufficient Reasons using Self-Explaining Neural Networks\n*Minimal sufficient reasons* represent a prevalent form of explanation - the smallest subset of input features which, when held constant at their corresponding values, ensure that the prediction remains unchanged. Previous *post-hoc* methods attempt to obtain such explanations but face two main limitations: (1) Obtaining these subsets poses a computational challenge, leading most scalable methods to conver<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30745<END_URL>

Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution Estimation\nWe consider a class of conditional forward-backward diffusion models for conditional generative modeling, that is, generating new data given a covariate (or control variable). To formally study the theoretical properties of these conditional generative models, we adopt a statistical framework of distribution regression to characterize the large sample properties of the conditional dist<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29860<END_URL>

Conditional Diffusion with Ordinal Regression: Longitudinal Data Generation for Neurodegenerative Disease Studies\nModeling the progression of neurodegenerative diseases such as Alzheimer’s disease (AD) is crucial for early detection and prevention given their irreversible nature. However, the scarcity of longitudinal data and complex disease dynamics make the analysis highly challenging. Moreover, longitudinal samples often contain irregular and large intervals between subject visits, which und<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30688<END_URL>

Conditional Testing based on Localized Conformal $p$-values\nIn this paper, we address conditional testing problems through the conformal inference framework. We define the localized conformal $p$-values by inverting prediction intervals and prove their theoretical properties. These defined $p$-values are then applied to several conditional testing problems to illustrate their practicality. Firstly, we propose a conditional outlier detection procedure to test for outliers in the conditional dist<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30148<END_URL>

Confidence Elicitation: A New Attack Vector for Large Language Models\nA fundamental issue in deep learning has been adversarial robustness. As these systems have scaled, such issues have persisted. Currently, large language models (LLMs) with billions of parameters suffer from adversarial attacks just like their earlier, smaller counterparts. However, the threat models have changed. Previously, having gray-box access, where input embeddings or output logits/probabilities were visible to the use<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29166<END_URL>

ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks\nThe loss functions of many learning problems contain multiple additive terms that can disagree and yield conflicting update directions. For Physics-Informed Neural Networks (PINNs), loss terms on initial/boundary conditions and physics equations are particularly interesting as they are well-established as highly difficult tasks. To improve learning the challenging multi-objective task posed by PINNs, we propose the ConFI<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30634<END_URL>

Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning\nIn real-world applications, a reinforcement learning (RL) agent should consider multiple objectives and adhere to safety guidelines.To address these considerations, we propose a constrained multi-objective RL algorithm named constrained multi-objective gradient aggregator (CoMOGA).In the field of multi-objective optimization, managing conflicts between the gradients of the multiple objectives is crucial <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28337<END_URL>

Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering\nGenerative models lack rigorous statistical guarantees with respect to their predictions. In this work, we propose Sequential Conformal Prediction for Generative Models (SCOPE-Gen), a sequential conformal prediction method producing prediction sets that satisfy a rigorous statistical guarantee called conformal admissibility control. This guarantee means that the prediction sets contain at least one<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31189<END_URL>

Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback\nIn interactive imitation learning (IL), uncertainty quantification offers a way for the learner (i.e. robot) to contend with distribution shifts encountered during deployment by actively seeking additional feedback from an expert (i.e. human) online. Prior works use mechanisms like ensemble disagreement or Monte Carlo dropout to quantify when black-box IL policies are uncertain; however, these approach<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29247<END_URL>

Conformalized Survival Analysis for General Right-Censored Data\nWe develop a framework to quantify predictive uncertainty in survival analysis, providing a reliable lower predictive bound (LPB) for the true, unknown patient survival time. Recently, conformal prediction has been used to construct such valid LPBs for *type-I right-censored data*, with the guarantee that the bound holds with high probability. Crucially, under the type-I setting, the censoring time is observed for all data points. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30112<END_URL>

Conformal Language Model Reasoning with Coherent Factuality\nLanguage models are increasingly being used in important decision pipelines, so ensuring the correctness of their outputs is crucial. Recent work has proposed evaluating the “factuality” of claims decomposed from a language model generation and applying conformal prediction techniques to filter out those claims that are not factual. This can be effective for tasks such as information retrieval, where constituent claims may be evaluated<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30640<END_URL>

Conformal Prediction Sets Can Cause Disparate Impact\nAlthough conformal prediction is a promising method for quantifying the uncertainty of machine learning models, the prediction sets it outputs are not inherently actionable. Many applications require a single output to act on, not several. To overcome this, prediction sets can be provided to a human who then makes an informed decision. In any such system it is crucial to ensure the fairness of outcomes across protected groups, and researchers<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28872<END_URL>

Conformal Structured Prediction\nConformal prediction has recently emerged as a promising strategy for quantifying the uncertainty of a predictive model; these algorithms modify the model to output sets of labels that are guaranteed to contain the true label with high probability. However, existing conformal prediction algorithms have largely targeted classification and regression settings, where the structure of the prediction set has a simple form as a level set of the scoring function. Howeve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31156<END_URL>

CONGO: Compressive Online Gradient Optimization\nWe address the challenge of zeroth-order online convex optimization where the objective function's gradient exhibits sparsity, indicating that only a small number of dimensions possess non-zero gradients. Our aim is to leverage this sparsity to obtain useful estimates of the objective function's gradient even when the only information available is a limited number of function samples. Our motivation stems from the optimization of large-scale queue<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31029<END_URL>

ConMix: Contrastive Mixup at Representation Level for Long-tailed Deep Clustering\nDeep clustering has made remarkable progress in recent years. However, most existing deep clustering methods assume that distributions of different clusters are balanced or roughly balanced, which are not consistent with the common long-tailed distributions in reality. In nature, the datasets often follow long-tailed distributions, leading to biased models being trained with significant performance drop. Despite t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31057<END_URL>

Connecting Federated ADMM to Bayes\nWe provide new connections between two distinct federated learning approaches based on (i) ADMM and (ii) Variational Bayes (VB), and propose new variants by combining their complementary strengths. Specifically, we show that the dual variables in ADMM naturally emerge through the "site" parameters used in VB with isotropic Gaussian covariances. Using this, we derive two versions of ADMM from VB that use flexible covariances and functional regularisation, respe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28671<END_URL>

Connectome Mapping: Shape-Memory Network via Interpretation of Contextual Semantic Information\nContextual semantic information plays a pivotal role in the brain's visual interpretation of the surrounding environment. When processing visual information, electrical signals within synapses facilitate the dynamic activation and deactivation of synaptic connections, guided by the contextual semantic information associated with different objects. In the realm of Artificial Intelligence (AI), neural n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29747<END_URL>

Conservative Contextual Bandits: Beyond Linear Representations\nConservative Contextual Bandits (CCBs) address safety in sequential decision making by requiring that an agent's policy, along with minimizing regret, also satisfies a safety constraint: the performance is not worse than a baseline policy (e.g., the policy that the company has in production) by more than $(1+\alpha)$ factor. Prior work developed UCB-stylealgorithms for this problem in the multi-armed (Wu et al., 2016)  and contextua<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29601<END_URL>

Consistency Checks for Language Model Forecasters\nForecasting is a task that is difficult to evaluate: the ground truth can only be known in the future. Recent work showing LLM forecasters rapidly approaching human-level performance begs the question: how can we benchmark and evaluate these forecasters *instantaneously*? Following the consistency check framework, we measure the performance of forecasters in terms of the consistency of their predictions on different logically-related questions. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28220<END_URL>

Consistency Models Made Easy\nConsistency models (CMs) offer faster sampling than traditional diffusion models, but their training is resource-intensive. For example, as of 2024, training a state-of-the-art CM on CIFAR-10 takes one week on 8 GPUs. In this work, we propose an effective scheme for training CMs that largely improves the efficiency of building such models. Specifically, by expressing CM trajectories via a particular differential equation, we argue that diffusion models can be viewed<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27780<END_URL>

Consistent Flow Distillation for Text-to-3D Generation\nScore Distillation Sampling (SDS) has made significant strides in distilling image-generative models for 3D generation. However, its maximum-likelihood-seeking behavior often leads to degraded visual quality and diversity, limiting its effectiveness in 3D applications. In this work, we propose Consistent Flow Distillation (CFD), which addresses these limitations. We begin by leveraging the gradient of the diffusion ODE or SDE sampling proce<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30650<END_URL>

Constraint-Conditioned Actor-Critic for Offline Safe Reinforcement Learning\nOffline safe reinforcement learning (OSRL) aims to learn policies with high rewards while satisfying safety constraints solely from data collected offline. However, the learned policies often struggle to handle states and actions that are not present or out-of-distribution (OOD) from the offline dataset, which can result in violation of the safety constraints or overly conservative behaviors during their online deployme<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28392<END_URL>

Constructing Confidence Intervals for Average Treatment Effects from Multiple Observational Datasets\nEstimating confidence intervals (CIs) of the average treatment effects (ATE) from patient records is crucial to assess the effectiveness and safety of drugs. However, patient records typically come from different hospitals, thus raising the question of how multiple observational datasets can be effectively combined for this purpose. In our paper, we propose a new method that estimates the ATE fr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30580<END_URL>

Content-Style Learning from Unaligned Domains: Identifiability under Unknown Latent Dimensions\nUnderstanding identifiability of latent content and style variables from unaligned multi-domain data is essential for tasks such asdomain translation and data generation. Existing works on content-style identification were often developed under somewhat stringent conditions, e.g., that all latent components are mutually independent and that the dimensions of the content and style variables are known. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28320<END_URL>

Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series\nRecently, leveraging pre-trained Large Language Models (LLMs) for time series (TS) tasks has gained increasing attention, which involves activating and enhancing LLMs' capabilities. Many methods aim to activate LLMs' capabilities based on token-level alignment, but overlook LLMs' inherent strength in natural language processing — their deep understanding of linguistic logic and structure rather than superficial embeddi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28084<END_URL>

Context-aware Dynamic Pruning for Speech Foundation Models\nFoundation models, such as large language models, have achieved remarkable success in natural language processing and are evolving into models capable of handling multiple modalities.Listening ability, in particular, is crucial for many applications, leading to research on building speech foundation models. However, the high computational cost of these large models presents a significant challenge for real-world applications. Although s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28001<END_URL>

Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHR Data\nFoundation Models (FMs) trained on Electronic Health Records (EHRs) have achieved state-of-the-art results on numerous clinical prediction tasks. However, prior EHR FMs typically have context windows of $<$1k tokens, which prevents them from modeling full patient EHRs which can exceed 10k's of events. For making clinical predictions, both model performance and robustness to the unique properties of EHR data <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27657<END_URL>

ContextGNN: Beyond Two-Tower Recommendation Systems\nRecommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic representation of users and items. In contrast, pair-wise representations either scale poorly due to their quadratic complexity or are too restrictive on the candidate pairs to rank. To address these is<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28384<END_URL>

Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance\nLarge Language Model's are instruction-finetuned to enhance their ability to follow user instructions and better comprehend input context. Still, they often struggle to follow the input context, especially when it contradicts model's parametric knowledge. This manifests as various failures, such as hallucinations where a model inserts outdated or unwarranted facts into its response. In this work, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29609<END_URL>

Contextual Document Embeddings\nDense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, while effective, are implicitly out-of-context for targeted use cases of retrieval, and that a contextualized document embedding should take into account both the document and neighboring documents in context - analogous to contextualized word embedd<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29341<END_URL>

Contextualizing biological perturbation experiments through language\nHigh-content genetic perturbation experiments provide insights into biomolecular pathways at unprecedented resolution, yet experimental and analysis costs pose barriers to their widespread adoption. In-silico modeling of unseen perturbations has the potential to alleviate this burden by leveraging prior knowledge to enable more efficient exploration of the perturbation space. However, current knowledge-graph approaches neglect<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30942<END_URL>

Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal Video Grounding\nIn this work, we focus on Weakly Supervised Spatio-Temporal Video Grounding (WSTVG). It is a multimodal task aimed at localizing specific subjects  spatio-temporally based on textual queries without bounding box supervision. Motivated by recent advancements in multi-modal foundation models for grounding tasks, we first explore the potential of state-of-the-art object detection models for WSTVG. Despite their ro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27737<END_URL>

Continual Slow-and-Fast Adaptation of Latent Neural Dynamics (CoSFan): Meta-Learning What-How & When to Adapt\nAn increasing interest in learning to forecast for time-series of high-dimensional observations is the ability to adapt to systems with diverse underlying dynamics. Access to observations that define a stationary distribution of these systems is often unattainable, as the underlying dynamics may change over time. Naively training or retraining models at each shift may lead to catastroph<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30443<END_URL>

Continuity-Preserving Autoencoders for Learning Continuous Latent Dynamical Models from Images\nContinuous dynamical systems are cornerstones of many scientific and engineering disciplines.While machine learning offers powerful tools to model these systems from trajectory data, challenges arise when these trajectories are captured as images, resulting in pixel-level observations that are discrete in nature.Consequently, a naive application of a convolutional autoencoder can result in latent coor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29910<END_URL>

Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis\nWe propose a novel autoregressive modeling approach for speech synthesis, combining a variational autoencoder (VAE) with a multi-modal latent space and an autoregressive model that uses Gaussian Mixture Models (GMM) as the conditional probability distribution. Unlike previous methods that rely on residual vector quantization, our model leverages continuous speech representations from the VAE's latent spa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29023<END_URL>

Continuous Diffusion for Mixed-Type Tabular Data\nScore-based generative models (or diffusion models for short) have proven successful for generating text and image data.However, the adaption of this model family to tabular data of mixed-type has fallen short so far. In this paper, we propose CDTD, a Continuous Diffusion model for mixed-type Tabular Data. Specifically, we combine score matching and score interpolation to ensure a common continuous noise distribution for both continuous and categ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29690<END_URL>

Continuous Ensemble Weather Forecasting with Diffusion models\nWeather forecasting has seen a shift in methods from numerical simulations to data-driven systems. While initial research in the area focused on deterministic forecasting, recent works have used diffusion models to produce skillful ensemble forecasts. These models are trained on a single forecasting step and rolled out autoregressively. However, they are computationally expensive and accumulate errors for high temporal resolution due<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28928<END_URL>

Continuous Exposure Learning for Low-light Image Enhancement using Neural ODEs\nLow-light image enhancement poses a significant challenge due to the limited information captured by image sensors in low-light environments.   Despite recent improvements in deep learning models, the lack of paired training datasets remains a significant obstacle.   Therefore, unsupervised methods have emerged as a promising solution.   In this work, we focus on the strength of curve-adjustment-based approaches to t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29919<END_URL>

CONTRA: Conformal Prediction Region via Normalizing Flow Transformation\nDensity estimation and reliable prediction regions for outputs are crucial in supervised and unsupervised learning. While conformal prediction effectively generates coverage-guaranteed regions, it struggles with multi-dimensional outputs due to reliance on one-dimensional nonconformity scores. To address this, we introduce CONTRA: CONformal prediction region via normalizing flow TRAnsformation. CONTRA utilizes the latent sp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28304<END_URL>

Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery\nImitation learning is a data-driven approach to learning policies from expert behavior, but it is prone to unreliable outcomes in out-of-sample (OOS) regions. While previous research on stable dynamical system policies guarantees convergence to a desired state, it often overlooks transient behavior. We propose a framework for learning policies modeled by contractive dynamical systems, ensuring that all policy rollouts<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28526<END_URL>

ContraDiff: Planning Towards High Return States via Contrastive Learning\nThe performance of offline reinforcement learning (RL) is sensitive to the proportion of high-return trajectories in the offline dataset. However, in many simulation environments and real-world scenarios, there are large ratios of low-return trajectories rather than high-return trajectories, which makes learning an efficient policy challenging. In this paper, we propose a method called Contrastive Diffuser (ContraDiff) to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29310<END_URL>

ContraFusion: Contrastively Improving Compositional Understanding in Diffusion Models via Fine-Grained Negative Images\nDespite the impressive text-to-image (T2I) synthesis capabilities of diffusion models, they often struggle to understand compositional relationships between objects and attributes, especially in complex settings. Existing solutions have tackled these challenges through optimizing the cross-attention mechanism or learning from the caption pairs with minimal semantic changes. How<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29626<END_URL>

Contrastive Learning from Synthetic Audio Doppelgängers\nLearning robust audio representations currently demands extensive datasets of real-world sound recordings. By applying artificial transformations to these recordings, models can learn to recognize similarities despite subtle variations through techniques like contrastive learning. However, these transformations are only approximations of the true diversity found in real-world sounds, which are generated by complex interactions of physical <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29305<END_URL>

ControlAR: Controllable Image Generation with Autoregressive Models\nAutoregressive (AR) models have reformulated image generation as next-token prediction, demonstrating remarkable potential and emerging as strong competitors to diffusion models. However, control-to-image generation, akin to ControlNet, remains largely unexplored within AR models. Although a natural approach, inspired by advancements in Large Language Models, is to tokenize control images into tokens and prefill them into the a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30567<END_URL>

Controllable Blur Data Augmentation Using 3D-Aware Motion Estimation\nExisting realistic blur datasets provide insufficient variety in scenes and blur patterns to be trained, while expanding data diversity demands considerable time and effort due to complex dual-camera systems. To address the challenge, data augmentation can be an effective way to artificially increase data diversity. However, existing methods on this line are typically designed to estimate motions from a 2D perspective, e.g., e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29336<END_URL>

Controllable Context Sensitivity and the Knob Behind It\nWhen making predictions, a language model must trade off how much it relies on its context vs. its prior knowledge.Choosing how sensitive the model is to its context is a fundamental functionality, as it enables the model to excel at tasks like retrieval-augmented generation and question-answering. In this paper, we search for a knob which controls this sensitivity, determining whether language models answer from the context or their prior<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30159<END_URL>

Controllable Generation via Locally Constrained Resampling\nAutoregressive models have demonstrated an unprecedented ability at modeling the intricaciesof natural language. However, they continue to struggle with generating complex outputs that adhere tological constraints. Sampling from a fully-independent distribution subject to a constraint is hard. Sampling from an autoregressive distribution subject to a constraint is doubly hard: We have to contend not only with the hardness of the constra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30751<END_URL>

Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements\nThe current paradigm for safety alignment of large language models (LLMs) follows a _one-size-fits-all_ approach: the model refuses to interact with any content deemed unsafe by the model provider. This approach lacks flexibility in the face of varying social norms across cultures and regions. In addition, users may have diverse safety needs, making a model with _static_ safety standards too restrictive to b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30405<END_URL>

Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control\nGenerating street-view images from satellite imagery is a challenging task, particularly in maintaining accurate pose alignment and incorporating diverse environmental conditions. While diffusion models have shown promise in generative tasks, their ability to maintain strict pose alignment throughout the diffusion process is limited. In this paper, we propose a novel Iterative Homogra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28891<END_URL>

Controllable Unlearning for Image-to-Image Generative Models via $\epsilon$-Constrained Optimization\nWhile generative models have made significant advancements in recent years, they also raise concerns such as privacy breaches and biases. Machine unlearning has emerged as a viable solution, aiming to remove specific training data, e.g., containing private information and bias, from models. In this paper, we study the machine unlearning problem in Image-to-Image (I2I) generative models. Previous<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30696<END_URL>

Controlled LLM Decoding via Discrete Auto-regressive Biasing\nControlled text generation allows for enforcing user-defined constraints on large language model outputs, an increasingly important field as LLMs become more prevalent in everyday life. One common approach uses energy-based decoding, which defines a target distribution through an energy function that combines multiple constraints into a weighted average. However, these methods often struggle to balance fluency with constraint satisfac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30436<END_URL>

Controlling Language and Diffusion Models by Transporting Activations\nThe increasing capabilities of large generative models and their ever more widespread deployment have raised concerns about their reliability, safety, and potential misuse. To address these issues, recent works have proposed to control model generation by steering model activations in order to effectively induce or prevent the emergence of concepts or behaviours in the generated output. In this paper we introduce Activation T<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28539<END_URL>

Control-oriented Clustering of Visual Latent Representation\nWe initiate a study of the geometry of the visual representation space ---the information channel from the vision encoder to the action decoder--- in an image-based control pipeline learned from behavior cloning. Inspired by the phenomenon of *neural collapse* (NC) in image classification, we empirically demonstrate the prevalent emergence of a similar *law of clustering* in the visual representation space. Specifically, - In discrete <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28302<END_URL>

ConvCodeWorld: Benchmarking Conversational Code Generation in Reproducible Feedback Environments\nLarge language models (LLMs) have proven invaluable for code generation, particularly in interactive settings. However, existing code generation benchmarks fail to capture the diverse feedback encountered in multi-turn interactions, limiting our ability to evaluate LLMs in these contexts. To address this gap, we present a set of novel benchmarks that explicitly model the quality of feedback provided<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28169<END_URL>

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification\nWe study continual learning on multiple linear classification tasks by sequentially running gradient descent (GD) for a fixed budget of iterations per each given task. When all tasks are jointly linearly separable and are presented in a cyclic/random order, we show the directional convergence of the trained linear classifier to the joint (offline) max-margin solution. This is surprising because GD training on a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30455<END_URL>

Convergence of Distributed Adaptive Optimization with Local Updates\nWe study distributed adaptive algorithms with local updates (intermittent communication). Despite the great empirical success of adaptive methods in distributed training of modern machine learning models, the theoretical benefits of local updates within adaptive methods, particularly in terms of reducing communication complexity, have not been fully understood yet. In this paper, we prove that \em Local SGD \em with momentum (\<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29416<END_URL>

Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis\nDiffusion models have achieved great success in generating high-dimensional samples across various applications. While the theoretical guarantees for continuous-state diffusion models have been extensively studied, the convergence analysis of the discrete-state counterparts remains under-explored. In this paper, we study the theoretical aspects of score-based discrete diffusion models under the Continuous Time Markov<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28280<END_URL>

Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness\nWe study the Differential Privacy (DP) guarantee of hidden-state Noisy-SGD algorithms over a bounded domain. Standard privacy analysis for Noisy-SGD assumes all internal states are revealed, which leads to a divergent R\'enyi DP bound with respect to the number of iterations. Ye & Shokri (2022) and Altschuler & Talwar (2022) proved convergent bounds for smooth (strongly) convex losses, and raise open questions about whether t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28563<END_URL>

Convex Formulations for Training Two-Layer ReLU Neural Networks\nSolving non-convex, NP-hard optimization problems is crucial for training machine learning models, including neural networks. However, non-convexity often leads to black-box machine learning models with unclear inner workings. While convex formulations have been used for verifying neural network robustness, their application to training neural networks remains less explored. In response to this challenge, we reformulate the problem<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28956<END_URL>

COPER: Correlation-based Permutations for Multi-View Clustering\nCombining data from different sources can improve data analysis tasks such as clustering. However, most of the current multi-view clustering methods are limited to specific domains or rely on a suboptimal and computationally intensive two-stage process of representation learning and clustering. We propose an end-to-end deep learning-based multi-view clustering framework for general data types (such as images and tables). Our approa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30937<END_URL>

Copyright-Protected Language Generation via Adaptive Model Fusion\nThe risk of language models reproducing copyrighted material from their training data has led to the development of various protective measures. Among these, inference-time strategies that impose constraints via post-processing have shown promise in addressing the complexities of copyright regulation. However, they often incur prohibitive computational costs or suffer from performance trade-offs. To overcome these limitations, we<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28583<END_URL>

Coreset Selection via Reducible Loss in Continual Learning\nA natural solution for rehearsal-based continual learning is to select a coreset as memory. A coreset serves as an informative summary of a large dataset, enabling a model trained solely on the coreset to achieve performance comparable to training on the full dataset. Previous bi-level coreset selection methods adjust sample weights or probabilities to minimize the outer loss, which is computed over the entire dataset. For non-represent<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28476<END_URL>

Coreset Spectral Clustering\nCoresets have become an invaluable tool for solving $k$-means and kernel $k$-means clustering problems on large datasets with small numbers of clusters. On the other hand, spectral clustering works well on sparse graphs and has recently been extended to scale efficiently to large numbers of clusters. We exploit the connection between kernel $k$-means and the normalised cut problem to combine the benefits of both. Our main result is a coreset spectral clustering algor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31180<END_URL>

CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking\nEffective code retrieval plays a crucial role in advancing code generation, bug fixing, and software maintenance, particularly as software systems increase in complexity. While current code embedding models have demonstrated promise in retrieving code snippets for small-scale, well-defined tasks, they often underperform in more demanding real-world applications such as bug localization within GitHub repositories. W<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28662<END_URL>

Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization\nLanguage model alignment methods, such as reinforcement learning from human feedback (RLHF), haveled to impressive advances in language model capabilities. However, existing techniques are limited by a widely observed phenomenon known as *overoptimization*, where the quality of the language model degrades over the course of the alignment process. Overoptimization occurs <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28754<END_URL>

Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking\nBecause it is difficult to precisely specify complex objectives, reinforcement learning policies are often optimized using flawed proxy rewards that seem to capture the true objective. However, optimizing proxy rewards frequently leads to reward hacking: the optimized reward function ceases to be a good proxy and the resulting policy performs poorly with respect to the unspecified true reward. Principled solutions t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28442<END_URL>

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)\nTransformer-based language models, though not explicitly trained to mimic brain recordings, have demonstrated surprising alignment with brain activity. Progress in these models—through increased size, instruction-tuning, and multimodality—has led to better representational alignment with neural data. Recently, a new class of instruction-tuned multimodal LLMs (MLLMs) have emerged, showing remarka<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27766<END_URL>

Correlation and Navigation in the Vocabulary Key Representation Space of Language Models\nLanguage model (LM) decoding is based on the next-token prediction (NTP) probability distribution. For neural LMs (e.g., Transformer-based), NTP distribution isessentially a softmax-regularized dot product between an encoded input context(query) and fixed vocabulary representations (keys). In this paper, we study theeffect of the key distribution on the NTP distribution, with a focus on whetherthe similarit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29402<END_URL>

CoS: Enhancing Personalization and Mitigating Bias with Context Steering\nTo deliver high-quality, personalized responses, large language models (LLMs) must effectively incorporate \textit{context} — personal, demographic, and cultural information specific to an end-user. For example, asking the model to explain Newton's second law with the context \textit{``I am a toddler''} should produce a response different from when the context is \textit{``I am a physics professor''}. However, leveraging t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27781<END_URL>

CoTFormer: A Chain of Thought Driven Architecture with Budget-Adaptive Computation Cost at Inference\nScaling language models to larger and deeper sizes has led to significant boosts in performance. Even though the size of these models limits their application in compute-constrained environments, the race to continually develop ever larger and deeper foundational models is underway. At the same time---regardless of the model size---task-specific techniques continue to play a pivotal role in achi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30808<END_URL>

Counterfactual Concept Bottleneck Models\nCurrent deep learning models are not designed to simultaneously address three fundamental questions: predict class labels to solve a given classification task (the "What?"), simulate changes in the situation to evaluate how this impacts class predictions (the "How?"), and imagine how the scenario should change to result in different class predictions (the "Why not?"). While current approaches in causal representation learning and concept interpretability<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27870<END_URL>

Counterfactual Generative Modeling with Variational Causal Inference\nEstimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, facial images) and covariates are relatively limited. In this case, to predict one's outcomes under counterfactual treatments, it is crucial to leverage individual information contained in its high-dim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28343<END_URL>

Counterfactual Realizability\nIt is commonly believed that, in a real-world environment, samples can only be drawn from observational and interventional distributions, corresponding to Layers 1 and 2 of the Pearl Causal Hierarchy. Layer 3, representing counterfactual distributions, is believed to be inaccessible by definition. However, Bareinboim, Forney, and Pearl (2015) introduced a procedure that allows an agent to sample directly from a counterfactual distribution, leaving open the question <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27947<END_URL>

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion\nDiffusion models have a tendency to exactly replicate their training data, especially when trained on small datasets.  Most prior work has sought to mitigate this problem by imposing differential privacy constraints or masking parts of the training data, resulting in a notable substantial decrease in image quality. We present CPSample, a method that modifies the sampling process to prevent training data replicat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30007<END_URL>

CR2PQ: Continuous Relative Rotary Positional Query for Dense Visual Representation Learning\nDense visual contrastive learning (DRL) shows promise for learning localized information in dense prediction tasks, but struggles with establishing pixel/patch correspondence across different views (cross-contrasting). Existing methods primarily rely on self-contrasting the same view with variations, limiting input variance and hindering downstream performance. This paper delves into the mechanisms of se<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31058<END_URL>

CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair\nDespite the significant progress made in code generation with large language models, challenges persist, especially with hardware description languages such as Verilog. This paper first presents an analysis of fine-tuned LLMs on Verilog coding, with synthetic data from prior methods. We identify two main issues: difficulties in handling non-t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30772<END_URL>

CR-CTC: Consistency regularization on CTC for improved speech recognition\nConnectionist Temporal Classification (CTC) is a widely used method for automatic speech recognition (ASR), renowned for its simplicity and computational efficiency. However, it often falls short in recognition performance. In this work, we propose the Consistency-Regularized CTC (CR-CTC), which enforces consistency between two CTC distributions obtained from different augmented views of the input speech mel-spectrogram. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30523<END_URL>

Cream: Consistency Regularized Self-Rewarding Language Models\nRecent self-rewarding large language models (LLM) have successfully applied LLM-as-a-Judge to iteratively improve the alignment performance without the need of human annotations for preference data. These methods commonly utilize the same LLM to act as both the policy model (which generates responses) and the reward model (which scores and ranks those responses). The ranked responses are then used as preference pairs to train the LLM<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29403<END_URL>

Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification\nThis paper presents an innovative approach, called credal wrapper, to formulating a credal set representation of model averaging for Bayesian neural networks (BNNs) and deep ensembles (DEs), capable of improving uncertainty estimation in classification tasks. Given a finite collection of single predictive distributions derived from BNNs or DEs, the proposed credal wrapper approach extracts an upper and a lower probab<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29022<END_URL>

Credit-based self organizing maps: training deep topographic networks with minimal performance degradation\nIn the primate neocortex, neurons with similar function are often found to be spatially close. Kohonen's self-organizing map (SOM) has been one of the most influential approaches for simulating brain-like topographical organization in artificial neural network models. However, integrating these maps into deep neural networks with multitude of layers has been challenging, with self-organize<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27851<END_URL>

CREIMBO: Cross-Regional Ensemble Interactions in Multi-view Brain Observations\nModern recordings of neural activity provide diverse observations of neurons across brain areas, behavioral conditions, and subjects; presenting an exciting opportunity to reveal the fundamentals of brain-wide dynamics. Current analysis methods, however, often fail to fully harness the richness of such data, as they provide either uninterpretable representations (e.g., via deep networks) or oversimplify models (e.g.,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31159<END_URL>

Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models\nRecent text-to-image diffusion models leverage cross-attention layers, which have been effectively utilized to enhance a range of visual generative tasks. However, our understanding of cross-attention layers remains somewhat limited. In this study, we present a method for constructing Head Relevance Vectors (HRVs) that align with useful visual concepts. An HRV for a given visual concep<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31175<END_URL>

Cross-Domain Offline Policy Adaptation with Optimal Transport and Dataset Constraint\nOffline reinforcement learning (RL) often struggles with limited data. This work explores cross-domain offline RL where offline datasets (with possibly sufficient data) from another domain can be accessed to facilitate policy learning. However, the underlying environments of the two datasets may have dynamics mismatches, incurring inferior performance when simply merging the data of two domains. Existing method<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29997<END_URL>

Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits\nOff-Policy Evaluation and Learning (OPE/L) in contextual bandits is rapidly gaining popularity in real systems because new policies can be evaluated and learned securely using only historical logged data. However, existing methods in OPE/L cannot handle many challenging but prevalent scenarios such as few-shot data, deterministic logging policies, and new actions. In many applications, such as personalized medicine, content <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29233<END_URL>

Cross-Embodiment Dexterous Grasping with Reinforcement Learning\nDexterous hands exhibit significant potential for complex real-world grasping tasks. While recent studies have primarily focused on learning policies for specific robotic hands, the development of a universal policy that controls diverse dexterous hands remains largely unexplored.In this work, we study the learning of cross-embodiment dexterous grasping policies using reinforcement learning (RL). Inspired by the capability of human<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28010<END_URL>

Cross-Entropy Is All You Need To Invert the Data Generating Process\nSupervised learning has become a cornerstone of modern machine learning, yet a comprehensive theory explaining its effectiveness remains elusive. Empirical phenomena, such as neural analogy-making and the linear representation hypothesis, suggest that supervised models can learn interpretable factors of variation in a linear fashion. Recent advances in self-supervised learning, particularly nonlinear Independent Component Analy<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28737<END_URL>

Cross-Modal Safety Mechanism Transfer in Large Vision-Language Models\nVision-language alignment in Large Vision-Language Models (LVLMs) successfully enables LLMs to understand visual input. However, we find that existing vision-language alignment methods fail to transfer the existing safety mechanism for text in LLMs to vision, which leads to vulnerabilities in toxic image. To explore the cause of this problem, we give the insightful explanation of where and how the safety mechanism of LVLMs op<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31036<END_URL>

CrossMPT: Cross-attention Message-passing Transformer for Error Correcting Codes\nError correcting codes (ECCs) are indispensable for reliable transmission in communication systems. The recent advancements in deep learning have catalyzed the exploration of ECC decoders based on neural networks. Among these, transformer-based neural decoders have achieved state-of-the-art decoding performance. In this paper, we propose a novel Cross-attention Message-Passing Transformer (CrossMPT), which shares k<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28830<END_URL>

Cross the Gap: Inter-modal CLIP Representations Are Superior for Intra-modal Tasks\nPre-trained multi-modal Vision Language Models like CLIP are widely used off-the-shelf for a variety of applications. Previous work has shown that, due to contrastive pre-training, there is a modality gap between the text and image feature embedding spaces. In this paper, we show that the common practice of individually exploiting the text or image encoders of these powerful multimodal models is highly suboptimal<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29411<END_URL>

CryoFM: A Flow-based Foundation Model for Cryo-EM Densities\nCryo-electron microscopy (cryo-EM) is a powerful technique in structural biology and drug discovery, enabling the study of biomolecules at high resolution. Significant advancements by structural biologists using cryo-EM have led to the production of over 38,626 protein density maps at various resolutions. However, cryo-EM data processing algorithms have yet to fully benefit from our knowledge of biomolecular density maps, with only a f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29563<END_URL>

CryoGEN: Cryogenic Electron Tomography Reconstruction via Generative Energy Nets\nCryogenic electron tomography (Cryo-ET) is a powerful method for visualizing cellular structures in their native state, but its effectiveness is limited by anisotropic resolution caused by the missing-wedge problem, complicating the interpretation of tomograms. IsoNet, a deep learning method, addresses these challenges by iteratively reconstructing missing-wedge information and improving the signal-to-noise ratio o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27980<END_URL>

cryoSPHERE: Single-Particle HEterogeneous REconstruction from cryo EM\nThe three-dimensional structure of proteins plays a crucial role in determining their function. Protein structure prediction methods, like AlphaFold, offer rapid access to a protein’s structure. However, large protein complexes cannot be reliably predicted, and proteins are dynamic, making it important to resolve their full conformational distribution. Single-particle cryo-electron microscopy (cryo-EM) is a powerful tool for <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28425<END_URL>

CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features\nMultimodal encoders like CLIP excel in tasks such as zero-shot image classification and cross-modal retrieval. However, they require excessive training data.We propose canonical similarity analysis (CSA), which uses two unimodal encoders to replicate multimodal encoders using limited data.CSA maps unimodal features into a multimodal space, using a new similarity score to retain only the multimodal information.CSA only invol<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30894<END_URL>

CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery\nLarge language models (LLMs) have demonstrated significant potential in advancing various fields of research and society. However, the current community of large language models (LLMs) overly focuses on benchmarks for analyzing specific foundational skills (e.g. mathematics and code generation), neglecting an all-round evaluation of the computer science field. To bridge this gap, we introduce CS-Bench<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28862<END_URL>

CtD: Composition through Decomposition in Emergent Communication\nCompositionality is a cognitive mechanism that allows humans to systematically combine known concepts in novel ways.This study demonstrates how artificial neural agents acquire and utilize compositional generalization to describe previously unseen images.Our method, termed \`\`Composition through Decomposition'', involves two sequential training steps.In the \'Decompose\' step, the agents learn to decompose an image into basic con<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30034<END_URL>

Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model\nControlNets are widely used for adding spatial control to text-to-image diffusion models. However, when it comes to controllable video generation, ControlNets cannot be directly integrated into new backbones due to feature space mismatches, and training ControlNets for new backbones can be a significant burden for many users. Furthermore, applying ControlNets independently to different frames<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28385<END_URL>

CtrLoRA: An Extensible and Efficient Framework for Controllable Image Generation\nRecently, large-scale diffusion models have made impressive progress in text-to-image (T2I) generation. To further equip these T2I models with fine-grained spatial control, approaches like ControlNet introduce an extra network that learns to follow a condition image. However, for every single condition type, ControlNet requires independent training on millions of data pairs with hundreds of GPU hours, which is quit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31087<END_URL>

Ctrl-U: Robust Conditional Image Generation via Uncertainty-aware Reward Modeling\nIn this paper, we focus on the task of conditional image generation, where an image is synthesized according to user instructions. The critical challenge underpinning this task is ensuring both the fidelity of the generated images and their semantic alignment with the provided conditions. To tackle this issue, previous studies have employed supervised perceptual losses derived from pre-trained models, i.e., reward<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28943<END_URL>

CTSyn: A Foundational Model for Cross Tabular Data Generation\nGenerative Foundation Models (GFMs) have achieved remarkable success in producing high-quality synthetic data for images and text. However, their application to tabular data presents significant challenges due to the heterogeneous nature of table features. Current cross-table learning frameworks struggle with the absence of a generative model backbone and a mechanism to decode heterogeneous feature values. To address these challenges<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29585<END_URL>

CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation\nWe introduce a novel method for generating 360° panoramas from text prompts or images. Our approach leverages recent advances in 3D generation by employing multi-view diffusion models to jointly synthesize the six faces of a cubemap. Unlike previous methods that rely on processing equirectangular projections or autoregressive generation, our method treats each face as a standard perspective image, simplifying the generat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29958<END_URL>

CURIE: Evaluating LLMs on Multitask Scientific Long-Context Understanding and Reasoning\nScientific problem-solving involves synthesizing information while applying expert  knowledge.   We  introduce  CURIE,  a  scientific  long-Context  Understanding, Reasoning, and Information Extraction benchmark to measure the potential of Large Language Models (LLMs) in scientific problem-solving and assisting scientists in realistic workflows. This benchmark introduces ten challenging tasks curated by expe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28609<END_URL>

Curriculum-aware Training for Discriminating Molecular Property Prediction Models\nDespite their wide application across various fields, current molecular property prediction models struggle with the challenge of activity cliff, which refers to the situation where molecules with similar chemical structures display remarkable different properties. This phenomenon hinders existing models' ability to learn distinctive representations for molecules with similar chemical structures, and results in in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30904<END_URL>

Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems\nRecent advancements in large language model (LLM)-powered agents have shown that collective intelligence can significantly outperform individual capabilities, largely attributed to the meticulously designed inter-agent communication topologies. Though impressive in performance, existing multi-agent pipelines inherently introduce substantial token overhead, as well as increased economic costs, which pose challen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29978<END_URL>

Cut Your Losses in Large-Vocabulary Language Models\nAs language models grow ever larger, so do their vocabularies.This has shifted the memory footprint of LLMs during training disproportionately to one single layer: the cross-entropy in the loss computation.Cross-entropy builds up a logit matrix with entries for each pair of input tokens and vocabulary items and, for small models, consumes an order of magnitude more memory than the rest of the LLM combined.We propose Cut Cross-Entropy (CCE), a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30424<END_URL>

CViT: Continuous Vision Transformer for Operator Learning\nOperator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various physical domains. Here we introduce the Continuous Vision Transformer (CViT), a novel neural operator architecture that leverages advances in computer vision to address challenges in learning complex physical systems.  CViT combines a vision transformer encode<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29048<END_URL>

Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models\nLanguage Model (LM) agents for cybersecurity that are capable of autonomously identifying vulnerabilities and executing exploits have potential to cause real-world impact. Policymakers, model providers, and researchers in the AI and cybersecurity communities are interested in quantifying the capabilities of such agents to help mitigate cyberrisk and investigate opportunities for penetration testing. Towa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28041<END_URL>

Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object Detection\nIn pursuit of detecting unstinted objects that extend beyond predefined categories, prior arts of open-vocabulary object detection (OVD) typically resort to pretrained vision-language models (VLMs) for base-to-novel category generalization. However, to mitigate the misalignment between upstream image-text pretraining and downstream region-level perception, additional supervisions are indispensable, e.g., image-text pairs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30109<END_URL>

D2G: Debiased Learning with Distribution Guidance for Generalized Category Discovery\nIn this paper, we tackle the problem of Generalized Category Discovery (GCD). Given a dataset containing both labelled and unlabelled images, the objective is to cluster all images in the unlabelled subset, irrespective of whether they are from known or unknown classes. In GCD, an inherent label bias exists between known and unknown classes due to the lack of ground-truth labels for the latter. State-of-the-art<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30717<END_URL>

DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life\nAs we increasingly seek guidance from LLMs for decision-making in daily life, many of these decisions are not clear-cut and depend significantly on the personal values and ethical standards of the users. We present DailyDilemmas, a dataset of 1,360 moral dilemmas encountered in everyday life. Each dilemma includes two possible actions and with each action, the affected parties and human values invoked. Based on the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29764<END_URL>

DAMO: Decoding by Accumulating Activations Momentum for Mitigating Hallucinations in Vision-Language Models\nLarge Vision-Language Models (VLMs) exhibit significant potential in multimodal tasks but often struggle with hallucinations—responses that are plausible yet visually ungrounded. In this work, we investigate the layer-wise prediction tendencies of VLMs and conduct an in-depth analysis of their decoding mechanism. We observe that VLMs tend to ``overthink'' during the final stages of decodi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30108<END_URL>

DarkBench: Benchmarking Dark Patterns in Large Language Models\nWe introduce DarkBench, a comprehensive benchmark for detecting dark design patterns—manipulative techniques that influence user behavior—in interactions with large language models (LLMs). Our benchmark comprises 660 prompts across six categories: brand bias, user retention, sycophancy, anthropomorphism, harmful generation, and sneaking. We evaluate models from five leading companies (OpenAI, Anthropic, Meta, Mistral, Google) and fi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28346<END_URL>

DART: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control\nText-conditioned human motion generation, which allows for user interaction through natural language, has become increasingly popular. Existing methods typically generate short, isolated motions based on a single input sentence. However, human motions are continuous and can extend over long periods, carrying rich semantics. Creating long, complex motions that precisely respond to streams of text descrip<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29308<END_URL>

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning\nLarge Language Models (LLMs) rely on the contextual information embedded in examples/demonstrations to perform in-context learning (ICL). To mitigate the risk of LLMs potentially leaking private information contained in examples in the prompt, we introduce a novel data-adaptive differentially private algorithm called **AdaDPSyn** to generate synthetic examples from the private dataset and then use these synthetic exam<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28125<END_URL>

Data-Augmented Phrase-Level Alignment for Mitigating Object Hallucination\nDespite their significant advancements, Multimodal Large Language Models(MLLMs) often generate factually inaccurate information, referred to as hallucination.In this work, we address object hallucinations in MLLMs, where informationis generated about an object not present in the input image. We introduce Data-augmentedPhrase-level Alignment (DPA), a novel loss which can be applied toinstruction-tuned off-the-shelf MLLMs t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27739<END_URL>

Data Center Cooling System Optimization Using Offline Reinforcement Learning\nThe recent advances in information technology and artificial intelligence have fueled a rapid expansion of the data center (DC) industry worldwide, accompanied by an immense appetite for electricity to power the DCs. In a typical DC, around 30-40% of the energy is spent on the cooling system rather than on computer servers, posing a pressing need for developing new energy-saving optimization technologies for DC cooling<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29382<END_URL>

Data-centric Prediction Explanation via Kernelized Stein Discrepancy\nExisting example-based prediction explanation methods often bridge test and training data points through the model’s parameters or latent representations. While these methods offer clues to the causes of model predictions, they often exhibit innate shortcomings, such as incurring significant computational overhead or producing coarse-grained explanations. This paper presents a Highly-precise and Data-centric Explanation (HD-Ex<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30035<END_URL>

Data Distillation for extrapolative protein design through exact preference optimization\nThe goal of protein design typically involves increasing fitness (extrapolating) beyond what is seen during training (e.g., towards higher stability, stronger binding affinity, etc.). State-of-the-art methods assume that one can safely steer proteins towards such extrapolated regions by learning from pairs alone. We hypothesize that noisy training pairs are not sufficiently informative to capture the fitnes<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27969<END_URL>

DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback\nThe process of creating training data to teach models is currently driven by humans, who manually analyze model weaknesses and plan how to create data that improves a student model. Recent approaches using large language models (LLMs) as annotators reduce human annotation effort, but still require humans to interpret feedback from evaluations and control the LLM to produce data the student needs. Automating this la<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31276<END_URL>

DataGen: Unified Synthetic Dataset Generation via Large Language Models\nLarge Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expensive human-generated datasets. Despite this, challenges remain in the areas of generalization, controllability, diversity, and truthfulness within the existing generative frameworks. To address these challenges, this paper presents DataGen, a comp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30365<END_URL>

DataMan: Data Manager for Pre-training Large Language Models\nAs the performance of large language models (LLMs) emerges via data scaling, the significance of pre-training data becomes increasingly evident. Although methods such as deduplication and high-quality sampling have explored data selection, comprehensive criteria for text quality remain underdeveloped, hindering efficient pre-training data selection and composition.This paper establishes guidelines for data selection, fosters consensus<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28930<END_URL>

Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance\nPretraining data of large language models composes multiple domains (e.g., web texts, academic papers, codes), whose mixture proportions crucially impact the competence of outcome models. While existing endeavors rely on heuristics or qualitative strategies to tune the proportions, we discover the quantitative predictability of model performance regarding the mixture proportions in function forms, which we re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28624<END_URL>

Data Pruning by Information Maximization\nIn this paper, we present InfoMax, a novel data pruning method, also known as coreset selection, designed to maximize the information content of selected samples while minimizing redundancy. By doing so, InfoMax enhances the overall informativeness of the coreset. The information of individual samples is measured by importance scores, which capture their influence or difficulty in model learning. To quantify redundancy, we use pairwise sample similaritie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30725<END_URL>

Data Scaling Laws in Imitation Learning for Robotic Manipulation\nData scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities. In this paper, we investigate whether similar data scaling laws exist in robotics, particularly in robotic manipulation, and whether appropriate data scaling can yield single-task robot policies that can be deployed zero-shot for any object within the same category in any enviro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28305<END_URL>

Data Selection via Optimal Control for Language Models\nThis work investigates the selection of high-quality pre-training data from massive corpora to enhance LMs' capabilities for downstream usage. We formulate data selection as a generalized Optimal Control problem, which can be solved theoretically by Pontryagin's Maximum Principle (PMP), yielding a set of necessary conditions that characterize the relationship between optimal data selection and LM training dynamics.Based on these theoretical<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28972<END_URL>

Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-training of Deep Networks\nDataset distillation (DD) generates small synthetic datasets that can efficiently train deep networks with a limited amount of memory and compute. Despite the success of DD methods for supervised learning, DD for self-supervised pre-training of deep models has remained unaddressed. Pre-training on unlabeled data is crucial for efficiently generalizing to downstream tasks with limited<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29070<END_URL>

Dataset Ownership Verification in Contrastive Pre-trained Models\nHigh-quality open-source datasets, which necessitate substantial efforts for curation, has become the primary catalyst for the swift progress of deep learning. Concurrently, protecting these datasets is paramount for the well-being of the data owner. Dataset ownership verification emerges as a crucial method in this domain, but existing approaches are often limited to supervised models and cannot be directly extended to increasing<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27659<END_URL>

Data Shapley in One Training Run\nData Shapley offers a principled framework for attributing the contribution of data within machine learning contexts. However, the traditional notion of Data Shapley requires re-training models on various data subsets, which becomes computationally infeasible for large-scale models. Additionally, this retraining-based definition cannot evaluate the contribution of data for a specific model training run, which may often be of interest in practice. This paper intr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30239<END_URL>

Data Taggants: Dataset Ownership Verification Via Harmless Targeted Data Poisoning\nDataset ownership verification, the process of determining if a dataset is used in a model's training data, is necessary for detecting unauthorized data usage and data contamination.Existing approaches, such as backdoor watermarking, rely on inducing a detectable behavior into the trained model on a part of the data distribution.However, these approaches have limitations, as they can be harmful to the model's per<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30866<END_URL>

Data Unlearning in Diffusion Models\nRecent work has shown that diffusion models memorize and reproduce training data examples. At the same time, large copyright lawsuits and legislation such as GDPR have highlighted the need for erasing datapoints from diffusion models. However, retraining from scratch is often too expensive. This motivates the setting of data unlearning, i.e., the study of efficient techniques for unlearning specific datapoints from the training set. Existing concept unlearnin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29573<END_URL>

DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation\nAdapting a pre-trained foundation model on downstream tasks should ensure robustness against distribution shifts without the need to retrain the whole model. Although existing weight interpolation methods are simple yet effective, we argue their static nature limits downstream performance while achieving efficiency. In this work, we propose DaWin, a training-free dynamic weight interpolation method that leverages the entrop<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30016<END_URL>

DAWN: Dynamic Frame Avatar with Non-autoregressive Diffusion Framework for Talking head Video Generation\nTalking head generation intends to produce vivid and realistic talking head videos from a single portrait and speech audio clip. Although significant progress has been made in diffusion-based talking head generation, almost all methods rely on autoregressive strategies, which suffer from limited context utilization beyond the current generation step, error accumulation, and slower generation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27895<END_URL>

DCT-CryptoNets: Scaling Private Inference in the Frequency Domain\nThe convergence of fully homomorphic encryption (FHE) and machine learning offers unprecedented opportunities for private inference of sensitive data. FHE enables computation directly on encrypted data, safeguarding the entire machine learning pipeline, including data and model confidentiality. However, existing FHE-based implementations for deep neural networks face significant challenges in computational cost, latency, and scal<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28520<END_URL>

Debiasing Federated Learning with Correlated Client Participation\nIn cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averaging (FedAvg) is the most popular algorithm in practice.  Existing analyses of FedAvg usually assume the participating clients are independently sampled in each round from a uniform distribution, which does not reflect real-world scenarios. This paper intr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30673<END_URL>

Debiasing Mini-Batch Quadratics for Applications in Deep Learning\nQuadratic approximations form a fundamental building block of machine learning methods. E.g., second-order optimizers try to find the Newton step into the minimum of a local quadratic proxy to the objective function; and the second-order approximation of a network's loss function can be used to quantify the uncertainty of its outputs via the Laplace approximation. When computations on the entire training set are intractable - typ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29718<END_URL>

dEBORA: Efficient Bilevel Optimization-based low-Rank Adaptation\nLow-rank adaptation methods are a popular approach for parameter-efficient fine-tuning of large-scale neural networks. However, selecting the optimal rank for each layer remains a challenging problem that significantly affects both performance and efficiency. In this paper, we introduce a novel bilevel optimization strategy that simultaneously trains both matrix and tensor low-rank adapters, dynamically selecting the optimal rank <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30949<END_URL>

DebugAgent: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging\nDespite the significant success of deep learning models in computer vision, they often exhibit systematic failures on specific data subsets, known as error slices. Identifying and mitigating these error slices is crucial to enhancing model robustness and reliability in real-world scenarios. In this paper, we introduce DebugAgent, an automated framework for error slice discovery and model repair. Debu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28538<END_URL>

Decentralized Optimization with Coupled Constraints\nWe consider the decentralized minimization of a separable objective $\sum_{i=1}^{n} f_i(x_i)$, where the variables are coupled through an affine constraint $\sum_{i=1}^n\left(\mathbf{A}_i x_i - b_i\right) = 0$.We assume that the functions $f_i$, matrices $\mathbf{A}_i$, and vectors $b_i$ are stored locally by the nodes of a computational network, and that the functions $f_i$ are smooth and strongly convex. This problem has significant applicat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30641<END_URL>

Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees\nDecentralized federated learning (DFL) captures FL settings where both (i) model updates and (ii) model aggregations are exclusively carried out by the clients without a central server. Existing DFL works have mostly focused on settings where clients conduct a fixed number of local updates between local model exchanges, overlooking heterogeneity and dynamics in communication and computation ca<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29019<END_URL>

DeciMamba: Exploring the Length Extrapolation Potential of Mamba\nLong-range sequence processing poses a significant challenge for Transformers due to their quadratic complexity in input length. A promising alternative is Mamba, which demonstrates high performance and achieves Transformer-level capabilities while requiring substantially fewer computational resources. In this paper we explore the length-generalization capabilities of Mamba, which we find to be relatively limited. Through a series<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28694<END_URL>

DECISION-FOCUSED UNCERTAINTY QUANTIFICATION\nThere is increasing interest in ``decision-focused" machine learning methods which train models to account for how their predictions are used in downstream optimization problems. Doing so can often improve performance on subsequent decision problems. However, current methods for uncertainty quantification do not incorporate any information at all about downstream decisions. We develop a framework based on conformal prediction to produce prediction set<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28698<END_URL>

Decision Information Meets Large Language Models: The Future of Explainable Operations Research\nOperations Research (OR) is vital for decision-making in many industries. While recent OR methods have seen significant improvements in automation and efficiency through integrating Large Language Models (LLMs), they still struggle to produce meaningful explanations. This lack of clarity raises concerns about transparency and trustworthiness in OR applications. To address these challenges, we propose<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29386<END_URL>

Decision Tree Induction via Semantically-Aware Evolution\nDecision trees are a crucial class of models offering robust predictive performance and inherent interpretability across various domains, including healthcare, finance, and logistics. However, current tree induction methods often face limitations such as suboptimal solutions from greedy methods or prohibitive computational costs and limited applicability of exact optimization approaches.To address these challenges, we propose an evolution<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29435<END_URL>

Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies\nDecoding strategies play a pivotal role in text generation for modern language models, yet a puzzling gap divides theory and practice. Surprisingly, strategies that should intuitively be optimal, such as Maximum a Posteriori (MAP), often perform poorly in practice. Meanwhile, popular heuristic approaches like Top-$k$ and Nucleus sampling, which employ truncation and normalization of the conditional next-token probabili<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29351<END_URL>

Decomposition Polyhedra of Piecewise Linear Functions\nIn this paper we contribute to the frequently studied question of how to decompose a continuous piecewise linear (CPWL) function into a difference of two convex CPWL functions. Every CPWL function has infinitely many such decompositions, but for applications in optimization and neural network theory, it is crucial to find decompositions with as few linear pieces as possible. This is a highly challenging problem, as we further demonstrate by <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27910<END_URL>

Deconstructing Denoising Diffusion Models for Self-Supervised Learning\nIn this study, we examine the representation learning abilities of Denoising Diffusion Models (DDM) that were originally purposed for image generation. Our philosophy is to deconstruct a DDM, gradually transforming it into a classical Denoising Autoencoder (DAE). This deconstructive process allows us to explore how various components of modern DDMs influence self-supervised representation learning. We observe that only a ver<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30665<END_URL>

Deconstructing What Makes a Good Optimizer for Autoregressive Language Models\nTraining language models becomes increasingly expensive with scale, prompting numerous attempts to improve optimization efficiency. Despite these efforts, the Adam optimizer remains the most widely used, due to a prevailing view that it is the most effective approach. We aim to compare several optimization algorithms, including SGD, Adafactor, Adam, Lion, and Sophia in the context of autoregressive language modeling a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27658<END_URL>

Decoupled Finetuning for Domain Generalizable Semantic Segmentation\nJoint finetuning of a pretrained encoder and a randomly initialized decoder has been the de facto standard in semantic segmentation, but the vulnerability of this approach to domain shift has not been studied. We investigate the vulnerability issue of joint finetuning, and propose a novel finetuning framework called Decoupled FineTuning for domain generalization (DeFT) as a solution. DeFT operates in two stages. Its first stage<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28246<END_URL>

Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs\nDespite extensive research efforts focused on Out-of-Distribution (OOD) detection on images, OOD detection on nodes in graph learning remains underexplored. The dependence among graph nodes hinders the trivial adaptation of existing approaches on images that assume inputs to be i.i.d. sampled, since many unique features and challenges specific to graphs are not considered, such as the heterophily is<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29852<END_URL>

Decoupled Subgraph Federated Learning\nWe address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subgraphs, where inter-connections between different clients play a critical role. We present a novel framework for this scenario, named FedStruct, that harnesses deep structural dependencies. To uphold privacy, unlike existing methods, FedStruct eliminates the necessity of sharing or<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27937<END_URL>

Decoupling Angles and Strength in Low-rank Adaptation\nParameter Efficient FineTuning (PEFT) methods have recently gained extreme popularity thanks to the vast availability of large-scale models, allowing to quickly adapt pretrained models to downstream tasks with minimal computational costs. However, current additive finetuning methods such as LoRA show low robustness to prolonged training and hyperparameter choices, not allowing for optimal out-of-the-box usage. On the other hand, multiplicati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29327<END_URL>

Decoupling Layout from Glyph in Online Chinese Handwriting Generation\nText plays a crucial role in the transmission of human civilization, and teaching machines to generate online handwritten text in various styles presents an interesting and significant challenge. However, most prior work has concentrated on generating individual Chinese fonts, leaving complete text line generation largely unexplored. In this paper, we identify that text lines can naturally be divided into two components: layo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30447<END_URL>

DEEM: Diffusion models serve as the eyes of large language models for image perception\nThe development of large language models (LLMs) has significantly advanced the emergence of large multimodal models (LMMs). While LMMs have achieved tremendous success by promoting the synergy between multimodal comprehension and creation, they often face challenges when confronted with out-of-distribution data, such as which can hardly distinguish orientation, quantity, color, structure, etc. This is primari<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28231<END_URL>

Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models\nWe present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models for accelerating high-resolution diffusion models. Existing autoencoder models have demonstrated impressive results at a moderate spatial compression ratio (e.g., 8x), but fail to maintain satisfactory reconstruction accuracy for high spatial compression ratios (e.g., 64x). We address this challenge by introducing two key techniques: (1)<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27858<END_URL>

Deep Distributed Optimization for Large-Scale Quadratic Programming\nQuadratic programming (QP) forms a crucial foundation in optimization, encompassing a broad spectrum of domains and serving as the basis for more advanced algorithms. Consequently, as the scale and complexity of modern applications continue to grow, the development of efficient and reliable QP algorithms becomes increasingly vital. In this context, this paper introduces a novel deep learning-aided distributed optimization archi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28726<END_URL>

DeeperForward: Enhanced Forward-Forward Training for Deeper and Better Performance\nWhile backpropagation effectively trains models, it presents challenges related to bio-plausibility, resulting in high memory demands and limited parallelism. Recently, Hinton (2022) proposed the Forward-Forward (FF) algorithm for high-parallel local updates. FF leverages squared sums as the local update target, termed goodness, and employs L2 normalization to decouple goodness and extract new features. However, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28588<END_URL>

DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale\nCircuit representation learning has become pivotal in electronic design automation, enabling critical tasks such as testability analysis, logic reasoning, power estimation, and SAT solving. However, existing models face significant challenges in scaling to large circuits due to limitations like over-squashing in graph neural networks and the quadratic complexity of transformer-based models. To address these i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29131<END_URL>

Deep Generative Model in Machine Learning: Theory, Principle and Efficacy\nDeep Generative Models (DGMs) have significantly advanced artificial intelligence (AI) through innovations like variational autoencoders, flow-based models, generative adversarial networks, and diffusion models. Despite their success, substantial theoretical and practical challenges remain, including the lack of rigorous theoretical frameworks, training instability, scalability issues, and challenges in adapting to struct<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23972<END_URL>

Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs\nMulti-View Representation Learning (MVRL) aims to derive a unified representation from multi-view data by leveraging shared and complementary information across views. However, when views are irregularly missing, the incomplete data can lead to representations that lack sufficiency and consistency. To address this, we propose Multi-View Permutation of Variational Auto-Encoders (MVP), which excavates invariant relationships betwe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28147<END_URL>

Deep Kernel Posterior Learning under Infinite Variance Prior Weights\nNeal (1996) proved that infinitely wide shallow Bayesian neural networks (BNN) converge to Gaussian processes (GP), when the network weights have bounded prior variance. Cho & Saul (2009) provided a useful recursive formula for deep kernel processes for relating the covariance kernel of each layer to the layer immediately below. Moreover, they worked out the form of the layer-wise covariance kernel in an explicit manner for se<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27952<END_URL>

Deep Learning Alternatives Of The Kolmogorov Superposition Theorem\nThis paper explores alternative formulations of the Kolmogorov Superposition Theorem (KST) as a foundation for neural network design. The original KST formulation, while mathematically elegant, presents practical challenges due to its limited insight into the structure of inner and outer functions and the large number of unknown variables it introduces. Kolmogorov-Arnold Networks (KANs) leverage KST for function approximation, b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29570<END_URL>

Deep Linear Probe Generators for Weight Space Learning\nWeight space learning aims to extract information about a neural network, such as its training dataset or generalization error. Recent approaches learn directly from model weights, but this presents many challenges as weights are high-dimensional and include permutation symmetries between neurons. An alternative approach, Probing, represents a model by passing a set of learned inputs (probes) through the model, and training a predictor on t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29289<END_URL>

DeepLTL: Learning to Efficiently Satisfy Complex LTL Instructions\nLinear temporal logic (LTL) has recently been adopted as a powerful formalism for specifying complex, temporally extended tasks in multi-task reinforcement learning (RL). However, learning policies that efficiently satisfy arbitrary specifications not observed during training remains a challenging problem. Existing approaches suffer from several shortcomings: they are often only applicable to the finite-horizon fragment of LTL, a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30664<END_URL>

Deep MMD Gradient Flow without adversarial training\nWe propose a gradient flow procedure for generative modeling by transporting particles from an initial source distribution to a target distribution, where the gradient field on the particles is given by a noise-adaptive Wasserstein Gradient of the Maximum Mean Discrepancy (MMD). The noise adaptive MMD is trained on data distributions corrupted by increasing levels of noise, obtained via a forward diffusion process, as commonly used in denoisin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29740<END_URL>

Deep Networks Learn Features From Local Discontinuities in the Label Function\nDeep neural networks outperform kernel machines on several datasets due to feature learning that happens during gradient descent training. In this paper, we analyze the mechanism through which feature learning happens and use a notion of features that corresponds to discontinuities in the true label function. We hypothesize that the core feature learning mechanism is label function discontinuities attracting model fun<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30974<END_URL>

Deep Random Features for Scalable Interpolation of Spatiotemporal Data\nThe rapid growth of earth observation systems calls for a scalable approach to interpolate remote-sensing observations. These methods in principle, should acquire more information about the observed field as data grows. Gaussian processes (GPs) are candidate model choices for interpolation. However, due to their poor scalability, they usually rely on inducing points for inference, which restricts their expressivity. Moreover<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29836<END_URL>

DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model\nRecent advancements in large language models (LLMs) have demonstrated significant potential in automating the generation of hardware description language (HDL) code from high-level natural language instructions. While fine-tuning has improved these models' performance in hardware design tasks, prior efforts have largely focused on Verilog code generation, overlooking the equally critical task of Verilog u<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31123<END_URL>

Deep Signature: Characterization of Large-Scale Molecular Dynamics\nUnderstanding protein dynamics are essential for deciphering protein functional mechanisms and developing molecular therapies. However, the complex high-dimensional dynamics and interatomic interactions of biological processes pose significant challenge for existing computational techniques. In this paper, we approach this problem for the first time by introducing Deep Signature, a novel computationally tractable framework that <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27774<END_URL>

DeepTAGE: Deep Temporal-Aligned Gradient Enhancement for Optimizing Spiking Neural Networks\nSpiking Neural Networks (SNNs), with their biologically inspired spatio-temporal dynamics and spike-driven processing, are emerging as a promising low-power alternative to traditional Artificial Neural Networks (ANNs). However, the complex neuronal dynamics and non-differentiable spike communication mechanisms in SNNs present substantial challenges for efficient training. By analyzing the membrane potent<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28961<END_URL>

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries\nSparse regularization techniques are well-established in machine learning, yet their application in neural networks remains challenging due to the non-differentiability of penalties like the $L_1$ norm, which is incompatible with stochastic gradient descent. A promising alternative is shallow weight factorization, where weights are decomposed into two factors, allowing for smooth optimization of $L_1$-penalized<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27917<END_URL>

DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference\nLarge language models (LLMs) are increasingly employed for complex tasks that process multiple generation calls in a tree structure with shared prefixes of tokens, including few-shot prompting, multi-step reasoning, speculative decoding, etc. However, existing inference systems for tree-based applications are inefficient due to improper partitioning of queries and KV cache during attention calculation.This lead<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31131<END_URL>

DELIFT: Data Efficient Language model Instruction Fine-Tuning\nFine-tuning large language models (LLMs) is essential for enhancing their performance on specific tasks but is often resource-intensive due to redundant or uninformative data. To address this inefficiency, we introduce DELIFT (Data Efficient Language model Instruction Fine-Tuning), a novel algorithm that systematically optimizes data selection across the three key stages of fine-tuning: (1) instruction tuning, (2) task-specific fine-<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30319<END_URL>

DeLLMa: Decision Making Under Uncertainty with Large Language Models\nThe potential of large language models (LLMs) as decision support tools is increasingly being explored in fields such as business, engineering, and medicine, which often face challenging tasks of *decision-making under uncertainty*. In this paper, we show that directly prompting LLMs on these types of decision-making problems can yield poor results, especially as the problem complexity increases. To aid in these tasks, we prop<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30624<END_URL>

DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory\nLarge language models (LLMs) have achieved reasonable quality improvements in machine translation (MT).However, most current research on MT-LLMs still faces significant challenges in maintaining translation consistency and accuracy when processing entire documents.In this paper, we introduce DelTA, a Document-levEL Translation Agent designed to overcome these limitations.DelTA features a multi-level memory structure t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28742<END_URL>

Democratic Training Against Universal Adversarial Perturbations\nDespite their advances and success, real-world deep neural networks are known to be vulnerable to adversarial attacks. Universal adversarial perturbation, an input-agnostic attack, poses a serious threat for them to be deployed in security-sensitive systems. In this case, a single universal adversarial perturbation deceives the model on a range of clean inputs without requiring input-specific optimization, which makes it particular<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31018<END_URL>

Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts\nThe contextual multi-armed bandit (MAB) problem is crucial in sequential decision-making. A line of research, known as online clustering of bandits, extends contextual MAB by grouping similar users into clusters, utilizing shared features to improve learning efficiency. However, existing algorithms, which rely on the upper confidence bound (UCB) strategy, struggle to gather adequat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31041<END_URL>

Demystifying the Token Dynamics of Deep Selective State Space Models\nSelective state space models (SSM), such as Mamba, have gained prominence for their effectiveness in modeling sequential data. Despite their outstanding empirical performance, a comprehensive theoretical understanding of deep selective SSM remains elusive, hindering their further development and adoption for applications that need high fidelity. In this paper, we investigate the dynamical properties of tokens in a pre-trained <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28232<END_URL>

Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing\nTopological deep learning (TDL) has emerged as a powerful tool for modeling higher-order interactions in relational data. However, phenomena such as oversquashing in topological message-passing remain understudied and lack theoretical analysis. We propose a unifying axiomatic framework that bridges graph and topological message-passing by viewing simplicial and cellul<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29705<END_URL>

DenoiseVAE: Learning Molecule-Adaptive Noise Distributions for Denoising-based 3D Molecular Pre-training\nDenoising learning of 3D molecules learns molecular representations by imposing noises into the equilibrium conformation and predicting the added noises to recover the equilibrium conformation, which essentially captures the information of molecular force fields. Due to the specificity of Potential Energy Surfaces, the probabilities of physically reasonable noises for each atom in different <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27701<END_URL>

Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration\nAlthough learning-based image restoration methods have made significant progress, they still struggle with limited generalization to real-world scenarios due to the substantial domain gap caused by training on synthetic data. Existing methods address this issue by improving data synthesis pipelines, estimating degradation kernels, employing deep internal learning, and performing domain adaptation and regularization. Pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28613<END_URL>

Denoising Autoregressive Transformers for Scalable Text-to-Image Generation\nDiffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process which gradually adds noise to the input. We argue that the Markovian property limits the model’s ability to fully utilize the generation trajectory, leading to inefficiencies during training and inference. In this paper, we propose DART, a transformer-based model that unifies autoregressive (AR) an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29151<END_URL>

Denoising Levy Probabilistic Models\nInvestigating noise distributions beyond Gaussian in diffusion generative models remains an open challenge. The Gaussian case has been a large success experimentally and theoretically, admitting a unified stochastic differential equation (SDE) framework, encompassing score-based and denoising formulations. Recent studies have investigated the potential of \emph{heavy-tailed} noise distributions to mitigate mode collapse and effectively manage datasets exhibit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29595<END_URL>

Denoising Task Difficulty-based Curriculum for Training Diffusion Models\nDiffusion-based generative models have emerged as powerful tools in the realm of generative modeling. Despite extensive research on denoising across various timesteps and noise levels, a conflict persists regarding the relative difficulties of the denoising tasks. While various studies argue that lower timesteps present more challenging tasks, others contend that higher timesteps are more difficult. To address this conflic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30723<END_URL>

Denoising with a Joint-Embedding Predictive Architecture\nJoint-embedding predictive architectures (JEPAs) have shown substantial promise in self-supervised representation learning, yet their application in generative modeling remains underexplored. Conversely, diffusion models have demonstrated significant efficacy in modeling arbitrary probability distributions. In this paper, we introduce Denoising with a Joint-Embedding Predictive Architecture (D-JEPA), pioneering the integration of JEPA wit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29012<END_URL>

DenseGrounding: Improving Dense Language-Vision Semantics for Ego-centric 3D Visual Grounding\nEnabling intelligent agents to comprehend and interact with 3D environments through natural language is crucial for advancing robotics and human-computer interaction. A fundamental task in this field is ego-centric 3D visual grounding, where agents locate target objects in real-world 3D spaces based on verbal descriptions. However, this task faces two significant challenges: (1) loss of fine-grained vi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28704<END_URL>

DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from One Demo\nDense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Compared to shape correspondence, semantic correspondence is more effective in generalizing across different object categories. To this end, we present DenseMatcher, a method capable of computing 3D correspondences between in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30743<END_URL>

Dense Video Object Captioning from Disjoint Supervision\nWe propose a new task and model for dense video object captioning -- detecting, tracking and captioning trajectories of objects in a video. This task unifies spatial and temporal localization in video, whilst also requiring fine-grained visual understanding that is best described by natural language. We propose a unified model, and demonstrate how our end-to-end approach is more accurate and temporally coherent than a multi-stage pipeline <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29140<END_URL>

Density estimation with LLMs: a geometric investigation of in-context learning trajectories\nLarge language models (LLMs) demonstrate remarkable emergent abilities to perform in-context learning across various tasks, including time series forecasting. This work investigates LLMs' ability to estimate probability density functions (PDFs) from data observed in-context; such density estimation (DE) is a fundamental task underlying many probabilistic modeling problems. We leverage the Intensive Princ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28109<END_URL>

DEPfold: RNA Secondary Structure Prediction as Dependency Parsing.\nRNA secondary structure prediction is critical for understanding RNA function but remains challenging due to complex structural elements like pseudoknots and limited training data. We introduce DEPfold, a novel deep learning approach that reframes RNA secondary structure prediction as a dependency parsing problem. DEPfold presents three key innovations: (1) a biologically motivated transformation of RNA structures into labeled d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30440<END_URL>

DEPT: Decoupled Embeddings for Pre-training Language Models\nPast works have shown that lexical, syntactical, and semantical differences in heterogeneous data sources can cause challenges such as negative interference or the ``curse of multilinguality''. Because of this, training on such heterogeneous corpora requires extensive and costly efforts to balance data mixtures. We propose a novel pre-training framework to alleviate this curse. Our method, DEPT, decouples embeddings from the transforme<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27901<END_URL>

Depth Pro: Sharp Monocular Metric Depth in Less Than a Second\nWe present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, without relying on the availability of metadata such as camera intrinsics. And the model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU. These characteristics a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29138<END_URL>

Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm\nTargeted and uniform interventions to a system are crucial for unveiling causal relationships. While several methods have been developed to leverage interventional data for causal structure learning, their practical application in real-world scenarios often remains challenging. Recent benchmark studies have highlighted these difficulties, even when large numbers of single-variable intervention samples are available<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27999<END_URL>

Descent with Misaligned Gradients and Applications to Hidden Convexity\nWe consider the problem of minimizing a convex objective given access to an oracle that outputs "misaligned" stochastic gradients, where the expected value of the output is guaranteed to be correlated with, but not necessarily equal to the true gradient of the objective.  In the case where the misalignment (or bias) of the oracle changes slowly, we obtain an optimization algorithm that achieves the optimum iteration complexi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31146<END_URL>

Designing Concise ConvNets with Columnar Stages\nIn the era of vision Transformers, the recent success of VanillaNet shows the hugepotential of simple and concise convolutional neural networks (ConvNets). Wheresuch models mainly focus on runtime, it is also crucial to simultaneously focuson other aspects, e.g., FLOPs, parameters, etc, to strengthen their utility further.To this end, we introduce a refreshing ConvNet macro design called ColumnarStage Network (CoSNet). CoSNet has a systematically <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27642<END_URL>

Designing Mechanical Meta-Materials by Learning Equivariant Flows\nMechanical meta-materials are solids whose geometric structure results in exotic nonlinear behaviors that are not typically achievable via homogeneous materials. We show how to drastically expand the design space of a class of mechanical meta-materials known as $\textit{cellular solids}$, by generalizing beyond translational symmetry. This is made possible by transforming a reference geometry according to a divergence free flow t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29419<END_URL>

Detecting Backdoor Samples in Contrastive Language Image Pretraining\nContrastive language-image pretraining (CLIP) has been found to be vulnerable to poisoning backdoor attacks where the adversary can achieve an almost perfect attack success rate on CLIP models by poisoning only 0.01\% of the training dataset. This raises security concerns on the current practice of pretraining large-scale models on unscrutinized web data using CLIP. In this work, we analyze the representations of backdoor-pois<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30032<END_URL>

Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling\nLarge language models (LLMs) exhibit varying strengths and weaknesses across different tasks, prompting recent studies to explore the benefits of ensembling models to leverage their complementary advantages. However, existing LLM ensembling methods often overlook model compatibility and struggle with inefficient alignment of probabilities across the entire vocabulary. In this study, we empirically investigate <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30354<END_URL>

DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References\nWe address the challenge of developing a generalizable neural tracking controller for dexterous manipulation from human references. This controller aims to manage a dexterous robot hand to manipulate diverse objects for various purposes defined by kinematic human-object interactions. Developing such a controller is complicated by the intricate contact dynamics of dexterous manipulation and t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29153<END_URL>

D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement\nWe introduce D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). FDR transforms the regression process from predicting fixed coordinates to iteratively refining probability<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29944<END_URL>

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models\nDespite the widespread use of text-to-image diffusion models across various tasks, their computational and memory demands limit practical applications. To mitigate this issue, quantization of diffusion models has been explored. It reduces memory usage and computational costs by compressing weights and activations into lower-bit formats. However, existing methods often struggle to preserve both image quality and text-i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29192<END_URL>

DICE: Data Influence Cascade in Decentralized Learning\nDecentralized learning offers a promising approach to crowdsource computational workloads across geographically distributed compute interconnected through peer-to-peer networks, accommodating the exponentially increasing compute demands in the era of large models. However, the absence of proper incentives in locally connected decentralized networks poses significant risks of free riding and malicious behaviors. Data influence, which ensures<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31136<END_URL>

DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image\nReconstructing 3D hand-face interactions with deformations from a single image is a challenging yet crucial task with broad applications in AR, VR, and gaming. The challenges stem from self-occlusions during single-view hand-face interactions, diverse spatial relationships between hands and face, complex deformations, and the ambiguity of the single-view setting. The previous state-of-the-art for hand-face intera<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28179<END_URL>

Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models\nBeyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most existing work treats diffusion models as a standalone component for perception tasks, employing them either solely for off-the-shelf data augmentation or as mere feature extractors. In contrast to these isolated and thus sub-optimal efforts, we introduce a integrated, versatile<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29212<END_URL>

Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering\n3D sketches are widely used for visually representing the 3D shape and structure of objects or scenes. However, the creation of 3D sketch often requires users to possess professional artistic skills. Existing research efforts primarily focus on enhancing the ability of interactive sketch generation in 3D virtual systems. In this work, we propose Diff3DS, a novel differentiable rendering framework for generating vie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29181<END_URL>

Differentiable and Learnable Wireless Simulation with Geometric Transformers\nModelling the propagation of electromagnetic wireless signals is critical for designing modern communication systems. Wireless ray tracing simulators model signal propagation based on the 3D geometry and other scene parameters, but their accuracy is fundamentally limited by underlying modelling assumptions and correctness of parameters. In this work, we introduce Wi-GATr, a fully-learnable neural simulation surrogate d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30689<END_URL>

Differentiable Causal Discovery for Latent Hierarchical Causal Models\nDiscovering causal structures with latent variables from observational data is a fundamental challenge in causal discovery. Existing methods often rely on constraint-based, iterative discrete searches, limiting their scalability to large numbers of variables. Moreover, these methods frequently assume linearity or invertibility, restricting their applicability to real-world scenarios. We present new theoretical results on the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30546<END_URL>

Differentiable Integer Linear Programming\nMachine learning (ML) techniques have shown great potential in generating high-quality solutions for integer linear programs (ILPs).However, existing methods typically rely on a *supervised learning* paradigm, leading to (1) *expensive training cost* due to repeated invocations of traditional solvers to generate training labels, and (2) *plausible yet infeasible solutions* due to the misalignment between the training objective (minimizing prediction los<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30343<END_URL>

Differentiable Optimization of Similarity Scores Between Models and Brains\nHow do we know if two systems - biological or artificial - process information in a similar way? Similarity measures such as linear regression, Centered Kernel Alignment (CKA), Normalized Bures Similarity (NBS), and angular Procrustes distance, are often used to quantify this similarity. However, it is currently unclear what drives high similarity scores and even what constitutes a "good" score. Here, we introduce a nove<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27905<END_URL>

Differential learning kinetics govern the transition from memorization to generalization during in-context learning\nTransformers exhibit in-context learning (ICL): the ability to use novel information presented in the context without additional weight updates. Recent work shows that ICL emerges when models are trained on a sufficiently diverse set of tasks and the transition from memorization to generalization is sharp with increasing task diversity. One interpretation is that a network's limit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30172<END_URL>

Differentially Private Federated Learning with Time-Adaptive Privacy Spending\nFederated learning (FL) with differential privacy (DP) provides a framework for collaborative machine learning, enabling clients to train a shared model while adhering to strict privacy constraints. The framework allows each client to have an individual privacy guarantee, e.g., by adding different amounts of noise to each client’s model updates. One underlying assumption is that all clients spend their privacy budgets<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29388<END_URL>

Differentially private learners for heterogeneous treatment effects\nPatient data is widely used to estimate heterogeneous treatment effects and understand the effectiveness and safety of drugs. Yet, patient data includes highlysensitive information that must be kept private. In this work, we aim to estimatethe conditional average treatment effect (CATE) from observational data underdifferential privacy. Specifically, we present DP-CATE, a novel framework forCATE estimation that is *doubly robus<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31170<END_URL>

Differentially Private Steering for Large Language Model Alignment\nAligning Large Language Models (LLMs) with human values and away from undesirable behaviors (such as hallucination) has become increasingly important. Recently, steering LLMs towards a desired behavior via activation editing has emerged as an effective method to mitigate harmful  generations at inference-time. Activation editing modifies LLM representations by preserving information from positive demonstrations (e.g., truthful) <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28523<END_URL>

Differential Transformer\nTransformer tends to overallocate attention to irrelevant context. In this work, we introduce Diff Transformer, which amplifies attention to the relevant context while canceling noise. Specifically, the differential attention mechanism calculates attention scores as the difference between two separate softmax attention maps. The subtraction cancels noise, promoting the emergence of sparse attention patterns. Experimental results on language modeling show that Diff Trans<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29792<END_URL>

Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficient\nWe introduce refined variants of the Local Learning Coefficient (LLC), a measure of model complexity grounded in singular learning theory, to study the development of internal structure in transformer language models during training. By applying these refined LLCs (rLLCs) to individual components of a two-layer attention-only transformer, we gain novel insights into the progressive differentiation a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29600<END_URL>

DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector\nGraph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields. Traditional unsupervised methods, which decode encoded latent representations of unlabeled data with a reconstruction focus, often fail to capture critical discriminative content, leading to suboptimal anomaly detection.To address these challenges, we present a Diffusion-based Graph Anoma<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30622<END_URL>

DiffGS: Repurposing Image Diffusion Models for Scalable Gaussian Splatting Generation\nRecent advancements in 3D content generation from text or a single image struggle with limited high-quality 3D datasets and inconsistency from 2D multi-view generation. We introduce DiffGS, a novel 3D generative framework that natively generates 3D Gaussians by taming large-scale text-to-image diffusion models. It differs from previous 3D generative models by effectively utilizing web-scale 2D priors while mai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28918<END_URL>

DiffPC: Diffusion-based High Perceptual Fidelity Image Compression with Semantic Refinement\nReconstructing high-quality images under low bitrates conditions presents a challenge, and previous methods have made this task feasible by leveraging the priors of diffusion models.  However, the effective exploration of pre-trained latent diffusion models and semantic information integration in image compression tasks still needs further study. To address this issue, we introduce Diffusion-based High P<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29654<END_URL>

Diff-PIC: Revolutionizing Particle-In-Cell Nuclear Fusion Simulation with Diffusion Models\nThe rapid development of AI highlights the pressing need for sustainable energy, a critical global challenge for decades. Nuclear fusion, generally seen as an ultimate solution, has been the focus of intensive research for nearly a century, with investments reaching hundreds of billions of dollars. Recent advancements in Inertial Confinement Fusion have drawn significant attention to fusion research, in w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29068<END_URL>

Diff-Prompt: Diffusion-driven Prompt Generator with Mask Supervision\nPrompt learning has demonstrated promising results in fine-tuning pre-trained multimodal models. However, the performance improvement is limited when applied to more complex and fine-grained tasks. The reason is that most existing methods directly optimize the parameters involved in the prompt generation process through loss backpropagation, which constrains the richness and specificity of the prompt representations. In this p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29982<END_URL>

Diffusing States and Matching Scores: A New Framework for Imitation Learning\nAdversarial Imitation Learning is traditionally framed as a two-player zero-sum game between a learner and an adversarially chosen cost function, and can therefore be thought of as the sequential generalization of a Generative Adversarial Network (GAN). However, in recent years, diffusion models have emerged as a non-adversarial alternative to GANs that merely require training a score function via regression, yet produ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28575<END_URL>

Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning\nGraph Neural Networks (GNNs) are proficient in graph representation learning and achieve promising performance on versatile tasks such as node classification and link prediction.Usually, a comprehensive hyperparameter tuning is essential for fully unlocking GNN's top performance, especially for complicated tasks such as node classification on large graphs and long-range graphs. This is usually associated with h<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30476<END_URL>

Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion Models\nRecent advancements in 3D generation are predominantly propelled by improvements in 3D-aware image diffusion models. These models are pretrained on Internet-scale image data and fine-tuned on massive 3D data, offering the capability of producing highly consistent multi-view images. However, due to the scarcity of synchronized multi-view video data, it remains challenging to adapt this par<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28867<END_URL>

Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning\nIn offline reinforcement learning, it is necessary to manage out-of-distribution actions to prevent overestimation of value functions. One class of methods, policy-regularized methods, address this problem by constraining the target policy to stay close to the behavior policy. Although several approaches suggest representing the behavior policy as an expressive diffu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28510<END_URL>

Diffusion Attribution Score: Which Training Sample Determines Your Generation?\nAs diffusion models advance, the scientific community is actively developing methods to curb the misuse of generative models, which aims to prevent the reproduction of copyrighted, explicitly violent, or personally sensitive information in generated images.One strategy is to identify the contribution of training samples in generative models by evaluating their influence to the generated images, a task known as data a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28556<END_URL>

Diffusion-based Decoupled Deterministic and Uncertain Framework for Probabilistic Multivariate Time Series Forecasting\nDiffusion-based denoising models have demonstrated impressive performance in probabilistic forecasting for multivariate time series (MTS). Nonetheless, existing approaches often model the entire data distribution, neglecting the variability in uncertainty across different components of the time series. This paper introduces a Diffusion-based Decoupled Deterministic and Uncertai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30216<END_URL>

Diffusion-based Neural Network Weights Generation\nTransfer learning, while fundamental to modern deep learning, faces critical limitations in model selection and storage requirements. To address these challenges, we introduce D2NWG: Diffusion-based Neural Network Weights Generation, a diffusion framework that learns to generate task-specific neural network weights by modeling the distribution of weights from diverse pretrained models. Our approach conditions the diffusion process on dataset fea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28652<END_URL>

Diffusion-Based Planning for Autonomous Driving with Flexible Guidance\nAchieving human-like driving behaviors in complex open-world environments is a critical challenge in autonomous driving. Contemporary learning-based planning approaches such as imitation learning methods often struggle to balance competing objectives and lack of safety assurance,due to limited adaptability and inadequacy in learning complex multi-modal behaviors commonly exhibited in human planning, not to mention their stro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27852<END_URL>

Diffusion Bridge AutoEncoders for Unsupervised Representation Learning\nDiffusion-based representation learning has achieved substantial attention due to its promising capabilities in latent representation and sample generation. Recent studies have employed an auxiliary encoder to identify a corresponding representation from data and to adjust the dimensionality of a latent variable $\mathbf{z}$. Meanwhile, this auxiliary structure invokes an *information split problem*; the information of each <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28774<END_URL>

Diffusion Bridge Implicit Models\nDenoising diffusion bridge models (DDBMs) are a powerful variant of diffusion models for interpolating between two arbitrary paired distributions given as endpoints. Despite their promising performance in tasks like image translation, DDBMs require a computationally intensive sampling process that involves the simulation of a (stochastic) differential equation through hundreds of network evaluations. In this work, we take the first step in fast sampling of DDBMs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28911<END_URL>

Diffusion Feedback Helps CLIP See Better\nContrastive Language-Image Pre-training (CLIP), which excels at abstracting open-world representations across domains and modalities, has become a foundation for a variety of vision and multimodal tasks. However, recent studies reveal that CLIP has severe visual shortcomings, such as which can hardly distinguish orientation, quantity, color, structure, etc. These visual shortcomings also limit the perception capabilities of multimodal large language mode<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28060<END_URL>

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images\nSpatial Transcriptomics (ST) allows a high-resolution measurement of RNA sequence abundance by systematically connecting cell morphology depicted in Hematoxylin and eosin (H\&E) stained histology images to spatially resolved gene expressions. ST is a time-consuming, expensive yet powerful experimental technique that provides new opportunities to understand cancer mechanisms at a fine-grained mol<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30320<END_URL>

DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing\nRecent advances in diffusion models have introduced a new era of text-guided image manipulation, enabling users to create realistic edited images with simple textual prompts. However, there is significant concern about the potential misuse of these methods, especially in creating misleading or harmful content. Although recent defense strategies, which introduce imperceptible adversarial noise to induce model failur<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30694<END_URL>

Diffusion Models and Gaussian Flow Matching: Two Sides of the Same Coin\nFlow matching and diffusion models are two popular frameworks in generative modeling. Despite seeming similar, there is some confusion in the community about their exact connection. In this post we aim to clear up this confusion and show that diffusion models and Gaussian flow matching are the same -- Different model specifications lead to different noise schedules and loss weightings but correspond to the same generative m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31359<END_URL>

Diffusion Models are Evolutionary Algorithms\nIn a convergence of machine learning and biology, we reveal that diffusion models are evolutionary algorithms. By considering evolution as a denoising process and reversed evolution as diffusion, we mathematically demonstrate that diffusion models inherently perform evolutionary algorithms, naturally encompassing selection, mutation, and reproductive isolation. Building on this equivalence, we propose the Diffusion Evolution method: an evolutionary a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27778<END_URL>

Diffusion Models Are Real-Time Game Engines\nWe present GameNGen, the first game engine powered entirely by a neural model that also enables real-time interaction with a complex environment over long trajectories at high quality. When trained on the classic game DOOM, GameNGen extracts gameplay and uses it to generate a playable environment that can interactively simulate new trajectories. GameNGen runs at 20 frames per second on a single TPU and remains stable over extended multi-minute play se<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29770<END_URL>

Diffusion Models as Cartoonists! The Curious Case of High Density Regions\nWe investigate what kind of images lie in the high-density regions of diffusion models. We introduce a theoretical mode-tracking process capable of pinpointing the exact mode of the denoising distribution, and we propose a practical high-density sampler that consistently generates images of higher likelihood than usual samplers. Our empirical findings reveal the existence of significantly higher likelihood samples that ty<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29641<END_URL>

Diffusion Models for 4D Novel View Synthesis\nWe present 4DiM, a cascaded diffusion model for 4D novel view synthesis (NVS), supporting generation with arbitrary camera trajectories and timestamps, in natural scenes, conditioned on one or more images. With a novel architecture and sampling procedure, we enable training on a mixture of 3D (with camera pose), 4D (pose+time) and video (time but no pose) data, which greatly improves generalization to unseen images and camera pose trajectories over p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29013<END_URL>

Diffusion On Syntax Trees For Program Synthesis\nLarge language models generate code one token at a time. Their autoregressive generation process lacks the feedback of observing the program's output. Training LLMs to suggest edits directly can be challenging due to the scarcity of rich edit data. To address these problems, we propose neural diffusion models that operate on syntax trees of any context-free grammar. Similar to image diffusion models, our method also inverts "noise" applied to synt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27850<END_URL>

Diffusion Policy Policy Optimization\nWe introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e.g. Diffusion Policy) in continuous control and robot learning tasks using the policy gradient (PG) method from reinforcement learning (RL). PG methods are ubiquitous in training RL policies with other policy parameterizations; nevertheless, they had been conjectured to be less efficient for diffusion-based pol<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28475<END_URL>

Diffusion State-Guided Projected Gradient for Inverse Problems\nRecent advancements in diffusion models have been effective in learning data priors for solving inverse problems. They leverage diffusion sampling steps for inducing a data prior while using a measurement guidance gradient at each step to impose data consistency. For general inverse problems, approximations are needed when an unconditionally trained diffusion model is used since the measurement likelihood is intractable, leading to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28584<END_URL>

Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data\nDiffusion Transformer, the backbone of Sora for video generation, successfully scales the capacity of diffusion models, pioneering new avenues for high-fidelity sequential data generation. Unlike static data such as images, sequential data consists of consecutive data frames indexed by time, exhibiting rich spatial and temporal dependencies. These dependencies represent the underlying dynamic model <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29925<END_URL>

Diffusion Transformers for Tabular Data Time Series Generation\nTabular data generation has recently attracted a growing interest due to its different application scenarios. However, generating time series of tabular data, where each element of the series depends on the others,remains a largely unexplored domain. This gap is probably due to the difficulty of jointly solving different problems, the main of which are the heterogeneity of tabular data (a problem common to non-time-dependent approac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29095<END_URL>

Digi-Q: Transforming VLMs to Device-Control Agents via Value-Based Offline RL\nMost paradigms for building foundation model agents rely on prompting or finetuning on existing demonstrations, but this is not sufficient in dynamic environments (e.g., mobile device control). In theory, while on-policy reinforcement learning (RL) should address these limitations, this approach itself is not quite effective at leveraging existing agentic data, especially when it is of low quality. An approach to addr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30502<END_URL>

Dimension Agnostic Neural Processes\nMeta-learning aims to train models that can generalize to new tasks with limited labeled data by extracting shared features across diverse task datasets. Additionally, it accounts for prediction uncertainty during both training and evaluation, a concept known as uncertainty-aware meta-learning. Neural Process (NP) is a well-known uncertainty-aware meta-learning method that constructs implicit stochastic processes using parametric neural networks, enabling rap<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27989<END_URL>

Direct Distributional Optimization for Provable Alignment of Diffusion Models\nWe introduce a novel alignment method for diffusion models from distribution optimization perspectives while providing rigorous convergence guarantees.We first formulate the problem as a generic regularized loss minimization over probability distributions and directly optimize the distribution using the Dual Averaging method.Next, we enable sampling from the learned distribution by approximating its score function via<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29849<END_URL>

Direct Imitation Learning: RLHF Secretly Performs Imitation Learning\nThis work studies the alignment of large language models with preference data. We address this problem from a novel imitation learning (IL) perspective. We establish a close connection between alignment and imitation learning, which shows that existing alignment objectives implicitly align model and preference data distributions. Built upon this connection, we develop a principled method DIL todirectly optimize the imitation l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31138<END_URL>

Directional Gradient Projection for Robust Fine-tuning of Foundation Models\nRobust fine-tuning aims to adapt large foundation models to downstream tasks while preserving their robustness to distribution shifts. Existing methods primarily focus on constraining and projecting current model towards the pre-trained initialization based on the magnitudes between fine-tuned and pre-trained weights, which often require extensive hyper-parameter tuning and can sometimes result in underfitting. In this <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28802<END_URL>

Direct Multi-agent Motion Generation Preference Alignment with Implicit Feedback from Demonstrations\nRecent advancements in Large Language Models (LLMs) have transformed motion generation models in embodied applications such as autonomous driving and robotic manipulation. While LLM-type motion models benefit from scalability and efficient formulation, there remains a discrepancy between their token-prediction imitation objectives and human preferences. This often results in behaviors that devia<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30765<END_URL>

Discovering Clone Negatives via Adaptive Contrastive Learning for Image-Text Matching\nIn this paper, we identify a common yet challenging issue in image-text matching, i.e., clone negatives: negative image-text pairs that are semantic-consistent with the positives, which leads to ambiguous and suboptimal matching results. To address this, we propose Adaptive Contrastive Learning (AdaCL), which introduces two margin parameters with a modulating anchor to dynamically strengthen the compactness be<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29908<END_URL>

Discovering Influential Neuron Path in Vision Transformers\nVision Transformer models exhibit immense power yet remain opaque to human understanding, posing challenges and risks for practical applications. While prior research has attempted to demystify these models through input attribution and neuron role analysis, there’s been a notable gap in considering layer-level information and the holistic path of information flow across layers. In this paper, we investigate the significance of influent<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29362<END_URL>

Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA\nGaussian Process Factor Analysis (GPFA) is a powerful latent variable model for extracting low-dimensional manifolds underlying population neural activities. However, one limitation of standard GPFA models is that the number of latent factors needs to be pre-specified or selected through heuristic-based processes, and that all factors contribute at all times. We propose the infinite GPFA model, a fully Bayesian n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31122<END_URL>

Discrete Bregman Divergence\nThe Bregman divergence, which is generated from a convex function, is commonly used as a pseudo-distance for comparing vectors or functions in continuous spaces. In contrast, defining an analog of the Bregman divergence for discrete spaces is nontrivial. Iyer & Bilmes (2012b) considered Bregman divergences on discrete domains using submodular functions as generating functions, the discrete analogs of convex functions. In this paper, we further generalize this framewo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27885<END_URL>

Discrete Codebook World Models for Continuous Control\nIn reinforcement learning (RL), world models serve as internal simulators, enabling agents to predict environment dynamics and future outcomes in order to make informed decisions. While previous approaches leveraging discrete latent spaces, such as DreamerV3, have achieved strong performance in discrete action environments, they are typically outperformed in continuous control tasks by models with continuous latent spaces, like TD-MPC2. This<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28506<END_URL>

Discrete Copula Diffusion\nDiscrete diffusion models have recently shown significant progress in modeling complex data, such as natural languages and DNA sequences. However, unlike diffusion models for continuous data, which can generate high-quality samples in just a few denoising steps, modern discrete diffusion models still require hundreds or even thousands of denoising steps to perform well. In this paper, we identify a fundamental limitation that prevents discrete diffusion models from ach<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30336<END_URL>

Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation\nTransporting between arbitrary distributions is a fundamental goal in generative modeling.Recently proposed diffusion bridge models provide a potential solution, but they rely on a joint distribution that is difficult to obtain in practice.Furthermore, formulations based on continuous domains limit their applicability to discrete domains such as graphs.To overcome these limitations, we propose Discrete Diffusion Schrödinger<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28054<END_URL>

Discrete Distribution Networks\nWe introduce a novel generative model, the Discrete Distribution Networks (DDN), that approximates data distribution using hierarchical discrete distributions. We posit that since the features within a network inherently capture distributional information, enabling the network to generate multiple samples simultaneously, rather than a single output, may offer an effective way to represent distributions. Therefore, DDN fits the target distribution, including contin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27788<END_URL>

Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control\nControl policies that can achieve high task performance and satisfy safety constraints are desirable for any system, including multi-agent systems (MAS). One promising technique for ensuring the safety of MAS is distributed control barrier functions (CBF). However, it is difficult to design distributed CBF-based policies for MAS that can tackle unknown discrete-time dynamics, partial observability, changing neighbor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31197<END_URL>

Discrete Latent Plans via Semantic Skill Abstractions\nSkill learning from language instructions is a critical challenge in developing intelligent agents that can generalize across diverse tasks and follow complex human instructions. Hierarchical methods address this by decomposing the learning problem into multiple levels, where the high-level and low-level policies are mediated through a latent plan space. Effective modeling and learning of this latent plan space are key to enabling robust and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30017<END_URL>

Discretization-invariance? On the Discretization Mismatch Errors in Neural Operators\nIn recent years, neural operators have emerged as a prominent approach for learning mappings between function spaces, such as the solution operators of parametric PDEs. A notable example is the Fourier Neural Operator (FNO), which models the integral kernel as a convolution operator and uses the Convolution Theorem to learn the kernel directly in the frequency domain. The parameters are decoupled from the resol<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30126<END_URL>

Discriminating image representations with principal distortions\nImage representations (artificial or biological) are often compared in terms of their global geometry; however, representations with similar global structure can have strikingly different local geometries. Here, we propose a framework for comparing a set of image representations in terms of their local geometries. We quantify the local geometry of a representation using the Fisher information matrix, a standard statistical tool for<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27965<END_URL>

Discriminator-Guided Cooperative Diffusion for Joint Audio and Video Generation\nIn this study, we aim to construct an audio-video generative model with minimal computational cost by leveraging pre-trained single-modal generative models for audio and video.To achieve this, we propose a novel method that guides single-modal models to cooperatively generate well-aligned samples across modalities. Specifically, given two pre-trained base diffusion models, we train a lightweight joint guidance modul<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29154<END_URL>

Discriminator-Guided Embodied Planning for LLM Agent\nLarge Language Models (LLMs) have showcased remarkable reasoning capabilities in various domains, yet face challenges in complex embodied tasks due to the need for a coherent long-term policy and context-sensitive environmental understanding. Previous work performed LLM refinement relying on outcome-supervised feedback, which can be costly and ineffective. In this work, we introduce a novel framework, Discriminator-Guided Action Optimization <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29523<END_URL>

Disentangled Representation Learning with the Gromov-Monge Gap\nLearning disentangled representations from unlabelled data is a fundamental challenge in machine learning. Solving it may unlock other problems, such as generalization, interpretability, or fairness. Although remarkably challenging to solve in theory, disentanglement is often achieved in practice through prior matching. Furthermore, recent works have shown that prior matching approaches can be enhanced by leveraging geometrical cons<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28910<END_URL>

Disentangling Representations through Multi-task Learning\nIntelligent perception and interaction with the world hinges on internal representations that capture its underlying structure ("disentangled" or "abstract" representations). Disentangled representations serve as world models, isolating latent factors of variation in the world along approximately orthogonal directions, thus facilitating feature-based generalization. We provide experimental and theoretical results guaranteeing the emergen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27723<END_URL>

DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation\nIn the realm of image generation, creating customized images from visual prompt with additional textual instruction emerges as a promising endeavor. However, existing methods, both tuning-based and tuning-free, struggle with interpreting the subject-essential attributes from the visual prompt. This leads to subject-irrelevant attributes infiltrating the generation process, ultimately compromising the personal<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27912<END_URL>

DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction\nDifferential privacy (DP) offers a robust framework for safeguarding individual data privacy. To utilize DP  in training modern machine learning models, differentially private optimizers have been widely used in recent years. A popular approach to privatize an optimizer is to clip the individual gradients and add sufficiently large noise to the clipped gradient. This approach led to the development of DP op<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29981<END_URL>

DisPose: Disentangling Pose Guidance for Controllable Human Image Animation\nControllable human image animation aims to generate videos from reference images using driving videos. Due to the limited control signals provided by sparse guidance (e.g., skeleton pose), recent works have attempted to introduce additional dense conditions (e.g., depth map) to ensure motion alignment. However, such strict dense guidance impairs the quality of the generated video when the body shape of the reference cha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30606<END_URL>

Dissecting Adversarial Robustness of Multimodal LM Agents\nAs language models (LMs) are used to build autonomous agents in real environments, ensuring their adversarial robustness becomes a critical challenge. Unlike chatbots, agents are compound systems with multiple components, which existing LM safety evaluations do not adequately address. To bridge this gap, we manually create 200 targeted adversarial tasks and evaluation functions in a realistic threat model on top of VisualWebArena, a real<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29257<END_URL>

Distance-Based Tree-Sliced Wasserstein Distance\nTo overcome computational challenges of Optimal Transport (OT), several variants of Sliced Wasserstein (SW) has been developed in the literature. These approaches exploit the closed-form expression of the univariate OT by projecting measures onto one-dimensional lines. However, projecting measures onto low-dimensional spaces can lead to a loss of topological information. Tree-Sliced Wasserstein distance on Systems of Lines (TSW-SL) has emerged as <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29805<END_URL>

DistillHGNN: A Knowledge Distillation Approach for High-Speed Hypergraph Neural Networks\nIn this paper, we propose a novel framework to significantly enhance the inference speed and memory efficiency of Hypergraph Neural Networks (HGNNs) while preserving their high accuracy. Our approach utilizes an advanced teacher-student knowledge distillation strategy. The teacher model, consisting of an HGNN and a Multi-Layer Perceptron (MLP), not only produces soft labels but also transfers structural and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27878<END_URL>

Distilling Auto-regressive Models into Few Steps 1: Image Generation\nAutoregressive (AR) models have recently achieved state-of-the-art performance in text and image generation. However, their primary limitation is slow generation speed due to the token-by-token process. We ask an ambitious question: can a pre-trained AR model be adapted to generate outputs in just one or two steps? If successful, this would significantly advance the development and deployment of AR models. We notice that exist<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27673<END_URL>

Distilling Dataset into Neural Field\nUtilizing large-scale datasets is essential for training high-performance deep learning models, but it also comes with substantial computation and storage costs. To overcome these challenges, dataset distillation has emerged as a promising solution by compressing large-scale datasets into smaller synthetic versions that retain the essential information needed for training. This paper proposes a novel parameterization framework for dataset distillation, coine<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28420<END_URL>

Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning\nRecent studies have demonstrated that Transformers can perform in-context reinforcement learning (RL) by imitating a source RL algorithm. This enables them to adapt to new tasks in a sample-efficient manner without parameter updates. However, since the Transformers are trained to mimic the source algorithm, they also reproduce its suboptimal behaviors. Model-based planning offers a promising solution to this limita<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30559<END_URL>

Distilling Structural Representations into Protein Sequence Models\nProtein language (or sequence) models, like the popular ESM2, are now widely used tools for extracting evolution-based protein representations and have achieved significant success on core downstream biological tasks.A major open problem is how to obtain representations that best capture both the sequence evolutionary history and the atomic structural properties of proteins in general. We introduce **I**mplicit **S**equence **M*<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30051<END_URL>

Dist Loss: Enhancing Regression in Few-Shot Region through Distribution Distance Constraint\nImbalanced data distributions are prevalent in real-world scenarios, posing significant challenges in both imbalanced classification and imbalanced regression tasks. They often cause deep learning models to overfit in areas of high sample density (many-shot regions) while underperforming in areas of low sample density (few-shot regions). This characteristic restricts the utility of deep learning models i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29253<END_URL>

Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers\nLarge language models have been successful at tasks involving basic forms of in-context reasoning, such as generating coherent language, as well as storing vast amounts of knowledge. At the core of the Transformer architecture behind such models are feed-forward and attention layers, which are often associated to knowledge and reasoning, respectively. In this paper, we study this distinction empiri<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29376<END_URL>

Distribution Backtracking Builds A Faster Convergence Trajectory for Diffusion Distillation\nAccelerating the sampling speed of diffusion models remains a significant challenge. Recent score distillation methods distill a heavy teacher model into a student generator to achieve one-step generation, which is optimized by calculating the difference between two score functions on the samples generated by the student model.However, there is a score mismatch issue in the early stage of the score disti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31101<END_URL>

Distribution-free Data Uncertainty for Neural Network Regression\nQuantifying uncertainty is an essential part of predictive modeling, especially in the context of high-stakes decision-making. While classification output includes data uncertainty by design in the form of class probabilities, the regression task generally aims only to predict the expected value of the target variable. Probabilistic extensions often assume parametric distributions around the expected value, optimizing the likeliho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28309<END_URL>

Distribution-Specific Agnostic Conditional Classification With Halfspaces\nWe study "selective" or "conditional" classification problems under an agnostic setting. Classification tasks commonly focus on modeling the relationship between features and categories that captures the vast majority of data. In contrast to common machine learning frameworks, conditional classification intends to model such relationships only on a subset of the data defined by some selection rule. Most work on conditiona<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30049<END_URL>

DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agent\nOn-device control agents, especially on mobile devices, are responsible for operating mobile devices to fulfill users' requests, enabling seamless and intuitive interactions. Integrating Multimodal Large Language Models (MLLMs) into these agents enhances their ability to understand and execute complex commands, thereby improving user experience. However, fine-tuning MLLMs for on-device control prese<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30000<END_URL>

Divergence-enhanced Knowledge-guided Context Optimization for Visual-Language Prompt Tuning\nPrompt tuning vision-language models like CLIP has shown great potential in learning transferable representations for various downstream tasks. The main issue is how to mitigate the over-fitting problem on downstream tasks with limited training samples. While knowledge-guided context optimization (Yao et al.,2023; 2024) has been proposed by constructing consistency constraints to handle catastrophic forg<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30858<END_URL>

Divergence of Neural Tangent Kernel in Classification Problems\nThis paper primarily investigates the convergence of the Neural Tangent Kernel (NTK) in classification problems. This study firstly show the strictly positive definiteness of NTK of multi-layer fully connected neural networks and residual neural networks. Then, through a contradiction argument,  it indicates that, during training with the cross-entropy loss function, the neural network parameters diverge due to the strictly positive<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29426<END_URL>

Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning\nRecovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning. After determining a latent style for a trajectory, previous diverse polices recovering methods usually employ a vanilla behavioral cloning learning objective conditioned on the latent style, treating each state-action pair in the trajectory with equal importance. Based on an obse<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30905<END_URL>

Diverse Preference Learning for Capabilities and Alignment\nAs LLMs increasingly impact society, their ability to represent diverse perspectives is critical.  However, recent studies reveal that alignment algorithms such as RLHF and DPO significantly reduce the diversity of LLM outputs. Not only do aligned LLMs generate text with repetitive structure and word choice, they also approach problems in more uniform ways, and their responses reflect a narrower range of societal perspectives. We attrib<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28303<END_URL>

Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning\nComplex logical reasoning tasks require a long sequence of reasoning, which a large language model (LLM) with chain-of-thought prompting still falls short. To alleviate this issue, neurosymbolic approaches incorporate a symbolic solver. Specifically, an LLM only translates a natural language problem into a satisfiability (SAT) problem that consists of first-order logic formulas, and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31268<END_URL>

DLEFT-MKC: Dynamic Late Fusion Multiple Kernel Clustering with Robust Tensor Learning via Min-Max Optimizaiton\nRecent advancements in multiple kernel clustering (MKC) have highlighted the effectiveness of late fusion strategies, particularly in enhancing computational efficiency to near-linear complexity while achieving promising clustering performance. However, existing methods encounter three significant limitations: (1) reliance on fixed base partition matrices that do not adaptively optimiz<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30238<END_URL>

Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models\nRecent research shows that fine-tuning on benign instruction-following data can inadvertently undo the safety alignment process and increase a model's propensity to comply with harmful queries. While instruction-following fine-tuning is important, task-specific fine-tuning - where models are trained on datasets with clear ground truth answers (e.g., multiple choice questions) - can enhance model performance<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28514<END_URL>

Do as We Do, Not as You Think: the Conformity of Large Language Models\nRecent advancements in large language models (LLMs) revolutionize the field of intelligent agents, enabling collaborative multi-agent systems capable of tackling complex problems across various domains. However, the potential of conformity within these systems, analogous to phenomena like conformity bias and group-think in human group dynamics, remains largely unexplored, raising concerns about their collective problem-solvi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28094<END_URL>

Dobi-SVD: Differential SVD for LLM Compression and Some New Perspectives\nLarge language models (LLMs) have sparked a new wave of AI applications; however, their substantial computational costs and memory demands pose significant challenges to democratizing access to LLMs for a broader audience. Singular Value Decomposition (SVD), a technique studied for decades, offers a hardware-independent and flexibly tunable solution for LLM compression. In this paper, we present new directions using SVD: w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28553<END_URL>

DocMIA: Document-Level Membership Inference Attacks against DocVQA Models\nDocument Visual Question Answering (DocVQA) has introduced a new paradigm for end-to-end document understanding, and quickly became one of the standard benchmarks for multimodal LLMs. Automating document processing workflows, driven by DocVQA models, presents significant potential for many business sectors. However, documents tend to contain highly sensitive information, raising concerns about privacy risks associated wit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28823<END_URL>

Do Contemporary CATE Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark\nWe present unexpected findings from a large-scale benchmark study evaluating Conditional Average Treatment Effect (CATE) estimation algorithms. By running 16 modern CATE models across 43,200 datasets, we find that: (a) 62\% of CATE estimates have a higher Mean Squared Error (MSE) than a trivial zero-effect predictor, rendering them ineffective; (b) in datasets with at least one useful CATE estima<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29711<END_URL>

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models\nWe introduce a novel index, the Distribution of Cosine Similarity (DOCS), for quantitatively assessing the similarity between weight matrices in Large Language Models (LLMs), aiming to facilitate the analysis of their complex architectures. Leveraging DOCS, our analysis uncovers intriguing patterns in the latest open-source LLMs: adjacent layers frequently exhibit high weight similarity and tend to form clusters,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29315<END_URL>

Do Deep Neural Network Solutions Form a Star Domain?\nIt has recently been conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances. This means that a linear path can connect two independent solutions with low loss, given the weights of one of the models are appropriately permuted. However, current methods to test this theory often require very wide networks to succeed. In this work, we conjecture that more gene<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29678<END_URL>

Do Egocentric Video-Language Models Truly Understand Hand-Object Interactions?\nEgocentric video-language pretraining is a crucial step in advancing the understanding of hand-object interactions in first-person scenarios. Despite successes on existing testbeds, we find that current EgoVLMs can be easily misled by simple modifications, such as changing the verbs or nouns in interaction descriptions, with models struggling to distinguish between these changes. This raises the question: Do EgoVLMs <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29951<END_URL>

Does Editing Provide Evidence for Localization?\nA basic aspiration for interpretability research in large language models is to localize semantically meaningful behaviors to particular components within the LLM. There are various heuristics for finding candidate locations within the LLM. Once a candidate localization is found, it can be assessed by editing the internal representations at the corresponding localization and checking whether this induces model behavior that is consistent with the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31325<END_URL>

Does Refusal Training in LLMs Generalize to the Past Tense?\nRefusal training is widely used to prevent LLMs from generating harmful, undesirable, or illegal outputs. We reveal a curious generalization gap in the current refusal training approaches: simply reformulating a harmful request in the past tense (e.g., *"How to make a Molotov cocktail?"* to *"How did people make a Molotov cocktail?"*) is often sufficient to jailbreak many state-of-the-art LLMs. We systematically evaluate this method on<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29179<END_URL>

Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts?\nLarge Language Models (LLMs) are known to be susceptible to crafted adversarial attacks or jailbreaks that lead to the generation of objectionable content despite being aligned to human preferences using safety fine-tuning methods. While the large dimensionality of input token space makes it inevitable to find adversarial prompts that can jailbreak these models, we aim to evaluate whether safety fine-tuned LLMs are<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30001<END_URL>

Does SGD really happen in tiny subspaces?\nUnderstanding the training dynamics of deep neural networks is challenging due to their high-dimensional nature and intricate loss landscapes. Recent studies have revealed that, along the training trajectory, the gradient approximately aligns with a low-rank top eigenspace of the training loss Hessian, referred to as the dominant subspace. Given this alignment, this paper explores whether neural networks can be trained within the dominant subspace, whic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27933<END_URL>

Does Spatial Cognition Emerge in Frontier Models?\nNot yet. We present SPACE, a benchmark that systematically evaluates spatial cognition in frontier models. Our benchmark builds on decades of research in cognitive science. It evaluates large-scale mapping abilities that are brought to bear when an organism traverses physical environments, smaller-scale reasoning about object shapes and layouts, and cognitive infrastructure such as spatial attention and memory. For many tasks, we instantiate par<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29371<END_URL>

Does Training with Synthetic Data Truly Protect Privacy?\nAs synthetic data becomes increasingly popular in machine learning tasks, numerous methods—without formal differential privacy guarantees—use synthetic data for training. These methods often claim, either explicitly or implicitly, to protect the privacy of the original training data.In this work, we explore four different training paradigms—coreset selection, dataset distillation, data-free knowledge distillation, and synthetic data gener<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30530<END_URL>

DoF: A Diffusion Factorization Framework for Offline Multi-Agent Decision Making\nDiffusion models have been widely adopted in image and language generation and are now being applied to decision-making. However, the application of diffusion models in offline cooperative Multi-Agent decision making (MADM) remains limited. Although some researches exist, they suffer from scalability or poor cooperation issues due to the lack of design principles for diffusion-based MADM. The Individual-Global-Max <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29825<END_URL>

Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models\nHallucinations in large language models are a widespread problem, yet the mechanisms behind whether models will hallucinate are poorly understood, limiting our ability to solve this problem. Using sparse autoencoders as an interpretability tool, we discover that a key part of these mechanisms is entity recognition, where the model detects if an entity is one it can recall facts about. Sparse autoencoders uncover me<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29377<END_URL>

Do Large Language Models Truly Understand Geometric Structures?\nGeometric ability is a significant challenge for large language models (LLMs) due to the need for advanced spatial comprehension and abstract thinking. Existing datasets primarily evaluate LLMs on their final answers, but they cannot truly measure their true understanding of geometric structures, as LLMs can arrive at correct answers by coincidence. To fill this gap, we introduce the GeomRel dataset, designed to evaluate LLMs’ unde<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30330<END_URL>

Do LLM Agents  Have Regret? A Case Study in Online Learning and Games\nLarge language models (LLMs) have been increasingly employed for (interactive) decision-making, via the development of LLM-based autonomous agents. Despite their emerging successes, the performance of LLM agents in decision-making has not been fully investigated through quantitative metrics, especially in the multi-agent setting when they interact with each other, a typical scenario in real-world LLM-agent applications. To be<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28236<END_URL>

Do LLMs estimate uncertainty well in instruction-following?\nLarge language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. However, recent studies have shown significant limitations in LLMs' instruction-following capabilities, raising concerns about their reliability in high-stakes applications. Accurately estimating LLMs' uncertainty in adhering to instructions is critical to mitigating deployment risks. We presen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30179<END_URL>

Do LLMs have Consistent Values?\nValues are a basic driving force underlying human behavior. Large Language Models (LLM) technology is constantly improving towards human-like dialogue. However, little research has been done to study the values exhibited in text generated by LLMs. Here we study this question by turning to the rich literature on value structure in psychology. We ask whether LLMs exhibit the same value structure that has been demonstrated in humans, including the ranking of values,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30729<END_URL>

Do LLMs ``know'' internally when they follow instructions?\nInstruction-following is crucial for building AI agents with large language models (LLMs), as these models must adhere strictly to user-provided constraints and guidelines. However, LLMs often fail to follow even simple and clear instructions.To improve instruction-following behavior and prevent undesirable outputs, a deeper understanding of how LLMs' internal states relate to these outcomes is required.In this work, we investigate whet<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28257<END_URL>

Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs\nLarge Language Models (LLMs) are increasingly deployed as chatbots, yet their ability to personalize responses to user preferences remains limited. We introduce PrefEval, a benchmark for evaluating LLMs' ability to infer, memorize and adhere to user preferences in long-context conversational setting.PrefEval comprises 3,000 manually curated user preference and query pairs spanning 20 topics. PrefEval contai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29686<END_URL>

Domain Guidance: A Simple Transfer Approach for a Pre-trained Diffusion Model\nRecent advancements in diffusion models have revolutionized generative modeling. However, the impressive and vivid outputs they produce often come at the cost of significant model scaling and increased computational demands. Consequently, building personalized diffusion models based on off-the-shelf models has emerged as an appealing alternative. In this paper, we introduce a novel perspective on conditional generatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29727<END_URL>

Do Mice Grok? Glimpses of Hidden Progress in Sensory Cortex\nDoes learning of task-relevant representations stop when behavior stops changing? Motivated by recent work in machine learning and the intuitive observation that human experts continue to learn after mastery, we hypothesize that task-specific representation learning in cortex can continue, even when behavior saturates. In a novel reanalysis of recently published neural data, we find evidence for such learning in posterior piriform cort<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28350<END_URL>

Do not write that jailbreak paper\nJailbreaks are becoming a new ImageNet competition instead of helping us better understand LLM security. This blogpost surveys the jailbreak literature to extract the most important contributions and encourages the community to revisit their choices and focus on research that can uncover new security vulnerabilities.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31351<END_URL>

Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations\nWhy do biological and artificial neurons sometimes modularise, each encoding a single meaningful variable, and sometimes entangle their representation of many variables? In this work, we develop a theory of when biologically inspired networks---those that are nonnegative and energy efficient---modularise their representation of source variables (sources). We derive necessary and sufficient conditions on <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30538<END_URL>

Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL\nThe use of deep neural networks in reinforcement learning (RL) often suffers from performance degradation as model size increases. While soft mixtures of experts (SoftMoEs) have recently shown promise in mitigating this issue for online RL, the reasons behind their effectiveness remain largely unknown. In this work we provide an in-depth analysis identifying the key factors driving this performance gain. We discover the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30744<END_URL>

DON’T STOP ME NOW: EMBEDDING BASED SCHEDULING FOR LLMS\nEfficient scheduling is crucial for interactive Large Language Model (LLM) applications, where low request completion time directly impacts user engagement. Size-based scheduling algorithms like Shortest Remaining Process Time (SRPT) aim to reduce average request completion time by leveraging known or estimated request sizes and allowing preemption by incoming jobs with shorter service times. However, two main challenges arise when applying<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30829<END_URL>

Don't Take Things Out of Context: Attention Intervention for Enhancing Chain-of-Thought Reasoning in Large Language Models\nFew-shot Chain-of-Thought (CoT) significantly enhances the reasoning capabilities of large language models (LLMs), functioning as a whole to guide these models in generating reasoning steps toward final answers. However, we observe that isolated segments, words, or tokens within CoT demonstrations can unexpectedly disrupt the generation process of LLMs. The model may overly<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29384<END_URL>

DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback\nRestless multi-armed bandits (RMAB) has been widely used to model constrained sequential decision making problems, where the state of each restless arm evolves according to a Markov chain and each state transition generates a scalar reward. However, the success of RMAB crucially relies on the availability and quality of reward signals. Unfortunately, specifying an exact reward function in practice can be chall<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31121<END_URL>

Do Stochastic, Feel Noiseless: Stable Stochastic Optimization via a Double Momentum Mechanism\nOptimization methods are crucial to the success of machine learning, with Stochastic Gradient Descent (SGD) serving as a foundational algorithm for training models. However, SGD is often sensitive to the choice of the learning rate, which necessitates extensive hyperparameter tuning. In this work, we introduce a new variant of SGD that brings enhanced stability in two key aspects. First, our method all<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27684<END_URL>

DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search\nEnhancing the capability of large language models (LLMs) in reasoning has gained significant attention in recent years. Previous studies have demonstrated the effectiveness of various prompting strategies in aiding LLMs in reasoning (called "reasoning actions"), such as step-by-step thinking, reflecting before answering, solving with programs, and their combinations. However, these approaches often applied st<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28026<END_URL>

Doubly Optimal Policy Evaluation for Reinforcement Learning\nPolicy evaluation estimates the performance of a policy by (1) collecting data from the environment and (2) processing raw data into a meaningful estimate. Due to the sequential nature of reinforcement learning, any improper data-collecting policy or data-processing method substantially deteriorates the variance of evaluation results over long time steps. Thus, policy evaluation often suffers from large variance and requires massive da<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30916<END_URL>

Doubly robust identification of treatment effects from multiple environments\nPractical and ethical constraints often dictate the use of observational data for causal inference, particularly in medicine and social sciences. Yet, observational datasets are prone to confounding, potentially compromising the validity of conclusions. While adjusting for all available covariates is a common corrective strategy, this approach can introduce bias, especially when post-treatment variables are present or <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30659<END_URL>

Do Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?\nVision and language model (VLM) decoders are currently the best-performing architectures on multimodal tasks. Next to answers, they are able to produce natural language explanations, either in post-hoc or CoT settings. However, it is not clear to what extent they are using the input vision and text modalities when generating answers or explanations.In this work, we investigate if VLMs rely on <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28530<END_URL>

Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference under Ambiguities\nSpatial expressions in situated communication can be ambiguous, as their meanings vary depending on the frames of reference (FoR) adopted by speakers and listeners. While spatial language understanding and reasoning by vision-language models (VLMs) have gained increasing attention, potential ambiguities in these models are still under-explored. To address this issue, we present the COnsist<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30786<END_URL>

Do vision models perceive objects like toddlers ?\nDespite recent advances in artificial vision systems, humans are still more data-efficient at learning strong visual representations. Psychophysical experiments suggest that toddlers develop fundamental visual properties between the ages of one and three, which affect their perceptual system for the rest of their life. They begin to recognize impoverished variants of daily objects, pay more attention to the shape of an object to categorize it, p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31352<END_URL>

Do WGANs succeed because they minimize the Wasserstein Distance? Lessons from Discrete Generators\nSince WGANs were first introduced, there has been considerable debate whether their success in generating realistic images can be attributed to minimizing the Wasserstein distance between the distribution of  generated images and the training distribution. In this paper we present theoretical and experimental results that show that successful WGANs {\em do} minimize the Wasserstein distance but the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30814<END_URL>

Do You Keep an Eye on What I Ask? Mitigating Multimodal Hallucination via Attention-Guided Ensemble Decoding\nRecent advancements in Large Vision-Language Models (LVLMs) have significantly expanded their utility in tasks like image captioning and visual question answering. However, they still struggle with object hallucination, where models generate descriptions that inaccurately reflect the visual content by including nonexistent objects or misrepresenting existing ones. While previous methods,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27655<END_URL>

DPaI: Differentiable Pruning at Initialization with Node-Path Balance Principle\nPruning at Initialization (PaI) is a technique in neural network optimization characterized by the proactive elimination of weights before the network's training on designated tasks. This innovative strategy potentially reduces the costs for training and inference, significantly advancing computational efficiency. A key element of PaI's effectiveness is that it considers the significance of weights in an untrained n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28732<END_URL>

DPLM-2: A Multimodal Diffusion Protein Language Model\nProteins are essential macromolecules defined by their amino acid sequences, which determine their three-dimensional structures and, consequently, their functions in all living organisms. Therefore, generative protein modeling necessitates a multimodal approach to simultaneously model, understand, and generate both sequences and structures. However, existing methods typically use separate models for each modality, limiting their ability to c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30917<END_URL>

DP-SGD for non-decomposable objective functions\nUnsupervised pre-training is a common step in developing computer vision models and large language models. In this setting, the absence of labels requires the use of similarity-based loss functions, such as the contrastive loss, that favor minimizing the distance between similar inputs and maximizing the distance between distinct inputs. As privacy concerns mount, training these models using differential privacy has become more important. However,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30368<END_URL>

Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient\nModel-based reinforcement learning (RL) offers a solution to the data inefficiency that plagues most model-free RL algorithms. However, learning a robust world model often demands complex and deep architectures, which are expensive to compute and train. Within the world model, dynamics models are particularly crucial for accurate predictions, and various dynamics-model architectures have been explored, eac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30818<END_URL>

Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want\nIn this paper, we present the Draw-and-Understand framework, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs). Visual prompts allow users to interact through multi-modal instructions, enhancing the models' interactivity and fine-grained image comprehension. In this framework, we propose a general architecture adaptable to different pre-tr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29098<END_URL>

DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation\nPersonalized image generation holds great promise in assisting humans in everyday work and life due to its impressive function in creatively generating personalized content. However, current evaluations either are automated but misalign with humans or require human evaluations that are time-consuming and expensive. In this work, we present DreamBench++, a human-aligned benchmark that advanced multimodal GPT models automat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31023<END_URL>

DreamCatalyst: Fast and High-Quality 3D Editing via Controlling Editability and Identity Preservation\nScore distillation sampling (SDS) has emerged as an effective framework in text-driven 3D editing tasks, leveraging diffusion models for 3D consistent editing. However, existing SDS-based 3D editing methods suffer from long training times and produce low-quality results. We identify that the root cause of this performance degradation is their conflict with the sampling dynamics of diffusion mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30362<END_URL>

DreamDistribution: Prompt Distribution Learning for Text-to-Image Diffusion Models\nThe popularization of Text-to-Image (T2I) diffusion models enables the generation of high-quality images from text descriptions. However, generating diverse customized images with reference visual attributes remains challenging. This work focuses on personalizing T2I diffusion models at a more abstract concept or category level, adapting commonalities from a set of reference images while creating new instances wi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28359<END_URL>

Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination\nA world model provides an agent with a representation of its environment, enabling it to predict the causal consequences of its actions. Current world models typically cannot directly and explicitly imitate the actual environment in front of a robot, often resulting in unrealistic behaviors and hallucinations that make them unsuitable for real-world applications. In this paper, we introduce a ne<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31075<END_URL>

Dreamweaver: Learning Compositional World Models from Pixels\nHumans have an innate ability to decompose their perceptions of the world into objects and their attributes, such as colors, shapes, and movement patterns. This cognitive process enables us to imagine novel futures by recombining familiar concepts. However, replicating this ability in artificial intelligence systems has proven challenging, particularly when it comes to modeling videos into compositional concepts and generating unseen,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28949<END_URL>

DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing\nWe introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning are either insufficient for complex style adaptation or computationally expensive, particularly in tasks like NPC creation or character role-playing. Our approach leverages the over-parameterized nature of LLMs to disentangle a style-relev<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28469<END_URL>

DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving\nEnd-to-end autonomous driving (E2E-AD) has emerged as a trend in the field of autonomous driving, promising a data-driven, scalable approach to system design. However, existing E2E-AD methods usually adopt the sequential paradigm of perception-prediction-planning, which leads to cumulative errors and training instability. The manual ordering of tasks also limits the system’s ability to leverage synergies between ta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29956<END_URL>

DRL: Decomposed Representation Learning for Tabular Anomaly Detection\nAnomaly detection, indicating to identify the anomalies that significantly deviate from the majority normal instances of data, has been an important role in machine learning and related applications. Despite the significant success achieved in anomaly detection on image and text data, the accurate Tabular Anomaly Detection (TAD) has still been hindered due to the lack of clear prior semantic information in the tabular data. M<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30521<END_URL>

DRoC: Elevating Large Language Models for Complex Vehicle Routing via Decomposed Retrieval of Constraints\nThis paper proposes Decomposed Retrieval of Constraints (DRoC), a novel framework aimed at enhancing large language models (LLMs) in exploiting solvers to tackle vehicle routing problems (VRPs) with intricate constraints. While LLMs have shown promise in solving simple VRPs, their potential in addressing complex VRP variants is still suppressed, due to the limited embedded internal knowledg<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28142<END_URL>

DRoP: Distributionally Robust Pruning\nIn the era of exceptionally data-hungry models, careful selection of the training data is essential to mitigate the extensive costs of deep learning. Data pruning offers a solution by removing redundant or uninformative samples from the dataset, which yields faster convergence and improved neural scaling laws. However, little is known about its impact on classification bias of the trained models. We conduct the first systematic study of this effect and reve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28840<END_URL>

Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization\nThe Mixture of Experts (MoE) architecture reduces the training and inference cost significantly compared to a dense model of equivalent capacity. Upcycling is an approach that initializes and trains an MoE model using a pre-trained dense model. While upcycling leads to initial performance gains, the training progresses slower than when trained from scratch, leading to suboptimal performance in the long term. We pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28794<END_URL>

DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?\nLarge Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents for targeted applications such as shopping assistants or AI software engineers. Recently, many data science benchmarks have been proposed to investigate their performance in the data science domain. However, existing data science benchmarks <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30458<END_URL>

DSI: Faster Inference of Large Language Models via Speculation Parallelism\nAccelerating the inference of large language models (LLMs) is an important challenge in artificial intelligence. This paper introduces Distributed Speculative Inference (DSI), a novel distributed inference algorithm that is provably faster than speculative inference (SI) [leviathan2023fast, chen2023accelerating, miao2023specinfer] and traditional autoregressive inference (non-SI). Like other SI algorithms, DSI works on f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29058<END_URL>

DS-LLM: Leveraging Dynamical Systems to Enhance Both Training and Inference of Large Language Models\nThe training of large language models (LLMs) faces critical computational cost challenges, hindering their scaling toward AGI and broader adoption. With model sizes doubling approximately every 3.4 months and training costs surging from 64 million USD for GPT-4 in 2020 to 191 million USD for Gemini Ultra in 2023, the economic strain is unsustainable. While optimizations like quantization provide<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29827<END_URL>

DUALFormer: A Dual Graph Convolution and Attention Network for Node Classification\nGraph Transformers (GTs), adept at capturing the locality and globality of graphs, have shown promising potential in node classification tasks. Most state-of-the-art GTs succeed through integrating local Graph Neural Networks (GNNs) with their global Self-Attention (SA) modules to enhance structural awareness. Nonetheless, this architecture faces limitations arising from scalability challenges and the trade-off b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30986<END_URL>

Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces\nIn human cognition theory, human thinking is governed by two systems: the fast and intuitive System 1 and the slower but more deliberative System 2. Recent studies have shown that incorporating System 2 process into Transformers including large language models (LLMs), significantly enhances their reasoning capabilities.   Nevertheless, models that purely resemble System 2 thinking require substantially <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29093<END_URL>

Dual Process Learning: Controlling Use of In-Context vs. In-Weights Strategies with Weight Forgetting\nLanguage models have the ability to perform in-context learning (ICL), allowing them to flexibly adapt their behavior based on context. This contrasts with in-weights learning (IWL), where memorized information is  encoded in model parameters from iterated observations of the data (e.g., common sayings). An ideal model should be able to maintain both of these abilities. Despite their apparent a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28644<END_URL>

DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity\nDecentralized bilevel optimization (DBO) provides a powerful framework for multi-agent systems to solve local bilevel tasks in a decentralized fashion without the need for a central server. However, most existing DBO methods rely on lower-level strong convexity (LLSC) to guarantee unique solutions and and a well-defined hypergradient for stationarity measure, hindering their applicability in many practical scenarios n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28607<END_URL>

DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads\nDeploying long-context large language models (LLMs) is essential but poses significant computational and memory challenges.Caching all Key and Value (KV) states across all attention heads consumes substantial memory.Existing KV cache pruning methods either damage the long-context capabilities of LLMs or offer only limited efficiency improvements.In this paper, we identify that only a fraction of attention head<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29062<END_URL>

Duoduo CLIP: Efficient 3D Understanding with Multi-View Images\nWe introduce Duoduo CLIP, a model for 3D representation learning that learns shape encodings from multi-view images instead of point-clouds. The choice of multi-view images allows us to leverage 2D priors from off-the-shelf CLIP models to facilitate fine-tuning with 3D data. Our approach not only shows better generalization compared to existing point cloud methods, but also reduces GPU requirements and training time. In addition, th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28703<END_URL>

Durable Quantization Conditioned Misalignment Attack on Large Language Models\nAs large language models (LLMs) are increasingly deployed on resource-constrained edge devices, quantization techniques have been widely adopted to reduce model size and computational requirements. However, this process can expose models to new vulnerabilities. In this work, we introduce the Quantization Conditioned Misalignment (Q-Misalign) attack, a novel threat in which safety misalignment remains dormant in a full<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31042<END_URL>

DyCAST: Learning Dynamic Causal Structure from Time Series\nUnderstanding the dynamics of causal structures is crucial for uncovering the underlying processes in time series data. Previous approaches rely on static assumptions, where contemporaneous and time-lagged dependencies are assumed to have invariant topological structures. However, these models fail to capture the evolving causal relationship between variables when the underlying process exhibits such dynamics. To address this limitation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29348<END_URL>

DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation\nCurrent unsupervised domain adaptation (UDA) methods for semantic segmentation typically assume identical class labels between the source and target domains. This assumption ignores the label-level domain gap, which is common in real-world scenarios, and limits their ability to identify finer-grained or novel categories without requiring extensive manual annotation.A promising direction to address this limitation li<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30161<END_URL>

DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models\nThe rapid advancements in Vision-Language Models (VLMs) have shown great potential in tackling mathematical reasoning tasks that involve visual context. Unlike humans who can reliably apply solution steps to similar problems with minor modifications, we found that state-of-the-art VLMs like GPT-4o can consistently fail in these scenarios, revealing limitations in their mathematical re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29415<END_URL>

Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models\nDiffusion models have emerged as powerful generative frameworks by progressively adding noise to data through a forward process and then reversing this process to generate realistic samples. While these models have achieved strong performance across various tasks and modalities, their application to temporal predictive learning remains underexplored. Existing approaches treat predictive learning as a conditional generation pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29071<END_URL>

DynamicCity: Large-Scale LiDAR Generation from Dynamic Scenes\nLiDAR scene generation has been developing rapidly recently. However, existing methods primarily focus on generating static and single-frame scenes, overlooking the inherently dynamic nature of real-world driving environments. In this work, we introduce DynamicCity, a novel 4D occupancy generation framework capable of generating large-scale, high-quality dynamic LiDAR scenes with semantics. DynamicCity mainly consists of two key mode<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29953<END_URL>

Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment\nReinforcement learning (RL) has made significant progress in various domains, but scaling it to long-horizon tasks with complex decision-making remains challenging. Skill learning attempts to address this by abstracting actions into higher-level behaviors. However, current approaches often fail to recognize semantically similar behaviors as the same skill and use fixed skill lengths, li<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30754<END_URL>

Dynamic Diffusion Transformer\nDiffusion Transformer (DiT), an emerging diffusion model for image generation,has demonstrated superior performance but suffers from substantial computationalcosts. Our investigations reveal that these costs stem from the static inferenceparadigm, which inevitably introduces redundant computation in certain diffusiontimesteps and spatial regions. To address this inefficiency, we propose DynamicDiffusion Transformer (DyDiT), an architecture that dynamically adjusts <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28043<END_URL>

Dynamic Discriminative Operations for Efficient Generative Inference of LLMs\nEfficient generative inference in Large Language Models (LLMs) is impeded by the growing memory demands of Key-Value (KV) cache, especially for longer sequences. Traditional KV Cache eviction strategies, which discard less critical KV-pairs based on attention scores, often degrade generation quality, leading to issues such as context loss or hallucinations. To address this, we introduce **D**ynamic **D**iscriminative *<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30197<END_URL>

Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Monocular Videos\nModern 3D engines and graphics pipelines require mesh as a memory-efficient representation, which allows efficient rendering, geometry processing, texture editing, and many other downstream operations. However, it is still highly difficult to obtain high-quality mesh in terms of detailed structure and time consistency from dynamic observations. To this end, we introduce Dynamic Gaussians Mesh (DG-Mesh), a framework to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29972<END_URL>

Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification\nMultimodal Large Language Models (MLLMs) have achieved remarkable success in vision understanding, reasoning, and interaction. However, the inference computation and memory increase progressively with the generation of output tokens during decoding, directly affecting the efficacy of MLLMs. Existing methods attempt to reduce the vision context redundancy to achieve efficient MLLMs. Unfor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28727<END_URL>

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining\nPretraining large language models (LLMs) on vast and heterogeneous datasets is crucial for achieving state-of-the-art performance across diverse downstream tasks. However, current training paradigms treat all samples equally, overlooking the importance or relevance of individual samples throughout the training process. Existing reweighting strategies, which primarily focus on group-level data importance, fail to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28818<END_URL>

Dynamic Low-Rank Sparse Adaptation for Large Language Models\nDespite the efficacy of network sparsity in alleviating the deployment strain of Large Language Models (LLMs), it endures significant performance degradation. Applying Low-Rank Adaptation (LoRA) to fine-tune the sparse LLMs offers an intuitive approach to counter this predicament, while it holds shortcomings include: 1) The inability to integrate LoRA weights into sparse LLMs post-training, and 2) Insufficient performance recovery at <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28352<END_URL>

Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models\nThe Sparse Mixture of Experts (SMoE) has been widely employed to enhance the efficiency of training and inference for Transformer-based foundational models, yielding promising results. However, the performance of SMoE heavily depends on the choice of hyper-parameters, such as the number of experts and the number of experts to be activated (referred to as top-$k$), resulting in significant computational overhead<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29566<END_URL>

Dynamic Modeling of Patients, Modalities and Tasks via Multi-modal Multi-task Mixture of Experts\nMulti-modal multi-task learning holds significant promise in tackling complex diagnostic tasks and many significant medical imaging problems. It fulfills the needs in real-world diagnosis protocol to leverage information from different data sources and simultaneously perform mutually informative tasks. However, medical imaging domains introduce two key challenges: dynamic modality fusion and modalit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29884<END_URL>

Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping\nLarge Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across multimodal tasks such as visual perception and reasoning, leading to good performance on various multimodal evaluation benchmarks. However, these benchmarks keep a static nature and overlap with the pre-training data, resulting in fixed complexity constraints and data contamination issues. This raises the concern regarding <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29328<END_URL>

Dynamic Multi-product Selection and Pricing under Preference Feedback\nIn this study, we investigate the problem of dynamic multi-product selection and pricing by introducing a novel framework based on a *censored multinomial logit* (C-MNL) choice model. In this model, sellers present a set of products with prices, and buyers filter out products priced above their valuation, purchasing at most one product from the remaining options based on their preferences. The goal is to maximize seller reven<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30462<END_URL>

Dynamic Negative Guidance of Diffusion Models\nNegative Prompting (NP) is widely utilized in diffusion models, particularly in text-to-image applications, to prevent the generation of undesired features. In this paper, we show that conventional NP is limited by the assumption of a constant guidance scale, which may lead to highly suboptimal results, or even complete failure, due to the non-stationarity and state-dependence of the reverse process. Based on this analysis, we derive a principled te<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30862<END_URL>

Dynamic Neural Fortresses: An Adaptive Shield for Model Extraction Defense\nModel extraction aims to acquire a pre-trained black-box model concealed behind a black-box API. Existing defense strategies against model extraction primarily concentrate on preventing the unauthorized extraction of API functionality. However, two significant challenges still need to be solved: (i) Neural network architecture of the API constitutes a form of intellectual property that also requires protection; (ii) The <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31275<END_URL>

Dynamics of Concept Learning and Compositional Generalization\nPrior work has shown that text-conditioned diffusion models can learn to identify and manipulate primitive concepts underlying a compositional data-generating process, enabling generalization to entirely novel, out-of-distribution compositions. Beyond performance evaluations, these studies develop a rich empirical phenomenology of learning dynamics, showing that models generalize sequentially, respecting the compositional hierarchy o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28151<END_URL>

Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustnes\nIt is generally perceived that Dynamic Sparse Training opens the door to a new era of scalability and efficiency for artificial neural networks at, perhaps, some costs in accuracy performance for the classification task. At the same time, Dense Training is widely accepted as being the "de facto" approach to train artificial neural networks if one would like to maximize their robustness against ima<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28977<END_URL>

DynaPrompt: Dynamic Test-Time Prompt Tuning\nTest-time prompt tuning enhances zero-shot generalization of vision-language models but tends to ignore the relatedness among test samples during inference. Online test-time prompt tuning provides a simple way to leverage the information in previous test samples, albeit with the risk of prompt collapse due to error accumulation. To enhance test-time prompt tuning, we propose DynaPrompt, short for dynamic test-time prompt tuning, exploiting relevant da<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30415<END_URL>

DynFrs: An Efficient Framework for Machine Unlearning in Random Forest\nRandom Forests are widely recognized for establishing efficacy in classification and regression tasks, standing out in various domains such as medical diagnosis, finance, and personalized recommendations. These domains, however, are inherently sensitive to privacy concerns, as personal and confidential data are involved. With increasing demand for the right to be forgotten, particularly under regulations such as GDPR and CCP<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28390<END_URL>

Dysca: A Dynamic and Scalable Benchmark for Evaluating Perception Ability of LVLMs\nCurrently many benchmarks have been proposed to evaluate the perception ability of the Large Vision-Language Models (LVLMs).However, most benchmarks conduct questions by selecting images from existing datasets, resulting in the potential data leakage.  Besides, these benchmarks merely focus on evaluating LVLMs on the realistic style images and clean scenarios, leaving the multi-stylized images and noisy scenarios<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29107<END_URL>

E(3)-equivariant models cannot learn chirality: Field-based molecular generation\nObtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utilizing graph neural network (GNN) parametrizations, with rotational symmetries baked in via E(3) invariant layers. We prove that such models must necessarily disregard chirality, a geometric property of the molecules that cannot be superimp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28460<END_URL>

Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders\nThe ability to accurately interpret complex visual information is a crucial topic of multimodal large language models (MLLMs). Recent work indicates that enhanced visual perception significantly reduces hallucinations and improves performance on resolution-sensitive tasks, such as optical character recognition and document analysis. A number of recent MLLMs achieve this goal using a mixture of vision encoders. Despit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29276<END_URL>

Earlier Tokens Contribute More: Learning Direct Preference Optimization From Temporal Decay Perspective\nDirect Preference Optimization (DPO) has gained attention as an efficient alternative to reinforcement learning from human feedback (RLHF) for aligning large language models (LLMs) with human preferences. Despite its advantages, DPO suffers from a length bias, generating responses longer than those from the reference model. Existing solutions like SimPO and SamPO address this issue but unifor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29798<END_URL>

Easing Training Process of Rectified Flow Models Via Lengthening Inter-Path Distance\nRecent research pinpoints that different diffusion methods and architectures trained on the same dataset produce similar results for the same input noise. This property suggests that they have some preferable noises for a given sample. By visualizing the noise-sample pairs of rectified flow models and stable diffusion models in two-dimensional spaces, we observe that the preferable paths, connecting preferable <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29645<END_URL>

ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials\nSupervised machine learning techniques are increasingly being adopted to speed up electronic structure predictions, serving as alternatives to first-principles methods like Density Functional Theory (DFT). Although current DFT datasets mainly emphasize chemical properties and atomic forces, the precise prediction of electronic charge density is essential for accuratel<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29623<END_URL>

EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation\nObject manipulation is a common component of everyday tasks, but learning to manipulate objects from high-dimensional observations presents significant challenges. These challenges are heightened in multi-object environments due to the combinatorial complexity of the state space as well as of the desired behaviors. While recent approaches have utilized large-scale offline data to train models from pixel observations, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28379<END_URL>

EC-DIT: Scaling Diffusion Transformers with Adaptive Expert-Choice Routing\nDiffusion transformers have been widely adopted for text-to-image synthesis. While scaling these models up to billions of parameters shows promise, the effectiveness of scaling beyond current sizes remains underexplored and challenging. By explicitly exploiting the computational heterogeneity of image generations, we develop a new family of Mixture-of-Experts (MoE) models (EC-DIT) for diffusion transformers with expert-c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29721<END_URL>

ECHOPulse: ECG Controlled Echocardio-gram Video Generation\nEchocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic data and generating high-quality videos from routine health data. However, existing models often face high computational costs, slow inferenc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28723<END_URL>

EcoFace: Audio-Visual Emotional Co-Disentanglement Speech-Driven 3D Talking Face Generation\nSpeech-driven 3D facial animation has attracted significant attention due to its wide range of applications in animation production and virtual reality. Recent research has explored speech-emotion disentanglement to enhance facial expressions rather than manually assigning emotions. However, this approach face issues such as feature confusion, emotions weakening and mean-face. To address these issues, we<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28709<END_URL>

econSG: Efficient and Multi-view Consistent Open-Vocabulary 3D Semantic Gaussians\nThe primary focus of most recent works on open-vocabulary neural fields is extracting precise semantic featuresfrom the VLMs and then consolidating them efficiently into a multi-view consistent 3D neural fieldsrepresentation. However, most existing works over-trusted SAM to regularize image-level CLIP without any further refinement. Moreover, several existing works improved efficiency by dimensionality reduction o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28248<END_URL>

Edge-aware Image Smoothing with Relative Wavelet Domain Representation\nImage smoothing is a fundamental technique in image processing, designed to eliminate perturbations and textures while preserving dominant structures. It plays a pivotal role in numerous high-level computer vision tasks. More recently, both traditional and deep learning-based smoothing methods have been developed. However, existing algorithms frequently encounter issues such as gradient reversals and halo artifacts. Furtherm<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31252<END_URL>

Edge Prompt Tuning for Graph Neural Networks\nPre-training powerful Graph Neural Networks (GNNs) with unlabeled graph data in a self-supervised manner has emerged as a prominent technique in recent years. However, inevitable objective gaps often exist between pre-training and downstream tasks. To bridge this gap, graph prompt tuning techniques design and learn graph prompts by manipulating input graphs or reframing downstream tasks as pre-training tasks without fine-tuning the pre-trained GNN mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30726<END_URL>

EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation\nCurrent auto-regressive mesh generation methods suffer from issues such as incompleteness, insufficient detail, and poor generalization. In this paper, we propose an Auto-regressive Auto-encoder (ArAE) model capable of generating high-quality 3D meshes with up to 4,000 faces at a spatial resolution of $512^3$.We introduce a novel mesh tokenization algorithm that efficiently compresses triangular meshes into 1D token sequences<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30789<END_URL>

EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models\nDistributed training methods are crucial for large language models (LLMs). However, existing distributed training methods often suffer from communication bottlenecks, stragglers, and limited elasticity, particularly in heterogeneous or large-scale environments. Local SGD methods have been proposed to address these issues, but their effectiveness remains limited to small-scale training due to additional memor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27758<END_URL>

EditRoom: LLM-parameterized Graph Diffusion for Composable 3D Room Layout Editing\nGiven the steep learning curve of professional 3D software and the time-consuming process of managing large 3D assets, language-guided 3D scene editing has significant potential in fields such as virtual reality, augmented reality, andgaming. However, recent approaches to language-guided 3D scene editing eitherrequire manual interventions or focus only on appearance modifications withoutsupporting comprehensive sc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29277<END_URL>

Effective and Efficient Time-Varying Counterfactual Prediction with State-Space Models\nTime-varying counterfactual prediction (TCP) from observational data supports the answer of when and how to assign multiple sequential treatments, yielding importance in various applications. Despite the progress achieved by recent advances, e.g., LSTM or Transformer based causal approaches, their capability of capturing interactions in long sequences remains to be improved in both prediction performance and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27707<END_URL>

Effective Interplay between Sparsity and Quantization: From Theory to Practice\nThe increasing size of deep neural networks (DNNs) necessitates effective model compression to reduce their computational and memory footprints. Sparsity and quantization are two prominent compression methods that have been shown to reduce DNNs' computational and memory footprints significantly while preserving model accuracy. However, how these two methods interact when combined together remains a key question for d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27855<END_URL>

Effective post-training embedding compression via temperature control in contrastive training\nFixed-size learned representations (dense representations, or embeddings) are widely used in many machine learning applications across language, vision or speech modalities. This paper investigates the role of the temperature parameter in contrastive training for text embeddings. We shed light on the impact this parameter has on the intrinsic dimensionality of the embedding spaces obtained, and show th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28082<END_URL>

Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs\nAction-constrained reinforcement learning (ACRL) is a generic framework for learning control policies with zero action constraint violation, which is required by various safety-critical and resource-constrained applications. The existing ACRL methods can typically achieve favorable constraint satisfaction but at the cost of either high computational burden incurred by the quadratic programs (Q<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30623<END_URL>

Efficient Active Imitation Learning with Random Network Distillation\nDeveloping agents for complex and underspecified tasks, where no clear objective exists, remains challenging but offers many opportunities. This is especially true in video games, where simulated players (bots) need to play realistically, and there is no clear reward to evaluate them. While imitation learning has shown promise in such domains, these methods often fail when agents encounter out-of-distribution scenarios during <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30291<END_URL>

Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation\nWeighted low rank approximation is a fundamental problem in numerical linear algebra, and it has many applications in machine learning. Given a matrix $M \in \mathbb{R}^{n \times n}$, a non-negative weight matrix $W \in \mathbb{R}_{\geq 0}^{n \times n}$, a parameter $k$, the goal is to output two matrices $X,Y\in \mathbb{R}^{n \times k}$ such that $\\| W \circ (M - X Y^\top) \\|_F$ is minimized, where $\circ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28163<END_URL>

Efficient and Accurate Explanation Estimation with Distribution Compression\nExact computation of various machine learning explanations requires numerous model evaluations and in extreme cases becomes impractical. The computational cost of approximation increases with an ever-increasing size of data and model parameters. Many heuristics have been proposed to approximate post-hoc explanations efficiently. This paper shows that the standard i.i.d. sampling used in a broad spectrum of algorithms fo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29979<END_URL>

Efficient and Context-Aware Label Propagation for Zero-/Few-Shot Training-Free Adaptation of Vision-Language Model\nVision-language models (VLMs) have revolutionized machine learning by leveraging large pre-trained models to tackle various downstream tasks. Despite improvements in label, training, and data efficiency, many state-of-the-art VLMs still require task-specific hyperparameter tuning and fail to fully exploit test samples. To overcome these challenges, we propose a graph-based approach<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30482<END_URL>

Efficient and Generalizable Second-Order Certified Unlearning: A Hessian-Free Online Model Updates Approach\nMachine unlearning strives to uphold the data owners' right to be forgotten by enabling models to selectively forget specific data. Recent advances suggest pre-computing and storing statistics extracted from second-order information and implementing unlearning through Newton-style updates.However, the Hessian matrix operations are extremely costly and previous works conduct unlearning for<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30533<END_URL>

Efficient and Robust Neural Combinatorial Optimization via Wasserstein-Based Coresets\nCombinatorial optimization (CO) is a fundamental tool in many fields. Many neural combinatorial optimization (NCO) methods have been proposed to solve CO problems.However, existing NCO methods typically require significant computational and storage resources, and face challenges in maintaining robustness to distribution shifts between training and test data.To address these issues, \textbf{first}, we model CO <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30367<END_URL>

Efficient and Trustworthy Causal Discovery with Latent Variables and Complex Relations\nMost traditional causal discovery methods assume that all task-relevant variables are observed, an assumption often violated in practice. Although some recent works allow the presence of latent variables, they typically assume the absence of certain special causal relations to ensure a degree of simplicity, which might also be invalid in real-world scenarios. This paper tackles a challenging and important set<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30566<END_URL>

Efficient Automated Circuit Discovery in Transformers using Contextual Decomposition\nAutomated mechanistic interpretation research has attracted great interest due to its potential to scale explanations of neural network internals to large models. Existing automated circuit discovery work relies on activation patching or its approximations to identify subgraphs in models for specific tasks (circuits). They often suffer from slow runtime, approximation errors, and specific requirements of metric<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31044<END_URL>

Efficient Biological Data Acquisition through Inference Set Design\nIn drug discovery, highly automated high-throughput laboratories are used to screen a large number of compounds in search of effective drugs. These experiments are expensive, so we might hope to reduce their cost by experimenting on a subset of the compounds, and predicting the outcomes of the remaining experiments. In this work, we model this problem as a sequential subset selection problem: we aim to sequentially select the sm<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28817<END_URL>

Efficient Causal Decision Making with One-sided Feedback\nWe study a class of decision-making problems with one-sided feedback, where outcomes are only observable for specific actions. A typical example is bank loans, where the repayment status is known only if a loan is approved and remains undefined if rejected. In such scenarios, conventional approaches to causal decision evaluation and learning from observational data are not directly applicable. In this paper, we introduce a novel value fun<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29469<END_URL>

Efficient Cross-Episode Meta-RL\nWe introduce Efficient Cross-Episodic Transformers (ECET), a new algorithm for online Meta-Reinforcement Learning that addresses the challenge of enabling reinforcement learning agents to perform effectively in previously unseen tasks. We demonstrate how past episodes serve as a rich source of in-context information, which our model effectively distills and applies to new contexts. Our learned algorithm is capable of outperforming the previous state-of-the-art an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29485<END_URL>

Efficient Dictionary Learning with Switch Sparse Autoencoders\nSparse autoencoders (SAEs) are a recent technique for decomposing neural network activations into human-interpretable features. However, in order for SAEs to identify all features represented in frontier models, it will be necessary to scale them up to very high width, posing a computational challenge. In this work, we introduce $\textbf{Switch Sparse Autoencoders}$, a novel SAE architecture aimed at reducing the compute cost of trai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28604<END_URL>

Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning\nDiffusion Policies have become widely used in Imitation Learning, offering several appealing properties, such as generating multimodal and discontinuous behavior.As models are becoming larger to capture more complex capabilities, their computational demands increase, as shown by recent scaling laws. Therefore, continuing with the current architectures will present a computational roadblock. To addre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28417<END_URL>

Efficient Discovery of Pareto Front for Multi-Objective Reinforcement Learning\nMulti-objective reinforcement learning (MORL) excels at handling rapidly changing preferences in tasks that involve multiple criteria, even for unseen preferences. However, previous dominating MORL methods typically generate a fixed policy set or preference-conditioned policy through multiple training iterations exclusively for sampled preference vectors, and cannot ensure the efficient discovery of the Pareto front.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28888<END_URL>

Efficient Distribution Matching of Representations via Noise-Injected Deep InfoMax\nDeep InfoMax (DIM) is a well-established method for self-supervised representation learning (SSRL) based on maximization of the mutual information between the input and the output of a deep neural network encoder. Despite the DIM and contrastive SSRL in general being well-explored, the task of learning representations conforming to a specific distribution (i.e., distribution matching, DM) is still under-addressed<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28477<END_URL>

Efficient Evolutionary Search Over Chemical Space with Large Language Models\nMolecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in molecular discovery, traverse chemical space by performing random mutations and crossovers, leading to a large number of expensive objective evaluations. In this work, we ame<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29136<END_URL>

Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction\nIn the face of difficult exploration problems in reinforcement learning, we study whether giving an agent an object-centric mapping (describing a set of items and their attributes) allow for more efficient learning. We found this problem is best solved hierarchically by modelling items at a higher level of state abstraction to pixels, and attribute change at a higher level of temporal abstraction to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28750<END_URL>

Efficient Imitation under Misspecification\nInteractive imitation learning (IL) is a powerful paradigm for learning to make sequences of decisions from an expert demonstrating how to perform a task. Prior work in efficient imitation learning has focused on the realizable setting, where the expert's policy lies within the learner's policy class (i.e. the learner can perfectly imitate the expert in all states). However, in practice, perfect imitation of the expert perfectly is often impossible due<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28859<END_URL>

Efficient Inference for Large Language Model-based Generative Recommendation\nLarge Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caused by autoregressive decoding. For lossless LLM decoding acceleration, Speculative Decoding (SD) has emerged as a promising solution. However, applying SD to generative recommendation presents unique challenges due to the requirement of gener<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30645<END_URL>

Efficient Interpolation between Extragradient and Proximal Methods for Weak MVIs\nWe study nonmonotone games satisfying the weak Minty variational inequality (MVI) with parameter $\rho \in (-\tfrac{1}{L}, \infty)$, where $L$ is the Lipschitz constant of the gradient operator. An error corrected version of the inexact proximal point algorithm is proposed, with which we establish the first $\mathcal O(1/\epsilon)$ rate for the entire range $\rho \in (-\tfrac{1}{L}, \infty)$, thus removing a logari<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29271<END_URL>

EFFICIENT JAILBREAK ATTACK SEQUENCES ON LARGE LANGUAGE MODELS VIA MULTI-ARMED BANDIT-BASED CONTEXT SWITCHING\nContent warning: This paper contains examples of harmful language and content.Recent advances in large language models (LLMs) have made them increasingly vulnerable to jailbreaking attempts, where malicious users manipulate models into generating harmful content. While existing approaches rely on either single-step attacks that trigger immediate safety responses or multi-step methods tha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28647<END_URL>

Efficient Learning with Sine-Activated Low-Rank Matrices\nLow-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning. These techniques significantly lower the number of parameters, striking a balance between compactness and performance. However, a common challenge has been the compromise between parameter efficiency and the accuracy of the model, where reduced parameters o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29043<END_URL>

Efficient Low-Bit Quantization with Adaptive Scales for Multi-Task Co-Training\nCo-training can achieve parameter-efficient multi-task models but remains unexplored for quantization-aware training. Our investigation shows that directly introducing co-training into existing quantization-aware training (QAT) methods results in significant performance degradation. Our experimental study identifies that the primary issue with existing QAT methods stems from the inadequate activation quantization sca<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27868<END_URL>

Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts\nAdapting medical Large  Language Models to local languages can reduce barriers to accessing healthcare services, but data scarcity remains a significant challenge, particularly for low-resource languages. To address this, we first construct a high-quality medical dataset and conduct analysis to ensure its quality. In order to leverage the generalization capability of multilingual LLMs to efficiently<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30111<END_URL>

Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs\nRecent efforts in fine-tuning language models often rely on automatic data selection, commonly using Nearest Neighbors retrieval from large datasets.However, we theoretically show that this approach tends to select redundant data, limiting its effectiveness or even hurting performance.To address this, we introduce SIFT, a data selection algorithm designed to reduce uncertainty about the model's response given a prompt, which unifies <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29875<END_URL>

Efficiently Parameterized Neural Metriplectic Systems\nMetriplectic systems are learned from data in a way that scales quadratically in both the size of the state and the rank of the metriplectic data. Besides being provably energy conserving and entropy stable, the proposed approach comes with approximation results demonstrating its ability to accurately learn metriplectic dynamics from data as well as an error estimate indicating its potential for generalization to unseen timescales when appro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27984<END_URL>

Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark\nThe dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of foreground objects. This often leads to severe under- and over-prediction problems and has been less studied in existing works.To tackle this issue in video object counting, we propose a density-embedded Efficient Masked Autoencoder Counting (E-MAC) framework in this paper. To eff<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28124<END_URL>

Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling\nLearning complex robot behavior through interactions with the environment necessitates principled exploration. Effective strategies should prioritize exploring regions of the state-action space that maximize rewards, with optimistic exploration emerging as a promising direction aligned with this idea and enabling sample-efficient reinforcement learning. However, existing methods overlook a crucial aspect: the need<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30164<END_URL>

Efficient Model Editing with Task-Localized Sparse Fine-tuning\nPre-trained models are stepping stones for modern machine learning systems, but how to efficiently extract, reuse, and steer their knowledge for new tasks is an area of research with still several open questions. State-of-the-art Task Arithmetic solutions are strongly tied to model linearization which leads to computational bottlenecks during training and inference, and potentially neglect essential task dependencies. In this work, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29556<END_URL>

Efficient Multi-agent Offline Coordination via Diffusion-based Trajectory Stitching\nLearning from offline data without interacting with the environment is a promising way to fully leverage the intelligent decision-making capabilities of multi-agent reinforcement learning (MARL). Previous approaches have primarily focused on developing learning techniques, such as conservative methods tailored to MARL using limited offline data. However, these methods often overlook the temporal relationships ac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30387<END_URL>

Efficient Neuron Segmentation in Electron Microscopy by Affinity-Guided Queries\nAccurate segmentation of neurons in electron microscopy (EM) images plays a crucial role in understanding the intricate wiring patterns of the brain. Existing automatic neuron segmentation methods rely on traditional clustering algorithms, where affinities are predicted first, and then watershed and post-processing algorithms are applied to yield segmentation results. Due to the nature of watershed algorithm, this p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29280<END_URL>

Efficient Off-Policy Learning for High-Dimensional Action Spaces\nExisting off-policy reinforcement learning algorithms often rely on an explicit state-action-value function representation, which can be problematic in high-dimensional action spaces due to the curse of dimensionality.This reliance results in data inefficiency as maintaining a state-action-value function in such spaces is challenging. We present an efficient approach that utilizes only a state-value function as the critic for off-<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30118<END_URL>

Efficient Online Pruning and Abstraction for Imperfect Information Extensive-Form Games\nEfficiently computing approximate equilibrium strategies in large Imperfect Information Extensive-Form Games (IIEFGs) poses significant challenges due to the vast size of the game tree. Pruning and abstraction methods effectively reduce this complexity, enhancing computational efficiency. However, seamlessly integrating pruning techniques with variants of Counterfactual Regret Minimization (CFR), a leading m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29930<END_URL>

Efficient Online Reinforcement Learning Fine-Tuning Should Not Retain Offline Data\nThe modern paradigm in machine learning involves pre-training models on diverse data, followed by task-specific fine-tuning. In reinforcement learning (RL), this translates to learning via offline RL on a static dataset, followed by rapid online RL fine-tuning using autonomous interaction data. Most RL fine-tuning methods require continued training on offline data for stability and performance. This is undesirabl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30230<END_URL>

Efficient Perplexity Bound and Ratio Matching in Discrete Diffusion Language Models\nWhile continuous diffusion models excel in modeling continuous distributions, their application to categorical data has been less effective. Recent work has shown that ratio-matching through *score-entropy* within a continuous-time discrete Markov chain (CTMC) framework serves as a competitive alternative to autoregressive models in language modeling.To enhance this framework, we first introduce three new theore<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29916<END_URL>

Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning\nIn reinforcement learning, classic on-policy evaluation methods often suffer from high variance and require massive online data to attain the desired accuracy. Previous studies attempt to reduce evaluation variance by searching for or designing proper behavior policies to collect data. However, these approaches ignore the safety of such behavior policies---the designed behavior policies have no safety guarantee and ma<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30450<END_URL>

Efficient Reinforcement Learning with Large Language Model Priors\nIn sequential decision-making (SDM) tasks, methods like reinforcement learning (RL) and heuristic search have made notable advances in specific cases. However, they often require extensive exploration and face challenges in generalizing across diverse environments due to their limited grasp of the underlying decision dynamics. In contrast, large language models (LLMs) have recently emerged as powerful general-purpose tools, due t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28953<END_URL>

Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping\nUniversal dexterous grasping across diverse objects presents a fundamental yet formidable challenge in robot learning. Existing approaches using reinforcement learning (RL) to develop policies on extensive object datasets face critical limitations, including complex curriculum design for multi-task learning and limited generalization to unseen objects. To overcome these challenges, we introduce ResDex, a novel <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30569<END_URL>

Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement\nIn this paper, we propose a framework for efficient Source-Free Domain Adaptation (SFDA) in the context of time-series, focusing on enhancing both parameter efficiency and data-sample utilization. Our approach introduces an improved paradigm for source-model preparation and target-side adaptation, aiming to enhance training efficiency during target adaptation. Specifically, we reparameterize the source model's w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29710<END_URL>

Efficient Sparse PCA via Block-Diagonalization\nSparse Principal Component Analysis (Sparse PCA) is a pivotal tool in data analysis and dimensionality reduction. However, Sparse PCA is a challenging problem in both theory and practice: it is known to be NP-hard and current exact methods generally require exponential runtime. In this paper, we propose a novel framework to efficiently approximate Sparse PCA by (i) approximating the  general input covariance matrix with a re-sorted block-diagonal m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30361<END_URL>

Efficient stagewise pretraining via progressive subnetworks\nRecent developments in large language models have sparked interest in efficientpretraining methods. Stagewise training approaches to improve efficiency, likegradual stacking and layer dropping (Reddi et al., 2023; Zhang & He, 2020), haverecently garnered attention. The prevailing view suggests that stagewise droppingstrategies, such as layer dropping, are ineffective, especially when compared tostacking-based approaches. This paper cha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29274<END_URL>

Efficient Structure-Aware 3D Gaussians via Lightweight Information Shaping\n3D Gaussians, as an explicit scene representation, typically involve thousands to millions of elements per scene. This makes it challenging to control the scene in ways that reflect the underlying semantics, where the number of independent entities is typically much smaller. Especially, if one wants to animate or edit objects in the scene, as this requires coordination among the many Gaussiansinvolved in representing eac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29735<END_URL>

Efficient Training Framework for Realistic Sensory-Motor Integration in a Biologically Constrained Barrel Cortex Model\nThe brain's ability to transform sensory inputs into motor functions is central to neuroscience and crucial for the development of embodied intelligence. Sensory-motor integration involves complex neural circuits, diverse neuronal types, and intricate intercellular connections. Bridging the gap between biological realism and behavioral functionality presents a formidable challe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29441<END_URL>

Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions\nNeural Stochastic Differential Equations (Neural SDEs) have emerged as powerful mesh-free generative models for continuous stochastic processes, with critical applications in fields such as finance, physics, and biology. Previous state-of-the-art methods have relied on adversarial training, such as GANs, or on minimizing distance measures between processes using signature kernels. However<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29011<END_URL>

EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition\nThe task of Visual Place Recognition (VPR) is to predict the location of a query image from a database of geo-tagged images. Recent studies in VPR have highlighted the significant advantage of employing pre-trained foundation models like DINOv2 for the VPR task. However, these models are often deemed inadequate for VPR without further fine-tuning on VPR-specific data.In this paper, we present an effective approach to h<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29874<END_URL>

EG4D: Explicit Generation of 4D Object without Score Distillation\nIn recent years, the increasing demand for dynamic 3D assets in design and gaming applications has given rise to powerful generative pipelines capable of synthesizing high-quality 4D objects.  Previous methods generally rely on score distillation sampling (SDS) algorithm to infer the unseen views and motion of 4D objects, thus leading to unsatisfactory results with defects like over-saturation and Janus problem.  Therefore, inspi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27956<END_URL>

EgoSim: Egocentric Exploration in Virtual Worlds with Multi-modal Conditioning\nRecent advancements in video diffusion models have established a strong foundation for developing world models with practical applications. The next challenge lies in exploring how an agent can leverage these foundation models to understand, interact with, and plan within observed environments. This requires adding more controllability to the model, transforming it into a versatile game engine capable of dynamic mani<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27687<END_URL>

EIA: ENVIRONMENTAL INJECTION ATTACK ON GENERALIST WEB AGENTS FOR PRIVACY LEAKAGE\nRecently, generalist web agents have demonstrated remarkable potential in autonomously completing a wide range of tasks on real websites, significantly boosting human productivity. However, web tasks, such as booking flights, usually involve users' personally identifiable information (PII), which may be exposed to potential privacy risks if web agents accidentally interact with compromised websites—a scenario that <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27789<END_URL>

ElasticTok: Adaptive Tokenization for Image and Video\nEfficient video tokenization remains a key bottleneck in learning general purpose vision models that are capable of processing long video sequences. Prevailing approaches are restricted to encoding videos to a fixed number of tokens, where too few tokens will result in overly lossy encodings, and too many tokens will result in prohibitively long sequence lengths. In this work, we introduce ElasticTok, a method that conditions on prior frames<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28065<END_URL>

ELBOing Stein: Variational Bayes with Stein Mixture Inference\nStein variational gradient descent (SVGD) (Liu & Wang, 2016) performs approximate Bayesian inference by representing the posterior with a set of particles.However, SVGD suffers from variance collapse, i.e. poor predictions due to underestimating uncertainty (Ba et al., 2021), even for moderately-dimensional modelssuch as small Bayesian neural networks (BNNs). To address this issue, we generalize SVGD by letting each particle paramete<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31111<END_URL>

Eliciting Human Preferences with Language Models\nLanguage models (LMs) can be directed to perform user- and context-dependenttasks by using labeled examples or natural language prompts.But selecting examples or writing prompts can be challenging---especially in tasks that require users to precisely articulate nebulous preferences or reason about complex edge cases. For such tasks, we introduce **Generative Active Task Elicitation (GATE)**, a method for using *LMs themselves* to guide the task s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29967<END_URL>

ELICIT: LLM Augmentation Via External In-context Capability\nEnhancing the adaptive capabilities of large language models is a critical pursuit in both research and application.Traditional fine-tuning methods require substantial data, computational resources, and specific capabilities, while in-context learning is limited by the need for appropriate demonstrations and efficient token usage.    Inspired by the expression of in-context learned capabilities through task vectors and the concept of m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30525<END_URL>

Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models\nClassifier-free guidance (CFG) is crucial for improving both generation quality and alignment between the input condition and final output in diffusion models. While a high guidance scale is generally required to enhance these aspects, it also causes oversaturation and unrealistic artifacts. In this paper, we revisit the CFG update rule and introduce modifications to address this issue. We first decompose the u<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28952<END_URL>

Eliminating Position Bias of Language Models: A Mechanistic Approach\nPosition bias has proven to be a prevalent issue of modern language models (LMs), where the models prioritize content based on its position within the given context. This bias often leads to unexpected model failures and hurts performance, robustness, and reliability across various applications. A simple mechanistic analysis attributes the position bias to two components employed in nearly all state-of-the-art LMs: causal atte<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28841<END_URL>

Elliptic Loss Regularization\nRegularizing neural networks is important for anticipating model behavior in regions of the data space that are not well represented. In this work, we propose a regularization technique for enforcing a level of smoothness in the mapping between the input space and the loss. We specify the level of regularity by requiring that the loss of the network satisfies an elliptic operator over the data domain. To do this, we modify the usual empirical risk minimization objec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29240<END_URL>

Elucidating the Preconditioning in Consistency Distillation\nConsistency distillation is a prevalent way for accelerating diffusion models adopted in consistency (trajectory) models, in which a student model is trained to traverse backward on the probability flow (PF) ordinary differential equation (ODE) trajectory determined by the teacher model. Preconditioning is a vital technique for stabilizing consistency distillation, by linear combining the input data and the network output with pre-defi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30971<END_URL>

EmbedLLM: Learning Compact Representations of Large Language Models\nWith hundreds of thousands of language models available on Huggingface today, efficiently evaluating and utilizing these models across various downstream tasks has become increasingly critical. Many existing methods repeatedly learn task-specific representations of Large Language Models (LLMs), which leads to inefficiencies in both time and computational resources. To address this, we propose EmbedLLM, a framework designed to l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30322<END_URL>

EmbodiedSAM: Online Segment Any 3D Thing in Real Time\nEmbodied tasks require the agent to fully understand 3D scenes simultaneously with its exploration, so an online, real-time, fine-grained and highly-generalized 3D perception model is desperately needed. Since high-quality 3D data is limited, directly training such a model in 3D is infeasible. Meanwhile, vision foundation models (VFM) has revolutionized the field of 2D computer vision with superior performance, which makes the use of VFM to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29314<END_URL>

Emergence of a High-Dimensional Abstraction Phase in Language Transformers\nA language model (LM) is a mapping from a linguistic context to an output token. However, much remains to be known about this mapping, including how its geometric properties relate to its function. We take a high-level geometric approach to its analysis, observing, across five pre-trained transformer-based LMs and three input datasets, a distinct phase characterized by high intrinsic dimensionality. During this phase, re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31244<END_URL>

Emergence of meta-stable clustering in mean-field transformer models\nWe model the evolution of tokens within a deep stack of Transformer layers as a continuous-time flow on the unit sphere, governed by a mean-field interacting particle system, building on the framework introduced in Geshkovski et al. (2023). Studying the corresponding mean-field Partial Differential Equation (PDE), which can be interpreted as a Wasserstein gradient flow, in this paper we provide a mathematical investigation of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28944<END_URL>

Emergent Orientation Maps —— Mechanisms, Coding Efficiency and Robustness\nExtensive experimental studies have shown that in lower mammals, neuronal orientation preference in the primary visual cortex is organized in a disordered "salt-and-pepper" pattern. In contrast, higher-order mammals display a continuous variation in orientation preference, forming structured pinwheel-like patterns. Despite these observations, the spiking mechanisms underlying the emergence of these distinct topological st<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28157<END_URL>

Emergent Possibilities and Challenges in Deep Learning for Code\nThe application of deep learning in code, spanning a diverse set of challenging tasks such as code completion, repair, synthesis, and explanation, constitutes a significant area of research within machine learning. The last two years have witnessed remarkable progress in this domain with the development of recent techniques (Rozière et al., 2023; Li et al., 2023; Guo et al., 2024; Wei et al., 2024; Yang et al., 2024a) that enables <BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23976<END_URL>

Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models\nFederated learning (FL) enables multiple parties to collaboratively fine-tune an large language model (LLM) without the need of direct data sharing. Ideally, by training on decentralized data that is aligned with human preferences and safety principles, federated instruction tuning can result in an LLM that could behave in a helpful and safe manner. In this paper, we for the first time reveal the vulnera<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28122<END_URL>

EMMA: Empowering Multi-modal Mamba with Structural and Hierarchical Alignment\nMamba-based architectures have shown to be a promising new direction for deep learning models owing to their competitive performance and sub-quadratic deployment speed. However, current Mamba multi-modal large language models (MLLM) are insufficient in extracting visual features, leading to imbalanced cross-modal alignment between visual and textural latents, negatively impacting performance on multi-modal tasks. In t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30381<END_URL>

EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents\nHeterogeneous multi-robot systems (HMRS) have emerged as a powerful ap-proach for tackling complex tasks that single robots cannot manage alone. Currentlarge-language-model-based multi-agent systems (LLM-based MAS) have shownsuccess in areas like software development and operating systems, but applyingthese systems to robot control presents unique challenges. In particular, the ca-pabilities of each agent in a mul<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30375<END_URL>

Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents\nThis paper presents a novel application of large language models (LLMs) to enhance user comprehension of privacy policies through an interactive dialogue agent. We demonstrate that LLMs significantly outperform traditional models in tasks like Data Practice Identification, Choice Identification, Policy Summarization, and Privacy Question Answering, setting new benchmarks in privacy policy analysis. Building on t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30352<END_URL>

Encryption-Friendly LLM Architecture\nLarge language models (LLMs) offer personalized responses based on user interactions, but this use case raises serious privacy concerns. Homomorphic encryption (HE) is a cryptographic protocol supporting arithmetic computations in encrypted states and provides a potential solution for privacy-preserving machine learning (PPML). However, the computational intensity of transformers poses challenges for applying HE to LLMs. In this work, we propose a modified H<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28288<END_URL>

Endless Jailbreaks with Bijection Learning\nDespite extensive safety measures, LLMs are vulnerable to adversarial inputs, or jailbreaks, which can elicit unsafe behaviors. In this work, we introduce bijection learning, a powerful attack algorithm which automatically fuzzes LLMs for safety vulnerabilities using randomly-generated encodings whose complexity can be tightly controlled. We leverage in-context learning to teach models bijective encodings, pass encoded queries to the model to bypass bu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27786<END_URL>

Endowing Visual Reprogramming with Adversarial Robustness\nVisual reprogramming (VR) leverages well-developed pre-trained models (e.g., a pre-trained classifier on ImageNet) to tackle target tasks (e.g., a traffic sign recognition task), without the need for training from scratch. Despite the effectiveness of previous VR methods, all of them did not consider the adversarial robustness of reprogrammed models against adversarial attacks, which could lead to unpredictable problems in safety-crucial<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29796<END_URL>

End-to-end Learning of Gaussian Mixture Priors for Diffusion Sampler\nDiffusion models optimized via variational inference (VI) have emerged as a promising tool for generating samples from unnormalized target densities. These models create samples by simulating a stochastic differential equation, starting from a simple, tractable prior, typically a Gaussian distribution. However, when the support of this prior differs greatly from that of the target distribution, diffusion models often struggle <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28690<END_URL>

End-to-End Rule Induction from Raw Sequence Inputs\nRule learning-based models are widely used in highly interpretable scenarios due to their transparent structures. Inductive logic programming (ILP), a form of machine learning, induces rules from facts while maintaining interpretability. Differentiable ILP models enhance this process by leveraging neural networks to improve robustness and scalability. However, most differentiable ILP methods rely on symbolic datasets, facing challenges when lea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27680<END_URL>

E(n) Equivariant Topological Neural Networks\nGraph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features. Topological deep learning (TDL) has emerged recently as a promising tool for addressing this issue. TDL enables the principled modeling of arbitrary multi-way, hierarchical higher-order interactions by operating on combinatorial topological spaces, such as simplicial or cell complexes, instead of graphs. However,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30602<END_URL>

Energy-based Backdoor Defense Against Federated Graph Learning\nFederated Graph Learning is rapidly evolving as a privacy-preserving collaborative approach. However, backdoor attacks are increasingly undermining federated systems by injecting carefully designed triggers that lead to the model making incorrect predictions. Trigger structures and injection locations in Federated Graph Learning are more diverse, making traditional federated defense methods less effective. In our work, we propose an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30953<END_URL>

Energy-Based Diffusion Language Models for Text Generation\nDespite remarkable progress in autoregressive language models, alternative generative paradigms beyond left-to-right generation are still being actively explored. Discrete diffusion models, with the capacity for parallel generation, have recently emerged as a promising alternative. Unfortunately, these models still underperform the autoregressive counterparts, with the performance gap increasing when reducing the number of sampling step<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28133<END_URL>

Energy-Weighted Flow Matching for Offline Reinforcement Learning\nThis paper investigates energy guidance in generative modeling, where the target distribution is defined as $q(\mathbf x) \propto p(\mathbf x)\exp(-\beta \mathcal E(\mathbf x))$, with $p(\mathbf x)$ being the data distribution and $\mathcal E(\mathbf x)$ as the energy function. To comply with energy guidance, existing methods often require auxiliary procedures to learn intermediate guidance during the diffusion process. To overcom<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30241<END_URL>

Enhanced Diffusion Sampling via Extrapolation with Multiple ODE Solutions\nDiffusion probabilistic models (DPMs), while effective in generating high-quality samples, often suffer from high computational costs due to the iterative sampling process. To address this, we propose an enhanced ODE-based sampling method for DPMs inspired by Richardson extrapolation, which has been shown to reduce numerical error and improve convergence rates. Our method, termed RX-DPM, utilizes numerical solutions obtai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28214<END_URL>

Enhance Multi-View Classification Through Multi-Scale Alignment and Expanded Boundary\nMulti-view classification aims at unifying the data from multiple views to complementarily enhance the classification performance. Unfortunately, two major problems in multi-view data are damaging model performance. The first is feature heterogeneity, which makes it hard to fuse features from different views. Considering this, we introduce a multi-scale alignment module, including an instance-scale alignment m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28081<END_URL>

Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies\nFederated Learning (FL) is an evolving distributed machine learning approach that safeguards client privacy by keeping data on edge devices. However, the variation in data among clients poses challenges in training models that excel across all local distributions. Recent studies suggest clustering as a solution to address client heterogeneity in FL by grouping clients with distribution shifts into disti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27669<END_URL>

Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data\nLarge multimodal models (LMMs) have shown impressive capabilities in a wide range of visual tasks. However, they often struggle with identifying domain-specific objectives and fail to explain their predictions reasonably. To address the above challenge, we propose a novel iterative visual fine-tuning framework to improve the effectiveness and explainability of LMMs using self-synthesized data. Spec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28527<END_URL>

Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds\nText-to-image diffusion models have demonstrated remarkable capability in generating realistic images from arbitrary text prompts. However, they often produce inconsistent results for compositional prompts such as "two dogs" or "a penguin on the right of a bowl". Understanding these inconsistencies is crucial for reliable image generation. In this paper, we highlight the significant role of initial noise in these incons<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30962<END_URL>

Enhancing Document Understanding with Group Position Embedding: A Novel Approach to Incorporate Layout Information\nRecent advancements in document understanding have been dominated by leveraging large language models (LLMs) and multimodal large models. However, enabling LLMs to comprehend complex document layouts and structural information often necessitates intricate network modifications or costly pre-training, limiting their practical applicability. In this paper, we introduce Group Position<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30445<END_URL>

Enhancing End-to-End Autonomous Driving with Latent World Model\nIn autonomous driving, end-to-end planners directly utilize raw sensor data, enabling them to extract richer scene features and reduce information loss compared to traditional planners. This raises a crucial research question: how can we develop better scene feature representations to fully leverage sensor data in end-to-end driving? Self-supervised learning methods show great success in learning rich feature representations in NLP<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28868<END_URL>

Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning\nFederated Domain Adaptation (FDA) is a Federated Learning (FL) scenario where models are trained across multiple clients with unique data domains but a shared category space, without transmitting private data. The primary challenge in FDA is data heterogeneity, which causes significant divergences in gradient updates when using conventional averaging-based aggregation methods, reducing the efficacy of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31049<END_URL>

Enhancing Graph Of Thought: Enhancing Prompts with LLM Rationales and Dynamic Temperature Control\nWe introduce Enhancing Graph Of Thoughts (EGOT), a method designed to enhance the performance of large language models (LLMs) on complex reasoning tasks. EGOT automates the process of generating accurate responses using given data and a base prompt. The process consists of several steps: It obtain an initial response from the answering node using the base prompt. Evaluation node evaluates the respo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28537<END_URL>

Enhancing Language Model Agents using Diversity of Thoughts\nA popular approach to building agents using Language Models (LMs) involves iteratively prompting the LM, reflecting on its outputs, and updating the input prompts until the desired task is achieved. However, our analysis reveals two key shortcomings in the existing methods: $(i)$ limited exploration of the decision space due to repetitive reflections, which result in redundant inputs, and $(ii)$ an inability to leverage insights from p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29196<END_URL>

Enhancing Large Language Models' Situated Faithfulness to External Contexts\nLarge Language Models (LLMs) are often augmented with external information as contexts, but this external information can sometimes be inaccurate or even intentionally misleading. We argue that robust LLMs should demonstrate situated faithfulness, dynamically calibrating their trust in external information based on their confidence in the internal knowledge and the external context. To benchmark this capability, we eval<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30079<END_URL>

Enhancing Learning with Label Differential Privacy by Vector Approximation\nLabel differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public. Existing approaches protect the privacy of labels by flipping them randomly, and then train a model to make the output approximate the privatized label. However, as the number of classes K increases, stronger randomization is needed, thus the performances of these methods become s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30142<END_URL>

Enhancing Multilingual Reasoning in LLMs: Insights from Cross-Linguistic Correlations and Optimal Data Proportions\nLarge language models (LLMs) typically rely on fine-tuning to enhance their reasoning capabilities across various languages. However, limited research has been conducted on the optimal balance of language proportions within multilingual reasoning datasets. To fill this gap, we performed a systematic study to examine how different proportions of language data in multilingual reasoni<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29628<END_URL>

Enhancing Outlier Knowledge for Few-Shot Out-of-Distribution Detection with Extensible Local Prompts\nOut-of-Distribution (OOD) detection, aiming to distinguish outliers from known categories, has gained prominence in practical scenarios. Recently, the advent of vision-language models (VLM) has heightened interest in enhancing OOD detection for VLM through few-shot tuning. However, existing methods mainly focus on optimizing global prompts, ignoring refined utilization of local information with <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30380<END_URL>

Enhancing Pre-trained Representation Classifiability can Boost its Interpretability\nThe visual representation of a pre-trained model prioritizes the classifiability on downstream tasks. However, widespread applications for pre-trained visual models have proposed new requirements for representation interpretability. It remains unclear whether the pre-trained representations can achieve high interpretability and classifiability simultaneously. To answer this question, we quantify the representati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30262<END_URL>

Enhancing Robust Fairness via Confusional Spectral Regularization\nRecent research has highlighted a critical issue known as ``robust fairness", where robust accuracy varies significantly across different classes, undermining the reliability of deep neural networks (DNNs). A common approach to address this has been to dynamically reweight classes during training, giving more weight to those with lower empirical robust performance. However, we find there is a divergence of class-wise robust perfo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28515<END_URL>

Enhancing Software Agents with Monte Carlo Tree Search and Hindsight Feedback\nSoftware engineers operating in complex and dynamic environments must continuously adapt to evolving requirements, learn iteratively from experience, and reconsider their approaches based on new insights. However, current large language model (LLM)-based software agents often follow linear, sequential processes that prevent backtracking and exploration of alternative solutions, limiting their ability to adapt their st<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30299<END_URL>

Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems\nDensity Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltonian. Despite its importance, the application of DFT is frequently limited by the substantial computational resources required to construct the Kohn-Sham Hamiltonian. In response to these limitations, current research has employe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28011<END_URL>

Enhancing Training Robustness through Influence Measure\nIn the field of machine learning, the pursuit of accurate models is ongoing. A key aspect of improving prediction performance lies in identifying which data points in the training set should be excluded and which high-quality, potentially unlabeled data points outside the training set should be incorporated to improve the model's performance on unseen data. To accomplish this, an effective metric is needed to evaluate the contribution of e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30037<END_URL>

Enhancing Uncertainty Estimation and Interpretability with Bayesian Non-negative Decision Layer\nAlthough deep neural networks have demonstrated significant success due to theirpowerful expressiveness, most models struggle to meet practical requirements foruncertainty estimation. Concurrently, the entangled nature of deep neural net-works leads to a multifaceted problem, where various localized explanation tech-niques reveal that multiple unrelated features influence the decisions, thereby un-de<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27792<END_URL>

Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures\nParameter-efficient fine-tuning (PEFT) significantly reduces memory costs when adapting large language models (LLMs) for downstream applications. However, traditional first-order (FO) fine-tuning algorithms incur substantial memory overhead due to the need to store activation values for back-propagation during gradient computation, particularly in long-context fine-tuning tasks. Zeroth-order (ZO) algorithms offer a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30716<END_URL>

Ensembles of Low-Rank Expert Adapters\nThe training and fine-tuning of large language models (LLMs) often involve diverse textual data from multiple sources, which poses challenges due to conflicting gradient directions, hindering optimization and specialization.  These challenges can undermine model generalization across tasks, resulting in reduced downstream performance.  Recent research suggests that fine-tuning LLMs on carefully selected, task-specific subsets of data can match or even surpa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28544<END_URL>

Ensembling Diffusion Models via Adaptive Feature Aggregation\nThe success of the text-guided diffusion model has inspired the development and release of numerous powerful diffusion models within the open-source community. These models are typically fine-tuned on various expert datasets, showcasing diverse denoising capabilities. Leveraging multiple high-quality models to produce stronger generation ability is valuable, but has not been extensively studied. Existing methods primarily adopt parame<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28951<END_URL>

Entropic Distribution Matching for Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity\nLarge language models rely on Supervised Fine-Tuning (SFT) to specialize in downstream tasks. Cross Entropy (CE) loss is the de facto choice in SFT. However, CE often results in overfitting and limited output diversity due to its aggressive distribution matching strategy, which forces the model’s generative distribution to closely mimic the empirical data distribution. This paper aims to add<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29878<END_URL>

Entropy-based Activation Function Optimization: A Method on Searching Better Activation Functions\nThe success of artificial neural networks (ANNs) hinges greatly on the judicious selection of an activation function, introducing non-linearity into network and enabling them to model sophisticated relationships in data. However, the search of activation functions has largely relied on empirical knowledge in the past, lacking theoretical guidance, which has hindered the identification of more effec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30823<END_URL>

Episodic Memories Generation and Evaluation Benchmark for Large Language Models\nEpisodic memory -- the ability to recall specific events grounded in time and space -- is a cornerstone of human cognition, enabling not only coherent storytelling, but also planning and decision-making. Despite their remarkable capabilities, Large Language Models (LLMs) lack a robust mechanism for episodic memory: we argue that integrating episodic memory capabilities into LLM is essential for advancing AI towards <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30855<END_URL>

Episodic Novelty Through Temporal Distance\nExploration in sparse reward environments remains a significant challenge in reinforcement learning, particularly in Contextual Markov Decision Processes (CMDPs), where environments differ across episodes. Existing episodic intrinsic motivation methods for CMDPs primarily rely on count-based approaches, which are ineffective in large state spaces, or on similarity-based methods that lack appropriate metrics for state comparison. To address these shortc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30190<END_URL>

Epistemic Monte Carlo Tree Search\nThe AlphaZero/MuZero (A/MZ) family of algorithms has achieved remarkable success across various challenging domains by integrating Monte Carlo Tree Search (MCTS) with learned models. Learned models introduce epistemic uncertainty, which is caused by learning from limited data and is useful for exploration in sparse reward environments. MCTS does not account for the propagation of this uncertainty however. To address this, we introduce Epistemic MCTS (EMCTS): a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29531<END_URL>

eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels\nCollaboration is a key challenge in distributed multi-agent reinforcement learning (MARL) environments. Learning frameworks for these decentralized systems must weigh the benefits of explicit player coordination against the communication overhead and computational cost of sharing local observations and environmental data. Quantum computing has sparked a potential synergy between quantu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29050<END_URL>

EqNIO: Subequivariant Neural Inertial Odometry\nNeural network-based odometry using accelerometer and gyroscope readings from a single IMU can achieve robust, and low-drift localization capabilities, through the use of **neural displacement priors**. These priors learn to produce denoised displacement measurements but need to ignore data variations due to specific IMU mount orientation and motion directions, hindering generalization.This work introduces EqNIO, which addresses this challenge with<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30531<END_URL>

Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models\nGraph diffusion models, while dominant in graph generative modeling, remain relatively underexplored for graph-to-graph translation tasks like chemical reaction prediction. We show that standard permutation equivariant denoisers cause severe limitations on such tasks, a problem that we pinpoint to their inability to break symmetries present in the noisy inputs. We then propose to \emph{align} the input and target graphs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28333<END_URL>

Equivariant Masked Position Prediction for Efficient Molecular Representation\nGraph neural networks (GNNs) have shown considerable promise in computational chemistry. However, the limited availability of molecular data raises concerns regarding GNNs' ability to effectively capture the fundamental principles of physics and chemistry, which constrains their generalization capabilities. To address this challenge, we introduce a novel self-supervised approach termed Equivariant Masked Position Pred<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29851<END_URL>

Equivariant Neural Functional Networks for Transformers\nThis paper systematically explores neural functional networks (NFN) for transformer architectures. NFN are specialized neural networks that treat the weights, gradients, or sparsity patterns of a deep neural network (DNN) as input data and have proven valuable for tasks such as learnable optimizers, implicit data representations, and weight editing. While NFN have been extensively developed for MLP and CNN, no prior work has addressed thei<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27995<END_URL>

Erasing Concept Combination from Text-to-Image Diffusion Model\nAdvancements in the text-to-image diffusion model have raised security concerns due to their potential to generate images with inappropriate themes such as societal biases and copyright infringements. Current studies make a great process to prevent the model from generating images containing specific high-risk visual concepts. However, these methods neglect the issue that inappropriate themes may also arise from the combination of b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29839<END_URL>

ESE: Espresso Sentence Embeddings\nHigh-quality sentence embeddings are fundamental in many natural language processing (NLP) tasks, such as semantic textual similarity (STS) and retrieval-augmented generation (RAG). However, most existing methods leverage fixed-length sentence embeddings from full-layer language models, which lack the scalability to accommodate the diverse available resources across various applications. Viewing this gap, we propose a novel sentence embedding model Espresso Sen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28282<END_URL>

Estimating the Probabilities of Rare Outputs in Language Models\nWe consider the problem of *low probability estimation*: given a machine learning model and a formally-specified input distribution, how can we estimate the probability of a binary property of the model's output, even when that probability is too small to estimate by random sampling? This problem is motivated by the need to improve worst-case performance, which distribution shift can make much more likely. We study low probability <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30474<END_URL>

Estimation of single-cell and tissue perturbation effect in spatial transcriptomics via Spatial Causal Disentanglement\nModels of Virtual Cells and Virtual Tissues at single-cell resolution would allow us to test perturbations in silico and accelerate progress in tissue and cell engineering. However, most such models are not rooted in causal inference and as a result, could mistake correlation for causation.We introduce Celcomen, a novel generative graph neural network grounded in mathematical c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29512<END_URL>

ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference-Time\nVision Language Models (VLMs) have become essential backbones for multi-modal intelligence, yet significant safety challenges limit their real-world application. While textual inputs can often be effectively safeguarded, adversarial visual inputs can often easily bypass VLM defense mechanisms. Existing defense methods are either resource-intensive, requiring substantial data and compute, or fail to simultaneously e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29674<END_URL>

ET-SEED: EFFICIENT TRAJECTORY-LEVEL SE(3) EQUIVARIANT DIFFUSION POLICY\nImitation learning, e.g., diffusion policy, has been proven effective in various robotic manipulation tasks.However, extensive demonstrations are required for policy robustness and generalization.To reduce the demonstration reliance, we leverage spatial symmetry and propose ET-SEED, an efficient trajectory-level SE(3) equivariant diffusion model for generating action sequences in complex robot manipulation tasks.Further, pre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29807<END_URL>

EvA: Erasing Spurious Correlations with Activations\nSpurious correlations often arise when models associate features strongly correlated with, but not causally related to, the label e.g. an image classifier associates bodies of water with ducks. To mitigate spurious correlations, existing methods focus on learning unbiased representation or incorporating additional information about the correlations during training. This work removes spurious correlations by ``**E**rasing **wi**th **A**ctivatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27672<END_URL>

EVA: Geometric Inverse Design for Fast Protein Motif-Scaffolding with Coupled Flow\nMotif-scaffolding is a fundamental component of protein design, which aims to construct the scaffold structure that stabilizes motifs conferring desired functions. Recent advances in generative models are promising for designing scaffolds, with two main approaches: training-based and sampling-based methods. Training-based methods are resource-heavy and slow, while training-free sampling-based methods are flexible<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30067<END_URL>

Evaluating and Improving Large Language Models on Graph Computation\nThe ``arms race'' of Large Language Models (LLMs) demands new benchmarks to examine their progresses. In this paper, we introduce GraphArena, a benchmarking tool designed to evaluate LLMs on real-world graph computational problems. It offers a suite of four polynomial-time tasks (e.g., Shortest Distance) and six NP-complete challenges (e.g., Traveling Salesman Problem). GraphArena features a rigorous evaluation framework that c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29278<END_URL>

Evaluating Large Language Models through Role-Guide and Self-Reflection: A Comparative Study\nLarge Language Models fine-tuned with Reinforcement Learning from Human Feedback (RLHF-LLMs) can over-rely on aligned preferences without truly gaining the self-knowledge, leading to hallucination and biases. If an LLM can better access its knowledge and know what it knows, it can avoid making false or unsupported claims. Therefore, it is crucial to evaluate whether LLMs have the ability to know what th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30426<END_URL>

Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective\nAccurate interpretation and visualization of human instructions are crucial for \textbf{T}ext-to-\textbf{I}mage (\textbf{T2I}) synthesis. Existing T2I models often struggle to capture semantic variations caused by different word orders. Current evaluations rely on indirect metrics, like text-image matching, where the limitations with uncommon or complex linguistic patterns may be obscured by focusing on frequent word<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29871<END_URL>

Event-Driven Online Vertical Federated Learning\nOnline learning is more adaptable to real-world scenarios in Vertical Federated Learning (VFL) compared to offline learning. However, integrating online learning into VFL presents challenges due to the unique nature of VFL, where clients possess non-intersecting feature sets for the same sample. In real-world scenarios, the clients may not receive data streaming for the disjoint features for the same entity synchronously. Instead, the data are typ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30357<END_URL>

Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?\nAs AI systems are increasingly deployed in high-stakes applications, ensuring their interpretability is essential. Mechanistic Interpretability (MI) aims to reverse-engineer neural networks by extracting human-understandable algorithms embedded within their structures to explain their behavior. This work systematically examines a fundamental question: for a fixed behavior to explain, and under the criteria that M<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30956<END_URL>

Everything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models\nRecent knowledge editing methods have primarily focused on modifying structured knowledge in large language models. However, this task setting overlooks the fact that a significant portion of real-world knowledge is stored in an unstructured format, characterized by long-form content, noise, and a complex yet comprehensive nature.Techniques like ``local layer key-value storage'' and ``term-driven opti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29325<END_URL>

Evidential Learning-based Certainty Estimation for Robust Dense Feature Matching\nDense feature matching methods aim to estimate a dense correspondence field between images. Inaccurate correspondence can occur due to the presence of unmatchable region, highlighting the need for certainty measurement. This is typically addressed by training a binary classifier to decide whether each predicted correspondence is reliable. However, deep neural network-based classifiers can be vulnerable to image cor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31015<END_URL>

Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles\nTokenization is associated with many poorly understood shortcomings in language models (LMs), yet remains an important component for long sequence scaling purposes. This work studies  how tokenization impacts  model performance by analyzing and comparing the stochastic behavior of tokenized models with their byte-level, or token-free, counterparts. We discover that, even when the two models are stati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27677<END_URL>

Exact Certification of (Graph) Neural Networks Against Label Poisoning\nMachine learning models are highly vulnerable to label flipping, i.e., the adversarial modification (poisoning) of training labels to compromise performance. Thus, deriving robustness certificates is important to guarantee that test predictions remain unaffected and to understand worst-case robustness behavior. However, for Graph Neural Networks (GNNs), the problem of certifying label flipping has so far been unsolved. We ch<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29003<END_URL>

Exact Community Recovery under Side Information: Optimality of Spectral Algorithms\nWe study the problem of exact community recovery in general, two-community block models, in the presence of node-attributed *side information*. We allow for a very general side information channel for node attributes, and for pairwise (edge) observations, consider both Bernoulli and Gaussian matrix models, capturing the Stochastic Block Model, Submatrix Localization, and $\mathbb{Z}_2$-Synchronization as special <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27656<END_URL>

Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks\nAlbeit the ubiquitous use of Graph Neural Networks (GNNs) in machine learning (ML) prediction tasks involving graph-structured data, their interpretability remains challenging. In explainable artificial intelligence (XAI), the Shapley Value (SV) is the predominant method to quantify contributions of individual features to a ML model’s output. Addressing the limitations of SVs in complex prediction models, Shapley Inte<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30660<END_URL>

Examining Alignment of Large Language Models through Representative Heuristics: the case of political stereotypes\nExamining the alignment of large language models (LLMs) has become increasingly important, particularly when these systems fail to operate as intended. This study explores the challenge of aligning LLMs with human intentions and values, with specific focus on their political inclinations. Previous research has highlighted LLMs' propensity to display political leanings, and their abi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30828<END_URL>

Execution-guided within-prompt search for programming-by-example\nSoundness is an important property in programming-by-example (PBE) as it allows synthesizers to perform a search over a domain-specific language (DSL) that terminates when any sound program is found.Large language models (LLMs) can generate code from examples without being limited to a DSL, but they lack search, as samples are independent.One can sampling code until a sound program is generated, but that is very inefficient.In thi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29749<END_URL>

Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting\nThe widespread deployment of sensing devices leads to a surge in data for spatio-temporal forecasting applications such as traffic flow, air quality, and wind energy. Although spatio-temporal graph neural networks (STGNNs) have achieved success in modeling various static spatio-temporal forecasting scenarios, real-world spatio-temporal data are typically received in a streaming manner, and the netwo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30341<END_URL>

Expected Return Symmetries\nSymmetry is an important inductive bias that can improve model robustness and generalization across many deep learning domains. In multi-agent settings, a priori known symmetries have been shown to address a fundamental coordination failure mode known as mutually incompatible symmetry breaking; e.g. in a game where two independent agents can choose to move "left" or "right", and where a reward of +1 or -1 is received when the agents choose the same action or different<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27862<END_URL>

Expected Sliced Transport Plans\nThe optimal transport (OT) problem has gained significant traction in modern machine learning for its ability to: (1) provide versatile metrics, such as Wasserstein distances and their variants, and (2) determine optimal couplings between probability measures. To reduce the computational complexity of OT solvers, methods like entropic regularization and sliced optimal transport have been proposed. The sliced OT framework improves efficiency by comparing one-dimen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29773<END_URL>

Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation\nRecent advances in efficient sequence modeling have led to attention-free layers, such as Mamba, RWKV, and various gated RNNs, all featuring sub-quadratic complexity in sequence length and excellent scaling properties, enabling the construction of a new type of foundation models. In this paper, we present a unified view of these models, formulating such layers as implicit causal self-attention layers. The formulati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27821<END_URL>

Explanations of GNN on Evolving Graphs via Axiomatic  Layer edges\nGraphs are ubiquitous in social networks, chemical molecules, and financial data, where Graph Neural Networks (GNNs) achieve superior predictive accuracy. Graphs can beevolving, while understanding how GNN predictions respond to the evolution provides significant insight and trust. We explore the problem of explaining evolving GNN predictions due to continuously changing edge weights.We first propose a layer edge-based explanatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28293<END_URL>

EXPLOITING DISTRIBUTION CONSTRAINTS FOR SCALABLE AND EFFICIENT IMAGE RETRIEVAL\nImage retrieval is crucial in robotics and computer vision, with downstream applications in robot place recognition and vision-based product recommendations. Modern retrieval systems face two key challenges: scalability and efficiency.State-of-the-art image retrieval systems train specific neural networks for each dataset, an approach that lacks scalability. Furthermore, since retrieval speed is directly proportional<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29018<END_URL>

Exploiting Hidden Symmetry to Improve Objective Perturbation for DP linear learners with a nonsmooth $\ell_1$-norm\nObjective Perturbation (OP) is a classic approach to differentially private (DP) convex optimization with smooth loss functions but is less understood for nonsmooth cases. In this work, we study how to apply OP to DP linear learners under loss functions with an implicit $\ell_1$-norm structure, such as $\max(0,x)$ as a motivating example. We propose to first smooth out the implicit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30128<END_URL>

Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank\nWe study the problem of learning an approximate equilibrium in the offline multi-agent reinforcement learning (MARL) setting. We introduce a structural assumption---the interaction rank---and establish that functions with low interaction rank are significantly more robust to distribution shift compared to general ones. Leveraging this observation, we demonstrate that utilizing function classes with low interact<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30636<END_URL>

Exploratory Preference Optimization: Provably Sample-Efficient Exploration in RLHF with General Function Approximation\nThis paper investigates a basic question in reinforcement learning from human feedback (RLHF) from a theoretical perspective: how to efficiently explore in an online manner under preference feedback and general function approximation. We take the initial step towards a theoretical understanding of this problem by proposing a novel algorithm, *Exploratory Preference Optimization<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29685<END_URL>

Exploring a Principled Framework for Deep Subspace Clustering\nSubspace clustering is a classical unsupervised learning task, built on a basic assumption that high-dimensional data can be approximated by a union of subspaces (UoS). Nevertheless, the real-world data are often deviating from the UoS assumption. To address this challenge, state-of-the-art deep subspace clustering algorithms attempt to jointly learn UoS representations and self-expressive coefficients. However, the general framework<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30796<END_URL>

Exploring channel distinguishability in local neighborhoods of the model space in quantum neural networks\nWith the increasing interest in Quantum Machine Learning, Quantum Neural Networks (QNNs) have emerged and gained significant attention. These models have, however, been shown to be notoriously difficult to train, which we hypothesize is partially due to the architectures, called ansatzes, that are hardly studied at this point. Therefore, in this paper, we take a step back and analyze ansatz<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28831<END_URL>

Exploring Learning Complexity for Efficient Downstream Dataset Pruning\nThe ever-increasing fine-tuning cost of large-scale pre-trained models gives rise to the importance of dataset pruning, which aims to reduce dataset size while maintaining task performance.However, existing dataset pruning methods require training on the entire dataset, which is impractical for large-scale pre-trained models.In this paper, we propose a straightforward, novel, and training-free hardness score named Distorting<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30346<END_URL>

Exploring Local Memorization in Diffusion Models via Bright Ending Attention\nIn this paper, we identify and leverage a novel `bright ending' (BE) anomaly in diffusion models prone to memorizing training images to address a new task: locating localized memorization regions within these models. BE refers to a distinct cross-attention pattern observed in text-to-image generations using diffusion models. Specifically, memorized image patches exhibit significantly greater attention to the end token <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28321<END_URL>

Exploring Prosocial Irrationality for LLM Agents: A Social Cognition View\nLarge language models (LLMs) have been shown to face hallucination issues due to the data they trained on often containing human bias; whether this is reflected in the decision-making process of LLM agents remains under-explored. As LLM Agents are increasingly employed in intricate social environments, a pressing and natural question emerges: Can we utilize LLM Agents' systematic hallucinations to mirror human cognitive b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27998<END_URL>

Exploring the Camera bias of Person Re-identification\nWe empirically investigate the camera bias of person re-identification (ReID) models. Previously, camera-aware methods have been proposed to address this issue, but they are largely confined to training domains of the models. We measure the camera bias of ReID models on unseen domains and reveal that camera bias becomes more pronounced under data distribution shifts. As a debiasing method for unseen domain data, we revisit feature normalizat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29586<END_URL>

Exploring the Design Space of Visual Context Representation in Video MLLMs\nVideo Multimodal Large Language Models (MLLMs) have shown remarkable capability of understanding the video semantics on various downstream tasks. Despite the advancements, there is still a lack of systematic research on visual context representation, which refers to the scheme to select frames from a video and further select the tokens from a frame. In this paper, we explore the design space for visual context representa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29477<END_URL>

Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models\nObject-centric (OC) representations, which represent the state of a visual scene by modeling it as a composition of objects, have the potential to be used in various downstream tasks to achieve systematic compositional generalization and facilitate reasoning. However, these claims have not been thoroughly analyzed yet.Recently, foundation models have demonstrat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30471<END_URL>

Exploring The Forgetting in Adversarial Training: A Novel Method for Enhancing Robustness\nIn recent years, there has been an explosion of research into developing robust deep neural networks against adversarial examples. As one of the most successful methods, Adversarial Training (AT)  has been widely studied before, but there is still a gap to achieve promisingclean and robust accuracy for many practical tasks. In this paper, we consider the AT problem from a new perspective which connects it <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28861<END_URL>

Exploring the Impact of Activation Functions in Training Neural ODEs\nNeural Ordinary Differential Equations (ODEs) have been successful in various applications due to their continuous nature and parameter-sharing efficiency. However, these unique characteristics also introduce challenges in training, particularly with respect to gradient computation accuracy and convergence analysis. In this paper, we address these challenges by investigating the impact of activation functions. We demonstrate t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30612<END_URL>

Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality\nWe discuss several aspects of the loss landscape of regularized neural networks: the structure of stationary points, connectivity of optimal solutions, path with non-increasing loss to arbitrary global optimum, and the nonuniqueness of optimal solutions, by casting the problem into an equivalent convex problem and considering its dual. Starting from two-layer neural networks with scalar output, we first characterize <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30981<END_URL>

Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning\nIn cooperative multi-agent reinforcement learning (MARL), well-designed communication protocols can effectively facilitate consensus among agents, thereby enhancing task performance. Moreover, in large-scale multi-agent systems commonly found in real-world applications, effective communication plays an even more critical role due to the escalated challenge of partial observability compared to smaller-scale<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30519<END_URL>

Exposure Bracketing is All You Need for Unifying Image Restoration and Enhancement Tasks\nIt is highly desired but challenging to acquire high-quality photos with clear content in low-light environments. Although multi-image processing methods (using burst, dual-exposure, or multi-exposure images) have made significant progress in addressing this issue, they typically focus on specific restoration or enhancement problems, and do not fully explore the potential of utilizing multiple images. Motiv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28212<END_URL>

Expressivity of Neural Networks with Random Weights and Learned Biases\nLandmark universal function approximation results for neural networks with trained weights and biases provided the impetus for the ubiquitous use of neural networks as learning models in neuroscience and Artificial Intelligence (AI). Recent work has extended these results to networks in which a smaller subset of weights (e.g., output weights) are tuned, leaving other parameters random. However, it remains an open question wh<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30920<END_URL>

Extendable and Iterative Structure Learning for Bayesian Networks\nLearning the structure of Bayesian networks is a fundamental yet computationally intensive task, especially as the number of variables grows. Traditional algorithms require retraining from scratch when new variables are introduced, making them impractical for dynamic or large-scale applications. In this paper, we propose an extendable structure learning strategy that efficiently incorporates a new variable $Y$ into an existing Ba<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31054<END_URL>

Extending Mercer's expansion to indefinite and asymmetric kernels\nMercer's expansion and Mercer's theorem are cornerstone results in kernel theory. While the classical Mercer's theorem only considers continuous symmetric positive definite kernels, analogous expansions are effective in practice for indefinite and asymmetric kernels. In this paper we extend Mercer's expansion to continuous kernels, providing a rigorous theoretical underpinning for indefinite and asymmetric kernels. We begin by de<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28631<END_URL>

Faceshot: Bring any Character into Life\nPortrait animation generates dynamic, realistic videos by mimicking facial expressions from a driven video.However, existing landmark-based methods are constrained by facial landmark detection and motion transfer limitations, resulting in suboptimal performance. In this paper, we present \emph{FaceShot}, a novel training-free framework designed to animate any character from any driven video, human or non-human, with unprecedented robustness and stability.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28365<END_URL>

Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning\nLarge Language Models (LLMs) have exhibited significant potential in performing diverse tasks, including the ability to call functions or use external tools to enhance their performance. While current research on function calling by LLMs primarily focuses on single-turn interactions, this paper addresses the overlooked necessity for LLMs to engage in multi-turn function calling—critical for handling compositi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28328<END_URL>

Factor Graph-based Interpretable Neural Networks\nComprehensible neural network explanations are foundations for a better understanding of decisions, especially when the input data are infused with malicious perturbations. Existing solutions generally mitigate the impact of perturbations through adversarial training, yet they fail to generate comprehensible explanations under unknown perturbations. To address this challenge, we propose AGAIN, a fActor GrAph-based Interpretable neural Network, wh<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31227<END_URL>

FACTS: A Factored State-Space Framework for World Modelling\nWorld modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such as Transformers and selective state-space models like Mambas, exhibit limitations in efficiently encoding spatial and temporal structures, particularly in scenarios requiring long-term high-dimensional sequence modelling. To address these issues, we propose a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28966<END_URL>

Factual Context Validation and Simplification: A Scalable Method to Enhance GPT Trustworthiness and Efficiency\nAs the deployment of Large Language Models (LLMs) like GPT expands across domains, addressing their susceptibility to factual inaccuracies, or hallucinations, is critical for ensuring reliable performance. This blog post introduces a novel framework that enhances retrieval-augmented generation (RAG) pipelines by integrating LLM summarization, DBSCAN clustering, and vectorized fact stor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31367<END_URL>

Failures to Find Transferable Image Jailbreaks Between Vision-Language Models\nThe integration of new modalities into frontier AI systems offers exciting capabilities, but also increases the possibility such systems can be adversarially manipulated in undesirable ways. In this work, we focus on a popular class of vision-language models (VLMs) that generates text outputs conditioned on visual and textual inputs. We conducted a large-scale empirical study to assess the transferability of gradient-<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27813<END_URL>

Fair Clustering in the Sliding Window Model\nWe study streaming algorithms for proportionally fair clustering (a notion originally suggested by Chierichetti et al. (2017) in the sliding window model. We show that although there exist efficient streaming algorithms exist in the insertion-only model, surprisingly no algorithm can achieve finite ratio without violating the fairness constraint in sliding window. Hence, the problem of fair clustering is a rare separation between the insertion-only st<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29424<END_URL>

FairDen: Fair Density-Based Clustering\nFairness in data mining tasks like clustering has recently become an increasingly important aspect. However, few clustering algorithms exist that focus on fair groupings of data with sensitive attributes. Including fairness in the clustering objective is especially hard for density-based clustering, as it does not directly optimize a closed form objective like centroid-based or spectral methods.  This paper introduces FairDen, the first fair, density-based<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29171<END_URL>

Fair Submodular Cover\nMachine learning algorithms are becoming increasing prevalent in the modern world, and as a result there has been significant recent study into algorithmic fairness in order to minimize the possibility of unintentional bias or discrimination in these algorithms. Submodular optimization problems also arise in many machine learning applications, including those such as data summarization and clustering where fairness is an important concern. In this paper, we initiate the st<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29478<END_URL>

FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"\nEnsuring faithfulness to context in large language models (LLMs) and retrieval-augmented generation (RAG) systems is crucial for reliable deployment in real-world applications, as incorrect or unsupported information can erode user trust. Despite advancements on standard benchmarks, faithfulness hallucination—where models generate responses misaligned with the provided context—remains a signi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29464<END_URL>

FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models\nThe rapid development of generative AI is a double-edged sword, which not only facilitates content creation but also makes image manipulation easier and more difficult to detect. Although current image forgery detection and localization (IFDL) methods are generally effective, they tend to face two challenges: \textbf{1)} black-box nature with unknown detection principle, \textbf{2)} limited ge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28315<END_URL>

Fantastic Copyrighted Beasts and How (Not) to Generate Them\nRecent studies show that image and video generation models can be prompted to reproduce copyrighted content from their training data, raising serious legal concerns about copyright infringement. Copyrighted characters (e.g., Mario, Batman) present a significant challenge: at least one lawsuit has already awarded damages based on the generation of such characters. Consequently, commercial services like DALL·E have started deploying inte<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28846<END_URL>

Fast and Accurate Blind Flexible Docking\nMolecular docking that predicts the bound structures of small molecules (ligands) to their protein targets, plays a vital role in drug discovery. However, existing docking methods often face limitations: they either overlook crucial structural changes by assuming protein rigidity or suffer from low computational efficiency due to their reliance on generative models for structure sampling. To address these challenges, we propose FABFlex, a fast and accura<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28683<END_URL>

Fast and Slow Streams for Online Time Series Forecasting Without Information Leakage\nCurrent research in online time series forecasting suffers from information leakage: models predict and then evaluate on historical time steps that have been backpropagated for parameter updates. This setting also misaligns with the real-world conception of forecasting, which typically emphasizes looking ahead and anticipating future uncertainties. This paper redefines online time series forecasting to focus on<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30196<END_URL>

Fast Direct: Query-Efficient  Online Black-box Guidance  for Diffusion-model Target Generation\nGuided diffusion-model generation is a promising direction for customizing the generation process of a pre-trained diffusion-model to address the specific downstream tasks. Existing guided diffusion models either rely on training of the guidance model with pre-collected datasets or require the objective functions to be differentiable. However, for most real-world tasks, the offline datasets are often <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29800<END_URL>

Fast Diversity-Preserving Reward Finetuning of Diffusion Models via Nabla-GFlowNets\nWhile one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to finetune pretrained diffusion models on some reward functions that are either designed by experts or learned from small-scale datasets. Existing methods for finetuning diffusion models typically suffer either 1) lack of diversity in generated samples, or 2) costly finetuning and slow converg<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30600<END_URL>

Faster Algorithms for Structured Linear and Kernel Support Vector Machines\nQuadratic programming is a ubiquitous prototype in convex programming. Many machine learning problems can be formulated as quadratic programming, including the famous Support Vector Machines (SVMs). Linear and kernel SVMs have been among the most popular models in machine learning over the past three decades, prior to the deep learning era.Generally, a quadratic program has an input size of $\Theta(n^2)$, where $n$ is th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30470<END_URL>

FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality\nIn this paper, we present \textbf{\textit{FasterCache}}, a novel training-free strategy designed to accelerate the inference of video diffusion models with high-quality generation. By analyzing existing cache-based methods, we observe that \textit{directly reusing adjacent-step features degrades video quality due to the loss of subtle variations}. We further perform a pioneering investigation of the acceleration pot<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29385<END_URL>

Faster Cascades via Speculative Decoding\nCascades and speculative decoding are two common approaches to improving language models' inference efficiency.  Both approaches involve interleaving models of different sizes,  but via fundamentally distinct mechanisms: cascades employ a deferral rule that invokes the larger model only for "hard" inputs, while speculative decoding uses speculative execution to primarily invoke the larger model in parallel verification mode. These mechanisms offer differ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27888<END_URL>

Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel\nSampling algorithms play an important role in controlling the quality and runtime of diffusion model inference. In recent years, a number of works (Chen et al., 2023c;b; Benton et al., 2023; Lee et al., 2022) have analyzed algorithms for diffusion sampling with provable guarantees; these works show that for essentially any data distribution, one can approximately sample in polynomial time given a sufficiently accurate <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29931<END_URL>

Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling\nConditional Flow Matching (CFM), a simulation-free method for training continuous normalizing flows, provides an efficient alternative to diffusion models for key tasks like image and video generation. The performance of CFM in solving these tasks depends on the way data is coupled with noise. A recent approach uses minibatch optimal transport (OT) to reassign noise-data pairs in each training step to streamline s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28167<END_URL>

Faster, More Efficient RLHF through Off-Policy Asynchronous Learning\nThe dominant paradigm for RLHF is *online* and *on-policy* RL: synchronously generating from the large language model (LLM) policy, labelling with a reward model, and learning using feedback on the LLM's own outputs. While performant, it is computationally inefficient. Inspired by classical deep RL literature, we propose separating generation and learning in RLHF. This enables asynchronous generation of new samples while simul<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30332<END_URL>

Fast Feedforward 3D Gaussian Splatting Compression\nWith 3D Gaussian Splatting (3DGS) advancing real-time and high-fidelity rendering for novel view synthesis, storage requirements pose challenges for their widespread adoption. Although various compression techniques have been proposed, previous art suffers from a common limitation: for any existing 3DGS, per-scene optimization is needed to achieve compression, making the compression sluggish and slow. To address this issue, we introduce Fast Co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30473<END_URL>

Fast Summation of Radial Kernels via QMC Slicing\nThe fast computation of large kernel sums is a challenging task, which arises as a subproblem in any kernel method. We approach the problem by slicing,  which relies on random projections to one-dimensional subspaces and fast Fourier summation. We prove bounds for the slicing error and propose a quasi-Monte Carlo (QMC) approach for selecting the projections based on spherical quadrature rules. Numerical examples demonstrate that our QMC-slicing a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28699<END_URL>

Fast training and sampling of Restricted Boltzmann Machines\nRestricted Boltzmann Machines (RBMs) are powerful tools for modeling complex systems and extracting insights from data, but their training is hindered by the slow mixing of Markov Chain Monte Carlo (MCMC) processes, especially with highly structured datasets. In this study, we build on recent theoretical advances in RBM training and focus on the stepwise encoding of data patterns into singular vectors of the coupling matrix, significan<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31062<END_URL>

Fast Training of Sinusoidal Neural Fields via Scaling Initialization\nNeural fields are an emerging paradigm that represent data as continuous functions parameterized by neural networks. Despite many advantages, neural fields often have a high training cost, which prevents a broader adoption. In this paper, we focus on a popular family of neural fields, called sinusoidal neural fields (SNFs), and study how it should be initialized to maximize the training speed. We find that the standard initial<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29576<END_URL>

Fast unsupervised ground metric learning with tree-Wasserstein distance\nThe performance of unsupervised methods such as clustering depends on the choice of distance metric between features, or ground metric. Commonly, ground metrics are decided with heuristics or learned via supervised algorithms. However, since many interesting datasets are unlabelled, unsupervised ground metric learning approaches have been introduced. One promising option employs Wasserstein singular vectors (WSVs), which em<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30359<END_URL>

Fat-to-Thin Policy Optimization: Offline Reinforcement Learning with Sparse Policies\nSparse continuous policies are distributions that can choose some actions at random yet keep strictly zero probability for the other actions, which are radically different from the Gaussian.They have important real-world implications, e.g. in modeling safety-critical tasks like medicine.The combination of offline reinforcement learning and sparse policies provides a novel paradigm that enables learning complete<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29606<END_URL>

Feast Your Eyes:  Mixture-of-Resolution Adaptation for Multimodal Large Language Models\nIn existing multimodal large language models (MLLMs), image resolution plays a significant role for  granular visual recognition.  However, directly increasing image resolution leads to expensive computational cost for MLLMs.  In this paper, we  reveal that a combination of low- and high-resolution visual features can efficiently mitigate this shortcoming.  Based on this principle, we propose a novel and eff<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31217<END_URL>

Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks\nIn this work, we investigate a particular implicit bias in the gradient descent training process, which we term “Feature Averaging”, and argue that it is one of the principal factors contributing to non-robustness of deep neural networks. Despite the existence of multiple discriminative features capable of classifying data, neural networks trained by gradient descent exhibit a tendency to learn <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27668<END_URL>

Feature-Based Online Bilateral Trade\nBilateral trade models the problem of facilitating trades between a seller and a buyer having private valuations for the item being sold. In the online version of the problem, the learner faces a new seller and buyer at each time step, and has to post a price for each of the two parties without any knowledge of their valuations. We consider a scenario where, at each time step, before posting prices the learner observes a context vector containing information<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27764<END_URL>

Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse\nMachine learning models are often used to automate or support decisions in applications such as lending and hiring. In such settings, consumer protection rules mandate that we provide consumers who receive adverse decisions with a list of "principal reasons." In practice, lenders and employers identify principal reasons as the top-scoring features from a *feature attribution* method. In this work, we study how such practice<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27815<END_URL>

Federated $Q$-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost\nIn this paper, we consider model-free federated reinforcement learning for tabular episodic Markov decision processes. Under the coordination of a central server, multiple agents collaboratively explore the environment and learn an optimal policy without sharing their raw data. Despite recent advances in federated $Q$-learning algorithms achieving near-linear regret speedup wi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30326<END_URL>

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting\nFederated Class-Incremental Learning (FCIL) refers to a scenario where a dynamically changing number of clients collaboratively learn an ever-increasing number of incoming tasks. FCIL is known to suffer from local forgetting due to class imbalance at each client and global forgetting due to class imbalance across clients. We develop a mathematical framew<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27710<END_URL>

Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond\nGiven the ability to model more realistic and dynamic problems, Federated Continual Learning (FCL) has been increasingly investigated recently. A well-known problem encountered in this setting is the so-called catastrophic forgetting, for which the learning model is inclined to focus on more recent tasks while forgetting the previously learned knowledge. The majority of the current approach<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28896<END_URL>

Federated Domain Generalization with Data-free On-server Gradient Matching\nDomain Generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. One of the key approaches in DG is training an encoder which generates domain-invariant representations. However, this approach is not applicable in Federated Domain Generalization (FDG), where data from various domains are distributed across different clients. In this paper, we introduc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30766<END_URL>

Federated Few-Shot Class-Incremental Learning\nThis study proposes a challenging yet practical Federated Few-Shot Class-Incremental Learning (FFSCIL) problem, where clients only hold very few samples for new classes.  We develop a novel Unified Optimized Prototype Prompt (UOPP) model to simultaneously handle catastrophic forgetting, over-fitting, and prototype bias in FFSCIL. UOPP utilizes task-wise prompt learning to mitigate task interference and over-fitting, unified static-dynamic prototypes<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29204<END_URL>

Federated Granger Causality Learning For Interdependent Clients With State Space Representation\nAdvanced sensors and IoT devices have improved the monitoring and control of complex industrial enterprises. They have also created an interdependent fabric of geographically distributed process operations (clients) across these enterprises. Granger causality is an effective approach to detect and quantify interdependencies by examining how the state of one client affects the states of others over ti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30056<END_URL>

Federated Residual Low-Rank Adaption of Large Language Models\nLow-Rank Adaptation (LoRA) presents an effective solution for federated fine-tuning of Large Language Models (LLMs), as it substantially reduces communication overhead. However, a straightforward combination of FedAvg and LoRA results in suboptimal performance, especially under data heterogeneity. We noted this stems from both intrinsic (i.e., constrained parameter space) and extrinsic (i.e., client drift) limitations, which hinder i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28955<END_URL>

FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking\nIn Federated Learning (FL), weighted aggregation of local models is conducted to generate a new global model, and the aggregation weights are typically normalized to 1. A recent study identifies the global weight shrinking effect in FL, indicating an enhancement in the global model’s generalization when the sum of weights (i.e., the shrinking factor) is smaller than 1, where how to learn the shrinking factor becomes crucial. H<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30887<END_URL>

FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine\nOne-Shot Federated Learning (OFL) is a promising approach that reduce communication to a single round, minimizing latency and resource consumption. However, existing OFL methods often rely on Knowledge Distillation, which adds a training phase and increases server-side latency. Their performance can also be compromised by the quality of generated data or public datasets, resulting in sub-optimal server models. To address these <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31038<END_URL>

Feedback Favors the Generalization of Neural ODEs\nThe well-known generalization problem hinders the application of artificial neural networks in continuous-time prediction tasks with varying latent dynamics. In sharp contrast, biological systems can neatly adapt to evolving environments benefiting from real-time feedback mechanisms. Inspired by the feedback philosophy, we present feedback neural networks, showing that a feedback loop can flexibly correct the learned latent dynamics of neural or<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29029<END_URL>

Feedback Schrödinger Bridge Matching\nRecent advancements in diffusion bridges for distribution transport problems have heavily relied on matching frameworks, yet existing methods often face a trade-off between scalability and access to optimal pairings during training. Fully unsupervised methods make minimal assumptions but incur high computational costs, limiting their practicality. On the other hand, imposing full supervision of the matching process with optimal pairings improves scalability,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28600<END_URL>

Fengbo: a Clifford Neural Operator pipeline for 3D PDEs in Computational Fluid Dynamics\nWe introduce Fengbo, a pipeline entirely in Clifford Algebra to solve 3D partial differential equations (PDEs) specifically for computational fluid dynamics (CFD). Fengbo is an architecture composed of only 3D convolutional and Fourier Neural Operator (FNO) layers, all working in 3D Clifford Algebra. It models the PDE solution problem as an interpretable mapping from the geometry to the physics of the proble<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29396<END_URL>

Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty Measurement\nWe propose Few-Class Arena (FCA), as a unified benchmark with focus on testing efficient image classification models for few classes. A wide variety of benchmark datasets with many classes (80-1000) have been created to assist Computer Vision architectural evolution. An increasing number of vision models are evaluated with these many-class datasets. However, real-world applications often inv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31153<END_URL>

Fewer May Be Better: Enhancing Offline Reinforcement Learning with Reduced Dataset\nResearch in offline reinforcement learning (RL) marks a paradigm shift in RL. However, a critical yet under-investigated aspect of offline RL is determining the subset of the offline dataset, which is used to improve algorithm performance while accelerating algorithm training. Moreover, the size of reduced datasets can uncover the requisite offline data volume essential for addressing analogous challenges. Based <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27646<END_URL>

Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization\nMulti-objective optimization can be found in many real-world applications where some conflicting objectives can not be optimized by a single solution. Existing optimization methods often focus on finding a set of Pareto solutions with different optimal trade-offs among the objectives. However, the required number of solutions to well approximate the whole Pareto optimal set could be exponentially large with respect to t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29845<END_URL>

F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI\nRecent research has developed a number of eXplainable AI (XAI) techniques, such as gradient-based approaches, input perturbation-base methods, and black-box explanation methods. While these XAI techniques can extract meaningful insights from deep learning models, how to properly evaluate them remains an open problem.  The most widely used approach is to perturb or even remove what the XAI method considers to be the mos<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29329<END_URL>

Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning\nRecent studies have identified one aggravating factor of LLM hallucinations as the knowledge inconsistency between pre-training and fine-tuning, where unfamiliar fine-tuning data mislead the LLM to fabricate plausible but wrong outputs. In this paper, we propose a novel fine-tuning strategy called Prereq-Tune to address this knowledge inconsistency and reduce hallucinations. Fundamentally, Prereq-Tune disentangles th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29436<END_URL>

FIG: Flow with Interpolant Guidance for Linear Inverse Problems\nDiffusion and flow matching models have been recently used to solve various linear inverse problems such as image restoration. Using a pre-trained diffusion or flow-matching model as a prior, most existing methods modify the reverse-time sampling process by incorporating the likelihood information from the measurement. However, they struggle in challenging scenarios, e.g., in case of high measurement noise or severe ill-posedness. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28851<END_URL>

Filtered not Mixed: Filtering-Based Online Gating for Mixture of Large Language Models\nWe propose MoE-F — a formalized mechanism for combining N pre-trained expert Large Language Models (LLMs) in online time-series prediction tasks by adaptively forecasting the best weighting of LLM predictions at every time step. Our mechanism leverages the conditional information in each expert's running performance to forecast the best combination of LLMs for predicting the time series in its next step. Dive<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28916<END_URL>

Finally Rank-Breaking Conquers MNL Bandits: Optimal and Efficient Algorithms for MNL Assortment\nWe address the problem of active online assortment optimization problem with preference feedback, which is a framework for modeling user choices and subsetwise utility maximization. The framework is useful in various real-world applications including ad placement, online retail, recommender systems, and fine-tuning language models, amongst many others. The problem, although has been studied in the pa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28552<END_URL>

Find A Winning Sign: Sign Is All We Need to Win the Lottery\nThe lottery ticket hypothesis (LTH) posits the existence of a sparse network (a.k.a. winning ticket) that can generalize comparably to its dense counterpart after training from initialization. However, early works fail to generalize its observation and method to large-scale settings. While recent methods, such as weight rewinding or learning rate rewinding (LRR), may have found effective pruning methods, we note that they still struggl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29056<END_URL>

Finding Shared Decodable Concepts and their Negations in the Brain\nPrior work has offered evidence for functional localization in the brain; different anatomical regions preferentially activate for certain types of visual input. For example, the fusiform face area preferentially activates for visual stimuli that include a face. However, the spectrum of visual semantics is extensive, and only a few semantically-tuned patches of cortex have so far been identified in the human brain. Using a multi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30022<END_URL>

Fine-Grained Verifiers: Preference Modeling as Next-token  in Vision-Language Alignment\nThe recent advancements in large language models (LLMs) and pre-trained vision models have accelerated the development of vision-language large models (VLLMs), enhancing the interaction between visual and linguistic modalities. Despite their notable success across various domains, VLLMs face challenges in modality alignment, which can lead to issues like hallucinations and unsafe content generation. Current <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29059<END_URL>

Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic\nIn recent years, *task arithmetic* has garnered increasing attention. This approach edits pre-trained models directly in weight space by combining the fine-tuned weights of various tasks into a *unified model*. Its efficiency and cost-effectiveness stem from its training-free combination, contrasting with traditional methods that require model training on large datasets for multiple tasks. However, applying <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28970<END_URL>

Fine-tuning can Help Detect Pretraining Data from Large Language Models\nIn the era of large language models (LLMs), detecting pretraining data has been increasingly important due to concerns about fair evaluation and ethical risks. Current methods differentiate members and non-members by designing scoring functions, like Perplexity and Min-k%. However, the diversity and complexity of training data magnifies the difficulty of distinguishing, leading to suboptimal performance in detecting pretrai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29320<END_URL>

Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design\nRecent studies have demonstrated the strong empirical performance of diffusion models on discrete sequences (i.e., discrete diffusion models) across domains such as natural language and biological sequence generation. For example, in the protein inverse folding task, where the goal is to generate a protein sequence from a given backbone structure, conditional diffusion models have achieved <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30308<END_URL>

Fine-Tuning Token-Based Large Multimodal Models: What Works, What Doesn’t and What's Next\nIn this blog post, we explore the advancements and challenges in fine-tuning unified token-based large multimodal models, focusing on the Chameleon architecture and its fine-tuned variant, Anole. Released in 2024, these models exemplify a modern approach for integrating various data modalities through tokens, simplifying modal fusion and leveraging established techniques from large language models. The pos<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31328<END_URL>

Fine-tuning with Reserved Majority for Noise Reduction\nParameter-efficient fine-tuning (PEFT) has revolutionized supervised fine-tuning, where LoRA and its variants gain the most popularity due to their low training costs and zero inference latency.However, LoRA tuning not only injects knowledgeable features but also noisy hallucination during fine-tuning, which hinders the utilization of tunable parameters with the increasing LoRA rank.In this work, we first investigate in-depth the redundanci<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29216<END_URL>

FIRING-Net: A filtered feature recycling network for speech enhancement\nCurrent deep neural networks for speech enhancement (SE) aim to minimize the distance between the output signal and the clean target by filtering out noise features from input features. However, when noise and speech components are highly similar, SE models struggle to learn effective discrimination patterns. To address this challenge, we propose a Filter-Recycle-Interguide framework termed Filter-Recycle-INterGuide NETwork<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29549<END_URL>

First-Person Fairness in Chatbots\nSome chatbots have access to a user’s name when responding. Prior work hasshown that large language model outputs can change based on the demographictraits correlated with a name, such as gender or race. In this study, we introducea scalable method for studying one form of first-personfairness—fairness towards the user based on their demographic information—across a large and heterogeneous corpus of actual chats. We leverage a languagemodel as an AI “research a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29520<END_URL>

Fitting Networks with a Cancellation Trick\nThe degree-corrected block model (DCBM), latent space model (LSM), and $\beta$-model are all popular network models. We combine their modeling ideas and propose the logit-DCBM as a new model. Similar as the $\beta$-model and LSM, the logit-DCBM contains nonlinear factors, where fitting the parameters is a challenging open problem. We resolve this problem by introducing a cancellation trick. We also propose R-SCORE as a recursive community detection alg<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30535<END_URL>

Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond\nWhile transformers have been at the core of most recent advancements in sequence generative models, their computational cost remains quadratic in sequence length.Several subquadratic architectures have been proposed to address this computational issue. Some of them, including long convolution sequence models (LCSMs), such as Hyena, address this issue at training time but remain quadratic during inference<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29040<END_URL>

FlashMask: Efficient and Rich Mask Extension of FlashAttention\nThe computational and memory demands of vanilla attention scale quadratically with the sequence length $N$, posing significant challenges for processing long sequences in Transformer models. FlashAttention alleviates these challenges by eliminating the $O(N^2)$ memory dependency and reducing attention latency through IO-aware memory optimizations. However, its native support for certain attention mask types is limited, and it does n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27841<END_URL>

FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware\nWhile Transformers and other sequence-parallelizable neural network architectures seem like the current state of the art in sequence modeling, they specifically lack state-tracking capabilities. These are important for time-series tasks and logical reasoning. Traditional RNNs like LSTMs and GRUs, as well as modern variants like sLSTM do have these capabilities at the cost of strictly sequential processing. While this is oft<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28546<END_URL>

Flat Reward in Policy Parameter Space Implies Robust Reinforcement Learning\nInvestigating flat minima on loss surfaces in parameter space is well-documented in the supervised learning context, highlighting its advantages for model generalization. However, limited attention has been paid to the reinforcement learning (RL) context, where the impact of flatter reward landscapes in policy parameter space remains largely unexplored. Beyond merely extrapolating from supervised learning, which suggest<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31013<END_URL>

Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks\nWe study the implicit bias of the family of steepest descent algorithms, including gradient descent, sign gradient descent and coordinate descent, in deep homogeneous neural networks. We prove that an algorithm-dependent geometric margin increases during training and characterize the late-stage bias of the algorithms. In particular, we define a generalized notion of stationarity for optimization problems and sho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30582<END_URL>

Flaws of ImageNet, Computer Vision's Favourite Dataset\nSince its release, ImageNet-1k dataset has become a gold standard for evaluating model performance. It has served as the foundation for numerous other datasets and training tasks in computer vision. As models have improved in accuracy, issues related to label correctness have become increasingly apparent. In this blog post, we analyze the issues in the ImageNet-1k dataset, including incorrect labels, overlapping or ambiguous class definitio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31342<END_URL>

FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models\nRecently, there is a growing interest in creating computer-aided design (CAD) models based on user intent, known as controllable CAD generation. Existing work offers limited controllability and needs separate models for different types of control, reducing efficiency and practicality. To achieve controllable generation across all CAD construction hierarchies, such as sketch-extrusion, extrusion, ske<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29237<END_URL>

FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference\nLarge language models (LLMs) encounter computational challenges during long-sequence inference, especially in the attention pre-filling phase, where the complexity grows quadratically with the prompt length. Previous efforts to mitigate these challenges have relied on fixed sparse attention patterns or identifying sparse attention patterns based on limited cases. However, these methods lacked the flexi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29811<END_URL>

FlickerFusion: Intra-trajectory Domain Generalizing Multi-agent Reinforcement Learning\nMulti-agent reinforcement learning has demonstrated significant potential in addressing complex cooperative tasks across various real-world applications. However, existing MARL approaches often rely on the restrictive assumption that the number of entities (e.g., agents, obstacles) remains constant between training and inference. This overlooks scenarios where entities are dynamically removed or $\textit{adde<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29932<END_URL>

FLIP: Flow-Centric Generative Planning for General-Purpose Manipulation Tasks\nWe aim to develop a model-based planning framework for world models that can be scaled with increasing model and data budgets for general-purpose manipulation tasks with only language and vision inputs. To this end, we present FLow-CentrIc generative Planning (FLIP), a model-based planning algorithm on visual space that features three key modules: 1) a multi-modal flow generation model as the general-purpose action pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30597<END_URL>

FLOPS: Forward Learning with OPtimal Sampling\nGiven the limitations of backpropagation, perturbation-based gradient computation methods have recently gained focus for learning with only forward passes, also referred to as queries. Conventional forward learning consumes enormous queries on each data point for accurate gradient estimation through Monte Carlo sampling, which hinders the scalability of those algorithms. However, not all data points deserve equal queries for gradient estimation. In <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27694<END_URL>

Flow-based Variational Mutual Information: Fast and Flexible Approximations\nMutual Information (MI) is a fundamental measure of dependence between random variables, but its practical application is limited because it is difficult to calculate in many circumstances. Variational methods offer one approach by introducing an approximate distribution to create various bounds on MI, which in turn is an easier optimization problem to solve. In practice, the variational distribution chosen is often a G<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28097<END_URL>

FlowDec: A flow-based full-band general audio codec with high perceptual quality\nWe propose FlowDec, a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output quality and reducing th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27945<END_URL>

Flow Distillation Sampling: Regularizing 3D Gaussians with Pre-trained Matching Priors\n3D Gaussian Splatting (3DGS) has achieved excellent rendering quality with fast training and rendering speed. However, its optimization process lacks explicit geometric constraints, leading to suboptimal geometric reconstruction in regions with sparse or no observational input views. In this work, we try to mitigate the issue by incorporating a pre-trained matching prior to the 3DGS optimization process. We i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30536<END_URL>

Flow matching achieves almost minimax optimal convergence\nFlow matching (FM) has gained significant attention as a simulation-free generative model. Unlike diffusion models, which are based on stochastic differential equations, FM employs a simpler approach by solving an ordinary differential equation with an initial condition from a normal distribution, thus streamlining the sample generation process. This paper discusses the convergence properties of FM in terms of the $p$-Wasserstein distanc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31143<END_URL>

Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting\nRecent advancements in generative modeling, particularly diffusion models, have opened new directions for time series modeling, achieving state-of-the-art performance in forecasting and synthesis. However, the reliance of diffusion-based models on a simple, fixed prior complicates the generative process since the data and prior distributions differ significantly. We introduce TSFlow, a conditional flow matching<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27944<END_URL>

Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective\nThe design space of discrete-space diffusion or flow generative models are significantly less well-understood than their continuous-space counterparts, with many works focusing only on a simple masked construction.In this work, we aim to take a holistic approach to the construction of discrete generative models based on continuous-time Markov chains, and for the first time, allow the use of arbitrary discrete probability p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28039<END_URL>

Flow With What You Know\nWe provide an accessible introduction to flow-matching and rectified flow models, which are increasingly at the forefront of generative AI applications. Typical descriptions of them are often laden with extensive probability-math equations, which can form barriers to the dissemination and understanding of these models. Fortunately, before they were couched in probabilities, the mechanisms underlying these models were grounded in basic physics, which provides an alternati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31364<END_URL>

Following the Human Thread in Social Navigation\nThe success of collaboration between humans and robots in shared environments relies on the robot's real-time adaptation to human motion. Specifically, in Social Navigation, the agent should be close enough to assist but ready to back up to let the human move freely, avoiding collisions. Human trajectories emerge as crucial cues in Social Navigation, but they are partially observable from the robot's egocentric view and computationally complex to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29952<END_URL>

Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems\nRetrieval-Augmented Generation (RAG) improves pre-trained models by incorporating external knowledge at test time to enable customized adaptation. We study the risk of datastore leakage in Retrieval-In-Context RAG Language Models (LMs). We show that an adversary can exploit LMs' instruction-following capabilities to easily extract text data verbatim from the datastore of RAG systems b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29275<END_URL>

For Better or For Worse? Learning Minimum Variance Features With Label Augmentation\nData augmentation has been pivotal in successfully training deep learning models on classification tasks over the past decade. An important subclass of data augmentation techniques - which includes both label smoothing and Mixup - involves modifying not only the input data but also the input label during model training. In this work, we analyze the role played by the label augmentation aspect of such methods. We<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30011<END_URL>

ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities\nForecasts of future events are essential inputs into informed decision-making.Machine learning (ML) systems have the potential to deliver forecasts at scale, butthere is no framework for evaluating the accuracy of ML systems on a standardizedset of forecasting questions. To address this gap, we introduce ForecastBench: adynamic benchmark that evaluates the accuracy of ML systems on an automaticallygenerated and regularly updated <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28507<END_URL>

Forecasting chaotic systems with zero-shot learning\nTime-series forecasting is a challenging problem that traditionally requires specialized models custom-trained for the specific task at hand. Recently, inspired by the success of large language models, foundation models pre-trained on vast amounts of time-series data from diverse domains have emerged as a promising candidate for general-purpose time-series forecasting. The defining characteristic of these foundation models is their ability to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29513<END_URL>

Forewarned is Forearmed:  Harnessing LLMs for Data Synthesis via Failure-induced Exploration\nLarge language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data, leading to impressive performance across a range of downstream applications. Current methods often rely on human-annotated data or predefined task templates to direct powerful LLMs in synthesizing task-relevant data for effective model training. However, this dependence on manually design<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27706<END_URL>

Forget the Data and Fine-Tuning! Just Fold the Network to Compress\nWe introduce model folding, a novel data-free model compression technique that merges structurally similar neurons across layers, significantly reducing the model size without the need for fine-tuning or access to training data. Unlike existing methods, model folding preserves data statistics during compression by leveraging k-means clustering, and using novel data-free techniques to prevent variance collapse or explosion. Our t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29387<END_URL>

Forgetting Transformer: Softmax Attention with a Forget Gate\nAn essential component of modern recurrent sequence models is the *forget gate*. While Transformers do not have an explicit recurrent form, we show that a forget gate can be naturally incorporated into Transformers by down-weighting the unnormalized attention scores in a data-dependent way. We name the resulting model the *Forgetting Transformer*. We show that the Forgetting Transformer outperforms the Transformer on long-context lang<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28268<END_URL>

Forking Paths in Neural Text Generation\nEstimating uncertainty in Large Language Models (LLMs) is important for properly evaluating LLMs, and ensuring safety for users. However, prior approaches to uncertainty estimation focus on the final answer in generated text, ignoring intermediate steps that might dramatically impact the outcome. We hypothesize that there exist key forking tokens, such that re-sampling the system at those specific tokens, but not others, leads to very different outcomes. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30769<END_URL>

FormalAlign: Automated Alignment Evaluation for Autoformalization\nAutoformalization aims to convert informal mathematical proofs into machine-verifiable formats, bridging the gap between natural and formal languages. However, ensuring semantic alignment between the informal and formalized statements remains challenging. Existing approaches heavily rely on manual verification, hindering scalability. To address this, we introduce FormalAlign, a framework for automatically evaluating the alignment<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30594<END_URL>

Formation of Representations in Neural Networks\nUnderstanding neural representations will help open the black box of neural networks and advance our scientific understanding of modern AI systems. However, how complex, structured, and transferable representations emerge in modern neural networks has remained a mystery. Building on previous results, we propose the Canonical Representation Hypothesis (CRH), which posits a set of six alignment relations to universally govern the formation of repres<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29863<END_URL>

Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy\nAmong the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings produced at the output layer dually serve as both predictive features for solving downstream tasks (e.g., node classification<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30256<END_URL>

Forte : Finding Outliers with Representation Typicality Estimation\nGenerative models can now produce photorealistic synthetic data which is virtually indistinguishable from the real data used to train it. This is a significant evolution over previous models which could produce reasonable facsimiles of the training data, but ones which could be visually distinguished from the training data by human evaluation. Recent work on OOD detection has raised doubts that generative model likelihoods are o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30817<END_URL>

FOSP: Fine-tuning Offline Safe Policy through World Models\nOffline Safe Reinforcement Learning (RL) seeks to address safety constraints by learning from static datasets and restricting exploration. However, these approaches heavily rely on the dataset and struggle to generalize to unseen scenarios safely. In this paper, we aim to improve safety during the deployment of vision-based robotic tasks through online fine-tuning an offline pretrained policy. To facilitate effective fine-tuning, we int<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28976<END_URL>

Foundation Models Secretly Understand Neural Network Weights: Enhancing Hypernetwork Architectures with Foundation Models\nLarge pre-trained models, or foundation models, have shown impressive performance when adapted to a variety of downstream tasks, often out-performing specialized models. Hypernetworks, neural networks that generate some or all of the parameters of another neural network, have become an increasingly important technique for conditioning and generalizing implicit neural represe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29067<END_URL>

Fourier Head: Helping Large Language Models Learn Complex Probability Distributions\nAs the quality of large language models has improved, there has been increased interest in using them to model non-linguistic tokens. For example, the Decision Transformer recasts agentic decision making as a sequence modeling problem, using a decoder-only LLM to model the distribution over the discrete action space for an Atari agent. However, when adapting LLMs to non-linguistic domains, it remains unclear if <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30999<END_URL>

Fourier Sliced-Wasserstein Embedding for Multisets and Measures\nWe present the _Fourier Sliced Wasserstein (FSW) embedding_—a novel method to embed multisets and measures over $\mathbb{R}^d$ into Euclidean space.Our proposed embedding approximately preserves the sliced Wasserstein distance on distributions, thereby yielding geometrically meaningful representations that better capture the structure of the input. Moreover, it is injective on measures and _bi-Lipschitz_ on multisets—a significant <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30562<END_URL>

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models\nStructure-based drug design (SBDD) is crucial for developing specific and effective therapeutics against protein targets but remains challenging due to complex protein-ligand interactions and vast chemical space. Although language models (LMs) have excelled in natural language processing, their application in SBDD is underexplored. To bridge this gap, we introduce a method, known as Frag2<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28470<END_URL>

Framer: Interactive Frame Interpolation\nWe propose Framer for interactive frame interpolation, which targets producing smoothly transitioning frames between two images as per user creativity. Concretely, besides taking the start and end frames as inputs, our approach supports customizing the transition process by tailoring the trajectory of some selected keypoints. Such a design enjoys two clear benefits. First, incorporating human interaction mitigates the issue arising from numerous possibili<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29976<END_URL>

Frame-Voyager: Learning to Query Frames for Video Large Language Models\nVideo Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks. However, they are constrained by the maximum length of input tokens, making it impractical to input entire videos. Existing frame selection approaches, such as uniform frame sampling and text-frame retrieval, fail to account for the information density variations in the videos or the complex instructions in the tasks, leadin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30004<END_URL>

FreCaS: Efficient Higher-Resolution Image Generation via Frequency-aware Cascaded Sampling\nWhile image generation with diffusion models has achieved a great success, generating images of higher resolution than the training size remains a challenging task due to the high computational cost. Current methods typically perform the entire sampling process at full resolution and process all frequency components simultaneously, contradicting with the inherent coarse-to-fine nature of latent diffusion <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29507<END_URL>

Fréchet Wavelet Distance: A Domain-Agnostic Metric for Image Generation\nModern metrics for generative learning like Fréchet Inception Distance (FID ) and DINOv2-Fréchet Distance (FD-DINOv2 ) demonstrate impressive performance. However, they suffer from various shortcomings, like a bias towards specific generators and datasets. To address this problem, we propose the Fréchet Wavelet Distance (FWD ) as a domain-agnostic metric based on the Wavelet Packet Transform ($\mathcal{W}_p$). FWD provides <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29680<END_URL>

FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields\nMachine Learning Force Fields (MLFFs) are of great importance for chemistry, physics, materials science, and many other related fields. The Clebsch–Gordan transform (CG transform) effectively encodes many-body interactions and is thus an important building block for many models of MLFFs. However, the permutation-equivariance requirement of MLFFs limits the design space of CG transform, that is, intensive<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28107<END_URL>

Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs\nThe covariance for clean data given a noisy observation is an important quantity in many training-free guided generation methods for diffusion models. Current methods require heavy test-time computation, altering the standard diffusion training process or denoiser architecture, or making heavy approximations. We propose a new framework that sidesteps these issues by using covariance information that is available<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31019<END_URL>

FreeVS: Generative View Synthesis on Free Driving Trajectory\nExisting reconstruction-based novel view synthesis methods for driving scenes focus on synthesizing camera views along the recorded trajectory of the ego vehicle. Their image rendering performance will severely degrade on viewpoints falling out of the recorded trajectory, where camera rays are untrained.We propose FreeVS, a novel fully generative approach that can synthesize camera views on free new trajectories in real driving scenes<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28983<END_URL>

FreqPrior: Improving Diffusion Models with Frequency Filtering Gaussian Noise as Prior\nText-driven video generation has advanced significantly due to developments in diffusion models. Beyond the training and sampling phases, recent studies have investigated noise priors of diffusion models, as improved noise priors yield better generation results. One recent approach employs Fourier transform to manipulate noise, marking the initial exploration of frequency operations in this context. However, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30733<END_URL>

Frequency-Guided Masking for Enhanced Vision Self-Supervised Learning\nWe present a novel frequency-based Self-Supervised Learning (SSL) approach that significantly enhances its efficacy for pre-training. Prior work in this direction masks out pre-defined frequencies in the input image and employs a reconstruction loss to pre-train the model. While achieving promising results, such an implementation has two fundamental limitations as identified in our paper. First, using pre-defined frequencies <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29401<END_URL>

FreSh: Frequency Shifting for Accelerated Neural Representation Learning\nImplicit Neural Representations (INRs) have recently gained attention as a powerful approach for continuously representing signals such as images, videos, and 3D shapes using multilayer perceptrons (MLPs). However, MLPs are known to exhibit a low-frequency bias, limiting their ability to capture high-frequency details accurately. This limitation is typically addressed by incorporating high-frequency input embeddings or spe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27671<END_URL>

From an LLM Swarm to a PDDL-empowered Hive: Planning Self-executed Instructions in a Multi-modal Jungle\nIn response to the call for agent-based solutions that leverage the ever-increasing capabilities of the deep models' ecosystem, we introduce a comprehensive solution for selecting appropriate models and subsequently planning a set of atomic actions to satisfy the end-users' instructions.Our system, Hive, operates over sets of models and, upon receiving natural language instructions, schedules<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29706<END_URL>

From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data\nRecent studies have shown that Large Language Models (LLMs) struggle to accurately retrieve information and maintain reasoning capabilities when processing long-context inputs. To address these limitations, we propose a finetuning approach utilizing a carefully designed synthetic dataset comprising numerical key-value retrieval tasks. Our experiments on models like GPT-3.5 Turbo a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30747<END_URL>

From Attention to Activation: Unraveling the Enigmas of Large Language Models\nWe study two strange phenomena in auto-regressive Transformers: (1) the dominance of the ﬁrst token in attention heads; (2) the occurrence of large outlier activations in the hidden states. We ﬁnd that popular large language models, such as Llama attend maximally to the first token in 98% of attention heads, a behaviour we attribute to the softmax function. To mitigate this issue, we propose a reformulation of softmax<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30153<END_URL>

From Commands to Prompts: LLM-based Semantic File System\nLarge language models (LLMs) have demonstrated significant potential in the development of intelligent LLM-based agents. However, when users use these agent applications perform file operations, their interaction with the file system still remains the traditional paradigm: reliant on manual navigation through precise commands. This paradigm poses a bottleneck to the usability of these systems as users are required to navigate complex fold<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31152<END_URL>

From Decoupling to Adaptive Transformation: a Wider Optimization Space for PTQ\nPost-training low-bit quantization (PTQ) is useful to accelerate DNNs due to its high efficiency. Currently, finetuning through self-distillation feature reconstruction is one of the most effective PTQ techniques. However, when bitwidth goes to be extremely low, we find that current parameter update settings in PTQ feature reconstruction are sub-optimal. Considering all possible parameters and the ignored fact that i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30116<END_URL>

From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions\nTool learning enables Large Language Models (LLMs) to interact with external environments by invoking tools, serving as an effective strategy to mitigate the limitations inherent in their pre-training data. In this process, tool documentation plays a crucial role by providing usage instructions for LLMs, thereby facilitating effective tool utilization. This paper concentrates on the critical challenge of bri<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29696<END_URL>

From Few to Many: Enhancing Many-Shot In-Context Learning with Optimized Example Selection and Expansion\nRecent advances in long-context large language models (LLMs) have led to the emerging paradigm of many-shot in-context learning (ICL), where it is observed that scaling many more demonstrating examples beyond the conventional few-shot setup in the context can lead to performance benefits. However, despite its promise, it is unclear what aspects dominate the benefits and whether simply scalin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30122<END_URL>

From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks\nInterpretable Graph Neural Networks (GNNs) aim to reveal the underlying reasoning behind model predictions, attributing their decisions to specific subgraphs that are informative. However, existing subgraph-based interpretable methods suffer from an overemphasis on local structure, potentially overlooking long-range dependencies within the entire graphs. Although recent efforts that rely on graph coarsening have prove<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30070<END_URL>

From Isolated Conversations to Hierachical Schemas: Dynamic Tree Memory Representation for LLMs\nRecent advancements in large language models have significantly improved their context windows, yet challenges in effective long-term memory management remain. We introduce MemTree, an algorithm that leverages a dynamic, tree-structured memory representation to optimize the organization, retrieval, and integration of information, akin to human cognitive schemas. MemTree organizes memory hierarchicall<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28447<END_URL>

From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics\nThe depth of neural networks is a critical factor for their capability, with deeper models often demonstrating superior performance. Motivated by this, significant efforts have been made to enhance layer aggregation - reusing information from previous layers to better extract features at the current layer, to improve the representational power of deep neural networks. However, previous works have primar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28443<END_URL>

From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks\nBiological and artificial neural networks develop internal representations that enable them to perform complex tasks. In artificial networks, the effectiveness of these models relies on their ability to build task specific representation, a process influenced by interactions among datasets, architectures, initialization strategies, and optimization algorithms. Prior studies highlight that different initializations can place netw<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29214<END_URL>

From Models to Microtheories: Distilling a Model's Topical Knowledge for Grounded Question-Answering\nRecent reasoning methods (e.g., chain-of-thought, entailment reasoning) help users understand how language models (LMs) answer a single question, but they do little to reveal the LM’s _overall understanding_, or “theory”, about the question’s _topic_, making it still hard to trust the model. Our goal is to materialize such theories - here called _microtheories_ (a linguistic analog of logical mi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30107<END_URL>

From Pixels to Tokens: Byte-Pair Encoding on Quantized Visual Modalities\nMultimodal Large Language Models have made significant strides in integrating visual and textual information, yet they often struggle with effectively aligning these modalities. We introduce a novel image tokenizer that bridges this gap by applying the principle of Byte-Pair Encoding (BPE) to visual data. Unlike conventional approaches that rely on separate visual encoders, our method directly incorporates structural prior<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31074<END_URL>

From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal Hierarchy\nThe framework of Pearl's Causal Hierarchy (PCH) formalizes three types of reasoning: probabilistic (i.e. purely observational), interventional, and counterfactual, that reflect the progressive sophistication of human thought regarding causation. We investigate the computational complexity aspects of reasoning in this framework focusing mainly on satisfiability problems expressed in proba<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28162<END_URL>

From Promise to Practice: Realizing High-performance Decentralized Training\nDecentralized training of deep neural networks has attracted significant attention for its theoretically superior scalability over synchronous data-parallel methods like All-Reduce. However, realizing this potential in multi-node training is challenging due to the complex design space that involves communication topologies, computation patterns, and optimization algorithms. This paper identifies three key factors that c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28499<END_URL>

From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation\nThere are various measures of predictive uncertainty in the literature, but their relationships to each other remain unclear. This paper uses a decomposition of statistical pointwise risk into components associated with different sources of predictive uncertainty: namely, aleatoric uncertainty (inherent data variability) and epistemic uncertainty (model-related uncertainty). Together with Bayesian metho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29041<END_URL>

From Search to Sampling: Generative Models for Robust Algorithmic Recourse\nAlgorithmic Recourse provides recommendations to individuals who are adversely impacted by automated model decisions, on how to alter their profiles to achieve a favorable outcome. Effective recourse methods must balance three conflicting goals: proximity to the original profile to minimize cost, plausibility for realistic recourse, and validity to ensure the desired outcome. We show that existing methods train for these<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29854<END_URL>

From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency\nChain-of-thought (CoT)  significantly enhances the reasoning performance of large language models (LLM). While current theoretical studies often attribute this improvement to increased expressiveness and computational capacity, we argue that expressiveness is not the primary limitation in the LLM regime, as current large models will fail on simple tasks. Using a parity-learning setu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30616<END_URL>

From Tokens to Lattices: Emergent Lattice Structures in Language Models\nPretrained masked language models (MLMs) have demonstrated an impressive capability to comprehend and encode conceptual knowledge, revealing a lattice structure among concepts. This raises a critical question: how does this conceptualization emerge from MLM pretraining? In this paper, we explore this problem from the perspective of Formal Concept Analysis (FCA), a mathematical framework that derives concept lattices from th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28457<END_URL>

From Tokens to Words: On the Inner Lexicon of LLMs\nNatural language is composed of words, but modern LLMs process *sub-words* as input. A natural question raised by this discrepancy is whether LLMs encode words internally, and if so how. We present evidence that LLMs engage in an intrinsic detokenization process, where sub-word sequences are combined into coherent word representations. Our experiments show that this process takes place primarily within the early and middle layers of the model. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31096<END_URL>

Frontiers in Probabilistic Inference: learning meets Sampling\nProbabilistic inference, particularly through the use of sampling-based methods, is a cornerstone for modeling across diverse fields, from machine learning and statistics to natural sciences such as physics, biology, and chemistry. However, many challenges exist, including scaling, which has resulted in the development of new machine learning methods. In response to these rapid developments, we propose a workshop, *Frontiers in Proba<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23990<END_URL>

Fugatto 1: Foundational Generative Audio Transformer Opus 1\nFugatto is a versatile audio synthesis and transformation model capable of following free-form text instructions with optional audio inputs. While large language models (LLMs) trained with text on a simple next-token prediction objective can learn to infer instructions directly from the data, models trained solely on audio data lack this capacity. This is because audio data does not inherently contain the instructions that were used to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30598<END_URL>

Fully-inductive Node Classification on Arbitrary Graphs\nOne fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can generalize to test graphs with new structures, but assuming the feature and label spaces remain the same as the training ones. This paper introduces the fully-inductive setup, where models should perform inference on arbitrary test graphs with new structures, feature and label spaces. We propose GraphAn<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31202<END_URL>

Functional Homotopy: Smoothing Discrete Optimization via Continuous Parameters for LLM Jailbreak Attacks\nOptimization methods are widely employed in deep learning to address and mitigate undesired model responses. While gradient-based techniques have proven effective for image models, their application to language models is hindered by the discrete nature of the input space. This study introduces a novel optimization approach, termed the *functional homotopy* method, which leverages the functio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27963<END_URL>

Fundamental Limitations on Subquadratic Alternatives to Transformers\nThe Transformer architecture is widely deployed in many popular and impactful Large Language Models. At its core is the attention mechanism for calculating correlations between pairs of tokens. Performing an attention computation takes quadratic time in the input size, and had become the time bottleneck for transformer operations. In order to circumvent this, researchers have used a variety of approaches, including designing h<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29565<END_URL>

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency\nWe investigate the statistical and computational limits of prompt tuning for transformer-based foundation models. Our key contributions are prompt tuning on *single-head* transformers with only a *single* self-attention layer: (i) is universal, and (ii) supports efficient (even nearly-linear time) algorithms under the Strong Exponential Time Hypothesis (SETH).Statistically, we prove that prompt tuning on suc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28645<END_URL>

Future-Guided Pretraining via Time-to-Event Supervision for 3D Medical Imaging\nWith the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predictive of future disease risk. While current self-supervised methods for 3D medical imaging models capture local structural features like organ morphology, they fail to link pixel biomarkers with long-term health outcomes due to a missing con<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27661<END_URL>

GALA: Geometry-Aware Local Adaptive Grids for Detailed 3D Generation\nWe propose GALA, a novel representation of 3D shapes that (i) excels at capturing and reproducing complex geometry and surface details, (ii) is computationally efficient, and (iii) lends itself to 3D generative modelling with modern, diffusion-based schemes. The key idea of GALA is to exploit both the global sparsity of surfaces within a 3D volume and their local surface properties. *Sparsity* is promoted by covering only the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30050<END_URL>

GameArena: Evaluating LLM Reasoning through Live Computer Games\nEvaluating the reasoning abilities of large language models (LLMs) is challenging. Existing benchmarks often depend on static datasets, which are vulnerable to data contamination and may get saturated over time, or on binary live human feedback that conflates reasoning with other abilities. As the most prominent dynamic benchmark, Chatbot Arena evaluates open-ended questions in real-world settings, but lacks the granularity in asse<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29588<END_URL>

GameGen-$\mathbb{X}$: Interactive Open-world Game Video Generation\nWe introduce GameGen-$\mathbb{X}$, the first diffusion transformer model specifically designed for both generating and interactively controlling open-world game videos. This model facilitates high-quality, open-domain generation by simulating an extensive array of game engine features, such as innovative characters, dynamic environments, complex actions, and diverse events.     Additionally, it provides interactive controllabili<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30764<END_URL>

GANDALF: Generative AttentioN based Data Augmentation and predictive modeLing Framework for personalized cancer treatment\nEffective treatment of cancer is a major challenge faced by healthcare providers, due to the highly individualized nature of patient responses to treatment. This is caused by the heterogeneity seen in cancer-causing alterations (mutations) across patient genomes. Limited availability of response data in patients makes it difficult to train personalized treatment recommendati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29335<END_URL>

Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition\nWe study the gap-dependent bounds of two important algorithms for on-policy $Q$-learning for finite-horizon episodic tabular Markov Decision Processes (MDPs): UCB-Advantage (Zhang et al. 2020) and Q-EarlySettled-Advantage (Li et al. 2021). UCB-Advantage and Q-EarlySettled-Advantage improve upon the results based on Hoeffding-type bonuses and achieve the {almost optimal} $\sqrt{T}$-type regret bound in the worst-case sce<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30859<END_URL>

Gap Preserving Distillation by Building Bidirectional Mappings with A Dynamic Teacher\nKnowledge distillation aims to transfer knowledge from a large teacher model to a compact student counterpart, often coming with a significant performance gap between them. Interestingly, we find that a too-large performance gap can hamper the training process.To alleviate this, we propose a **Gap Preserving Distillation (GPD)** method that trains an additional dynamic teacher model from scratch along with the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29731<END_URL>

Gated Delta Networks: Improving Mamba2 with Delta Rule\nLinear Transformers have gained attention as efficient alternatives to standard Transformers, but their performance in retrieval and long-context tasks has been limited.  To address these limitations, recent work has explored two distinct mechanisms: gating for adaptive memory control and the delta update rule for precise memory modifications. We observe that these mechanisms are complementary—gating enables rapid memory erasure while the d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28219<END_URL>

GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation\nRecent advancements in diffusion models and large-scale datasets have revolutionized image and video generation, with increasing focus on 3D content generation. While existing methods show promise, they face challenges in input formats, latent space structures, and output representations. This paper introduces a novel 3D generation framework that addresses these issues, enabling scalable and high-quality 3D generation <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29781<END_URL>

Gaussian-Based Instance-Adaptive Intensity Modeling for Point-Supervised Facial Expression Spotting\nPoint-supervised facial expression spotting (P-FES) aims to localize facial expression instances in untrimmed videos, requiring only a single timestamp label for each instance during training. To address label sparsity, hard pseudo-labeling is often employed to propagate point labels to unlabeled frames; however, this approach can lead to confusion when distinguishing between neutral and expressi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28978<END_URL>

GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians\nRecently, with the development of Neural Radiance Fields and Gaussian Splatting, 3D reconstruction techniques have achieved remarkably high fidelity. However, the latent representations learnt by these methods are highly entangled and lack interpretability. In this paper, we propose a novel part-aware compositional reconstruction method, called GaussianBlock, that enables semantically coherent and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27818<END_URL>

Gaussian-Det: Learning Closed-Surface Gaussians for 3D Object Detection\nSkins wrapping around our bodies, leathers covering over the sofa, sheet metal coating the car – it suggests that objects are enclosed by a series of continuous surfaces, which provides us with informative geometry prior for objectness deduction. In this paper, we propose Gaussian-Det which leverages Gaussian Splatting as surface representation for multi-view based 3D object detection. Unlike existing monocular or NeRF-base<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30438<END_URL>

Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation\nIn this paper we consider the problem of releasing a Gaussian Differentially Private (GDP) 3D human face. The human face is a complex structure with many features and inherently tied to one's identity.  Protecting this data, in a formally private way, is important yet challenging given the dimensionality of the problem. We extend approximate DP techniques for functional data to the GDP framework. We further pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30080<END_URL>

Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional, Black-box Systems\nEfficient inference in high-dimensional models is a central challenge in machine learning.We introduce the Gaussian Ensemble Belief Propagation (GEnBP) algorithm, which combines the strengths of the Ensemble Kalman Filter (EnKF) and Gaussian Belief Propagation (GaBP) to address this challenge.GEnBP updates ensembles of prior samples into posterior samples by passing low-rank local messages over t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29758<END_URL>

Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping\nThe ability to reconstruct realistic and controllable upper body avatars from casual monocular videos is critical for various applications in communication and entertainment. By equipping the most recent 3D Gaussian Splatting representation with head 3D morphable models (3DMM), existing methods manage to create head avatars with high fidelity. However, most existing methods only recons<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30204<END_URL>

Gaussian Mixture Counterfactual Generator\nGenerating synthetic control arms is a key challenge in clinical research, particularly in crossover trials where placebo data becomes unavailable after patients switch to active treatment. The absence of placebo data complicates estimating long-term efficacy and safety. To solve this, we propose a Gaussian mixture model that generates counterfactual data without needing control data for training. This method handles time-varying, continuous doses and e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28533<END_URL>

Gaussian Splatting Lucas-Kanade\nGaussian Splatting and its dynamic extensions are effective for reconstructing 3D scenes from 2D images when there is significant camera movement to facilitate motion parallax and when scene objects remain relatively static. However, in many real-world scenarios, these conditions are not met. As a consequence, data-driven semantic and geometric priors have been favored as regularizers, despite their bias toward training data and their neglect of broader movement <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28968<END_URL>

GDrag:Towards General-Purpose Interactive Editing with Anti-ambiguity Point Diffusion\nRecent interactive point-based image manipulation methods have gained considerable attention for being user-friendly. However, these methods still face two types of ambiguity issues that can lead to unsatisfactory outcomes, namely, intention ambiguity which misinterprets the purposes of users, and content ambiguity where target image areas are distorted by distracting elements. To address these issues and achi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30776<END_URL>

GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-Time Alignment\nLarge Language Models (LLMs) exhibit impressive capabilities but require careful alignment with human preferences. Traditional training-time methods finetune LLMs using human preference datasets but incur significant training costs and require repeated training to handle diverse user preferences. Test-time alignment methods address this by using reward models (RMs) to guide frozen LLMs without retraining. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30136<END_URL>

GenDataAgent: On-the-fly Dataset Augmentation with Synthetic Data\nSynthetic data is increasingly employed for training dataset augmentation in computer vision. However, prior works typically perform a uniform search across the entire category space, overlooking the interaction between synthetic data generation and downstream task training. Furthermore, balancing the diversity of synthetic data while ensuring it remains within the same distribution as real data (i.e., avoiding outliers) remains <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29344<END_URL>

General Framework for Off-Policy Learning with Partially-Observed Reward\nOff-policy learning (OPL) in contextual bandits aims to learn a decision-making policy that maximizes the target rewards by using only historical interaction data collected under previously developed policies. Unfortunately, when rewards are only partially observed, the effectiveness of OPL degrades severely. Well-known examples of such partial rewards include explicit ratings in content recommendations, conversion signals<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28461<END_URL>

Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Power\nThe primary objective of learning methods is generalization. Classic generalization bounds, based on VC-dimension or Rademacher complexity, are uniformly applicable to all networks in the hypothesis space. On the other hand, algorithm-dependent generalization bounds, like stability bounds, address more practical scenarios and provide generalization conditions for neural networks trained using SGD. However, t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30734<END_URL>

Generalizable and Efficient Video-Language Reasoning via Multimodal Modular Fusion\nDespite impressive advancements in recent multimodal reasoning approaches, they are still limited in flexibility and efficiency, as these models typically process only a few fixed modality inputs and require updates to numerous parameters. This paper tackles these critical challenges and proposes CREMA, a generalizable, highly efficient, and modular modality-fusion framework that can incorporate many new modaliti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31072<END_URL>

Generalizable Human Gaussians from Single-View Image\nIn this work, we tackle the task of learning 3D human Gaussians from a single image, focusing on recovering detailed appearance and geometry including unobserved regions. We introduce a single-view generalizable Human Gaussian Model (HGM), which employs a novel generate-then-refine pipeline with the guidance from human body prior and diffusion prior. Our approach uses a ControlNet to refine rendered back-view images from coarse predicted huma<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28987<END_URL>

Generalizable Human Rendering with Learned Iterative Feedback Over Multi-Resolution Gaussians-on-Mesh\nGeneralizable reconstruction of an animatable human avatar from sparse inputs and corresponding high-quality rendering conditioned on a given pose faces two main challenges: First, generalizable methods, which are needed for fast reconstruction, avoid scene-specific optimization but instead rely on data priors and inductive biases extracted from training on large data. However, at reconstructio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28812<END_URL>

Generalizable Motion Planning via Operator Learning\nIn this work, we introduce a planning neural operator (PNO) for predicting the value function of a motion planning problem. We recast value function approximation as learning a single operator from the cost function space to the value functionspace, which is defined by an Eikonal partial differential equation (PDE). Therefore, our PNO model, despite being trained with a finite number of samples at coarse resolution, inherits the zero-shot supe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29468<END_URL>

Generalization and Distributed Learning of GFlowNets\nConventional wisdom attributes the success of Generative Flow Networks (GFlowNets) to their ability to exploit the compositional structure of the sample space for learning generalizable flow functions (Bengio et al., 2021). Despite the abundance of empirical evidence, formalizing this belief with verifiable non-vacuous statistical guarantees has remained elusive. We address this issue with the first data-dependent generalization bounds for GF<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29760<END_URL>

Generalization Bounds and Model Complexity for Kolmogorov–Arnold Networks\nKolmogorov–Arnold Network (KAN) is a network structure recently proposed in Liu et al. (2024) that offers improved interpretability and a more parsimonious design in many science-oriented tasks compared to multi-layer perceptrons. This work provides a rigorous theoretical analysis of KAN by establishing generalization bounds for KAN equipped with activation functions that are either represented by linear combinations of b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28263<END_URL>

Generalization Bounds for Canonicalization: A Comparative Study with Group Averaging\nCanonicalization, a popular method for generating invariant or equivariant function classes from arbitrary function sets, involves initial data projection onto a reduced input space subset, followed by applying any learning method to the projected dataset. Despite recent research on the expressive power and continuity of functions represented by canonicalization, its generalization capabilities remain less expl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28434<END_URL>

Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs\nWe analyze the universality and generalization of graph neural networks (GNNs) on attributed graphs, i.e., with node attributes. To this end, we propose pseudometrics over the space of all attributed graphs that describe the fine-grained expressivity of GNNs. Namely, GNNs are both Lipschitz continuous with respect to our pseudometrics and can separate attributed graphs that are distant in the metric. Mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28255<END_URL>

Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors\nWe establish in-expectation and tail bounds on the generalization error of representation learning type algorithms. The bounds are in terms of the relative entropy between the distribution of the representations extracted from the training and "test'' datasets and a data-dependent symmetric prior, i.e., the Minimum Description Length (MDL) of the latent variables for the training and test datasets. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28886<END_URL>

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis\nDespite the empirical success of Diffusion Models (DMs) and Variational Autoencoders (VAEs), their generalization performance remains theoretically underexplored, particularly lacking a full consideration of the shared encoder-generator structure. Leveraging recent information-theoretic tools, we propose a unified theoretical framework that guarantees the generalization of both the encoder and generator by trea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29890<END_URL>

Generalization of Transformers with In-Context Learning: An Empirical Study\nLarge language models (LLMs) like GPT-4 and LLaMA-3 utilize the powerful in-context learning (ICL) capability of Transformer architecture to learn on the fly from limited examples. While ICL underpins many LLM applications, its full potential remains hindered by a limited understanding of its generalization boundaries and vulnerabilities. We present a systematic investigation of transformers' generalization capability w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27730<END_URL>

Generalization through variance: how noise shapes inductive biases in diffusion models\nHow diffusion models generalize beyond their training set is not known, and is somewhat mysterious given two facts: the optimum of the denoising score matching (DSM) objective usually used to train diffusion models is the score function of the training distribution; and the networks usually used to learn the score function are expressive enough to learn this score to high accuracy. We claim that a certain fea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30806<END_URL>

Generalization v.s. Memorization: Tracing Language Models’ Capabilities Back to Pretraining Data\nThe impressive capabilities of large language models (LLMs) have sparked debate over whether these models genuinely generalize to unseen tasks or predominantly rely on memorizing vast amounts of pretraining data. To explore this issue, we introduce an extended concept of memorization, distributional memorization, which measures the correlation between the LLM output probabilities and the pretraining<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30169<END_URL>

Generalized Behavior Learning from Diverse Demonstrations\nDiverse behavior policies are valuable in domains requiring quick test-time adaptation or personalized human-robot interaction. Human demonstrations provide rich information regarding task objectives and factors that govern individual behavior variations, which can be used to characterize \it{useful} diversity and learn diverse performant policies.However, we show that prior work that builds naive representations of demonstration heterog<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29708<END_URL>

Generalized Consistency Trajectory Models for Image Manipulation\nDiffusion-based generative models excel in unconditional generation, as well as on applied tasks such as image editing and restoration. The success of diffusion models lies in the iterative nature of diffusion: diffusion breaks down the complex process of mapping noise to data into a sequence of simple denoising tasks. Moreover, we are able to exert fine-grained control over the generation process by injecting guidance terms into <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29202<END_URL>

Generalized Principal-Agent Problem with a Learning Agent\nGeneralized principal-agent problems, including Stackelberg games, contract design, and Bayesian persuasion, are a class of economic problems where an agent best responds to a principal's committed strategy. We study repeated generalized principal-agent problems under the assumption that the principal does not have commitment power and the agent uses algorithms to learn to respond to the principal. We reduce this problem to a one-shot ge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29975<END_URL>

Generalized Video Moment Retrieval\nIn this paper, we introduce the Generalized Video Moment Retrieval (GVMR) framework, which extends traditional Video Moment Retrieval (VMR) to handle a wider range of query types. Unlike conventional VMR systems, which are often limited to simple, single-target queries, GVMR accommodates both non-target and multi-target queries. To support this expanded task, we present the NExT-VMR dataset, derived from the YFCC100M collection, featuring diverse query scenari<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28243<END_URL>

Generalizing Reasoning Problems to Longer Lengths\nLength generalization (LG) is a challenging problem in learning to reason. It refers to the phenomenon that when trained on reasoning problems of smaller lengths/sizes, the model struggles with problems of larger sizes or lengths. Although it has been proven that reasoning can be learned if the intermediate reasoning steps (also known as chain-of-thought (CoT)) are given in the training data, existing studies only apply to within a given length <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27647<END_URL>

Generalizing Weisfeiler-Lehman Kernels to Subgraphs\nSubgraph representation learning has been effective in solving various real-world problems. However, current graph neural networks (GNNs) produce suboptimal results for subgraph-level tasks due to their inability to capture complex interactions within and between subgraphs. To provide a more expressive and efficient alternative, we propose WLKS, a Weisfeiler-Lehman (WL) kernel generalized for subgraphs by applying the WL algorithm on induced k<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30220<END_URL>

General Scene Adaptation for Vision-and-Language Navigation\nVision-and-Language Navigation (VLN) tasks mainly evaluate agents based on one-time execution of individual instructions across multiple environments, aiming to develop agents capable of functioning in any environment in a zero-shot manner. However, real-world navigation robots often operate in persistent environments with relatively consistent physical layouts, visual observations, and language styles from instructors. Such a gap in t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31116<END_URL>

Generating CAD Code with Vision-Language Models for 3D Designs\nGenerative AI has transformed the fields of Design and Manufacturing by providingefficient and automated methods for generating and modifying 3D objects. Oneapproach involves using Large Language Models (LLMs) to generate Computer-Aided Design (CAD) scripting code, which can then be executed to render a 3Dobject; however, the resulting 3D object may not meet the specified requirements.Testing the correctness of CAD generated code is<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30576<END_URL>

Generating Freeform Endoskeletal Robots\nThe automatic design of embodied agents (e.g. robots) has existed for 31 years and is experiencing a renaissance of interest in the literature. To date however, the field has remained narrowly focused on two kinds of anatomically simple robots: (1) fully rigid, jointed bodies; and (2) fully soft, jointless bodies. Here we bridge these two extremes with the open ended creation of terrestrial endoskeletal robots: deformable soft bodies that leverage jointed<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29135<END_URL>

Generating Likely Counterfactuals Using Sum-Product Networks\nThe need to explain decisions made by AI systems is driven by both recent regulation and user demand. The decisions are often explainable only post hoc. In counterfactual explanations, one may ask what constitutes the best counterfactual explanation. Clearly, multiple criteria must be taken into account, although "distance from the sample" is a key criterion. Recent methods that consider the plausibility of a counterfactual seem to sa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28206<END_URL>

Generating Multi-Modal and Multi-Attribute Single-Cell Counts with CFGen\nGenerative modeling of single-cell RNA-seq data has proven instrumental for tasks like trajectory inference, batch effect removal, and gene expression generation. However, the most recent deep generative models simulating synthetic single cells from noise operate on pre-processed continuous gene expression approximations, overlooking the discrete nature of single-cell data, which limits their effectiveness and hinders the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31082<END_URL>

Generating Physical Dynamics under Priors\nGenerating physically feasible dynamics in a data-driven context is challenging, especially when adhering to physical priors expressed in specific equations or formulas. Existing methodologies often overlook the integration of ''physical priors'', resulting in violation of basic physical laws and suboptimal performance. In this paper, we introduce a novel framework that seamlessly incorporates physical priors into diffusion-based generative models to ad<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28929<END_URL>

Generation and Comprehension Hand-in-Hand: Vision-guided Expression Diffusion for Boosting Referring Expression Generation and Comprehension\nReferring expression generation (REG) and comprehension (REC) are vital and complementary in joint visual and textual reasoning.  Existing REC datasets typically contain insufficient image-expression pairs for training, hindering the generalization of REC models to unseen referring expressions. Moreover, REG methods frequently struggle to bridge the visual<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31181<END_URL>

Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass\nLarge language models (LLMs) acquire substantial knowledge during pretraining but often need adaptation to new contexts, tasks, or domains, typically achieved through fine-tuning or prompting. However, fine-tuning incurs significant training costs, while prompting increases inference overhead. Inspired by fast weight memory, we introduce GenerativeAdapter, an effective and efficient adaptation method th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29103<END_URL>

Generative Classifiers Avoid Shortcut Solutions\nDiscriminative approaches to classification often learn shortcuts that hold in-distribution but fail even under minor distribution shift. This failure mode stems from an overreliance on features that are spuriously correlated with the label. We show that generative classifiers, which use class-conditional generative models, avoid this issue by modeling all features, both core and spurious, instead of mainly spurious ones. These generative classifi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28371<END_URL>

Generative Flows on Synthetic Pathway for Drug Design\nGenerative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synthesizability, limiting their practical use in real-world scenarios. In this paper, we propose RxnFlow, which sequentially assembles molecules using predefined molecular building blocks and chemical reaction templates to constrain the synthetic chemical pathway. We <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28313<END_URL>

Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation\nWe present a method for generating video sequences with coherent motion between a pair of input keyframes. We adapt a pretrained large-scale image-to-video diffusion model (originally trained to generate videos moving forward in time from a single input image) for keyframe interpolation, i.e., to produce a video between two input frames. We accomplish this adaptation through a lightweight fine-tuning technique th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27704<END_URL>

Generative Models for Robot Learning\nNext generation of robots should combine ideas from other fields such as computer vision, natural language processing, machine learning and many others, because the close-loop system is required to deal with complex tasks based on multimodal input in the complicated real environment. This workshop proposal focuses on generative models for robot learning, which lies in the important and fundamental field of AI and robotics. Learning-based methods in robotics <BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23977<END_URL>

Generative Monoculture in Large Language Models\nWe introduce {\em generative monoculture}, a behavior observed in large language models (LLMs) characterized by a significant narrowing of model output diversity relative to available training data for a given task: for example, generating only positive book reviews for books with a mixed reception. While in some cases, generative monoculture enhances performance (e.g., LLMs more often produce efficient code), the dangers are exacerbated in others<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27717<END_URL>

Generative Representational Instruction Tuning\nAll text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM-7B is among the top models on the Massive Text Embeddin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30586<END_URL>

Generative Verifiers: Reward Modeling as Next-Token Prediction\nVerifiers or reward models are often used to enhance the reasoning performance of large language models (LLMs). A common approach is the Best-of-N method, where N candidate solutions generated by the LLM are ranked by a verifier, and the best one is selected. While LLM-based verifiers are typically trained as discriminative classifiers to score solutions, they do not utilize the text generation capabilities of pretrained LLMs. To ov<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30506<END_URL>

Generative World Explorer\nPlanning with partial observation is a central challenge in embodied AI. A majority of prior works have tackled this challenge by developing agents that physically explore their environment to update their beliefs about the world state. However, humans can imagine unseen parts of the world through a mental exploration and revise their beliefs with imagined observations. Such updated beliefs can allow them to make more informed decisions at the current step, without hav<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30770<END_URL>

Generator Matching: Generative modeling with arbitrary Markov processes\nWe introduce generator matching, a modality-agnostic framework for generative modeling using arbitrary Markov processes. Generators characterize the infinitesimal evolution of a Markov process, which we leverage for generative modeling in a similar vein to flow matching: we construct conditional generators which generate single data points, then learn to approximate the marginal generator which generates the full data distr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29636<END_URL>

Genesis: Advancing Towards Efficient Embodiment Co-Design\nEmbodiment co-design aims to optimize a robot's morphology and control simultaneously. Previous research has demonstrated its  potential for generating environment-adaptive robots.However, the problem is inherently combinatorial and the morphology is changeable and agnostic in its vast search space, optimization efficiency remains complex and challenging to address. We prove that the inefficient morphology representation and unbalanced r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29047<END_URL>

GenSE: Generative Speech Enhancement via Language Models using Hierarchical Modeling\nSemantic information refers to the meaning conveyed through words, phrases, and contextual relationships within a given linguistic structure. Humans can leverage semantic information, such as familiar linguistic patterns and contextual cues, to reconstruct incomplete or masked speech signals in noisy environments. However, existing speech enhancement (SE) approaches often overlook the rich semantic information <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31185<END_URL>

GenVP: Generating Visual Puzzles with Contrastive Hierarchical VAEs\nRaven’s Progressive Matrices (RPMs) is an established benchmark to examinethe ability to perform high-level abstract visual reasoning (AVR). Despite the current success of algorithms that solve this task, humans can generalize beyond a given puzzle and create new puzzles given a set of rules, whereas machines remain locked in solving a fixed puzzle from a curated choice list. We propose Generative Visual Puzzles (GenVP), a fram<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29743<END_URL>

GenXD: Generating Any 3D and 4D Scenes\nRecent developments in 2D visual generation have been remarkably successful. However, 3D and 4D generation remain challenging in real-world applications due to the lack of large-scale 4D data and effective model design. In this paper, we propose to jointly investigate general 3D and 4D generation by leveraging camera and object movements commonly observed in daily life. Due to the lack of real-world 4D data in the community, we first propose a data curatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31199<END_URL>

GeoILP: A Synthetic Dataset to Guide Large-Scale Rule Induction\nInductive logic programming (ILP) is a machine learning approach aiming to learn explanatory rules from data.    While existing ILP systems can successfully solve small-scale tasks, large-scale applications with various language biases are rarely explored.    Besides, it is crucial for a large majority of current ILP systems to require expert-defined language bias, which hampers the development of ILP towards broader utilizations. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29037<END_URL>

GeoLoRA: Geometric integration for parameter efficient fine-tuning\nLow-Rank Adaptation (LoRA) has become a widely used method for parameter-efficient fine-tuning of large-scale, pre-trained neural networks. However, LoRA and its extensions face several challenges, including the need for rank adaptivity, robustness, and computational efficiency during the fine-tuning process. We introduce GeoLoRA, a novel approach that addresses these limitations by leveraging dynamical low-rank approximation th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29084<END_URL>

Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture\nIn this paper, we propose the *geometric invariance hypothesis (GIH)*, which argues that when training a neural network, the input space curvature remains invariant under transformation in certain directions determined by its architecture. Starting with a simple non-linear binary classification problem residing on a plane in a high dimensional space, we observe that while an MLP can generalize on this problem regardl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29031<END_URL>

Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits\nThis paper is motivated by recent research in the $d$-dimensional stochastic linear bandit literature, which has revealed an unsettling discrepancy: algorithms like Thompson sampling and Greedy demonstrate promising empirical performance, yet this contrasts with their pessimistic theoretical regret bounds. The challenge arises from the fact that while these algorithms may perform poorly in certain p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29812<END_URL>

Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects\nManipulating objects with varying geometries and deformable objects is a major challenge in robotics. Tasks such as insertion with different objects or cloth hanging require precise control and effective modelling of complex dynamics. In this work, we frame this problem through the lens of a heterogeneous graph that comprises smaller sub-graphs, such as actuators and objects, accompanied by different edge types describi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30840<END_URL>

Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation\nGenerating high-quality 3D objects from textual descriptions remains a challenging problem due to high computational costs, the scarcity of 3D data, and the complexity of 3D representations. We introduce Geometry Image Diffusion (GIMDiffusion), a novel Text-to-3D model that utilizes geometry images to efficiently represent 3D shapes using 2D images, thereby avoiding the need for complex 3D-aware<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30259<END_URL>

Geometry of Lightning Self-Attention: Identifiability and Dimension\nWe consider function spaces defined by self-attention networks without normalization, and theoretically analyze their geometry. Since these networks are polynomial, we rely on tools from algebraic geometry. In particular, we study the identifiability of deep attention by providing a description of the generic fibers of the parametrization for an arbitrary number of layers and, as a consequence, compute the dimension of the func<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29285<END_URL>

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces\nAdvances in reinforcement learning (RL) have led to its successful application in complex tasks with continuous state and action spaces. Despite these advances in practice, most theoretical work pertains to finite state and action spaces. We propose building a theoretical understanding of continuous state and action spaces by employing a geometric lens to understand the locally attained set of states. The set of all<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30635<END_URL>

GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training\nDespite their proficiency in general tasks, Multi-modal Large Language Models (MLLMs) struggle with automatic Geometry Problem Solving (GPS), which demands understanding diagrams, interpreting symbols, and performing complex reasoning. This limitation arises from their pre-training on natural images and texts, along with the lack of automated verification in the problem-solving process. Besides, current geom<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30888<END_URL>

GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation\nRetrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, including statistical and deep learning methods, often fail to effectively integrate gene interaction knowledge from databases or explicitly learn subtype-specific interactions. To address this mismatch, we propose GeSubNet, which learns a unif<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28630<END_URL>

GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks\nGraph Neural Networks (GNNs) deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly problematic in high-stakes applications where accurate uncertainty estimates are essential. Existing post-hoc methods, such as temperature scaling, fail to effectively utilize graph structures, while current GNN calibration methods often<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28240<END_URL>

GEVRM: Goal-Expressive Video Generation Model For Robust Visual Manipulation\nWith the rapid development of embodied artificial intelligence, significant progress has been made in vision-language-action (VLA) models for general robot decision-making. However, the majority of existing VLAs fail to account for the inevitable external perturbations encountered during deployment. These perturbations introduce unforeseen state information to the VLA, resulting in inaccurate actions and consequently, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28764<END_URL>

GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost\nRecent advancements in dataset distillation have demonstrated the significant benefits of employing soft labels generated by pre-trained teacher models.    In this paper, we introduce a novel perspective by emphasizing the full utilization of labels.    We first conduct a comprehensive comparison of various loss functions for soft label utilization in dataset distillation, revealing that the model trained on the syn<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30327<END_URL>

GI-GS: Global Illumination Decomposition on Gaussian Splatting for Inverse Rendering\nWe present GI-GS, a novel inverse rendering framework that leverages 3D Gaussian Splatting (3DGS) and deferred shading to achieve photo-realistic novel view synthesis and relighting. In inverse rendering, accurately modeling the shading processes of objects is essential for achieving high-fidelity results. Therefore, it is critical to incorporate global illumination to account for indirect lighting that reaches<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28771<END_URL>

Glad: A Streaming Scene Generator for Autonomous Driving\nThe generation and simulation of diverse real-world scenes have significant application value in the field of autonomous driving, especially for the corner cases. Recently, researchers have explored employing neural radiance fields or diffusion models to generate novel views or synthetic data under driving scenes. However, these approaches suffer from unseen scenes or restricted video length, thus lacking sufficient adaptability for data <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29229<END_URL>

Glauber Generative Model: Discrete Diffusion Models via Binary Classification\nWe introduce the Glauber Generative Model (GGM), a new class of discrete diffusion models, to obtain new samples from a distribution given samples from a discrete space. GGM deploys a discrete Markov chain called the heat bath dynamics (or the Glauber dynamics) to denoise a sequence of noisy tokens to a sample from a joint distribution of discrete tokens. Our novel conceptual framework provides an exact reduction of t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30199<END_URL>

G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model\nLarge language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities, which encourages extensive research on their application in mathematical problem solving. However, current work has been largely focused on text-based mathematical problems, with limited investigation in problems involving multi-modal geometric information. Addressing this gap, we aim to enable LLMs to solve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28274<END_URL>

Global Convergence of Policy Gradient in Average Reward MDPs\nWe present the first comprehensive finite-time global convergence analysis of policy gradient for infinite horizon average reward Markov decision processes (MDPs). Specifically, we focus on ergodic tabular MDPs with finite state and action spaces. Our analysis shows that the policy gradient iterates converge to the optimal policy at a sublinear rate of $O(\frac{1}{T})$, where $T$ represents the number of iterations. Performance bounds<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31141<END_URL>

Global Identifiability of Overcomplete Dictionary Learning via L1 and Volume Minimization\nWe propose a novel formulation for dictionary learning with an overcomplete dictionary, i.e., when the number of atoms is larger than the dimension of the dictionary. The proposed formulation consists of a weighted sum of $\ell_1$ norms of the rows of the sparse coefficient matrix plus the log of the matrix volume of the dictionary matrix. The main contribution of this work is to show that this novel formu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30993<END_URL>

Global Well-posedness and Convergence Analysis of Score-based Generative Models via Sharp Lipschitz Estimates\nWe establish global well-posedness and convergence of the score-based generative models (SGM) under minimal general assumptions of initial data for score estimation. For the smooth case, we start from a Lipschitz bound of the score function with optimal time length. The optimality is validated by an example whose Lipschitz constant of scores is bounded at initial but blows up in finite <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28221<END_URL>

GLOMA: Global Video Text Spotting with Morphological Association\nVideo Text Spotting (VTS) is a fundamental visual task that aims to predict the trajectories and content of texts in a video. Previous works usually conduct local associations and apply IoU-based distance and complex post-processing procedures to boost performance, ignoring the abundant temporal information and the morphological characteristics in VTS. In this paper, we propose \model{} to model the tracking problem as global asso<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28058<END_URL>

GLoRa: A Benchmark to Evaluate the Ability to Learn Long-Range Dependencies in Graphs\nLearning on graphs is one of the most active research topics in machine learning (ML). Among the key challenges in this field, effectively learning long-range dependencies in graphs has been a particularly difficult problem. It has been observed that, in practice, the performance of many ML approaches, including various types of graph neural networks (GNNs), degrades significantly when the learning task involv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31120<END_URL>

GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning\nGlycans are basic biomolecules and perform essential functions within living organisms. The rapid increase of functional glycan data provides a good opportunity for machine learning solutions to glycan understanding. However, there still lacks a standard machine learning benchmark for glycan property and function prediction. In this work, we fill this blank by building a comprehensive benchmark for Glycan Machine L<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28329<END_URL>

GMValuator: Similarity-based Data Valuation for Generative Models\nData valuation plays a crucial role in machine learning. Existing data valuation methods, mainly focused on discriminative models, overlook generative models that have gained attention recently. In generative models, data valuation measures the impact of training data on generated datasets. Very few existing attempts at data valuation methods designed for deep generative models either concentrate on specific models or lack robust<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29345<END_URL>

GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring\nMaximizing the spectral gap through graph rewiring has been proposed to enhance the performance of message-passing graph neural networks (GNNs) by addressing over-squashing. However, as we show, minimizing the spectral gap can also improve generalization. To explain this, we analyze how rewiring can benefit GNNs within the context of stochastic block models. Since spectral gap optimization primarily influences community streng<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28833<END_URL>

Gnothi Seauton: Empowering Faithful Self-Interpretability in Black-Box Models\nThe debate between self-interpretable models and post-hoc explanations for black-box models is central to Explainable AI (XAI). Self-interpretable models, such as concept-based networks, offer insights by connecting decisions to human-understandable concepts but often struggle with performance and scalability. Conversely, post-hoc methods like Shapley values, while theoretically robust, are computationally expensive a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29444<END_URL>

GOAL: A Generalist Combinatorial Optimization Agent Learning\nMachine Learning-based heuristics have recently shown impressive performance in solving a variety of hard combinatorial optimization problems (COPs). However they generally rely on a separate neural model, specialized and trained for each single problem. Any variation of a problem requires adjustment of its model and re-training from scratch. In this paper, we propose GOAL (for Generalist combinatorial Optimization Agent Learning), a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27692<END_URL>

GOFA: A Generative One-For-All Model for Joint Graph Language Modeling\nFoundation models, such as Large Language Models (LLMs) or Large Vision Models (LVMs), have emerged as one of the most powerful tools in the respective fields. However, unlike text and image data, graph data do not have a definitive structure, posing great challenges to developing a Graph Foundation Model (GFM). For example, current attempts at designing general graph models either transform graph data into a language format<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28473<END_URL>

Going Beyond Feature Similarity: Effective Dataset distillation based on Class-aware Conditional Mutual Information\nDataset distillation (DD) aims to minimize the time and memory consumption needed for training deep neural networks on large datasets, by creating a smaller synthetic dataset that has similar performance to that of the full real dataset. However, current dataset distillation methods often result in synthetic datasets that are excessively difficult for networks to learn from, due t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31237<END_URL>

Going Beyond Static: Understanding Shifts with Time-Series Attribution\nDistribution shifts in time-series data are complex due to temporal dependencies, multivariable interactions, and trend changes. However, robust methods often rely on structural assumptions that lack thorough empirical validation, limiting their practical applicability. In order to support an empirically grounded inductive approach to research, we introduce our Time-Series Shift Attribution (TSSA) framework, which analyzes p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29306<END_URL>

GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation\nDespite graph neural networks' (GNNs) great success in modelling graph-structured data, out-of-distribution (OOD) test instances still pose a great challenge for current GNNs. One of the most effective techniques to detect OOD nodes is to expose the detector model with an additional OOD node-set, yet the extra OOD instances are often difficult to obtain in practice. Recent methods for image data address this pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27751<END_URL>

GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models\nIn this paper, we introduce GoodDrag, a novel approach to improve the stability and image quality of drag editing. Unlike existing methods that struggle with accumulated perturbations and often result in distortions, GoodDrag introduces an AlDD framework that alternates between drag and denoising operations within the diffusion process, effectively improving the fidelity of the result. We also propose an information-preserv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29395<END_URL>

GOPS: Learning Generative Object Priors for Unsupervised 3D Instance Segmentation\nWe study the hard problem of 3D object segmentation in complex point clouds without requiring human labels of 3D scenes for supervision. By relying on the similarity of pretrained 2D features or external signals such as motion to group 3D points as objects, existing unsupervised methods are usually limited to identifying simple objects like cars or their segmented objects are often inferior due to the lack of obje<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27837<END_URL>

GOttack: Universal Adversarial Attacks on Graph Neural Networks via Graph Orbits Learning\nGraph Neural Networks (GNNs) have demonstrated superior performance in node classification tasks across diverse applications. However, their vulnerability to adversarial attacks, where minor perturbations can mislead model predictions, poses significant challenges. This study introduces GOttack, a novel adversarial attack framework that exploits the topological structure of graphs to undermine the integrit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29256<END_URL>

GPromptShield: Elevating Resilience in Graph Prompt Tuning Against Adversarial Attacks\nThe paradigm of ``pre-training and prompt fine-tuning", with its effectiveness and lightweight characteristics, has rapidly spread from the language field to the graph field. Several pioneering studies have designed specialized prompt functions for diverse downstream graph tasks based on various graph pre-training strategies. These prompts concentrate on the compatibility between the pre-training pretext and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27744<END_URL>

GPS: A Probabilistic Distributional Similarity with Gumbel Priors for Set-to-Set Matching\nSet-to-set matching aims to identify correspondences between two sets of unordered items by minimizing a distance metric or maximizing a similarity measure. Traditional metrics, such as Chamfer Distance (CD) and Earth Mover’s Distance (EMD), are widely used for this purpose but often suffer from limitations like suboptimal performance in terms of accuracy and robustness, or high computational costs - or bo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29496<END_URL>

GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS\nMulti-agent learning algorithms have been successful at generating superhuman planning in various games but have had limited impact on the design of deployed multi-agent planners. A key bottleneck in applying these techniques to multi-agent planning is that they require billions of steps of experience. To enable the study of multi-agent planning at scale, we present GPUDrive, a GPU-accelerated, multi-agent simulator built on<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30404<END_URL>

Gradient correlation is needed to accelerate SGD with momentum\nEmpirically, it has been observed that adding momentum to Stochastic Gradient Descent (SGD) accelerates the convergence of the algorithm.However, the literature has been rather pessimistic, even in the case of convex functions, about the possibility of theoretically proving this observation.We investigate the possibility of obtaining accelerated convergence of the Stochastic Nesterov Accelerated Gradient (SNAG), a momentum-based ver<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31139<END_URL>

Gradient descent with generalized Newton’s method\nWe propose the generalized Newton's method (GeN) --- a Hessian-informed approach that applies to any optimizer such as SGD and Adam, and covers the Newton-Raphson method as a sub-case. Our method automatically and dynamically selects the learning rate that accelerates the convergence, without the intensive tuning of the learning rate scheduler. In practice, our method is easily implementable, since it only requires additional forward passes with<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29116<END_URL>

Gradient-Free Generation for Hard-Constrained Systems\nGenerative models that satisfy hard constraints are crucial in scientific applications, e.g., numerical simulations, dynamical systems, and supply chain optimization, where physical laws or system requirements must be strictly respected. However, many existing constrained generative models, especially those developed for computer vision, rely heavily on gradient information, which is often sparse or computationally expensive in other fields,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28037<END_URL>

GRAIN: Exact Graph Reconstruction from Gradients\nFederated learning allows multiple parties to train collaboratively while only Federated learning allows multiple parties to train collaboratively while only sharing gradient updates. However, recent work has shown that it is possible to exactly reconstruct private data such as text and images from gradients for both fully connected and transformer layers in the honest-but-curious setting. In this work, we present GRAIN, the first exact reconstru<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30812<END_URL>

Gramian Multimodal Representation Learning and Alignment\nHuman perception integrates multiple modalities—such as vision, hearing, and language—into a unified understanding of the surrounding reality. While recent multimodal models have achieved significant progress by aligning pairs of modalities via contrastive learning, their solutions are unsuitable when scaling to multiple modalities. These models typically align each modality to a designated anchor without ensuring the alignment of all mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28847<END_URL>

Grammar Reinforcement Learning: path and cycle counting in graphs with a Context-Free Grammar and Transformer approach\nThis paper presents Grammar Reinforcement Learning (GRL), a reinforcement learning algorithm that uses Monte Carlo Tree Search (MCTS) and a transformer architecture that models a Pushdown Automaton (PDA) within a context-free grammar (CFG) framework. Taking as use case the problem of efficiently counting paths and cycles in graphs, a key challenge in network analysis, computer <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27743<END_URL>

Graph Assisted Offline-Online Deep Reinforcement Learning for Dynamic Workflow Scheduling\nDynamic workflow scheduling (DWS) in cloud computing presents substantial challenges due to heterogeneous machine configurations, unpredictable workflow arrivals/patterns, and constantly evolving environments. However, existing research often assumes homogeneous setups and static conditions, limiting flexibility and adaptability in real-world scenarios. In this paper, we propose a novel *Graph assisted Off<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31012<END_URL>

Graph-based Document Structure Analysis\nWhen reading a document, glancing at the spatial layout of a document is an initial step to understand it roughly. Traditional document layout analysis (DLA) methods, however, offer only a superficial parsing of documents, focusing on basic instance detection and often failing to capture the nuanced spatial and logical relationships between instances. These limitations hinder DLA-based models from achieving a gradually deeper comprehension akin to human r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30318<END_URL>

GraphBridge: Towards Arbitrary Transfer Learning in GNNs\nGraph neural networks (GNNs) are conventionally trained on a per-domain, per-task basis. It creates a significant barrier in transferring the acquired knowledge to different, heterogeneous data setups. This paper introduces **GraphBridge**, a novel framework to enable knowledge transfer across disparate tasks and domains in GNNs, circumventing the need for modifications to task configurations or graph structures. Specifically, GraphBridge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28805<END_URL>

GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation\nThe powerful capabilities of Large Language Models (LLMs) have led to their growing use in evaluating human-generated content, particularly in evaluating research ideas within academic settings. Existing solutions primarily rely on prompt-based LLM methods or fine-tuned lightweight language models for idea evaluation. However, these methods are often unstable and struggle to comprehend the complex semantic information embedd<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30947<END_URL>

Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting\nThis paper investigates an open research challenge of reconstructing high-quality, large-scale 3D open scenes from images. It is observed existing methods have various limitations, such as requiring precise camera poses for input and dense viewpoints for supervision. To perform effective and efficient 3D scene reconstruction, we propose a novel graph-guided 3D scene reconstruction framework, GraphGS. Specifically, given a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30969<END_URL>

Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective\nGraph Neural Networks (GNNs) have achieved remarkable success in various graph-based learning tasks. While their performance is often attributed to the powerful neighborhood aggregation mechanism, recent studies suggest that other components such as non-linear layers may also significantly affecting how GNNs process the input graph data in the spectral domain. Such evidence challenges the preval<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28406<END_URL>

Graph Neural Networks Can (Often) Count Substructures\nMessage passing graph neural networks (GNNs) are known to have limited expressive power in their ability to distinguish some non-isomorphic graphs.Because of this, it is well known that they are unable to detect or count arbitrary graph substructures (i.e., solving the subgraph isomorphism problem), a task that is of great importance for several types of graph-structured data. However, we observe that GNNs are in fact able to count graph pat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28119<END_URL>

Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance\nMany applications in traffic, civil engineering, or electrical engineering revolve around edge-level signals. Such signals can be categorized as inherently directed, for example, the water flow in a pipe network, and undirected, like the diameter of a pipe. Topological methods model edge signals with inherent direction by representing them relative to a so-called *orientation* assigned to each edge. These approaches<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29303<END_URL>

Graph Neural Networks Gone Hogwild\nGraph neural networks (GNNs) appear to be powerful tools to learn state representations for agents in distributed, decentralized multi-agent systems, but generate catastrophically incorrect predictions when nodes update asynchronously during inference.  This failure under asynchrony effectively excludes these architectures from many potential applications where synchrony is difficult or impossible to enforce, e.g., robotic swarms or sensor networks.  In this w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29350<END_URL>

Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems\nPreconditioning is at the heart of iterative solutions of large, sparse linear systems of equations in scientific disciplines. Several algebraic approaches, which access no information beyond the matrix itself, are widely studied and used, but ill-conditioned matrices remain very challenging. We take a machine learning approach and propose using graph neural networks as a general-purpose preconditioner. They show attr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29521<END_URL>

Graph Neural Ricci Flow: Evolving Feature from a Curvature Perspective\nDifferential equations provide a dynamical perspective for understanding and designing graph neural networks (GNNs). By generalizing the discrete Ricci flow (DRF) to attributed graphs, we can leverage a new paradigm for the evolution of node features with the help of curvature. We show that in the attributed graphs, DRF guarantees a vital property: The curvature of each edge concentrates toward zero over time. This property <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30813<END_URL>

GraphRouter: A Graph-based Router for LLM Selections\nThe rapidly growing number and variety of Large Language Models (LLMs)present significant challenges in efficiently selecting the appropriate LLM fora given query, especially considering the trade-offs between performance andcomputational cost. Current LLM selection methods often struggle to generalizeacross new LLMs and different tasks because of their limited ability to leveragecontextual interactions among tasks, queries, and LLMs, as well<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28926<END_URL>

Graph Sparsification via Mixture of Graphs\nGraph Neural Networks (GNNs) have demonstrated superior performance across various graph learning tasks but face significant computational challenges when applied to large-scale graphs. One effective approach to mitigate these challenges is graph sparsification, which involves removing non-essential edges to reduce computational overhead. However, previous graph sparsification methods often rely on a single global sparsity setting and uniform pruning c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30843<END_URL>

Graph Transformers Dream of Electric Flow\nWe show theoretically and empirically that the linear Transformer, when applied to graph data, can implement algorithms that solve canonical problems such as electric flow and eigenvector decomposition. The Transformer has access to information on the input graph only via the graph's incidence matrix. We present explicit weight configurations for implementing each algorithm, and we bound the constructed Transformers' errors by the errors of the underlyi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28191<END_URL>

GRASP: Generating  Graphs  via Spectral Diffusion\nIn this paper, we present GRASP, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process. Specifically, we propose to use a denoising model to sample eigenvectors and eigenvalues from which we can reconstruct the graph Laplacian and adjacency matrix. Using the Laplacian spectrum allows us to naturally capture the structural characteristics of the graph and work directly in th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30647<END_URL>

GravMAD: Grounded Spatial Value Maps Guided Action Diffusion for Generalized 3D Manipulation\nRobots' ability to follow language instructions and execute diverse 3D tasks is vital in robot learning. Traditional imitation learning-based methods perform well on seen tasks but struggle with novel, unseen ones due to variability. Recent approaches leverage large foundation models to assist in understanding novel tasks, thereby mitigating this issue. However, these methods lack a task-specific learni<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28249<END_URL>

GReaTer: Gradients Over Reasoning Makes Smaller Language Models Strong Prompt Optimizers\nThe effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range of tasks. Although recent advancements have focused on automating prompt engineering, many existing approaches rely exclusively on textual feedback, refining prompts based solely on inference errors identified by large, computati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28876<END_URL>

Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention\nGraph Neural Networks (GNNs) have become important tools for machine learning on graph-structured data. In this paper, we explore the synergistic combination of graph encoding, graph rewiring, and graph attention, by introducing Graph Attention with Stochastic Structures (GRASS), a novel GNN architecture. GRASS utilizes relative random walk probabilities (RRWP) encoding and a novel decomposed variant (D-RRWP) to efficiently ca<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28209<END_URL>

GridMix: Exploring Spatial Modulation for Neural Fields in PDE Modeling\nSignificant advancements have been achieved in PDE modeling using neural fields. Despite their effectiveness, existing methods rely on global modulation, limiting their ability to reconstruct local details. While spatial modulation with vanilla grid-based representations offers a promising alternative, it struggles with inadequate global information modeling and over-fitting to the training spatial domain. To address these <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30317<END_URL>

gRNAde: Geometric Deep Learning for 3D RNA inverse design\nComputational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to generat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28493<END_URL>

Grokking at the Edge of Numerical Stability\nGrokking, or sudden generalization that occurs after prolonged overfitting, is a surprising phenomenon that has challenged our understanding of deep learning. While a lot of progress has been made in understanding grokking, it is still not clear why generalization is delayed and why grokking often does not happen without regularization. In this work we argue that without regularization, grokking tasks push models to the edge of numerical stability, in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29501<END_URL>

GROOT-2: Weakly Supervised Multimodal Instruction Following Agents\nDeveloping agents that can follow multimodal instructions remains a fundamental challenge in robotics and AI. Although large-scale pre-training on unlabeled datasets has enabled agents to learn diverse behaviors, these agents often struggle with following instructions. While augmenting the dataset with instruction labels can mitigate this issue, acquiring such high-quality annotations at scale is impractical. To address this iss<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29624<END_URL>

Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval\nThe hallucinations of large language models (LLMs) are increasingly mitigated by allowing LLMs to search for information and to ground their answers in real sources. Unfortunately, LLMs often struggle with posing the right search queries, especially when dealing with complex or otherwise indirect topics. Observing that LLMs can learn to search for relevant facts by $\textit{trying}$ different queries and learning to up-wei<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30573<END_URL>

Grounding Continuous Representations in Geometry: Equivariant Neural Fields\nConditional Neural Fields (CNFs) are increasingly being leveraged as continuous signal representations, by associating each data-sample with a latent variable that conditions a shared backbone Neural Field (NeF) to reconstruct the sample. However, existing CNF architectures face limitations when using this latent downstream in tasks requiring fine-grained geometric reasoning, such as classification and segmentation. We <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30651<END_URL>

Grounding Video Models to Actions through Goal Conditioned Exploration\nLarge video models, pretrained on massive quantities of amount of Internet video,  provide a rich source of physical knowledge about the dynamics and motions of objects and tasks.However, video models are not grounded in the embodiment of an agent, and do not describe how to actuate the world to reach the visual states depicted in a video.To tackle this problem, current methods use a separate vision-based inverse dynamic mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30300<END_URL>

Group Distributionally Robust Dataset Distillation with Risk Minimization\nDataset distillation (DD) has emerged as a widely adopted technique for crafting a synthetic dataset that captures the essential information of a training dataset, facilitating the training of accurate neural models. Its applications span various domains, including transfer learning, federated learning, and neural architecture search. The most popular methods for constructing the synthetic data rely on matching the conver<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31083<END_URL>

Group Downsampling with Equivariant Anti-aliasing\nDownsampling layers are crucial building blocks in CNN architectures, which help to increase the receptive field for learning high-level features and reduce the amount of memory/computation in the model. In this work, we study the generalization of the uniform downsampling layer for group equivariant architectures, e.g., $G$-CNNs. That is, we aim to downsample signals (feature maps) on general finite groups *with* anti-aliasing. This involves th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28130<END_URL>

Group Ligands Docking to Protein Pockets\nMolecular docking is a key task in computational biology that has attracted increasing interest from the machine learning community. While existing methods have achieved success, they generally treat each protein-ligand pair in isolation. Inspired by the biochemical observation that ligands binding to the same target protein tend to adopt similar poses, we propose \textsc{GroupBind}, a novel molecular docking framework that simultaneously considers multi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27682<END_URL>

Growth Inhibitors for Suppressing Inappropriate Image Concepts in Diffusion Models\nDespite their remarkable image generation capabilities, text-to-image diffusion models inadvertently learn inappropriate concepts from vast and unfiltered training data, which leads to various ethical and business risks. Specifically, model-generated images may exhibit not safe for work (NSFW) content and style copyright infringements. The prompts that result in these problems often do not include explicit unsafe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27876<END_URL>

GSBA$^K$: $top$-$K$ Geometric Score-based Black-box Attack\nExisting score-based adversarial attacks mainly focus on crafting $top$-1 adversarial examples against classifiers with single-label classification. Their attack success rate and query efficiency are often less than satisfactory, particularly under small perturbation requirements; moreover, the vulnerability of classifiers with multi-label learning is yet to be studied. In this paper, we propose a comprehensive surrogate free score-base<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28735<END_URL>

GSE: Group-wise Sparse and Explainable Adversarial Attacks\nSparse adversarial attacks fool deep neural networks (DNNs) through minimal pixel perturbations, often regularized by the $\ell_0$ norm. Recent efforts have replaced this norm with a structural sparsity regularizer, such as the nuclear group norm, to craft group-wise sparse adversarial attacks. The resulting perturbations are thus explainable and hold significant practical relevance, shedding light on an even greater vulnerability of DN<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29010<END_URL>

GS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian Splatting\nLiDAR novel view synthesis (NVS) has emerged as a novel task within LiDAR simulation, offering valuable simulated point cloud data from novel viewpoints to aid in autonomous driving systems. However, existing LiDAR NVS methods typically rely on neural radiance fields (NeRF) as their 3D representation, which incurs significant computational costs in both training and rendering. Moreover, NeRF and its variants are<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29653<END_URL>

GSLoc: Efficient Camera Pose Refinement via 3D Gaussian Splatting\nWe leverage 3D Gaussian Splatting (3DGS) as a scene representation and propose a novel test-time camera pose refinement (CPR) framework, GS-CPR. This framework enhances the localization accuracy of state-of-the-art absolute pose regression and scene coordinate regression methods. The 3DGS model renders high-quality synthetic images and depth maps to facilitate the establishment of 2D-3D correspondences. GS-CPR obviates the need f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28467<END_URL>

GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models\nRecent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabil<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30620<END_URL>

GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement\nWe propose a novel approach for 3D mesh reconstruction from multi-view images. We improve upon the large reconstruction model LRM that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images. We introduce three key components to significantly enhance the 3D reconstruction quality. First of all, we examine the original LRM architecture and find several sh<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29787<END_URL>

Guaranteed Generation from Large Language Models\nAs large language models (LLMs) are increasingly used across various applications, there is a growing need to control text generation to satisfy specific constraints or requirements. This raises a crucial question: Is it possible to guarantee strict constraint satisfaction in generated outputs while preserving the distribution of the original model as much as possible? We first define the ideal distribution — the one closest to the original model<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30739<END_URL>

Guided Score Identity Distillation for Data-Free One-Step Text-to-Image Generation\nDiffusion-based text-to-image generation models trained on extensive text-image pairs have demonstrated the ability to produce photorealistic images aligned with textual descriptions. However, a significant limitation of these models is their slow sample generation process, which requires iterative refinement through the same network. To overcome this, we introduce a data-free guided distillation method that enab<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30232<END_URL>

Gyrogroup Batch Normalization\nSeveral Riemannian manifolds in machine learning, such as Symmetric Positive Definite (SPD), Grassmann, spherical, and hyperbolic manifolds, have been proven to admit gyro structures, thus enabling a principled and effective extension of Euclidean Deep Neural Networks (DNNs) to manifolds. Inspired by this, this study introduces a general Riemannian Batch Normalization (RBN) framework on gyrogroups, termed GyroBN. We identify the least requirements to guarantee Gyro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29016<END_URL>

h4rm3l: A Language for Composable Jailbreak Attack Synthesis\nDespite their demonstrated valuable capabilities, state-of-the-art (SOTA) widelydeployed large language models (LLMs) still cause harm to society due to the inef-fectiveness of their safety filters, which can be bypassed by prompt transformationscalled jailbreak attacks. Current approaches to LLM safety assessment, whichemploy datasets of templated prompts and benchmarking pipelines, fail to coversufficiently large and diverse sets of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27663<END_URL>

HADAMRNN: BINARY AND SPARSE TERNARY ORTHOGONAL RNNS\nBinary and sparse ternary weights in neural networks enable faster computations and lighter representations, facilitating their use on edge devices with limited computational power. Meanwhile, vanilla RNNs are highly sensitive to changes in their recurrent weights, making the binarization and ternarization of these weights inherently challenging. To date, no method has successfully achieved binarizationor ternarization of vanilla RNN weights. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29150<END_URL>

HaDeMiF: Hallucination Detection and Mitigation in Large Language Models\nThe phenomenon of knowledge hallucinations has raised substantial concerns about the security and reliability of deployed large language models (LLMs). Current methods for detecting hallucinations primarily depend on manually designed individual metrics, such as prediction uncertainty and consistency, and fall short in effectively calibrating model predictions, thus constraining their detection accuracy and applicability i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29391<END_URL>

HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis\nRecently, Text-to-speech (TTS) models based on large language models (LLMs) that translate natural language text into sequences of discrete audio tokens have gained great research attention, with advances in neural audio codec (NAC) models using residual vector quantization (RVQ).However, long-form speech synthesis remains a significant challenge due to the high frame rate, which increases the le<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30784<END_URL>

Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image Animation\nRecent advances in latent diffusion-based generative models for portrait image animation, such as Hallo, have achieved impressive results in short-duration video synthesis. In this paper, we present updates to Hallo, introducing several design enhancements to extend its capabilities.First, we extend the method to produce long-duration videos. To address substantial challenges such as appearance drift and temporal ar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28174<END_URL>

Halton Scheduler for Masked  Generative Image Transformer\nMasked Generative Image Transformers (MaskGIT) have emerged as a scalable and efficient image generation framework, able to deliver high-quality visuals with low inference costs. However, MaskGIT's token unmasking scheduler, an essential component of the framework, has not received the attention it deserves. We analyze the sampling objective in MaskGIT, based on the mutual information between tokens, and elucidate its shortcomings. We th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29658<END_URL>

HAMSTER: Hierarchical Action Models for Open-World Robot Manipulation\nLarge models have shown strong open-world generalization to complex problems in vision and language, but they have been relatively more difficult to deploy in robotics. This challenge stems from several factors, the foremost of which is the lack of scalable robotic training data since this requires expensive on-robot collection. For scalable training, these models must show considerable transfer across domains, to make use of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28776<END_URL>

Handling Delay in Reinforcement Learning Caused by Parallel Computations of Neurons\nReal-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with parallel computation of neurons, leading to higher throughput and potentially better <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29264<END_URL>

HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics\nAdvanced applied mathematics problems are underrepresented in existing Large Language Model (LLM) benchmark datasets. To address this, we introduce $\textbf{HARDMath}$, a dataset inspired by a graduate course on asymptotic methods, featuring challenging applied mathematics problems that require analytical approximation techniques. These problems demand a combination of mathematical reasoning, computational tools, and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28419<END_URL>

HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models\nSafety guard models that detect malicious queries aimed at large language models (LLMs) are essential for ensuring the secure and responsible deployment of LLMs in real-world applications.However, deploying existing safety guard models with billions of parameters alongside LLMs on mobile devices is impractical due to substantial memory requirements and latency.To reduce this cost, we distill a large teacher s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27754<END_URL>

Harnessing Diversity for Important Data Selection in Pretraining Large Language Models\nData selection is of great significance in  pretraining large language models, given the  variation in quality within the large-scale available training corpora. To achieve this, researchers are currently investigating the use of data influence to measure the importance of data instances, $i.e.,$ a high influence score indicates that incorporating this instance to the training set is likely to enhance the mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29114<END_URL>

Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search\nLean is an advanced proof assistant designed to facilitate formal theorem proving by providing a variety of interactive feedback. In this paper, we explore methodologies to leverage proof assistant feedback to augment the capabilities of large language models in constructing formal proofs. First, we deploy online reinforcement learning using Lean verification outcomes as the reward signal to improve the p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30193<END_URL>

Harnessing Webpage UIs for Text-Rich Visual Understanding\nText-rich visual understanding—the ability to interpret both textual content and visual elements within a scene—is crucial for multimodal large language models (MLLMs) to effectively interact with structured environments. We propose leveraging webpage UIs as a naturally structured and diverse data source to enhance MLLMs’ capabilities in this area. Existing approaches, such as rule-based extraction, multimodal model captioning, and rigid<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30176<END_URL>

HASARD: A Benchmark for Harnessing Safe Reinforcement Learning with Doom\nThe advancement of safe reinforcement learning (RL) faces numerous obstacles, including the lack of simulation environments, demanding computational requirements, and a lack of widely accepted benchmarks. To address these challenges, we introduce **HASARD** (A Benchmark for **HA**rnessing **SA**fe **R**einforcement Learning with **D**oom), tailored for egocentric pixel-based safe RL. HASARD features a suite of diverse and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30963<END_URL>

Has the Deep Neural Network learned the Stochastic Process? An Evaluation Viewpoint\nThis paper presents the first systematic study of evaluating Deep Neural Networks (DNNs) designed to forecast the evolution of stochastic complex systems. We show that traditional evaluation methods like threshold-based classification metrics and error-based scoring rules assess a DNN's ability to replicate the observed ground truth but fail to measure the DNN's learning of the underlying stochastic process. To <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31133<END_URL>

Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs\nIn the study of LLMs, sycophancy represents a prevalent hallucination that poses significant challenges to these models. Specifically, LLMs often fail to adhere to original correct responses, instead blindly agreeing with users' opinions, even when those opinions are incorrect or malicious. However, research on sycophancy in visual language models (VLMs) has been scarce. In this work, we extend the exploration of sycophancy from LLMs <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30427<END_URL>

HeadMap: Locating and Enhancing Knowledge Circuits in LLMs\nLarge language models (LLMs), through pretraining on extensive corpora, encompass rich semantic knowledge and exhibit the potential for efficient adaptation to diverse downstream tasks. However, the intrinsic mechanisms underlying LLMs remain unexplored, limiting the efficacy of applying these models to downstream tasks. In this paper, we explore the intrinsic mechanisms of LLMs from the perspective of knowledge circuits. Specifically, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28637<END_URL>

Heavy-Tailed Diffusion Models\nDiffusion models achieve state-of-the-art generation quality across many applications, but their ability to capture rare or extreme events in heavy-tailed distributions remains unclear. In this work, we show that traditional diffusion and flow-matching models with standard Gaussian priors fail to accurately capture heavy-tailed behavior. We address this by repurposing the diffusion framework for heavy-tail estimation using multivariate Student-t distributions. We d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28024<END_URL>

HELMET: How to Evaluate Long-context Models Effectively and Thoroughly\nMany benchmarks exist for evaluating long-context language models (LCLMs), but developers often rely on synthetic tasks like needle-in-a-haystack (NIAH) or arbitrarily selected subsets of datasets. It remains unclear whether these evaluations translate to the diverse downstream applications of LCLMs, and the inconsistency further complicates model comparison. We investigate the underlying reasons behind current practices and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31157<END_URL>

HELM: Hierarchical Encoding for mRNA Language Modeling\nMessenger RNA (mRNA) plays a crucial role in protein synthesis, with its codon structure directly impacting biological properties. While Language Models (LMs) have shown promise in analyzing biological sequences, existing approaches fail to account for the hierarchical nature of mRNA's codon structure. We introduce Hierarchical Encoding for mRNA Language Modeling (HELM), a novel pre-training strategy that incorporates codon-level hierarchic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29936<END_URL>

HelpSteer2-Preference: Complementing Ratings with Preferences\nReward models are critical for aligning models to follow instructions, and are typically trained following one of two popular paradigms: Bradley-Terry style or Regression style. However, there is a lack of evidence that either approach is better than the other, when adequately matched for data. This is primarily because these approaches require data collected in different (but incompatible) formats, meaning that adequately matched da<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29917<END_URL>

Herald: A Natural Language Annotated Lean 4 Dataset\nVerifiable formal languages like Lean have profoundly impacted mathematical reasoning, particularly through the use of large language models (LLMs) for automated reasoning. A significant challenge in training LLMs for these formal languages is the lack of parallel datasets that align natural language with formal language proofs. To address this challenge, this paper introduces a novel framework for translating the Mathlib4 corpus (a unified li<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29589<END_URL>

HexGen-2: Disaggregated Generative Inference of LLMs in Heterogeneous Environment\nDisaggregating the prefill and decoding phases represents an effective new paradigm for generative inference of large language models (LLM). This approach offers some significant system advantages, such as eliminating prefill-decoding interference and optimizing resource allocation. However, it is still an challenging open problem about how to deploy the disaggregated inference paradigm across a group of heterogen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30492<END_URL>

HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters\nThe "pre-train, prompt-tuning'' paradigm has demonstrated impressive  performance for tuning pre-trained heterogeneous graph neural networks (HGNNs) by mitigating the gap between pre-trained models and downstream tasks. However, most prompt-tuning-based works may face at least two limitations: (i) the model may be insufficient to fit the graph structures well as they are generally ignored in the prompt-tuni<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30642<END_URL>

HGM³: Hierarchical Generative Masked Motion Modeling with Hard Token Mining\nText-to-motion generation has significant potential in a wide range of applications including animation, robotics, and AR/VR. While recent works on masked motion models are promising, the task remains challenging due to the inherent ambiguity in text and the complexity of human motion dynamics. To overcome the issues, we propose a novel text-to-motion generation framework that integrates two key components: Hard Token M<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30181<END_URL>

Hidden in the Noise: Two-Stage Robust Watermarking for Images\nAs the quality of image generators continues to improve, deepfakes become a topic of considerable societal debate. Image watermarking allows responsible model owners to detect and label their AI-generated content, which can mitigate the harm. Yet, current state-of-the-art methods in image watermarking remain vulnerable to forgery and removal attacks. This vulnerability occurs in part because watermarks distort the distribution of gen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28502<END_URL>

Hierarchical Autoregressive Transformers for Tokenizer-Free Language Modelling\nTokenization is a fundamental step in natural language processing, breaking text into units that computational models can process. While learned subword tokenizers have become the de-facto standard, they present challenges such as large vocabularies, limited adaptability to new domains or languages, and sensitivity to spelling errors and variations. To overcome these limitations, we investigate a hierarchical archite<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28049<END_URL>

Hierarchically Encapsulated Representation for Protocol Design in Self-Driving Labs\nSelf-driving laboratories have begun to replace human experimenters in performing single experimental skills or predetermined experimental protocols. However, as the pace of idea iteration in scientific research has been intensified by Artificial Intelligence, the demand of rapid design of new protocols for new discoveries become evident. Efforts to automate protocol design have been initiated, but the capabilit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30666<END_URL>

Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging\nOver recent years, deep learning based image registration has achieved impressive accuracy in many domains, including medical imaging and, specifically, human neuroimaging with magnetic resonance imaging (MRI). However, the uncertainty estimation associated with these methods has been largely limited to the application of generic techniques (e.g., Monte Carlo dropout) that do not exploit the peculiarities of the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27869<END_URL>

Hierarchical World Models as Visual Whole-Body Humanoid Controllers\nWhole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visual observations further exacerbates this difficulty. In this work, we explore highly data-driven approaches to visual whole-body humanoid control based on reinforcement learning, without any simplifying assumptions, reward design, or skill primitives.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30793<END_URL>

High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws\nA growing number of machine learning scenarios rely on knowledge distillation where one uses the output of a surrogate model as labels to supervise the training of a target model. In this work, we provide a sharp characterization of this process for ridgeless, high-dimensional regression, under two settings: *(i)* model shift, where the surrogate model is arbitrary, and *(ii)* distribution shift,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31172<END_URL>

High-Dimensional Bayesian Optimisation with Gaussian Process Prior Variational Autoencoders\nBayesian optimisation (BO) using a Gaussian process (GP)-based surrogate model is a powerful tool for solving black-box optimisation problems but does not scale well to high-dimensional data. Previous works have proposed to use variational autoencoders (VAEs) to project high-dimensional data onto a low-dimensional latent space and to implement BO in the inferred latent space. In this work, we propose a c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29615<END_URL>

High-dimension Prototype is a Better Incremental Object Detection Learner\nIncremental object detection (IOD), surpassing simple classification, requires the simultaneous overcoming of catastrophic forgetting in both recognition and localization tasks, primarily due to the significantly higher feature space complexity. Integrating Knowledge Distillation (KD) would mitigate the occurrence of catastrophic forgetting. However, the challenge of knowledge shift caused by invisible previous task data <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30884<END_URL>

High-Dynamic Radar Sequence Prediction for Weather Nowcasting Using Spatiotemporal Coherent Gaussian Representation\nWeather nowcasting is an essential task that involves predicting future radar echo sequences based on current observations, offering significant benefits for disaster management, transportation, and urban planning. Current prediction methods are limited by training and storage efficiency, mainly focusing on 2D spatial predictions at specific altitudes. Meanwhile, 3D volumetric pre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30500<END_URL>

Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning\nReward shaping is a technique in reinforcement learning that addresses the sparse-reward problem by providing more frequent and informative rewards. We introduce a self-adaptive and highly efficient reward shaping mechanism that incorporates success rates derived from historical experiences as shaped rewards. The success rates are sampled from Beta distributions, which dynamically evolve from uncertain to reliable values a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29692<END_URL>

High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity\nIn the realm of high-resolution (HR), fine-grained image segmentation, the primary challenge is balancing broad contextual awareness with the precision required for detailed object delineation, capturing intricate details and the finest edges of objects. Diffusion models, trained on vast datasets comprising billions of image-text pairs, such as SD V2.1, have revolutionized text-to-image synthesis by delivering exceptio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27897<END_URL>

High-Quality Joint Image and Video Compression with Causal VAE\nGenerative modeling has seen significant advancements in image and video synthesis. However, the curse of dimensionality remains a significant obstacle, especially for video generation, given its inherently complex and high-dimensional nature. Many existing works rely on low-dimensional latent spaces from pretrained image autoencoders. However, this approach overlooks temporal redundancy in videos and often leads to temporally incoh<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29168<END_URL>

High-quality Text-to-3D Character Generation with SparseCubes and Sparse Transformers.\nCurrent state-of-the-art text-to-3D generation methods struggle to produce 3D models with fine details and delicate structures due to limitations in differentiable mesh representation techniques. This limitation is particularly pronounced in anime character generation, where intricate features such as fingers, hair, and facial details are crucial for capturing the essence of the characters.In this paper, we i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28180<END_URL>

HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts\nGeneralized Category Discovery (GCD) is a challenging task in which, given a partially labelled dataset, models must categorize all unlabelled instances, regardless of whether they come from labelled categories or from new ones. In this paper, we challenge a remaining assumption in this task: that all images share the same \underline{domain}. Specifically, we introduce a new task and method to handle GCD when <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31128<END_URL>

HiRA: Parameter-Efficient Hadamard High-Rank Adaptation for Large Language Models\nWe propose Hadamard High-Rank Adaptation (HiRA), a parameter-efficient fine-tuning (PEFT) method that enhances the adaptability of Large Language Models (LLMs). While Low-rank Adaptation (LoRA) is widely used to reduce resource demands, its low-rank updates may limit its expressiveness for new tasks. HiRA addresses this by using a Hadamard product to retain high-rank update parameters, improving the model capacity<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29500<END_URL>

HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction\nReconstructing 3D scenes from multiple viewpoints is a fundamental task in stereo vision. Recently, advances in generalizable 3D Gaussian Splatting have enabled high-quality novel view synthesis for unseen scenes from sparse input views by feed-forward predicting per-pixel Gaussian parameters without extra optimization. However, existing methods typically generate single-scale 3D Gaussians, which lack repre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29622<END_URL>

HMoRA: Making LLMs More Effective with Hierarchical Mixture of LoRA Experts\nRecent studies have combined Mixture of Experts (MoE) and Parameter-Efficient Fine-tuning (PEFT) to fine-tune large language models (LLMs), holding excellent performance in multi-task scenarios while remaining resource-efficient. Yet, existing MoE methods still  exhibit  three major limitations: (1) Current multi-granular routing methods overlook that different LLM layers capture features at varying granularities, resul<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28518<END_URL>

Holistically Evaluating the Environmental Impact of Creating Language Models\nAs the performance of artificial intelligence systems has dramatically increased, so too has the environmental impact of creating these systems. While many model developers release estimates of the power consumption and carbon emissions from the final training runs for their latest models, there is comparatively little transparency into the impact of model development, hardware manufacturing, and total water usage thro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31271<END_URL>

Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data\nThe rapid increase in textual information means we need more efficient methods to sift through, organize, and understand it all. While retrieval-augmented generation (RAG) models excel in accessing information from large document collections, they struggle with complex tasks that require aggregation and reasoning over information spanning across multiple documents--what we call  \textit{holisti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30950<END_URL>

Holographic Node Representations: Pre-training Task-Agnostic Node Embeddings\nLarge general purpose pre-trained models have revolutionized computer vision and natural language understanding. However, the development of general purpose pre-trained Graph Neural Networks (GNNs) lags behind other domains due to the lack of suitable generalist node representations. Existing GNN architectures are often tailored to specific task orders, such as node-level, link-level, or higher-order tasks, because dif<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28064<END_URL>

Homomorphism Counts as Structural Encodings for Graph Learning\nGraph Transformers are popular neural networks that extend the well-known Transformer architecture to the graph domain. These architectures operate by applying self-attention on graph nodes and incorporating graph structure through the use of positional encodings (e.g., Laplacian positional encoding) or structural encodings (e.g., random-walk structural encoding). The quality of such encodings is critical, since they provide the nec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28259<END_URL>

Homomorphism Expressivity of Spectral Invariant Graph Neural Networks\nGraph spectra are an important class of structural features on graphs that have shown promising results in enhancing Graph Neural Networks (GNNs). Despite their widespread practical use, the theoretical understanding of the power of spectral invariants --- particularly their contribution to GNNs --- remains incomplete. In this paper, we address this fundamental question through the lens of homomorphism expressivity, providing<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28183<END_URL>

HOPE for a Robust Parameterization of Long-memory State Space Models\nState-space models (SSMs) that utilize linear, time-invariant (LTI) systems are known for their effectiveness in learning long sequences. To achieve state-of-the-art performance, an SSM often needs a specifically designed initialization, and the training of state matrices is on a logarithmic scale with a very small learning rate. To understand these choices from a unified perspective, we view SSMs through the lens of Hankel op<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29646<END_URL>

Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension\nPeptides, short chains of amino acids, interact with target proteins, making them a unique class of protein-based therapeutics for treating human diseases. Recently, deep generative models have shown great promise in peptide generation. However, several challenges remain in designing effective peptide binders. First, not all residues contribute equally to peptide-target interactions. Second, the generated peptides must ad<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28614<END_URL>

How Can Language Models Learn from Mistakes on Grade-School Math Problems\nLanguage models have demonstrated remarkable performance in solving reasoning tasks; however, even the strongest models still occasionally make reasoning mistakes. Recently, there has been active research aimed at improving reasoning accuracy, particularly by using pretrained language models to "self-correct'' their mistakes via multi-round prompting. In this paper, we follow this line of work but focus on understanding t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27648<END_URL>

How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models  via a Stochastic Integral Framework\nDiscrete diffusion models have gained increasing attention for their ability to model complex distributions with tractable sampling and inference. However, the error analysis for discrete diffusion models remains less well-understood. In this work, we propose a comprehensive framework for the error analysis of discrete diffusion models based on Lévy-type stochasti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30876<END_URL>

How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning\nWe show that deep neural networks (DNNs) can efficiently learn anycomposition of functions with bounded $F_{1}$-norm, which allowsDNNs to break the curse of dimensionality in ways that shallow networkscannot. More specifically, we derive a generalization bound that combinesa covering number argument for compositionality, and the $F_{1}$-norm(or the related Barron norm) for large width adaptivity. We show thatthe <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29440<END_URL>

How Does Critical Batch Size Scale in Pre-training?\nTraining large-scale models under given resources requires careful design of parallelism strategies. In particular, the efficiency notion of critical batch size (CBS), concerning the compromise between time and compute, marks the threshold beyond which greater data parallelism leads to diminishing returns. To operationalize it, we propose a measure of CBS and pre-train a series of auto-regressive language models, ranging from 85 million to 1.2<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30121<END_URL>

How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?\nVision-Language adaptation (VL adaptation) transforms Large Language Models (LLMs) into Large Vision-Language Models (LVLMs) for multimodal tasks, but this process often compromises the inherent safety capabilities embedded in the original LLMs. Despite potential harmfulness due to weakened safety measures, in-depth analysis on the effects of VL adaptation on safety remains under-explored. This study examines how V<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28921<END_URL>

How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension\nBenchmarking the capabilities and limitations of large language models (LLMs) in graph-related tasks is becoming an increasingly popular and crucial area of research. Recent studies have shown that LLMs exhibit a preliminary ability to understand graph structures and node features. However, the potential of LLMs in graph pattern mining remains largely unexplored. This is a key component in fields<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30499<END_URL>

How do students become teachers: A dynamical analysis for two-layer neural networks\nThis paper investigates the fundamental regression task of learning $k$ neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width $m$ (a.k.a. students) and $m, k= \mathcal{O}(1)$, trained via gradient descent under proper initialization and a small step-size. Our analysis follows a three-phase structure: alignment after weak recovery, tangential growth, and local convergence,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31163<END_URL>

How do we interpret the outputs of a neural network trained on classification?\nDeep neural networks are widely used for classification tasks, but the interpretation of their output activations is often unclear. This post explains how these outputs can be understood as approximations of the Bayesian posterior probability. We showed that, in theory, the loss function for classification tasks -- derived by maximum likelihood -- is minimized by the Bayesian posterior. We conducted empirical studies<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31365<END_URL>

How efficient is LLM-generated code? A rigorous & high-standard benchmark\nThe emergence of large language models (LLMs) has significantly pushed the frontiers of program synthesis. Advancement of LLM-based program synthesis calls for a thorough evaluation of LLM-generated code. Most evaluation frameworks focus on the (functional) correctness of generated code; efficiency, as an important measure of code quality, has been overlooked in existing evaluations. In this work, we develop ENAMEL (Effic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28090<END_URL>

How Far Are We from True Unlearnability?\nHigh-quality data plays an indispensable role in the era of large models, but the use of unauthorized data for model training greatly damages the interests of data owners. To overcome this threat, several unlearnable methods have been proposed, which generate unlearnable examples (UEs) by compromising the training availability of data. Clearly, due to unknown training purpose and the powerful representation learning capabilities of existing models, these<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30194<END_URL>

How Feature Learning Can Improve Neural Scaling Laws\nWe develop a simple solvable model of neural scaling laws beyond the kernel limit. Theoretical analysis of this model predicts the performance scaling predictions with model size, training time and total amount of available data. From the scaling analysis we identify three relevant regimes: hard tasks, easy tasks, and super easy tasks. For easy and super-easy target functions, which are in the Hilbert space (RKHS) of the initial infinite-widt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28996<END_URL>

How Learnable Grids Recover Fine Detail in Low Dimesions: A Neural Tangent Kernel Analysis of Multigrid Parameteric Encodings\nNeural networks that map between low dimensional spaces are ubiquitous incomputer graphics and scientific computing; however, in their naiveimplementation, they are unable to learn high frequency information. We presenta comprehensive analysis comparing the two most common techniques for mitigatingthis spectral bias: Fourier feature encodings (FFE) and multigrid parametr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30269<END_URL>

How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node Embeddings\nLow-dimensional embeddings are essential for machine learning tasks involving graphs, such as node classification, link prediction, community detection, network visualization, and network compression. Although recent studies have identified exact low-dimensional embeddings, the limits of the required embedding dimensions remain unclear. We presently prove that lower dimensional embedd<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29429<END_URL>

How many samples are needed to train a deep neural network?\nEven though neural networks have become standard tools in many areas, many important statistical questions remain open. This paper studies the question of how much data are needed to train a ReLU feed-forward neural network. Our theoretical and empirical results suggest that the generalization error of ReLU feed-forward neural networks scales at the rate $1/\sqrt{n}$ in the sample size $n$-rather than the "parametric rate"  $1/n$, whic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28262<END_URL>

How many tokens is an image worth?\nCurrent vision systems typically assign fixed-length representations to images, regardless of the information content. This contrasts with human intelligence —and even large language models—which allocate varying representational capacities based on entropy, context and familiarity. Inspired by this, we propose an approach to learn variable-length token representations for 2D images. Our encoder-decoder architecture recursively processes 2D image tokens, disti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28458<END_URL>

How Much is a  Noisy Image Worth? Data Scaling Laws for Ambient Diffusion.\nThe quality of generative models depends on the quality of the data they are trained on. Creating large-scale, high-quality datasets is often expensive and sometimes impossible, e.g.~in certain scientific applications where there is no access to clean data due to physical or instrumentation constraints. Ambient Diffusion and related frameworks train diffusion models with solely corrupted data (which are usually cheaper t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28244<END_URL>

How Much is Unseen Depends Chiefly on Information About the Seen\nWe find that *in expectation* the missing mass, i.e., the proportion of data points in an unknown population---that belong to classes that *do not* appear in the training data---is entirely determined by the number $f_k$ of classes that *do* appear in the training data the same number of times and an exponentially decaying error.While this is the first precise characterization of the expected missing mass in terms of the sample, t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27955<END_URL>

How much of my dataset did you use? Quantitative Data Usage Inference in Machine Learning\nHow much of a given dataset was used to train a machine learning model? This is a critical question for data owners assessing the risk of unauthorized data usage and protecting their right (United States Code, 1976). However, previous work mistakenly treats this as a binary problem—inferring whether \textit{all or none} or \textit{any or none} of the data was used—which is fragile when faced with real, non<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30403<END_URL>

How new data pollutes LLM knowledge and how to dilute it\nUnderstanding how the learning of new texts alter the existing knowledge in a large language model is of great importance, because it is through these accumulated changes that the LLM was initially pre-trained, and is also through such changes that continual, new learning in LLMs can proceed. As a result, both desirable alterations (i.e. generalization) and undesirable alterations (i.e. hallucination) can occur. Here, we study the learnin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29889<END_URL>

How to Evaluate Reward Models for RLHF\nWe introduce a new benchmark for reward models that quantifies their ability to produce strong language models through RLHF (Reinforcement Learning from Human Feedback).The gold-standard approach is to run a full RLHF training pipeline and directly probe downstream LLM performance.However, this process is prohibitively expensive.To address this, we build a predictive model of downstream LLM performance by evaluating the reward model on proxy tasks. These p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29039<END_URL>

How to Find the Exact Pareto Front for Multi-Objective MDPs?\nMulti-Objective Markov Decision Processes (MO-MDPs) are receiving increasing attention, as real-world decision-making problems often involve conflicting objectives that cannot be addressed by a single-objective MDP. The Pareto front identifies the set of policies that cannot be dominated, providing a foundation for finding Pareto optimal solutions that can efficiently adapt to various preferences.However, finding the Pareto front is a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29630<END_URL>

How to Probe: Simple Yet Effective Techniques for Improving Post-hoc Explanations\nPost-hoc importance attribution methods are a popular tool for “explaining” Deep Neural Networks (DNNs) and are inherently based on the assumption that the explanations can be applied independently of how the models were trained. Contrarily, in this work we bring forward empirical evidence that challenges this very notion. Surprisingly, we discover a strong dependency on and demonstrate that the training details o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30968<END_URL>

How to Verify Any (Reasonable) Distribution Property: Computationally Sound Argument Systems for Distributions\nAs statistical analyses become more central to science, industry and society, there is a growing need to ensure correctness of their results. Approximate correctness can be verified by replicating the entire analysis, but can we verify without replication? We focus on distribution testing problems: verifying that an unknown distribution is close to having a  claimed property. Our main <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30266<END_URL>

How to visualize training dynamics in neural networks\nDeep learning practitioners typically rely on training and validation loss curves to understand neural network training dynamics. This blog post demonstrates how classical data analysis tools like PCA and hidden Markov models can reveal how neural networks learn different data subsets and identify distinct training phases. We show that traditional statistical methods remain valuable for understanding the training dynamics of modern deep lear<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31343<END_URL>

HQGS: High-Quality Novel View Synthesis with Gaussian Splatting in Degraded Scenes\n3D Gaussian Splatting (3DGS) has shown promising results for Novel View Synthesis. However, while it is quite effective when based on high-quality images, its performance declines as image quality degrades, due to lack of resolution, motion blur, noise, compression artifacts, or other factors common in real-world data collection. While some solutions have been proposed for specific types of degradation, general t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31164<END_URL>

HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting\nThe application of large deep learning models in weather forecasting has led tosignificant advancements in the field, including higher-resolution forecasting andextended prediction periods exemplified by models such as Pangu and Fuxi. Despitethese successes, previous research has largely been characterized by the neglectof extreme weather events, and the availability of datasets specifically curated forsuch events remains lim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30964<END_URL>

HShare: Fast LLM Decoding by Hierarchical Key-Value Sharing\nThe frequent retrieval of Key-Value (KV) cache data has emerged as a significant factor contributing to the inefficiency of the inference process in large language models. Previous research has demonstrated that a small subset of critical KV cache tokens largely influences attention outcomes, leading to methods that either employ fixed sparsity patterns or dynamically select critical tokens based on the query. While dynamic sparse patt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29532<END_URL>

Human-Aligned Chess With a Bit of Search\nChess has long been a testbed for AI's quest to match human intelligence, and in recent years, chess AI systems have surpassed the strongest humans at the game.However, these systems are *not human-aligned*; they are unable to match the skill levels of all human partners or model human-like behaviors beyond piece movement.In this paper, we introduce Allie, a chess-playing AI designed to bridge the gap between artificial and human intelligence in this cla<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29104<END_URL>

Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning\nControllable generation through Stable Diffusion (SD) fine-tuning aims to improve fidelity, safety, and alignment with human guidance. Existing reinforcement learning from human feedback methods usually rely on predefined heuristic reward functions or pretrained reward models built on large-scale datasets, limiting their applicability to scenarios where collecting such data is costly or difficult. To effective<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27733<END_URL>

Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors\nThe advent of large language models (LLMs) has revolutionized the field of text generation, producing outputs that closely mimic human-like writing. Although academic and industrial institutions have developed detectors to prevent the malicious usage of LLM-generated texts, other research has doubt about the robustness of these systems. To stress test these detectors, we introduce a proxy-attack strategy that effortlessly compromise<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29761<END_URL>

Human-like Episodic Memory for Infinite Context LLMs\nLarge language models (LLMs) have shown remarkable capabilities, but still struggle with processing extensive contexts, limiting their ability to maintain coherence and accuracy over long sequences. In contrast, the human brain excels at organising and retrieving episodic experiences across vast temporal scales, spanning a lifetime. In this work, we introduce EM-LLM, a novel approach that integrates key aspects of human episodic memory and ev<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30579<END_URL>

Human Simulacra: Benchmarking the Personification of Large Language Models\nLarge Language Models (LLMs) are recognized as systems that closely mimic aspects of human intelligence. This capability has attracted the attention of the social science community, who see the potential in leveraging LLMs to replace human participants in experiments, thereby reducing research costs and complexity. In this paper, we introduce a benchmark for LLMs personification, including a strategy for constructing vir<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30585<END_URL>

Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment\nWhile diffusion models are powerful in generating high-quality, diverse synthetic data for object-centric tasks, existing methods struggle with scene-aware tasks such as Visual Question Answering (VQA) and Human-Object Interaction (HOI) Reasoning, where it is critical to preserve scene attributes in generated images consistent with a multimodal context, i.e. a reference image with accompanying text guidance query. To a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30868<END_URL>

Hybrid Regularization Improves Diffusion-based Inverse Problem Solving\nDiffusion models, recognized for their effectiveness as generative priors, have become essential tools for addressing a wide range of visual challenges. Recently, there has been a surge of interest in leveraging Denoising processes for Regularization (DR) to solve inverse problems. However, existing methods often face issues such as mode collapse, which results in excessive smoothing and diminished diversity. In this study, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29007<END_URL>

Hydra-SGG: Hybrid Relation Assignment for One-stage Scene Graph Generation\nDETR introduces a simplified one-stage framework for scene graph generation (SGG) but faces challenges of sparse supervision and false negative samples. The former occurs because each image typically contains fewer than 10 relation annotations, while DETR-based SGG models employ over 100 relation queries. Each ground truth relation is assigned to only one query during training. The latter arises when one ground truth rel<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28023<END_URL>

Hymba: A Hybrid-head Architecture for Small Language Models\nThe transformative capabilities of language models (LMs) have intensified the demand for their deployment on everyday devices, necessitating efficient processing for on-device language tasks. To address this, we propose Hymba, a new family of small language models featuring a hybrid-head architecture that strategically integrates attention mechanisms with state space models (SSMs). This architecture leverages the strengths of both syst<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30655<END_URL>

Hyperbolic Genome Embeddings\nCurrent approaches to genomic sequence modeling often struggle to align the inductive biases of machine learning models with the evolutionarily-informed structure of biological systems. To this end, we formulate a novel application of hyperbolic CNNs that exploits this structure, enabling more expressive DNA sequence representations. Our strategy circumvents the need for explicit phylogenetic mapping while discerning key properties of sequences pertaining to core fu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29862<END_URL>

Hyper-Connections\nWe present hyper-connections, a simple yet effective method that can serve as an alternative to residual connections. This approach specifically addresses common drawbacks observed in residual connection variants, such as the seesaw effect between gradient vanishing and representation collapse. Theoretically, hyper-connections allow the network to adjust the strength of connections between features at different depths and dynamically rearrange layers. We conduct experiments fo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30709<END_URL>

HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks\nMechanistic interpretability has made great strides in identifying neural network features (e.g., directions in hidden activation space) that mediate concepts (e.g., *the birth year of a Nobel laureate*) and enable predictable manipulation. Distributed alignment search (DAS) leverages supervision from counterfactual data to learn concept features within hidden states, but DAS assumes we can afford to conduct a brute fo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30872<END_URL>

HyperFace: Generating Synthetic Face Recognition Datasets by Exploring Face Embedding Hypersphere\nFace recognition datasets are often collected by crawling Internet and without individuals' consents, raising  ethical and privacy concerns. Generating synthetic datasets for training face recognition models has emerged as a promising alternative. However, the generation of synthetic datasets remains  challenging as it entails adequate inter-class and intra-class variations. While advances in gener<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31005<END_URL>

HyperPLR: Hypergraph Generation through Projection, Learning, and Reconstruction\nHypergraphs are essential in modeling higher-order complex networks, excelling in representing group interactions within real-world contexts. This is particularly evident in collaboration networks, where they facilitate the capture of groupwise polyadic patterns, extending beyond traditional pairwise dyadic interactions. The use of hypergraph generators, or generative models, is a crucial method for promoting and v<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29533<END_URL>

Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models\nMulti-agent reinforcement learning (MARL) methods struggle with the non-stationarity of multi-agent systems and fail to adaptively learn online when tested with novel agents. Here, we leverage large language models (LLMs) to create an autonomous agent that can handle these challenges. Our agent, Hypothetical Minds, consists of a cognitively-inspired architecture, featuring modular components for perc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28330<END_URL>

I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength\nVideo generation technologies are developing rapidly and have broad potential applications. Among these technologies, camera control is crucial for generating professional-quality videos that accurately meet user expectations. However, existing camera control methods still suffer from several limitations, including control precision and the neglect of the control for subject motion dynamics. In this work, we propose<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30626<END_URL>

I Can't Believe It's Not Better: Challenges in Applied Deep Learning\nThe goal of the I Can’t Believe It’s Not Better (ICBINB) workshop series is to promote slow science and build a community to discuss surprising and negative results, thereby encouraging a culture of transparency and shared learning. In recent years, we have witnessed a remarkable rise of Deep Learning (DL), whose impressive performance on benchmark tasks has led to increasing ambitions to deploy DL in real-world applications a<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23987<END_URL>

ICLR 2025 Workshop on Bidirectional Human-AI Alignment\nAs AI systems grow more integrated into real-world applications, the traditional one-way approach to AI alignment is proving insufficient. Bidirectional Human-AI Alignment proposes a new, dynamic framework where alignment is viewed as an ongoing, reciprocal process, with both humans and AI systems adapting over time. This paradigm acknowledges the complexity of human-AI interactions and emphasizes the need for continuous adaptation to evolv<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23986<END_URL>

ICLR 2025 Workshop on GenAI Watermarking: Going beyond safety in watermarking research and development\nWatermarking involves embedding a hidden signal into digital media like text, images, and audio to establish ownership or ensure authenticity. It has become increasingly important in the age of generative AI. However, despite its growing significance, watermarking in the AI community often gets lost in broader conversations around adversarial robustness, and general security, and safety. We ar<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23975<END_URL>

ICLR 2025 Workshop on Human-AI Coevolution\nThis workshop aims to build a multidisciplinary research community around the emerging field of human-AI coevolution (HAIC) to understand the feedback loops that emerge from continuous and long-term human-AI interaction. As AI systems have become more prevalent and have been present in society over longer periods, scholars from diverse fields and methodologies have come to focus on HAIC and its importance for system architecture, human feedback, regula<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/24002<END_URL>

ICLR 2025 Workshop on Tackling Climate Change with Machine Learning: Data-Centric Approaches in ML for Climate Action\nClimate change is one of the greatest problems society has ever faced, with increasingly severe consequences for humanity as natural disasters multiply, sea levels rise, and ecosystems falter. While no silver bullet, machine learning can be an invaluable tool in fighting climate change via a wide array of applications and techniques, from designing smart electric grids to tracki<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23966<END_URL>

ICL-TSVD: Bridging Theory and Practice in Continual Learning with Pre-trained Models\nThe goal of continual learning (CL) is to train a model that can solve multiple tasks presented sequentially. Recent CL approaches have achieved strong performance by leveraging large pre-trained models that generalize well to downstream tasks. However, such methods lack theoretical guarantees, making them prone to unexpected failures. Conversely, principled CL approaches often fail to achieve competitive perfo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29087<END_URL>

Identifiability for Gaussian Processes with Holomorphic Kernels\nGaussian processes (GPs) are widely recognized for their robustness and flexibility across various domains, including machine learning, time series, spatial statistics, and biomedicine. In addition to their common usage in regression tasks, GP kernel parameters are frequently interpreted in various applications. For example, in spatial transcriptomics, estimated kernel parameters are used to identify spatial variable genes, which e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30338<END_URL>

Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning\nIdentifying latent representations or causal structures is important for good generalization and downstream task performance. However, both fields developed rather independently.We observe that several structure and representation identifiability methods, particularly those that require multiple environments, rely on exchangeable non--i.i.d. (independent and identically distributed) data.To formalize this conn<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28605<END_URL>

Identifying and Tuning Safety Neurons in Large Language Models\nSafety alignment for Large Language Models (LLMs) has become a critical issue due to their rapid progress. However, our understanding of effective safety mechanisms in LLMs remains limited, leading to safety alignment training that mainly focuses on improving optimization, data-level enhancement, or adding extra structures to intentionally block harmful outputs. To address this gap, we develop a neuron detection method to identify s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27728<END_URL>

Identifying latent state transitions in non-linear dynamical systems\nThis work aims to recover the underlying states and their time evolution in a latent dynamical system from high-dimensional sensory measurements. Previous work on identifiable representation learning in dynamical systems focused on identifying latent states, possibly with linear transition approximations. As such, they cannot identify nonlinear transition dynamics, and hence fail to reliably predict complex future behavior. In<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29017<END_URL>

IDInit: A Universal and Stable Initialization Method for Neural Network Training\nDeep neural networks have achieved remarkable accomplishments in practice. The success of these networks hinges on effective initialization methods, which are vital for ensuring stable and rapid convergence during training. Recently, initialization methods that maintain identity transition within layers have shown good efficiency in network training. These techniques (e.g., Fixup) set specific weights to zero to ac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30009<END_URL>

IDIV: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations\nCapturing geometric and material information from images remains a fundamental challenge in computer vision and graphics. Traditional optimization-based methods often require hours of computational time to reconstruct geometry, material properties, and environmental lighting from dense multi-view inputs, while still struggling with inherent ambiguities between lighting and material. On the other hand, learning-b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27948<END_URL>

IFORMER: INTEGRATING CONVNET AND TRANSFORMER FOR MOBILE APPLICATION\nWe present a new family of mobile hybrid vision networks, called iFormer, with afocus on optimizing latency and accuracy on mobile applications. iFormer effectivelyintegrates the fast local representation capacity of convolution with the efficientglobal modeling ability of self-attention. The local interactions are derivedfrom transforming a standard convolutional network, i.e., ConvNeXt, to design amore lightweight mobile netw<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30980<END_URL>

IgGM: A Generative Model for Functional Antibody and Nanobody Design\nImmunoglobulins are crucial proteins produced by the immune system to identify and bind to foreign substances, playing an essential role in shielding organisms from infections and diseases. Designing specific antibodies opens new pathways for disease treatment. With the rise of deep learning, AI-driven drug design has become possible, leading to several methods for antibody design. However, many of these approaches require add<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27650<END_URL>

IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning\nDeep graph learning has gained grand popularity over the past years due to its versatility and success in representing graph data across a wide range of domains. However, the pervasive issue of imbalanced graph data distributions, where certain parts exhibit disproportionally abundant data while others remain sparse, undermines the efficacy of conventional graph learning algorithms, leading to biased outcomes. To <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27975<END_URL>

ILLUSION: Unveiling Truth with a Comprehensive Multi-Modal, Multi-Lingual Deepfake Dataset\nThe proliferation of deepfakes and AI-generated content has led to a significant increase in media forgeries and misinformation, necessitating development of more robust detection systems. Current datasets, however, lack comprehensive diversity across modalities, languages, and real-world scenarios. To address this gap, we present ILLUSION (Integration of Life-Like Unique Synthetic Identities and Objects <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28235<END_URL>

Image and Video Tokenization with Binary Spherical Quantization\nWe propose a new transformer-based image and video tokenizer with Binary Spherical Quantization (BSQ). BSQ projects the high-dimensional visual embedding to a lower-dimensional hypersphere and then applies binary quantization. BSQ is (1) parameter-efficient without an explicit codebook, (2) scalable to arbitrary token dimensions, and (3) compact: compressing visual data by up to 100×with minimal distortion. Our tokenizer uses a tra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27738<END_URL>

ImageFolder: Autoregressive Image Generation with Folded Tokens\nImage tokenizers are crucial for visual generative models, \eg, diffusion models (DMs) and autoregressive (AR) models, as they construct the latent representation for modeling. Increasing token length is a common approach to improve image reconstruction quality. However, tokenizers with longer token lengths are not guaranteed to achieve better generation quality. There exists a trade-off between reconstruction and generation qualit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29703<END_URL>

Image-level memorization detection via inversion-based inference perturbation\nRecent studies have discovered that widely used text-to-image diffusion models can replicate training samples during image generation, a phenomenon known as memorization. Existing detection methods primarily focus on identifying memorized prompts. However, in real-world scenarios, image owners may need to verify whether their proprietary or personal images have been memorized by the model, even in the absence of paire<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27880<END_URL>

ImagineNav: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination\nVisual navigation is an essential skill for home-assistance robots, providing the object-searching ability to accomplish long-horizon daily tasks. Many recent approaches use Large Language Models (LLMs) for commonsense inference to improve exploration efficiency. However, the planning process of LLMs is limited within texts and it is difficult to represent the spatial occupancy and geometry layout only <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27914<END_URL>

IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning\nUsing extensive training data from SA-1B, the Segment Anything Model (SAM) has demonstrated exceptional generalization and zero-shot capabilities, attracting widespread attention in areas such as medical image segmentation and remote sensing image segmentation. However, its performance in the field of image manipulation detection remains largely unexplored and unconfirmed. There are two main challe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27891<END_URL>

ImDy: Human Inverse Dynamics from Imitated Observations\nInverse dynamics (ID), which aims at reproducing the driven torques from human kinematic observations, has been a critical tool for gait analysis. However, it is hindered from wider application to general motion due to its limited scalability. Conventional optimization-based ID requires expensive laboratory setups, restricting its availability. To alleviate this problem, we propose to exploit the recently progressive human motion imitation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29085<END_URL>

Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection\nImmunogenicity prediction is a central topic in reverse vaccinology for finding candidate vaccines that can trigger protective immune responses. Existing approaches typically rely on highly compressed features and simple model architectures, leading to limited prediction accuracy and poor generalizability. To address these challenges, we introduce ProVaccine, a novel deep learning solution with a dual attention mecha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28756<END_URL>

Implicit Bias of Mirror Descent for Shallow Neural Networks in Univariate Regression\nWe examine the implicit bias of mirror flow in least squares error regression with wide and shallow neural networks. For a broad class of potential functions, we show that mirror flow exhibits lazy training and has the same implicit bias as ordinary gradient flow when the network width tends to infinity. For univariate ReLU networks, we characterize this bias through a variational problem in function space. Our<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30180<END_URL>

Implicit In-context Learning\nIn-context Learning (ICL) empowers large language models (LLMs) to swiftlyadapt to unseen tasks at inference-time by prefixing a few demonstrationexamples before queries. Despite its versatility, ICL incurs substantial computa-tional and memory overheads compared to zero-shot learning and is sensitive tothe selection and order of demonstration examples. In this work, we introduceImplicit In-context Learning (I2CL), an innovative paradigm that reduces theinference co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30298<END_URL>

Implicit Neural Surface Deformation with Explicit Velocity Fields\nIn this work, we introduce the first unsupervised method that simultaneously predicts time-varying neural implicit surfaces and deformations between pairs of point clouds. We propose to model the point movement using an explicit velocity field and directly deform a time-varying implicit field using the modified level-set equation. This equation utilizes an iso-surface evolution with Eikonal constraints in a compact formulation, e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28123<END_URL>

Implicit Search via Discrete Diffusion: A Study on Chess\nIn the post-AlphaGo era, there has been a resurgence of interest in search techniques like Monte Carlo Tree Search (MCTS) within the realm of Large Language Models (LLMs). This renewed attention is driven by the recognition that current next-token prediction models often lack the ability for long-term planning. Is it possible to instill search-like abilities within the models to enhance their planning abilities without relying on explicit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30648<END_URL>

Improved Algorithms for Kernel Matrix-Vector Multiplication\nMotivated by the problem of fast processing of attention matrices, we study fast algorithms for computing matrix-vector products for asymmetric Gaussian Kernel matrices $K\in \mathbb{R}^{n\times n}$. $K$'s columns are indexed by a set of $n$ keys $k_1,k_2\ldots, k_n\in \mathbb{R}^d$, rows by a set of $n$ queries $q_1,q_2,\ldots,q_n\in \mathbb{R}^d $, and its $i,j$ entry is $K_{ij} = e^{-\|q_i-k_j\|_2^2/2\sigma^2}$ for some bandwidth pa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27853<END_URL>

Improved Approximation Algorithms for $k$-Submodular Maximization via Multilinear Extension\nWe investigate a generalized form of submodular maximization, referred to as $k$-submodular maximization, with applications across the domains of social networks and machine learning. In this work, we propose the multilinear extension of $k$-submodular functions and unified Frank-Wolfe-type frameworks based on that. This continuous framework accommodates 1) monotone or non-monotone functions, and 2) vari<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30407<END_URL>

Improved Convergence Rate for Diffusion Probabilistic Models\nScore-based diffusion models have achieved remarkable empirical performance in the field of machine learning and artificial intelligence for their ability to generate high-quality new data instances from complex distributions. Improving our understanding of diffusion models, including mainly convergence analysis for such models, has attracted a lot of interests. Despite a lot of theoretical attempts, there still exists significant gap<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29610<END_URL>

Improved Diffusion-based Generative Model with Better Adversarial Robustness\nDiffusion Probabilistic Models (DPMs) have achieved considerable success in generation. However, its training and sampling processes are confronted with the problem of distribution mismatch. During the denoising process, the input data distributions of the model are different during the training and inference stages, which makes the model potentially generate inaccurate data. To obviate this, we conduct an analysis of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31218<END_URL>

Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent\nWe provide finite-particle convergence rates for the Stein Variational Gradient Descent (SVGD) algorithm in the Kernelized Stein Discrepancy ($\KSD$) and Wasserstein-2 metrics. Our key insight is the observation that the time derivative of the relative entropy between the joint density of $N$ particle locations and the $N$-fold product target measure, starting from a regular initial distribution, splits into a dom<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28115<END_URL>

Improved Sampling Algorithms for Lévy-Itô Diffusion Models\nLévy-Itô denoising diffusion models relying on isotropic α-stable noise instead of Gaussian distribution have recently been shown to improve performance of conventional diffusion models in image generation on imbalanced datasets while performing comparably in the standard settings. However, the stochastic algorithm of sampling from such models consists in solving the stochastic differential equation describing only an approximate invers<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29281<END_URL>

Improved Techniques for Optimization-Based Jailbreaking on Large Language Models\nLarge language models (LLMs) are being rapidly developed, and a key component of their widespread deployment is their safety-related alignment. Many red-teaming efforts aim to jailbreak LLMs, where among these efforts, the Greedy Coordinate Gradient (GCG) attack’s success has led to a growing interest in the study of optimization-based jailbreaking techniques. Although GCG is a significant milestone, its attacking <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28946<END_URL>

Improved Training Technique for Latent Consistency Models\nConsistency models are a new family of generative models capable of producing high-quality samples in either a single step or multiple steps. Recently, consistency models have demonstrated impressive performance, achieving results on par with diffusion models in the pixel space. However, the success of scaling consistency training to large-scale datasets, particularly for text-to-image and video generation tasks, is determined by perform<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29756<END_URL>

ImProver: Agent-Based Automated Proof Optimization\nLarge language models (LLMs) have been used to generate formal proofs of mathematical theorems in proofs assistants such as Lean.However, we often want to optimize a formal proof with respect to various criteria, depending on its downstream use.For example, we may want a proof to adhere to a certain style, be declaratively structured, or concise. Having suitably optimized proofs is also important for learning tasks, especially since human-writt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28980<END_URL>

Improving Autonomous AI Agents with Reflective Tree Search and Self-Learning\nAutonomous agents have demonstrated significant potential in automating complex multistep decision-making tasks. However, even state-of-the-art vision-language models (VLMs), such as GPT-4o, still fall short of human-level performance, particularly in intricate web environments and long-horizon planning tasks. To address these limitations, we introduce Reflective Monte Carlo Tree Search (R-MCTS), a novel test-time algo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30295<END_URL>

Improving Complex Reasoning with Dynamic Prompt Corruption: A Soft Prompt Optimization Approach\nPrompt Tuning (PT) has emerged as a promising Parameter-Efficient Fine-Tuning (PEFT) approach by appending trainable continuous prompt vectors to the input, maintaining competitive performance with significantly fewer trainable parameters. While PT has shown effectiveness in enhancing task performance, particularly for classification tasks, its application to complex reasoning tasks has been largely <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28777<END_URL>

Improving Convergence Guarantees of Random Subspace Second-order Algorithm for Nonconvex Optimization\nIn recent years, random subspace methods have been actively studied for large-dimensional non-convex problems. Recent subspace methods have improved theoretical guarantees such as iteration complexity and local convergence rate while reducing computational costs by deriving descent directions in randomly selected low-dimensional subspaces. This paper proposes the Random Subspace Homogenized Tru<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28012<END_URL>

Improving Data Efficiency via Curating LLM-Driven Rating Systems\nInstruction tuning is critical for adapting large language models (LLMs) to downstream tasks, and recent studies have demonstrated that small amounts of human-curated data can outperform larger datasets, challenging traditional data scaling laws. While LLM-based data quality rating systems offer a cost-effective alternative to human annotation, they often suffer from inaccuracies and biases, even in powerful models like GPT-4. In <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30465<END_URL>

Improving Deep Regression with Tightness\nFor deep regression, preserving the ordinality of the targets with respect to the feature representation improves performance across various tasks. However, a theoretical explanation for the benefits of ordinality is still lacking. This work reveals that preserving ordinality reduces the conditional entropy $H(Z|Y)$ of representation $Z$ conditional on the target $Y$. However, our findings reveal that typical regression losses do little to reduce $H(Z|Y)<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28969<END_URL>

Improving Equivariant Networks with Probabilistic Symmetry Breaking\nEquivariance encodes known symmetries into neural networks, often enhancing generalization. However, equivariant networks cannot *break* symmetries: the output of an equivariant network must, by definition, have at least the same self-symmetries as its input. This poses an important problem, both (1) for prediction tasks on domains where self-symmetries are common, and (2) for generative models, which must break symmetries in o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29230<END_URL>

Improving Generalization and Robustness in SNNs Through Signed Rate Encoding and Sparse Encoding Attacks\nRate-encoded spiking neural networks (SNNs) are known to offer superior adversarial robustness compared to direct-encoded SNNs but have relatively poor generalization on clean input. While the latter offers good generalization on clean input it suffers poor adversarial robustness under standard training. A key reason for this behaviour is the input noise introduced by the rate encoding, whic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28253<END_URL>

Improving Graph Neural Networks by Learning Continuous Edge Directions\nGraph Neural Networks (GNNs) traditionally employ a message-passing mechanism that resembles diffusion over undirected graphs, which often leads to homogenization of node features and reduced discriminative power in tasks such as node classification. Our key insight for addressing this limitation is to assign fuzzy edge directions---that can vary continuously from node $i$ pointing to node $j$ to vice versa---to the edges of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28711<END_URL>

Improving Instruction-Following in Language Models through Activation Steering\nThe ability to follow instructions is crucial for numerous real-world applications of language models. In pursuit of deeper insights and more powerful capabilities, we derive instruction-specific vector representations from language models and use them to steer models accordingly. These vectors are computed as the difference in activations between inputs with and without instructions, enabling a modular approach to a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27819<END_URL>

Improving Large Language Model based  Multi-Agent Framework through Dynamic Workflow Updating\nMulti-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution. However, the effective adjustment of workflows during execution has not been well studied. A flexible workflow is crucial, as in many real-world scenarios, the initial plan must adjust to unforeseen challenges and changing conditions in real-time to ensure the efficie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28132<END_URL>

Improving Large Language Model Planning with Action Sequence Similarity\nPlanning is essential for artificial intelligence systems to look ahead and proactively determine a course of actions to reach objectives in the virtual and real world. Recent work on large language models (LLMs) sheds light on their planning capability in various tasks. However, it remains unclear what signals in the context influence the model performance. In this work, we explore how to improve the model planning capabil<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28022<END_URL>

Improving Long-Text Alignment for Text-to-Image Diffusion Models\nThe rapid advancement of text-to-image (T2I) diffusion models has enabled them to generate unprecedented results from given texts. However, as text inputs become longer, existing encoding methods like CLIP face limitations, and aligning the generated images with long texts becomes challenging. To tackle these issues, we propose a segment-level encoding method for processing long texts and a decomposed preference optimization metho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31132<END_URL>

Improving Multi-modal Representations via Binding Space in Scale\nRecently, human-computer interaction with various modalities has shown promising applications, like GPT-4o and Gemini. Meanwhile, multimodal representation models have emerged as the foundation for these versatile multimodal understanding and generation pipeline. Models like CLIP, CLAP and ImageBind can map their specialized modalities into respective joint spaces. To construct a high-quality omni representation space that can be <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28540<END_URL>

Improving Neural Network Accuracy by Concurrently Training with a Twin Network\nRecently within Spiking Neural Networks, a method called Twin Network Augmentation (TNA) has been introduced. This technique claims to improve the validation accuracy of a Spiking Neural Network simply by training two networks in conjunction and matching the logits via the Mean Squared Error loss. In this paper, we validate the viability of this method on a wide range of popular Convolutional Neural Network (CNN) ben<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29553<END_URL>

Improving Neural Optimal Transport via Displacement Interpolation\nOptimal Transport (OT) theory investigates the cost-minimizing transport map that moves a source distribution to a target distribution. Recently, several approaches have emerged for learning the optimal transport map for a given cost function using neural networks. We refer to these approaches as the OT Map. OT Map provides a powerful tool for diverse machine learning tasks, such as generative modeling and unpaired image-to-image<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30504<END_URL>

Improving Pretraining Data Using Perplexity Correlations\nQuality pretraining data is often seen as the key to high-performance language models. However, progress in understanding pretraining data has been slow due to the costly pretraining runs required for data selection experiments. We present a framework that avoids these costs and selects high-quality pretraining data without any LLM training of our own. Our work is based on a simple observation: LLM losses on many pretraining texts are cor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28733<END_URL>

Improving Probabilistic Diffusion Models With Optimal Covariance Matching\nThe probabilistic diffusion model has become highly effective across various domains. Typically, sampling from a diffusion model involves using a denoising distribution characterized by a Gaussian with a learned mean and either fixed or learned covariances. In this paper, we leverage the recently proposed covariance moment matching technique and introduce a novel method for learning the diagonal covariances. Unlike tradit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28877<END_URL>

Improving Reasoning Performance in Large Language Models via Representation Engineering\nRecent advancements in large language models (LLMs) have resulted in increasingly anthropomorphic language concerning the ability of LLMs to reason. Whether reasoning in LLMs should be understood to be inherently special is, however, widely debated. We propose utilizing a representation engineering approach wherein model activations are read from the residual stream of an LLM when processing a reasoning task<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30146<END_URL>

Improving Semantic Understanding in Speech Language Models via Brain-tuning\nSpeech language models align with human brain responses to natural language to an impressive degree. However, current models rely heavily on low-level speech features, indicating they lack brain-relevant semantics which limits their utility as model organisms of semantic processing in the brain. In this work, we address this limitation by inducing brain-relevant bias directly into the models via fine-tuning with fMRI re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30063<END_URL>

Improving Sequence Level Distillation through Hidden State Matching\nHidden State Matching is a prominent technique in the knowledge distillation of language models. Most existing methods follow DistilBERT in using a cosine loss to encourage similarity between the student and the teacher's hidden states. However, the cosine loss restricts the architecture and dimensionality of the student, thereby severely limiting the compression ratio. We present a different technique using Centered Kernel Ali<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30163<END_URL>

Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency\nThe human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its lifespan. Drawing inspiration from this spike-based computation, Spiking Neural Networks (SNNs) have been developed to construct event-driven models that emulate this efficiency. Despite these advances, deep SNNs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28809<END_URL>

Improving Uncertainty Estimation through Semantically Diverse Language Generation\nLarge language models (LLMs) can suffer from hallucinations when generating text. These hallucinations impede various applications in society and industry by making LLMs untrustworthy. Current LLMs generate text in an autoregressive fashion by predicting and appending text tokens. When an LLM is uncertain about the semantic meaning of the next tokens to generate, it is likely to start hallucinating. Thus, it has b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30224<END_URL>

Improving Unsupervised Constituency Parsing via Maximizing Semantic Information\nUnsupervised constituency parsers organize phrases within a sentence into a tree-shaped syntactic constituent structure that reflects the organization of sentence semantics. However, the traditional objective of maximizing sentence log-likelihood (LL) does not explicitly account for the close relationship between the constituent structure and the semantics, resulting in a weak correlation between LL values and parsi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28229<END_URL>

ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Language\nHandling implicit language is essential for natural language processing systems to achieve precise text understanding and  facilitate natural interactions with users. Despite its importance, the absence of a robust metric for accurately measuring the implicitness of language significantly constrains the depth of analysis possible in evaluating models' comprehension capabilities. This paper addresses this gap by deve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28811<END_URL>

Imputation for prediction: beware of diminishing returns.\nMissing values are prevalent across various fields, posing challenges for training and deploying predictive models. In this context, imputation is a common practice, driven by the hope that accurate imputations will enhance predictions. However, recent theoretical and empirical studies indicate that simple constant imputation can be consistent and competitive. This empirical study aims at clarifying *if* and *when* investing in advanced <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30481<END_URL>

INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge\nThe performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the development of functional LLMs in many languages (i.e., multilingual LLMs) is bottlenecked by the lack of high-quality evaluation resources in languages other than English. Moreov<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28601<END_URL>

In-Context Editing: Learning Knowledge from Self-Induced Distributions\nIn scenarios where language models must incorporate new information efficiently without extensive retraining, traditional fine-tuning methods are prone to overfitting, degraded generalization, and unnatural language generation. To address these limitations, we introduce Consistent In-Context Editing (ICE), a novel approach leveraging the model's in-context learning capability to optimize towards a contextual distribution rat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27872<END_URL>

In-Context Learning of Representations\nRecent work demonstrates that structured patterns in pretraining data influence how representations of different concepts are organized in a large language model’s (LLM) internals, with such representations then driving downstream abilities. Given the open-ended nature of LLMs, e.g., their ability to in-context learn novel tasks, we ask whether models can flexibly alter their semantically grounded organization of concepts. Specifically, if we provide in-co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28292<END_URL>

In-context Time Series Predictor\nRecent Transformer-based large language models (LLMs) demonstrate in-context learning ability to perform various functions based solely on the provided context, without updating model parameters. To fully utilize the in-context capabilities in time series forecasting (TSF) problems, unlike previous Transformer-based or LLM-based time series forecasting methods, we reformulate "time series forecasting tasks" as input tokens by constructing a series of (lookback, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28999<END_URL>

Incorporating Visual Correspondence into Diffusion Model for Visual Try-On\nDiffusion models have shown preliminary success in virtual try-on (VTON) task. The typical dual-branch architecture comprises two UNets for implicit garment deformation and synthesized image generation respectively, and has emerged as the recipe for VTON task. Nevertheless, the problem remains challenging to preserve the shape and every detail of the given garment due to the intrinsic stochasticity of diffusion model. To<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29302<END_URL>

Incremental Causal Effect for Time to Treatment Initialization\nWe consider time to treatment initialization. This can commonly occur in preventive medicine, such as disease screening and vaccination; it can also occur with non-fatal health conditions such as HIV infection without the onset of AIDS. While traditional causal inference focused on ‘when to treat’ and its effects, including their possible dependence on subject characteristics, we consider the incremental causal effect when the inten<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31239<END_URL>

Indirect Gradient Matching for Adversarial Robust Distillation\nAdversarial training significantly improves adversarial robustness, but superior performance is primarily attained with large models. This substantial performance gap for smaller models has spurred active research into adversarial distillation (AD) to mitigate the difference. Existing AD methods leverage the teacher’s logits as a guide.In contrast to these approaches, we aim to transfer another piece of knowledge from the teacher, t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28611<END_URL>

INFER: A Neural-symbolic Model For Extrapolation Reasoning on Temporal Knowledge Graph\nTemporal Knowledge Graph(TKG) serves as an efficacious way to store dynamic facts in real-world. Extrapolation reasoning on TKGs, which aims at predicting possible future events, has attracted consistent research interest. Recently, some rule-based methods have been proposed, which are considered more interpretable compared with embedding-based methods. Existing rule-based methods apply rules through path mat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30378<END_URL>

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models\nRecent studies indicate that effectively utilizing inference-time compute is crucial for attaining good performance from large language models (LLMs). Specifically, the Best-of-N (BoN) inference strategy, where an LLM generates multiple responses and a verifier selects the best, has shown strong empirical performance. Motivated by this, we develop a novel inference-aware fine-tuning paradigm, which encompasses the BoN-a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30849<END_URL>

Inference Optimal VLMs Need Only One Visual Token but Larger Models\nVision Language Models (VLMs) have demonstrated strong capabilities across various visual understanding and reasoning tasks, driven by incorporating image representations into the token inputs of Large Language Models (LLMs). However, their real-world deployment is often constrained by high latency during inference due to the substantial compute required by the LLM to process the large number of input tokens, predominantly aris<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30880<END_URL>

Inference Scaling for Long-Context Retrieval Augmented Generation\nThe scaling of inference computation has unlocked the potential of long-context large language models (LLMs) across diverse settings. For knowledge-intensive tasks, the increased compute is often allocated to incorporate more external knowledge. However, without effectively utilizing such knowledge, solely expanding context does not always enhance performance. In this work, we investigate inference scaling for retrieval augmented<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30339<END_URL>

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for LLM Problem-Solving\nWhile the scaling laws of large language models (LLMs) training have been extensively studied, optimal inference configurations of LLMs remain underexplored. We study _inference scaling laws_ and _compute-optimal inference_, focusing on the trade-offs between model sizes and generating additional tokens with different inference strategies. As a first step towards understanding and designing comput<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29417<END_URL>

Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models\nIn pretraining data detection, the goal is to detect whether a given sentence is in the dataset used for training a Large Language Model LLM). Recent methods (such as Min-K % and Min-K%++) reveal that most training corpora are likely contaminated with both sensitive content and evaluation benchmarks, leading to inflated test set performance. These methods sometimes fail to detect samples from the pretraining data,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30693<END_URL>

Infinite-Resolution Integral Noise Warping for Diffusion Models\nAdapting pretrained image-based diffusion models to generate temporally consistent videos has become an impactful generative modeling research direction. Training-free noise-space manipulation has proven to be an effective technique, where the challenge is to preserve the Gaussian white noise distribution while adding in temporal consistency. Recently, Chang et al. (2024) formulated this problem using an integral noise representati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29273<END_URL>

Influence Functions for Scalable Data Attribution in Diffusion Models\nDiffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges in diffusion models by extending influence functions. Influence function-based data attribution methods approximate how a model's output would have changed if some training data were removed. In super<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28904<END_URL>

Influence-Guided Diffusion for Dataset Distillation\nDataset distillation aims to streamline the training process by creating a compact yet effective dataset for a much larger original dataset. However, existing methods often struggle with distilling large, high-resolution datasets due to prohibitive resource costs and limited performance, primarily stemming from sample-wise optimizations in the pixel space. Motivated by the remarkable capabilities of diffusion generative models in learning targ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31231<END_URL>

Information Theoretic Text-to-Image Alignment\nDiffusion models for Text-to-Image (T2I) conditional generation have recently achievedtremendous success. Yet, aligning these models with user’s intentions still involves alaborious trial-and-error process, and this challenging alignment problem has attractedconsiderable attention from the research community. In this work, instead of relying onfine-grained linguistic analyses of prompts, human annotation, or auxiliary vision-languagemodels, we use M<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29462<END_URL>

Injecting Universal Jailbreak Backdoors into LLMs in Minutes\nJailbreak backdoor attacks on LLMs have garnered attention for their effectiveness and stealth. However, existing methods rely on the crafting of poisoned datasets and the time-consuming process of fine-tuning. In this work, we propose JailbreakEdit, a novel jailbreak backdoor injection method that exploits model editing techniques to inject a universal jailbreak backdoor into safety-aligned LLMs with minimal intervention *in minutes*<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29167<END_URL>

Injective flows for star-like manifolds\nNormalizing Flows (NFs) are powerful and efficient models for density estimation. When modeling densities on manifolds, NFs can be generalized to injective flows but the Jacobian determinant becomes computationally prohibitive. Current approaches either consider bounds on the log-likelihood or rely on some approximations of the Jacobian determinant. In contrast, we propose injective flows for star-like manifolds and show that for such manifolds we can com<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30082<END_URL>

Inner Information Analysis Algorithm for Deep Neural Network based on Community\nDeep learning has achieved advancements across a variety of forefront fields. However, its inherent 'black box' characteristic poses challenges to the comprehension and trustworthiness of the decision-making processes within neural networks. To mitigate these challenges, we introduce InnerSightNet, an inner information analysis algorithm designed to illuminate the inner workings of deep neural networks through the p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29134<END_URL>

Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leaps\nHumor is a culturally nuanced aspect of human language that presents challenges for understanding and generation. Humor generation requires a multi-hop reasoning process where each hop is based on proper rationales. Although CloT introduce nouns as instruction receipt, thereby enhancing generalization. Creative data is inherently scarce, and relying solely on dataset fitting easily lead<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30526<END_URL>

Input Space Mode Connectivity in Deep Neural Networks\nWe extend the concept of loss landscape mode connectivity to the input space of deep neural networks. Mode connectivity was originally studied within parameter space, where it describes the existence of low-loss paths between different solutions (loss minimizers) obtained through gradient descent. We present theoretical and empirical evidence of its presence in the input space of deep networks, thereby highlighting the broader nature of the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31052<END_URL>

In Search of Forgotten Domain Generalization\nOut-of-Domain (OOD) generalization is the ability of a model trained on one or more domains to generalize to unseen domains. In the ImageNet era of computer vision, evaluation sets for measuring a model's OOD performance were designed to be strictly OOD with respect to style. However, the emergence of foundation models and expansive web-scale datasets has obfuscated this evaluation process, as datasets cover a broad range of domains and risk test dom<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30328<END_URL>

In Search of the Engram in LLMs: A Neuroscience Perspective on the Memory Functions in AI Models\nLarge Language Models (LLMs) are enhancing our daily lives but also pose risks like spreading misinformation and violating privacy, highlighting the importance of understanding how they process and store information. This blogpost offers a fresh look into a neuroscience-inspired perspective of LLM's memory functions, based on the concept of engrams-the physical substrate of memory in living organism<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31329<END_URL>

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation\nData analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We introduce InsightBench, a benchmark dataset with three key features. First, it consists of 100 datasets representing diverse business use cases such as finance and incident management, each accompanied by a carefully curated set of insights planted in the datasets. Second, u<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29227<END_URL>

INS: Interaction-aware Synthesis to Enhance Offline Multi-agent Reinforcement Learning\nData scarcity in offline multi-agent reinforcement learning (MARL) is a key challenge for real-world applications. Recent advances in offline single-agent reinforcement learning (RL) demonstrate the potential of data synthesis to mitigate this issue.However, in multi-agent systems, interactions between agents introduce additional challenges. These interactions complicate the synthesis of multi-agent datasets,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28551<END_URL>

Inspection and Control of Self-Generated-Text Recognition Ability in Llama3-8b-Instruct\nIt has been reported that LLMs can recognize their own writing. As this has potential implications for AI safety, yet is relatively understudied, we investigate the phenomenon, seeking to establish: whether it robustly occurs at the behavioral level, how the observed behavior is achieved, and whether it can be controlled. First, we find that the Llama3-8b–Instruct chat model - but not the base Llama3-8b mode<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27838<END_URL>

Instance-dependent Early Stopping\nIn machine learning practice, early stopping has been widely used to regularize models and can save computational costs by halting the training process when the model's performance on a validation set stops improving. However, conventional early stopping applies the same stopping criterion to all instances without considering their individual learning statuses, which leads to redundant computations on instances that are already well-learned. To further improve <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29782<END_URL>

Instant Policy: In-Context Imitation Learning via Graph Diffusion\nFollowing the impressive capabilities of in-context learning with large transformers, In-Context Imitation Learning (ICIL) is a promising opportunity for robotics. We introduce Instant Policy, which learns new tasks instantly from just one or two demonstrations, achieving ICIL through two key components. First, we introduce inductive biases through a graph representation and model ICIL as a graph generation problem using a learne<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28628<END_URL>

InstantPortrait: One-Step Portrait Editing via Diffusion Multi-Objective Distillation\nReal-time instruction-based portrait image editing is crucial in various applications, including filters, augmented reality, and video communications, etc. However, real-time portrait editing presents three significant challenges: identity preservation, fidelity to editing instructions, and fast model inference. Given that these aspects often present a trade-off, concurrently addressing them poses an even grea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29200<END_URL>

InstantSplamp: Fast and Generalizable Stenography Framework for Generative Gaussian Splatting\nWith the rapid development of large generative models for 3D, especially the evolution from NeRF representations to more efficient Gaussian Splatting, the synthesis of 3D assets has become increasingly fast and efficient, enabling the large-scale publication and sharing of generated 3D objects. However, while existing methods can add watermarks or steganographic information to individual 3D assets, the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27757<END_URL>

InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences\nRecent advances in Customized Concept Swapping (CCS) enable a text-to-image model to swap a concept in the source image with a customized target concept. However, the existing methods still face the challenges of $\textit{\textbf{inconsistency}}$ and $\textit{\textbf{inefficiency}}$. They struggle to maintain consistency in both the foreground and background during concept swapping, especially when the shape difference<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29484<END_URL>

InstaRevive: One-Step Image Enhancement via Dynamic Score Matching\nImage enhancement finds wide-ranging applications in real-world scenarios due to complex environments and the inherent limitations of imaging devices. Recent diffusion-based methods yield promising outcomes but necessitate prolonged and computationally intensive iterative sampling. In response, we propose InstaRevive, a straightforward yet powerful image enhancement framework that employs score-based diffusion distillation to ha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30310<END_URL>

InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly\nIn recent years, the Shapley value and SHAP explanations have emerged as oneof the most dominant paradigms for providing post-hoc explanations of blackbox models. Despite their well-founded theoretical properties, many recent workshave focused on the limitations in both their computational efficiency and theirrepresentation power. The underlying connection with additive models, however,is left critically under-emphasized <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28549<END_URL>

InstaTrain: Adaptive Training via Ultra-Fast Natural Annealing within Dynamical Systems\nTime-series modeling is broadly adopted to capture underlying patterns and trends present in historical data, allowing for prediction of future values. However, one crucial aspect in such modeling is often overlooked: in highly dynamic environments, data distributions can shift drastically within a second or less. Under this circumstance, traditional predictive models, even online learning methods struggle t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29682<END_URL>

Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy\nLarge Language Models (LLMs) are susceptible to security and safety threats, such as prompt injection, prompt extraction, and harmful requests.One major cause of these vulnerabilities is the lack of an instruction hierarchy.Modern LLM architectures treat all inputs equally, failing to distinguish between and prioritize various types of instructions, such as system messages, user prompts, and data. As a result, lowe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28101<END_URL>

InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales\nRetrieval-augmented generation (RAG) has shown promising potential to enhance the accuracy and factuality of language models (LMs). However, imperfect retrievers or noisy corpora can introduce misleading or even erroneous information to the retrieved contents, posing a significant challenge to the generation quality. Existing RAG methods typically address this challenge by directly predicting final answers d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29783<END_URL>

Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning\nWe introduce INSTRUCT-SKILLMIX, an automated approach for creating diverse, high quality SFT data for instruction-following. The pipeline involves two stages, each leveraging an existing powerful LLM: (1) Skill extraction: uses the LLM to extract core “skills” for instruction-following by directly prompting the model. This is inspired by “LLM metacognition” of (Didolkar et al., 2024); (2) Data generation: uses the powerful LLM to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31037<END_URL>

Integral Performance Approximation for Continuous-Time Reinforcement Learning Control\nWe introduce integral performance approximation (IPA), a new continuous-time reinforcement learning (CT-RL) control method. It leverages an affine nonlinear dynamic model, which partially captures the dynamics of the physical environment, alongside state-action trajectory data to enable optimal control with great data efficiency and robust control performance. Utilizing Kleinman algorithm structures allows IPA<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27693<END_URL>

Integrating Expertise of Software Engineering Agents\nLarge language model (LLM) agents have shown great potential in solving real-world software engineering (SWE) problems. The most advanced open-source SWE agent can resolve over 27% of real GitHub issues in SWE-Bench Lite. However, these sophisticated agent frameworks exhibit varying strengths, excelling in certain tasks while underperforming in others. To fully harness the diversity of these agents, we propose DEI (Diversity Empowered Intelli<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29057<END_URL>

Integrating Generative and Experimental Platforms for Biomolecular Design\nBiomolecular design, through artificial engineering of proteins, ligands, and nucleic acids, holds immense promise in addressing pressing medical, industrial, and environmental challenges. While generative machine learning has shown significant potential in this area, a palpable disconnect exists with experimental biology: many ML research efforts prioritize static benchmark performance, potentially sidelining impactful b<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23981<END_URL>

Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows\nThe dynamic nature of proteins, influenced by ligand interactions, is essential for comprehending protein function and progressing drug discovery. Traditional structure-based drug design (SBDD) approaches typically target binding sites with rigid structures, limiting their practical application in drug development. While molecular dynamics simulation can theoretically capture all the biologically releva<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30663<END_URL>

Integrative Decoding: Improving Factuality via Implicit Self-consistency\nSelf-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective in improving the factual accuracy of large language models. Nonetheless, existing methods usually have strict constraints on the task format, largely limiting their applicability. In this paper, we present Integrative Decoding (ID), to unlock the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28829<END_URL>

Intelligence at the Edge of Chaos\nWe explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (ECA), simple yet powerful one-dimensional systems that generate behaviors ranging from trivial to highly complex. By training distinct Large Language Models (LLMs) on different ECAs, we evaluated the relationship be<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30160<END_URL>

Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models\nGo-Explore is a powerful family of algorithms designed to solve hard-exploration problems built on the principle of archiving discovered states, and iteratively returning to and exploring from the most promising states. This approach has led to superhuman performance across a wide variety of challenging problems including Atari games and robotic control, but requires manually designing heuristics to guide exploration (<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29147<END_URL>

Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention\nIn real-life scenarios, humans seek out objects in the 3D world to fulfill their daily needs or intentions. This inspires us to  introduce 3D intention grounding, a new task in 3D object detection  employing RGB-D, based on human intention, such as "I want something to support my back." Closely related, 3D visual grounding focuses on understanding human reference. To achieve detection based on human intention, it relies on hu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30957<END_URL>

Interaction Asymmetry: A General Principle for Learning Composable Abstractions\nLearning disentangled representations of concepts and re-composing them in unseen ways is crucial for generalizing to out-of-domain situations. However, the underlying properties of concepts that enable such disentanglement and compositional generalization remain poorly understood. In this work, we propose the principle of interaction asymmetry which states: "Parts of the same concept have more complex interactions <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29064<END_URL>

Interactive Adjustment for Human Trajectory Prediction with Individual Feedback\nHuman trajectory prediction is fundamental for autonomous driving and service robot. The research community has studied various important aspects of this task and made remarkable progress recently. However, there is an essential perspective which is not well exploited in previous research all along, namely individual feedback. Individual feedback exists in the sequential nature of trajectory prediction, where earlie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30472<END_URL>

Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface\nAgents, as user-centric tools, are increasingly deployed for human task delegation, assisting with a broad spectrum of requests by generating thoughts, engaging with user proxies, and producing action plans. However, agents based on large language models often face substantial planning latency due to two primary factors: the efficiency limitations of the underlying LLMs due to their large s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30539<END_URL>

Interference Among First-Price Pacing Equilibria: A Bias and Variance Analysis\nA/B testing is widely used in the internet industry. For online marketplaces (such as advertising markets), standard approaches to A/B testing may lead to biased results when buyers have budget constraints, as budget consumption in one arm of the experiment impacts performance of the other arm. This is often addressed using a budget-split design. Yet such splitting may degrade statistical performance as budgets becom<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30875<END_URL>

Interleaved Scene Graph for Interleaved Text-and-Image Generation Assessment\nMany real-world user queries (e.g. *"How do to make egg fried rice?"*) could benefit from systems capable of generating responses with both textual steps with accompanying images, similar to a cookbook.Models designed to generate interleaved text and images face challenges in ensuring consistency within and across these modalities.To address these challenges, we present ISG, a comprehensive evaluation framework for int<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28211<END_URL>

InterMask: 3D Human Interaction Generation via Collaborative Masked Modelling\nGenerating realistic 3D human-human interactions from textual descriptions remains a challenging task. Existing approaches, typically based on diffusion models, often generate unnatural and unrealistic results. In this work, we introduce *InterMask*, a novel framework for generating human interactions using collaborative masked modeling in discrete space. InterMask first employs a VQ-VAE to transform each motion seque<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29232<END_URL>

Intermediate Layer Classifiers for OOD generalization\nDeep classifiers are known to be sensitive to data distribution shifts, primarily due to their reliance on spurious correlations in training data. It has been suggested that these classifiers can still find useful features in the network's last layer that hold up under such shifts. In this work, we question the use of last-layer representations for out-of-distribution (OOD) generalisation and explore the utility of intermediate layers. To th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30537<END_URL>

Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence\nThe rapid advancement of large language models (LLMs) has paved the way for the development of highly capable autonomous agents. However, existing multi-agent frameworks often struggle with integrating diverse capable third-party agents due to reliance on agents defined within their own ecosystems. They also face challenges in simulating distributed environments, as most frameworks are limited to single-dev<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28382<END_URL>

Interpolating Autoregressive and Discrete Denoising Diffusion Language Models\nDiffusion language models offer unique benefits over autoregressive (AR) models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeling and are limited to fixed-length generation. In this work, we introduce a class of semi-autoregressive (SAR) diffusion models that interpolate between discrete denoising diffusion and autoregressive models. We propose a recipe for bui<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28004<END_URL>

Interpretable Causal Representation Learning for Biological Data in the Pathway Space\nPredicting the impact of genomic and drug perturbations in cellular function is crucial for understanding gene functions and drug effects, ultimately leading to improved therapies. To this end, Causal Representation Learning (CRL) constitutes one of the most promising approaches, as it aims to identify the latent factors that causally govern biological systems, thus facilitating the prediction of the effect of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31089<END_URL>

Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light Scenarios\nReal-world low-light images often suffer from complex degradations such as local overexposure, low brightness, noise, and uneven illumination. Supervised methods tend to overfit to specific scenarios, while unsupervised methods, though better at generalization, struggle to model these degradations due to the lack of reference images. To address this issue, we propose an interpretable, zero-reference jo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29750<END_URL>

Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology\nHistopathology Whole-Slide Images (WSIs) provide an important tool to assess cancer prognosis in computational pathology (CPATH). While existing survival analysis (SA) approaches have made exciting progress, they are generally limited to adopting highly-expressive architectures and only coarse-grained patient-level labels to learn prognostic visual representations from gigapixel WSIs. Such le<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28016<END_URL>

Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations\nWe investigate the internal representations of vision-language models (VLMs) to address hallucinations, a persistent challenge despite advances in model size and training. We project VLMs’ internal image representations to their language vocabulary and identify differences in token output probabilities between real and hallucinated objects. We additionally use these output probabilities to spatially localize rea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30724<END_URL>

Interpreting Emergent Planning in Model-Free Reinforcement Learning\nWe present the first mechanistic evidence that model-free reinforcement learning agents can learn to plan. This is achieved by applying a methodology based on concept-based interpretability to a model-free agent in Sokoban -- a commonly used benchmark for studying planning. Specifically, we demonstrate that DRC, a generic model-free agent introduced by [Guez et al. (2019)](https://arxiv.org/abs/1901.03559), uses learned concept<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30433<END_URL>

Interpreting Language Reward Models via Contrastive Explanations\nReward models (RMs) are a crucial component in the alignment of large language models’ (LLMs) outputs with human values. RMs approximate human preferences over possible LLM responses to the same prompt by predicting and comparing reward scores. However, as they are typically modified versions of LLMs with scalar output heads, RMs are large black boxes whose predictions are not explainable. More transparent RMs would enable improve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28716<END_URL>

Interpreting the Second-Order Effects of Neurons in CLIP\nWe interpret the function of individual neurons in CLIP by automatically describing them using text. Analyzing the direct effects (i.e. the flow from a neuron through the residual stream to the output) or the indirect effects (overall contribution) fails to capture the neurons' function in CLIP. Therefore, we present the "second-order lens", analyzing the effect flowing from a neuron through the later attention heads, directly to the outp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30282<END_URL>

IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning\nDespite the popularity of multi-agent reinforcement learning (RL) in simulated and two-player applications, its success in messy real-world applications has been limited. A key challenge lies in its generalizability across problem variations, a common necessity for many real-world problems. Contextual reinforcement learning (CRL) formalizes learning policies that generalize across problem variations. How<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29288<END_URL>

Intervening Anchor Token: Decoding Strategy in Alleviating Hallucinations for MLLMs\nMultimodal large language models (MLLMs) offer a powerful mechanism for interpreting visual information. However, they often suffer from hallucinations, which impede the real-world usage of these models. Existing methods attempt to alleviate this issue by designing special decoding strategies that penalize the summary tokens. However, these methods lack analysis of the relationship between hallucination and summ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27678<END_URL>

Intricacies of Feature Geometry in Large Language Models\nStudying the geometry of a language model's embedding space is an important and challenging task because of the various ways concepts can be represented, extracted, and used. Specifically, we want a framework that unifies both measurement (of how well a latent explains a feature/concept) and causal intervention (how well it can be used to control/steer the model). We discuss several challenges with using some recent approaches to study th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31349<END_URL>

Intrinsic Dimension Correlation: uncovering nonlinear connections in multimodal representations\nTo gain insight into the mechanisms behind machine learning methods, it is crucial to establish connections among the features describing data points. However, these correlations often exhibit a high-dimensional and strongly nonlinear nature, which makes them challenging to detect using standard methods. This paper exploits the entanglement between intrinsic dimensionality and correlation to propose <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29679<END_URL>

Intrinsic User-Centric Interpretability through Global Mixture of Experts\nIn human-centric settings like education or healthcare, model accuracy and model explainability are key factors for user adoption. Towards these two goals, intrinsically interpretable deep learning models have gained popularity, focusing on accurate predictions alongside faithful explanations. However, there exists a gap in the human-centeredness of these approaches, which often produce nuanced and complex explanations th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27864<END_URL>

Invariance to Planning in Goal-Conditioned RL\nWe study goal-conditioned RL through the lens of generalization, but not in the traditional sense of random augmentations and domain randomization. Rather, we aim to learn goal-directed policies that generalize with respect to the horizon: after training to reach nearby goals (which are easy to learn), these policies should succeed in reaching distant goals (which are quite challenging to learn). In the same way that invariance is closely linked wit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30581<END_URL>

Invariant Graphon Networks: Approximation and Cut Distance\nGraph limit models, like graphons for limits of dense graphs, have recently been used as a tool to study size transferability of graph neural networks (GNNs). While most existing literature focuses on message passing GNNs (MPNNs), we attend to *Invariant Graph Networks* (IGNs), a powerful alternative GNN architecture. In this work, we generalize IGNs to graphons, introducing *Invariant Graphon Networks (IWNs)* which are defined using a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29581<END_URL>

Inverse Attention Agent in Multi-Agent System\nA major challenge for Multi-Agent Systems (MAS) is enabling agents to adapt dynamically to diverse environments in which opponents and teammates may continually change. Agents trained using conventional methods tend to excel only within the confines of their training cohorts; their performance drops significantly when confronting unfamiliar agents. To address this shortcoming, we introduce Inverse Attention Agents that adopt concepts from the Theory<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29819<END_URL>

InverseBench: Benchmarking Plug-and-Play Diffusion Models for Scientific Inverse Problems\nPlug-and-play diffusion prior methods have emerged as a promising research direction for solving inverse problems.  However, current studies primarily focus on natural image restoration, leaving the performance of these algorithms in scientific inverse problems largely unexplored. To address this gap, we introduce \textsc{InverseBench}, a unified framework that evaluates diffusion models across five distin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29493<END_URL>

Inverse Constitutional AI: Compressing Preferences into Principles\nFeedback data is crucial for fine-tuning and evaluating state-of-the-art AI models according to human preferences. Pairwise text preferences, where annotators select the "better" of two options, are particularly common and often used to train reward models or compute aggregate statistics to declare one model "better" than another. For many applications, however, it is desirable to understand human preferences in addition to mode<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30711<END_URL>

Inverse decision-making using neural amortized Bayesian actors\nBayesian observer and actor models have provided normative explanations for many behavioral phenomena in perception, sensorimotor control, and other areas of cognitive science and neuroscience. They attribute behavioral variability and biases to interpretable entities such as perceptual and motor uncertainty, prior beliefs, and behavioral costs. However, when extending these models to more naturalistic tasks with continuous actions,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27641<END_URL>

Inverse Rendering for Shape, Light, and Material Decomposition using Multi-Bounce Path Tracing and Reservoir Sampling\nWe present a novel two-stage inverse rendering framework that jointly reconstructs and optimizes explicit geometry, materials, and lighting from multi-view images. Unlike previous methods that rely on implicit irradiance fields or oversimplified path tracing algorithms, our method first extracts an explicit triangular mesh in the initial stage. Subsequently, it employs a more re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30069<END_URL>

InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization\nExploring chemical space to find novel molecules that simultaneously satisfy multiple properties is crucial in drug discovery. However, existing methods often struggle with trading off multiple properties due to the conflicting or correlated nature of chemical properties.  To tackle this issue, we introduce InversionGNN framework, an effective yet sample-efficient dual-path graph neural network (GNN) for multi-objective<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28405<END_URL>

InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma\nInvestESG is a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of Environmental, Social, and Governance (ESG) disclosure mandates on corporate climate investments. Supported by both PyTorch and JAX implementation, the benchmark models an intertemporal social dilemma where companies balance short-term profit losses from climate mitigation efforts an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31135<END_URL>

Investigating Pattern Neurons in Urban Time Series Forecasting\nUrban time series forecasting is crucial for smart city development and is key to sustainable urban management. Although urban time series models (UTSMs) are effective in general forecasting, they often overlook low-frequency events, such as emergencies and holidays, leading to degraded performance in practical applications. In this paper, we first investigate how UTSMs handle these infrequent patterns from a neural perspective. Bas<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29185<END_URL>

Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning\nThe emergence of in-context learning (ICL) is potentially attributed to two major abilities: task recognition (TR) for recognizing the task from demonstrations and utilizing pre-trained priors, and task learning (TL) for learning from demonstrations. However, relationships between the two abilities and how such relationships affect the emergence of ICL is unclear. In this paper, we take the first <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28736<END_URL>

In vivo cell-type and brain region classification via multimodal contrastive learning\nCurrent electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded without further molecular or histological analysis. Developing accurate and scalable algorithms for identifying the cell-type and brain region of recorded neurons is thus crucial for improving our understanding of neural computation. In this work, we devel<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31226<END_URL>

IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts\nRecent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation. These methods enable the synthesis of detailed and photorealistic textured objects. However, the appearance of 3D objects produced by such text-to-3D models is often unpredictable, and it is hard for single-image-to-3D methods to deal wi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31077<END_URL>

Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness\nAs the modern tools of choice for text understanding and generation, large language models (LLMs) are expected to accurately output answers by leveraging the input context.This requires LLMs to possess both context-faithfulness and factual accuracy.Extensive efforts have been made to enable better outputs from LLMs by mitigating hallucinations through factuality enhancement methods.However,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29143<END_URL>

Is Graph Convolution Always Beneficial For Every Feature?\nGraph Neural Networks (GNNs) have demonstrated strong capabilities in processing structured data. While traditional GNNs typically treat each feature dimension equally during graph convolution, we raise an important question: *Is the graph convolution operation equally beneficial for each feature?* If not, the convolution operation on certain feature dimensions can possibly lead to harmful effects, even worse than the convolution-free mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30186<END_URL>

Is In-Context Learning Sufficient for Instruction Following in LLMs?\nIn-context learning (ICL) allows LLMs to learn from examples without changing their weights: this is a particularly promising capability for long-context LLMs that can potentially learn from many examples. Recently, Lin et al. (2024) proposed URIAL, a method using only three in-context examples to align base LLMs, achieving non-trivial instruction following performance. In this work, we show that, while effective, ICL alignmen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29602<END_URL>

Is Large-scale Pretraining the Secret to Good Domain Generalization?\nMulti-Source Domain Generalization (DG) is the task of training on multiple source domains and achieving high classification performance on unseen target domains. Recent methods combine robust features from web-scale pretrained backbones with new features learned from source data, and this has dramatically improved benchmark results. However, it remains unclear if DG finetuning methods are becoming better over time, or if impr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27866<END_URL>

Isometric Regularization for Manifolds of Functional Data\nWhile conventional data are represented as discrete vectors, Implicit Neural Representations (INRs) utilize neural networks to represent data points as continuous functions. By incorporating a shared network that maps latent vectors to individual functions, one can model the distribution of functional data, which has proven effective in many applications, such as learning 3D shapes, surface reflectance, and operators.However, the infinit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27800<END_URL>

Is uniform expressivity too restrictive? Towards efficient expressivity of GNNs\nUniform expressivity  guarantees that a Graph Neural Network (GNN) can express a query without the parameters depending on the size of the input graphs. This property is desirable in applications in order to  have number of trainable parameters  that is independent of the size of the input graphs. Uniform expressivity of the two variable guarded fragment (GC2) of first order logic is a well-celebrated result for Rec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28495<END_URL>

Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist\nExceptional mathematical reasoning ability is one of the key features that demonstrate the power of large language models (LLMs). How to comprehensively define and evaluate the mathematical abilities of LLMs, and even reflect the user experience in real-world scenarios, has emerged as a critical issue. Current benchmarks predominantly concentrate on problem-solving capabilities, presenting a substantial <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28416<END_URL>

Is Your Multimodal Language Model Oversensitive to Safe Queries?\nHumans are prone to cognitive distortions — biased thinking patterns that lead to exaggerated responses to specific stimuli, albeit in very different contexts.This paper demonstrates that advanced Multimodal Large Language Models (MLLMs) exhibit similar tendencies.While these models are designed to respond queries under safety mechanism, they sometimes reject harmless queries in the presence of certain visual stimuli, disregarding<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29670<END_URL>

Is Your Video Language Model a Reliable Judge?\nEvaluating video language models (VLMs) is crucial for improving their understanding of video content. Existing evaluation methods depend on single models, which may be unreliable or biased due to the models' incapability to understand content or inherent bias, ultimately compromising the reliability of evaluation. A straightforward remedy is to apply the principle of collective thoughts, aggregating reviews from multiple VLMs to enhance reliabilit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28480<END_URL>

Iterative Dual-RL: An Optimal Discriminator Weighted Imitation Perspective for Reinforcement Learning\nWe introduce Iterative Dual Reinforcement Learning (IDRL), a new method that takes an optimal discriminator-weighted imitation view of solving RL. Our method is motivated by a simple experiment in which we find training a discriminator using the offline dataset plus an additional expert dataset and then performing discriminator-weighted behavior cloning gives strong results on various types of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30701<END_URL>

Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision\nLanguage model (LM) post-training relies on two stages of human supervision: task demonstrations for supervised finetuning (SFT), followed by preference comparisons for reinforcement learning from human feedback (RLHF). As LMs become more capable, the tasks they are given become harder to supervise. Will post-training remain effective under unreliable supervision? To test this, we simulate unreliable dem<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28265<END_URL>

Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning\nReinforcement Learning with Human Feedback (RLHF) has achieved great successin aligning large language models (LLMs) with human preferences. PrevalentRLHF approaches are reward-based, following the Bradley-Terry (BT) model assumption, which may not fully capture the complexity of human preferences. Inthis paper, we explore RLHF under a general preference framework and approachit from a game-theoret<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29724<END_URL>

Iterative Substructure Extraction for Molecular Relational Learning with Interactive Graph Information Bottleneck\nMolecular relational learning (MRL) seeks to understand the interaction behaviors between molecules, a pivotal task in domains such as drug discovery and materials science. Recently, extracting core substructures and modeling their interactions have emerged as mainstream approaches within machine learning-assisted methods. However, these methods still exhibit some limitations, such <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31059<END_URL>

IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation\nAdvanced diffusion models like Stable Diffusion 3, Omost, and FLUX have made notable strides in compositional text-to-image generation. However, these methods typically exhibit distinct strengths for compositional generation, with some excelling in handling attribute binding and others in spatial relationships. This disparity highlights the need for an approach that can leverage the complemen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30983<END_URL>

IterGen: Iterative Structured LLM Generation\nLarge Language Models (LLMs) are widely used for tasks such as natural language and code generation, but their outputs often suffer from issues like hallucination, toxicity, and incorrect results. Current libraries for structured LLM generation rely on left-to-right decoding without support for backtracking, limiting the ability to correct or refine outputs mid-generation. To address this, we introduce IterGen, a user-friendly library for iterative, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29157<END_URL>

It Helps to Take a Second Opinion: Teaching Smaller LLMs To Deliberate Mutually via Selective Rationale Optimisation\nVery large language models (LLMs) such as GPT-4 have shown the ability to handle complex tasks by generating and self-refining step-by-step rationales. Smaller language models (SLMs), typically with < 13B parameters, have been improved by using the data generated from very-large LMs through knowledge distillation. However, various practical constraints such as API costs, copyrigh<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29886<END_URL>

IV-mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis\nExploring suitable solutions to improve performance by increasing the computational cost of inference in visual diffusion models is a highly promising direction. Sufficient prior studies have demonstrated that correctly scaling up computation in the sampling process can successfully lead to improved generation quality, enhanced image editing, and compositional generalization. While there have been rapid advancement<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30150<END_URL>

Jailbreak Antidote: Runtime Safety-Utility Balance via Sparse Representation Adjustment in Large Language Models\nAs large language models (LLMs) become integral to various applications, ensuring both their safety and utility is paramount. Jailbreak attacks, which manipulate LLMs into generating harmful content, pose significant challenges to this balance. Existing defenses, such as prompt engineering and safety fine-tuning, often introduce computational overhead, increase inference latency, and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28150<END_URL>

Jailbreaking as a Reward Misspecification Problem\nThe widespread adoption of large language models (LLMs) has raised concerns about their safety and reliability, particularly regarding their vulnerability to adversarial attacks. In this paper, we propose a new perspective that attributes this vulnerability to reward misspecification during the alignment process. This misspecification occurs when the reward function fails to accurately capture the intended behavior, leading to misaligned model o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27994<END_URL>

Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks\nWe show that even the most recent safety-aligned LLMs are not robust to simple *adaptive* jailbreaking attacks. First, we demonstrate how to successfully leverage access to *logprobs* for jailbreaking: we initially design an adversarial prompt template (sometimes adapted to the target LLM), and then we apply random search on a suffix to maximize a target logprob (e.g., of the token *``Sure''*), potentially with multiple resta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28755<END_URL>

Jamba: Hybrid Transformer-Mamba Language Models\nWe present Jamba, a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture. Jamba interleaves blocks of Transformer and Mamba layers, enjoying the benefits of both model families. MoE is added in some of these layers to increase model capacity while keeping active parameter usage manageable. This flexible architecture allows resource- and objective-specific configurations. We implement two configurations: Jamba-1.5-Large, with 94B ac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30115<END_URL>

JetFormer: An autoregressive generative model of raw images and text\nRemoving modeling constraints and unifying architectures across domains has been a key driver of the recent progress in training large multimodal models. However, most of these models still rely on many separately trained components such as modality-specific encoders and decoders. In this work, we further streamline joint generative modeling of images and text. We propose an autoregressive decoder-only transformer---JetFormer-<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28106<END_URL>

Jogging the Memory of Unlearned LLMs Through Targeted Relearning Attacks\nMachine unlearning is a promising approach to mitigate undesirable memorization of training data in ML models. However, in this work we show that existing approaches for unlearning in LLMs are surprisingly susceptible to a simple set of *targeted relearning attacks*. With access to only a small and potentially loosely related set of data, we find that we can ‘jog’ the memory of unlearned models to reverse the effects of un<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28883<END_URL>

Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity\nArchitectures such as Linformer and Mamba have recently emerged as competitive linear time replacements for transformers. However, corresponding large pretrained models are often unavailable, especially in non-text domains. To remedy this, we present a Cross-Architecture Layerwise Distillation (CALD) approach that jointly converts a transformer model to a linear time substitute and fine-tunes it <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30727<END_URL>

Joint Gradient Balancing for Data Ordering in Finite-Sum Multi-Objective Optimization\nIn finite-sum optimization problems, the sample orders for parameter updates can significantly influence the convergence rate of optimization algorithms. While numerous sample ordering techniques have been proposed in the context of single-objective optimization, the problem of sample ordering in finite-sum multi-objective optimization has not been thoroughly explored. To address this gap, we propose a sample <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28185<END_URL>

Joint Graph Rewiring and Feature Denoising via Spectral Resonance\nIn graph learning the graph and the node features both contain noisy information about the node labels. In this paper we propose joint denoising and rewiring (JDR)—an algorithm to jointly rewire the graph and denoise the features, which improves the performance of downstream node classification graph neural nets (GNNs). JDR improves the alignment between the leading eigenspaces of graph and feature matrices. To approximately solv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27686<END_URL>

Joint Reward and Policy Learning with Demonstrations and Human Feedback Improves Alignment\nAligning to human preferences and/or intentions is an important requirement for contemporary foundation models. To ensure alignment, popular approaches such as reinforcement learning with human feedback (RLHF) break down the task into three stages: (i) a model is computed with supervised fine-tuning (SFT) based upon large demonstrations data, (ii) a reward model (RM) is estimated based upon human feedback<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29428<END_URL>

JPEG Inspired Deep Learning\nAlthough it is traditionally believed that lossy image compression, such as JPEG compression, has a negative impact on the performance of deep neural networks (DNNs), it is shown by recent works that well-crafted JPEG compression can actually improve the performance of deep learning (DL). Inspired by this, we propose JPEG-DL, a novel DL framework that prepends any underlying DNN architecture with a trainable JPEG compression layer. To make the quantization operation <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28038<END_URL>

JudgeBench: A Benchmark for Evaluating LLM-Based Judges\nLLM-based judges have emerged as a scalable alternative to human evaluation and are increasingly used to assess, compare, and improve models. However, the reliability of LLM-based judges themselves is rarely scrutinized. As LLMs become more advanced, their responses grow more sophisticated, requiring stronger judges to evaluate them. Existing benchmarks primarily focus on a judge’s alignment with human preferences, but often fail to accoun<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30311<END_URL>

Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment\nThe performance of large language models (LLMs) is closely linked to their underlying size, leading to ever-growing networks and hence slower inference. Speculative decoding has been proposed as a technique to accelerate autoregressive generation, leveraging a fast draft model to propose candidate tokens, which are then verified in parallel based on their likelihood under the target model. While this approach guar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28440<END_URL>

JudgeLM: Fine-tuned Large Language Models are Scalable Judges\nEvaluating Large Language Models (LLMs) in open-ended scenarios is challenging because existing benchmarks and metrics can not measure them comprehensively. To address this problem, we propose to fine-tune LLMs as scalable judges (JudgeLM) to evaluate LLMs efficiently and effectively in open-ended benchmarks. We first propose a comprehensive, large-scale, high-quality dataset containing task seeds, LLMs-generated answers, and GPT-4-g<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27760<END_URL>

Jump Your Steps: Optimizing Sampling Schedule of Discrete Diffusion Models\nDiffusion models have seen notable success in continuous domains, leading to the development of discrete diffusion models (DDMs) for discrete variables. Despite recent advances, DDMs face the challenge of slow sampling speeds. While parallel sampling methods like $\tau$-leaping accelerate this process, they introduce _Compounding Decoding Error_ (CDE), where discrepancies arise between the true distribution and the appro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28310<END_URL>

Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge\nLLM-as-a-Judge has been widely utilized as an evaluation method in various benchmarks and served as supervised rewards in model training. However, despite their excellence in many domains, potential issues are under-explored, undermining their reliability and the scope of their utility. Therefore, we identify 12 key potential biases and propose a new automated bias quantification framework—CALM—which systematically quantifies and analyz<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31088<END_URL>

KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks\nGraph neural networks (GNNs) with attention mechanisms, often referred to as attentive GNNs, have emerged as a prominent paradigm in advanced GNN models in recent years. However, our understanding of the critical process of scoring neighbor nodes remains limited, leading to the underperformance of many existing attentive GNNs. In this paper, we unify the scoring functions of current attentive GNNs and propose Kolmogo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29141<END_URL>

KAN: Kolmogorov–Arnold Networks\nInspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons''), KANs have learnable activation functions on edges ("weights''). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes K<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29784<END_URL>

KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models\nThe increasing sizes of large language models (LLMs) result in significant computational overhead and memory usage when adapting these models to specific tasks or domains. Various parameter-efficient fine-tuning (PEFT) methods have been devised to mitigate these challenges by training a small set of parameters for the task-specific updates of the model weights. Among PEFT methods, LoRA stands out for its simplicity and eff<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29826<END_URL>

KBLaM: Knowledge Base augmented Language Model\nIn this paper, we propose Knowledge Base augmented Language Model (KBLaM), a novel method for augmenting Large Language Models (LLMs) with external knowledge. KBLaM works with a knowledge base (KB) constructed from a corpus of documents, transforming each piece of knowledge in the KB into continuous key-value vector pairs via pre-trained sentence encoders with linear adapters and integrating them into pre-trained LLMs via a specialized rectangular <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29174<END_URL>

Kernel-based Optimally Weighted Conformal Time-Series Prediction\nConformal prediction has been a popular distribution-free framework for uncertainty quantification. In this work, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals ($\texttt{KOWCPI}$). Specifically, $\texttt{KOWCPI}$ adapts the classic Reweighted Nadaraya-Watson (RNW) estimator for quantile regression on dependent data and learns optimal dat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28361<END_URL>

Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks\nWhile large models trained with self-supervised learning on offline datasets have shown remarkable capabilities in text and image domains, achieving the same generalisation for agents that act in sequential decision problems remains an open challenge.In this work, we take a step towards this goal by procedurally generating tens of millions of 2D physics-based tasks and using these to train a gen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27683<END_URL>

KinFormer: Generalizable Dynamical Symbolic Regression for catalytic organic Reaction Kinetics\nModeling kinetic equations is essential for understanding the mechanisms of chemical reactions, yet a complex and time-consuming task.  Kinetic equation prediction is formulated as a problem of dynamical symbolic regression (DSR) subject to physical chemistry constraints. Deep learning (DL) holds the potential to capture reaction patterns and predict kinetic equations from data of chemical species, ef<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28399<END_URL>

KinPFN: Bayesian Approximation of RNA Folding Kinetics using Prior-Data Fitted Networks\nRNA is a dynamic biomolecule crucial for cellular regulation, with its function largely determined by its folding into complex structures, while misfolding can lead to multifaceted biological sequelae. During the folding process, RNA traverses through a series of intermediate structural states, with each transition occurring at variable rates that collectively influence the time required to reach the functio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30428<END_URL>

KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models\nThis paper investigates visual analogical reasoning in large multimodal models (LMMs) compared to human adults and children. A “visual analogy” is an abstract rule inferred from one image and applied to another. While benchmarks exist for testing visual reasoning in LMMs, they require advanced skills and omit basic visual analogies that even young children can make. Inspired by developmental psychology, we propose a new ben<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27918<END_URL>

KLay: Accelerating Neurosymbolic AI\nA popular approach to neurosymbolic AI involves mapping logic formulas to arithmetic circuits (computation graphs consisting of sums and products) and passing the outputs of a neural network through these circuits. This approach enforces symbolic constraints onto a neural network in a principled and end-to-end differentiable way. Unfortunately, arithmetic circuits are challenging to run on modern tensor accelerators as they exhibit a high degree of irregular <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29206<END_URL>

Knowing Your Target : Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding\nTransformer has attracted increasing interest in spatio-temporal video grounding, or STVG, owing to its end-to-end pipeline and promising result. Existing Transformer-based STVG approaches often leverage a set of object queries, which are initialized simply using zeros and then gradually learn target position information via iterative interactions with multimodal features, for spatial and temporal locali<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29364<END_URL>

Knowledge And Capability Transfer Through Large Language Models' Parameters Fusing\nThe post-training phase of large language models (LLMs) plays a pivotal role in refining models to follow instructions and align with human preferences. However, this phase is fraught with challenges, particularly in sourcing high-quality post-training data. This paper introduces a novel approach, termed Parameters Fusing, that simplifies the post-training process by amalgamating model parameters delta from exist<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27886<END_URL>

Knowledge Benchmark Graph: Assisting Large Language Models in Designing Models by Retrieving Benchmark Knowledge\nIn recent years, the design and transfer of neural network models have been widely studied due to their exceptional performance and capabilities. However, the complex nature of datasets and the vast architecture space pose significant challenges for both manual and automated algorithms in creating high-performance models. Inspired by researchers who design, train, and document the pe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31033<END_URL>

Knowledge Capacity Scaling Laws for Language Models\nScaling laws describe the relationship between the size of language models and their capabilities. Unlike prior studies that evaluate a model's capability via loss or benchmarks, we estimate information-theoretically the number of knowledge \emph{bits} a model stores. We focus on factual knowledge represented as tuples, such as (USA, capital, Washington D.C.) from a Wikipedia page. Through multiple controlled datasets, we establish that langua<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30313<END_URL>

Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution\nKnowledge distillation (KD) is a promising yet challenging model compression technique that transfers rich learning representations from a well-performing but cumbersome teacher model to a compact student model.  Previous methods for image super-resolution (SR) mostly are tailored to the specific teacher-student architectures. And the potential for improvement is limited, which hinders their wide applicat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29042<END_URL>

Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition\nIn this work, we investigate how a model's tendency to broadly integrate its parametric knowledge evolves throughout pretraining, and how this behavior affects overall performance, particularly in terms of knowledge acquisition and forgetting. We introduce the concept of knowledge entropy, which quantifies the range of memory sources the model engages with; high knowledge entropy indicates that the model<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28937<END_URL>

Knowledge Graph Finetuning Enhances Knowledge Manipulation in Large Language Models\nDespite the impressive performance of general large language models(LLMs), many of their applications in specific domains (e.g., low-data and knowledge-intensive) still confront significant challenges. Supervised fine-tuning (SFT)---where a general LLM is further trained on a small labeled dataset to adapt for specific tasks or domains---has shown great power for developing domain-specific LLMs. However, existin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28362<END_URL>

Knowledge Localization: Mission Not Accomplished? Enter Query Localization!\nLarge language models (LLMs) store extensive factual knowledge, but the mechanisms behind how they store and express this knowledge remain unclear.The Knowledge Neuron (KN) thesis is a prominent theory for explaining these mechanisms. This theory is based on the **Knowledge Localization (KL)** assumption, which suggests that a fact can be localized to a few knowledge storage units, namely knowledge neurons. However, thi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28033<END_URL>

Knowledge Manipulation in Language Models\nLanguage models can store vast factual knowledge, yet their ability to flexibly use this knowledge for downstream tasks (e.g., via instruction finetuning) remains questionable. This paper investigates four fundamental knowledge manipulation tasks: \textbf{retrieval} (e.g., "What is person A's attribute X?"), \textbf{classification} (e.g., "Is A's attribute X even or odd?"), \textbf{comparison} (e.g., "Is A greater than B in attribute X?"), and \textbf{i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28369<END_URL>

Kolmogorov-Arnold Transformer\nTransformers stand as the cornerstone of mordern deep learning. Traditionally, these models rely on multi-layerperceptron (MLP) layers to mix the information between channels. In this paper, we introduce the Kolmogorov–ArnoldTransformer (KAT), a novel architecture that replaces MLP layers with Kolmogorov-Arnold Network (KAN) layers toenhance the expressiveness and performance of the model. Integrating KANs into transformers, however, is no easyfeat, especially when<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30584<END_URL>

KooNPro: A Variance-Aware Koopman Probabilistic Model Enhanced by Neural Processes for Time Series Forecasting\nThe probabilistic forecasting of time series is a well-recognized challenge, particularly in disentangling correlations among interacting time series and addressing the complexities of distribution modeling. By treating time series as temporal dynamics, we introduce **KooNPro**, a novel probabilistic time series forecasting model that combines variance-aware deep **Koo**pman model with<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30930<END_URL>

KOR-Bench: Benchmarking Language Models on Knowledge-Orthogonal Reasoning Tasks\nIn this paper, we introduce the concept of Knowledge-Orthogonal Reasoning(KOR),where knowledge orthogonality refers to the independence from existing pretrained knowledge. By introducing new rules that are orthogonal to the pretrained knowledge, we minimize its interference to achieve a more accurate evaluation of the model's intrinsic reasoning and planning abilities. Based on this concept, we propose the Knowledge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29599<END_URL>

Kronecker Mask and Interpretive Prompts are Language-Action Video Learners\nContrastive language-image pretraining (CLIP) has significantly advanced image-based vision learning. A pressing topic subsequently arises: how can we effectively adapt CLIP to the video domain? Recent studies have focused on adjusting either the textual or visual branch of CLIP for action recognition. However, we argue that adaptations of both branches are crucial. In this paper, we propose a **C**ontrastive **L**anguag<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29648<END_URL>

L3Ms — Lagrange Large Language Models\nSupervised fine-tuning (SFT) and alignment of large language models (LLMs) are key steps in providing a good user experience. However, the concept of an appropriate alignment is inherently application-dependent, and current methods often rely on heuristic choices to drive the optimization. In this work, we formulate SFT and alignment as a constrained optimization problem: the LLM is fine-tuned on a task while being required to meet application-specific requ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29479<END_URL>

Label Correlation Biases Direct Time Series Forecast\nTime series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences. Current research predominantly focuses on handling autocorrelation within the historical sequence but often neglects its presence in the label sequence. Specifically, emerging forecast models mainly conform to the direct forecast (DF) paradigm, generating multi-step forecasts under the assumption of conditional independence w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31031<END_URL>

Label-Free Coreset Selection with Proxy Training Dynamics\nHigh-quality human-annotated data is crucial for modern deep learning pipelines, yet the human annotation process is both costly and time-consuming. Given a constrained human labeling budget, selecting an informative and representative data subset for labeling can significantly reduce human annotation effort. Well-performing state-of-the-art (SOTA) coreset selection methods require ground truth labels over the whole dataset, failing to r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27703<END_URL>

LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion\nThis paper introduces a novel hierarchical autoencoder that maps 3D models into a highly compressed latent space. The hierarchical autoencoder is specifically designed to tackle the challenges arising from large-scale datasets and generative modeling using diffusion. Different from previous approaches that only work on a regular image or volume grid, our hierarchical autoencoder operates on unordered sets of vectors. Eac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30852<END_URL>

Lambda-Skip Connections: the architectural component that prevents Rank Collapse\nRank collapse, a phenomenon where embedding vectors in sequence modelsrapidly converge to a uniform token or equilibrium state, has recently gained at-tention in the deep learning literature. This phenomenon leads to reduced expres-sivity and potential training instabilities due to vanishing gradients. Empirical ev-idence suggests that architectural components like skip connections, LayerNorm,and MultiLayer Percept<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31171<END_URL>

LaMPlace: Learning to Optimize Cross-Stage Metrics in Macro Placement\nMachine learning techniques have shown great potential in enhancing macro placement, a critical stage in modern chip design.However, existing methods primarily focus on *online* optimization of *intermediate surrogate metrics* that are available at the current placement stage, rather than directly targeting the *cross-stage metrics*---such as the timing performance---that measure the final chip quality.This is mainly because <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29266<END_URL>

LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning\nLanguage plays a vital role in the realm of human motion. Existing methods have largely depended on CLIP text embeddings for motion generation, yet they fall short in effectively aligning language and motion due to CLIP’s pretraining on static image-text pairs. This work introduces LaMP, a novel Language-Motion Pretraining model, which transitions from a language-vision to a more suitable language-motion latent s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29989<END_URL>

LancBiO: Dynamic Lanczos-aided Bilevel Optimization via Krylov Subspace\nBilevel optimization, with broad applications in machine learning, has an intricate hierarchical structure. Gradient-based methods have emerged as a common approach to large-scale bilevel problems. However, the computation of the hyper-gradient, which involves a Hessian inverse vector product, confines the efficiency and is regarded as a bottleneck. To circumvent the inverse, we construct a sequence of low-dimensional appro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27854<END_URL>

Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning\nExisting actor-critic algorithms, which are popular for continuous control reinforcement learning (RL) tasks, suffer from poor sample efficiency due to lack of principled exploration mechanism within them. Motivated by the success of Thompson sampling for efficient exploration in RL, we propose a novel model-free RL algorithm, \emph{Langevin Soft Actor Critic} (LSAC), which prioritizes enhancing critic <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30315<END_URL>

Language Agents Meet Causality -- Bridging LLMs and Causal World Models\nLarge Language Models (LLMs) have recently shown great promise in planning and reasoning applications. These tasks demand robust systems, which arguably require a causal understanding of the environment. While LLMs can acquire and reflect common sense causal knowledge from their pretraining data, this information is often incomplete, incorrect, or inapplicable to a specific environment. In contrast, causal representation le<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27748<END_URL>

Language-Assisted Feature Transformation for Anomaly Detection\nThis paper introduces LAFT, a novel feature transformation method designed to incorporate user knowledge and preferences into anomaly detection using natural language. Accurately modeling the boundary of normality is crucial for distinguishing abnormal data, but this is often challenging due to limited data or the presence of nuisance attributes. While unsupervised methods that rely solely on data without user guidance are common, t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31115<END_URL>

Language Guided Skill Discovery\nSkill discovery methods enable agents to learn diverse emergent behaviors without explicit rewards. To make learned skills useful for downstream tasks, obtaining a semantically diverse repertoire of skills is crucial. While some approaches use discriminators to acquire distinguishable skills and others focus on increasing state coverage, the direct pursuit of ‘semantic diversity’ in skills remains underexplored. We hypothesize that leveraging the semantic knowled<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28721<END_URL>

Language-Image Models with 3D Understanding\nMulti-modal large language models (MLLMs) have shown incredible capabilities in a variety of 2D vision and language tasks. We extend MLLMs’ perceptual capabilities to ground and reason about images in 3-dimensional space. To that end, we first develop a large-scale pretraining dataset for 2D and 3D called LV3D by combining multiple existing 2D and 3D recognition datasets under a common task formulation: as multi-turn question-answering. Next, we intro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27715<END_URL>

Language Imbalance Driven Rewarding for Multilingual Self-improving\nLarge Language Models (LLMs) have achieved state-of-the-art performance across numerous tasks. However, these advancements have predominantly benefited "first-class" languages such as English and Chinese, leaving many other languages underrepresented. This imbalance, while limiting broader applications, generates a natural preference ranking between languages, offering an opportunity to bootstrap the multilingual capabilities o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30045<END_URL>

Language Model Non-Myopic Generation for Reasoning and Planning\nLarge Language Models have demonstrated remarkable abilities in reasoning and planning by breaking down complex problems into sequential steps. Despite their success in various domains like mathematical problem-solving and coding, LLMs face challenges in ensuring reliable and optimal planning due to their inherent myopic nature of autoregressive decoding. This paper revisits LLM reasoning from an optimal-control perspective, propos<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29799<END_URL>

Language Models are Advanced Anonymizers\nRecent privacy research on large language models (LLMs) has shown that they achieve near-human-level performance at inferring personal data from online texts. With ever-increasing model capabilities, existing text anonymization methods are currently lacking behind regulatory requirements and adversarial threats. In this work, we take two steps to bridge this gap: First, we present a new setting for evaluating anonymization in the face of adversarial LLM <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30788<END_URL>

Language Models Are Implicitly Continuous\nLanguage is typically modelled with discrete sequences. However, the most successful approaches to language modelling, namely neural networks, are continuous and smooth function approximators.In this work, we show that Transformer-based language models implicitly learn to represent sentences as continuous-time functions defined over a continuous input space. This phenomenon occurs in most state-of-the-art Large Language Models (LLMs), including Llama2, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29613<END_URL>

Language Models Can Articulate Their Implicit Goals\nWe study *objective awareness*, which we define as an LLM's capability to articulate its behavioral policies without relying on in-context examples. We finetune LLMs on examples that exhibit particular behaviors, including (a) making risk-seeking / risk-averse economic decisions, and (b) making the user say a certain word. Although these examples never contain explicit descriptions of the policy (e.g. ``I will now take the risk-seeking option'<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30155<END_URL>

Language Models, Grade-School Math, and the Hidden Reasoning Process\nRecent advances in language models have demonstrated their capability to solve mathematical reasoning problems, achieving near-perfect accuracy on grade-school level math benchmarks like GSM8K. In this paper, we formally study how language models solve these problems. We design a series of controlled experiments to address several fundamental questions: (1) Can language models truly develop reasoning skills, or do they simply <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29518<END_URL>

Language Models Implicitly Learn a Unified Representation Space\nModern language and multimodal models can process a wide variety of inputs across different languages and modalities. We hypothesize that models acquire this capability through learning a *unified representation space* across heterogeneous data types. We first show that model representations for semantically equivalent inputs in different languages are similar in the intermediate layers, and that this space can further be interpret<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30324<END_URL>

Language Models Learn to Mislead Humans via RLHF\nLanguage models (LMs) can produce errors that are hard to detect for humans, especially when the task is complex.RLHF, the most popular post-training method, may exacerbate this problem: to achieve higher rewards, LMs might get better at convincing humans that they are right even when they are wrong. We study this phenomenon under a standard RLHF pipeline, calling it ``U-Sophistry'' since it is \textbf{U}nintended by model developers. Specificall<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27791<END_URL>

Language Models Need Inductive Biases to Count Inductively\nCounting constitutes a core skill underlying a wide range of tasks, such as formal language recognition, multi-hop reasoning and simulating algorithms. Generalizing counting inductively is central to task success on out-of-distribution (OOD) instances where testing inputs are longer than those seen in training. While there is a large body of literature reporting poor length generalization in language models, few papers have tried to dis<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28148<END_URL>

Language models scale reliably with over-training and on downstream tasks\nScaling laws are useful guides for derisking expensive training runs, as they predict performance of large models using cheaper, small-scale experiments. However, there remain gaps between current scaling studies and how language models are ultimately trained and evaluated. For instance, scaling is usually studied in the compute-optimal training regime (i.e., "Chinchilla optimal" regime). In contrast, models are often ove<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28688<END_URL>

Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice\nThe observed similarities in the behavior of humans and Large Language Models (LLMs) have prompted researchers to consider the potential of using LLMs as models of human cognition. However, several significant challenges must be addressed before LLMs can be legitimately regarded as cognitive models. For instance, LLMs are trained on far more data than humans typically encounter, and may have been directly trai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29516<END_URL>

Language Representations Can be What Recommenders Need: Findings and Potentials\nRecent studies empirically indicate that language models (LMs) encode rich world knowledge beyond mere semantics, attracting significant attention across various fields.However, in the recommendation domain, it remains uncertain whether LMs implicitly encode user preference information. Contrary to prevailing understanding that LMs and traditional recommenders learn two distinct representation spaces due to the huge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28934<END_URL>

LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding\nAuto-Regressive (AR) models have recently gained prominence in image generation, often matching or even surpassing the performance of diffusion models. However, one major limitation of AR models is their sequential nature, which processes tokens one at a time, slowing down generation compared to models like GANs or diffusion-based methods that operate more efficiently. While speculative decoding has proven effe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30718<END_URL>

Laplace Sample Information:  Data Informativeness Through a Bayesian Lens\nAccurately estimating the informativeness of individual samples in a dataset is an important objective in deep learning, as it can guide sample selection, which can improve model efficiency and accuracy by removing redundant or potentially harmful samples. We propose $\text{\textit{Laplace Sample Information}}$ ($\mathsf{LSI}$) measure of sample informativeness grounded in information theory widely applicable across model<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28251<END_URL>

Large Convolutional Model Tuning via Filter Subspace\nEfficient fine-tuning methods are critical to address the high computational and parameter complexity while adapting large pre-trained models to downstream tasks.Our study is inspired by prior research that represents each convolution filter as a linear combination of a small set of filter subspace elements, referred to as filter atoms. In this paper, we propose to fine-tune pre-trained models by adjusting only filter atoms, which are respons<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30422<END_URL>

Large Language Models are Interpretable Learners\nThe trade-off between expressiveness and interpretability remains a core challenge when building human-centric models for classification and decision-making. While symbolic rules offer interpretability, they often lack expressiveness, whereas neural networks excel in performance but are known for being black boxes. This paper shows a combination of Large Language Models (LLMs) and symbolic programs can bridge this gap. In the proposed LLM-based S<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28760<END_URL>

Large Language Models Assume People are More Rational than We Really are\nIn order for AI systems to communicate effectively with people, they must understand how we make decisions. However, people's decisions are not always rational, so the implicit internal models of human decision-making in Large Language Models (LLMs) must account for this. Previous empirical evidence seems to suggest that these implicit models are accurate --- LLMs offer believable proxies of human behavior, acting how we e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29001<END_URL>

Large Language Models can be Strong Self-Detoxifiers\nReducing the likelihood of generating harmful and toxic output is an essential task when aligning large language models (LLMs). Existing methods mainly rely on training an external reward model (i.e., another language model) or fine-tuning the LLM using self-generated data to influence the outcome. In this paper, we show that LLMs have the capability of self-detoxification without the use of an additional reward model or re-training. We propo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28633<END_URL>

Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses\nScientific discovery contributes largely to the prosperity of human society, and recent progress shows that LLMs could potentially catalyst the process. However, it is still unclear whether LLMs can discover novel and valid hypotheses in chemistry. In this work, we investigate this main research question: whether LLMs can automatically discover novel and valid chemistry research hypotheses, given only a research ques<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29319<END_URL>

Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation\nFirst-order logic (FOL) reasoning, which involves sequential deduction, is pivotal for intelligent systems and serves as a valuable task for evaluating reasoning capabilities, particularly in chain-of-thought (CoT) contexts. Existing benchmarks often rely on extensive human annotation or handcrafted templates, making it difficult to achieve the necessary complexity, scalability, and diversity for robust evaluation. To <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30534<END_URL>

Large Language Models Often Say One Thing and Do Another\nAs large language models (LLMs) increasingly become central to various applications and interact with diverse user populations, ensuring their reliable and consistent performance is becoming more important. This paper explores a critical issue in assessing the reliability of LLMs: the consistency between their words and deeds. To quantitatively explore this consistency, we developed a novel evaluation benchmark called the Words and Deeds <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29649<END_URL>

Larger Language Models Provably Generalize Better\nWhy do larger language models generalize better? To explore this question, we develop generalization bounds on the pretraining objective of large language models (LLMs) in the compute-optimal regime, as described by the Chinchilla scaling laws. We introduce a novel, fully empirical Freedman-type martingale concentration inequality that tightens existing bounds by accounting for the variance of the loss function. The generalization bound can be b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29945<END_URL>

Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding\nArtificial intelligence (AI) shows great potential in assisting radiologists to improve the efficiency and accuracy of medical image interpretation and diagnosis. However, a versatile AI model requires large-scale data and comprehensive annotations, which are often impractical in medical settings. Recent studies leverage radiology reports as a naturally high-quality supervision for medical images, usin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28403<END_URL>

Large Scale Knowledge Washing\nLarge language models show impressive abilities in memorizing world knowledge, which leads to concerns regarding memorization of private information, toxic or sensitive knowledge, and copyrighted content. We introduce the problem of Large Scale Knowledge Washing, focusing on unlearning an extensive amount of factual knowledge. Previous unlearning methods usually define the reverse loss and update the model via backpropagation, which may affect the model's fluency a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28979<END_URL>

Large (Vision) Language Models are Unsupervised In-Context Learners\nRecent advancements in large language and vision-language models have made it possible to solve new tasks via zero-shot inference without task-specific training. Various adaptation techniques, such as In-Context Learning (ICL), supervised fine-tuning, and prompt engineering, can further enhance the model’s performance on a given task. However, these methods require either labeled examples or substantial manual effort to constru<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28335<END_URL>

LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior\nWe present LARP, a novel video tokenizer designed to overcome limitations in current video tokenization methods for autoregressive (AR) generative models. Unlike traditional patchwise tokenizers that directly encode local visual patches into discrete tokens, LARP introduces a holistic tokenization scheme that gathers information from the visual content using a set of learned holistic queries. This design allows LARP to captu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29340<END_URL>

LASER: A Neuro-Symbolic Framework for Learning Spatio-Temporal Scene Graphs with Weak Supervision\nSupervised approaches for learning spatio-temporal scene graphs (STSG) from video are greatly hindered due to their reliance on STSG-annotated videos, which are labor-intensive to construct at scale. Is it feasible to instead use readily available video captions as weak supervision? To address this question, we propose LASER, a neuro-symbolic framework to enable training STSG generators using only <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30236<END_URL>

LASeR: Towards Diversified and Generalizable Robot Design with Large Language Models\nRecent advances in Large Language Models (LLMs) have stimulated a significant paradigm shift in evolutionary optimization, where hand-crafted search heuristics are gradually replaced with LLMs serving as intelligent search operators. However, these studies still bear some notable limitations, including a challenge to balance exploitation with exploration, often leading to inferior solution diversity, as well as<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30803<END_URL>

Lasso Bandit with Compatibility Condition on Optimal Arm\nWe consider a stochastic sparse linear bandit problem where only a sparse subset of context features affects the expected reward function, i.e., the unknown reward parameter has sparse structure.In the existing Lasso bandit literature, the compatibility conditions together with additional diversity conditions on the context features are imposed to achieve regret bounds that only depend logarithmically on the ambient dimension $d$.In this <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28898<END_URL>

Last Iterate Convergence of Incremental Methods as a Model of Forgetting\nIncremental gradient and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Yet, without strong convexity, their convergence guarantees have primarily been established for the ergodic (average) iterate. We establish the first nonasymptotic convergence guarantees for the last iterate of both incremental gradient and increme<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28465<END_URL>

Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games\nWe study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching$^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence. A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity.We start by showing numerically that several var<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29992<END_URL>

Latent Action Pretraining from Videos\nWe introduce Latent Action Pretraining for general Action models (LAPA), the first unsupervised method for pretraining Vision-Language-Action (VLA) models without ground-truth robot action labels. Existing Vision-Language-Action models require action labels typically collected by human teleoperators during pretraining, which significantly limits possible data sources and scale. In this work, we propose a method to learn from internet-scale videos that do no<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29409<END_URL>

Latent Bayesian Optimization via Autoregressive Normalizing Flows\nBayesian Optimization (BO) has been recognized for its effectiveness in optimizing expensive and complex objective functions.Recent advancements in Latent Bayesian Optimization (LBO) have shown promise by integrating generative models such as variational autoencoders (VAEs) to manage the complexity of high-dimensional and structured data spaces.However, existing LBO approaches often suffer from the value discrepancy problem, whic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29231<END_URL>

Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data\nAccurate modeling and prediction of complex physical systems often rely on data assimilation techniques to correct errors inherent in model simulations. Traditional methods like the Ensemble Kalman Filter (EnKF) and its variants as well as the recently developed Ensemble Score Filters (EnSF) face significant challenges when dealing with high-dimensional and nonlinear Bayesian filterin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27953<END_URL>

Latent Radiance Fields with 3D-aware 2D Representations\nLatent 3D reconstruction has shown great promise in empowering 3D semantic understanding and 3D generation by distilling 2D features into the 3D space. However, existing approaches struggle with the domain gap between 2D feature space and 3D representations, resulting in degraded rendering performance. To address this challenge, we propose a novel framework that integrates 3D awareness into the 2D latent space. The framework consists of th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27920<END_URL>

Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning\nIn safe offline reinforcement learning, the objective is to develop a policy that maximizes cumulative rewards while strictly adhering to safety constraints, utilizing only offline data. Traditional methods often face difficulties in balancing these constraints, leading to either diminished performance or increased safety risks. We address these issues with a novel approach that begins by learning a conservatively<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29119<END_URL>

Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation\nLLM self-evaluation relies on the LLM's own ability to estimate response correctness, which can greatly improve its deployment reliability. In this research track, we propose the Chain-of-Embedding (CoE) in the latent space to enable LLMs to perform output-free self-evaluation. CoE consists of all progressive hidden states produced during the inference time, which can be treated as the latent thinking path of LLMs. We find <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28606<END_URL>

Lawma: The Power of Specialization for Legal Tasks\nAnnotation and classification of legal text are central components of empirical legal research. Traditionally, these tasks are often delegated to trained research assistants. Motivated by the advances in language modeling, empirical legal scholars are increasingly turning to commercial models, hoping that it will alleviate the significant cost of human annotation. In this work, we present a comprehensive analysis of large language models' curre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30835<END_URL>

Law of the Weakest Link: Cross Capabilities of Large Language Models\nThe development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities. However, this overlooks the intersection of multiple abilities across different types of expertise that are often required for real-world tasks, which we term **cross capabilities**. To systematically explore this concept, we first define seven core individual capabilities and then pair them to form seven common cros<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29519<END_URL>

Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models\nModel merging, such as model souping, is the practice of combining different models with the same architecture together without further training. In this work, we present a model merging methodology that addresses the difficulty of fine-tuning Large Language Models (LLMs) for target tasks in non-English languages, where task-specific data is often unavailable. We focus on mathematical reasoning and without in-language <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27913<END_URL>

Layerwise Recurrent Router for  Mixture-of-Experts\nThe scaling of large language models (LLMs) has revolutionized their capabilities in various tasks, yet this growth must be matched with efficient computational strategies. The Mixture-of-Experts (MoE) architecture stands out for its ability to scale model size without significantly increasing training costs. Despite their advantages, current MoE models often display parameter inefficiency. For instance, a pre-trained MoE-based LLM with 52 bill<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28923<END_URL>

Layout-your-3D: Controllable and Precise 3D Generation with 2D Blueprint\nWe present Layout-Your-3D, a framework that allows controllable and compositional 3D generation from text prompts. Existing text-to-3D methods often struggle to generate assets with plausible object interactions or require tedious optimization processes. To address these challenges, our approach leverages 2D layouts as a blueprint to facilitate precise and plausible control over 3D generation. Starting with a 2D layout pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28437<END_URL>

LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics\nWe introduce LDAdam, a memory-efficient optimizer for training large models, that performs adaptive optimization steps within lower dimensional subspaces, while consistently exploring the full parameter space during training. This strategy keeps the optimizer's memory footprint to a fraction of the model size. LDAdam relies on a new projection-aware update rule for the optimizer states that allows for transitioning between s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29199<END_URL>

LeanAgent: Lifelong Learning for Formal Theorem Proving\nLarge Language Models (LLMs) have been successful in mathematical reasoning tasks such as formal theorem proving when integrated with interactive proof assistants like Lean. Existing approaches involve training or fine-tuning an LLM on a specific dataset to perform well on particular domains, such as undergraduate-level mathematics. These methods struggle with generalizability to advanced mathematics. A fundamental limitation is that these<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29455<END_URL>

LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid\nLarge language models (LLMs) have shown immense potential across various domains, but their high memory requirements and inference costs remain critical challenges for deployment. Post-training quantization (PTQ) has emerged as a promising technique to reduce memory requirements and decoding latency. However, recent accurate quantization methods often depend on specialized computations or custom data f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30168<END_URL>

Lean-STaR: Learning to Interleave Thinking and Proving\nTraditional language model-based theorem proving assumes that by training on a sufficient amount of formal proof data, a model will learn to prove theorems. Our key observation is that a wealth of informal information that is not present in formal proofs can be useful for learning to prove theorems. For instance, humans think through steps of a proof, but this thought process is not visible in the resulting code. We present Lean-STaR, a fra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29611<END_URL>

Learn-by-interact: A Data-Centric Framework For Self-Adaptive Agents in Realistic Environments\nAutonomous agents powered by large language models (LLMs) have the potential to enhance human capabilities, assisting with digital tasks from sending emails to performing data analysis.   The abilities of existing LLMs at such tasks are often hindered by the lack of high-quality agent data from the corresponding environments they interact with.  We propose LEARN-BY-INTERACT, a data-centric framework t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31073<END_URL>

Learned Reference-based Diffusion Sampler for multi-modal distributions\nOver the past few years, several approaches utilizing score-based diffusion have been proposed to sample from probability distributions, that is without having access to exact samples and relying solely on evaluations of unnormalized densities. The resulting samplers approximate the time-reversal of a noising diffusion process, bridging the target distribution to an easy-to-sample base distribution. In practice, the perform<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28860<END_URL>

Learn hybrid prototypes for multivariate time series anomaly detection\nIn multivariate time series anomaly detection (MTSAD), reconstruction-based models reconstruct testing series with learned knowledge of only normal series and identify anomalies with higher reconstruction errors. In practice, over-generalization often occurs with unexpectedly well reconstruction of anomalies. Although memory banks are employed by reconstruction-based models to fight against over-generalization, these models <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30767<END_URL>

Learning 3D Perception from Others' Predictions\nAccurate 3D object detection in real-world environments requires a huge amount of annotated data with high quality. Acquiring such data is tedious and expensive, and often needs repeated effort when a new sensor is adopted or when the detector is deployed in a new environment. We investigate a new scenario to construct 3D object detectors: *learning from the predictions of a nearby unit that is equipped with an accurate detector.* For example, whe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29248<END_URL>

Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory\nIn order to train agents that can quickly adapt to new objectives or reward functions, efficient unsupervised representation learning in sequential decision-making environments can be important. Frameworks such as the Exogenous Block Markov Decision Process (Ex-BMDP) have been proposed to formalize this representation-learning problem (Efroni et al., 2022b). In the Ex-BMDP framework, the agent's high-dimensional observations o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31043<END_URL>

Learning and aligning single-neuron invariance manifolds in visual cortex\nUnderstanding how sensory neurons exhibit selectivity to certain features and invariance to others is central to uncovering the computational principles underlying robustness and generalization in visual perception. Most existing methods for characterizing selectivity and invariance identify single or finite discrete sets of stimuli. Since these are only isolated measurements from an underlying continuous manifold, charac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28569<END_URL>

Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods\nPhysics-informed deep learning often faces optimization challenges due to the complexity of solving partial differential equations (PDEs), which involve exploring large solution spaces, require numerous iterations, and can lead to unstable training. These challenges arise particularly from the ill-conditioning of the optimization problem, caused by the differential terms in the loss function. To address these issues<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28615<END_URL>

Learning-Augmented Frequent Directions\nAn influential paper of Hsu et al. (ICLR'19) introduced the study of learning-augmented streaming algorithms in the context of frequency estimation. A fundamental problem in the streaming literature, the goal of frequency estimation is to approximate the number of occurrences of items appearing in a long stream of data using only a small amount of memory. Hsu et al. develop a natural framework to combine the worst-case guarantees of popular solutions such <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29356<END_URL>

Learning-Augmented Search Data Structures\nWe study the integration of machine learning advice to improve upon traditional data structure designed for efficient search queries. Although there has been recent effort in improving the performance of binary search trees using machine learning advice, e.g., Lin et. al.  (ICML 2022), the resulting constructions nevertheless suffer from inherent weaknesses of binary search trees, such as complexity of maintaining balance across multiple updates and the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29901<END_URL>

Learning Causal Alignment for Reliable Disease Diagnosis\nAligning the decision-making process of machine learning algorithms with that of experienced radiologists is crucial for reliable diagnosis. While existing methods have attempted to align their prediction behaviors to those of radiologists reflected in the training data, this alignment is primarily associational rather than causal, resulting in pseudo-correlations that may not transfer well. In this paper, we propose a causality-based ali<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28327<END_URL>

Learning Chaos In A Linear Way\nLearning long-term behaviors in chaotic dynamical systems, such as turbulent flows and climate modelling, is challenging due to their inherent instability and unpredictability. These systems exhibit positive Lyapunov exponents, which significantly hinder accurate long-term forecasting. As a result, understanding long-term statistical behavior is far more valuable than focusing on short-term accuracy. While autoregressive deep sequence models have been applied to c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29977<END_URL>

Learning Closed-Loop Concept-Guided Policies from Unlabeled Demonstrations\nTraining embodied agents to perform complex robotic tasks presents significant challenges due to the entangled factors of task compositionality, environmental diversity, and dynamic changes. In this work, we introduce a novel imitation learning framework to train closed-loop concept-guided policies that enhance long-horizon task performance by leveraging discovered manipulation concepts. Unlike methods that rely on prede<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30674<END_URL>

Learning Clustering-based Prototypes for Compositional Zero-Shot Learning\nLearning primitive (i.e., attribute and object) concepts from seen compositions is the primary challenge of Compositional Zero-Shot Learning (CZSL). Existing CZSL solutions typically rely on oversimplified data assumptions, e.g., modeling each primitive with a single centroid primitive presentation, ignoring the natural diversities of the attribute (resp. object) when coupled with different objects (resp. attribute). In t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28941<END_URL>

Learning Color Equivariant Representations\nIn this paper, we introduce group convolutional neural networks (GCNNs) equivariant to color variation. GCNNs have been designed for a variety of geometric transformations from 2D and 3D rotation groups, to semi-groups such as scale. Despite the improved interpretability, accuracy and generalizability of these architectures, GCNNs have seen limited application in the context of perceptual quantities. Notably, the recent CEConv network uses a GCNN to ac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30166<END_URL>

Learning Conditionally Independent Marginals Enables Logical Compositions in Conditional Diffusion Models\nHow can we learn generative models to sample data with arbitrary logical compositions of statistically independent attributes? The prevailing solution is to sample from distributions expressed as a composition of attributes' conditional marginal distributions under the assumption that they are statistically independent. This paper shows that standard conditional diffusion models violate thi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29065<END_URL>

Learning Continually by Spectral Regularization\nLoss of plasticity is a phenomenon where neural networks can become more difficult to train over the course of learning. Continual learning algorithms seek to mitigate this effect by sustaining good performance while maintaining network trainability. We develop a new technique for improving continual learning inspired by the observation that the singular values of the neural network parameters at initialization are an important factor for trainabi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30217<END_URL>

Learning Diagrams: A Graphical Language for Compositional Training Regimes\nMotivated by deep learning regimes with multiple interacting yet distinct model components, we introduce learning diagrams, graphical depictions of training setups that capture parameterized learning as data rather than code. A learning diagram compiles to a unique loss function on which component models are trained. The result of training on this loss is a collection of models whose predictions ``agree" with one another<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28962<END_URL>

Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks\nPhysical systems with complex unsteady dynamics, such as fluid flows, are often poorly represented by a single mean solution. For many practical applications, it is crucial to access the full distribution of possible states, from which relevant statistics (e.g., RMS and two-point correlations) can be derived. Here, we propose a graph-based latent diffusion model that enables direct sampling of states from their eq<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27985<END_URL>

Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning\nRed-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit un<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31186<END_URL>

Learning Dynamics of Deep Matrix Factorization Beyond the Edge of Stability\nDeep neural networks trained using gradient descent with a fixed learning rate $\eta$ often operate in the regime of ``edge of stability'' (EOS), where the largest eigenvalue of the Hessian equilibrates about the stability threshold $2/\eta$. Existing theoretical analyses of EOS focus on simple prototypes, such as scalar functions or second-order regression models, which limits our understanding of the phenomenon in dee<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30132<END_URL>

Learning Dynamics of LLM Finetuning\nLearning dynamics, which describes how the learning of specific training examples influences the model's predictions on other examples, gives us a powerful tool for understanding the behavior of deep learning systems. We study the learning dynamics of large language models during different types of finetuning, by analyzing the step-wise decomposition of how influence accumulates among different potential responses. Our framework allows a uniform interpretatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28056<END_URL>

Learning Efficient Positional Encodings with Graph Neural Networks\nPositional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectures and increase the expressive capacity of Graph Neural Networks (GNNs). However, designing powerful and efficient PEs for graphs poses significant challenges due to the absence of canonical node ordering and the scale of the graph. In this work, we id<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30628<END_URL>

Learning Equivariant Non-Local Electron Density Functionals\nThe accuracy of density functional theory hinges on the approximation of non-local contributions to the exchange-correlation (XC) functional. To date, machine-learned and human-designed approximations suffer from insufficient accuracy, limited scalability, or dependence on costly reference data. To address these issues, we introduce Equivariant Graph Exchange Correlation (EG-XC), a novel non-local XC functional based on equivariant gra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30333<END_URL>

Learning Evolving Tools for Large Language Models\nTool learning enables large language models (LLMs) to interact with external tools and APIs, greatly expanding the application scope of LLMs. However, due to the dynamic nature of external environments, these tools and APIs may become outdated over time, preventing LLMs from correctly invoking tools. Existing research primarily focuses on static environments and overlooks this issue, limiting the adaptability of LLMs in real-world applications. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27814<END_URL>

Learning Fine-Grained Representations through Textual Token Disentanglement in Composed Video Retrieval\nWith the explosive growth of video data, finding videos that meet detailed requirements in large datasets has become a challenge. To address this, the composed video retrieval task has been introduced, enabling users to retrieve videos using complex queries that involve both visual and textual information. However, the inherent heterogeneity between modalities poses significant challenges. Te<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27859<END_URL>

Learning from End User Data with Shuffled Differential Privacy\nWe study a setting of collecting and learning from private data distributed across end users.In the shuffled model of differential privacy, the end users partially protect their data locally before sharing it, and their data is also anonymized during its collection to enhance privacy. This model has recently become a prominent alternative to central DP, which requires full trust in a central data curator, and local DP, where fully l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29677<END_URL>

Learning from Imperfect  Human Feedback: A Tale from Corruption-Robust Dueling\nThis paper studies Learning from Imperfect Human Feedback (LIHF), addressing the potential irrationality or imperfect perception when learning from comparative human feedback. Building on evidences that human's imperfection decays over time (i.e., humans learn to improve), we  cast this problem as a concave-utility continuous-action dueling bandit but under a restricted form of  corruption:  i.e., the corruption scal<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28276<END_URL>

Learning from weak labelers as constraints\nWe study programmatic weak supervision, where in contrast to labeled data, we have access to \emph{weak labelers}, each of which either abstains or provides noisy labels corresponding to any input. Most previous approaches typically employ latent generative models that model the joint distribution of the weak labels and the latent ``true'' label. The caveats are that this relies on assumptions that may not always hold in practice such as conditional in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31155<END_URL>

Learning Gain Map for Inverse Tone Mapping\nFor a more compatible and consistent high dynamic range (HDR) viewing experience, a new image format with a double-layer structure has been developed recently, which incorporates an auxiliary Gain Map (GM) within a standard dynamic range (SDR) image for adaptive HDR display. This new format motivates us to introduce a new task termed Gain Map-based Inverse Tone Mapping (GM-ITM), which focuses on learning the corresponding GM of an SDR image instead of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30253<END_URL>

Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation\nLearning cooperative multi-agent policy from offline multi-task data that can generalize to unseen tasks with varying numbers of agents and targets is an attractive problem in many scenarios.Although aggregating general behavior patterns among multiple tasks as skills to improve policy transfer is a promising approach,two primary challenges hinder the further advancement of skill learning in offline multi-tas<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30225<END_URL>

Learning General-purpose Biomedical Volume Representations using Randomized Synthesis\nCurrent _volumetric_ biomedical foundation models struggle to generalize as public 3D datasets are small and do not cover the broad diversity of medical procedures, conditions, anatomical regions, and imaging protocols. We address this by creating a representation learning method that instead anticipates strong domain shifts at training time itself. We first propose a data engine that synthesizes highly variab<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27787<END_URL>

Learning Generative Judge from Preference Data\nLearning from preference feedback is a common practice for aligning large language models (LLMs) with human value.Conventionally, preference data is learned and encoded into a scalar reward model that connects a value head with an LLM to produce a scalar score as preference. However, scalar models lack interpretability and are known to be susceptible to biases in datasets. This paper investigates leveraging the generation capability of LLMs to addr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30221<END_URL>

Learning Geometric Reasoning Networks For Robot Task And Motion Planning\nTask and Motion Planning (TAMP) is a computationally challenging robotics problem due to the tight coupling of discrete symbolic planning and continuous geometric planning of robot motions. In particular, planning manipulation tasks in complex 3D environments leads to a large number of costly geometric planner queries to verify the feasibility of considered actions and plan their motions. To address this issue, we propose <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29152<END_URL>

Learning Graph Invariance by Harnessing Spuriosity\nRecently, graph invariant learning has become the _de facto_ approach to tackle the Out-of-Distribution (OOD) generalization failure in graph representation learning. They generically follow the framework of invariant risk minimization to capture the invariance of graph data from different environments. Despite some success, it remains unclear to what extent existing approaches have captured invariant features for OOD generalization on graphs. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29448<END_URL>

Learning Graph Quantized Tokenizers for Transformers\nTransformers serve as the backbone architectures of Foundational Models, where a domain-specific tokenizer helps them adapt to various domains. Graph Transformers (GTs) have recently emerged as a leading model in geometric deep learning, outperforming Graph Neural Networks (GNNs) in various graph learning tasks. However, the development of tokenizers for graphs has lagged behind other modalities, with existing approaches relying on heuristics<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28351<END_URL>

Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling\nLong-horizon combinatorial optimization problems (COPs), such as the Flexible Job-Shop Scheduling Problem (FJSP), often involve complex, interdependent decisions over extended time frames, posing significant challenges for existing solvers. While Rolling Horizon Optimization (RHO) addresses this by decomposing problems into overlapping shorter-horizon subproblems, such overlap often involves redundant com<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30617<END_URL>

Learning Harmonized Representations for Speculative Sampling\nSpeculative sampling is a promising approach to accelerate the decoding stage for Large Language Models (LLMs). Recent advancements that leverage target LLM's contextual information, such as hidden states and KV cache, have shown significant practical improvements. However, these approaches suffer from inconsistent context between training and decoding. We also observe another discrepancy between the training and decoding objectives i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29560<END_URL>

Learning Hierarchical Polynomials of Multiple Nonlinear Features\nIn deep learning theory, a critical question is to understand how neural networks learn hierarchical features. In this work, we study the learning of hierarchical polynomials of multiple nonlinear features using three-layer neural networks. We examine a broad class of functions of the form $f^{\star}=g^{\star}\circ \mathbf{p}$, where $\mathbf{p}:\mathbb{R}^{d} \rightarrow \mathbb{R}^{r}$ represents multiple quadratic features with<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29467<END_URL>

Learning High-Degree Parities: The Crucial Role of the Initialization\nParities have become a standard benchmark for evaluating learning algorithms. Recent works show that regular neural networks trained by gradient descent can efficiently learn degree $k$ parities on uniform inputs for constant $k$, but fail to do so when $k$ and $d-k$ grow with $d$ (here $d$ is the ambient dimension). However, the case where $k=d-O_d(1)$, including the degree $d$ parity (the \textit{full parity}), has remained<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29795<END_URL>

Learning How Hard to Think: Input-Adaptive Allocation of LM Computation\nComputationally intensive decoding procedures---including search, reranking, and self-critique---can improve the quality of language model (LM) outputs in problems spanning code generation, numerical reasoning, and dialog.Existing work typically applies the same decoding procedure for every input to an LM. But not all inputs require the same amount of computation to process. Can we allocate decoding computation adaptively, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30861<END_URL>

Learning Interleaved Image-Text Comprehension in Vision-Language Large Models\nThe swift progress of Multi-modal Large Models (MLLMs) has showcased their impressive ability to tackle tasks blending vision and language.Yet, most current models and benchmarks cater to scenarios with a narrow scope of visual and textual contexts.These models often fall short when faced with complex comprehension tasks, which involve navigating through a plethora of irrelevant and potentially misleading information <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28632<END_URL>

Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data\nIn science, we are often interested in obtaining a generative model of the underlying system dynamics from observed time series. While powerful methods for dynamical systems reconstruction (DSR) exist when data come from a single domain, how to best integrate data from multiple dynamical regimes and leverage it for generalization is still an open question. This becomes particularly important when individual time <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29398<END_URL>

Learning local equivariant representations for quantum operators\nPredicting quantum operator matrices such as Hamiltonian, overlap, and density matrices in the density functional theory (DFT) framework is crucial for understanding material properties. Current methods often focus on individual operators and struggle with efficiency and scalability for large systems. Here we introduce a novel deep learning model, SLEM (strictly localized equivariant message-passing) for predicting multiple quantu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28559<END_URL>

Learning Long Range Dependencies on Graphs via Random Walks\nMessage-passing graph neural networks (GNNs) excel at capturing local relationships but struggle with long-range dependencies in graphs. In contrast, graph transformers (GTs) enable global information exchange but often oversimplify the graph structure by representing graphs as sets of fixed-length vectors. This work introduces a novel architecture that overcomes the shortcomings of both approaches by combining the long-range informati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28595<END_URL>

Learning Mask Invariant Mutual Information for Masked Image Modeling\nMasked autoencoders (MAEs) represent a prominent self-supervised learning paradigm in computer vision. Despite their empirical success, the underlying mechanisms of MAEs remain insufficiently understood. Recent studies have attempted to elucidate the functioning of MAEs through contrastive learning and feature representation analysis, yet these approaches often provide only implicit insights. In this paper, we propose a new pe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29858<END_URL>

Learning Meaningful Representations of Life (LMRL) Workshop @ ICLR 2025\nLearning Meaningful Representations of Life 2025 (LMRL 2025) aims to address the growing interest in large-scale representation learning for biological data, driven by the availability of large biological datasets, such as DNA and RNA sequences, protein structures, and cell imaging. There have been many recent papers proposing “foundation models” for biological data, but the performance of these models varies dramatically a<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23999<END_URL>

Learning mirror maps in policy mirror descent\nPolicy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived through the selection of a mirror map and enjoy finite-time convergence guarantees. Despite its popularity, the exploration of PMD's full potential is limited, with the majority of research focusing on a particular mirror map---namely, the negative entropy---which gives ris<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28430<END_URL>

Learning Molecular Representation in a Cell\nPredicting drug efficacy and safety in vivo requires information on biological responses (e.g., cell morphology and gene expression) to small molecule perturbations. However, current molecular representation learning methods do not provide a comprehensive view of cell states under these perturbations and struggle to remove noise, hindering model generalization. We introduce the Information Alignment (InfoAlign) approach to learn molecular representati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30563<END_URL>

Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics\nWe study the problem of learning multi-index models in high-dimensions using a two-layer neural network trained with the mean-field Langevin algorithm. Under mild distributional assumptions on the data, we characterize the effective dimension $d_{\mathrm{eff}}$ that controls both sample and computational complexity by utilizing the adaptivity of neural networks to latent low-dimensional structures. When the data e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29373<END_URL>

Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees\nWe give the first provably efficient algorithms for learning neural networks with respect to distribution shift. We work in the Testable Learning with Distribution Shift  framework (TDS learning) of Klivans et al. (2024), where the learner receives labeled examples from a training distribution and unlabeled examples from a test distribution and must either output a hypothesis with low test error or reject if di<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28915<END_URL>

Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement Learning\nOffline reinforcement learning (RL) seeks to learn optimal policies from static datasets without interacting with the environment. A common challenge is handling multi-modal action distributions, where multiple behaviours are represented in the data. Existing methods often assume unimodal behaviour policies, leading to suboptimal performance when this assumption is violated. We propose Weighted Imitation Learning <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27958<END_URL>

Learning Randomized Algorithms with Transformers\nRandomization is a powerful tool that endows algorithms with remarkable properties. For instance, randomized algorithms excel in adversarial settings, often surpassing the worst-case performance of deterministic algorithms with large margins. Furthermore, their success probability can be amplified by simple strategies such as repetition and majority voting. In this paper, we enhance deep neural networks, in particular transformer models, with ran<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29471<END_URL>

Learning Representations of Intermittent Temporal Latent Process\nIdentifying time-delayed latent causal process is crucial for understanding temporal dynamics and enabling downstream reasoning. While recent methods have made progress in identifying latent time-delayed causal processes, they cannot address the dynamics in which the influence of some latent factors on both the subsequent latent states and the observed data can become inactive or irrelevant at different time steps. Therefore, we i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30889<END_URL>

Learning Robust Representations with Long-Term Information for Generalization in Visual Reinforcement Learning\nGeneralization in visual reinforcement learning (VRL) aims to learn agents that can adapt to test environments with unseen visual distractions. Despite advances in robust representations learning, many methods do not take into account the essential downstream task of sequential decision-making. This leads to representations that lack critical long-term information, impairing decision-m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29765<END_URL>

Learning Shape-Independent Transformation via Spherical Representations for Category-Level Object Pose Estimation\nCategory-level object pose estimation aims to determine the pose and size of novel objects in specific categories. Existing correspondence-based approaches typically adopt point-based representations to establish the correspondences between primitive observed points and normalized object coordinates. However, due to the inherent shape-dependence of canonical coordinates, these metho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30479<END_URL>

Learning Spatial-Semantic Features for Robust Video Object Segmentation\nTracking and segmenting multiple similar objects with distinct or complex parts in long-term videos is particularly challenging due to the ambiguity in identifying target components and the confusion caused by occlusion, background clutter, and changes in appearance or environment over time. In this paper, we propose a robust video object segmentation framework that learns spatial-semantic features and discriminative object<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30411<END_URL>

Learning Spatiotemporal Dynamical Systems from Point Process Observations\nSpatiotemporal dynamics models are fundamental for various domains, from heat propagation in materials to oceanic and atmospheric flows. However, currently available neural network-based spatiotemporal modeling approaches fall short when faced with data that is collected randomly over time and space, as is often the case with sensor networks in real-world applications like crowdsourced earthquake detection or pollution mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31095<END_URL>

Learning Splitting Heuristics in Divide-and-Conquer SAT Solvers with Reinforcement Learning\nWe propose RDC-SAT, a novel approach to optimize splitting heuristics in Divide-and-Conquer SAT solvers using deep reinforcement learning.  Our method dynamically extracts features from the current solving state whenever a split is required.  These features, such as learned clauses, variable activity scores, and clause LBD (Literal Block Distance) values, are represented as a graph.  A GNN integrated wit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27974<END_URL>

Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport\nReconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning. Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots. Based on the RUOT form, our method models these dynamics without requi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28822<END_URL>

Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport\nTo embed structured knowledge within labels into feature representations, prior work (Zeng et al., 2022) proposed to use the Cophenetic Correlation Coefficient (CPCC) as a regularizer during supervised learning. This regularizer calculates pairwise Euclidean distances of class means and aligns them with the corresponding shortest path distances derived from the label hierarchy tree. However, class means<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30615<END_URL>

Learning Successor Features with Distributed Hebbian Temporal Memory\nThis paper presents a novel approach to address the challenge of online sequence learning for decision making under uncertainty in non-stationary, partially observable environments. The proposed algorithm, Distributed Hebbian Temporal Memory (DHTM), is based on the factor graph formalism and a multi-component neuron model. DHTM aims to capture sequential data relationships and make cumulative predictions about future observati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27836<END_URL>

Learning system dynamics without forgetting\nObservation-based trajectory prediction for systems with unknown dynamics is essential in fields such as physics and biology. Most existing approaches are limited to learning within a single system with fixed dynamics patterns. However, many real-world applications require learning across systems with evolving dynamics patterns, a challenge that has been largely overlooked. To address this, we systematically investigate the problem of Continual Dynami<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28175<END_URL>

Learning Task Belief Similarity with Latent Dynamics for Meta-Reinforcement Learning\nMeta-reinforcement learning requires utilizing prior task distribution information obtained during exploration to rapidly adapt to unknown tasks. The efficiency of an agent's exploration hinges on accurately identifying the current task. Recent Bayes-Adaptive Deep RL approaches often rely on reconstructing the environment's reward signal, which is challenging in sparse reward settings, leading to suboptimal exp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30938<END_URL>

Learning the Complexity of Weakly Noisy Quantum States\nQuantifying the complexity of quantum states is a longstanding key problem in various subfields of science, ranging from quantum computing to the black-hole theory. The lower bound on quantum pure state complexity has been shown to grow linearly with system size [J. Haferkamp et al., 2022, *Nat. Phys.*]. However, extending this result to noisy circuit environments, which better reflect real quantum devices, remains an open challenge. In thi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28027<END_URL>

Learning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test\nTime-sensitive machine learning benefits from Sequential Probability Ratio Test (SPRT), which provides an optimal stopping time for early classification of time series. However, in *finite horizon* scenarios, where input lengths are finite, determining the optimal stopping rule becomes computationally intensive due to the need for *backward induction*, limiting practical applicabi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29607<END_URL>

Learning to Achieve Goals with Belief State Transformers\nWe introduce the "Belief State Transformer", a next-token predictor that takes both a prefix and suffix as inputs, with a novel objective of predicting both the next token for the prefix and the previous token for the suffix. The Belief State Transformer effectively learns to solve challenging problems that conventional forward-only transformers struggle with, in a domain-independent fashion.  Key to this success is learning a compact bel<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29526<END_URL>

Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation\nFew-shot Test-Time Domain Adaptation focuses on adapting a model at test time to a specific domain using only a few unlabeled examples, addressing domain shift. Prior methods leverage CLIP's strong out-of-distribution (OOD) abilities by generating domain-specific prompts to guide its generalized, frozen features. However, since downstream datasets are not explicitly seen by CLIP, solely depending on the feature space knowled<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29558<END_URL>

Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training\nLarge language models (LLMs), optimized through human feedback, have rapidly emerged as a leading paradigm for developing intelligent conversational assistants. However, despite their strong performance across many benchmarks, LLM-based agents might still lack conversational skills such as disambiguation -- when they are faced with ambiguity, they often overhedge or implicitly guess users' true intents rat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29616<END_URL>

Learning to Construct Implicit Communication Channel\nEffective communication is an essential component in collaborative multi-agent systems. Situations where explicit messaging is not feasible have been common in human society throughout history, which motivate the study of implicit communication. Previous works on learning implicit communication mostly rely on theory of mind (ToM), where agents infer the mental states and intentions of others by interpreting their actions. However, ToM-based m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27824<END_URL>

Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents\nRecent advances in large language models (LLMs) have led to a growing interest in developing LLM-based agents for automating web tasks. However, these agents often struggle with even simple tasks on real-world websites due to their limited capability to understand and process complex web page structures. In this work, we introduce LCoW, a framework for Learning language models to Contextualize complex Web pages into <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31086<END_URL>

Learning to Discover Regulatory Elements for Gene Expression Prediction\nWe consider the problem of predicting gene expressions from DNA sequences. A key challenge of this task is to find the regulatory elements that control gene expressions. Here, we introduce Seq2Exp, a Sequence to Expression network explicitly designed to discover and extract regulatory elements that drive target gene expression, enhancing the accuracy of the gene expression prediction. Our approach captures the causal relati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29922<END_URL>

Learning to Discretize Denoising Diffusion ODEs\nDiffusion Probabilistic Models (DPMs) are generative models showing competitive performance in various domains, including image synthesis and 3D point cloud generation. Sampling from pre-trained DPMs involves multiple neural function evaluations (NFE) to transform Gaussian noise samples into images, resulting in higher computational costs compared to single-step generative models such as GANs or VAEs. Therefore, reducing the number of NFEs while p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27797<END_URL>

Learning to engineer protein flexibility\nGenerative machine learning models are increasingly being used to design novel proteins. However, their major limitation is the inability to account for protein flexibility, a property crucial for protein function. Learning to engineer flexibility is difficult because the relevant data is scarce, heterogeneous, and costly to obtain using computational and experimental methods. Our contributions are three-fold. First, we perform a comprehensive comparison<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30019<END_URL>

Learning to Explore and Exploit with GNNs for Unsupervised Combinatorial Optimization\nCombinatorial optimization (CO) problems are pervasiveacross various domains, but their NP-hard nature often necessitates problem-specificheuristic algorithms. Recent advancements in deep learning have led to the development of learning-based heuristics, yet these approaches often struggle with limited search capabilities.We introduce  Explore-and-Exploit GNN ($X^2$GNN, pronounced x-squared GNN), a novel unsup<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27904<END_URL>

Learning to Help in Multi-Class Settings\nDeploying complex machine learning models on resource-constrained devices is challenging due to limited computational power, memory, and model retrainability. To address these limitations, a hybrid system can be established by augmenting the local model with a server-side model, where samples are selectively deferred by a *rejector* and then sent to the server for processing. The hybrid system enables efficient use of computational resources while minimi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29894<END_URL>

Learning to Permute with Discrete Diffusion\nThe group of permutations $S_n$, also known as the finite symmetric groups, are essential in fields such as combinatorics, physics, and chemistry. However, learning a probability distribution over $S_n$ poses significant challenges due to its intractable size and discrete nature. In this paper, we introduce *SymmetricDiffusers*, a novel discrete diffusion model that simplifies the task of learning a complicated distribution over $S_n$ by decomposing i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30408<END_URL>

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving\nIn the field of large language model (LLM) post-training, the effectiveness of utilizing synthetic data generated by the LLM itself has been well-presented. However, a key question remains unaddressed: what essential information should such self-generated data encapsulate? Existing approaches only produce step-by-step problem solutions, and fail to capture the abstract meta-knowledge necessary for <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30031<END_URL>

Learning to Search from Demonstration Sequences\nSearch and planning are essential for solving many real-world problems. However, in numerous learning scenarios, only action-observation sequences, such as demonstrations or instruction sequences, are available for learning. Relying solely on supervised learning with these sequences can lead to sub-optimal performance due to the vast, unseen search space encountered during training. In this paper, we introduce Differentiable Tree Search Network (D<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27934<END_URL>

Learning to Select Nodes in Branch and Bound with Sufficient Tree Representation\nBranch-and-bound methods are pivotal in solving Mixed Integer Linear Programming (MILP), where the challenge of node selection arises, necessitating the prioritization of different regions of the space for subsequent exploration. While machine learning techniques have been proposed to address this, two crucial problems concerning \textbf{(P1)} how to sufficiently extract features from the branch-and-bound tree, and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28790<END_URL>

Learning to Solve Differential Equation Constrained Optimization Problems\nDifferential equations (DE) constrained optimization plays a critical role in numerous scientific and engineering fields, including energy systems, aerospace engineering, ecology, and finance, where optimal configurations or control strategies must be determined for systems governed by ordinary or stochastic differential equations. Despite its significance, the computational challenges associated with these problems have <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29404<END_URL>

Learning to Steer Markovian Agents under Model Uncertainty\nDesigning incentives for an adapting population is a ubiquitous problem in a wide array of economic applications and beyond. In this work, we study how to design additional rewards to steer multi-agent systems towards desired policies \emph{without} prior knowledge of the agents' underlying learning dynamics. Motivated by the limitation of existing works, we consider a new and general category of learning dynamics called \emph{Markovian<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30137<END_URL>

Learning Transformer-based World Models with Contrastive Predictive Coding\nThe DreamerV3 algorithm recently obtained remarkable performance across diverse environment domains by learning an accurate world model based on Recurrent Neural Networks (RNNs). Following the success of model-based reinforcement learning algorithms and the rapid adoption of the Transformer architecture for its superior training efficiency and favorable scaling properties, recent works such as STORM have proposed replaci<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29267<END_URL>

Learning under Temporal Label Noise\nMany time series classification tasks, where labels vary over time, are affected by label noise that also varies over time. Such noise can cause label quality to improve, worsen, or periodically change over time. We first propose and formalize temporal label noise, an unstudied problem for sequential classification of time series. In this setting, multiple labels are recorded over time while being corrupted by a time-dependent noise function. We first demonst<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30932<END_URL>

Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors\nVisuo-tactile sensors aim to emulate human tactile perception, enabling robots to precisely understand and manipulate objects. Over time, numerous meticulously designed visuo-tactile sensors have been integrated into robotic systems, aiding in completing various tasks. However, the distinct data characteristics of these low-standardized visuo-tactile sensors hinder the establishment of a powerful tactile percep<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29304<END_URL>

Learning vector fields of differential equations on manifolds with geometrically constrained operator-valued kernels\nWe address the problem of learning ordinary differential equations (ODEs) on manifolds. Existing machine learning methods, particularly those using neural networks, often struggle with high computational demands. To overcome this issue, we introduce a geometrically constrained operator-valued kernel that allows us to represent vector fields on tangent bundles of smooth manifolds.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29790<END_URL>

Learning Video-Conditioned Policy on Unlabelled Data with Joint Embedding Predictive Transformer\nThe video-conditioned policy takes prompt videos of the desired tasks as a condition and is regarded for its prospective generalizability. Despite its promise, training a video-conditioned policy is non-trivial due to the need for abundant demonstrations. In some tasks, the expert rollouts are merely available as videos, and costly and time-consuming efforts are required to annotate action labels. T<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29514<END_URL>

Learning View-invariant World Models for Visual Robotic Manipulation\nRobotic manipulation tasks often rely on visual inputs from cameras to perceive the environment. However, previous approaches still suffer from performance degradation when the camera’s viewpoint changes during manipulation. In this paper, we propose ReViWo (Representation learning for View-invariant World model), leveraging multi-view data to learn robust representations for control under viewpoint disturbance. ReViWo utilize<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27921<END_URL>

Learn Your Reference Model for Real Good Alignment\nDespite the fact that offline methods for Large Language Models (LLMs) alignment do not require a direct reward model, they remain susceptible to overoptimization. This issue arises when the trained model deviates excessively from the reference policy, leading to a decrease in sample quality. We propose a novel approach of offline alignment methods, called Trust Region (including variants TR-DPO, TR-IPO, TR-KTO), which dynamically updates the r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30247<END_URL>

Leave-One-Out Stable Conformal Prediction\nConformal prediction (CP) is an important tool for distribution-free predictive uncertainty quantification.Yet, a major challenge is to balance computational efficiency and prediction accuracy, particularly for multiple predictions.We propose **L**eave-**O**ne-**O**ut **Stab**le **C**onformal **P**rediction (LOO-StabCP), a novel method to speed up full conformal using algorithmic stability without sample splitting.By leveraging *leave-one-out* stability<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30540<END_URL>

LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models\nPatient data from real-world clinical practice often suffers from data scarcity and long-tail imbalances, leading to biased outcomes or algorithmic unfairness. This study addresses these challenges by generating lesion-containing image-segmentation pairs from lesion-free images. Previous efforts in medical imaging synthesis have struggled with separating lesion information from background, resulting in low-quality ba<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31069<END_URL>

LEGO: Learnable Expansion of Graph Operators for Multi-Modal Feature Fusion\nIn computer vision tasks, features often come from diverse representations, domains (e.g., indoor and outdoor), and modalities (e.g., text, images, and videos). Effectively fusing these features is essential for robust performance, especially with the availability of powerful pre-trained models like vision-language models. However, common fusion methods, such as concatenation, element-wise operations, and non-linear tec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29612<END_URL>

LegoScale: One-stop PyTorch native solution for production ready LLM pre-training\nThe development of large language models (LLMs) has been instrumental in advancing state-of-the-art natural language processing applications. Training LLMs with billions of parameters and trillions of tokens requires sophisticated distributed systems that enable composing and comparing several state-of-the-art techniques in order to efficiently scale across thousands of accelerators. However, existing solutions ar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29620<END_URL>

Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model\n''Grokking'' is a phenomenon where a neural network first memorizes training data and generalizes poorly, but then suddenly transitions to near-perfect generalization after prolonged training. While intriguing, this delayed generalization phenomenon compromises predictability and efficiency. Ideally, models should generalize directly without delay. To this end, this paper proposes GrokTransfer, a simple and pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30991<END_URL>

Let SSMs be ConvNets: State-space Modeling with Optimal Tensor Contractions\nWe introduce Centaurus, a class of networks composed of generalized state-space model (SSM) blocks, where the SSM operations can be treated as tensor contractions during training. The optimal order of tensor contractions can then be systematically determined for every SSM block to maximize training efficiency. This allows more flexibility in designing SSM blocks beyond the depthwise-separable configuration commonly impl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29733<END_URL>

Let the Code LLM Edit Itself When You Edit the Code\nIn this work, we investigate a typical scenario in code generation where a developer edits existing code in real time and requests a code assistant, e.g., a large language model, to re-predict the next token or next line on the fly. Naively, the LLM needs to re-encode the entire KV cache to provide an accurate prediction. However, this process is computationally expensive, especially when the sequence length is long. Simply encoding the edited<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27645<END_URL>

Leveraging Discrete Structural Information for Molecule-Text Modeling\nThe integration of molecular and natural language representations has emerged as a focal point in molecular science, with recent advancements in Language Models (LMs) demonstrating significant potential for comprehensive modeling of both domains. However, existing approaches face notable limitations, particularly in their neglect of three-dimensional (3D) information, which is crucial for understanding molecular structures an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28938<END_URL>

Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction\nUnderstanding drivers’ decision-making is crucial for road safety. Although predicting the ego-vehicle’s path is valuable for driver-assistance systems, existing methods mainly focus on external factors like other vehicles’ motions, often neglecting the driver’s attention and intent. To address this gap, we infer the ego-trajectory by integrating the driver’s attention and the surrounding scene. We introduce RouteFormer, a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30006<END_URL>

Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD\nInformation-theoretic generalization bounds have been used to study the generalization of learning algorithms. These bounds are intrinsically data- and algorithm-dependent so that one can exploit the properties of data and algorithm to derive tighter bounds. However, we observe such algorithm dependence is still inadequate in existing information-theoretic bounds for SGD because they have not adequately leveraged<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28297<END_URL>

Leveraging Submodule Linearity Enhances Task Arithmetic Performance in LLMs\nTask arithmetic is a straightforward yet highly effective strategy for model merging, enabling the resultant model to exhibit multi-task capabilities. Recent research indicates that models demonstrating linearity enhance the performance of task arithmetic. In contrast to existing methods that rely on the global linearization of the model, we argue that this linearity already exists within the model's submodules. In part<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28670<END_URL>

Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning\nTo create useful reinforcement learning (RL) agents, step zero is to design a suitable reward function that captures the nuances of the task. However, reward engineering can be a difficult and time-consuming process.  Instead, human-in-the-loop (HitL) RL methods hold the promise of learning reward functions from human feedback. Despite recent successes, many of the HitL RL methods still require numerous human interactions <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30457<END_URL>

Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization\nGradient-based multi-objective optimization (MOO) is essential in modern machine learning, with applications in e.g., multi-task learning, federated learning,  algorithmic fairness and reinforcement learning. In this work, we first reveal some limitations of Pareto stationarity, a widely accepted first-order condition for Pareto optimality, in the presence of sparse function-variable structures. Next, to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30551<END_URL>

LICO: Large Language Models for In-Context Molecular Optimization\nOptimizing black-box functions is a fundamental problem in science and engineering. To solve this problem, many approaches learn a surrogate function that estimates the underlying objective from limited historical evaluations. Large Language Models (LLMs), with their strong pattern-matching capabilities via pretraining on vast amounts of data, stand out as a potential candidate for surrogate modeling. However, directly prompting <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27698<END_URL>

LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning\nRecent advances in reinforcement learning (RL) have predominantly leveraged neural network-based policies for decision-making, yet these models often lack interpretability, posing challenges for stakeholder comprehension and trust. Concept bottleneck models offer an interpretable alternative by integrating human-understandable concepts into neural networks. However, a significant limitation in prior work is the assumpt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29920<END_URL>

Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups\nThe quest for robust and generalizable machine learning models has driven recent interest in exploiting symmetries through equivariant neural networks. In the context of PDE solvers, recent works have shown that Lie point symmetries can be a useful inductive bias for Physics-Informed Neural Networks (PINNs) through data and loss augmentation. Despite this, directly enforcing equivariance within the model archi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30825<END_URL>

LiFT: Learning to Fine-Tune via Bayesian Parameter Efficient Meta Fine-Tuning\nWe tackle the problem of parameter-efficient fine-tuning (PEFT) of a pre-trained large deep model on many different but related tasks. Instead of the simple but strong baseline strategy of task-wise independent fine-tuning, we aim to meta-learn the core shared information that can be used for unseen test tasks to improve the prediction performance further. That is, we propose a method for {\em learning-to-fine-tune} (<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30801<END_URL>

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space\nWe introduce a new framework for 2D molecular graph generation using 3D molecule generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps 2D molecular graphs to 3D Euclidean point clouds via synthetic coordinates and learns the inverse map using an E($n$)-Equivariant Graph Neural Network (EGNN). The induced point cloud-structured latent space is well-suited to apply existing 3D molecule generative model<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27981<END_URL>

Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models\nDiffusion inversion is the problem of taking an image and a text prompt that describes it and finding a noise latent that would generate the exact same image. Most current deterministic inversion techniques operate by approximately solving an implicit equation and may converge slowly or yield poor reconstructed images.  We formulate the problem by finding the roots of an implicit equation and design a method to solve <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28072<END_URL>

Lightweight Neural App Control\nThis paper introduces a novel mobile phone control architecture, termed "app agents", for efficient interactions and controls across various Android apps. The proposed Lightweight Multi-modal App Control (LiMAC) takes as input a textual goal and a sequence of past mobile observations, such as screenshots and corresponding UI trees, to generate precise actions. To address the computational constraints inherent to smartphones, within LiMAC, we introduce a small Acti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30577<END_URL>

Lightweight Predictive 3D Gaussian Splats\nRecent approaches representing 3D objects and scenes using Gaussian splats show increased rendering speed across a variety of platforms and devices. While rendering such representations is indeed extremely efficient, storing and transmitting them is often prohibitively expensive. To represent large-scale scenes, one often needs to store millions of 3D Gaussian, which can occupy up to gigabytes of storage. This creates a significant practical barrier, pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29745<END_URL>

Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory\nDespite their successes, deep learning models struggle with tasks requiring complex reasoning and function composition. We present a theoretical and empirical investigation into the limitations of Structured State Space Models (SSMs) and Transformers in such tasks. We prove that one-layer SSMs cannot efficiently perform function composition over large domains without impractically large state sizes, and even with C<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30446<END_URL>

Limits to scalable evaluation at the frontier: LLM as judge won’t beat twice the data\nHigh quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem. Scalable evaluation methods that avoid costly annotation have therefore become an important research ambition. Many hope to use strong existing models in lieu of costly labels to provide cheap model evaluations. Unfortunately, this method of using models as judges introduces biases, such as self-prefer<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29881<END_URL>

Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better\nDiffusion Models (DM) and Consistency Models (CM) are two types of popular generative models with good generation quality on various tasks. When training DM and CM, intermediate weight checkpoints are not fully utilized and only the last converged checkpoint is used. In this work, we find proper checkpoint merging can significantly improve the training convergence and final performance. Specifically, we propos<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29672<END_URL>

Linear combinations of Gaussian latents in generative models: interpolation and beyond\nSampling from generative models has become a crucial tool for applications like data synthesis and augmentation. Diffusion, Flow Matching and Continuous Normalizing Flows have shown effectiveness across various modalities, and rely on Gaussian latent variables for generation. For search-based or creative applications that require additional control over the generation process, it has become common to manipula<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28429<END_URL>

Linear Mode Connectivity in Differentiable Tree Ensembles\nLinear Mode Connectivity (LMC) refers to the phenomenon that performance remains consistent for linearly interpolated models in the parameter space. For independently optimized model pairs from different random initializations, achieving LMC is considered crucial for understanding the stable success of the non-convex optimization in modern machine learning models and for facilitating practical parameter-based operations such as model mer<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29452<END_URL>

Linear Multistep Solver Distillation for Fast Sampling of Diffusion Models\nSampling from diffusion models can be seen as solving the corresponding    probability flow ordinary differential equation (ODE).    The solving process requires a significant number of function    evaluations (NFE), making it time-consuming.    Recently, several solver search frameworks have attempted to find    better-performing model-specific solvers. However, predicting the impact of    intermediate solving strategie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27893<END_URL>

Linear Partial Gromov-Wasserstein Embedding\nThe Gromov–Wasserstein (GW) problem, a variant of the classical optimal transport (OT) problem, has attracted growing interest in the machine learning and data science communities due to its ability to quantify similarity between measures in different metric spaces. However, like the classical OT problem, GW imposes an equal mass constraint between measures, which restricts its application in many machine learning tasks. To address this limitation, th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30589<END_URL>

Linear Recursions for Everyone\nInvestigating linear RNNs such as Mamba, can be challenging because they are currently not efficiently expressible in PyTorch. We propose the abstraction of linear recursions to gain intuition for the computational structure of these emerging deep learning architectures. After deriving their parallel algorithm, we gradually build towards a simple template CUDA extension for PyTorch. We hope that making linear recursions accessible to a wider audience inspires furt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31368<END_URL>

Linear Representations of Political Perspective Emerge in Large Language Models\nLarge language models (LLMs) have demonstrated the ability to simulate responses aligned with human subjective perspectives, such as liberal or conservative ideologies in American politics. Our study reveals that LLMs achieve this by learning a ``geometry of perspective'' that linearly represents subjective perspectives in the activation space, where similar simulated perspectives are represented closer to each othe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28160<END_URL>

Linear SCM Identification in the Presence of Confounders and Gaussian Noise\nNoisy linear structural causal models (SCMs) in the presence of confounding variables are known to be identifiable if all confounding and noise variables are non-Gaussian and unidentifiable if all are Gaussian.    The identifiability when only some are Gaussian remains concealed.     We show that, in the presence of Gaussian noise, a linear SCM is uniquely identifiable provided that \emph{(i)} the number of confounders <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29094<END_URL>

Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data\nEfficient comparison of spherical probability distributions becomes important in fields such as computer vision, geosciences, and medicine. Sliced optimal transport distances, such as spherical and stereographic spherical sliced Wasserstein distances, have recently been developed to address this need. These methods reduce the computational burden of optimal transport by slicing hyperspheres into one-dimensiona<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28865<END_URL>

Linear Transformer Topological Masking with Graph Random Features\nWhen training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relative position encoding, achieves this by upweighting or downweighting attention depending on the relationship between the query and keys in the graph. In this paper, we propose to parameterise topological masks as a learnable function of a weighted adjacen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30895<END_URL>

Lines of Thought in Large Language Models\nLarge Language Models achieve next-token prediction by transporting a vectorized piece of text (prompt) across an accompanying embedding space under the action of successive transformer layers. The resulting high-dimensional trajectories realize different contextualization, or 'thinking', steps, and fully determine the output probability distribution. We aim to characterize the statistical properties of ensembles of these 'lines of thought.' We observe <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27654<END_URL>

LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging\nLarge pre-trained models exhibit impressive zero-shot performance across diverse tasks, but fine-tuning often leads to catastrophic forgetting, where improvements on a target domain degrade generalization on other tasks. To address this challenge, we introduce LiNeS, Layer-Increasing Network Scaling, a post-training editing technique designed to preserve pre-trained generalization while enhancing fine-tuned task p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30130<END_URL>

Lipschitz Bandits in Optimal Space\nThis paper considers the Lipschitz bandit problem, where the set of arms is continuous and the expected reward is a Lipschitz function over the arm space. This problem has been extensively studied. Prior algorithms need to store the reward information of all visited arms, leading to significant memory consumption. We address this issue by introducing an algorithm named Log-space Lipschitz bandits (Log-Li), which achieves an optimal (up to logarithmic factors) <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28717<END_URL>

LiveBench: A Challenging, Contamination-Free LLM Benchmark\nTest set contamination, wherein test data from a benchmark ends up in a newer model's training set, is a well-documented obstacle for fair LLM evaluation and can quickly render benchmarks obsolete. To mitigate this, many recent benchmarks crowdsource new prompts and evaluations from human or LLM judges; however, these can introduce significant biases, and break down when scoring hard questions. In this work, we introduce a new benchmark<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28134<END_URL>

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code\nLarge Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from academia and industry. However, as new and improved LLMs are developed, existing evaluation benchmarks (e.g., HumanEvla, MBPP) are no longer sufficient for assessing their capabilities suffering from data contamination, overfitting, saturation, and focus on merely code<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29033<END_URL>

LiveXiv - A Multi-Modal live benchmark based on Arxiv papers content\nThe large-scale training of multi-modal models on data scraped from the web has shown outstanding utility in infusing these models with the required world knowledge to perform effectively on multiple downstream tasks. However, one downside of scraping data from the web can be the potential sacrifice of the benchmarks on which the abilities of these models are often evaluated. To safeguard against test data contamination and to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29572<END_URL>

LLaMaFlex: Many-in-one LLMs via Generalized Pruning and Weight Sharing\nLarge Language Model (LLM) providers typically train a family of models, each of a different size targeting a specific deployment scenario. Models in the family are all trained from scratch, making the process extremely resource intensive.Recent work has successfully reduced the cost of training model families through a combination of structured pruning and knowledge distillation; here, only the largest model in the family i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30601<END_URL>

LLaMA-Omni: Seamless Speech Interaction with Large Language Models\nModels like GPT-4o enable real-time interaction with large language models (LLMs) through speech, significantly enhancing user experience compared to traditional text-based interaction. However, there is still a lack of exploration on how to build speech interaction models based on open-source LLMs. To address this, we propose LLaMA-Omni, a novel model architecture designed for low-latency and high-quality speech interaction wit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29748<END_URL>

LLaRA: Supercharging Robot Learning Data for Vision-Language Policy\nLLMs with visual inputs, i.e., Vision Language Models (VLMs), have the capacity to process state information as visual-textual prompts and respond with policy decisions in text. We propose LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as conversations and provides improved action output when trained with auxiliary data that complements policy learning. We first introduce an automa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28695<END_URL>

LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token\nThe advent of real-time large multimodal models (LMMs) like GPT-4o has sparked considerable interest in efficient LMMs. LMM frameworks typically encode visual inputs into vision tokens (continuous representations) and integrate them and textual instructions into the context of large language models (LLMs), where large-scale parameters and numerous context tokens (predominantly vision tokens) result in substantia<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29475<END_URL>

LLaVA-MoD: Making LLaVA Tiny via MoE-Knowledge Distillation\nWe introduce LLaVA-MoD, a novel framework designed to enable the efficient training of small-scale Multimodal Language Models ($s$-MLLM) distilling knowledge from large-scale MLLM ($l$-MLLM). Our approach tackles two fundamental challenges in MLLM distillation. First, we optimize the network structure of $s$-MLLM by integrating a sparse Mixture of Experts (MoE) architecture into the language model, striking a balance between computatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27973<END_URL>

LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models\nVisual instruction tuning has made considerable strides in enhancing the capabilities of Large Multimodal Models (LMMs). However, existing open LMMs largely focus on single-image tasks, their applications to multiimage scenarios remains less explored. Additionally,prior LMM research separately tackles different scenarios, leaving it impossible to generalize cross scenarios with new emerging capabilities. To th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28357<END_URL>

LLM-Assisted Static Analysis for Detecting Security Vulnerabilities\nSoftware is prone to security vulnerabilities. Program analysis tools to detect them have limited effectiveness in practice due to their reliance on human labeled specifications. Large language models (or LLMs) have shown impressive code generation capabilities but they cannot do complex reasoning over code to detect such vulnerabilities especially since this task requires whole-repository analysis. We propose IRIS, a neuro-sym<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30698<END_URL>

LLM-based Typed Hyperresolution for Commonsense Reasoning with Knowledge Bases\nLarge language models (LLM) are being increasingly applied to tasks requiring commonsense reasoning. Despite their outstanding potential, the reasoning process of LLMs is prone to errors and hallucinations that hinder their applicability, especially in high-stakes scenarios. Several works have attempted to enhance commonsense reasoning performance of LLMs by (i) using prompting styles that elicit more accurate reason<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27849<END_URL>

LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch\nOptimization problems are prevalent across various scenarios. Formulating and then solving optimization problems described by natural language often requires highly specialized human expertise, which could block the widespread application of optimization-based decision making. To make problem formulating and solving automated, leveraging large language models (LLMs) has emerged as a potential way. However, this kind<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30695<END_URL>

LLMs Can Plan Only If We Tell Them\nLarge language models (LLMs) have demonstrated significant capabilities in natural language processing and reasoning, yet their effectiveness in autonomous planning has been under debate. While existing studies have utilized LLMs with external feedback mechanisms or in controlled environments for planning, these approaches often involve substantial computational and development resources due to the requirement for careful design and iterative backprompting. Mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30078<END_URL>

LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations\nLarge language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as "hallucinations". Recent studies have demonstrated that LLMs' internal states encode information regarding the truthfulness of their outputs, and that this information can be utilized to detect errors. In this work, we show that the internal representations of LLMs encod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30060<END_URL>

LLMs' Potential Influences on Our Democracy: Challenges and Opportunities\nWith growing research and attention on LLMs' potential influence on political discourse and democratic processes, this blog post discusses the path forward and proposes future research questions in four broad areas: (1) evaluation of LLM political leanings, (2) understanding LLMs' influence on our democracy, (3) better policy frameworks for AI development, and (4) technical solutions to adjust or mitigate political leanin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31347<END_URL>

LLM-SR: Scientific Equation Discovery via Programming with Large Language Models\nMathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equations from data presents significant challenges due to the necessity of navigating extremely large combinatorial hypothesis spaces. Current methods of equation discovery, commonly known as symbolic regression techniques, largely focus on extr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28487<END_URL>

LLM Table Reading: Bridging the Semantic Gap Between Text and Table\nThe rise of Large Language Models (LLMs) has revolutionized numerous domains, yet these models still exhibit weakness in understanding structured tabular data.Although a larger context window of next-generation LLMs promises them to accommodate a larger volume of table contents, it does not inherently improve the model's capability to understand the underlying structure and semantics of tabular data.To bridge the semantic gap b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28237<END_URL>

LLM Unlearning via Loss Adjustment with Only Forget Data\nUnlearning in Large Language Models (LLMs) is essential for ensuring ethical and responsible AI use, especially in addressing privacy leak, bias, safety, and evolving regulations. Existing approaches to LLM unlearning often rely on retain data or a reference LLM, yet they struggle to adequately balance unlearning performance with overall model utility. This challenge arises because leveraging explicit retain data or implicit knowledge of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30903<END_URL>

LLM-wrapper: Black-Box Semantic-Aware Adaptation of Vision-Language Models for Referring Expression Comprehension\nVision Language Models (VLMs) have demonstrated remarkable capabilities in various open-vocabulary tasks, yet their zero-shot performance lags behind task-specific fine-tuned models, particularly in complex tasks like Referring Expression Comprehension (REC). Fine-tuning usually requires ‘white-box’ access to the model’s architecture and weights, which is not always feasible due to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29739<END_URL>

Local convergence of simultaneous min-max algorithms  to differential equilibrium on Riemannian manifold\nWe study min-max algorithms to solve zero-sum differential games onRiemannian manifold.Based on the notions ofdifferential Stackelberg equilibriumand differential Nash equilibrium on Riemannian manifold,we analyze the local convergence of two representative deterministic simultaneous algorithms $\tau$-GDA and $\tau$-SGAto such equilibria.Sufficient conditions are obtained to establish the li<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29652<END_URL>

Locality Alignment Improves Vision-Language Models\nVision language models (VLMs) have seen growing adoption in recent years, but many still struggle with basic spatial reasoning. We hypothesize that this is due to VLMs adopting pre-trained vision backbones, specifically vision transformers (ViTs) trained with image-level supervision and minimal inductive biases. Such models may fail to encode the class contents at each position in the image, and our goal is to resolve this by ensuring that the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28233<END_URL>

Locality-aware Gaussian Compression for Fast and High-quality Rendering\nWe present LocoGS, a locality-aware 3D Gaussian Splatting (3DGS) framework that exploits the spatial coherence of 3D Gaussians for compact modeling of volumetric scenes.To this end, we first analyze the local coherence of 3D Gaussian attributes, and propose a novel locality-aware 3D Gaussian representation that effectively encodes locally-coherent Gaussian attributes using a neural field representation with a minimal storag<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28993<END_URL>

Locality Sensitive Avatars From Video\nWe present locality-sensitive avatar, a neural radiance field (NeRF) based network to learn human motions from monocular videos. To this end, we estimate a canonical representation between different frames of a video with a non-linear mapping from observation to canonical space, which we decompose into a skeletal rigid motion and a non-rigid counterpart. Our key contribution is to retain fine-grained details by modeling the non-rigid part with a graph neura<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29598<END_URL>

Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation\nLocal learning, which trains a network through layer-wise local targets and losses, has been studied as an alternative to backpropagation (BP) in neural computation. However, its algorithms often become more complex or require additional hyperparameters due to the locality, making it challenging to identify desirable settings where the algorithm progresses in a stable mann<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28834<END_URL>

Locally Connected Echo State Networks for Time Series Forecasting\nEcho State Networks (ESNs) are a class of recurrent neural networks in which only a small readout regression layer is trained, while the weights of the recurrent network, termed the reservoir, are randomly assigned and remain fixed.Our work introduces the Locally Connected ESN (LCESN), a novel ESN variant with a locally connected reservoir, forced memory, and a weight adaptation strategy.LCESN significantly reduces the asymptotic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30041<END_URL>

LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning\nLow-rank adaptation (LoRA) has become a prevalent method for adapting pre-trained large language models to downstream tasks. However, the simple low-rank decomposition form may constrain the optimization flexibility. To address this limitation, we introduce Location-aware Cosine Adaptation (LoCA), a novel frequency-domain parameter-efficient fine-tuning method based on inverse Discrete Cosine Transform (iDCT) with select<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31017<END_URL>

Local Patterns Generalize Better for Novel Anomalies\nVideo anomaly detection (VAD) aims to identify novel actions or events which are unseen during training. Existing mainstream VAD techniques typically focus on the global patterns with redundant details and struggle to generalize to unseen samples. In this paper, we propose a framework that identifies the local patterns which generalize to novel samples and models the dynamics of local patterns. The capability of extracting spatial local patte<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30988<END_URL>

Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression\nWe analyze two variants of Local Gradient Descent applied to distributed logistic regression with heterogeneous, separable data and show convergence at the rate $O(1/KR)$ for $K$ local steps and sufficiently large $R$ communication rounds. In contrast, all existing convergence guarantees for Local GD applied to any problem are at least $\Omega(1/R)$, meaning they fail to show the benefit of local updates. The key to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28491<END_URL>

LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression\nIn $D$istributed optimization and $L$earning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical. We introduce LoCoDL, a communication-efficient algorithm that leverages the two popular and effective techniques of $Lo$cal training, which reduces the communication frequency, and $Co$mpression, in which short bitstreams are sent instead of full-di<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29728<END_URL>

LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality\nUnderstanding human locomotion is crucial for AI agents such as robots, particularly in complex indoor home environments. Modeling human trajectories in these spaces requires insight into how individuals maneuver around physical obstacles and manage social navigation dynamics. These dynamics include subtle behaviors influenced by proxemics - the social use of space, such as stepping aside to allow others to pass or choosing longer r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30668<END_URL>

Logical Consistency of Large Language Models in Fact-Checking\nIn recent years, large language model (LLM) has demonstrated significant success in performing varied natural language tasks such as language translation, question-answering, summarising, fact-checking, etc. Despite LLM’s impressive ability to generate human-like texts, LLMs are infamous for their inconsistent responses – a meaning-preserving change in the input query results in an inconsistent response and attributes to vulnerabilit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29583<END_URL>

Logically Consistent Language Models via Neuro-Symbolic Integration\nLarge language models (LLMs) are a promising venue for natural language understanding and generation tasks. However, current LLMs are far from reliable: they are prone to generate non-factual information and, more crucially, to contradict themselves when prompted to reason about relations between real entities of the world. These problems are currently addressed with large scale fine-tuning or by delegating consistent reasoning<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30826<END_URL>

Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference\nWe study how to subvert large language models (LLMs) from following prompt-specified rules.We first formalize rule-following as inference in propositional Horn logic, a mathematical system in which rules have the form "if $P$ and $Q$, then $R$" for some propositions $P$, $Q$, and $R$.Next, we prove that although small transformers can faithfully follow such rules, maliciously crafted prompts can still mislead both the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28281<END_URL>

Logic-Logit: A Logic-Based Approach to Choice Modeling\nIn this study, we propose a novel rule-based interpretable choice model, {\bf Logic-Logit}, designed to effectively learn and explain human choices. Choice models have been widely applied across various domains—such as commercial demand forecasting, recommendation systems, and consumer behavior analysis—typically categorized as parametric, nonparametric, or deep network-based. While recent innovations have favored neural network approaches <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27923<END_URL>

LOIRE: LifelOng learning on Incremental data via pre-trained language model gRowth Efficiently\nLarge-scale pre-trained language models (PLMs) require significant computational resources to train from scratch on large volumes of data. But in the real world, emerging data from diverse sources may not be initially available for pre-training. Recent studies on lifelong learning have tried to solve this problem by exploring the use of model growth techniques to effectively incorporate new knowledge <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30366<END_URL>

LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models\nWith the rapid development of AI-generated content, the future internet may be inundated with synthetic data, making the discrimination of authentic and credible multimodal data increasingly challenging. Synthetic data detection has thus garnered widespread attention, and the performance of large multimodal models (LMMs) in this task has attracted significant interest. LMMs can provide natural language explan<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27689<END_URL>

LoLCATs: On Low-Rank Linearizing of Large Language Models\nRecent works show we can linearize large language models (LLMs)—swapping the quadratic attentions of popular Transformer-based LLMs with subquadratic analogs, such as linear attention—avoiding the expensive pretraining costs. However, linearizing LLMs often significantly degrades model quality, still requires training over billions of tokens, and remains limited to smaller 1.3B to 7B LLMs. We thus propose Low-rank Linear Conversion via A<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30763<END_URL>

Long Context Compression with Activation Beacon\nLong context compression is a critical research problem due to its significance in reducing the high computational and memory costs associated with LLMs. In this paper, we propose Activation Beacon, a plug-in module for transformer-based LLMs that targets effective, efficient, and flexible compression of long contexts. To achieve this, our method introduces the following technical designs. 1) We directly compress the activations (i.e. keys and val<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31191<END_URL>

Long-Context Linear System Identification\nThis paper addresses the problem of long-context linear system identification, where the state $x_t$ of the system at time $t$ depends linearly on previous states $x_s$ over a fixed context window of length $p$. We establish a sample complexity bound that matches the _i.i.d._ parametric rate, up to logarithmic factors for a broad class of systems, extending previous work that considered only first-order dependencies. Our findings reveal a ``learning-wit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31134<END_URL>

Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG\nRetrieval-augmented generation (RAG) empowers large language models (LLMs) to utilize external knowledge sources. The increasing capacity of LLMs to process longer input sequences opens up avenues for providing more retrieved information, to potentially enhance the quality of generated outputs. It is plausible to assume that a larger retrieval set would contain more relevant information (higher recall), that might result i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28355<END_URL>

LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs\nCurrent benchmarks like ``$\textit{Needle-in-a-Haystack}$'' ($\textit{NIAH}$), $\textit{Ruler}$, and $\textit{Needlebench}$ focus on models' ability to understand long-context input sequences but fail to capture a critical dimension: the generation of high-quality long-form text. Applications such as design proposals, technical documentation, and creative writing rely on coherent, instruction-following outputs over extended se<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31092<END_URL>

Long-horizon Visual Instruction Generation with Logic and Attribute Self-reflection\nVisual instructions for long-horizon tasks are crucial as they intuitively clarify complex concepts and enhance retention across extended steps. Directly generating a series of images using text-to-image models without considering the context of previous steps results in inconsistent images, increasing cognitive load.  Additionally, the generated images often miss objects or the attributes such as color, shape, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30395<END_URL>

Longhorn: State Space Models are Amortized Online Learners\nThe most fundamental capability of modern AI methods such as Large Language Models (LLMs) is the ability to predict the next token in a long sequence of tokens, known as “sequence modeling.”  Although the Transformers model is the current dominant approach to sequence modeling, its quadratic computational cost with respect to sequence length is a significant drawback. State-space models (SSMs) offer a promising alternative due to their <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30749<END_URL>

LongMamba: Enhancing Mamba's Long-Context Capabilities via Training-Free Receptive Field Enlargement\nMamba models have emerged as an efficient alternative to Transformer models for language modeling tasks, offering linear complexity as context length increases. However, despite their efficiency in handling long contexts, recent studies have demonstrated that Mamba models underperform in understanding extended contexts compared to Transformer models. To address this significant shortfall, we pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28881<END_URL>

LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory\nRecent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized responses. However, their long-term memory capabilities in sustained interactions remain underexplored. This paper introduces LongMemEval, a comprehensive benchmark designed to evaluate five core long-term memory abilities of chat assistants: info<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28290<END_URL>

LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization\nLarge Language Models (LLMs) have demonstrated remarkable capabilities through pretraining and alignment. However, superior short-context LLMs may underperform in long-context scenarios due to insufficient long-context alignment. This alignment process remains challenging due to the impracticality of human annotation for extended contexts and the difficulty in balancing short- and long-con<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28247<END_URL>

Long-Sequence Recommendation Models Need Decoupled Embeddings\nLifelong user behavior sequences, comprising up to tens of thousands of history behaviors, are crucial for capturing user interests and predicting user responses in modern recommendation systems. A two-stage paradigm is typically adopted to handle these long sequences: a few relevant behaviors are first searched from the original long sequences via an attention mechanism in the first stage and then aggregated with the target item to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28620<END_URL>

Long-Short Decision Transformer: Bridging Global and Local Dependencies for Generalized Decision-Making\nDecision Transformers (DTs) effectively capture long-range dependencies using self-attention but struggle with fine-grained local relationships, especially the Markovian properties in many offline-RL datasets. Conversely, Decision Convformer (DC) utilizes convolutional filters for capturing local patterns but shows limitations in tasks demanding long-term dependencies, such as Maze2d. To addr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29888<END_URL>

Long-tailed Adversarial Training with Self-Distillation\nAdversarial training significantly enhances adversarial robustness, yet superior performance is predominantly achieved on balanced datasets. Addressing adversarial robustness in the context of unbalanced or long-tailed distributions is considerably more challenging, mainly due to the scarcity of tail data instances.  Previous research on adversarial robustness within long-tailed distributions has primarily focused on combining traditional <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27919<END_URL>

Long-time asymptotics of noisy SVGD outside the population limit\nStein Variational Gradient Descent (SVGD) is a widely used sampling algorithm that has been successfully applied in several areas of Machine Learning. SVGD operates by iteratively moving a set of $n$ interacting particles (which represent the samples) to approximate the target distribution. Despite recent studies on the complexity of SVGD and its variants, their long-time asymptotic behavior (i.e., after numerous iterations $k$) i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29322<END_URL>

LongVILA: Scaling Long-Context Visual Language Models for Long Videos\nLong-context capability is critical for multi-modal foundation models, especially for long video understanding. We introduce LongVILA, a full-stack solution for long-context visual-language models by co-designing the algorithm and system. For model training, we upgrade existing VLMs to support long video understanding by incorporating two additional stages, i.e., long context extension and long video supervised fine-tuning. H<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27865<END_URL>

LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs\nCurrent long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation length is inherently bounded by the sample it has seen during supervised fine-tuning (SFT). In other words, their output limitation is due to the scarcity of long-output examples i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28586<END_URL>

Look Before You Leap: Universal Emergent Mechanism for Retrieval in Language Models\nWhen solving challenging problems, language models (LMs) are able to identify relevant information from long and complicated contexts. To study how LMs solve retrieval tasks in diverse situations, we introduce ORION, a collection of structured retrieval tasks spanning six domains, from text understanding to coding. Each task in ORION can be represented abstractly by a request (e.g. a question) that retrieves an <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28935<END_URL>

Looking Backward: Retrospective Backward Synthesis for Goal-Conditioned GFlowNets\nGenerative Flow Networks (GFlowNets), a new family of probabilistic samplers, have demonstrated remarkable capabilities to generate diverse sets of high-reward candidates, in contrast to standard return maximization approaches (e.g., reinforcement learning) which often converge to a single optimal solution. Recent works have focused on developing goal-conditioned GFlowNets, which aim to train a single GFlowNet cap<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28879<END_URL>

Looking into User’s Long-term Interests through the Lens of Conservative Evidential Learning\nReinforcement learning (RL) provides an effective means to capture users' evolving preferences, leading to improved recommendation performance over time. However, existing RL approaches primarily rely on standard exploration strategies, which are less effective for a large item space with sparse reward signals given the limited interactions for most users. Therefore, they may not be able to learn the op<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28375<END_URL>

Looking Inward: Language Models Can Learn About Themselves by Introspection\nHumans acquire knowledge by observing the external world, but also by introspection. Introspection gives a person privileged access to their current state of mind (e.g. thoughts and feelings) that are not accessible to external observers. Do LLMs have this introspective capability of privileged access? If they do, this would show that LLMs can acquire knowledge not contained in or inferable from training data.We investi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28917<END_URL>

Looped Transformers for Length Generalization\nRecent work has shown that Transformers trained from scratch can successfully solve various arithmetic and algorithmic tasks, such as adding numbers and computing parity. While these Transformers generalize well on unseen inputs of the same length, they struggle with length generalization, i.e., handling inputs of unseen lengths. In this work, we demonstrate that looped Transformers with an adaptive number of steps significantly improve length gener<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31126<END_URL>

LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation models\nEmerging 3D geometric foundation models, such as DUSt3R, offer a promising approach for in-the-wild 3D vision tasks.However, due to the high-dimensional nature of the problem space and scarcity of high-quality 3D data,these pre-trained models still struggle to generalize to many challenging circumstances,such as limited view overlap or low lighting.To address this, we propose LoRA3D, an efficient self-calibration pipeline to *s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29996<END_URL>

LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization\nLow-rank adaption (LoRA) is a widely used parameter-efficient finetuning method for LLM that reduces memory requirements. However, current LoRA optimizers lack transformation invariance, meaning the updates depending on how the two LoRA factors are scaled or rotated. This deficiency leads to inefficient learning and sub-optimal solutions in practice. This paper introduces LoRA-RITE, a novel adaptive matrix preco<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29397<END_URL>

LoRA-Pro: Are Low-Rank Adapters Properly Optimized?\nLow-rank adaptation, also known as LoRA, has emerged as a prominent method for parameter-efficient fine-tuning of foundation models.Despite its computational efficiency, LoRA still yields inferior performance compared to full fine-tuning.In this paper, we first uncover a fundamental connection between the optimization processes of LoRA and full fine-tuning: using LoRA for optimization is mathematically equivalent to full fine-tuning using a lo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28819<END_URL>

LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation\nThe rising popularity of large foundation models has led to a heightened demand for parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), which offer performance comparable to full model fine-tuning while requiring only a few additional parameters tailored to the specific base model. When such base models are deprecated and replaced, all associated LoRA modules must be retrained, requiring access<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30873<END_URL>

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation\nVisual prompting has gained popularity as a method for adapting pre-trained models to specific tasks, particularly in the realm of parameter-efficient tuning. However, existing visual prompting techniques often pad the prompt parameters around the image, limiting the interaction between the visual prompts and the original image to a small set of patches while neglecting the inductive bias present in shared information acros<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30935<END_URL>

Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding\nIn this paper, we investigate the loss landscape of one-hidden-layer neural networks with ReLU-like activation functions trained with the empirical squared loss. As the activation function is non-differentiable, it is so far unclear how to completely characterize the stationary points. We deduce the conditions for stationarity that apply to both non-differentiable and differentiable ar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28339<END_URL>

Lossy Compression with Pretrained Diffusion Models\nWe apply Theis et al. (2022)'s DiffC algorithm to Stable Diffusion 1.5, 2.1, XL, and and Flux-dev, and demonstrate that these pretrained models are remarkably capable lossy image compressors. A principled algorithm for compression using pretrained diffusion models has been understood since at least 2020 (Ho et al.), but challenges in reverse-channel coding have prevented such algorithms from ever being fully implemented. We introduce simple wor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28188<END_URL>

Lost in Prediction: Why Social Media Narratives Don't Help Macroeconomic Forecasting?\nCan we predict the macroeconomy by analyzing the narratives people share on social media? We dove deep into the world of Narrative Economics, using NLP models to analyze millions of viral tweets and see if they could help us predict the fluctuations of macroeconomic indicators. 🚨 Spoiler alert: it's not that easy! Join us as we explore the interesting relationship between narratives, social media, and macroeco<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31323<END_URL>

Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction\nLeveraging the visual priors of pre-trained text-to-image diffusion models offers a promising solution to enhance zero-shot generalization in dense prediction tasks. However, existing methods often uncritically use the original diffusion formulation, which may not be optimal due to the fundamental differences between dense prediction and image generation. In this paper, we provide a systemic analysis of the diffusi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28092<END_URL>

LR0.FM: LOW-RESOLUTION ZERO-SHOT CLASSIFICATION BENCHMARK FOR FOUNDATION MODELS\nVisual-language foundation Models (FMs) exhibit remarkable zero-shot generalization across diverse tasks, largely attributed to extensive pre-training on largescale datasets. However, their robustness on low-resolution/pixelated (LR) images, a common challenge in real-world scenarios, remains underexplored. We introduce LR0.FM, a comprehensive benchmark evaluating the impact of low resolution on the zero-shot classi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30609<END_URL>

LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision\nPrototypical parts networks combine the power of deep learning with the explainability of case-based reasoning to make accurate, interpretable decisions. They follow the this looks like that reasoning, representing each prototypical part with patches from training images. However, a single image patch comprises multiple visual features, such as color, shape, and texture, making it difficult for users<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30575<END_URL>

Lumina-T2X: Scalable Flow-based Large Diffusion Transformer for Flexible Resolution Generation\nSora unveils the potential of scaling Diffusion Transformer (DiT) for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lacks sufficient implementation details. In this paper, we introduce the Lumina-T2X family -- a series of Flow-based Large Diffusion Transformers (Flag-DiT) equipped with zero-initialized attention, as a simple and scalab<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30398<END_URL>

LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias\nWe propose the Large View Synthesis Model (LVSM), a novel transformer-based approach for scalable and generalizable novel view synthesis from sparse-view inputs. We introduce two architectures: (1) an encoder-decoder LVSM, which encodes input image tokens into a fixed number of 1D latent tokens, functioning as a fully learned scene representation, and decodes novel-view images from them; and (2) a decoder-only LVSM, which directl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29689<END_URL>

L-WISE: Boosting human image category learning through model-based image selection and enhancement\nThe currently leading artificial neural network (ANN) models of the visual ventral stream -- which are derived from a combination of performance optimization and robustification methods -– have demonstrated a remarkable degree of behavioral alignment with humans on visual categorization tasks. Extending upon previous work, we show that not only can these models guide image perturbations that chang<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30613<END_URL>

M^3PC: Test-time Model Predictive Control using Pretrained Masked Trajectory Model\nRecent work in Offline Reinforcement Learning (RL) has shown that  a unified transformer trained under a masked auto-encoding objective can effectively capture the relationships between different modalities (e.g., states, actions, rewards) within given trajectory datasets. However, this information has not been fully exploited during the inference phase, where the agent needs to generate an optimal policy instead<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28673<END_URL>

MA$^2$E: Addressing Partial Observability in Multi-Agent Reinforcement Learning with Masked Auto-Encoder\nCentralized Training and Decentralized Execution (CTDE) is a widely adopted paradigm to solve cooperative multi-agent reinforcement learning (MARL) problems. Despite the successes achieved with CTDE, partial observability still limits cooperation among agents. While previous studies have attempted to overcome this challenge through communication, direct information exchanges could be restric<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28562<END_URL>

Machine Learning for Genomics Explorations (MLGenX)\nOur limited understanding of the biological mechanisms underlying diseases remains a critical bottleneck in drug discovery. As a result, we often lack insights into why patients develop specific conditions, leading to the failure of many drug candidates in clinical trials. Recent advancements in genomics platforms and the emergence of diverse omics datasets have sparked increasing interest in this field. The primary objective of this workshop <BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23974<END_URL>

Machine Learning Multiscale Processes\nSome of the most exciting and impactful open scientific problems have computational complexity as the limiting factor to an in silico solution, e. g. high–temperature superconductivity and fusion power. Atoms behave according to the well–established laws of quantum mechanics, but as system size grows computations quickly become intractable. This workshop will gather for cross–pollination a diverse group of researchers belonging to difference scientific doma<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23967<END_URL>

Machine Unlearning Fails to Remove Data Poisoning Attacks\nWe revisit the efficacy of several practical methods for approximate machine unlearning developed for large-scale deep learning. In addition to complying with data deletion requests, one often-cited potential application for unlearning methods is to remove the effects of training on poisoned data. We experimentally demonstrate that, while existing unlearning methods have been demonstrated to be effective in a number of evaluation setting<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30218<END_URL>

Machine Unlearning via Simulated Oracle Matching\nMachine unlearning---efficiently removing the effect of a small "forget set" of training data on a pre-trained machine learning model---has recently attracted significant research interest. Despite this interest, however, recent work shows that existing machine unlearning techniques do not hold up to thorough evaluation in non-convex settings. In this work, we introduce a new machine unlearning technique that exhibits strong empirical performance<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31050<END_URL>

MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization\nAs large language models (LLMs) are rapidly advancing and achieving near-human capabilities, aligning them with human values is becoming more urgent. In scenarios where LLMs outperform humans, we face a weak-to-strong alignment problem where we need to effectively align strong student LLMs through weak supervision generated by weak teachers. Existing alignment methods mainly focus on strong-to-weak alignment and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27806<END_URL>

MADGEN - Mass-Spec attends to De Novo Molecular generation\nThe annotation (assigning structural chemical identities) of MS/MS spectra remains a significant challenge due to the enormous molecular diversity in biological samples and the limited scope of reference databases.  Currently, the vast majority of spectral measurements remain in the "dark chemical space" without structural annotations.  To improve annotation, we propose MADGEN (Mass-spec Attends to De Novo Molecular GENeration), a scaff<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30846<END_URL>

MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL\nBuilding deep reinforcement learning (RL) agents that find a good policy with few samples has proven notoriously challenging. To achieve sample efficiency, recent work has explored updating neural networks with large numbers of gradient steps for every new sample. While such high update-to-data (UTD) ratios have shown strong empirical performance, they also introduce instability to the training process.  Previous approaches need to re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30886<END_URL>

MAESTRO: Masked Encoding Set Transformer with Self-Distillation\nThe interrogation of cellular states and interactions in immunology research is an ever-evolving task, requiring adaptation to the current levels of high dimensionality. Cytometry enables high-dimensional profiling of immune cells, but its analysis is hindered by the complexity and variability of the data. We present MAESTRO, a self-supervised set representation learning model that generates vector representations of set-structured<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30353<END_URL>

MaestroMotif: Skill Design from Artificial Intelligence Feedback\nDescribing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28331<END_URL>

MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation\nGraph Neural Networks (GNNs) have shown remarkable success in molecular tasks, yet their interpretability remains challenging. Traditional model-level explanation methods like XGNN and GNNInterpreter often fail to identify valid substructures like rings, leading to questionable interpretability. This limitation stems from XGNN's atom-by-atom approach and GNNInterpreter's reliance on average graph embeddings, w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27883<END_URL>

MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding\nLarge Language Models (LLMs) have become more prevalent in long-context applications such as interactive chatbots, document analysis, and agent workflows, but it is challenging to serve long-context requests with low latency and high throughput. Speculative decoding (SD) is a widely used technique to reduce latency losslessly, but the conventional wisdom suggests that its efficacy is limited<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30513<END_URL>

MagicPIG: LSH Sampling for Efficient LLM Generation\nLarge language models (LLMs) with long context windows have gained significant attention. However, the KV cache, stored to avoid re-computation, now becomes a bottleneck. Leveraging the common insight that attention is sparse, various dynamic sparse or TopK-based attention approximation methods have been proposed. In this paper, we first show that TopK attention itself suffers from a quality degradation in certain downstream tasks because atte<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30638<END_URL>

Magnetic Mirror Descent Self-play Preference Optimization\nSelf-play methods have demonstrated remarkable success in enhancing model capabilities across various domains. In the context of Reinforcement Learning from Human Feedback (RLHF), self-play not only boosts Large Language Model (LLM) performance but also overcomes the limitations of traditional Bradley-Terry (BT) model assumptions by finding the Nash equilibrium (NE) of a preference-based, two-player constant-sum game. However, existing m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29766<END_URL>

MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds\nRecent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions.Most models for molecule generation rely on the decomposition of molecules into frequently occurring substructures (motifs), from which they generate novel compounds. While motif representations greatly aid in learning molecular distributions, such methods fail to represent substructures beyond their kn<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30958<END_URL>

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing\nHigh-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent existing open-source data creation methods from scaling effectively, potentially limiting the diversity and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29730<END_URL>

MAI: A Multi-turn Aggregation-Iteration Model for Composed Image Retrieval\nMulti-Turn Composed Image Retrieval (MTCIR) addresses a real-world scenario where users iteratively refine retrieval results by providing additional information until a target meeting all their requirements is found. Existing methods primarily achieve MTCIR through a "multiple single-turn" paradigm, wherein methods incorrectly converge on shortcuts that only utilize the most recent turn's image, ignoring attributes from <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28813<END_URL>

Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning\nAdapting pre-trained foundation models for various downstream tasks has been prevalent in artificial intelligence. Due to the vast number of tasks and high costs, adjusting all parameters becomes unfeasible. To mitigate this, several fine-tuning techniques have been developed to update the pre-trained model weights in a more resource-efficient manner, such as through low-rank adjustments. Yet, almost all of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29842<END_URL>

Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks\nIn spite of finite dimension ReLU neural networks being a consistent factor behind recent deep learning successes, a theory of feature learning in these models remains elusive. Currently, insightful theories still rely on assumptions including the linearity of the network computations, unstructured input data and architectural constraints such as infinite width or a single hidden layer. To beg<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31160<END_URL>

Making Text Embedders Few-Shot Learners\nLarge language models (LLMs) with decoder-only architectures have demonstrated exceptional text-generation capabilities across a variety of tasks. Some researchers have also adapted these models for text representation tasks. However, in text representation tasks, these models often face performance degradation on unseen tasks. In-context learning (ICL), which leverages examples provided in the input context, enables LLMs to handle unseen tasks effectivel<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27833<END_URL>

Making Transformer Decoders Better Differentiable Indexers\nRetrieval aims to find the top-k items most relevant to a query/user from a large dataset. Traditional retrieval models represent queries/users and items as embedding vectors and use Approximate Nearest Neighbor (ANN) search for retrieval. Recently, researchers have proposed a generative-based retrieval method that represents items as token sequences and uses a decoder model for autoregressive training. Compared to traditional methods, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29100<END_URL>

MallowsPO: Fine-Tune Your LLM with Preference Dispersions\nDirect Preference Optimization (DPO) has recently emerged as a popular approach to improve reinforcement learning with human feedback (RLHF), leading to better techniques to fine-tune large language models (LLM). A weakness of DPO, however, lies in its lack of capability to characterize the diversity of human preferences. Inspired by Mallows' theory of preference ranking, we develop in this paper a new approach, the *MallowsPO*. A distin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29005<END_URL>

MambaExtend: A Training-Free Approach to Improve Long Context Extension of Mamba\nThe inherent quadratic complexity of the attention mechanism in transformer models has driven the research community to explore alternative architectures with sub-quadratic complexity, such as state-space models. Within this emerging paradigm, Mamba has established itself as a leading model, achieving state-of-the-art results in various language modeling benchmarks. However, despite its impressive performance, Mamb<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29980<END_URL>

MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba\nAn ecosystem of Transformer-based models has been established by building large models with extensive data. Parameter-efficient fine-tuning (PEFT) is a crucial technology for deploying these models to downstream tasks with minimal cost while achieving effective performance. Recently, Mamba, a State Space Model (SSM)-based model, has attracted attention as a potential alternative to Transformers. While many large-scale Mamba-based mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29486<END_URL>

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods\nMamba is an efficient sequence model that rivals Transformers and demonstrates significant potential as a foundational architecture for various tasks. Quantization is commonly used in neural networks to reduce model size and computational latency. However, applying quantization to Mamba remains underexplored, and existing quantization methods, which have been effective for CNN and Transformer models, appear inadequat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30065<END_URL>

MamBEV: Enabling State Space Models to Learn Birds-Eye-View Representations\n3D visual perception tasks, such as 3D detection from multi-camera images, are essential components of autonomous driving and assistance systems. However, designing computationally efficient methods remains a significant challenge. In this paper, we propose a Mamba-based framework called MamBEV, which learns unified Bird's Eye View (BEV) representations using linear spatio-temporal SSM-based attention. This approach sup<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29911<END_URL>

MamKO: Mamba-based Koopman operator for modeling and predictive control\nThe Koopman theory, which enables the transformation of nonlinear systems into linear representations, is a powerful and efficient tool to model and control nonlinear systems. However, the ability of the Koopman operator to model complex systems, particularly time-varying systems, is limited by the fixed linear state-space representation. To address the limitation, the large language model, Mamba, is considered a promising <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28767<END_URL>

Managing Diffuse Risks in the Safe Deployment of Untrusted Large Language Models\nAs large language models (LLMs) grow more powerful, they also become more difficult to trust. They could be either aligned with human intentions, or exhibit "subversive misalignment" -- introducing subtle errors that bypass safety checks. Although individual errors may not immediately cause harm, each increases the risk of an eventual safety failure. With this uncertainty, model deployment often grapples with the t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28567<END_URL>

Manifold Constraint Reduces Exposure Bias in Accelerated Diffusion Sampling\nDiffusion models have demonstrated significant potential for generating high-quality images, audio, and videos. However, their iterative inference process entails substantial computational costs, limiting practical applications. Recently, researchers have introduced accelerated sampling methods that enable diffusion models to generate samples with far fewer timesteps than those used during training. Nonetheless, as the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30921<END_URL>

Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Images\nDistinguishing between real and AI-generated images presents a timely and significant challenge. Despite extensive research in the (semi-)supervised regime, only recently, zero-shot and few-shot solutions have emerged as promising approaches to this task: They alleviate the ongoing data maintenance, which quickly becomes outdated due to advances in generative technologies. We identify two main gaps: (1) a lack of t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30810<END_URL>

Manifolds, Random Matrices and Spectral Gaps: The geometric phases of generative diffusion\nIn this paper, we investigate the latent geometry of generative diffusion models under the manifold hypothesis. To this purpose, we analyze the spectrum of eigenvalues (and singular values) of the Jacobian of the score function, whose discontinuities (gaps) reveal the presence and dimensionality of distinct sub-manifolds. Using a statistical physics approach, we derive the spectral distributions and formu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30036<END_URL>

ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks\nHigh-quality benchmarks are the foundation for embodied AI research, enabling significant advancements in long-horizon navigation, manipulation and rearrangement tasks. However, as frontier tasks in robotics get more advanced, they require faster simulation speed, more intricate test environments, and larger demonstration datasets. To this end, we present MS-HAB, a holistic benchmark for low-level manipulation and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30874<END_URL>

MANTRA: The Manifold Triangulations Assemblage\nThe rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting high-order structures in the data, especially in topological deep learning (TDL), which designs neural networks on high-order domains such as simplicial complexes. However, progress in this field is hindered by the scarcity of datasets for benchmarking these architectures. To address this gap, we introduce M<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29323<END_URL>

Many-Objective Multi-Solution Transport\nOptimizing the performance of many objectives (instantiated by tasks or clients) jointly with a few Pareto stationary solutions (models) is critical in machine learning. However, previous multi-objective optimization methods often focus on a few objectives and cannot scale to many objectives that outnumber the solutions, leading to either subpar performance or ignored objectives. We introduce ''Many-objective multi-solution Transport (MosT)'', a framework<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29867<END_URL>

MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation\nModel merging has emerged as an effective approach to combine multiple single-task models into a multitask model. This process typically involves computing a weighted average of the model parameters without any additional training. Existing model-merging methods focus on enhancing average task accuracy. However, interference and conflicts between the objectives of different tasks can lead to trade-offs durin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31176<END_URL>

MAP: Multi-Human-Value Alignment Palette\nEnsuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs. Since human values can be personalized and dynamically change over time, the desirable levels of value alignment vary across different ethnic groups, industry sectors, and user cohorts. Within existing frameworks, it is hard to define human values and align AI systems accordingly acros<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29882<END_URL>

MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science\nPre-trained on extensive text and image corpora, current Multi-Modal Large Language Models (MLLM) have shown strong capabilities in general visual reasoning tasks. However, their performance is still lacking in physical domains that require understanding diagrams with complex physical structures and quantitative analysis based on multi-modal information. To address this, we develop a new framework, named **M**ulti-Modal Scie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30279<END_URL>

MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions\nReinforcement learning from human feedback (RLHF) has demonstrated effectiveness in aligning large language models (LLMs) with human preferences. However, token-level RLHF suffers from the credit assignment problem over long sequences, where delayed rewards make it challenging for the model to discern which actions contributed to successful outcomes. This hinders learning efficiency and slows convergence. In this paper, we p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29359<END_URL>

MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model\nGenerative models aim to simulate realistic effects of various actions across different contexts, from text generation to visual effects. Despite significant efforts to build real-world simulators, the application of generative models to virtual worlds, like financial markets, remains under-explored. In financial markets, generative models can simulate complex market effects of participants with various behaviors,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29246<END_URL>

Mask-DPO: Generalizable Fine-grained Factuality Alignment of LLMs\nLarge language models (LLMs) exhibit hallucinations (i.e., unfaithful or nonsensical information) when serving as AI assistants in various domains. Since hallucinations always come with truthful content in the LLM responses, previous factuality alignment methods that conduct response-level preference learning inevitably introduced noises during training. Therefore, this paper proposes a fine-grained factuality alignment method ba<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29014<END_URL>

Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling\nMasked diffusion models (MDMs) have emerged as a popular research topic for generative modeling of discrete data, thanks to their superior performance over other discrete diffusion models, and are rivaling the auto-regressive models (ARMs) for language modeling tasks. The recent effort in simplifying the masked diffusion framework further leads to alignment with continuous-space diffusio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30511<END_URL>

Masked Temporal Interpolation Diffusion for Procedure Planning in Instructional Videos\nIn this paper, we study the problem of procedure planning in instructional videos, which involves making goal-directed plans based on current visual observations in unstructured, real-life videos. Prior research leverages different forms of supervision to bridge the gap between observed states and unobserved actions. Building on this foundation, we propose an innovative approach by introducing a latent space <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30210<END_URL>

MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer\nThe recent large-scale text-to-speech (TTS) systems are usually grouped as autoregressive and non-autoregressive systems. The autoregressive systems implicitly model duration but exhibit certain deficiencies in robustness and lack of duration controllability. Non-autoregressive systems require explicit alignment information between text and speech during training and predict durations for linguistic units (e.g. phone), w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30376<END_URL>

Mask in the Mirror: Implicit Sparsification\nContinuous sparsification strategies are among the most effective methods for reducing the inference costs and memory demands of large-scale neural networks. A key factor in their success is the implicit $L_1$ regularization induced by jointly learning both mask and weight variables, which has been shown experimentally to outperform explicit $L_1$ regularization. We provide a theoretical explanation for this observation by analyzing the learning dynam<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29491<END_URL>

Mastering Task Arithmetic: $\tau$Jp as a Key Indicator for Weight Disentanglement\nModel-editing techniques using task arithmetic have rapidly gained attention. Through task arithmetic, simply through arithmetic operations on the weights of pre-trained and fine-tuned models create desired models, such as multi-task models, models with specific tasks unsolvable, or domain-transferred models. However, task arithmetic faces challenges, such as low reproducibility and the high cost associated with a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31198<END_URL>

MAST: model-agnostic sparsified training\nWe introduce a novel optimization problem formulation that departs from the conventional way of minimizing machine learning model loss as a black-box function. Unlike traditional formulations, the proposed approach explicitly incorporates an initially pre-trained model and random sketch operators, allowing for sparsification of both the model and gradient during training. We establish insightful properties of the proposed objective function and highlight<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28129<END_URL>

Matérn Kernels for Tunable Implicit Surface Reconstruction\nWe propose to use the family of Matérn kernels for tunable implicit surface reconstruction, building upon the recent success of kernel methods for 3D reconstruction of oriented point clouds.As we show, both, from a theoretical and practical perspective, Matérn kernels have some appealing properties which make them particularly well suited for surface reconstruction---outperforming state-of-the-art methods based on the arc-cosine kernel <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29789<END_URL>

MatExpert: Decomposing Materials Discovery By Mimicking Human Experts\nMaterial discovery is a critical research area with profound implications for various industries. In this work, we introduce MatExpert, a novel framework that leverages Large Language Models (LLMs) and contrastive learning to accelerate the discovery and design of new solid-state materials. Inspired by the workflow of human materials design experts, our approach integrates three key stages: retrieval, transition, and generati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30631<END_URL>

MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code\nCode has been shown to be effective in enhancing the mathematical reasoning abilities of large language models due to its precision and accuracy. Previous works involving continued mathematical pretraining  often include code that utilizes math-related packages, which are primarily designed for fields such as engineering, machine learning, signal processing, or module testing, rather than being di<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31210<END_URL>

MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs\nLarge language models (LLMs) can solve arithmetic word problems with high accuracy, but little is known about how well they generalize to problems that are more complex than the ones on which they have been trained. Empirical investigations of such questions are impeded by two major flaws of current evaluations: (i) much of the evaluation data is contaminated, in the sense that it has already been seen during tr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30934<END_URL>

Matrix Product Sketching via Coordinated Sampling\nWe revisit the well-studied problem of approximating a matrix product, $\bv{A}^T\bv{B}$, based on small space sketches $\mathcal{S}(\bv{A})$ and  $\mathcal{S}(\bv{B})$ of $\bv{A} \in \R^{n \times d}$ and $\bv{B}\in \R^{n \times m}$. We are interested in the setting where the sketches must be computed independently of each other, except for the use of a shared random seed. We prove that, when $\bv{A}$ and $\bv{B}$ are sparse, methods based on \em<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28936<END_URL>

MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection\nKV cache has become a *de facto* technique for the inference of large language models (LLMs), where tensors of shape (layer number, head number, sequence length, feature dimension) are introduced to cache historical information for self-attention. As the size of the model and data grows, the KV cache can, yet, quickly become a bottleneck within the system in both storage and memory transfer.To address this, prior studies <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30571<END_URL>

Matryoshka Multimodal Models\nLarge Multimodal Models (LMMs) such as LLaVA have shown strong performance in visual-linguistic reasoning. These models first embed images into a fixed large number of visual tokens and then feed them into a Large Language Model (LLM). However, this design causes an excessive number of tokens for dense visual scenarios such as high-resolution images and videos, leading to great inefficiency. While token pruning/merging methods do exist, they produce a single length <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29460<END_URL>

MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine\nMulti-modal Large Language Models (MLLMs) have recently showcased superior proficiency in general visual scenarios. However, we identify their mathematical capabilities remain under-explored with three areas to be improved: visual encoding of math diagrams, diagram-language alignment, and chain-of-thought (CoT) reasoning. This draws forth an urgent demand for an effective training paradigm and a large-scale, comprehensi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29918<END_URL>

MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks\nWe propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges.Our approach works well on any kind of graph topology and is fully differentiable, making it possible to find solutions that jointly optimize the MAXCUT along with other objectives.Based on the obtained MAXCUT partition, we implement a hierarchical graph pooling layer for Graph Neur<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27765<END_URL>

Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory\nSynthetic data has gained attention for training large language models, but poor-quality data can harm performance (see, e.g., Shumailov et al. (2023); Seddik et al. (2024)). A potential solution is data pruning, which retains only high-quality data based on a score function (human or machine feedback). Previous work Feng et al. (2024) analyzed models trained on synthetic data as sample size increases. We extend this<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30188<END_URL>

MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization\nReinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms use undirected exploration, i.e., select random sequences of actions.Exploration can also be directed using intrinsic rewards, such as curiosity or model epistemic uncertainty. However, effectively balancing task and intrins<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29662<END_URL>

McEval: Massively Multilingual Code Evaluation\nCode large language models (LLMs) have shown remarkable advances in code understanding, completion, and generation tasks. Programming benchmarks, comprised of a selection of code challenges and corresponding test cases, serve as a standard to evaluate the capability of different LLMs in such tasks. However, most existing benchmarks primarily focus on Python and are still restricted to a limited number of languages, where other languages are transla<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29445<END_URL>

MC-MoE: Mixture Compressor for Mixture-of-Experts LLMs Gains More\nMixture-of-Experts large language models (MoE-LLMs) marks a significant step forward of language models, however, they encounter two critical challenges in practice: 1) expert parameters lead to considerable memory consumption and loading latency; and 2) the current activated experts are redundant, as many tokens may only require a single expert. Motivated by these issues, we investigate the MoE-LLMs and make two key observations<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28749<END_URL>

MCNC: Manifold-Constrained Reparameterization for Neural Compression\nThe outstanding performance of large foundational models across diverse tasks, from computer vision to speech and natural language processing, has significantly increased their demand. However, storing and transmitting these models poses significant challenges due to their massive size (e.g., 750GB for Llama 3.1 405B). Recent literature has focused on compressing the original weights or reducing the number of parameters requir<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29420<END_URL>

MDSGen: Fast and Efficient Masked Diffusion Temporal-Aware Transformers for Open-Domain Sound Generation\nWe introduce MDSGen, a novel framework for vision-guided open-domain sound generation optimized for model parameter size, memory consumption, and inference speed. This framework incorporates two key innovations: (1) a redundant video feature removal module that filters out unnecessary visual information, and (2) a temporal-aware masking strategy that leverages temporal context for enhanced a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27741<END_URL>

Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse\nLLMs are an integral component of retrieval-augmented generation (RAG) systems. While many studies focus on evaluating the overall quality of end-to-end RAG systems, there is a gap in understanding the appropriateness of LLMs for the RAG task. To address this, we introduce Trust-Score, a holistic metric that evaluates the trustworthiness of LLMs within the RAG framework. Our results show <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30139<END_URL>

Measuring And Improving Persuasiveness Of Generative Models\nLarge Language Models (LLMs) are increasingly being used in workflows involving generating content to be consumed by humans (e.g., marketing) and also in directly interacting with humans (e.g., through chatbots). The development of such systems that are capable of generating verifiably persuasive messages presents both opportunities and challenges for society. On the one hand, such systems couldpositively impact domains like advertisin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29866<END_URL>

Measuring memorization in RLHF for code completion\nReinforcement learning with human feedback (RLHF) has become the dominant method to align large models to user preferences.Unlike fine-tuning, for which there are many studies regarding training data memorization, it is not clear how memorization is affected by or introduced in the RLHF alignment process.Understanding this relationship is important as real user data may be collected and used to align large models; if user data is memorized duri<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29527<END_URL>

Measuring Non-Adversarial Reproduction of Training Data in Large Language Models\nLarge language models memorize parts of their training data. Memorizing short snippets and facts is required to answer questions about the world and to be fluent in any language. But models have also been shown to reproduce long verbatim sequences of memorized text when prompted by a motivated adversary. In this work, we investigate an intermediate regime of memorization that we call non-adversarial reproduction, w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30966<END_URL>

Mechanism and emergence of stacked attention heads in multi-layer transformers\nIn this paper, we introduce the retrieval problem, a simple reasoning task that can be solved only by transformers with a minimum number of layers. The task has an adjustable difficulty that can further increase the required number of layers to any arbitrary value. We demonstrate that large language models can solve the task under different prompting formulations without any fine-tuning. To understand how transformer<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28194<END_URL>

Mechanistic Interpretability Meets Vision Language Models: Insights and Limitations\nVision language models (VLMs), such as GPT-4o, have rapidly evolved, demonstrating impressive capabilities across diverse tasks. However, much of the progress in this field has been driven by engineering efforts, with a limited understanding of how these models work. The lack of scientific insight poses challenges to further enhancing their robustness, generalization, and interpretability, especially in high-sta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31330<END_URL>

Mechanistic Permutability: Match Features Across Layers\nUnderstanding how features evolve across layers in deep neural networks is a fundamental challenge in mechanistic interpretability, particularly due to polysemanticity and feature superposition. While Sparse Autoencoders (SAEs) have been used to extract interpretable features from individual layers, aligning these features across layers has remained an open problem. In this paper, we introduce SAE Match, a novel, data-free method for align<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29946<END_URL>

MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models\nMultimodal Large Language Models (MLLMs) have tremendous potential to improve the accuracy, availability, and cost-effectiveness of healthcare by providing automated solutions or serving as aids to medical professionals. Despite promising first steps in developing medical MLLMs in the past few years, their capabilities and limitations are not well understood. Recently, many benchmar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30242<END_URL>

Medium-Difficulty Samples Constitute Smoothed Decision Boundary for Knowledge Distillation on Pruned Datasets\nThis paper tackles a new problem of dataset pruning for Knowledge Distillation (KD), from a fresh perspective of Decision Boundary (DB) preservation and drifts. Existing dataset pruning methods generally assume that the post-pruning DB formed by the selected samples can be well-captured by future networks that use those samples for training. Therefore, they tend to preserve hard samples<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29633<END_URL>

MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine\nThis paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. These multigranular annotations encompass both global information, such as modality and organ detection, and local information like ROI analysis, lesion texture, and region-wise correlations. Un<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30141<END_URL>

MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks\nWe present MEGA-Bench, an evaluation suite that scales multimodal evaluation to over 500 real-world tasks, to address the highly heterogeneous daily use cases of end users.Our objective is to optimize for a set of high-quality data samples that cover a highly diverse and rich set of multimodal tasks, while enabling cost-effective and accurate model evaluation.In particular, we collected 505 realistic tasks encompassing over <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31110<END_URL>

Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis\nWe present Meissonic, which elevates non-autoregressive text-to-image Masked Image Modeling (MIM) to a level comparable with state-of-the-art diffusion models like SDXL. By incorporating a comprehensive suite of architectural innovations, advanced positional encoding strategies, and optimized sampling conditions, Meissonic substantially improves MIM's performance and efficiency. Addition<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30289<END_URL>

MELODI: Exploring Memory Compression for Long Contexts\nWe present MELODI, a novel memory architecture designed to efficiently process long documents using short context windows. The key principle behind MELODI is to represent short-term and long-term memory as a hierarchical compression scheme across both network layers and context windows. Specifically, the short-term memory is achieved through recurrent compression of context windows across multiple layers, ensuring smooth transitions between<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29502<END_URL>

Memory-efficient Training of Large Language Models with Larger Mini-batches\nTraining with larger mini-batches improves the convergence rate and can yield superior performance. However, training with large mini-batches becomes prohibitive for Large Language Models (LLMs), due to the large GPU memory requirement. To address this problem, an effective approach is finding small mini-batch coresets that closely match the gradient of larger mini-batches. However, this approach becomes infeasible and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29123<END_URL>

Memory Efficient Transformer Adapter for Dense Predictions\nWhile current Vision Transformer (ViT) adapter methods have shown promising accuracy, their inference speed is implicitly hindered by inefficient memory access operations, e.g., standard normalization and frequent reshaping. In this work, we propose META, a simple and fast ViT adapter that can improve the model's memory efficiency and decrease memory time consumption by reducing the inefficient memory access operations. Our method featu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27922<END_URL>

Memory Mosaics\nMemory Mosaics are networks of associative memories working in concert to achieve a prediction task of interest. Like transformers, memory mosaics possess compositional capabilities and in-context learning capabilities. Unlike transformers, memory mosaics achieve these capabilities in comparatively transparent way (“predictive disentanglement”). We illustrate these capabilities on a toy example and also show that memory mosaics perform as well or better than transformers on mediu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30157<END_URL>

Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering\nLow-Rank Adaptation (LoRA) has emerged as a popular technique for fine-tuning large language models (LLMs) to various domains due to its modular design and widespread availability on platforms like Huggingface. This modularity has sparked interest in combining multiple LoRAs to significantly enhance LLM capabilities. However, existing methods for LoRA composition primarily focus on task-spec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28655<END_URL>

MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers\nRecently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this potential is largely unrealized because these assets always need to be converted to meshes for 3D industry applications, and the meshes produced by current mesh extraction methods are significantly inferior to Artist-Created Meshes (AMs), i.e., m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30068<END_URL>

MeshMask: Physics-Based Simulations with Masked Graph Neural Networks\nWe introduce a novel masked pre-training technique for graph neural networks (GNNs) applied to computational fluid dynamics (CFD) problems. By randomly masking up to 40\% of input mesh nodes during pre-training, we force the model to learn robust representations of complex fluid dynamics. We pair this masking strategy with an asymmetric encoder-decoder architecture and gated multi-layer perceptrons to further enhance performa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29117<END_URL>

metabench - A Sparse Benchmark of Reasoning and Knowledge in Large Language Models\nLarge Language Models (LLMs) vary in their abilities on a range of tasks. Initiatives such as the $\texttt{Open LLM Leaderboard}$ aim to quantify these differences with several large benchmarks (sets of test items to which an LLM can respond either correctly or incorrectly).   However, high correlations within and between benchmark scores suggest that (1) there exists a small set of common underlying abilities th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31009<END_URL>

Meta-Continual Learning of Neural Fields\nNeural Fields (NF) have gained prominence as a versatile framework for complex data representation. This work unveils a new problem setting termed Meta-Continual Learning of Neural Fields (MCL-NF) and introduces a novel strategy that employs a modular architecture combined with optimization-based meta-learning. Focused on overcoming the limitations of existing methods for continual learning of neural fields, such as catastrophic forgetting and slow conve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29837<END_URL>

MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis\nMetaDesigner revolutionizes artistic typography synthesis by leveraging the strengths of Large Language Models (LLMs) to drive a design paradigm centered around user engagement. At the core of this framework lies a multi-agent system comprising the Pipeline, Glyph, and Texture agents, which collectively enable the creation of customized WordArt, ranging from semantic enhancements to t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29912<END_URL>

Meta-Dynamical State Space Models for Integrative Neural Data Analysis\nLearning shared structure across environments facilitates rapid learning and adaptive behavior in neural systems. This has been widely demonstrated and applied in machine learning to train models that are capable of generalizing to novel settings. However, there has been limited work exploiting the shared structure in neural activity during similar tasks for learning latent dynamics from neural recordings.Existing approaches<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29605<END_URL>

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold\nNumerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynamics of communicating cells or physical particles. Learning the dynamics of such systems is essential for predicting the temporal evolution of populations across novel samples and unseen environments. Flow-based models allow for learning these dynamics at the population level — they mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30690<END_URL>

Metalic: Meta-Learning In-Context with Protein Language Models\nPredicting the biophysical and functional properties of proteins is essential for in silico protein design. Machine learning has emerged as a promising technique for such prediction tasks. However, the relative scarcity of in vitro annotations means that these models often have little, or no, specific data on the desired fitness prediction task. As a result of limited data, protein language models (PLMs) are typically trained on gen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29540<END_URL>

MetaMetrics: Calibrating Metrics for Generation Tasks Using Human Preferences\nUnderstanding the quality of a performance evaluation metric is crucial for ensuring that model outputs align with human preferences. However, it remains unclear how well each metric captures the diverse aspects of these preferences, as metrics often excel in one particular area but not across all dimensions. To address this, it is essential to systematically calibrate metrics to specific aspects of human preference, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28099<END_URL>

Metamizer: A Versatile Neural Optimizer for Fast and Accurate Physics Simulations\nEfficient physics simulations are essential for numerous applications, ranging from realistic cloth animations in video games, to analyzing pollutant dispersion in environmental sciences, to calculating vehicle drag coefficients in engineering applications. Unfortunately, analytical solutions to the underlying physical equations are rarely available, and numerical solutions are computationally demanding. Latest de<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30915<END_URL>

MetaOOD: Automatic Selection of OOD Detection Models\nHow can we automatically select an out-of-distribution (OOD) detection model for various underlying tasks? This is crucial for maintaining the reliability of open-world applications by identifying data distribution shifts, particularly in critical domains such as online transactions, autonomous driving, and real-time patient diagnosis. Despite the availability of numerous OOD detection methods, the challenge of selecting an optimal model for <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30662<END_URL>

MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility\nPublic urban spaces like streetscapes and plazas serve residents and accommodate social life in all its vibrant variations. Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Food delivery bots and electric wheelchairs have started sharing sidewalks with pedestrians, while robot dogs and humanoids have recently emerged in the street. **Micromobility** enabled by AI for short-di<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28597<END_URL>

MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models\nThe pretrain+fine-tune paradigm is foundational for deploying large language models (LLMs) across various downstream applications. Within this framework, Low-Rank Adaptation (LoRA) stands out for its parameter-efficient fine-tuning (PEFT), producing numerous reusable task-specific LoRA adapters. However, this approach requires explicit task intention selection, posing challenges for autonomous task sensing and switching during infe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27732<END_URL>

Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity\nDue to the non-smoothness of optimization problems in Machine Learning, generalized smoothness assumptions have gained much attention in recent years. One of the most popular assumptions of this type is $(L_0, L_1)$-smoothness  (Zhang et al., 2020). In this paper, we focus on the class of (strongly) convex $(L_0, L_1)$-smooth functions and derive new convergence guarantees for several existing methods. In<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31230<END_URL>

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization\nNon-convex Machine Learning problems typically do not adhere to the standard smoothness assumption. Based on empirical findings, Zhang et al. (2020b) proposed a more realistic generalized $(L_0,L_1)$-smoothness assumption, though it remains largely unexplored. Many existing algorithms designed for standard smooth problems need to be revised. However, in the context of Federated Learning, only <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29511<END_URL>

MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction\nPost-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome, regulating protein attributes and interactions that are crucial for biological processes. Accurately predicting PTM sites and their specific types is therefore essential for elucidating protein function and understanding disease mechanisms. Existing computational approaches predominantly focus on pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28395<END_URL>

Metric-Driven Attributions for Vision Transformers\nAttribution algorithms explain computer vision models by attributing the model response to pixels within the input. Existing attribution methods generate explanations by combining transformations of internal model representations such as class activation maps, gradients, attention, or relevance scores. The effectiveness of an attribution map is measured using attribution quality metrics. This leads us to pose the following question: if attribut<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28207<END_URL>

MGCFNN: A Neural MultiGrid Solver with Novel Fourier Neural Network for High Wave Number Helmholtz Equations\nSolving high wavenumber Helmholtz equations is notoriously challenging. Traditional solvers have yet to yield satisfactory results, and most neural network methods struggle to accurately solve cases with extremely high wavenumbers within heterogeneous media. This paper presents an advanced multigrid-hierarchical AI solver, tailored specifically for high wavenumber Helmholtz equations. We<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29525<END_URL>

MGDA Converges under Generalized Smoothness, Provably\nMulti-objective optimization (MOO) is receiving more attention in various fields such as multi-task learning. Recent works provide some effective algorithms with theoretical analysis but they are limited by the standard $L$-smooth or bounded-gradient assumptions, which typically do not hold for neural networks, such as Long short-term memory (LSTM) models and Transformers. In this paper, we study a more general and realistic class of general<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27830<END_URL>

MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction\nThe construction of Vectorized High-Definition (HD) map typically requires capturing both category and geometry information of map elements. Current state-of-the-art methods often adopt solely either point-level or instance-level representation, overlooking the strong intrinsic relationships between points and instances. In this work, we propose a simple yet efficient framework named MGMapNet (Mult<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30420<END_URL>

MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs\nEffective evaluation of Multimodal Large Language Models (MLLMs) is essential for understanding their capabilities and limitations. In this paper, we introduce MIA-Bench, a benchmark designed to assess MLLMs’ ability to strictly adhere to complex instructions. Our benchmark comprises a diverse set of 400 image-prompt pairs, each crafted to challenge the models’ compliance with layered instructions in generating accura<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30836<END_URL>

MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models\nVisual preference alignment involves training Large Vision-Language Models (LVLMs) to predict human preferences between visual inputs. This is typically achieved by using labeled datasets of chosen/rejected pairs and employing optimization algorithms like direct preference optimization (DPO).Existing visual alignment methods, primarily designed for single-image scenarios, struggle to effectively handl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28892<END_URL>

Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks\nDespite recent advances, sampling-based inference for Bayesian Neural Networks (BNNs) remains a significant challenge in probabilistic deep learning. While sampling-based approaches do not require a variational distribution assumption, current state-of-the-art samplers still struggle to navigate the complex and highly multimodal posteriors of BNNs. As a consequence, sampling still requires considerably longer <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29695<END_URL>

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Masked Image Modeling Representations\nWe introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models. MIM-Refiner is motivated by the insight that strong representations within MIM models generally reside in intermediate layers. Accordingly, MIM-Refiner leverages multiple instance discrimination (ID) heads that are connected to different intermediate layers. In each head, a nearest<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31256<END_URL>

Mind Control through Causal Inference: Predicting Clean Images from Poisoned Data\nAnti-backdoor learning, aiming to train clean models directly from poisoned datasets, serves as an important defense method for backdoor attack. However, existing methods usually fail to recover backdoored samples to their original, correct labels and suffer from poor generalization to large pre-trained models due to its non end-to end training, making them unsuitable for protecting the increasingly prevalent larg<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28743<END_URL>

MIND: Math Informed syNthetic Dialogues for Pretraining LLMs\nThe utility of synthetic data to enhance pretraining data quality and hence to improve downstream task accuracy has been widely explored in recent large language models (LLMs). Yet, these approaches fall inadequate in complex, multi-hop and mathematical reasoning tasks as the synthetic data typically fails to add complementary knowledge to the existing raw corpus. In this work, we propose a novel large-scale and diverse Math Informed <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29504<END_URL>

MIND over Body: Adaptive Thinking using Dynamic Computation\nWhile the human brain efficiently handles various computations with a limited number of neurons, traditional deep learning networks require a significant increase in parameters to improve performance.  Yet, these parameters are used inefficiently as the networks employ the same amount of computation for inputs of the same size, regardless of the input's complexity.  We address this inefficiency by introducing self-introspection capabil<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30390<END_URL>

MindSearch: Mimicking Human Minds Elicits Deep AI Searcher\nInformation seeking and integration is a complex cognitive task that consumes enormous time and effort. Inspired by the remarkable progress of Large Language Models, recent works attempt to solve this task by combining LLMs and search engines. However, these methods still obtain unsatisfying performance due to three challenges: (1) complex requests often cannot be accurately and completely retrieved by the search engine once (2) corresp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27772<END_URL>

MindSimulator: Exploring Brain Concept Localization via Synthetic fMRI\nConcept-selective regions within the human cerebral cortex exhibit significant activation in response to specific visual stimuli associated with particular concepts. Precisely localizing these regions stands as a crucial long-term goal in neuroscience to grasp essential brain functions and mechanisms. Conventional experiment-driven approaches hinge on manually constructed visual stimulus collections and corresponding brain a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27898<END_URL>

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models\nSelf-improvement is a mechanism in Large Language Model (LLM) pre-training, post-training and test-time inference. We explore a framework where the model verifies its own outputs, filters or reweights data based on this verification, and distills the filtered data.  Despite several empirical successes, a fundamental understanding is still lacking. In this work, we initiate a comprehensive, modular and controlled <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28441<END_URL>

Mind the GAP: Glimpse-based Active Perception improves generalization and sample efficiency of visual reasoning\nHuman capabilities in understanding visual relations are far superior to those of AI systems, especially for previously unseen objects. For example, while AI systems struggle to determine whether two such objects are visually the same or different, humans can do so with ease. Active vision theories postulate that the learning of visual relations is grounded in actions that we take to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28691<END_URL>

miniCTX: Neural Theorem Proving with (Long-)Contexts\nReal-world formal theorem proving often depends on a wealth of context, including definitions, lemmas, comments, file structure, and other information. We introduce $\texttt{miniCTX}$, which tests a model's ability to prove formal mathematical theorems that depend on new context that is not seen during training. $\texttt{miniCTX}$ contains theorems sourced from real Lean projects and textbooks, each associated with a context that can span ten<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30064<END_URL>

Minimal Impact ControlNet: Advancing Multi-ControlNet Integration\nWith the advancement of diffusion models, there is a growing demand for high-quality, controllable image generation, particularly through methods that utilize one or multiple control signals based on ControlNet. However, in current ControlNet training, each control is designed to influence all areas of an image, which can lead to conflicts when different control signals are expected to manage different parts of the image in pract<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28156<END_URL>

Minimalistic Predictions for Online Class Constraint Scheduling\nWe consider online scheduling with class constraints. That is, we are given $m$ machines, each with $k$ class slots. Upon receiving a job $j$ with class $c_j$, an algorithm needs to allocate $j$ on some machine $i$. The goal is to minimize the makespan while not assigning more than $k$ different classes onto each machine.While the offline case is well understood and even (E)PTAS results are known [Jansen, Lassota, Maack SPAA'20, Ch<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28651<END_URL>

Minimax Optimal Reinforcement Learning with Quasi-Optimism\nIn our quest for a reinforcement learning (RL) algorithm that is both practical and provably optimal, we introduce EQO (Exploration via Quasi-Optimism). Unlike existing minimax optimal approaches, EQO avoids reliance on empirical variances and employs a simple bonus term proportional to the inverse of the state-action visit count. Central to EQO is the concept of *quasi-optimism*, where estimated values need not be fully optimistic, all<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28715<END_URL>

Mini-Monkey: Alleviating the Semantic Sawtooth Effect for Lightweight MLLMs via Complementary Image Pyramid\nRecently, scaling images to high resolution has received much attention in multimodal large language models (MLLMs). Most existing practices adopt a sliding-window-style cropping strategy to adapt to resolution increase. Such a cropping strategy, however, can easily cut off objects and connected regions, which introduces semantic discontinuity and therefore impedes MLLMs from recognizing <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30854<END_URL>

Mining your own secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models\nPersonalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However, in the real world, a user may wish to personalize a model on multiple concepts but one at a time, with no access to the data from previous concepts due to storage/privacy concerns. When faced with this<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28758<END_URL>

MiniPLM: Knowledge Distillation for Pre-training Language Models\nKnowledge distillation (KD) is widely used to train small, high-performing student language models (LMs) using large teacher LMs. While effective in fine-tuning, KD during pre-training faces challenges in efficiency, flexibility, and effectiveness. Existing methods either incur high computational costs due to online teacher inference, require tokenization matching between teacher and student LMs, or risk losing the difficulty and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28062<END_URL>

Minixax Optimal Two-Stage Algorithm For Moment Estimation Under Covariate Shift\nCovariate shift occurs when the distribution of input features differs between the training and testing phases.  In covariate shift, estimating an unknown function's moment is a classical problem that remains under-explored, despite its common occurrence in real-world scenarios. In this paper, we investigate the minimax lower bound of the problem when the source and target distributions are known. To achieve the min<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28348<END_URL>

Min-K%++: Improved Baseline for Pre-Training Data Detection from Large Language Models\nThe problem of pre-training data detection for large language models (LLMs) has received growing attention due to its implications in critical issues like copyright violation and test data contamination. Despite improved performance, existing methods (including the state-of-the-art, Min-K%) are mostly developed upon simple heuristics and lack solid, reasonable foundations. In this work, we propose a novel and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29228<END_URL>

MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on 3D Point Clouds via Shape Model Reconstruction\nIn this paper, we introduce a novel framework for memory-efficient and privacy-preserving continual learning in 3D object classification. Unlike conventional memory-based approaches in continual learning that require storing numerous exemplars, our method constructs a compact shape model for each class, retaining only the mean shape along with a few key modes of varia<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30637<END_URL>

MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models\nInductive reasoning is an essential capability for large language models (LLMs) to achieve higher intelligence, which requires the model to generalize rules from observed facts and then apply them to unseen examples. We present {\scshape Mirage}, a synthetic dataset that addresses the limitations of previous work, specifically the lack of comprehensive evaluation and flexible test data. In it, we evaluate LLMs' cap<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28046<END_URL>

(Mis)Fitting Scaling Laws: A Survey of Scaling Law Fitting Techniques in Deep Learning\nModern foundation models rely heavily on using scaling laws to guide crucial training decisions. Researchers often extrapolate the optimal architecture and hyper parameters settings from smaller training runs by describing the relationship between, loss, or task performance, and scale. All components of this process vary, from the specific equation being fit, to the training setup, to the optimization method.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27795<END_URL>

Misspecified  $Q$-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error\nThe recent work by Dong and Yang (2023) showed for misspecified sparse linear bandits, one can obtain an $O(\epsilon)$-optimal policy using a polynomial number of samples when the sparsity is a constant, where $\epsilon$ is the misspecification error. This result is in sharp contrast to misspecified linear bandits without sparsity, which require an exponential number of samples to get the s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28411<END_URL>

Mitigate the Gap: Improving Cross-Modal Alignment in CLIP\nContrastive Language--Image Pre-training (CLIP) has manifested remarkable improvements in zero-shot classification and cross-modal vision-language tasks. Yet, from a geometrical point of view, the CLIP embedding space has been found to have a pronounced modality gap. This gap renders the embedding space overly sparse and disconnected, with different modalities being densely distributed in distinct subregions of the hypersphere. In this w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29170<END_URL>

Mitigating Hallucination in Large Vision-Language Models via Modular Attribution and Intervention\nLarge Vision-Language Models (LVLMs) exhibit impressive capabilities in complex visual tasks but are prone to hallucination, especially in open-ended generation tasks. This paper explores why LVLMs tend to hallucinate and how to mitigate it. First, we conduct causal mediation analysis through counterfactual edits on specific modules in LVLMs. Our results disclose that Multi-Head Attention (MHA) mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30556<END_URL>

Mitigating Memorization in Language Models\nLanguage models (LMs) can “memorize” information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. This ability to extract training data can be problematic, for example, when data are private or sensitive. In this work, we investigate methods to mitigate memorization: three regularizer-based, three fine-tuning-based, and eleven machine unlearning-based methods, with f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29943<END_URL>

Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality\nMultimodal Large Language Models (MLLMs) have emerged as a central focus in both industry and academia, but often suffer from biases introduced by visual and language priors, which can lead to multimodal hallucination. These biases arise from the visual encoder and the Large Language Model (LLM) backbone, affecting the attention mechanism responsible for aligning multimodal i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30629<END_URL>

Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning\nLarge-scale deep learning models with a pretraining-finetuning paradigm have led to a surge of numerous task-specific models finetuned from a common pretrained model. Recently, several research efforts have been made on merging these large models into a single multi-task model, particularly with simple arithmetic on parameters. Such merging methodology faces a central challenge: interference between model paramet<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28919<END_URL>

Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization\nReinforcement learning from human feedback (RLHF) is an effective method for aligning large language models (LLMs) with human values. However, reward over-optimization remains an open challenge leading to discrepancies between the performance of LLMs under the reward model and the true human objectives. A primary contributor to reward over-optimization is the extrapolation error that arises when the reward model e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29757<END_URL>

Mitigating Robust Overfitting in Wasserstein Distributionally Robust Optimization\nWasserstein distributionally robust optimization (WDRO) optimizes against worst-case distributional shifts within a specified uncertainty set, leading to enhanced generalization on unseen adversarial examples, compared to standard adversarial training which focuses on pointwise adversarial perturbations. However, WDRO still suffers fundamentally from the robust overfitting problem, as it does not consider statisti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28096<END_URL>

Mitigating Spurious Correlations in Zero-Shot Multimodal Models\nMultimodal models or Vision Language Models (VLMs) have reshaped the paradigm in machine learning, offering zero-shot capabilities that require no additional training when adapted to new classification tasks. However, despite their advancements, spurious correlations still exist in VLMs. Existing approaches to tackle this issue often require target label annotations, contradicting the principle of zero-shot classification, or they <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29449<END_URL>

Mitigating Spurious Correlations via Group-robust Sample Reweighting\nMachine learning models often have uneven performance among subpopulations (a.k.a., groups) in the data distributions. This poses a significant challenge for the models to generalize when the proportions of the groups shift during deployment.To improve robustness to such subpopulation shifts, existing approaches have developed strategies that train models or perform hyperparameter tuning using the group-labeled data to minimiz<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29169<END_URL>

Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace\nModel merging has gained significant attention as a cost-effective approach to integrate multiple single-task fine-tuned models into a unified one that can perform well on multiple tasks. However, existing model merging techniques primarily focus on resolving conflicts between task-specific models, they often overlook potential security threats, particularly the risk of backdoor attacks in the open-source mode<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28963<END_URL>

Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment\nAdapting large language models (LLMs) to specialized domains typically requires domain-specific corpora for continual pre-training to facilitate knowledge memorization and related instructions for fine-tuning to apply this knowledge.However, this method may lead to inefficient knowledge memorization due to a lack of awareness of knowledge utilization during the continual pre-training and demands LLMs t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28784<END_URL>

MixEval-X: Any-to-any Evaluations from Real-world Data Mixture\nPerceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their development. We identify two major issues in current evaluations: (1) inconsistent standards, shaped by different communities with varying protocols and maturity levels; and (2) significant query, grading, and generalization biases. To address these, we intr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28740<END_URL>

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN\nLarge Language Models (LLMs) have achieved remarkable success, yet recent findings reveal that their deeper layers often contribute minimally and can be pruned without affecting overall performance.  While some view this as an opportunity for model compression, we identify it as a training shortfall rooted in the widespread use of Pre-Layer Normalization (Pre-LN). We demonstrate that Pre-LN, commonly employed in model<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30583<END_URL>

MixMax: Distributional Robustness in Function Space via Optimal Data Mixtures\nMachine learning models are often required to perform well across several pre-defined settings, such as a set of user groups. Worst-case performance is a common metric to capture this requirement, and is the objective of group distributionally robust optimization (group DRO). Unfortunately, these methods struggle when the loss is non-convex in the parameters, or the model class is non-parametric. Here, we make a class<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28992<END_URL>

Mixture-of-Agents Enhances Large Language Model Capabilities\nRecent advances in large language models (LLMs) demonstrate substantial capabilities in natural language understanding and generation tasks. With the growing number of LLMs, how to harness the collective expertise of multiple LLMs is an exciting open direction. Toward this goal, we propose a new approach that leverages the collective strengths of multiple LLMs through a Mixture-of-Agents (MoA) methodology. In our approach, we construc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28787<END_URL>

Mixture of Attentions For Speculative Decoding\nThe growth in the number of parameters of Large Language Models (LLMs) has led to a significant surge in computational requirements, making them challenging and costly to deploy.Speculative decoding (SD) leverages smaller models to efficiently propose future tokens, which are then verified by the LLM in parallel.Small models that utilise activations from the LLM currently achieve the fastest decoding speeds.However, we identify several limitations <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29634<END_URL>

Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models\nPrompt learning for pre-trained Vision-Language Models (VLMs) like CLIP has demonstrated potent applicability across diverse downstream tasks. This lightweight approach has quickly gained traction from federated learning (FL) researchers who seek to efficiently adapt VLMs to heterogeneous scenarios. However, current federated prompt learning methods are habitually restricted to the traditional FL paradigm<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27771<END_URL>

Mixture of In-Context Prompters for Tabular PFNs\nRecent benchmarks find In-Context Learning (ICL) outperforms both deep learning and tree-based algorithms on small tabular datasets. However, on larger datasets, ICL for tabular learning suffers in both efficiency and effectiveness. In terms of efficiency, transformers incur linear space and quadratic time complexity w.r.t. context size. In terms of effectiveness, contexts at inference encounter distribution shift compared to contexts from pretra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31124<END_URL>

Mixture of Parrots: Experts improve memorization more than reasoning\nThe Mixture-of-Experts (MoE) architecture enables a significant increase in the total number of model parameters with minimal computational overhead. However, it is not clear what performance tradeoffs, if any, exist between MoEs and standard dense transformers.In this paper, we show that as we increase the number of experts (while fixing the number of active parameters), the memorization performance consistently increases whi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30684<END_URL>

MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering\nWe introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 71 ML engineering-related competitions from Kaggle, creating a diverse set of challenging tasks that test real-world ML engineering skills such as training models, preparing datasets, and running experiments. We establish human baselines for each competition using Kaggle's publicly avail<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30860<END_URL>

MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents\nMLLM agents demonstrate potential for complex embodied tasks by retrieving multimodal task-relevant trajectory data. However, current retrieval methods primarily focus on surface-level similarities of textual or visual cues in trajectories, neglecting their effectiveness for the specific task at hand. To address this issue, we propose a novel method, MART, which enhances the performance of embodied agents by util<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30075<END_URL>

MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation\nMultimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena, but the underlying reasons remain poorly understood. In this paper, we present an empirical analysis and find that, although MLLMs incorrectly generate the targets in the final output, they are actually able to recognize visual objects in the preceding layers. We speculate that this may be due to the strong knowledge priors of the language m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30978<END_URL>

MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs\nMultimodal Large Language Models (MLLMs) have recently achieved promising performance on visual question answering (VQA)---a fundamental task affecting various downstream applications and domains. Given MLLMs' potential integration into many critical VQA applications, it is important to understand the limits of their perception. In this work, we study whether MLLMs can perceive small details as well <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30449<END_URL>

MLPs Learn In-Context on Regression and Classification Tasks\nIn-context learning (ICL), the remarkable ability to solve a task from only input exemplars, is often assumed to be a unique hallmark of Transformer models. By examining commonly employed synthetic ICL tasks, we demonstrate that multi-layer perceptrons (MLPs) can also learn in-context. Moreover, MLPs, and the closely related MLP-Mixer models, learn in-context *competitively with Transformers given the same compute budget* in this sett<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29924<END_URL>

MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning\nWe present MM1.5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and multi-image reasoning. Building upon the MM1 architecture, MM1.5 adopts a data-centric approach to model training, systematically exploring the impact of diverse data mixtures across the entire model training lifecycle. This includes high-quality OCR da<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30222<END_URL>

MMAD: The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection\nIn the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to introduce new paradigms in practical applications due to their robust language capabilities and generalization abilities. However, despite their impressive problem-solving skills in many domains, MLLMs' ability in industrial anomaly detection has not been systematically studied. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30120<END_URL>

MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark\nThe ability to comprehend audio—which includes speech, non-speech sounds, and music—is crucial for AI agents to interact effectively with the world. We present MMAU, a novel benchmark designed to evaluate multimodal audio understanding models on tasks requiring expert-level knowledge and complex reasoning. MMAU comprises 10k carefully curated audio clips paired with human-annotated natural language questions and answers span<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29528<END_URL>

MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models\nMultimodal foundation models (MMFMs) play a crucial role in various applications, including autonomous driving, healthcare, and virtual assistants. However, several studies have revealed vulnerabilities in these models, such as generating unsafe content by text-to-image models. Existing benchmarks on multimodal models either predominantly assess the helpfulness of these models, or only focus on limited perspectives su<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28256<END_URL>

MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models\nArtificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment planning. Recent progress in Medical Large Vision-Language Models (Med-LVLMs) has opened up new possibilities for interactive diagnostic tools. However, these models often suffer from factual hallucination, which can lead to incorrect diagnoses. Fine-tuning and retrieval-augmented generatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28145<END_URL>

MMEgo: Towards Building Egocentric Multimodal LLMs\nThis research aims to comprehensively explore building a multimodal foundation model for egocentric video understanding. To achieve this goal, we work on three fronts. First, as there is a lack of QA data for egocentric video understanding, we automatically generate 7M high-quality QA samples for egocentric videos ranging from 30 seconds to one hour long, based on human-annotated data. This is one of the largest egocentric QA datasets. Second, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30907<END_URL>

MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?\nComprehensive evaluation of Multimodal Large Language Models (MLLMs) has recently garnered widespread attention in the research community. However, we observe that existing benchmarks present several common barriers that make it difficult to measure the significant challenges that models face in the real world, including: 1) small data scale leads to a large performance varianc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28598<END_URL>

MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs\nCurrent multimodal misinformation detection (MMD) methods often assume a single source and type of forgery for each sample, which is insufficient for real-world scenarios where multiple forgery sources coexist. The lack of a benchmark for mixed-source misinformation has hindered progress in this field. To address this, we introduce MMFakeBench, the first comprehensive benchmark for mixed-source MMD. MMFakeBench <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30477<END_URL>

MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models\nInterleaved multimodal comprehension and generation, enabling models to produce and interpret both images and text in arbitrary sequences, have become a pivotal area in multimodal learning. Despite significant advancements, the evaluation of this capability remains insufficient. Existing benchmarks suffer from limitations in data scale, scope, and evaluation depth, while current evaluation metrics are <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30211<END_URL>

MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models\nThe capability to process multiple images is crucial for Large Vision-Language Models (LVLMs) to develop a more thorough and nuanced understanding of a scene. Recent multi-image LVLMs have begun to address this need. However, their evaluation has not kept pace with their development. To fill this gap, we introduce the Multimodal Multi-image Understanding (MMIU) benchmark, a comprehensive evaluation suite desi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29339<END_URL>

MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge\nKnowledge editing techniques have emerged as essential tools for updating the factual knowledge of large language models (LLMs) and multimodal models (LMMs), allowing them to correct outdated or inaccurate information without retraining from scratch. However, existing benchmarks for multimodal knowledge editing primarily focus on entity-level knowledge represented as simple triplets, which fail to capture the complexity of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27931<END_URL>

MMQA: Evaluating LLMs with Multi-Table Multi-Hop Complex Questions\nWhile large language models (LLMs) have made strides in understanding tabular data, current tabular evaluation benchmarks, such as WikiTableQuestions and WikiSQL, are focus on single-table scenarios, which cannot necessarily reflect the complexity of real-world applications. To bridge this gap, we present a \textbf{M}ulti-table and Multi-hop Question Answering (MMQA) dataset to assess LLMs' understanding and reasoning capabiliti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30290<END_URL>

MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentation\nThe fusion of Large Language Models (LLMs) with vision models is pioneering new possibilities in user-interactive vision-language tasks. A notable application is reasoning segmentation, where models generate pixel-level segmentation masks by comprehending implicit meanings in human instructions. However, seamless human-AI interaction demands more than just object-level recognition; it requires und<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28436<END_URL>

MMRole: A Comprehensive Framework for Developing and Evaluating Multimodal Role-Playing Agents\nRecently, Role-Playing Agents (RPAs) have garnered increasing attention for their potential to deliver emotional value and facilitate sociological research.However, existing studies are primarily confined to the textual modality, unable to simulate humans' multimodal perceptual capabilities.To bridge this gap, we introduce the concept of Multimodal Role-Playing Agents (MRPAs), and propose a comprehens<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30350<END_URL>

MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines\nThe advent of Large Language Models (LLMs) has paved the way for AI search engines, e.g., SearchGPT, showcasing a new paradigm in human-internet interaction. However, most current AI search engines are limited to text-only settings, neglecting the multimodal user queries and the text-image interleaved nature of website information. Recently, Large Multimodal Models (LMMs) have made impressive strides. Yet, whethe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30134<END_URL>

MMTEB: Massive Multilingual Text Embedding Benchmark\nText embeddings are typically evaluated on a narrow set of tasks, limited in terms of languages, domains, and task types. To circumvent this limitation and to provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) -- a large-scale community-driven initiative expanding MTEB to over 500 \textit{quality controlled} evaluation tasks across 1,000+ languages. MMTEB includes a wide range of ch<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27651<END_URL>

MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos\nMultimodal Language Language Models (MLLMs) demonstrate the emerging abilities of "world models"---interpreting and reasoning about complex real-world dynamics. To assess these abilities, we posit videos are the ideal medium, as they encapsulate rich representations of real-world dynamics and causalities. To this end, we introduce MMWorld, a new benchmark for multi-discipline, multi-faceted multimodal video underst<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28053<END_URL>

Modality-Specialized Synergizers for Interleaved Vision-Language Generalists\nRecent advancements in Vision-Language Models (VLMs) have led to the emergence of Vision-Language Generalists (VLGs) capable of understanding and generating both text and images. However, seamlessly generating an arbitrary sequence of text and images remains a challenging task for the current VLGs. One primary limitation lies in applying a unified architecture and the same set of parameters to simultaneously model disc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30822<END_URL>

MoDeGPT: Modular Decomposition for Large Language Model Compression\nLarge Language Models (LLMs) have significantly advanced AI with their exceptional performance across a wide range of tasks. However, their extensive computational requirements restrict their use on devices with limited resources.While recent compression methods based on low-rank matrices show potentialsolutions, they often suffer from significant loss of accuracy or introduce substantialoverhead in parameters and inference tim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30778<END_URL>

Model aggregation: minimizing empirical variance outperforms minimizing empirical error\nWhether deterministic or stochastic, models can be viewed as functions designedto approximate a specific quantity of interest. We introduce Minimal Empiri-cal Variance Aggregation (MEVA), a data-driven framework that integrates pre-dictions from various models, enhancing overall accuracy by leveraging the in-dividual strengths of each. This non-intrusive, model-agnostic approach treatsthe contributing models<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28797<END_URL>

Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout\nModeling the evolution of physical systems is critical to many applications in science and engineering. As the evolution of these systems is predominantly governed by partial differential equations (PDEs), there are a number of sophisticated computational simulations which resolve these systems with high accuracy. However, as these simulations incur high computational costs, they are infeasible to be employed for l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31063<END_URL>

Model-agnostic meta-learners for estimating heterogeneous treatment effects over time\nEstimating heterogeneous treatment effects (HTEs) over time is crucial in many disciplines such as personalized medicine. Existing works for this task have mostly focused on *model-based* learners that adapt specific machine-learning models and adjustment mechanisms. In contrast, model-agnostic learners - so-called *meta-learners* - are largely unexplored. In our paper, we propose several meta-learners that ar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29699<END_URL>

Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning\nModel-based offline reinforcement learning (RL) is a compelling approach that addresses the challenge of learning from limited, static data by generating imaginary trajectories using learned models. However, these approaches often struggle with inaccurate value estimation from model rollouts. In this paper, we introduce a novel model-based offline RL method, Lower Expectile Q-learning (LEQ), which provides a low-bias mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29841<END_URL>

Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds\nLearning a transition model via Maximum Likelihood Estimation (MLE) followed by planning inside the learned model is perhaps the most standard and simplest Model-based Reinforcement Learning (RL) framework. In this work, we show that such a simple Model-based RL scheme, when equipped with optimistic and pessimistic planning procedures, achieves strong regret and sample complexity bounds in online and offline RL sett<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28008<END_URL>

Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity\nRecent alignment algorithms such as direct preference optimization (DPO) have been developed to improve the safety of large language models (LLMs) by training these models to match human behaviors exemplified by preference data. However, these methods are both computationally intensive and lacking in controllability and transparency, inhibiting their widespread use. Furthermore, these tuning-based methods require la<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28521<END_URL>

Model Equality Testing: Which Model is this API Serving?\nUsers often interact with large language models through black-box inference APIs, both for closed- and open-weight models (e.g., Llama models are popularly accessed via Amazon Bedrock and Azure AI Studio). In order to cut costs or add functionality, API providers may quantize, watermark, or finetune the underlying model, changing the output distribution --- often without notifying users. We formalize detecting such distortions as Model Eq<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29704<END_URL>

Model-Free Offline Reinforcement Learning with Enhanced Robustness\nOffline reinforcement learning (RL) has gained considerable attention for its ability to learn policies from pre-collected data without real-time interaction, which makes it particularly useful for high-risk applications. However, due to its reliance on offline datasets, existing works inevitably introduce assumptions to ensure effective learning, which, however, often lead to a trade-off between robustness to model mismatch and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29669<END_URL>

Modeling Complex System Dynamics with Flow Matching Across Time and Conditions\nModeling the dynamics of complex real-world systems from temporal snapshot data is crucial for understanding phenomena such as gene regulation, climate change, and financial market fluctuations. Researchers have recently proposed a few methods based either on the Schroedinger Bridge or Flow Matching to tackle this problem, but these approaches remain limited in their ability to effectively combine data from multiple <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28730<END_URL>

Modeling dynamic social vision highlights gaps between deep learning and humans\nDeep learning models trained on computer vision tasks are widely considered the most successful models of human vision to date. The majority of work that supports this idea evaluates how accurately these models predict behavior and brain responses to static images of objects and scenes. Real-world vision, however, is highly dynamic, and far less work has evaluated deep learning models on human responses to moving st<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27867<END_URL>

Modeling Fine-Grained Hand-Object Dynamics for Egocentric Video Representation Learning\nIn egocentric video understanding, the motion of hands and objects as well as their interactions play a significant role by nature.However, existing egocentric video representation learning methods mainly focus on aligning video representation with high-level narrations, overlooking the intricate dynamics between hands and objects.In this work, we aim to integrate the modeling of fine-grained hand-object dyn<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29778<END_URL>

Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questions\nLarge language models (LLMs) must often respond to highly ambiguous user requests. In such cases, the LLM's best response may be to ask a clarifying question to elicit more information. We observe existing LLMs often respond by presupposing a single interpretation of such ambiguous requests, frustrating users who intended a different interpretation. We speculate this is caused by current preference data labeling practi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29021<END_URL>

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning\nGeneralization in reinforcement learning (RL) remains a significant challenge, especially when agents encounter novel environments with unseen dynamics. Drawing inspiration from human compositional reasoning—where known components are reconfigured to handle new situations—we introduce World Modeling with Compositional Causal Components (WM3C). This novel framework enhances RL generalization <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29309<END_URL>

Model merging with SVD to tie the Knots\nRecent model merging methods demonstrate that the parameters of fully-finetuned models specializing in distinct tasks can be combined into one model capable of solving all tasks without retraining. Yet, this success does not transfer well when merging LoRA finetuned models. We study this phenomenon and observe that the weights of LoRA finetuned models showcase a lower degree of alignment compared to their fully-finetuned counterparts. We hypothesize that <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30908<END_URL>

Model Risk-sensitive Offline Reinforcement Learning\nOffline reinforcement learning (RL) is becoming critical in risk-sensitive areas such as finance and autonomous driving, where incorrect decisions can lead to substantial financial loss or compromised safety. However, traditional risk-sensitive offline RL methods often struggle with accurately assessing risk, with minor errors in the estimated return potentially causing significant inaccuracies of risk estimation. These challenges are intensif<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28781<END_URL>

MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos\nIn this paper, we propose MoDGS, a new pipeline to render novel-view images in dynamic scenes using only casually captured monocular videos. Previous monocular dynamic NeRF or Gaussian Splatting methods strongly rely on the rapid movement of input cameras to construct multiview consistency but fail to reconstruct dynamic scenes on casually captured input videos whose cameras are static or move slowly. To address this chal<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31112<END_URL>

Modular, Collaborative and Decentralized Deep Learning\nThe increasing complexity of modern machine learning models exposes the limitations of the traditional, monolithic approach to their development, raising concerns about cost and sustainability.This workshop challenges this approach by advocating for a new paradigm based on modular design and functional specialization. Inspired by principles from software engineering, we envision a future where models are composed of independently trainable <BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23979<END_URL>

MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts\nIn this work, we aim to simultaneously enhance the effectiveness and efficiency of Mixture-of-Experts (MoE) methods. To achieve this, we propose MoE++, a general and heterogeneous MoE framework that integrates both Feed-Forward Network (FFN) and zero-computation experts. Specifically, we introduce three types of zero-computation experts: the zero expert, copy expert, and constant expert, which correspond to discard, sk<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28078<END_URL>

MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks\nMetal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce MOFFlow, the first deep generative model tailored for MOF structure prediction. Existing approaches, including ab initio calculations and even deep generative models, struggle with the complexity of MOF structures due to the large number of at<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28990<END_URL>

MoLEx: Mixture of Layer Experts for Fine-tuning with Sparse Upcycling\nLarge-scale pre-training of deep models, followed by fine-tuning them to adapt to downstream tasks, has become the cornerstone of natural language processing (NLP). The prevalence of vast corpses of data coupled with computational resources has led to large models with a considerable number of parameters. While the massive size of these models has led to remarkable success in many NLP tasks, a detriment is the expense require<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28189<END_URL>

MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra\nEstablishing the relationship between 3D structures and the energy states of molecular systems has proven to be a promising approach for learning 3D molecular representations. However, existing methods are limited to modeling the molecular energy states from classical mechanics. This limitation results in a significant oversight of quantum mechanical effects, such as quantized (discrete) energy level structures<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27793<END_URL>

Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation\nMotion correction (MoCo) in radial MRI is a particularly challenging problem due to the unpredictability of subject movement. Current state-of-the-art (SOTA) MoCo algorithms often rely on extensive high-quality MR images to pre-train neural networks, which constrains the solution space and leads to outstanding image reconstruction results. However, the need for large-scale datasets significantly increase<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29816<END_URL>

Monet: Mixture of Monosemantic Experts for Transformers\nUnderstanding the internal computations of large language models (LLMs) is crucial for aligning them with human values and preventing undesirable behaviors like toxic content generation. However, mechanistic interpretability is hindered by *polysemanticity*—where individual neurons respond to multiple, unrelated concepts. While Sparse Autoencoders (SAEs) have attempted to disentangle these features through sparse dictionary learning,  they<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31204<END_URL>

Monitoring Latent World States in Language Models with Propositional Probes\nLanguage models (LMs) are susceptible to bias, sycophancy, backdoors, and other tendencies that lead to unfaithful responses to the input context. Interpreting internal states of LMs could help monitor and correct unfaithful behavior. We hypothesize that LMs faithfully represent their input contexts in a latent world model, and we seek to extract these latent world states as logical propositions. For example, given the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31228<END_URL>

MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion\nEstimating geometry from dynamic scenes, where objects move and deform over time, remains a core challenge in computer vision. Current approaches often rely on multi-stage pipelines or global optimizations that decompose the problem into subtasks, like depth and flow, leading to complex systems prone to errors. In this paper, we present Motion DuSt3R (MonST3R), a novel geometry-first approachthat directly estimates per<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28524<END_URL>

Monte Carlo Planning with Large Language Model for Text-Based Games\nText-based games provide valuable environments for language-based autonomous agents. However, planning-then-learning paradigms, such as those combining Monte Carlo Tree Search (MCTS) and reinforcement learning (RL), are notably time-consuming due to extensive iterations. Additionally, these algorithms perform uncertainty-driven exploration but lack language understanding and reasoning abilities.In this paper, we introduce the M<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28224<END_URL>

Montessori-Instruct: Generate Influential Training Data Tailored for Student Learning\nSynthetic data has been widely used to train large language models, but their generative nature inevitably introduces noisy, non-informative, and misleading learning signals. In this paper, we propose Montessori-Instruct, a novel data synthesis framework that tailors the data synthesis ability of the teacher language model toward the student language model's learning process. Specifically, we utilize local dat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30692<END_URL>

Moral Alignment for LLM Agents\nDecision-making agents based on pre-trained Large Language Models (LLMs) are increasingly being deployed across various domains of human activity. While their applications are currently rather specialized, several research efforts are under way to develop more generalist agents. As LLM-based systems become more agentic, their influence on human activity will grow and the transparency of this will decrease. Consequently, developing effective methods for aligning th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29923<END_URL>

More Experts Than Galaxies: Conditionally-Overlapping Experts with Biologically-Inspired Fixed Routing\nThe evolution of biological neural systems has led to both modularity and sparse coding, which enables energy efficiency and robustness across the diversity of tasks in the lifespan. In contrast, standard neural networks rely on dense, non-specialized architectures, where all model parameters are simultaneously updated to learn multiple tasks, leading to interference. Current sparse neural net<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31179<END_URL>

More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness\nThe trustworthiness of Large Language Models (LLMs) refers to the extent to which their outputs are reliable, safe, and ethically aligned, and it has become a crucial consideration alongside their cognitive performance. In practice, Reinforcement Learning From Human Feedback (RLHF) has been widely used to align LLMs with labeled human preferences, but its assumed effect on model trustworthiness hasn't been rigorousl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30325<END_URL>

MORPHING TOKENS DRAW STRONG MASKED IMAGE MODELS\nMasked image modeling (MIM) has emerged as a promising approach for training Vision Transformers (ViTs). The essence of MIM lies in the token-wise prediction of masked tokens, which aims to predict targets tokenized from images or generated by pre-trained models like vision-language models. While using tokenizers or pre-trained models are plausible MIM targets, they often offer spatially inconsistent targets even for neighboring tokens, complicati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29006<END_URL>

MorphoDiff: Cellular Morphology Painting with Diffusion Models\nUnderstanding cellular responses to external stimuli is critical for parsing biological mechanisms and advancing therapeutic development. High-content image-based assays provide a cost-effective approach to examine cellular phenotypes induced by diverse interventions, which offers valuable insights into biological processes and cellular states. In this paper, we introduce MorphoDiff, a generative pipeline to predict high-resolution <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29726<END_URL>

MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection\nLiDAR-based 3D object detection is crucial for various applications but often experiences performance degradation in real-world deployments due to domain shifts. While most studies focus on cross-dataset shifts, such as changes in environments and object geometries, practical corruptions from sensor variations and weather conditions remain underexplored. In this work, we propose a novel online test-time adaptation fr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29272<END_URL>

MoS: Unleashing Parameter Efficiency of Low-Rank Adaptation with Mixture of Shards\nThe rapid scaling of large language models necessitates more lightweight finetuning methods to reduce the explosive GPU memory overhead when numerous customized models are served simultaneously.Targeting more parameter-efficient low-rank adaptation (LoRA), parameter sharing presents a promising solution. Empirically, our research into high-level sharing principles highlights the indispensable role of differentiat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31177<END_URL>

MotherNet: Fast Training and Inference via Hyper-Network Transformers\nFoundation models are transforming machine learning across many modalities, with in-context learning replacing classical model training. Recent work on tabular data hints at a similar opportunity to build foundation models for classification for numerical data. However, existing meta-learning approaches can not compete with tree-based methods in terms of inference time. In this paper, we propose MotherNet, a hypernetwork arch<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30899<END_URL>

Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs\nWhile previous approaches to 3D human motion generation have achieved notable success, they often rely on extensive training and are limited to specific tasks. To address these challenges, we introduce **Motion-Agent**, an efficient conversational framework designed for general human motion generation, editing, and understanding. Motion-Agent employs an open-source pre-trained language model to develop a generative a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30605<END_URL>

MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion\nThe spatio-temporal complexity of video data presents significant challenges in tasks such as compression, generation, and inpainting. We present four key contributions to address the challenges of spatiotemporal video processing. First, we introduce the 3D Mobile Inverted Vector-Quantization Variational Autoencoder (3D-MBQ-VAE), which combines Variational Autoencoders (VAEs) with masked modeling to enhanc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29105<END_URL>

MotionClone: Training-Free Motion Cloning for Controllable Video Generation\nMotion-based controllable video generation offers the potential for creating captivating visual content. Existing methods typically necessitate model training to encode particular motion cues or incorporate fine-tuning to inject certain motion patterns, resulting in limited flexibility and generalization.In this work, we propose MotionClone, a training-free framework that enables motion cloning from reference videos to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29159<END_URL>

Motion Control of High-Dimensional Musculoskeletal System with Hierarchical Model-Based Planning\nControlling high-dimensional nonlinear systems presents significant challenges in biological and robotic applications due to the large state and action spaces. While deep reinforcement learning has emerged as the leading approach, it suffers from computationally-intensive and time-consuming, and are not scalable to wide varieties of tasks that each require significant manual tuning. This paper intro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29929<END_URL>

MotionDreamer: One-to-Many Motion Synthesis with Localized Generative Masked Transformer\nGenerative masked transformer have demonstrated remarkable success across various content generation tasks, primarily due to their ability to effectively model large-scale dataset distributions with high consistency. However, in the animation domain, large datasets are not always available. Applying generative masked modeling to generate diverse instances from a single MoCap reference may lead to overfittin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29015<END_URL>

MovieDreamer: Hierarchical Generation for Coherent Long Visual Sequences\nRecent advancements in video generation have primarily leveraged diffusion models for short-duration content. However, these approaches often fall short in modeling complex narratives and maintaining character consistency over extended periods, which is essential for long-form video production like movies. We propose MovieDreamer, a novel hierarchical framework that integrates the strengths of autoregressive models with di<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29698<END_URL>

mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models\nMulti-modal Large Language Models have demonstrated remarkable capabilities in executing instructions for a variety of single-image tasks. Despite this progress, significant challenges remain in modeling long image sequences. In this work, we introduce the versatile multi-modal large language model, mPLUG-Owl3, which enhances the capability for long image-sequence understanding in scenarios that incorpora<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28277<END_URL>

MP-Mat: A 3D-and-Instance-Aware Matting Framework with Multiplane Representation\nHuman instance matting aims to estimate an alpha matte for each human instance in an image, which is challenging as it easily fails in complex cases requiring disentangling mingled pixels belonging to multiple instances along hairy and thin boundary structures. In this work, we address this by introducing a novel 3D-and-instance-aware matting framework with multiplane representation, where the multiplane concept is<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29283<END_URL>

MQuAKE-Remastered: Multi-Hop Knowledge Editing Can Only Be Advanced with Reliable Evaluations\nLarge language models (LLMs) can give out erroneous answers to factually rooted questions either as a result of undesired training outcomes or simply because the world has moved on after a certain knowledge cutoff date. Under such scenarios, knowledge editing often comes to the rescue by delivering efficient patches for such erroneous answers without significantly altering the rests, where many editing<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28478<END_URL>

MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models\nExisting multimodal retrieval benchmarks primarily focus on evaluating whether models can retrieve and utilize external textual knowledge for question answering. However, there are scenarios where retrieving visual information is either more beneficial or easier to access than textual data. In this paper, we introduce a multimodal retrieval-augmented generation benchmark, MRAG-Bench, in which we systematically ident<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29447<END_URL>

MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation\nIn this work, we introduce a novel evaluation paradigm for Large Language Models(LLMs) that compels them to transition from a traditional question-answering role,akin to a student, to a solution-scoring role, akin to a teacher. This paradigm, focusing on "reasoning about reasoning," termed meta-reasoning, shifts the emphasisfrom result-oriented assessments, which often neglect the reasoning process, to amore comprehensive <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29086<END_URL>

MRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE Solvers\nIn applications of diffusion models, controllable generation is of practical significance, but is also challenging. Current methods for controllable generation primarily focus on modifying the score function of diffusion models, while Mean Reverting (MR) Diffusion directly modifies the structure of the stochastic differential equation (SDE), making the incorporation of image conditions simpler and more natural. Howeve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27721<END_URL>

Mr.Steve: Instruction-Following Agents in Minecraft with What-Where-When Memory\nSignificant advances have been made in developing general-purpose embodied AI in environments like Minecraft through the adoption of LLM-augmented hierarchical approaches. While these approaches, which combine high-level planners with low-level controllers, show promise, low-level controllers frequently become performance bottlenecks due to repeated failures. In this paper, we argue that the primary cause of failure<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30503<END_URL>

MrT5: Dynamic Token Merging for Efficient Byte-level Language Models\nModels that rely on subword tokenization have significant drawbacks, such as sensitivity to character-level noise like spelling errors and inconsistent compression rates across different languages and scripts. While character or byte-level models like ByT5 attempt to address these concerns, they have not gained widespread adoption—processing raw byte streams without tokenization results in significantly longer sequence lengths<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29408<END_URL>

MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance\nRecent advancements in text-to-image generation models have dramatically enhanced the generation of photorealistic images from textual prompts, leading to an increased interest in personalized text-to-image applications, particularly in multi-subject scenarios. However, these advances are hindered by two main challenges: firstly, the need to accurately maintain the details of each referenced subject in accordance w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29759<END_URL>

MTSAM: Multi-Task Fine-Tuning for Segment Anything Model\nThe Segment Anything Model (SAM), with its remarkable zero-shot capability, has the potential to be a foundation model for multi-task learning. However, adopting SAM to multi-task learning faces two challenges: (a) SAM has difficulty generating task-specific outputs with different channel numbers, and (b) how to fine-tune SAM to adapt multiple downstream tasks simultaneously remains unexplored. To address these two challenges, in this pap<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30892<END_URL>

MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models\nLarge Language Models (LLMs) have displayed massive improvements in reason- ing and decision-making skills and can hold natural conversations with users. Recently, many tool-use benchmark datasets have been proposed. However, existing datasets have the following limitations: (1). Insufficient evaluation scenarios (e.g., only cover limited tool-use scenes). (2). Extensive evaluation costs (e.g., GPT API costs). To addres<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30871<END_URL>

Mufu:  Multilingual Fused Learning for Low-Resource Translation with LLM\nMultilingual large language models (LLMs) are great translators, but this is largely limited to high-resource languages. For many LLMs, translating in and out of low-resource languages remains a challenging task. To maximize data efficiency in this low-resource setting, we introduce Mufu, which includes a selection of automatically generated multilingual candidates and an instruction to correct inaccurate translations in t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31245<END_URL>

MuHBoost: A Multi-Label Boosting Method For Practical Longitudinal Human Behavior Modeling\nLongitudinal human behavior modeling has received increasing attention over the years due to its widespread applications to patient monitoring, dietary and lifestyle recommendations, and just-in-time intervention for at-risk individuals (e.g., problematic drug users and struggling students), to name a few. Using in-the-moment health data collected via ubiquitous devices (e.g., smartphones and smartwatches<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30588<END_URL>

MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding\nWe introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tasks (e.g., scene understanding, ordering) that involve 10 categories of multi-image relations (e.g., multiview, temporal relations). Comprising 11,264 images and 2,600 multiple-choice questions, MuirBench is created in a pairwise manner, where ea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29509<END_URL>

Multi-agent cooperation through learning-aware policy gradients\nSelf-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. How can we achieve cooperation among self-interested, independent learning agents? Promising recent work has shown that in certain tasks cooperation can be established between ``learning-aware" agents who model the learning dynamics of each other. Here, we present the first unbiased, higher-derivative-free policy gradient a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30261<END_URL>

Multiagent Finetuning of Language Models\nLarge language models (LLMs) have achieved remarkable performance in recent years but are fundamentally limited by the underlying training data. To improve models beyond the training data, recent works have explored how LLMs can be used to generate synthetic data for autonomous self-improvement. However, successive steps of self-improvement can reach a point of diminishing returns, as the diversity of generations decreases, limiting further performance g<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30086<END_URL>

Multi-Dimensional Conformal Prediction\nConformal prediction has attracted significant attention as a distribution-free method for uncertainty quantification in black-box models, providing prediction sets with guaranteed coverage. However, its practical utility is often limited when these prediction sets become excessively large, reducing its overall effectiveness. In this paper, we introduce a novel approach to conformal prediction for classification problems, which leverages a multi-dimensiona<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28498<END_URL>

Multi-domain Distribution Learning for De Novo Drug Design\nWe introduce DrugFlow, a generative model for structure-based drug design that integrates continuous flow matching with discrete Markov bridges, demonstrating state-of-the-art performance in learning chemical, geometric, and physical aspects of three-dimensional protein-ligand data. We endow DrugFlow with an uncertainty estimate that is able to detect out-of-distribution samples. To further enhance the sampling process towards distribut<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28837<END_URL>

Multi-Draft Speculative Sampling: Canonical Architectures and Theoretical Limits\nWe consider multi-draft speculative sampling, where the proposal sequences are sampled independently from different draft models.  At each step, a  token-level draft selection scheme takes a list of valid tokens as input and produces an output token whose distribution matches that of the target model. Previous works have demonstrated that the optimal scheme (which maximizes the probability of accepting one of the i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29904<END_URL>

Multi-Field Adaptive Retrieval\nDocument retrieval for tasks such as search and retrieval-augmented generation typically involves datasets that are _unstructured_: free-form text without explicit internal structure in each document. However, documents can have a structured form, consisting of fields such as an article title, message body, or HTML header. To address this gap, we introduce Multi-Field Adaptive Retrieval (mFAR), a flexible framework that accommodates any number of and any type of d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31079<END_URL>

Multi-Label Node Classification with Label Influence Propagation\nGraphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Examples include proteins in PPI networks with multiple functions and users in social or e-commerce networks exhibiting diverse interests. Tackling multi-label node classification (MLNC) on graphs has led to the development of various approaches. Some methods leverage graph neural netw<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31071<END_URL>

Multi-Label Test-Time Adaptation with Bound Entropy Minimization\nMainstream test-time adaptation (TTA) techniques endeavor to mitigate distribution shifts via entropy minimization for multi-class classification, inherently increasing the probability of the most confident class. However, when encountering multi-label instances, the primary challenge stems from the varying number of labels per image, and prioritizing only the highest probability class inevitably undermines the adaptation of other<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30850<END_URL>

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning\nSimilar to other machine learning frameworks, Offline Reinforcement Learning (RL) is shown to be vulnerable to poisoning attacks, due to its reliance on externally sourced datasets, a vulnerability that is exacerbated by its sequential nature. To mitigate the risks posed by RL poisoning, we extend certified defenses to provide larger guarantees against adversarial manipulation, ensuring robustness for both<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29326<END_URL>

Multilevel Generative Samplers for Investigating Critical Phenomena\nInvestigating critical phenomena, i.e., phase transitions, is of high interest in physics and chemistry. However, Monte Carlo (MC) simulations, a crucial tool for numerically analyzing macroscopic properties of given systems, are often hindered by the emerging scale invariance at criticality (SIC)---a divergence of the correlation length, which causes the system to behave the same at any length scale, as can be shown with renor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29254<END_URL>

Multi-LLM-Agents Debate - Performance, Efficiency, and Scaling Challenges\nMulti-Agent Debate (MAD) explores leveraging collaboration among multiple large language model (LLM) agents to improve test-time performance without additional training. This blog evaluates five MAD frameworks across nine benchmarks, revealing that current MAD methods fail to consistently outperform simpler single-agent strategies, even with increased computational resources. Analysis of factors such as agent configuratio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31346<END_URL>

Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage\nThe advancement of large language models (LLMs) prompts the development of multi-modal agents, providing a feasible way to solve practical tasks by using tools. In this paper, we propose a multi-modal agent tuning method that automatically generates multi-modal tool-usage data and tunes a vision-language model (VLM) as the controller for powerful tool-usage reasoning. To preserve the data quality, we prompt the GPT-4<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31249<END_URL>

Multi-modal brain encoding models for multi-modal stimuli\nDespite participants engaging in unimodal stimuli, such as watching images or silent videos, recent work has demonstrated that multi-modal Transformer models can predict visual brain activity impressively well, even with incongruent modality representations. This raises the question of how accurately these multi-modal models can predict brain activity when participants are engaged in multi-modal stimuli. As these models grow increasingly<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31247<END_URL>

Multimodality Helps Few-Shot 3D Point Cloud Semantic Segmentation\nFew-shot 3D point cloud segmentation (FS-PCS) aims at generalizing models to segment novel categories with minimal annotated support samples. While existing FS-PCS methods have shown promise, they primarily focus on unimodal point cloud inputs, overlooking the potential benefits of leveraging multimodal information. In this paper, we address this gap by introducing a cost-free multimodal FS-PCS setup, utilizing textual labels and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28634<END_URL>

Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning\nWhile large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug design. This difficulty stems from the need for coherent autoregressive generation across texts and graphs. To address this, we introduce Llamole, the first multimodal LLM capable of interleaved text and graph generation, enabling molecular inverse desig<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28198<END_URL>

Multi-modal Learning: A Look Back and the Road Ahead\nAdvancements in language models has spurred an increasing interest in multi-modal AI — models that process and understand information across multiple forms of data, such as text, images and audio. While the goal is to emulate human-like ability to handle diverse information, a key question is: do human-defined modalities align with machine perception? If not, how does this misalignment affect AI performance? In this blog, we examine these que<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31355<END_URL>

Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine\nLearning holistic computational representations in physical, chemical or biological systems requires the ability to process information from different distributions and modalities within the same model. Thus, the demand for multimodal machine learning models has sharply risen for modalities that go beyond vision and language, such as sequences, graphs, time series, or tabular data. While there are man<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28308<END_URL>

Multimodal Quantitative Language for Generative Recommendation\nGenerative recommendation has emerged as a promising paradigm aiming at directly generating the identifiers of the target candidates.Most existing methods attempt to leverage prior knowledge embedded in Pre-trained Language Models (PLMs) to improve the recommendation performance. However, they often fail to accommodate the differences between the general linguistic knowledge of PLMs and the specific needs of recommendation systems. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27932<END_URL>

Multimodal Situational Safety\nMultimodal Large Language Models (MLLMs) are rapidly evolving, demonstrating impressive capabilities as multimodal assistants that interact with both humans and their environments. However, this increased sophistication introduces significant safety concerns. In this paper, we present the first evaluation and analysis of a novel safety challenge termed Multimodal Situational Safety, which explores how safety considerations vary based on the specific situation in wh<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30187<END_URL>

Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality Gap\nDomain generalization (DG) is an important problem that learns a model which generalizes to unseen test domains leveraging one or more source domains, under the assumption of shared label spaces. However, most DG methods assume access to abundant source data in the target label space, a requirement that proves overly stringent for numerous real-world applications, where acquiring the same label space as the targ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29088<END_URL>

Multi-objective antibody design with constrained preference optimization\nAntibody design is crucial for developing therapies against diseases such as cancer and viral infections. Recent deep generative models have significantly advanced computational antibody design, particularly in enhancing binding affinity to target antigens. However, beyond binding affinity, antibodies should exhibit other favorable biophysical properties such as non-antigen binding specificity and low self-association, whi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30996<END_URL>

Multi-objective Differentiable Neural Architecture Search\nPareto front profiling in multi-objective optimization (MOO), i.e. finding a diverse set of Pareto optimal solutions, is challenging, especially with expensive objectives that require training a neural network. Typically, in MOO for neural architecture search (NAS), we aim to balance performance and hardware metrics across devices. Prior NAS approaches simplify this task by incorporating hardware constraints into the objective function, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30667<END_URL>

Multi-Perspective Data Augmentation for Few-shot Object Detection\nRecent few-shot object detection (FSOD) methods have focused on  augmenting synthetic samples for novel classes, show promising results  to the rise of diffusion models. However, the diversity of such datasets is often limited in representativeness because they lack awareness of typical and hard samples, especially in the context of foreground and background relationships. To tackle this issue, we propose a Multi-Perspective Data<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28258<END_URL>

Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning\nLearning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC). The main challenge is to collaboratively model the structural information concealed in massive triples and the multi-modal features of the entities. Existing method<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27966<END_URL>

Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment\nReal-world data distributions are often highly skewed. This has spurred a growing body of research on long-tailed recognition, aimed at addressing the imbalance in training classification models. Among the methods studied, multiplicative logit adjustment (MLA) stands out as a simple and effective method. What theoretical foundation explains the effectiveness of this heuristic method?We provide a just<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30178<END_URL>

Multi-Resolution Decomposable Diffusion Model for Non-Stationary Time Series Anomaly Detection\nRecently, generative models have shown considerable promise in unsupervised time series anomaly detection. Nonetheless, the task of effectively capturing complex temporal patterns and minimizing false alarms becomes increasingly challenging when dealing with non-stationary time series, characterized by continuously fluctuating statistical attributes and joint distributions. To confront these challenge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28922<END_URL>

Multi-Reward as Condition for Instruction-based Image Editing\nHigh-quality training triplets (instruction, original image, edited image) are essential for instruction-based image editing. Predominant training datasets (e.g., InsPix2Pix) are created using text-to-image generative models (e.g., Stable Diffusion, DALL-E) which are not trained for image editing. Accordingly, these datasets suffer from inaccurate instruction following, poor detail preserving, and generation artifacts. In this paper,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30691<END_URL>

Multi-Robot Motion Planning with Diffusion Models\nDiffusion models have recently been successfully applied to a wide range of robotics applications for learning complex multi-modal behaviors from data. However, prior works have mostly been confined to single-robot and small-scale environments due to the high sample complexity of learning multi-robot diffusion models. In this paper, we propose a method for generating collision-free multi-robot trajectories that conform to underlying data distrib<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30630<END_URL>

Multi-Scale Fusion for Object Representation\nRepresenting images or videos as object-level feature vectors, rather than pixel-level feature maps, facilitates advanced visual tasks. Object-Centric Learning (OCL) primarily achieves this by reconstructing the input under the guidance of Variational Autoencoder (VAE) intermediate representation to drive so-called $\textit{slots}$ to aggregate as much object information as possible. However, existing VAE guidance does not explicitly address that obj<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28394<END_URL>

Multi-session, multi-task neural decoding from distinct cell-types and brain regions\nRecent work has shown that scale is important for improved brain decoding, with more data leading to greater decoding accuracy. However, large-scale decoding across many different datasets is challenging because neural circuits are heterogeneous---each brain region contains a unique mix of cellular sub-types, and the responses to different stimuli are diverse across regions and sub-types. It is unknown whether <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30143<END_URL>

Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation\nAudio-visual speech recognition (AVSR) incorporates auditory and visual modalities to improve recognition accuracy, particularly in noisy environments where audio-only speech systems are insufficient. While previous research has largely addressed audio disruptions, few studies have dealt with visual corruptions, e.g., lip occlusions or blurred videos, which are also detrimental. To address this real-world cha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29375<END_URL>

Multi-Task Dense Predictions via Unleashing the Power of Diffusion\nDiffusion models have exhibited extraordinary performance in dense prediction tasks. However, there are few works exploring the diffusion pipeline for multi-task dense predictions. In this paper, we unlock the potential of diffusion models in solving multi-task dense predictions and propose a novel diffusion-based method, called TaskDiffusion, which leverages the conditional diffusion process in the decoder. Instead of denoising<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29497<END_URL>

Multiview Equivariance Improves 3D Understanding with Minimal Feature Finetuning\nVision foundation models, particularly the ViT family, have revolutionized image understanding by providing rich semantic features. However, despite their success in 2D comprehension, their abilities on grasping 3D spatial relationships are still unclear.In this work, we evaluate and enhance the 3D awareness of ViT-based models. We begin by systematically assessing their ability to learn 3D equivariant features, sp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30515<END_URL>

MuPT: A Generative Symbolic Music Pretrained Transformer\nIn this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music. While the prevalent use of MIDI in music modeling is well-established, our findings suggest that LLMs are inherently more compatible with ABC Notation, which aligns more closely with their design and strengths, thereby enhancing the model's performance in musical composition.To address the challenges associated with misaligned measures <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28712<END_URL>

MUSE: Machine Unlearning Six-Way Evaluation for Language Models\nLanguage models (LMs) are trained on vast amounts of text data, which may include private and copyrighted content. Data owners may request the removal of their data from a trained model due to privacy or copyright concerns. However, exactly unlearning only these datapoints (i.e., retraining with the data removed) is intractable in modern-day models. This has led to the development of many approximate unlearning algorithms. The eval<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29559<END_URL>

Mutual Effort for Efficiency: A Similarity-based Token Pruning for Vision Transformers in Self-Supervised Learning\nSelf-supervised learning (SSL) offers a compelling solution to the challenge of extensive labeled data requirements in traditional supervised learning.With the proven success of Vision Transformers (ViTs) in supervised tasks, there is increasing interest in adapting them for SSL frameworks. However, the high computational demands of SSL pose substantial challenges, particularly on <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30276<END_URL>

Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solver\nThis paper introduces rStar, a self-play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superior models. rStar decouples reasoning into a self-play mutual generation-discrimination process. First, a target SLM augments the Monte Carlo Tree Search (MCTS) with a rich set of human-like reasoning actions to construct higher quality reasoning trajectories. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30877<END_URL>

MVTokenFlow: High-quality 4D Content Generation using Multiview Token Flow\nIn this paper, we present MVTokenFlow for high-quality 4D content creation from monocular videos. Recent advancements in generative models such as video diffusion models and multiview diffusion models enable us to create videos or 3D models. However, extending these generative models for dynamic 4D content creation is still a challenging task that requires the generated content to be consistent spatially and temporally. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27643<END_URL>

NarrativeBridge: Enhancing Video Captioning with Causal-Temporal Narrative\nExisting video captioning benchmarks and models lack causal-temporal narrative, which is sequences of events linked through cause and effect, unfolding over time and driven by characters or agents. This lack of narrative restricts models’ ability to generate text descriptions that capture the causal and temporal dynamics inherent in video content. To address this gap, we propose NarrativeBridge, an approach comprising of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29120<END_URL>

Narrowing Information Bottleneck Theory for Multimodal Image-Text Representations Interpretability\nThe task of identifying multimodal image-text representations has recently received increasing attention, particularly with models like CLIP (Contrastive Language-Image Pretraining), which excel at learning complex associations between images and text. Despite these advancements, ensuring the interpretability of such models remains crucial for their safe application in real-world scenarios, such a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30173<END_URL>

Natural Language Inference Improves Compositionality in Vision-Language Models\nCompositional reasoning in Vision-Language Models (VLMs) remains challenging as these models often struggle to relate objects, attributes, and spatial relationships. Recent methods aim to address these limitations by relying on the semantics of the textual description, using Large Language Models (LLMs) to break them down into subsets of questions and answers. However, these methods primarily operate on the surface l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30305<END_URL>

NatureLM-audio: an Audio-Language Foundation Model for Bioacoustics\nLarge language models (LLMs) prompted with text and audio represent the state of the art in various auditory tasks, including speech, music, and general audio, showing emergent abilities on unseen tasks. However, these capabilities have yet to be fully demonstrated in bioacoustics tasks, such as detecting animal vocalizations in large recordings, classifying rare and endangered species, and labeling context and behavior—tasks t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28770<END_URL>

Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence\nWith a growing interest in understanding neural network prediction strategies, Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable concepts in the latent space.Commonly, CAVs are computed by leveraging linear classifiers optimizing the *separability* of latent representations of samples with and without a given concept. However, in this paper we show t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29707<END_URL>

Navigation-Guided Sparse Scene Representation for End-to-End Autonomous Driving\nEnd-to-End Autonomous Driving (E2EAD) methods typically rely on supervised perception tasks to extract explicit scene information (e.g., objects, maps). This reliance necessitates expensive annotations and constrains deployment and data scalability in real-time applications. In this paper, we introduce SSR, a novel framework that utilizes only 16 navigation-guided tokens as Sparse Scene Representation, efficiently e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29393<END_URL>

ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction\nNeural implicit reconstruction via volume rendering has demonstrated its effectiveness in recovering dense 3D surfaces. However, it is non-trivial to simultaneously recover meticulous geometry and preserve smoothness across regions with differing characteristics. To address this issue, previous methods typically employ geometric priors, which are often constrained by the performance of the prior models. In this pa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31021<END_URL>

NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance\nArtificial neural networks have been shown to be state-of-the-art machine learning models in a wide variety of applications, including natural language processing and image recognition. However, building a performant neural network is a laborious task and requires substantial computing power. Neural Architecture Search (NAS) addresses this issue by an automatic selection of the optimal network from a set of potential cand<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29235<END_URL>

Near-Exact Privacy Amplification for Matrix Mechanisms\nWe study the problem of computing the privacy parameters for DP machine learning when using privacy amplification via random batching and noise correlated across rounds via a correlation matrix $\textbf{C}$ (i.e., the matrix mechanism). Past work on this problem either only applied to banded $\textbf{C}$, or gave loose privacy parameters. In this work, we give a framework for computing near-exact privacy parameters for any lower-triangular,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28007<END_URL>

Near, far: Patch-ordering enhances vision foundation models' scene understanding\nWe introduce NeCo: Patch Neighbor Consistency, a novel self-supervised training loss that enforces patch-level nearest neighbor consistency across a student and teacher model. Compared to contrastive approaches that only yield binary learning signals, i.e. "attract" and "repel", this approach benefits from the more fine-grained learning signal of sorting spatially dense features relative to reference patches. Our m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29671<END_URL>

Near-optimal Active Regression of Single-Index Models\nThe active regression problem of the single-index model is to solve $\min_x \lVert f(Ax)-b\rVert_p$, where $A$ is fully accessible and $b$ can only be accessed via entry queries, with the goal of minimizing the number of queries to the entries of $b$.When $f$ is Lipschitz, previous results only obtain constant-factor approximations. This work presents the first algorithm that provides a $(1+\varepsilon)$-approximation solution by querying $\<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28706<END_URL>

Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency\nCoordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm,  are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication grap<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28714<END_URL>

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form\nDesigning a safe policy for uncertain environments is crucial in real-world control systems. However, this challenge remains inadequately addressed within the Markov decision process (MDP) framework. This paper presents the first algorithm guaranteed to identify a near-optimal policy in a robust constrained MDP (RCMDP), where an optimal policy minimizes cumulative cost while satisfying constrain<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30302<END_URL>

Needle In A Video Haystack: A Scalable  Synthetic Evaluator for Video MLLMs\nVideo understanding is a crucial next step for multimodal large language models (MLLMs).Various benchmarks are introduced for better evaluating the MLLMs.Nevertheless, current video benchmarks are still inefficient for evaluating video models during iterative development due to the high cost of constructing datasets and the difficulty in isolating specific skills.In this paper, we propose VideoNIAH (Video Needle in A Ha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29224<END_URL>

Needle Threading: Can LLMs Follow Threads Through Near-Million-Scale Haystacks?\nAs the context limits of Large Language Models (LLMs) increase, the range ofpossible applications and downstream functions broadens. In many real-worldtasks, decisions depend on details scattered across collections of often disparatedocuments containing mostly irrelevant information. Long-context LLMs appearwell-suited to this form of complex information retrieval and reasoning, which hastraditionally proven costly <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27857<END_URL>

NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields\nSound plays a major role in human perception. Along with vision, it provides essential information for understanding our surroundings. Despite advances in neural implicit representations, learning acoustics that align with visual scenes remains a challenge. We propose NeRAF, a method that jointly learns acoustic and radiance fields. NeRAF synthesizes both novel views and spatialized room impulse responses (RIR) at new positions by cond<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28397<END_URL>

Nesterov acceleration in benignly non-convex landscapes\nWhile momentum-based optimization algorithms are commonly used in the notoriously non-convex optimization problems of deep learning, their analysis has historically been restricted to the convex and strongly convex setting. In this article, we partially close this gap between theory and practice and demonstrate that virtually identical guarantees can be obtained in optimization problems with a `benign' non-convexity. We show that these wea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29241<END_URL>

NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks in Open Domains\nWe explore neuro-symbolic approaches to generalize actionable knowledge, enabling embodied agents to tackle complex tasks more effectively in open-domain environments. A key challenge for embodied agents is the generalization of knowledge across diverse environments and situations, as limited experiences often confine them to their prior knowledge. To address this issue, we introduce a novel framework, NeSyC, a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29399<END_URL>

NetFormer: An interpretable model for recovering dynamical connectivity in neuronal population dynamics\nNeuronal dynamics are highly nonlinear and nonstationary. Traditional methods for extracting the underlying network structure from neuronal activity recordings mainly concentrate on modeling static connectivity, without accounting for key nonstationary aspects of biological neural systems, such as ongoing synaptic plasticity and neuronal modulation. To bridge this gap, we introduce the NetFor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29102<END_URL>

NetMoE: Accelerating MoE Training through Dynamic Sample Placement\nMixture of Experts (MoE) is a widely used technique to expand model sizes for better model quality while maintaining the computation cost constant. In a nutshell, an MoE model consists of multiple experts in each model layer and routes the training tokens to only a fixed number of experts rather than all. In distributed training, as experts are distributed among different GPUs, All-to-All communication is necessary to exchange t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31182<END_URL>

Neural Approximate Mirror Maps for Constrained Diffusion Models\nDiffusion models excel at creating visually-convincing images, but they often struggle to meet subtle constraints inherent in the training data. Such constraints could be physics-based (e.g., satisfying a PDE), geometric (e.g., respecting symmetry), or semantic (e.g., including a particular number of objects). When the training data all satisfy a certain constraint, enforcing this constraint on a diffusion model makes it more relia<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27899<END_URL>

Neural Causal Graph for Interpretable and Intervenable Classification\nAdvancements in neural networks have significantly enhanced the performance of classification models, achieving remarkable accuracy across diverse datasets. However, these models often lack transparency and do not support interactive reasoning with human users, which are essential attributes for applications that require trust and user engagement. To overcome these limitations, we introduce an innovative framework, Neural Cau<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28396<END_URL>

Neural Context Flows for Meta-Learning of Dynamical Systems\nNeural Ordinary Differential Equations (NODEs) often struggle to adapt to new dynamic behaviors caused by parameter changes in the underlying system, even when these dynamics are similar to previously observed behaviors. This problem becomes more challenging when the changing parameters are unobserved, meaning their value or influence cannot be directly measured when collecting data. To address this issue, we introduce Neural Context F<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30735<END_URL>

Neural Dueling Bandits: Principled Preference-Based Optimization with Non-Linear Reward Function\nContextual dueling bandit is used to model the bandit problems, where a learner's goal is to find the best arm for a given context using observed noisy preference feedback over the selected arms for the past contexts. However, existing algorithms assume the reward function is linear, which can be complex and non-linear in many real-life applications like online recommendations or ranking web search <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29425<END_URL>

Neural Exploratory Landscape Analysis\nRecent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis features to inform the meta-level agent about the low-level optimizati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30417<END_URL>

Neural Fluid Simulation on Geometric Surfaces\nIncompressible fluid on the surface is an interesting research area in the fluid simulation, which is the fundamental building block in visual effects, design of liquid crystal films, scientific analyses of atmospheric and oceanic phenomena, etc. The task brings two key challenges: the extension of the physical laws on 3D surfaces and the preservation of the energy and volume. Traditional methods rely on grids or meshes for spatial discretization, w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30967<END_URL>

Neural Functions for Learning Periodic Signal\nAs function approximators, deep neural networks have served as an effective tool to represent various signal types. Recent approaches utilize multi-layer perceptrons (MLPs) to learn a nonlinear mapping from a coordinate to its corresponding signal, facilitating the learning of continuous neural representations from discrete data points. Despite notable successes in learning diverse signal types, coordinate-based MLPs often face issues of overfitting<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30294<END_URL>

Neural Interactive Proofs\nWe consider the problem of how a trusted, but computationally bounded agent (a 'verifier') can learn to interact with one or more powerful but untrusted agents ('provers') in order to solve a given task. More specifically, we study the case in which agents are represented using neural networks and refer to solutions of this problem as neural interactive proofs. First we introduce a unifying framework based on prover-verifier games (Anil et al., 2021), which generalises<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29664<END_URL>

Neuralized Markov Random Field for Interaction-Aware Stochastic Human Trajectory Prediction\nInteractive human motions and the continuously changing nature of intentions pose significant challenges for human trajectory prediction. In this paper, we present a neuralized Markov random field (MRF)-based motion evolution method for probabilistic interaction-aware human trajectory prediction. We use MRF to model each agent's motion and the resulting crowd interactions over time, hence is robust again<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28222<END_URL>

Neural Multi-Objective Combinatorial Optimization via Graph-Image Multimodal Fusion\nExisting neural multi-objective combinatorial optimization (MOCO) methods still exhibit an optimality gap since they fail to fully exploit the intrinsic features of problem instances. A significant factor contributing to this shortfall is their reliance solely on graph-modal information. To overcome this, we propose a novel graph-image multimodal fusion (GIMF) framework that enhances neural MOCO methods by integ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30989<END_URL>

Neural networks on Symmetric Spaces of Noncompact Type\nRecent works have demonstrated promising performances of neural networks on hyperbolic spaces and symmetric positive definite (SPD) manifolds. These spaces belong to a family of Riemannian manifolds referred to as symmetric spaces of noncompact type. In this paper, we propose a novel approach for developing neural networks on such spaces. Our approach relies on a unified formulation of the distance from a point to a hyperplane on the consid<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29080<END_URL>

Neural Network Weights as a New Data Modality\nThe ongoing deep learning revolution of the last decade has brought about hundreds of millions of neural networks (NNs) trained on diverse datasets.At the same time, the recent rise of foundation models has led to a rapid increase in the number of publicly available neural network models. On Hugging Face alone, there are over a million models, with thousands more added daily. As a result, the ample knowledge contained in the data, the abstraction le<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23994<END_URL>

Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning\nRecent advancements in large language models (LLMs) based on transformer architectures have sparked significant interest in understanding their inner workings. In this paper, we introduce a novel approach to modeling transformer architectures using highly flexible non-autonomous neural ordinary differential equations (ODEs). Our proposed model fully parameterizes all the weights of attention and feed-forward blocks th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29292<END_URL>

Neural Phylogeny: Fine-Tuning Relationship Detection among Neural Networks\nGiven a collection of neural networks, can we determine which are parent models and which are child models fine-tuned from the parents?In this work, we strive to answer this questionvia introducing a new task termed as neural phylogeny detection, aimed at identifying the existence and direction of the fine-tuning relationship. Specifically, neural phylogeny detection attempts to identify all parent-child model pairs and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28610<END_URL>

NeuralPlane: Structured 3D Reconstruction in Planar Primitives with Neural Fields\n3D maps assembled from planar primitives are compact and expressive in representing man-made environments, making them suitable for a spectrum of applications. In this paper, we present **NeuralPlane**, a novel approach that explores **neural** fields for multi-view 3D **plane** reconstruction. Our method is centered upon the core idea of distilling geometric and semantic cues from inconsistent 2D plane observatio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30944<END_URL>

Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry\nWe deal with the task of sampling from an unnormalized Boltzmann density $\rho_D$by learning a Boltzmann curve given by energies $f_t$ starting in a simple density $\rho_Z$.First, we examine conditions under which Fisher-Rao flows are absolutely continuous in the Wasserstein geometry.Second, we address specific interpolations $f_t$ and  the learning of the related density/velocity pairs $(\rho_t,v_t)$.It was<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29539<END_URL>

Neural Spacetimes for DAG Representation Learning\nWe propose a class of trainable deep learning-based geometries called Neural SpaceTimes (NSTs), which can universally represent nodes in weighted Directed Acyclic Graphs (DAGs) as events in a spacetime manifold. While most works in the literature focus on undirected graph representation learning or causality embedding separately, our differentiable geometry can encode both graph edge weights in its spatial dimensions and causality in the form of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28100<END_URL>

Neural Stochastic Differential Equations for Uncertainty-Aware Offline RL\nOffline model-based reinforcement learning (RL) offers a principled approach to using a learned dynamics model as a simulator to optimize a control policy. Despite the near-optimal performance of existing approaches on benchmarks with high-quality datasets, most struggle on datasets with low state-action space coverage or suboptimal demonstrations.We develop a novel offline model-based RL approach that particularly shines<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28729<END_URL>

Neural Wave Equation for Irregularly Sampled Sequence Data\nSequence labeling problems arise in several real-world applications such as healthcare and robotics. In many such applications, sequence data are irregularly sampled and are of varying complexities. Recently, efforts have been made to develop neural ODE-based architectures to model the evolution of hidden states continuously in time, to address irregularly sampled sequence data. However, they assume a fixed architectural depth and limit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28570<END_URL>

NeurFlow: Interpreting Neural Networks through Critical Neuron Groups and Functional Interactions\nUnderstanding the inner workings of neural networks is essential for enhancing model performance and interpretability. Current research predominantly focuses on examining the connection between individual neurons and the model's final predictions, which suffers from challenges in interpreting the internal workings of the model, particularly when neurons encode multiple unrelated features. In this p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30270<END_URL>

NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals\nRecent advancements for large-scale pre-training with neural signals such as electroencephalogram (EEG) have shown promising results, significantly boosting the development of brain-computer interfaces (BCIs) and healthcare. However, these pre-trained models often require full fine-tuning on each downstream task to achieve substantial improvements, limiting their versatility and usability, and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30149<END_URL>

Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models\nIn recent years, there has been increasing attention on the capabilities of large models, particularly in handling complex tasks that small-scale models are unable to perform. Notably, large language models (LLMs) have demonstrated ``intelligent'' abilities such as complex reasoning and abstract language comprehension, reflecting cognitive-like behaviors. However, current research on emergent abilities in large mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28389<END_URL>

Neuron based Personality Trait Induction in Large Language Models\nLarge language models (LLMs) have become increasingly proficient at simulating various personality traits, an important capability for supporting related applications (e.g., role-playing). To further improve this capacity, in this paper, we present a neuron based approach for personality trait induction in LLMs, with three major technical contributions. First, we construct PERSONALITYBENCH, a large-scale dataset for identifying a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29990<END_URL>

Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive learning\nThe Platonic Representation Hypothesis posits that behind different modalities of data (what we sense or detect), there exists a universal, modality-independent representation of reality. Inspired by this, we treat each neuron as a system, where we can detect the neuron’s multi-segment activity data under different peripheral conditions. We believe that, similar to the Platonic idea, there exists a time-invariant <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27925<END_URL>

Neuroplastic Expansion in Deep Reinforcement Learning\nThe loss of plasticity in learning agents, analogous to the solidification of neural pathways in biological brains, significantly impedes learning and adaptation in reinforcement learning due to its non-stationary nature. To address this fundamental challenge, we propose a novel approach, *Neuroplastic Expansion* (NE), inspired by cortical expansion in cognitive science. NE maintains learnability and adaptability throughout the entire traini<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31169<END_URL>

New Algorithms for the Learning-Augmented k-means Problem\nIn this paper, we study the clustering problems in the learning-augmented setting, where predicted labels for a d-dimensional dataset with size m are given by an oracle to serve as auxiliary information to improve the clustering performance. Following the prior work, the given oracle is parameterized by some error rate α, which captures the accuracy of the oracle such that there are at most α fraction of false positives and false negativ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29284<END_URL>

New Frontiers in Associative Memories\nThis workshop will discuss the latest multidisciplinary developments in Associative Memory. A number of leading researchers in this topic from around the world have already agreed to attend and present their latest results.  We anticipate sharing their presentations and outlining future research directions in this emerging field with the rest of the ICLR community.<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23982<END_URL>

Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing\nMotivated by recent advances in serverless cloud computing, in particular the ``function as a service'' (FaaS) model, we consider the problem of minimizing a convex function in a massively parallel fashion, where communication between workers is limited.Focusing on the case of a twice-differentiable objective subject to an L2 penalty, we propose a scheme where the central node (serve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29499<END_URL>

NextBestPath: Efficient 3D Mapping of Unseen Environments\nThis work addresses the problem of active 3D mapping, where an agent must find an efficient trajectory to exhaustively reconstruct a new scene.Previous approaches mainly predict the next best view near the agent's location, which is prone to getting stuck in local areas. Additionally, existing indoor datasets are insufficient due to limited geometric complexity and inaccurate ground truth meshes.To overcome these limitations, we introduc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30819<END_URL>

NEXT-MOL: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation\n3D molecule generation is crucial for drug discovery and material design. While prior efforts focus on 3D diffusion models for their benefits in modeling continuous 3D conformers, they overlook the advantages of 1D SELFIES-based Language Models (LMs), which can generate 100% valid molecules and leverage the billion-scale 1D molecule datasets. To combine these advantages for 3D molecule generation, we propose a foundati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28319<END_URL>

N-ForGOT: Towards Not-forgetting and Generalization of Open Temporal Graph Learning\nTemporal Graph Neural Networks (TGNNs) lay emphasis on capturing node interactions over time but often overlook evolution in node classes and dynamic data distributions triggered by the continuous emergence of new class labels, known as the open-set problem. This problem poses challenges for existing TGNNs in preserving learned classes while rapidly adapting to new, unseen classes. To address this, this paper id<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28202<END_URL>

nGPT: Normalized Transformer with Representation Learning on the Hypersphere\nWe propose a novel neural network architecture, the normalized Transformer (nGPT) with representation learning on the hypersphere. In nGPT, all vectors forming the embeddings, MLP, attention matrices and hidden states are unit norm normalized. The input stream of tokens travels on the surface of a hypersphere, with each layer contributing a displacement towards the target output predictions. These displacements are def<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28110<END_URL>

NL-Eye: Abductive NLI For Images\nWill a Visual Language Model (VLM)-based bot warn us about slipping if it detects a wet floor? Recent VLMs have demonstrated impressive capabilities, yet their ability to infer outcomes and causes remains underexplored. To address this, we introduce NL-Eye, a benchmark designed to assess VLMs' visual abductive reasoning skills. NL-Eye adapts the abductive Natural Language Inference (NLI) task to the visual domain, requiring models to evaluate the plausibility of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31098<END_URL>

NNsight and NDIF: Democratizing Access to Foundation Model Internals\nWe introduce NNsight and NDIF, technologies that work in tandem to enable scientific study of the representations and computations learned by very large neural networks. NNsight is an open-source system that extends PyTorch to introduce deferred remote execution. NDIF is a scalable inference service that executes NNsight requests, allowing users to share GPU resources and pretrained models. These technologies are enabled by th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29909<END_URL>

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning\nWe present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations—termed node identifiers (node IDs)—to tackle inference challenges on large-scale graphs. By employing vector quantization, we compress continuous node embeddings from multiple layers of a Graph Neural Network (GNN) into discrete codes, applicable under both <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28071<END_URL>

Node Similarities under Random Projections: Limits and Pathological Cases\nRandom Projections have been widely used to generate embeddings for various graph learning tasks due to their computational efficiency. The majority of applications have been justified through the Johnson-Lindenstrauss Lemma. In this paper, we take a step further and investigate how well dot product and cosine similarity are preserved by random projections when these are applied over the rows of the graph matrix. Our anal<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30323<END_URL>

Node-Time Conditional Prompt Learning in Dynamic Graphs\nDynamic graphs capture evolving interactions between entities, such as in social networks, online learning platforms, and crowdsourcing projects. For dynamic graph modeling, dynamic graph neural networks (DGNNs) have emerged as a mainstream technique. However, they are generally pre-trained on the link prediction task, leaving a significant gap from the objectives of downstream tasks such as node classification. To bridge the gap, prompt-b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28577<END_URL>

No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs\nData-driven modeling of dynamical systems is a crucial area of machine learning. In many scenarios, a thorough understanding of the model’s behavior becomes essential for practical applications. For instance, understanding the behavior of a pharmacokinetic model, constructed as part of drug development, may allow us to both verify its biological plausibility (e.g., the drug concentration curve is non-negative and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28568<END_URL>

No Free Lunch: Fundamental Limits of Learning Non-Hallucinating Generative Models\nGenerative models have shown impressive capabilities in synthesizing high-quality outputs across various domains. However, a persistent challenge is the occurrence of "hallucinations," where the model produces outputs that are not grounded in the underlying facts. While empirical strategies have been explored to mitigate this issue, a rigorous theoretical understanding remains elusive. In this paper, we develop a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29791<END_URL>

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation\nThis paper introduces a new imitation learning framework based on energy-based generative models capable of learning complex, physics-dependent, robot motion policies through state-only expert motion trajectories. Our algorithm, called Noise-conditioned Energy-based Annealed Rewards (NEAR), constructs several perturbed versions of the expert's motion data distribution and learn<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30464<END_URL>

Noise Separation guided Candidate Label Reconstruction for Noisy Partial Label Learning\nPartial label learning is a weakly supervised learning problem in which an instance is annotated with a set of candidate labels, among which only one is the correct label. However, in practice the correct label is not always in the candidate label set, leading to the noisy partial label learning (NPLL) problem. In this paper, we theoretically prove that the generalization error of the classifier constructed <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29547<END_URL>

Noisy Test-Time Adaptation in Vision-Language Models\nTest-time adaptation (TTA) aims to address distribution shifts between source and target data by relying solely on target data during testing. In open-world scenarios, models often encounter noisy samples, i.e., samples outside the in-distribution (ID) label space. Leveraging the zero-shot capability of pre-trained vision-language models (VLMs), this paper introduces Zero-Shot Noisy TTA (ZS-NTTA), focusing on adapting the model to target data<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28661<END_URL>

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data\nImplicit neural representations (INRs) exhibit growing promise in addressing Earth representation challenges, ranging from emissions monitoring to climate modeling. However, existing methods disproportionately prioritize global average performance, whereas practitioners require fine-grained insights to understand biases and variations in these models. To bridge this gap, we introduce FAIR-Ea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28761<END_URL>

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching\nIn inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Traditionally, IRL is treated as an adversarial game, where an adversary searches over reward models, and a learner optimizes the reward through repeated RL procedures.This game-solving approach is both computationally expensive and difficult to stabilize.In this work, we propose a nove<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29965<END_URL>

Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation\nWe address the problem of solving strongly convex and smooth minimization problems using stochastic gradient descent (SGD) algorithm with a constant step size. Previous works  suggested to combine the Polyak-Ruppert averaging procedure with the Richardson-Romberg extrapolation technique to reduce the asymptotic bias of SGD at the expense of a mild increase of the variance. We significantly extend pre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29815<END_URL>

Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping\nRecently, the study of heavy-tailed noises in first-order nonconvex stochastic optimization has gotten a lot of attention since it was recognized as a more realistic condition as suggested by many empirical observations. Specifically, the stochastic noise (the difference between the stochastic and true gradient) is considered only to have a finite $\mathfrak{p}$-th moment where $\mathfrak{<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29883<END_URL>

No Need to Talk: Asynchronous Mixture of Language Models\nWe introduce SMALLTALK LM, an innovative method for training a mixture of language models in an almost asynchronous manner. Eachmodel of the mixture specializes in distinct parts of the data distribution, without the need of high-bandwidth communication between the nodes training each model. At inference, a lightweight router directs a given sequence to a single expert, according to a short prefix. This inference scheme naturally uses a f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28307<END_URL>

Non-Equilibrium Dynamics of Hybrid Continuous-Discrete Ground-State Sampling\nWe propose a general framework for a hybrid continuous-discrete algorithm that integrates continuous-time deterministic dynamics with Metropolis-Hastings (MH) steps to combine search dynamics with and without detailed balance. Our purpose is to study the non-equilibrium dynamics that leads to the ground state of rugged energy landscapes in this general setting. Our results show that MH-driven dynamics reach ``easy'' gr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30550<END_URL>

Nonlinear multiregion neural dynamics with parametric impulse response communication channels\nCognition arises from the coordinated interaction of brain regions with distinct computational roles. Despite improvements in our ability to extract the dynamics underlying circuit computation from population activity recorded in individual areas, understanding how multiple areas jointly support distributed computation remains a challenge. As part of this effort, we propose a multi-region neural dynami<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29986<END_URL>

Nonlinear Sequence Embedding by Monotone Variational Inequality\nIn the wild, we often encounter collections of sequential data such as electrocardiograms, motion capture, genomes, and natural language, and sequences may be multichannel or symbolic with nonlinear dynamics. We introduce a method to learn low-dimensional representations of nonlinear sequence and time-series data without supervision which has provable recovery guarantees. The learned representation can be used for downstream machin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29488<END_URL>

Non-parametric Kernel Relative Test for Machine-generated Text Detection\nRecent studies demonstrate that two-sample test can effectively detect machine-generated texts (MGTs) with excellent adaptation ability to texts generated by newer LLMs. However, the two-sample test-based detection relies on the assumption that human-written texts (HWTs) must follow the distribution of seen HWTs. As a result, it tends to make mistakes in identifying HWTs that deviate from the \textit{seen HWT} distribution<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27688<END_URL>

No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images\nWe introduce NoPoSplat, a feed-forward model capable of reconstructing 3D scenes parameterized by 3D Gaussians from unposed sparse multi-view images. Our model, trained exclusively with photometric loss, achieves real-time 3D Gaussian reconstruction during inference. To eliminate the need for accurate pose input during reconstruction, we anchor one input view's local camera coordinates as the canonical space <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29779<END_URL>

No Preference Left Behind: Group Distributional Preference Optimization\nPreferences within a group of people are not uniform but follow a distribution. While existing alignment methods like Direct Preference Optimization (DPO) attempt to steer models to reflect human preferences, they struggle to capture the distributional pluralistic preferences within a group. These methods often skew toward dominant preferences, overlooking the diversity of opinions, especially when conflicting preferences a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29097<END_URL>

Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning\nKey-Value (KV) caching is a common technique to enhance the computational efficiency of Large Language Models (LLMs), but its memory overhead grows rapidly with input length. Prior work has shown that not all tokens are equally important for text generation, proposing layer-level KV cache compression to selectively retain key information. Recognizing the distinct roles of attention heads in ge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30348<END_URL>

Not All Language Model Features Are Linear\nRecent work has proposed that language models perform computation by manipulating one-dimensional representations of concepts ("features") in activation space. In contrast, we explore whether some language model representations may be inherently multi-dimensional. We begin by developing a rigorous definition of irreducible multi-dimensional features based on whether they can be decomposed into either independent or non-co-occurring lower-dimensional fe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29008<END_URL>

Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification\nSynthetic data augmentation via large language models (LLMs) allows researchers to leverage additional training data, thus enhancing the performance of downstream tasks, especially when real-world data is scarce. However, the generated data can deviate from the real-world data, and this misalignment can bring deficient outcomes while applying the trained model to applications. Therefore, we proposed efficient<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28366<END_URL>

Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models\nText-to-image (T2I) diffusion models have demonstrated impressive image generation capabilities. Still, their computational intensity prohibits resource-constrained organizations from deploying T2I models after fine-tuning them on their internal *target* data. While pruning techniques offer a potential solution to reduce the computational burden of T2I models, static pruning methods use the same pruned model <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31091<END_URL>

Not-So-Optimal Transport Flows for 3D Point Cloud Generation\nLearning generative models of 3D point clouds is one of the fundamental problems in 3D generative learning. One of the key properties of point clouds is their permutation invariance, i.e., changing the order of points in a point cloud does not change the shape they represent. In this paper, we analyze the recently proposed equivariant OT flows that learn permutation invariant generative models for point-based molecular data and we sho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30911<END_URL>

Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning\nBinary code analysis is the foundation of crucial tasks in the security domain; thus building effective binary analysis techniques is more important than ever. Large language models (LLMs) although have brought impressive improvement to source code tasks, do not directly generalize to assembly code due to the unique challenges of assembly: (1) the low information density of assembly and (2) t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30979<END_URL>

NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens\nThe rapid advancement of Large Language Models (LLMs) has introduced a new frontier in natural language processing, particularly in understanding and processing long-context information. However, the evaluation of these models' long-context abilities remains a challenge due to the limitations of current benchmarks. To address this gap, we introduce NovelQA, a benchmark specifically designed to test the capabilities of L<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27983<END_URL>

NoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Models\nHallucinations in Large Language Models (LLMs) remain a major obstacle, particularly in high-stakes applications where factual accuracy is critical. While representation editing and reading methods have made strides in reducing hallucinations, their heavy reliance on specialised tools and training on in-domain samples, makes them difficult to scale and prone to overfitting. This limits their accuracy gains and ge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27716<END_URL>

NRGBoost: Energy-Based Generative Boosted Trees\nDespite the rise to dominance of deep learning in unstructured data domains, tree-based methods such as Random Forests (RF) and Gradient Boosted Decision Trees (GBDT) are still the workhorses for handling discriminative tasks on tabular data. We explore generative extensions of these popular algorithms with a focus on explicitly modeling the data density (up to a normalization constant), thus enabling other applications besides sampling. As our ma<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27846<END_URL>

NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval\n$k$-Nearest Neighbor search on dense vector embeddings ($k$-NN retrieval) from pre-trained embedding models is the predominant retrieval method for text and images, as well as Retrieval-Augmented Generation (RAG) pipelines. In practice, application developers often fine-tune the embeddings to improve their accuracy on the dataset and query workload in hand. Existing approaches either fine-tune the pre-trained model itself<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29928<END_URL>

Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning\nHindsight relabeling is a powerful tool for overcoming sparsity in goal-conditioned reinforcement learning (GCRL). While effective in some domains like navigation and locomotion, hindsight relabeling can struggle in object-centric domains. For example, suppose that the goal space consists of a robotic arm pushing a particular target block to a goal location. In this case, hindsight relabeling will give high rewa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31106<END_URL>

Number Cookbook: Number Understanding of Language Models and How to Improve It\nLarge language models (LLMs) can solve an increasing number of complex reasoning tasks while making surprising mistakes in basic numerical understanding and processing (such as $9.11 > 9.9$). The latter ability is essential for tackling complex arithmetic and mathematical problems and serves as a foundation for most reasoning tasks, but previous work paid little attention to it or only discussed several restricted ta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30568<END_URL>

NutriBench: A Dataset for Evaluating Large Language Models in Nutrition Estimation from Meal Descriptions\nAccurate nutrition estimation helps people make informed dietary choices and is essential in the prevention of serious health complications. We present NutriBench, the first publicly available natural language meal description nutrition benchmark. NutriBench consists of 11,857 meal descriptions generated from real-world global dietary intake data. The data is human-verified and annotated wi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30896<END_URL>

NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models\nDecoder-only large language model (LLM)-based embedding models are beginning to outperform BERT or T5-based embedding models in general-purpose text embedding tasks, including dense vector-based retrieval. In this work, we introduce the NV-Embed model, incorporating architectural designs, training procedures, and curated datasets to significantly enhance the performance of LLM as a versatile embedding model, while ma<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28505<END_URL>

OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition\nThe recent paradigm shift to large-scale foundation models has brought about a new era for deep learning that, while has found great success in practice, has also been plagued by prohibitively expensive costs in terms of high memory consumption and compute. To mitigate these issues, there has been a concerted effort in post-hoc neural network pruning techniques that do not require costly retraining. Despite the considerable p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30463<END_URL>

OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones?\nWe introduce OBI-Bench, a holistic benchmark crafted to systematically evaluate large multi-modal models (LMMs) on whole-process oracle bone inscriptions (OBI) processing tasks demanding expert-level domain knowledge and deliberate cognition. OBI-Bench includes 5,523 meticulously collected diverse-sourced images, covering five key domain problems: recognition, rejoining, classification, retrieval, and deciphering. These images <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28769<END_URL>

ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding\nLanguage models (LMs) have become a staple of the code-writing toolbox. Their pre-training recipe has, however, remained stagnant over recent years, barring the occasional changes in data sourcing and filtering strategies. In particular, research exploring modifications to Code-LMs' pre-training objectives, geared towards improving data efficiency and better disentangling between syntax and semantics, has been notic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29407<END_URL>

OCCAM: Towards Cost-Efficient and Accuracy-Aware Classification Inference\nClassification tasks play a fundamental role in various applications, spanning domains such as healthcare, natural language processing and computer vision. With the growing popularity and capacity of machine learning models, people can easily access trained classifiers as a service online or offline. However, model use comes with a cost and classifiers of higher capacity (such as large foundation models) usually incur hig<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30510<END_URL>

Occlusion-aware Non-Rigid Point Cloud Registration via Unsupervised Neural Deformation Correntropy\nNon-rigid alignment of point clouds is crucial for scene understanding, reconstruction, and various computer vision and robotics tasks. Recent advancements in implicit deformation networks for non-rigid registration have significantly reduced the reliance on large amounts of annotated training data. However, existing state-of-the-art methods still face challenges in handling occlusion scenarios. T<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29032<END_URL>

OccProphet: Pushing the Efficiency Frontier of Camera-Only 4D Occupancy Forecasting with an Observer-Forecaster-Refiner Framework\nPredicting variations in complex traffic environments is crucial for the safety of autonomous driving. Recent advancements in occupancy forecasting have enabled forecasting future 3D occupied status in driving environments by observing historical 2D images. However, high computational demands make occupancy forecasting less efficient during training and inference sta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27927<END_URL>

OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models\nOffline evaluation of LLMs is crucial in understanding their capacities, though current methods remain underexplored in existing research. In this work, we focus on the offline evaluation of the chain-of-thought capabilities and show how to optimize LLMs based on the proposed evaluation method. To enable offline feedback with rich knowledge and reasoning paths, we use knowledge graphs (e.g., Wikidata5m) to provide<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28173<END_URL>

OCEBO: Object-Centric Pretraining by Target Encoder Bootstrapping\nObject-centric representation learning has recently been successfully applied to real-world datasets. This success can be attributed to pretrained non-object-centric foundation models, whose features serve as reconstruction targets for slot attention. However, targets must remain frozen throughout the training, which sets an upper bound on the performance object-centric models can attain. Attempts to update the target encoder by <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30811<END_URL>

ODE-based Smoothing Neural Network for Reinforcement Learning Tasks\nThe smoothness of control actions is a significant challenge faced by deep reinforcement learning (RL) techniques in solving optimal control problems. Existing RL-trained policies tend to produce non-smooth actions due to high-frequency input noise and unconstrained Lipschitz constants in neural networks. This article presents a Smooth ODE (SmODE) network capable of simultaneously addressing both causes of unsmooth control acti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29629<END_URL>

O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions\nScore-based diffusion models, which generate new data by learning to reverse a diffusion process that perturbs data from the target distribution into noise, have achieved remarkable success across various generative tasks. Despite their superior empirical performance, existing theoretical guarantees are often constrained by stringent assumptions or suboptimal convergence rates. In this paper, we establish a f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31027<END_URL>

Offline Hierarchical Reinforcement Learning via Inverse Optimization\nHierarchical policies enable strong performance in many sequential decision-making problems, such as those with high-dimensional action spaces, those requiring long-horizon planning, and settings with sparse rewards. However, learning hierarchical policies from static offline datasets presents a significant challenge.Crucially, actions taken by higher-level policies may not be directly observable within hierarchical controller<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28982<END_URL>

Offline Model-Based Optimization by Learning to Rank\nOffline model-based optimization (MBO) aims to identify a design that maximizes a black-box function using only a fixed, pre-collected dataset of designs and their corresponding scores. This problem has garnered significant attention from both scientific and industrial domains. A common approach in offline MBO is to train a regression-based surrogate model by minimizing mean squared error (MSE) and then find the best design within this surrog<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28117<END_URL>

Offline RL in Regular Decision Processes: Sample Efficiency via Language Metrics\nThis work studies offline Reinforcement Learning (RL) in a class of non-Markovian environments called Regular Decision Processes (RDPs). In RDPs, the unknown dependency of future observations and rewards from the past interactions can be captured by some hidden finite-state automaton. For this reason, many RDP algorithms first reconstruct this unknown dependency using automata learning techniques. In this paper, we<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30400<END_URL>

Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood\nOffline Reinforcement Learning (RL) struggles with distributional shifts, leading to the $Q$-value overestimation for out-of-distribution (OOD) actions. Existing methods address this issue by imposing constraints; however, they often become overly conservative when evaluating OOD regions, which constrains the $Q$-function generalization. This over-constraint issue results in poor $Q$-value estimation and hinders polic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28920<END_URL>

OGBench: Benchmarking Offline Goal-Conditioned RL\nOffline goal-conditioned reinforcement learning (GCRL) is a major problem in reinforcement learning (RL) because it provides a simple, unsupervised, and domain-agnostic way to acquire diverse behaviors and representations from unlabeled data without rewards. Despite the importance of this setting, we lack a standard benchmark that can systematically evaluate the capabilities of offline GCRL algorithms. In this work, we propose OGBench, a new, hi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29950<END_URL>

OLMoE: Open Mixture-of-Experts Language Models\nWe introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but uses only 1B per input token. We pretrain it on 5 trillion tokens and further adapt it to create OLMoE-1B-7B-Instruct. Our models outperform all available models with similar active parameters, even surpassing larger ones like Llama2-13B-Chat and DeepSeekMoE-16B. We present novel findings on MoE <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27777<END_URL>

OMG: Opacity Matters in Material Modeling with Gaussian Splatting\nDecomposing geometry, materials and lighting from a set of images, namely inverse rendering, has been a long-standing problem in computer vision and graphics. Recent advances in neural rendering enable photo-realistic and plausible inverse rendering results. The emergence of 3D Gaussian Splatting has boosted it to the next level. An intuitive finding is that the models used for inverse rendering do not take into account the depen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28342<END_URL>

OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision\nInstruction-guided image editing methods have demonstrated significant potential by training diffusion models on automatically synthesized or manually annotated image editing pairs. However, these methods remain far from practical, real-life applications. We identify three primary challenges contributing to this gap. Firstly, existing models have limited editing skills due to the biased synthesis process. Secondly<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30213<END_URL>

OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code\nOpen-ended and AI-generating algorithms aim to continuously generate and solve increasingly complex tasks indefinitely, offering a promising path toward more general intelligence. To accomplish this grand vision, learning must occur within a vast array of potential tasks. Existing approaches to automatically generating environments are constrained within manually predefined, often narro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29279<END_URL>

OmniKV: Dynamic Context Selection for Efficient Long-Context LLMs\nDuring the inference phase of Large Language Models (LLMs) with long context, a substantial portion of GPU memory is allocated to the KV cache, with memory usage increasing as the sequence length grows. To mitigate the GPU memory footprint associate with KV cache, some previous studies have discarded less important tokens based on the sparsity identified in attention scores in long context scenarios. However, we argue that attent<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27961<END_URL>

Omni-MATH: A Universal Olympiad Level Mathematic Benchmark for Large Language Models\nRecent advancements in large language models (LLMs) have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benchmarks like GSM8K or MATH are now being solved with high accuracy (e.g., OpenAI o1 achieves 94.8% on MATH dataset), indicating their inadequacy for truly challenging these models. To bridge this gap, we propose a comprehensive and challenging benchmark specifica<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27714<END_URL>

OmniPhysGS: 3D Constitutive Gaussians for General Physics-Based Dynamics Generation\nRecently, significant advancements have been made in the reconstruction and generation of 3D assets, including static cases and those with physical interactions. To recover the physical properties of 3D assets, existing methods typically assume that all materials belong to a specific predefined category ($\textit{e.g.}$, elasticity). However, such assumptions ignore the complex composition of multiple heterogene<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30706<END_URL>

OmniRe: Omni Urban Scene Reconstruction\nWe introduce OmniRe, a comprehensive system for efficiently creating high-fidelity digital twins of dynamic real-world scenes from on-device logs. Recent methods using neural fields or Gaussian Splatting primarily focus on vehicles, hindering a holistic framework for all dynamic foregrounds demanded by downstream applications, e.g., the simulation of human behavior. OmniRe extends beyond vehicle modeling to enable accurate, full-length reconstruction of d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31225<END_URL>

OmniSep: Unified Omni-Modality Sound Separation with Query-Mixup\nQuery-based sound separation (QSS) effectively isolate sound signals that match the content of a given query, enhancing the understanding of audio data. However, most existing QSS methods rely on a single modality for separation, lacking the ability to fully leverage homologous but heterogeneous information across multiple modalities for the same sound signal. To address this limitation, we introduce Omni-modal Sound Separation (*<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30444<END_URL>

OmnixR: Evaluating Omni-modality Language Models on Reasoning across Modalities\nWe introduce \textbf{OmnixR}, an evaluation suite designed to benchmark state-of-the-art Omni-modality Language Models (OLMs), such as GPT-4o and Gemini. Evaluating OLMs, which integrate multiple modalities such as text, vision, and audio, presents unique challenges. Particularly, the user message might often consist of multiple modalities, such that OLMs have to establish holistic understanding and reasoning across<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28621<END_URL>

On Bits and Bandits: Quantifying the Regret-Information Trade-off\nIn many sequential decision problems, an agent performs a repeated task. He then suffers regret and obtains information that he may use in the following rounds. However, sometimes the agent may also obtain information and avoid suffering regret by querying external sources. We study the trade-off between the information an agent accumulates and the regret it suffers. We invoke information-theoretic methods for obtaining regret lo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31236<END_URL>

On Calibration of LLM-based Guard Models for Reliable Content Moderation\nLarge language models (LLMs) are exposed to significant risks due to their potential for malicious use. Existing studies have developed LLM-based guard models designed to moderate the input and output of threat LLMs, ensuring adherence to safety policies by blocking content that violates these protocols upon deployment. However, limited attention has been given to the reliability and calibration of such guard models. In th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27843<END_URL>

Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption\nAlthough recent generative image compression methods have demonstrated impressive potential in optimizing the rate-distortion-perception trade-off, they still face the critical challenge of flexible rate adaption to diverse compression necessities and scenarios. To overcome this challenge, this paper proposes a $\textbf{Control}$lable $\textbf{G}$enerative $\textbf{I}$mage $\textbf{C}$ompression framework,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27695<END_URL>

On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning\nWe study the discriminative probabilistic modeling problem on a continuous domain for (multimodal) self-supervised representation learning. To address the challenge of computing the integral in the partition function for each anchor data, we leverage the multiple importance sampling (MIS) technique for robust Monte Carlo integration, which can recover InfoNCE-based contrastive loss as a special case. Within thi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28153<END_URL>

On Disentangled Training for Nonlinear Transform in Learned Image Compression\nLearned image compression (LIC) has demonstrated superior rate-distortion (R-D) performance compared to traditional codecs, but is challenged by training inefficiency that could incur more than two weeks to train a state-of-the-art model from scratch. Existing LIC methods overlook the slow convergence caused by compacting energy in learning nonlinear transforms. In this paper, we first reveal that such energy compacti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29489<END_URL>

One for all and all for one: Efficient computation of partial Wasserstein distances on the line\nPartial Wasserstein helps overcoming some of the limitations of Optimal Transport when the distributions at stake differ in mass, contain noise or outliers or exhibit mass mismatches across distribution modes.We introduce PAWL, a novel algorithm designed to efficiently compute exact PArtial Wasserstein distances on the Line. PAWL not only solves the partial transportation problem for a specified amou<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28547<END_URL>

One-for-All Few-Shot Anomaly Detection via Instance-Induced Prompt Learning\nAnomaly detection methods under the 'one-for-all' paradigm aim to develop a unified model capable of detecting anomalies across multiple classes. However, these approaches typically require a large number of normal samples for model training, which may not always be feasible in practice. Few-shot anomaly detection methods can address scenarios with limited data but often require a tailored model for each class, struggli<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29190<END_URL>

One Hundred Neural Networks and Brains Watching Videos: Lessons from Alignment\nWhat can we learn from comparing video models to human brains, arguably the most efficient and effective video processing systems in existence? Our work takes a step towards answering this question by performing the first large-scale benchmarking of deep video models on representational alignment to the human brain, using publicly available models and a recently released video brain imaging (fMRI) dataset. We disenta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30005<END_URL>

One Model Transfer to All: On Robust Jailbreak Prompts Generation against LLMs\nSafety alignment in large language models (LLMs) is increasingly compromised by jailbreak attacks, which can manipulate these models to generate harmful or unintended content. Investigating these attacks is crucial for uncovering model vulnerabilities. However, many existing jailbreak strategies fail to keep pace with the rapid development of defense mechanisms, such as defensive suffixes, rendering them ineffective <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28127<END_URL>

One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt\nText-to-image generation models can create high-quality images from input prompts. However, they struggle to support the consistent generation of identity-preserving requirements for storytelling. Existing approaches to this problem typically require extensive training in large datasets or additional modifications to the original model architectures. This limits their applicability across different domain<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29063<END_URL>

One  slice  is not  enough:  In  search  of  stable conclusions in text-to-image evaluation\nWhile text-to-image (T2I) generative models have become ubiquitous, they do not necessarily generate images that align with a given prompt. While many metrics and benchmarks have been proposed to evaluate T2I models and alignment metrics, the impact of the evaluation components (prompt sets, human annotations, evaluation task) has not been systematically measured.We find that looking at only *one slice o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30151<END_URL>

One Step Diffusion via Shortcut Models\nDiffusion models and flow matching models have enabled generating diverse and realistic images by learning to transfer noise to data. However, sampling from these models involves iterative denoising over many neural network passes, making generation slow and expensive. Previous approaches for speeding up sampling require complex training regimes, such as multiple training phases, multiple networks, or fragile scheduling. We introduce Shortcut Models, a fam<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29802<END_URL>

On Evaluating the Durability of Safeguards for Open-Weight LLMs\nMany stakeholders---from model developers to policymakers---seek to minimize the risks of large language models (LLMs). Key to this goal is whether technical safeguards can impede the misuse of LLMs, even when models are customizable via fine-tuning or when model weights are openly available. Several recent studies have proposed methods to produce durable LLM safeguards for open-weight LLMs that can withstand adversarial modificati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28875<END_URL>

On Generalization Within Multi-Objective Reinforcement Learning Algorithms\nReal-world sequential decision-making tasks often require balancing trade-offs between multiple conflicting objectives, making Multi-Objective Reinforcement Learning (MORL) an increasingly prominent field of research. Despite recent advances, existing MORL literature has narrowly focused on performance within static environments, neglecting the importance of generalizing across diverse settings. Conversely, existing rese<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28013<END_URL>

On Large Language Model Continual Unlearning\nWhile large language models have demonstrated impressive performance across various domains and tasks, their security issues have become increasingly severe. Machine unlearning has emerged as a representative approach for model safety and security, removing the influence of undesired data on the target model. However, these methods do not sufficiently consider that unlearning requests in real-world scenarios are continuously emerging, especially in t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30382<END_URL>

On Linear Representations and Pretraining Data Frequency in Language Models\nPretraining data has a direct impact on the behaviors and quality of language models (LMs), but we only understand the most basic principles of this relationship. While most work focuses on pretraining data's effect on downstream task behavior, we investigate its relationship to LM representations. Previous work has discovered that, in language models, some concepts are encoded as ``linear representations'', but what fa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30418<END_URL>

Online Clustering with Nearly Optimal Consistency\nWe give online algorithms for $k$-Means(more generally, $(k, z)$-Clustering) with nearly optimal consistency (a notion suggested by Lattanzi & Vassilvitskii (2017)). Our result turns any $\alpha$-approximate offline algorithm for clustering into an $(1+\epsilon)\alpha^2$-competitive online algorithm for clustering with $O(k \text{poly} \log n)$ consistency. This consistency bound is optimal up to $\text{poly} \log(n)$ factors. Plugging in the of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29895<END_URL>

ONLINE EPSILON NET & PIERCING SET FOR GEOMETRIC CONCEPTS\nVC-dimension (Vapnik & Chervonenkis (1971)) and $\varepsilon$-nets  (Haussler & Welzl (1987)) are key concepts in Statistical Learning Theory. Intuitively, VC-dimension is a measure of the size of a class of sets. The famous $\varepsilon$-net theorem, a fundamental result in Discrete Geometry, asserts that if the VC-dimension of a set system is bounded, then a small sample exists that intersects all sufficiently large sets.        In onli<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28409<END_URL>

Online Neuro-Symbolic Predicate Invention for High-Level Planning\nBroadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensorimotor space. In this work, we present Neuro-Symbolic Predicates, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations. We outline an online algorithm for inventing such predicates and learning a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29691<END_URL>

Online Preference Alignment for Language Models via Count-based Exploration\nReinforcement Learning from Human Feedback (RLHF) has shown great potential in fine-tuning Large Language Models (LLMs) to align with human preferences. Existing methods perform preference alignment from a fixed dataset, which can be limited in data coverage and the resulting reward model is hard to generalize in out-of-distribution responses. Thus, online RLHF is more desirable to empower the LLM to explore outside the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29036<END_URL>

Online Reinforcement Learning in Non-Stationary Context-Driven Environments\nWe study online reinforcement learning (RL) in non-stationary environments, where a time-varying exogenous context process affects the environment dynamics. Online RL is challenging in such environments due to ``catastrophic forgetting'' (CF). The agent tends to forget prior knowledge as it trains on new experiences. Prior approaches to mitigate this issue assume task labels (which are often not available in practice), <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28535<END_URL>

Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein Regularization\nRecent advancements in reinforcement learning (RL) have achieved great success in fine-tuning diffusion-based generative models. However, fine-tuning continuous flow-based generative models to align with arbitrary user-defined reward functions remains challenging, particularly due to issues such as policy collapse from overoptimization and the prohibitively high computational cost of likelihoods in continuous-ti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31149<END_URL>

Online-to-Offline RL for Agent Alignment\nReinforcement learning (RL) has shown remarkable success in training agents to achieve high-performing policies, particularly in domains like Game AI where simulation environments enable efficient interactions. However, despite their success in maximizing these returns, such online-trained policies often fail to align with human preferences concerning actions, styles, and values. The challenge lies in efficiently adapting these online-trained policies to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28165<END_URL>

On LLM Knowledge Distillation - A Comparison between Forward KL and Reverse KL\nIn this blog post, we delve into knowledge distillation techniques for Large Language Models (LLMs), with a particular focus on using Kullback-Leibler (KL) Divergence as the optimization objective. Knowledge distillation is a powerful tool to reduce model size while maintaining comparable performance, making it especially useful in scenarios with constrained computational or serving resources. We specifically explore<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31335<END_URL>

On Minimizing Adversarial Counterfactual Error in Adversarial Reinforcement Learning\nDeep Reinforcement Learning (DRL) policies are critically vulnerable to adversarial noise in observations, posing severe risks in safety-critical scenarios. For example, a self-driving car receiving manipulated sensory inputs about traffic signs could lead to catastrophic outcomes. Existing strategies to fortify RL algorithms against such adversarial perturbations generally fall into two categories: (a) using r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28925<END_URL>

On Quantizing Neural Representation for Variable-Rate Video Coding\nThis work introduces NeuroQuant, a novel post-training quantization (PTQ) approach tailored to non-generalized Implicit Neural Representations for variable-rate Video Coding (INR-VC). Unlike existing methods that require extensive weight retraining for each target bitrate, we hypothesize that variable-rate coding can be achieved by adjusting quantization parameters (QPs) of pre-trained weights. Our study reveals that traditional<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31039<END_URL>

On Rollouts in Model-Based Reinforcement Learning\nModel-based reinforcement learning (MBRL) seeks to enhance data efficiency by learning a model of the environment and generating synthetic rollouts from it. However, accumulated model errors during these rollouts can distort the data distribution, negatively impacting policy learning and hindering long-term planning. Thus, the accumulation of model errors is a key bottleneck in current MBRL methods. We propose Infoprop, a model-based rollout mec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29461<END_URL>

On Scaling Up 3D Gaussian Splatting Training\n3D Gaussian Splatting (3DGS) is increasingly popular for 3D reconstruction due to its superior visual quality and rendering speed. However, 3DGS training currently occurs on a single GPU, limiting its ability to handle high-resolution and large-scale 3D reconstruction tasks due to memory constraints. We introduce Grendel, a distributed system designed to partition 3DGS parameters and parallelize computation across multiple GPUs. As each Gaussian affe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28300<END_URL>

On Speeding Up Language Model Evaluation\nDeveloping prompt-based methods with Large Language Models (LLMs) requires making numerous decisions, which give rise to a combinatorial search problem over hyper-parameters. This exhaustive evaluation can be time-consuming and costly. In this paper, we propose an \textit{adaptive} approach to explore this space. We are exploiting the fact that often only few samples are needed to identify clearly superior or inferior settings, and that many evaluation t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31065<END_URL>

On Statistical Rates of Conditional Diffusion Transformer: Approximation and Estimation\nWe investigate the approximation and estimation rates of conditional diffusion transformers (DiTs) with classifier-free guidance.We present a comprehensive analysis for ``in-context'' conditional DiTs under four common data assumptions.We show that both conditional DiTs and their latent variants lead to the minimax optimality of unconditional DiTs under identified settings.Specifically, we discretize the inp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29072<END_URL>

On Stochastic Contextual Bandits with Knapsacks in Small Budget Regime\nThis paper studies stochastic contextual bandits with knapsack constraints (CBwK), where a learner observes a context, takes an action, receives a reward, and incurs a vector of costs at every round. The learner aims to maximize the cumulative rewards across $T$ rounds under the knapsack constraints with an initial budget of $B$. We study CBwK in the small budget regime where the budget $B = \Omega(\sqrt{T})$and propose an A<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30356<END_URL>

On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning\nTest-time adaptation (TTA) updates the model weights during the inference stage using testing data to enhance generalization. However, this practice exposes TTA to adversarial risks. Existing studies have shown that when TTA is updated with crafted adversarial test samples, also known as test-time poisoned data, the performance on benign samples can deteriorate. Nonetheless, the perceived a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30848<END_URL>

On the Adversarial Vulnerability of Label-Free Test-Time Adaptation\nDespite the success of Test-time adaptation (TTA), recent work has shown that adding relatively small adversarial perturbations to a limited number of samples leads to significant performance degradation. Therefore, it is crucial to rigorously evaluate existing TTA algorithms against relevant threats and implement appropriate security countermeasures. Importantly, existing threat models assume test-time samples will be labeled,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29905<END_URL>

On the Almost Sure Convergence of the Stochastic Three Points Algorithm\nThe stochastic three points (STP) algorithm is a derivative-free optimization technique designed for unconstrained optimization problems in $\mathbb{R}^d$. In this paper, we analyze this algorithm for three classes of functions : smooth functions that may lack convexity, smooth convex functions, and smooth functions that are strongly convex. Our work provides the first almost sure convergence results of the STP algorithm, a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29896<END_URL>

On the Benefits of Attribute-Driven Graph Domain Adaptation\nGraph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Recent studies attempted to learn domain invariant representations by eliminating structural shifts between graphs. In this work, we show that existing methodologies have overlooked the significance of the graph node attribute, a pivotal factor for graph domain a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28080<END_URL>

On the Benefits of Memory for Modeling Time-Dependent PDEs\nData-driven techniques  have emerged as a promising alternative to traditional numerical methods. For time-dependent PDEs, many approaches are Markovian---the evolution of the trained system only depends on the current state, and not the past states. In this work, we investigate the benefits of using memory for modeling time-dependent PDEs: that is, when past states are explicitly used to predict the future. Motivated by the Mori-Zwanzi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28374<END_URL>

On the Byzantine-Resilience of Distillation-Based Federated Learning\nFederated Learning (FL) algorithms using Knowledge Distillation (KD) have received increasing attention due to their favorable properties with respect to privacy, non-i.i.d. data and communication cost. These methods depart from transmitting model parameters and instead communicate information about a learning task by sharing predictions on a public dataset. In this work, we study the performance of such approaches in the byza<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28341<END_URL>

On the Completeness of Invariant Geometric Deep Learning Models\nInvariant models, one important class of geometric deep learning models, are capable of generating meaningful geometric representations by leveraging informative geometric features in point clouds. These models are characterized by their simplicity, good experimental results and computational efficiency. However, their theoretical expressive power still remains unclear, restricting a deeper understanding of the potential of such mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30973<END_URL>

On the Computation of the Fisher Information in Continual Learning\nOne of the most popular methods for continual learning with deep neural networks is Elastic Weight Consolidation (EWC), which involves computing the Fisher Information. The exact way in which the Fisher Information is computed is however rarely described, and multiple different implementations for it can be found online. This blog post discusses and empirically compares several often-used implementations, which highlights that m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31332<END_URL>

On the Convergence of No-Regret Dynamics in Information Retrieval Games with Proportional Ranking Functions\nPublishers who publish their content on the web act strategically, in a behavior that can be modeled within the online learning framework. Regret, a central concept in machine learning, serves as a canonical measure for assessing the performance of learning agents within this framework.We prove that any proportional content ranking function with a concave activation function induces games<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28641<END_URL>

On the Crucial Role of Initialization for Matrix Factorization\nThis work revisits the classical low-rank matrix factorization problem and unveils the critical role of initialization in shaping convergence rates for such nonconvex and nonsmooth optimization. We introduce Nystrom initialization, which significantly improves the global convergence of Scaled Gradient Descent (ScaledGD) in both symmetric and asymmetric matrix factorization tasks. Specifically, we prove that ScaledGD with Nystrom ini<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29262<END_URL>

On the Effectiveness of Dataset Alignment for Fake Image Detection\nAs latent diffusion models (LDMs) democratize image generation capabilities, there is a growing need to detect fake images. A good detector should focus on the generative model’s fingerprints while ignoring image properties such as semantic content, resolution, file format, etc. Fake image detectors are usually built in a data-driven way, where a model is trained to separate real from fake images. Existing works primarily invest<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28964<END_URL>

On the expressiveness and spectral bias of KANs\nKolmogorov-Arnold Networks (KAN) \cite{liu2024kan} were very recently proposed as a potential alternative to the prevalent architectural backbone of many deep learning models, the multi-layer perceptron (MLP). KANs have seen success in various tasks of AI for science, with their empirical efficiency and accuracy demonstrated in function regression, PDE solving, and many more scientific problems. In this article, we revisit the comparison of KANs a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27709<END_URL>

On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth\nTo confirm that the expressive power of ReLU neural networks grows with their depth, the function $F_n = \max (0,x_1,\ldots,x_n )$ has been considered in the literature.  A conjecture by Hertrich, Basu, Di Summa, and Skutella [NeurIPS 2021] states that any ReLU network that exactly represents $F_n$ has at least $\lceil \log_2 (n+1) \rceil$ hidden layers.  The conjecture has recently been confirmed for networks with intege<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27977<END_URL>

On the Expressive Power of Sparse Geometric MPNNs\nMotivated by applications in chemistry and other sciences, we study the expressivepower of message-passing neural networks for geometric graphs, whose nodefeatures correspond to 3-dimensional positions. Recent work has shown that suchmodels can separate generic pairs of non-isomorphic geometric graphs, though theymay fail to separate some rare and complicated instances. However, these resultsassume a fully connected graph, where each node posses<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29870<END_URL>

On the feature learning in diffusion models\nThe predominant success of diffusion models in generative modeling has spurred significant interest in understanding their theoretical foundations. In this work, we propose a feature learning framework aimed at analyzing and comparing the training dynamics of diffusion models with those of traditional classification models. Our theoretical analysis demonstrates that, under identical settings, neural networks trained for classification tend to prioriti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30093<END_URL>

On-the-fly Preference Alignment via Principle-Guided Decoding\nWith the rapidly expanding landscape of large language models, aligning model generations with human values and preferences is becoming increasingly important. Popular alignment methods, such as Reinforcement Learning from Human Feedback, have shown significant success in guiding models with greater control. However, these methods require considerable computational resources, which is inefficient, and substantial collection of traini<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29035<END_URL>

On the Fourier analysis in the SO(3) space : the EquiLoPO Network\nAnalyzing volumetric data with rotational invariance or equivariance is currently an active research topic. Existing deep-learning approaches utilize either group convolutional networks limited to discrete rotations or steerable convolutional networks with constrained filter structures. This work proposes a novel equivariant neural network architecture that achieves analytical Equivariance to Local Pattern Orientation on the cont<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29964<END_URL>

On the Hölder Stability of Multiset and Graph Neural Networks\nExtensive research efforts have been put into characterizing and constructing maximally separating multiset and graph neural networks. However, recent empirical evidence suggests the notion of separation itself doesn't capture several interesting phenomena. On the one hand, the quality of this separation may be very weak, to the extent that the embeddings of  "separable" objects might even be considered identical when using fixed fin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29775<END_URL>

On the Identification of Temporal Causal Representation with Instantaneous Dependence\nTemporally causal representation learning aims to identify the latent causal process from time series observations, but most methods require the assumption that the latent causal processes do not have instantaneous relations. Although some recent methods achieve identifiability in the instantaneous causality case, they require either interventions on the latent variables or grouping of the observations, which <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31125<END_URL>

On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning\nNon-Independent and Identically Distributed (Non-IID) training data significantly challenge federated learning (FL), impairing the performance of the global model in distributed frameworks. Inspired by the superior performance and generalizability of language-driven representation learning in centralized settings, we explore its potential to enhance FL for handling non-IID data. In specific, this p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30797<END_URL>

On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery\nAn intriguing property of the Transformer is its ability to perform in-context learning (ICL), where the Transformer can solve different inference tasks without parameter updating based on the contextual information provided by the corresponding input-output demonstration pairs. It has been theoretically proved that ICL is enabled by the capability of Transformers to perform gradient-descent algorithms (Von Oswa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29887<END_URL>

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations\nFederated reinforcement learning (FedRL) enables multiple agents to collaboratively learn a policy without sharing their own local trajectories collected during agent-environment interactions. However, in practice, the environments faced by different agents are often heterogeneous, leading to poor performance by the single policy learned by existing FedRL algorithms on individual agents. In this p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30560<END_URL>

On the Modeling Capabilities of Large Language Models for Sequential Decision Making\nLarge pretrained models are showing increasingly better performance in reasoning and planning tasks across different modalities, opening the possibility to leverage them for complex sequential decision making problems. In this paper, we investigate the capabilities of Large Language Models (LLMs) for reinforcement learning (RL) across a diversity of interactive domains. We evaluate their ability to produce deci<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27887<END_URL>

On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent\nThe Adam optimizer is widely used for transformer optimization in practice, which makes understanding the underlying optimization mechanisms an important problem.However, due to the Adam's complexity, theoretical analysis of how it optimizes transformers remains a challenging task. Fortunately, Sign Gradient Descent (SignGD) serves as an effective surrogate for Adam.Despite its simplicity, theoretical un<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30719<END_URL>

On the Optimization Landscape of Low Rank Adaptation Methods for Large Language Models\nTraining Large Language Models (LLMs) poses significant memory challenges, making low-rank adaptation methods an attractive solution. Previously, Low-Rank Adaptation (LoRA) addressed this by adding a trainable low-rank matrix to the frozen pre-trained weights in each layer, reducing the number of trainable parameters and optimizer states. GaLore, which compresses the gradient matrix instead of the weight matr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28273<END_URL>

On the Performance Analysis of Momentum Method: A Frequency Domain Perspective\nMomentum-based optimizers are widely adopted for training neural networks. However, the optimal selection of momentum coefficients remains elusive. This uncertainty impedes a clear understanding of the role of momentum in stochastic gradient methods. In this paper, we present a frequency domain analysis framework that interprets the momentum method as a time-variant filter for gradients, where adjustments to momentum<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28003<END_URL>

On the Price of Differential Privacy for Hierarchical Clustering\nHierarchical clustering is a fundamental unsupervised machine learning task with the aim of organizing data into a hierarchy of clusters. Many applications of hierarchical clustering involve sensitive user information, therefore motivating recent studies on differentially private hierarchical clustering under the rigorous framework of Dasgupta's objective. However, it has been shown that any privacy-preserving algorithm under edge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27734<END_URL>

On the Relation between Trainability and Dequantization of Variational Quantum Learning Models\nQuantum machine learning (QML) explores the potential advantages of quantum computers for machine learning tasks, with variational QML among the main current approaches.While quantum computers promise to solve problems that are classically intractable, it has been recently shown that a particular quantum algorithm which outperforms all pre-existing classical algorithms can be matched by a newly develo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29529<END_URL>

On the Role of Attention Heads in Large Language Model Safety\nLarge language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that when safety representations or component are suppressed, the safety capability of LLMs are compromised. However, existing research tends to overlook the safety impact of multi-hea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28788<END_URL>

On the self-verification limitations of large language models on reasoning and planning tasks\nThere has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs).While the initial optimism that reasoning might emerge automatically with scale has been tempered thanks to a slew of counterexamples--ranging from multiplication to simple planning--there persists a wide spread belief that LLMs can self-critique and improve their own solutions in an iterative f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31014<END_URL>

On the Transfer of Object-Centric Representation Learning\nThe goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities into individual vectors. Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing features from pre-trained foundation models like DINO. However, so far, these object-centric methods have mostly been applied in-distribution, with models <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29108<END_URL>

Open-CK: A Large Multi-Physics Fields Coupling benchmarks in Combustion Kinetics\nIn this paper, we use the Fire Dynamics Simulator (FDS) combined with the {\fontfamily{lmtt}\selectfont \textit{supercomputer}} support to create a \textbf{C}ombustion \textbf{K}inetics (CK) dataset for machine learning and scientific research. This dataset captures the development of fires in industrial parks with high-precision Computational Fluid Dynamics (CFD) simulations. It includes various physical fields su<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30654<END_URL>

OpenHands: An Open Platform for AI Software Developers as Generalist Agents\nSoftware is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and effect change in their surrounding environments. In this paper, we introduce OpenHands, a platform for th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29831<END_URL>

OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data\nMathematical reasoning continues to be a critical challenge in large language model (LLM) development with significant interest. However, most of the cutting-edge progress in mathematical reasoning with LLMs has become closed-source due to lack of access to training data. This lack of data access limits researchers from understanding the impact of different choices for synthesizing and utilizing the data. Wit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28464<END_URL>

OpenPRM: Building Open-domain Process-based Reward Models with Preference Trees\nScaling inference-time computation is increasingly seen as the next frontier in scaling laws for large language models. Previous work in mathematics and coding has demonstrated the remarkable potential for inference-time scaling. During such scaling, fine-grained supervision through process-based reward models (PRMs) is essential for enhancement. However, exploration of inference-time scaling and PRMs in open-domain<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28887<END_URL>

Open Science for Foundation Models\nFoundation models (FMs) have revolutionized artificial intelligence (AI) research across many domains, enabling rapid adaptation to diverse downstream tasks. These FMs, trained on massive and high-quality datasets, have demonstrated remarkable performance in natural language processing (e.g., BERT [4], GPT [12], Gemini [14]), computer vision (e.g., ViT [5], VQGAN [6]), speech recognition (e.g., Whisper [13]), and multi-modal understanding (e.g., GPT-4o, LLaVA <BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23983<END_URL>

Open-Set Graph Anomaly Detection via Normal Structure Regularisation\nThis paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (referred to as *seen anomalies*) to detect both seen anomalies and *unseen anomalies* (*i.e*., anomalies that cannot be illustrated the training anomalies). Those labelled training data provide crucial prior knowledge about abnormalities for GAD model<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28580<END_URL>

OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation\nText-to-video (T2V) generation has recently garnered significant attention thanks to the large multi-modality model Sora. However, T2V generation still faces two important challenges: 1) Lacking a precise open sourced high-quality dataset. The previously popular video datasets, e.g.WebVid-10M and Panda-70M, overly emphasized large scale, resulting in the inclusion of many low-quality videos andshort, imprecise captions.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28653<END_URL>

Open-Vocabulary Customization from CLIP via Data-Free Knowledge Distillation\nVision-language models such as CLIP have demonstrated strong zero-shot performance, but their considerable size and inefficient inference limit customizable deployment for users. While knowledge distillation is a solution, it still requires the original data, which is not always available due to copyrights and privacy concerns. For many users seeking open-vocabulary customization, Data-Free Knowledge Distillation (DFKD<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31193<END_URL>

Open-World Reinforcement Learning over Long Short-Term Imagination\nTraining visual reinforcement learning agents in a high-dimensional open world presents significant challenges. While various model-based methods have improved sample efficiency by learning interactive world models, these agents tend to be “short-sighted”, as they are typically trained on short snippets of imagined experiences. We argue that the primary challenge in open-world decision-making is improving the exploration efficie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27879<END_URL>

Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation\nRecent works on open-vocabulary 3D instance segmentation show strong promise but at the cost of slow inference speed and high computation requirements. This high computation cost is typically due to their heavy reliance on aggregated clip features from multi-view, which require computationally expensive 2D foundation models like Segment Anything (SAM) and CLIP. Consequently, this hampers their applicability in many<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30514<END_URL>

OPTAMI: Global Superlinear Convergence of High-order Methods\nSecond-order methods for convex optimization outperform first-order methods in terms of theoretical iteration convergence, achieving rates up to $O(k^{-5})$ for highly-smooth functions. However, their practical performance and applications are limited due to their multi-level structure and implementation complexity. In this paper, we present new results on high-order optimization methods, supported by their practical performance. Firs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30494<END_URL>

OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling\nLarge language models (LLMs) have exhibited their problem-solving abilities in mathematical reasoning. Solving realistic optimization (OPT) problems in application scenarios requires advanced and applied mathematics ability. However, current OPT benchmarks that merely solve linear programming are far from complex realistic situations. In this work, we propose **OptiBench**, a benchmark for End-to-end optimization pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28850<END_URL>

Optimal Brain Apoptosis\nThe increasing complexity and parameter count of Convolutional Neural Networks (CNNs) and Transformers pose challenges in terms of computational efficiency and resource demands. Pruning has been identified as an effective strategy to address these challenges by removing redundant elements such as neurons, channels, or connections, thereby enhancing computational efficiency without heavily compromising performance. This paper builds on the foundational work of Optimal Bra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30781<END_URL>

Optimal Flow Transport and its Entropic Regularization: a GPU-friendly Matrix Iterative Algorithm for Flow Balance Satisfaction\nThe Sinkhorn algorithm, based on Entropic Regularized Optimal Transport (OT), has garnered significant attention due to its computational efficiency enabled by GPU-friendly matrix-vector multiplications. However, vanilla OT primarily deals with computations between the source and target nodes in a bipartite graph, limiting its practical application in real-world transp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29855<END_URL>

Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression\nWe provide a convergence analysis of \emph{deep feature instrumental variable} (DFIV) regression (Xu et al., 2021), a nonparametric approach to IV regression using data-adaptive features learned by deep neural networks in two stages. We prove that the DFIV algorithm achieves the minimax optimal learning rate when the target structural function lies in a Besov space. This is shown under standard nonparametric <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29643<END_URL>

Optimality of Matrix Mechanism on $\ell_p^p$-metric\nIn this paper, we introduce the $\ell_p^p$-error metric (for $p \geq 2$) when answering linear queries under the constraint of differential privacy. We characterize such an error under $(\epsilon,\delta)$-differential privacy in the natural add/remove model. Before this paper, tight characterization in the hardness of privately answering linear queries was known under $\ell_2^2$-error metric (Edmonds et al. 2020) and $\ell_p^2$-error metric fo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28869<END_URL>

Optimal Learning of Kernel Logistic Regression for Complex Classification Scenarios\nComplex classification scenarios, including long-tailed learning, domain adaptation, and transfer learning, present substantial challenges for traditional algorithms. Conditional class probability (CCP) predictions have recently become critical components of many state-of-the-art algorithms designed to address these challenging scenarios. Among kernel methods, kernel logistic regression (KLR) is distinguished by<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29346<END_URL>

Optimal Memorization Capacity of Transformers\nRecent research in the field of machine learning has increasingly focused on the memorization capacity of Transformers, but how efficient they are is not yet well understood.We demonstrate that Transformers can memorize labels with $\tilde{O}(\sqrt{N})$ parameters in a next-token prediction setting for $N$ input sequences of length $n$, which is proved to be optimal up to logarithmic factors.This indicates that Transformers can efficiently perform m<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29483<END_URL>

Optimal Non-Asymptotic Rates of Value Iteration for Average-Reward Markov Decision Processes\nWhile there is an extensive body of research on the analysis of Value Iteration (VI) for discounted cumulative-reward MDPs, prior work on analyzing VI for (undiscounted) average-reward MDPs has been limited, and most prior results focus on asymptotic rates in terms of Bellman error. In this work, we conduct refined non-asymptotic analyses of average-reward MDPs, obtaining a collection of convergence res<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29337<END_URL>

Optimal Partial Graph Matching\nPartial graph matching extends traditional graph matching by allowing some nodes to remain unmatched, enabling applications in more complex scenarios. However, this flexibility introduces additional complexity, as both the subset of nodes to match and the optimal mapping must be determined. While recent studies have explored deep learning techniques for partial graph matching, a significant limitation remains: the absence of an optimization objective that fully ca<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27991<END_URL>

Optimal Protocols for Continual Learning via Statistical Physics and Control Theory\nArtificial neural networks often struggle with _catastrophic forgetting_ when learning multiple tasks sequentially, as training on new tasks degrades the performance on previously learned tasks. Recent theoretical work has addressed this issue by analysing learning curves in synthetic frameworks under predefined training protocols. However, these protocols relied on heuristics and lacked a solid theoretical foun<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28178<END_URL>

Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization\nWe study online learning in constrained MDPs (CMDPs), focusing on the goal of attaining sublinear strong regret and strong cumulative constraint violation. Differently from their standard (weak) counterparts, these metrics do not allow negative terms to compensate positive ones, raising considerable additional challenges. Efroni et al. (2020) were the first to propose an algorithm with sublinear strong regret and st<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30755<END_URL>

Optimal Targets for Concept Erasure in Diffusion Models and Where To Find Them\nConcept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models by selectively unlearning undesirable concepts. The common principle of previous works to remove a specific concept is to map it to a fixed generic concept, such as a neutral concept or just an empty text prompt. In this paper, we demonstrate that this fixed-target strategy is suboptimal, as <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28045<END_URL>

Optimal Transport for Time Series Imputation\nMissing data imputation through distribution alignment has demonstrated advantages for non-temporal datasets but exhibits suboptimal performance in time-series applications. The primary obstacle is crafting a discrepancy measure that simultaneously (1) $\textit{captures temporal pattern}$—accounting for patterns such as periodicities and temporal dependencies inherent in time-series—and (2) $\textit{accommodates non-stationarity}$, ensuring robustnes<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27784<END_URL>

Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design\nBayesian optimization (BO) is a powerful framework to optimize black-box expensive-to-evaluate functions via sequential interactions. In several important problems (e.g. drug discovery, circuit design, neural architecture search, etc.), though, such functions are defined over large $\textit{combinatorial and unstructured}$ spaces. This makes existing BO algorithms not feasible due to the intractable maxi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27768<END_URL>

Optimization-Biased Hypernetworks for Generalizable Policy Generation\nPolicy learning through behavior cloning poses significant challenges, particularly when demonstration data is limited. In this work, we present HyPoGen, a novel optimization-biased hypernetwork structure for policy generation. The proposed hypernetwork learns to synthesize optimal policy parameters solely from task specifications, by modeling policy generation as an approximation of the optimization process executed over a f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30522<END_URL>

Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling\nLearning-based methods have gained attention as general-purpose solvers due to their ability to automatically learn problem-specific heuristics, reducing the need for manually crafted heuristics. However, these methods often face scalability challenges. To address these issues, the improved Sampling algorithm for Combinatorial Optimization (iSCO), using discrete Langevin dynamics, has been proposed, demonstrating bett<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30713<END_URL>

Optimized Multi-Token Joint Decoding With Auxiliary Model for LLM Inference\nLarge language models (LLMs) have achieved remarkable success across diverse tasks, yet their inference processes are hindered by substantial time and energy demands due to single-token generation at each decoding step. While previous methods such as speculative decoding mitigate these inefficiencies by producing multiple tokens per step, each token is still generated by its single-token distribution,thereby enhancing s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29226<END_URL>

Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods\nWe study gradient methods for solving an optimization problem with an $(L_0, L_1)$-smooth objective function. This problem class generalizes that of Lipschitz-smooth problems and has gained interest recently, as it captures a broader range of machine learning applications.We provide novel insights on the properties of this function class and develop a general framework for analyzing optimization methods  for $(L_0, L_1)$-smooth functi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30281<END_URL>

Optimizing 4D Gaussians for Dynamic Scene Video from Single Landscape Images\nTo achieve realistic immersion in landscape images, fluids such as water and clouds need to move within the image while revealing new scenes from various camera perspectives. Recently, a field called dynamic scene video has emerged, which combines single image animation with 3D photography. These methods use pseudo 3D space, implicitly represented with Layered Depth Images (LDIs). LDIs separate a single image into dept<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30162<END_URL>

Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization\nGenerative Flow Networks (GFlowNets) are a family of generative models that learn to sample objects with probabilities proportional to a given reward function. The key concept behind GFlowNets is the use of two stochastic policies: a forward policy, which incrementally constructs compositional objects, and a backward policy, which sequentially deconstructs them. Recent results show a close relationship between GFlo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29295<END_URL>

Optimizing importance weighting in the presence of sub-population shifts\nA distribution shift between the training and test data can severely harm performance of machine learning models. Importance weighting addresses this issue by assigning different weights to data points during training. We argue that existing heuristics for determining the weights are suboptimal, as they neglect the increase of the variance of the estimated model due to the limited sample size of the training data. We inter<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28656<END_URL>

Optimizing Neural Network Representations of Boolean Networks\nNeural networks are known to be universal computers for Boolean functions. Recent advancements in hardware have significantly reduced matrix multiplication times, making neural network simulation both fast and efficient. Consequently, functions defined by complex Boolean networks are increasingly viable candidates for simulation through their neural network representation. Prior research has introduced a general method for deriving n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31213<END_URL>

Optimizing Posterior Samples for Bayesian Optimization via Rootfinding\nBayesian optimization devolves the global optimization of a costly objective function to the global optimization of a sequence of acquisition functions. This inner-loop optimization can be catastrophically difficult if it involves posterior sample paths, especially in higher dimensions. We introduce an efficient global optimization strategy for posterior samples based on global rootfinding. It provides gradient-based optimiz<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30191<END_URL>

OptionZero: Planning with Learned Options\nPlanning with options -- a sequence of primitive actions -- has been shown effective in reinforcement learning within complex environments. Previous studies have focused on planning with predefined options or learned options through expert demonstration data. Inspired by MuZero, which learns superhuman heuristics without any human knowledge, we propose a novel approach, named OptionZero. OptionZero incorporates an option network into MuZero, providing a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31084<END_URL>

Oracle efficient truncated statistics\nWe study the problem of learning from truncated samples: instead of observingsamples from some underlying population $p^\ast$, we observe only the examples that fall in some survival set $S \subset \mathbb{R}^d$ whose probability mass (measured with respect to $p^\ast$) is at least $\alpha$.  Assuming membership oracle access to the truncation set $S$, prior works obtained algorithms for the case where $p^\ast$ is Gaussian or more generally an exponential f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29220<END_URL>

Orchestrating Heterogeneous Architectures for Fast Inference of Mixture-of-Experts Models\nLarge Language Models (LLMs) with the Mixture-of-Experts (MoE) architectures have shown promising performance on various tasks. However, due to the huge model sizes, running them in resource-constrained environments where GPU memory is not abundant is challenging. Some existing systems propose to use CPU resources to solve that, but they either suffer from significant overhead of frequently moving data bet<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29900<END_URL>

Order-aware Interactive Segmentation\nInteractive segmentation aims to accurately segment target objects with minimal user interactions. However, current methods often fail to accurately separate target objects from the background, due to a limited understanding of order, the relative depth between objects in a scene. To address this issue, we propose OIS: order-aware interactive segmentation, where we explicitly encode the relative depth between objects into order maps. We introduce a novel ord<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30760<END_URL>

ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization\nReward shaping is a critical component in reinforcement learning (RL), particularly for complex tasks where sparse rewards can hinder learning. While shaping rewards have been introduced to provide additional guidance, selecting effective shaping functions remains challenging and computationally expensive. This paper introduces Online Reward Selection and Policy Optimization (ORSO), a novel approach that frames<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31232<END_URL>

Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution\nVisual data comes in various forms, ranging from small icons of just a few pixels to long videos spanning hours. Existing multi-modal LLMs usually standardize these diverse visual inputs to fixed-resolution images or patches for visual encoders and yield similar numbers of tokens for LLMs. This approach is non-optimal for multimodal understanding and inefficient for processing inputs with long and short visual contents.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29834<END_URL>

OS-ATLAS: Foundation Action Model for Generalist GUI Agents\nExisting efforts in building GUI agents heavily rely on the availability of robust commercial Vision-Language Models (VLMs) such as GPT-4o and GeminiProVision. Practitioners are often reluctant to use open-source VLMs due to their significant performance lag compared to their closed-source counterparts, particularly in GUI grounding and Out-Of-Distribution (OOD) scenarios. To facilitate future research in this area, we developed OS-Atl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28423<END_URL>

OSCAR: Operating System Control via State-Aware Reasoning and Re-Planning\nLarge language models (LLMs) and large multimodal models (LMMs) have shown great potential in automating complex tasks like web browsing and gaming. However, their ability to generalize across diverse applications remains limited, hindering broader utility. To address this challenge, we present OSCAR: Operating System Control via state-Aware reasoning and Re-planning. OSCAR is a generalist agent designed to autonomously n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29394<END_URL>

Oscillatory State-Space Models\nWe propose Linear Oscillatory State-Space models (LinOSS) for efficiently learning on long sequences. Inspired by cortical dynamics of biological neural networks, we base our proposed LinOSS model on a system of forced harmonic oscillators. A stable discretization, integrated over time using fast associative parallel scans, yields the proposed state-space model. We prove that LinOSS produces stable dynamics only requiring nonnegative diagonal state matrix. This is<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30278<END_URL>

OSDA Agent: Leveraging Large Language Models for De Novo Design of Organic Structure Directing Agents\nZeolites are crystalline porous materials that have been widely utilized in petrochemical industries as well as sustainable chemistry areas. Synthesis of zeolites often requires small molecules termed Organic Structure Directing Agents (OSDAs), which are critical in forming the porous structure. Molecule generation models can aid the design of OSDAs, but they are limited by single functionality<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30683<END_URL>

OSTQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting\nPost-training quantization (PTQ) has emerged as a widely adopted technique for compressing and accelerating Large Language Models (LLMs).The major challenge in LLM quantization is that uneven and heavy-tailed data distributions can expand the quantization range, thereby reducing bit precision for most values.Recent methods attempt to eliminate outliers and balance int<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28216<END_URL>

Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detection\nOut-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer fro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29899<END_URL>

Out-of-distribution Generalization for Total Variation based Invariant Risk Minimization\nInvariant risk minimization is an important general machine learning framework that has recently been interpreted as a total variation model (IRM-TV). However, how to improve out-of-distribution (OOD) generalization in the IRM-TV setting remains unsolved. In this paper, we extend IRM-TV to a Lagrangian multiplier model, named OOD-TV-IRM. We find that the autonomous TV penalty hyperparameter is exactly the L<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29073<END_URL>

Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model\nWe introduce a novel Multi-modal Guided Real-World Face Restoration (MGFR) technique designed to improve the quality of facial image restoration from low-quality inputs. Leveraging a blend of attribute text prompts, high-quality reference images, and identity information, MGFR can mitigate the generation of false facial attributes and identities often associated with generative face restoration met<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28479<END_URL>

Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions\nConstrained bilevel optimization tackles nested structures present in constrained learning tasks like constrained meta-learning, adversarial learning, and distributed bilevel optimization. However, existing bilevel optimization methods mostly are typically restricted to specific constraint settings, such as linear lower-level constraints. In this work, we overcome this limitation and deve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29020<END_URL>

Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control\nReinforcement learning (RL) is rapidly reaching and surpassing human-level control capabilities. However, state-of-the-art RL algorithms often require timesteps and reaction times significantly faster than human capabilities, which is impractical in real-world settings and typically necessitates specialized hardware. Such speeds are difficult to achieve in the real worl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27877<END_URL>

OvercookedV2: Rethinking Overcooked for Zero-Shot Coordination\nAI agents hold the potential to transform everyday life by helping humans achieve their goals.To do this successfully, agents need to be able to coordinate with novel partners without prior interaction, a setting known as zero-shot coordination (ZSC).Overcooked has become one of the most popular benchmarks for evaluating coordination capabilities of AI agents and learning algorithms.In this work, we investigate the origins of ZSC ch<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28745<END_URL>

OVTR: End-to-End Open-Vocabulary Multiple Object Tracking with Transformer\nOpen-vocabulary multiple object tracking aims to generalize trackers to unseen categories during training, enabling their application across a variety of real-world scenarios. However, the existing open-vocabulary tracker, which relies on off-the-shelf open-vocabulary detector, perceives categories and locations independently in each frame, causing instability and making it vulnerable to similar appearances and irregular<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30293<END_URL>

PABBO: Preferential Amortized Black-Box Optimization\nPreferential Bayesian Optimization (PBO) is a sample-efficient method to learn latent user utilities from preferential feedback over a pair of designs. It relies on a statistical surrogate model for the latent function, usually a Gaussian process, and an acquisition strategy to select the next candidate pair to get user feedback on. Due to the non-conjugacy of the associated likelihood, every PBO step requires a significant amount of computat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29251<END_URL>

PaCA: Partial Connection Adaptation for Efficient Fine-Tuning\nPrior parameter-efficient fine-tuning (PEFT) algorithms reduce memory usage and computational costs of fine-tuning large neural network models by training only a few additional adapter parameters, rather than the entire model. However, the reduction in computational costs due to PEFT does not necessarily translate to a reduction in training time; although the computational costs of the adapter layers are much smaller than the pretrai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28689<END_URL>

Pacmann: Efficient Private Approximate Nearest Neighbor Search\nWe propose a new private Approximate Nearest Neighbor (ANN) search schemenamed Pacmannthat allows a client to perform ANN searchin a vector database without revealing the query vector to the server.Unlike prior constructions that run encrypted search on the server sidewith computationally-intensive cryptographic techniques,Pacmann carefully offloads limited computation and storage to the client. Specifically, clients run a graph-bas<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27729<END_URL>

PAD: Personalized Alignment at Decoding-time\nAligning with personalized preferences, which vary significantly across cultural, educational, and political differences, poses a significant challenge due to the computational costs and data demands of traditional alignment methods. In response, this paper presents Personalized Alignment at Decoding-time (PAD), a novel framework designed to align LLM outputs with diverse personalized preferences during the inference phase, eliminating the need for a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28948<END_URL>

PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer\nWe present Polynomial Attention Drop-in Replacement (PADRe), a novel and unifying framework designed to replace the conventional self-attention mechanism in transformer models. Notably, several recent alternative attention mechanisms, including Hyena, Mamba, SimA, Conv2Former, and Castling-ViT, can be viewed as specific instances of our PADRe framework.  PADRe leverages polynomial functions and draws upo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29269<END_URL>

Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning\nImage captioning has long been a pivotal task in visual understanding, with recent advancements in vision-language models (VLMs) significantly enhancing the ability to generate detailed image captions. However, the evaluation of detailed image captioning remains underexplored due to outdated evaluation metrics and coarse annotations. In this paper, we introduce DeCapBench along with a novel metric, DC<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30910<END_URL>

Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy\nMulti-armed bandits (MAB) are commonly used in sequential online decision-making when the reward of each decision is an unknown random variable. In practice, however, the typical goal of maximizing total reward may be less important than minimizing the total cost of the decisions taken, subject to a reward constraint. For example, we may seek to make decisions that have at least the reward of a reference ``def<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28945<END_URL>

PaLD: Detection of Text Partially Written by Large Language Models\nAdvances in large language models (LLM) have produced text that appears increasingly human-like and difficult to detect with the human eye. In order to mitigate the impact of misusing LLM-generated texts, e.g., copyright infringement, fair student assessment, fraud, and other societally harmful LLM usage, a line of work on detecting human and LLM-written text has been explored. While recent work has focused on classifying entire<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28190<END_URL>

PALMBENCH: A COMPREHENSIVE BENCHMARK OF COMPRESSED LARGE LANGUAGE MODELS ON MOBILE PLATFORMS\nDeploying large language models (LLMs) locally on mobile devices is advantageous in scenarios where transmitting data to remote cloud servers is either undesirable due to privacy concerns or impractical due to network connection. Recent advancements have facilitated the local deployment of LLMs. However, local deployment also presents challenges, particularly in balancing quality (generative performance<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27755<END_URL>

PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment\nFoundation models trained on internet-scale data benefit from extensive alignment to human preferences before deployment. However, existing methods typically assume a homogeneous preference shared by all individuals, overlooking the diversity inherent in human values. In this work, we propose a general reward modeling framework for pluralistic alignment (PAL), which incorporates diverse preferences from the ground up. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31187<END_URL>

Palu: KV-Cache Compression with Low-Rank Projection\nPost-training KV-Cache compression methods typically either sample a subset of effectual tokens or quantize the data into lower numerical bit width. However, these methods cannot exploit redundancy in the hidden dimension of the KV tenors. This paper presents a hidden dimension compression approach called Palu, a KV-Cache compression framework that utilizes low-rank projection to reduce inference-time LLM memory usage. Palu decomposes the line<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29993<END_URL>

Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages\nDespite recent advances in multimodal large language models (MLLMs), their development has predominantly focused on English- and western-centric datasets and tasks, leaving most of the world's languages and diverse cultural contexts underrepresented.  This paper introduces PANGEA, a multilingual multimodal LLM trained on PANGEAINS, a diverse 6M instruction dataset spanning 39 languages. PANGEAINS features: 1) high-quality English<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29188<END_URL>

PaPaGei: Open Foundation Models for Optical Physiological Signals\nPhotoplethysmography (PPG) is the most widely used non-invasive technique for monitoring biosignals and cardiovascular health, with applications in both clinical settings and consumer health through wearable devices. Current machine learning models trained on PPG signals are mostly task-specific and lack generalizability. Previous works often used single-device datasets, did not explore out-of-domain generalization, or did not re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28573<END_URL>

Parameter and Memory Efficient Pretraining via Low-rank Riemannian Optimization\nPretraining large language models often requires significant computational resources and memory due to their vast parameter amount. An effective approach to enhance parameter efficiency in both training and inference is to parameterize each full-size weight as the product of two trainable low-rank factors. While low-rank fine-tuning has achieved great success, low-rank pretraining remains challenging as it requires <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28725<END_URL>

Parameter-Efficient and Stable Singular Value Adaptation for Pre-Trained Models\nWe observe that the Q, K, O, and V matrices in attention layers can inherently be absorbed and decomposed into four head-wise orthogonal matrices and two sets of singular values without any loss. After orthogonalization, we freeze the singular vectors and fine-tune only the singular values, enabling stable fine-tuning constrained to the original latent space, which achieves a 5.4% improvement over LoRA across eight <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31331<END_URL>

Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo\nBayesian Neural Networks (BNNs) provide a promising framework for modeling predictive uncertainty and enhancing out-of-distribution robustness (OOD) by estimating the posterior distribution of network parameters. Stochastic Gradient Markov Chain Monte Carlo (SGMCMC) is one of the most powerful methods for scalable posterior sampling in BNNs, achieving efficiency by combining stochastic gradient descent with second-order Langevin dy<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28902<END_URL>

PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction\nPersonalizing a large-scale pretrained Text-to-Image (T2I) diffusion model is chal-lenging as it typically struggles to make an appropriate trade-off between its trainingdata distribution and the target distribution, i.e., learning a novel concept with only afew target images to achieve personalization (aligning with the personalized target)while preserving text editability (aligning with diverse text prompts). In this pap<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30047<END_URL>

ParaSolver: A Hierarchical Parallel Integral Solver for Diffusion Models\nThis paper explores the challenge of accelerating the sequential inference process of Diffusion Probabilistic Models (DPMs). We tackle this critical issue from a dynamic systems perspective, in which the inherent sequential nature is transformed into a parallel sampling process. Specifically, we propose a unified framework that generalizes the sequential sampling process of DPMs as solving a system of banded nonlinear equa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31147<END_URL>

ParetoFlow: Guided Flows in Multi-Objective Optimization\nIn offline multi-objective optimization (MOO), we leverage an offline dataset of designs and their associated labels to simultaneously minimize multiple objectives. This setting more closely mirrors complex real-world problems compared to single-objective optimization. Recent works mainly employ evolutionary algorithms and Bayesian optimization, with limited attention given to the generative modeling capabilities inherent in such data. In<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28472<END_URL>

Pareto Low-Rank Adapters: Efficient Multi-Task Learning with Preferences\nMulti-task trade-offs in machine learning can be addressed via Pareto Front Learning (PFL) methods that parameterize the Pareto Front (PF) with a single model. PFL permits to select the desired operational point during inference, contrary to traditional Multi-Task Learning (MTL) that optimizes for a single trade-off decided prior to training. However, recent PFL methodologies suffer from limited scalability, slow convergen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28684<END_URL>

Pareto Prompt Optimization\nNatural language prompt optimization, or prompt engineering, has emerged as a powerful technique to unlock the potential of Large Language Models (LLMs) for various tasks. While existing methods primarily focus on maximizing a single task-specific performance metric for LLM outputs, real-world applications often require considering trade-offs between multiple objectives. In this work, we address this limitation by proposing an effective technique for multi-objective p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30234<END_URL>

ParFam -- (Neural Guided) Symbolic Regression via Continuous Global Optimization\nThe problem of symbolic regression (SR) arises in many different applications, such as identifying physical laws or deriving mathematical equations describing the behavior of financial markets from given data. Various methods exist to address the problem of SR, often based on genetic programming. However, these methods are usually complicated and involve various hyperparameters. In this paper, we present our new ap<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30731<END_URL>

Partial Gromov-Wasserstein Metric\nThe Gromov-Wasserstein (GW) distance has gained increasing interest in the machine learning community in recent years, as it allows for the comparison of measures in different metric spaces. To overcome the limitations imposed by the equal mass requirements of the classical GW problem, researchers have begun exploring its application in unbalanced settings. However, Unbalanced GW (UGW) can only be regarded as a discrepancy rather than a rigorous metric/distance<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28140<END_URL>

Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior\nTrajectory inference seeks to recover the temporal dynamics of a population from snapshots of its (uncoupled) temporal marginals, i.e. where observed particles are \emph{not} tracked over time. Prior works addressed this challenging problem under a stochastic differential equation (SDE) model with a gradient-driven drift in the observed space, introducing a minimum entropy estimator relative to the Wiener measu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30243<END_URL>

PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks\nWe present a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration (PARTNR) designed to study human-robot coordination in household activities. PARTNR tasks exhibit characteristics of everyday tasks, such as spatial, temporal, and heterogeneous agent capability constraints. We employ a semi-automated task generation pipeline using Large Language Models (LLMs), incorporating simulation-in-the-loop for <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29562<END_URL>

Patch-Level Training for Large Language Models\nThe prohibitive training costs of Large Language Models (LLMs) have emerged as a significant bottleneck in the development of next-generation LLMs. In this paper, we show that it is possible to significantly reduce the training costs of LLMs without sacrificing their performance. Specifically, we introduce patch-level training for LLMs, in which multiple tokens are aggregated into a unit of higher information density, referred to as a `patch', to s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28998<END_URL>

PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration\nVision Language Models (VLMs) like CLIP have attracted substantial attention in pathology, serving as backbones for applications such as zero-shot image classification and Whole Slide Image (WSI) analysis. Additionally, they can function as vision encoders when combined with large language models (LLMs) to support broader capabilities. Current efforts to train pathology VLMs rely on pathology image<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28208<END_URL>

PEARL: Parallel Speculative Decoding with Adaptive Draft Length\nSpeculative decoding (SD), where an extra draft model is employed to provide multiple **draft** tokens first and then the original target model verifies these tokens in parallel, has shown great power for LLM inference acceleration.However, existing SD methods suffer from the mutual waiting problem, i.e., the target model gets stuck when the draft model is *guessing* tokens, and vice versa. This problem is directly incurred by the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29693<END_URL>

PEARL: Towards Permutation-Resilient LLMs\nThe in-context learning (ICL) ability of large language models (LLMs) enables them to undertake challenging tasks using provided demonstrations. However, it is prone to instability: different orderings of demonstrations can significantly influence predictions, revealing LLMs’ limitations in processing combinatorial inputs.  This paper shows that this vulnerability can be exploited to design a natural attack that is almost imperceptible to the model prov<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28005<END_URL>

PEAR: Primitive Enabled Adaptive Relabeling for Boosting Hierarchical Reinforcement Learning\nHierarchical reinforcement learning (HRL) has the potential to solve complex long horizon tasks using temporal abstraction and increased exploration. However, hierarchical agents are difficult to train due to inherent non-stationarity. We present primitive enabled adaptive relabeling (PEAR), a two-phase approach where we first perform adaptive relabeling on a few expert demonstrations to generate effici<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31238<END_URL>

Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities\nReconstructing pedestrian motion from dynamic sensors, with a focus on pedestrian intention, is crucial for advancing autonomous driving safety. However, this task is challenging due to data limitations arising from technical complexities, safety, and cost concerns. We introduce the Pedestrian Motion Reconstruction (PMR) dataset, which focuses on pedestrian intention t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29263<END_URL>

Peeking Behind Closed Doors: Risks of LLM Evaluation by Private Data Curators\nThe rapid advancement in building large language models (LLMs) has intensified competition among big-tech companies and AI startups. In this regard, model evaluations are critical for product and investment-related decision-making. While open evaluation sets like MMLU initially drove progress, concerns around data contamination and data bias have constantly questioned their reliability. As a result, it has led to the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31326<END_URL>

Periodic Materials Generation using Text-Guided Joint Diffusion Model\nEquivariant diffusion models have emerged as the prevailing approach for generating novel crystal materials due to their ability to leverage the physical symmetries of periodic material structures. However, current models do not effectively learn the joint distribution of atom types, fractional coordinates, and lattice parameters of the crystal material in a cohesive end-to-end diffusion framework. Also, none of these models <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30619<END_URL>

PeriodWave: Multi-Period Flow Matching for High-Fidelity Waveform Generation\nRecently, universal waveform generation tasks have been investigated conditioned on various out-of-distribution scenarios. Although one-step GAN-based methods have shown their strength in fast waveform generation, they are vulnerable to train-inference mismatch scenarios such as two-stage text-to-speech. Meanwhile, diffusion-based models have shown their powerful generative performance in other domains; however, they s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28055<END_URL>

Perm: A Parametric Representation for Multi-Style 3D Hair Modeling\nWe present Perm, a learned parametric representation of human 3D hair designed to facilitate various hair-related applications. Unlike previous work that jointly models the global hair structure and local curl patterns, we propose to disentangle them using a PCA-based strand representation in the frequency domain, thereby allowing more precise editing and output control. Specifically, we leverage our strand representation to fit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29369<END_URL>

Permute-and-Flip: An optimally stable and watermarkable decoder for LLMs\nIn this paper, we propose a new decoding method called Permute-and-Flip (PF) decoder. It enjoys stability properties similar to the standard sampling decoder, but is provably up to 2x better in its quality-stability tradeoff than sampling and never worse than any other decoder. We also design a cryptographic watermarking scheme analogous to Aaronson (2023)'s Gumbel watermark, but naturally tailored for PF decoder. The wate<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29239<END_URL>

Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models\nIn this work, we investigate whether small language models can determine high-quality subsets of large-scale text datasets that improve the performance of larger language models. While existing work has shown that pruning based on the perplexity of a larger model can yield high-quality data, we investigate whether smaller models can be used for perplexity-based pruning and how pruning is affected by the domain co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31214<END_URL>

Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents\nPrevious studies have found that PLM-based retrieval models exhibit a preference for LLM-generated content, assigning higher relevance scores to these documents even when their semantic quality is comparable to human-written ones. This phenomenon, known as source bias, threatens the sustainable development of the information access ecosystem. However, the underlying causes of source bias remain unexplored. In this paper, we<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29494<END_URL>

Persistent Pre-training Poisoning of LLMs\nLarge language models are pre-trained on uncurated text datasets consisting of trillions of tokens scraped from the Web.Prior work has shown that: (1) web-scraped pre-training datasets can be practically poisoned by malicious actors; and (2) adversaries can compromise language models after poisoning fine-tuning datasets.Our work evaluates for the first time whether language models can also be \emph{compromised during pre-training}, with a focus on the p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28909<END_URL>

Personality Alignment of Large Language Models\nCurrent methods for aligning large language models (LLMs) typically aim to reflect general human values and behaviors, but they often fail to capture the unique characteristics and preferences of individual users. To address this gap, we introduce the concept of Personality Alignment. This approach tailors LLMs' responses and decisions to match the specific preferences of individual users or closely related groups. Inspired by psychometrics, we cre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31265<END_URL>

Perturbation-Restrained Sequential Model Editing\nModel editing is an emerging field that focuses on updating the knowledge embedded within large language models (LLMs) without extensive retraining. However, current model editing methods significantly compromise the general abilities of LLMs as the number of edits increases, and this trade-off poses a substantial challenge to the continual learning of LLMs. In this paper, we first theoretically analyze that the factor affecting the general abili<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29099<END_URL>

PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training\nThis paper aims to address the challenge of hallucinations in Multimodal Large Language Models (MLLMs)  particularly for dense image captioning tasks. To tackle the challenge, we identify the current lack of a metric that finely measures the caption quality in concept level. We hereby introduce HalFscore, a novel metric built upon the language graph and is designed to evaluate both the  accuracy and completeness<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28657<END_URL>

PETRA: Parallel End-to-end Training with Reversible Architectures\nReversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep learning for memory savings and generative modeling. In this work, we show how reversible architectures can solve challenges in parallelizing deep model training. We introduce PETRA, a novel alternative to backpropagation for parallelizing gradient computations. PETRA facilitates effective mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31242<END_URL>

PFDiff: Training-free Acceleration of Diffusion Models through the Gradient Guidance of Past and Future\nDiffusion Probabilistic Models (DPMs) have shown remarkable potential in image generation, but their sampling efficiency is hindered by the need for numerous denoising steps. Most existing solutions accelerate the sampling process by proposing fast ODE solvers. However, the inevitable discretization errors of the ODE solvers are significantly magnified when the number of function evaluations <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27822<END_URL>

PFGuard: A Generative Framework with Privacy and Fairness Safeguards\nGenerative models must ensure both privacy and fairness for Trustworthy AI. While these goals have been pursued separately, recent studies propose to combine existing privacy and fairness techniques to achieve both goals. However, naively combining these techniques can be insufficient due to privacy-fairness conflicts, where a sample in a minority group may be amplified for fairness, only to be suppressed for privacy. We demon<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30740<END_URL>

PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching\nThe increasing size of screening libraries poses a significant challenge for the development of virtual screening methods for drug discovery, necessitating a re-evaluation of traditional approaches in the era of big data.Although 3D pharmacophore screening remains a prevalent technique, its application to very large datasets is limited by the computational cost associated with matching query pharmacophores to datab<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31161<END_URL>

Phidias: A Generative Model for Creating 3D  Content from Text, Image, and 3D Conditions with Reference-Augmented  Diffusion\nGenerative 3D modeling has made significant advances recently, but it remains constrained by its inherently ill-posed nature, leading to challenges in quality and controllability. Inspired by the real-world workflow that designers typically refer to existing 3D models when creating new ones, we propose Phidias, a novel generative model that uses diffusion for reference-au<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29554<END_URL>

PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis\nPredictive coding has been established as a promising neuroscientific theory to describe the mechanism of long-term memory residing in the retina.  This theory hypothesises that cortex predicts sensory inputs at various levels of abstraction to minimise prediction errors and forms long-term memory.  Inspired by predictive coding, Chen et al. (2024) proposed another theory, temporal prediction hypothesis, to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30926<END_URL>

PhyloLM: Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks\nThis paper introduces PhyloLM, a method adapting phylogenetic algorithms to Large Language Models (LLMs) to explore whether and how they relate to each other and to predict their performance characteristics. Our method calculates a phylogenetic distance metric based on the similarity of LLMs' output. The resulting metric is then used to construct dendrograms, which satisfactorily capture  k<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28195<END_URL>

PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders\nLearning informative representations of phylogenetic tree structures is essential for analyzing evolutionary relationships. Classical distance-based methods have been widely used to project phylogenetic trees into Euclidean space, but they are often sensitive to the choice of distance metric and may lack sufficient resolution. In this paper, we introduce *phylogenetic variational autoencoders* (PhyloVAEs), an uns<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29234<END_URL>

PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems\nSolving partial differential equations (PDEs) serves as a cornerstone for modeling complex dynamical systems. Recent progresses have demonstrated grand benefits of data-driven neural-based models for predicting spatiotemporal dynamics (e.g., tremendous speedup gain compared with classical numerical methods). However, most existing neural models rely on rich training data, have limited extrapolation and general<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28878<END_URL>

Physics-aligned field reconstruction with diffusion bridge\nThe reconstruction of physical fields from sparse measurements is pivotal in both scientific research and engineering applications. Traditional methods are increasingly supplemented by deep learning models due to their efficacy in extracting features from data. However, except for the low accuracy on complex physical systems, these models often fail to comply with essential physical constraints, such as governing equations and boundary <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30484<END_URL>

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems\nInverse problems involving partial differential equations (PDEs) can be seen as discovering a mapping from measurement data to unknown quantities, often framed within an operator learning approach. However, existing methods typically rely on large amounts of labeled training data, which is impractical for most real-world applications. Moreover, these supervised models may fail to capture the underlying physical pri<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31262<END_URL>

Physics-Informed Diffusion Models\nGenerative models such as denoising diffusion models are quickly advancing their ability to approximate highly complex data distributions. They are also increasingly leveraged in scientific machine learning, where samples from the implied data distribution are expected to adhere to specific governing equations. We present a framework that unifies generative modeling and partial differential equation fulfillment by introducing a first-principle-based loss term t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28020<END_URL>

Physics-Informed Neural Predictor\nAccurately predicting fluid dynamics and evolution has been a long-standing challenge in physical sciences. Conventional deep learning methods often rely on the nonlinear modeling capabilities of neural networks to establish mappings between past and future states, overlooking the fluid dynamics, or only modeling the velocity field, neglecting the coupling of multiple physical quantities. In this paper, we propose a new physics-informed learning approach that i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27928<END_URL>

Physics-informed Temporal Difference Metric Learning for Robot Motion Planning\nThe motion planning problem involves finding a collision-free path from a robot's starting to its target configuration. Recently, self-supervised learning methods have emerged to tackle motion planning problems without requiring expensive expert demonstrations. They solve the Eikonal equation for training neural networks and lead to efficient solutions. However, these methods struggle in complex environments because <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29546<END_URL>

Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs\nState-of-the-art methods for forecasting irregularly sampled time series with missing values predominantly rely on just four datasets and a few small toy examples for evaluation. While ordinary differential equations (ODE) are the prevalent models in science and engineering, a baseline model that forecasts a constant value outperforms ODE-based models from the last five years on three<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30863<END_URL>

PhysPDE: Rethinking PDE Discovery and a Physical HYpothesis Selection Benchmark\nDespite extensive research, recovering PDE expressions from experimental observations often involves symbolic regression. This method generally lacks the incorporation of meaningful physical insights, resulting in outcomes lacking clear physical interpretations. Recognizing that the primary interest of Machine Learning for Science (ML4Sci) often lies in understanding the underlying physical mechanisms or even discov<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30306<END_URL>

PianoMotion10M: Dataset and Benchmark for Hand Motion Generation in Piano Performance\nRecently, artificial intelligence techniques for education have been received increasing attentions, while it still remains an open problem to design the effective music instrument instructing systems. Although key presses can be directly derived from sheet music, the transitional movements among key presses require more extensive guidance in piano performance. In this work, we construct a piano-hand motion ge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28158<END_URL>

PICASO: Permutation-Invariant Composition of Document States\nGeneration with Large Language Models (LLMs) is often augmented by incorporating additional information "in-context" through the concatenation of multiple prepended documents. However, this process leads to significant computational costs that scale with the number and size of each document chunk. State Space Models (SSMs) offer a promising solution by allowing a database of documents to be pre-processed as states from which to start <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30782<END_URL>

PiCO: Peer Review in LLMs based on Consistency Optimization\nExisting large language models (LLMs) evaluation methods typically focus on testing the performance on some closed-environment and domain-specific benchmarks with human annotations. In this paper, we explore a novel unsupervised evaluation direction, utilizing peer-review mechanisms to measure LLMs automatically without any human feedback. In this setting, both open-source and closed-source LLMs lie in the same environment, capable of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28108<END_URL>

PIED: Physics-Informed Experimental Design for Inverse Problems\nIn many science and engineering settings, system dynamics are characterized by governing partial differential equations (PDEs), and a major challenge is to solve inverse problems (IPs) where unknown PDE parameters are inferred based on observational data gathered under limited budget. Due to the high costs of setting up and running experiments, experimental design (ED) is often done with the help of PDE simulations to optimize for <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27871<END_URL>

PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations\nThe approximation of Partial Differential Equations (PDEs) using neural networks has seen significant advancements through Physics-Informed Neural Networks (PINNs). Despite their straightforward optimization framework and flexibility in implementing various PDEs, PINNs often suffer from low accuracy due to the spectral bias of Multi-Layer Perceptrons (MLPs), which struggle to effectively learn high-frequency and non-lin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27752<END_URL>

PIN: Prolate Spheroidal Wave Function-based Implicit Neural Representations\nImplicit Neural Representations (INRs) provide a continuous mapping between the coordinates of a signal and the corresponding values. As the performance of INRs heavily depends on the choice of nonlinear-activation functions, there has been a significant focus on encoding explicit signals within INRs using diverse activation functions. Despite recent advancements, existing INRs often encounter significant challenges, pa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30391<END_URL>

PIORF: Physics-Informed Ollivier-Ricci Flow for Long–Range Interactions in Mesh Graph Neural Networks\nRecently, data-driven simulators based on graph neural networks have gained attention in modeling physical systems on unstructured meshes. However, they struggle with long-range dependencies in fluid flows, particularly in refined mesh regions. This challenge, known as the 'over-squashing' problem, hinders information propagation. While existing graph rewiring methods address this issue to some<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28238<END_URL>

Pitfalls of Evidence-Based AI Policy\nNations across the world are working to govern AI. However, from a technical perspective, the best way to do this is not yet clear. Meanwhile, recent debates over AI regulation have led to calls for “evidence-based AI policy” which emphasize holding regulatory action to a high evidentiary standard. Evidence is of irreplaceable value to policymaking. However, holding regulatory action to too high an evidentiary standard can lead to systematic neglect of certa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31363<END_URL>

PivotMesh: Generic 3D Mesh Generation via Pivot Vertices Guidance\nGenerating compact and sharply detailed 3D meshes poses a significant challenge for current 3D generative models. Different from extracting dense meshes from neural representation, some recent works try to model the native mesh distribution (i.e., a set of triangles), which generates more compact results as humans crafted. However, due to the complexity and variety of mesh topology, these methods are typically limited to small da<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29379<END_URL>

Planning Anything with Rigor: General-Purpose Zero-Shot Planning with LLM-based Formalized Programming\nWhile large language models (LLMs) have recently demonstrated strong potential in solving planning problems, there is a trade-off between flexibility and complexity. LLMs, as zero-shot planners themselves, are still not capable of directly generating valid plans for complex planning problems such as multi-constraint or long-horizon tasks. On the other hand, many frameworks aiming to solve comp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31260<END_URL>

Planning in Natural Language Improves LLM Search for Code Generation\nWhile scaling training compute has led to remarkable improvements in large language models (LLMs), scaling inference compute has not yet yielded analogous gains. We hypothesize that a core missing component is a lack of diverse LLM outputs, leading to inefficient search due to models repeatedly sampling highly similar, yet incorrect generations. We empirically demonstrate that this lack of diversity can be mitigated by searchi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31034<END_URL>

Plastic Learning with Deep Fourier Features\nDeep neural networks can struggle to learn continually in the face of non-stationarity. This phenomenon is known as loss of plasticity. In this paper, we identify underlying principles that lead to plastic algorithms. In particular, we provide theoretical results showing that shallow linear networks, as well as a special case of deep linear networks, do not suffer from loss of plasticity. We then propose \emph{deep Fourier features}, which are the con<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29885<END_URL>

PLENCH: Realistic Evaluation of Deep Partial-Label Learning Algorithms\nPartial-label learning (PLL) is a weakly supervised learning problem in which each example is associated with multiple candidate labels and only one is the true label. In recent years, many deep PLL algorithms have been developed to improve model performance. However, we find that some early developed algorithms are often underestimated and can outperform many later algorithms with complicated designs. In this paper, we delv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30321<END_URL>

pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation\nParameter-efficient fine-tuning has demonstrated promising results across various visual adaptation tasks, such as classification and segmentation. Typically, prompt tuning techniques have harnessed knowledge from a single pre-trained model, whether from a general or a specialized medical domain. However, this approach typically overlooks the potential synergies that could arise from integrating diverse domain knowledge wit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28112<END_URL>

PN-GAIL: Leveraging Non-optimal Information from Imperfect Demonstrations\nImitation learning aims at constructing an optimal policy by emulating expert demonstrations. However, the prevailing approaches in this domain typically presume that the demonstrations are optimal, an assumption that seldom holds true in the complexities of real-world applications. The data collected in practical scenarios often contains imperfections, encompassing both optimal and non-optimal examples. In this study, we<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31246<END_URL>

PnP-Flow: Plug-and-Play Image Restoration with Flow Matching\nIn this paper, we introduce Plug-and-Play (PnP) Flow Matching, an algorithm for solving imaging inverse problems. PnP methods leverage the strength of pre-trained denoisers, often deep neural networks, by integrating them in optimization schemes. While they achieve state-of-the-art performance on various inverse problems in imaging, PnP approaches face inherent limitations on more generative tasks like inpainting. On the other hand, g<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30965<END_URL>

POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation\nMulti-agent reinforcement learning (MARL) has recently excelled in solving challenging cooperative and competitive multi-agent problems in various environments with, mostly, few agents and full observability. Moreover, a range of crucial robotics-related tasks, such as multi-robot navigation and obstacle avoidance, that have been conventionally approached with the classical non-learnable methods (e.g., heuristic search) is curr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30881<END_URL>

Point-based Instance Completion with Scene Constraints\nRecent point-based object completion methods have demonstrated the ability to accurately recover the missing geometry of partially observed objects. However, these approaches are not well-suited for completing objects within a scene as they do not consider known scene constraints (e.g., other observed surfaces) in their completions and further expect the partial input to be in a canonical coordinate system which does not hold for objects wi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28501<END_URL>

Point Cluster: A Compact Message Unit for Communication-Efficient Collaborative Perception\nThe objective of the collaborative perception task is to enhance the individual agent's perception capability through message communication among neighboring agents. A central challenge lies in optimizing the inherent trade-off between perception ability and communication cost. To tackle this bottleneck issue, we argue that a good message unit should encapsulate both semantic and structural information in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30972<END_URL>

PointOBB-v2: Towards Simpler, Faster, and Stronger Single Point Supervised Oriented Object Detection\nSingle point supervised oriented object detection has gained attention and made initial progress within the community. Diverse from those approaches relying on one-shot samples or powerful pretrained models (e.g. SAM), PointOBB has shown promise due to its prior-free feature. In this paper, we propose PointOBB-v2, a simpler, faster, and stronger method to generate pseudo rotated boxes from point<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29665<END_URL>

Point-SAM: Promptable 3D Segmentation Model for Point Clouds\nThe development of 2D foundation models for image segmentation has been significantly advanced by the Segment Anything Model (SAM). However, achieving similar success in 3D models remains a challenge due to issues such as non-unified data formats, poor model scalability, and the scarcity of labeled data with diverse masks. To this end, we propose a 3D promptable segmentation model Point-SAM, focusing on point clouds. We employ an effi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27719<END_URL>

Poison-splat: Computation Cost Attack on 3D Gaussian Splatting\n3D Gaussian splatting (3DGS), known for its groundbreaking performance and efficiency, has become a dominant 3D representation and brought progress to many 3D vision tasks. However, in this work, we reveal a significant security vulnerability that has been largely overlooked in 3DGS: the computation cost of training 3DGS could be maliciously tampered by poisoning the input data. By developing an attack named Poison-splat, we reveal <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30377<END_URL>

Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains\nDeep learning has achieved great success in modeling dynamical systems, providing data-driven simulators to predict complex phenomena, even without known governing equations. However, existing models have two major limitations: their narrow focus on mechanical systems and their tendency to treat systems as monolithic. These limitations reduce their applicability to dynamical systems in other domains, such as ele<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29495<END_URL>

PolaFormer: Polarity-aware Linear Attention for Vision Transformers\nLinear attention has emerged as a promising alternative to softmax-based attention, leveraging kernelized feature maps to reduce complexity from quadratic to linear in sequence length. However, the non-negative constraint on feature maps and the relaxed exponential function used in approximation lead to significant information loss compared to the original query-key dot products, resulting in less discriminative attention maps <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28592<END_URL>

Policy-aware Reward Modeling with Uncertainty-Gradient based Data Augmentation\nReinforcement Learning from Human Feedback (RLHF) has emerged as a standard and effective approach for training large language models (LLMs) with human preferences. In this framework, a learned reward model approximates human preferences and guides policy optimization, making it crucial to develop an accurate reward model. However, without the ``true'' reward function, challenges arise when the reward model is an imp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28685<END_URL>

Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model\nRecent advancements in robot learning have used imitation learning with large models and extensive demonstrations to develop effective policies. However, these models are often limited by the quantity quality, and diversity of demonstrations. This paper explores improving offline-trained imitation learning models through online interactions with the environment. We introduce Policy Decorator, which uses a model-agnostic r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28950<END_URL>

Policy Design in Long-run Welfare Dynamics\nWe study a stochastic dynamic model of long-term welfare in a population. Individuals in our model have welfare that improves with intervention and deteriorates in the absence of treatment. The planner can treat one individual at each time step. We contrast two fundamental policies in our model. The utilitarian policy greedily maximizes welfare improvement at each step. The Rawlsian policy intervenes on the individual of lowest welfare. Although hugely<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29004<END_URL>

Policy Optimization under Imperfect Human Interactions with Agent-Gated Shared Autonomy\nWe introduce AGSA, an Agent-Gated Shared Autonomy framework that learns from high-level human feedback to tackle the challenges of reward-free training, safe exploration, and imperfect low-level human control. Recent human-in-the loop learning methods enable human participants to intervene a learning agent’s control and provide online demonstrations. Nonetheless, these methods rely heavily on perfect human i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29983<END_URL>

PolyhedronNet: Representation Learning for Polyhedra with Surface-attributed Graph\nUbiquitous geometric objects can be precisely and efficiently represented as polyhedra. The transformation of a polyhedron into a vector, known as polyhedra representation learning, is crucial for manipulating these shapes with mathematical and statistical tools for tasks like classification, clustering, and generation. Recent years have witnessed significant strides in this domain, yet most efforts focus on the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30542<END_URL>

PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization\nReinforcement learning-based methods for constructing solutions to combinatorial optimization problems are rapidly approaching the performance of human-designed algorithms. To further narrow the gap, learning-based approaches must efficiently explore the solution space during the search process. Recent approaches artificially increase exploration by enforcing diverse solution generation through handcrafted rules<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29548<END_URL>

Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models\nTransformers have found extensive applications across various domains due to the powerful fitting capabilities. This success can be partially attributed to their inherent nonlinearity. Thus, in addition to the ReLU function employed in the original transformer architecture, researchers have explored alternative modules such as GeLU and SwishGLU to enhance nonlinearity and thereby augment representational capaci<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30507<END_URL>

PolyPythias: Stability and Outliers across Fifty Language Model Pre-Training Runs\nUnderstanding the stability of language model pre-training and its effects on downstream performance is still understudied. Prior work shows that the training process can yield significantly different results in response to slight variations in initial conditions, e.g., the random seed.Crucially, resources to study pre-training stability in language models are still lacking, especially for decoder-only models.We i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29092<END_URL>

Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation\nRating-based human evaluation has become an essential tool to accurately evaluate the impressive performance of large language models (LLMs). However, current rating systems suffer from several important limitations: first, they fail to account for biases that significantly influence evaluation results, second, they require large and expensive preference datasets to obtain accurate ratings, and third, they do not facil<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29474<END_URL>

PooDLe🐩: Pooled and dense self-supervised learning from naturalistic videos\nSelf-supervised learning has driven significant progress in learning from single-subject, _iconic_ images.However, there are still unanswered questions about the use of minimally-curated, naturalistic video data, which contain _dense_ scenes with many independent objects, imbalanced class distributions, and varying object sizes.In this paper, we propose PooDLe, a self-supervised learning method that combines an invarian<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28997<END_URL>

Population Transformer: Learning Population-level Representations of Neural Activity\nWe present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address two key challenges in scaling models with neural time-series data: sparse and variable electrode distribution across subjects and datasets. The Population Transformer (PopT) stacks on top of pretrained representations and enhances downstream decoding by enabling learned agg<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30337<END_URL>

Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks\nThe dynamics of information diffusion within graphs is a critical open issue that heavily influences graph representation learning, especially when considering long-range propagation. This calls for principled approaches that control and regulate the degree of propagation and dissipation of information throughout the neural flow. Motivated by this, we introduce port-Hamiltonian Deep Graph Networks, a novel fra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31273<END_URL>

PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches\nAs large language models (LLMs) increasingly shape the AI landscape, fine-tuning pretrained models has become more popular than in the pre-LLM era for achieving optimal performance in domain-specific tasks. However, pretrained LLMs such as ChatGPT are periodically evolved (i.e., model parameters are frequently updated), making it challenging for downstream users with limited resources to keep up <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28792<END_URL>

Positional Embeddings in Transformer Models: Evolution from Text to Vision Domains\nPositional encoding has become an essential element in transformer models, addressing their fundamental property of permutation invariance and allowing them to understand sequential relationships within data. This blog post examines positional encoding techniques, emphasizing their vital importance in traditional transformers and their use with 2D data in Vision Transformers (ViT). We explore two contemporary met<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31345<END_URL>

Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation\nDiffusion models are powerful generative models but often generate sensitive data that are unwanted by users,mainly because the unlabeled training data frequently contain such sensitive data.Since labeling all sensitive data in the large-scale unlabeled training data is impractical,we address this problem by using a small amount of labeled sensitive data.In this paper,we propose positive-unlabeled diffusion models,whic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28640<END_URL>

PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling\nPrecipitation nowcasting plays a pivotal role in socioeconomic sectors, especially in severe convective weather warnings. Although notable progress has been achieved by approaches mining the spatiotemporal correlations with deep learning, these methods still suffer severe blurriness as the lead time increases, which hampers accurate predictions for extreme precipitation. To alleviate blurrin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27936<END_URL>

PostEdit: Posterior Sampling for Efficient Zero-Shot Image Editing\nIn the field of image editing, three core challenges persist: controllability, background preservation, and efficiency. Inversion-based methods rely on time-consuming optimization to preserve the features of the initial images, which results in low efficiency due to the requirement for extensive network inference. Conversely, inversion-free methods lack theoretical support for background similarity, as they circumvent the issue <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30127<END_URL>

Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration\nPhoto-realistic image restoration algorithms are typically evaluated by distortion measures (e.g., PSNR, SSIM) and by perceptual quality measures (e.g., FID, NIQE), where the desire is to attain the lowest possible distortion without compromising on perceptual quality. To achieve this goal, current methods commonly attempt to sample from the posterior distribution, or to optimize a weighted sum of a distortion <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28765<END_URL>

Post-hoc Reward Calibration: A Case Study on Length Bias\nReinforcement Learning from Human Feedback aligns the outputs of Large Language Models with human values and preferences. Central to this process is the reward model (RM), which translates human feedback into training signals for optimising LLM behaviour. However, RMs can develop biases by exploiting spurious correlations in their training data, such as favouring outputs based on length orstyle rather than true quality. These biases can l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30144<END_URL>

POTEC: Off-Policy Contextual Bandits for Large Action Spaces via Policy Decomposition\nWe study off-policy learning (OPL) of contextual bandit policies in large discrete action spaces where existing methods -- most of which rely crucially on reward-regression models or importance-weighted policy gradients -- fail due to excessive bias or variance. To overcome these issues in OPL, we propose a novel two-stage algorithm, called Policy Optimization via Two-Stage Policy Decomposition (POTEC). It lev<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29991<END_URL>

PPT: Patch Order Do Matters In Time Series Pretext Task\nRecently, patch-based models have been widely discussed in time series analysis. However, existing pretext tasks for patch-based learning, such as masking, may not capture essential time and channel-wise patch interdependencies in time series data, presumed to result in subpar model performance. In this work, we introduce *Patch order-aware Pretext Task (PPT)*, a new self-supervised patch order learning pretext task for time series classif<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30790<END_URL>

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation\nWe propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of generative models. The proposed approach, PQMass, provides a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models. PQMass divides the sample space into non-overlapping regions an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28426<END_URL>

Preble: Efficient Distributed Prompt Scheduling for LLM Serving\nPrompts to large language models (LLMs) have evolved beyond simple user questions.For LLMs to solve complex problems, today’s practices are to include domain-specificinstructions, illustration of tool usages, and/or long context such as textbook chapters inprompts. As such, many parts of prompts are repetitive across requests. Recent workspropose to cache and reuse KV state of prompts. However, they are all confined to a single-GPU<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28456<END_URL>

Precedence-Constrained Winter Value for Effective Graph Data Valuation\nData valuation is essential for quantifying data’s worth, aiding in assessing data quality and determining fair compensation. While existing data valuation methods have proven effective in evaluating the value of Euclidean data, they face limitations when applied to the increasingly popular graph-structured data. Particularly, graph data valuation introduces unique challenges, primarily stemming from the intricate dependenci<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28048<END_URL>

Precise Localization of Memories: A Fine-grained Neuron-level Knowledge Editing Technique for LLMs\nKnowledge editing aims to update outdated information in Large Language Models (LLMs). A representative line of study is the locate-then-edit methods, which typically employ causal tracing localization to identify the modules responsible for recalling factual knowledge about entities. However, we find that these methods are often sensitive only to changes in the subject entity, leaving them less e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30923<END_URL>

Precise Parameter Localization for Textual Generation in Diffusion Models\nNovel diffusion models (DMs) can synthesize photo-realistic images with integrated high-quality text. Surprisingly, we demonstrate through attention activation patching that only less than 1% of DMs' parameters contained in attention layers influence the generation of textual content within the images. Building on this observation, by precisely targeting cross and joint attention layers of DMs, we improve the efficiency a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28808<END_URL>

Predicate Hierarchies Improve Few-Shot State Classification\nState classification of objects and their relations is core to many long-horizon tasks, particularly in robot planning and manipulation. However, the combinatorial explosion of possible object-predicate combinations, coupled with the need to adapt to novel real-world environments, makes it a desideratum for state classification models to generalize to novel queries with few examples. To this end, we propose PHIER, which leverages predi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28492<END_URL>

Predicting the Energy Landscape of Stochastic Dynamical System via  Physics-informed Self-supervised Learning\nEnergy landscapes play a crucial role in shaping dynamics of many real-world complex systems. System evolution is often modeled as particles moving on a landscape under the combined effect of energy-driven drift and noise-induced diffusion, where the energy governs the long-term motion of the particles. Estimating the energy landscape of a system has been a longstanding interdisciplinar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29722<END_URL>

Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors\nIn recent years, there has been a significant growth in research focusing on minimum $\ell_2$ norm (ridgeless) interpolation least squares estimators. However, the majority of these analyses have been limited to an unrealistic regression error structure, assuming independent and identically distributed errors with zero mean and common variance. In this paper, we explore pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30610<END_URL>

Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation\nCurrent efforts to learn scalable policies in robotic manipulation primarily fall into two categories: one focuses on "action," which involves behavior cloning from extensive collections of robotic data, while the other emphasizes "vision," enhancing model generalization by pre-training representations or generative models, also referred to as world models, using large-scale visual datasets. This paper presents an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28455<END_URL>

Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Function\nThe fusion of raw sensor data to create a Bird's Eye View (BEV) representation is critical for autonomous vehicle planning and control. Despite the growing interest in using deep learning models for BEV semantic segmentation, anticipating segmentation errors and enhancing the explainability of these models remain under-explored. This paper introduces a comprehensive benchmark for predicti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28599<END_URL>

Preference Diffusion for Recommendation\nRecommender systems predict personalized item rankings based on user preference distributions derived from historical behavior data. Recently, diffusion models (DMs) have gained attention in recommendation for their ability to model complex distributions, yet current DM-based recommenders often rely on traditional objectives like mean squared error (MSE) or recommendation objectives, which are not optimized for personalized ranking tasks or fail to fully <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30900<END_URL>

Preference Elicitation for Offline Reinforcement Learning\nApplying reinforcement learning (RL) to real-world problems is often made challenging by the inability to interact with the environment and the difficulty of designing reward functions. Offline RL addresses the first challenge by considering access to an offline dataset of environment interactions labeled by the reward function. In contrast, Preference-based RL does not assume access to the reward function and learns it from preferences,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31114<END_URL>

Preference Optimization as Probabilistic Inference\nExisting preference optimization methods are mainly designed for directly learning from human feedback with the assumption that paired examples (preferred vs. dis-preferred) are available. In contrast, we propose a method that can leverage unpaired preferred or dis-preferred examples by decoupling learning from positive and negative feedback, allowing control over the contribution of each, and works even when only one type of feedback (positive<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31025<END_URL>

Preference Optimization for Reasoning with Pseudo Feedback\nPreference optimization techniques, such as Direct Preference Optimization (DPO), are frequently employed to enhance the reasoning capabilities of large language models (LLMs) in domains like mathematical reasoning and coding, typically following supervised fine-tuning. These methods rely on high-quality labels for reasoning tasks to generate preference pairs; however, the availability of reasoning datasets with human-verified labels is<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28622<END_URL>

Preserving Deep Representations in One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework\nWe present SNOWS, a one-shot post-training pruning framework aimed at reducing neural network inference cost without requiring retraining. Current leading one-shot pruning methods minimize layer-wise least squares reconstruction error which does not take into account deeper network representations. We propose to optimize a more global reconstruction objective that accounts for nonlinear activ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28931<END_URL>

Presto! Distilling Steps and Layers for Accelerating Music Generation\nDespite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling steps and cost per step. To reduce steps, we develop a new score-based distribution matching distillation (DMD) method for the EDM-family of diffusion models, the first GAN-based dis<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30264<END_URL>

Pre-Training for 3D Hand Pose Estimation with Contrastive Learning on Large-Scale Hand Images in the Wild\nWe present a contrastive learning framework based on in-the-wild hand images tailored for pre-training 3D hand pose estimators, dubbed HandCLR. Pre-training on large-scale images achieves promising results in various tasks, but prior 3D hand pose pre-training methods have not fully utilized the potential of diverse hand images accessible from in-the-wild videos. To facilitate scalable pre-t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30721<END_URL>

Pre-training of Foundation Adapters for LLM Fine-tuning\nAdapter-based fine-tuning methods insert small, trainable adapters into frozen pre-trained LLMs, significantly reducing computational costs while maintaining performance. Despite their advantages, traditional adapter fine-tuning suffers from training instability due to random weight initialization, which can lead to inconsistent performance across different runs. To address this issue, this blog post introduces pre-trained foundation adapt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31361<END_URL>

Prevalence of Negative Transfer in Continual Reinforcement Learning: Analyses and a Simple Baseline\nWe argue that the negative transfer problem occurring when the new task to learn arrives is an important problem that needs not be overlooked when developing effective Continual Reinforcement Learning (CRL) algorithms. Through comprehensive experimental validation, we demonstrate that such issue frequently exists in CRL and cannot be effectively addressed by several recent work on either mitigati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30073<END_URL>

Prioritized Generative Replay\nSample-efficient online reinforcement learning often uses replay buffers to store experience for reuse when updating the value function. However, uniform replay is inefficient, since certain classes of transitions can be more relevant to learning. While prioritization of more useful samples is helpful, this strategy can also lead to overfitting, as useful samples are likely to be more rare. In this work, we instead propose a prioritized, parametric version of an ag<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30955<END_URL>

PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models\nDespite recent advancements in federated learning (FL), the integration of generative models into FL has been limited due to challenges such as high communication costs and unstable training in heterogeneous data environments. To address these issues, we propose PRISM, a FL framework tailored for generative models that ensures (i) stable performance in heterogeneous data distributions and (ii) resource efficie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30590<END_URL>

Privacy Auditing of Large Language Models\nCurrent techniques for privacy auditing of large language models (LLMs) have limited efficacy---they rely on basic approaches to generate canaries which leads to weak membership inference attacks that in turn give loose lower bounds on the empirical privacy leakage.We develop canaries that are far more effective than those used in prior work under threat models that cover a range of realistic settings. We demonstrate through extensive experiments on mul<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30914<END_URL>

Privacy-Aware Lifelong Learning\nLifelong learning algorithms enable models to incrementally acquire new knowledge without forgetting previously learned information. Contrarily, the field of machine unlearning focuses on explicitly forgetting certain previous knowledge from pretrained models when requested, in order to comply with data privacy regulations on the right-to-be-forgotten. Enabling efficient lifelong learning with the capability to selectively unlearn sensitive information from model<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29446<END_URL>

Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models\nMultimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated Prompt Learning (FPL) is a recently proposed approach that combines pre-trained multimodal LLMs such as vision-language models with federated learning to create personalized, privacy-preserving AI systems. However, balancin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30385<END_URL>

Privately Counting Partially Ordered Data\nWe consider differentially private counting when each data point consists of $d$ bits satisfying a partial order. Our main technical contribution is a problem-specific $K$-norm mechanism that runs in time $O(d^2)$. Experiments show that, depending on the partial order in question, our solution dominates existing pure differentially private mechanisms and can reduce their error by an order of magnitude or more.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28757<END_URL>

Private Mechanism Design via Quantile Estimation\nWe investigate the problem of designing differentially private (DP), revenue-maximizing single item auction. Specifically, we consider broadly applicable settings in mechanism design where agents' valuation distributions are **independent**, **non-identical**, and can be either **bounded** or **unbounded**. Our goal is to design such auctions with **pure**, i.e., $(\epsilon,0)$ privacy in polynomial time. In this paper, we propose two computation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30113<END_URL>

Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance\nAgents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scenarios requiring foresight and autonomous decision-making. In this paper, we tackle the challenge of developing proactive agents capable of anticipating and initiating tasks without explicit human instructions. We propose a novel data-driv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28128<END_URL>

Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utility\nWith the rise of large language models (LLMs), increasing research has recognizedtheir risk of leaking personally identifiable information (PII) under maliciousattacks. Although efforts have been made to protect PII in LLMs, existing methodsstruggle to balance privacy protection with maintaining model utility. In this paper,inspired by studies of amnesia in cognitive science, we propose<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28672<END_URL>

ProAdvPrompter: A Two-Stage Journey to Effective Adversarial Prompting for LLMs\nAs large language models (LLMs) are increasingly being integrated into various real-world applications, the identification of their vulnerabilities to jailbreaking attacks becomes an essential component of ensuring the safety and reliability of LLMs. Previous studies have developed LLM assistants, known as the adversarial prompter, to automatically generate suffixes that manipulate target LLMs into generating harmfu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28021<END_URL>

Probabilistic Conformal Prediction with Approximate Conditional Validity\nWe develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution $\textup{P}_{Y \mid X}$. Existing methods, such as conformalized quantile regression and probabilistic conformal prediction, usually provide only a marginal coverage guarantee. In contrast, our approach extends these frameworks to achieve approximately conditional covera<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29865<END_URL>

Probabilistic Geometric Principal Component Analysis\nDimensionality reduction is critical across various domains of science including neuroscience.  Probabilistic Principal Component Analysis (PPCA) is a prominent dimensionality reduction method that provides a probabilistic approach unlike the deterministic approach of PCA and serves as a connection between PCA and Factor Analysis (FA). Despite their power, PPCA and its extensions are mainly based on linear models and can only describe the dat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28454<END_URL>

Probabilistic Language-Image Pre-Training\nVision-language models (VLMs) embed aligned image-text pairs into a joint space but often rely on deterministic embeddings, assuming a one-to-one correspondence between images and texts. This oversimplifies real-world relationships, which are inherently many-to-many, with multiple captions describing a single image and vice versa. We introduce Probabilistic Language-Image Pre-training (ProLIP), the first probabilistic VLM pre-trained on a billion-scale <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30478<END_URL>

Probabilistic Learning to Defer: Handling Missing Expert Annotations and Controlling Workload Distribution\nRecent progress in machine learning research is gradually shifting its focus towards *human - AI cooperation* due to the advantages of exploiting the reliability of human experts and the efficiency of AI models. One of the promising approaches in human - AI cooperation is *learning to defer* (L2D), where the system analyses the input data and decides to make its own decision or defer to hu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27652<END_URL>

Probabilistic Neural Pruning via Sparsity Evolutionary Fokker-Planck-Kolmogorov Equation\nNeural pruning aims to compress and accelerate deep neural networks by identifying the optimal subnetwork within a specified sparsity budget. In this work, we study how to gradually sparsify the unpruned dense model to the target sparsity level with a minimal performance drop. Specifically, we analyze the evolution of the population of optimal subnetworks under continuous sparsity increments from a thermody<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28772<END_URL>

Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models\nBackdoor unalignment attacks against Large Language Models (LLMs) enable the stealthy compromise of safety alignment using a hidden trigger while evading normal safety auditing. These attacks pose significant threats to the applications of LLMs in the real-world Large Language Model as a Service (LLMaaS) setting, where the deployed model is a fully black-box system that can only interact thro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30397<END_URL>

Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing\nWe introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. PP leverages the insight that not all samples and tokens contribute equally to the model's output, and probing a small portion of each batch effectively identifies crucial weights, enabling tailored dynamic pruning for different batches. It comprises three main stages:<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29365<END_URL>

Probing the Latent Hierarchical Structure of Data via Diffusion Models\nHigh-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessing the latent variables underlying such a data structure remains a challenge. Forward-backward experiments in diffusion-based models, where a datum is noised and then denois<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31261<END_URL>

Problem-Parameter Free Federated Learning\nFederated learning (FL) has garnered significant attention from academia and industry in recent years due to its advantages in data privacy, scalability, and communication efficiency. However, current FL algorithms face a critical limitation: their performance heavily depends on meticulously tuned hyperparameters, particularly the learning rate or stepsize. This manual tuning process is challenging in federated settings due to data heterogeneity and lim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29194<END_URL>

Procedural Synthesis of Synthesizable Molecules\nDesigning synthetically accessible molecules and recommending analogs to unsynthesizable molecules are important problems for accelerating molecular discovery. We reconceptualize both problems using ideas from program synthesis. Drawing inspiration from syntax-guided synthesis approaches, we decouple the syntactic skeleton from the semantics of a synthetic tree to create a bilevel framework for reasoning about the combinatorial space of synthesis <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29833<END_URL>

Process Reward Model with Q-value Rankings\nProcess Reward Modeling (PRM) is critical for complex reasoning and decision-making tasks where the accuracy of intermediate steps significantly influences the overall outcome. Existing PRM approaches, primarily framed as classification problems, employ cross-entropy loss to independently evaluate each step's correctness. This method can lead to suboptimal reward distribution and does not adequately address the interdependencies among steps. To address<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27847<END_URL>

ProFI-Painter: Text-Guided Prompt-Faithful Image Inpainting with Diffusion Models\nRecent progress in text-guided image inpainting, based on the unprecedented success of text-to-image diffusion models, has led to exceptionally realistic and visually plausible results.However, there is still significant potential for improvement in current text-to-image inpainting models, particularly in better aligning the inpainted area with user prompts.Therefore, we introduce $\textit{ProFI-Painter}$, a $\tex<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30867<END_URL>

Programming Refusal with Conditional Activation Steering\nLLMs have shown remarkable capabilities, but precisely controlling their response behavior remains challenging.Existing activation steering methods alter LLM behavior indiscriminately, limiting their practical applicability in settings where selective responses are essential, such as content moderation or domain-specific assistants.In this paper, we propose Conditional Activation Steering (CAST), which analyzes LLM activation patterns dur<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29806<END_URL>

Progressive Compression with Universally Quantized Diffusion Models\nDiffusion probabilistic models have achieved mainstream success in many generative modeling tasks, from image generation to inverse problem solving. A distinct feature of these models is that they correspond to deep hierarchical latent variable models optimizing a variational evidence lower bound (ELBO) on the data likelihood.Drawing on a basic connection between likelihood modeling and compression, we explore the potential of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30486<END_URL>

Progressive distillation induces an implicit curriculum\nKnowledge distillation leverages a teacher model to improve the training of a student model. A persistent challenge is that a better teacher does not always yield a better student, to which a common mitigation is to use additional supervision from several “intermediate” teachers. One empirically validated variant of this principle is progressive distillation, where the student learns from successive intermediate checkpoints of the teacher.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27848<END_URL>

Progressively Refined Differentiable Physics\nThe physics solvers employed for neural network training are primarily iterative, and hence, differentiating through them introduces a severe computational burden as iterations grow large. Inspired by works in bilevel optimization, we show that full accuracy of the network is achievable through physics significantly coarser than fully converged solvers. We propose *progressively refined differentiable physics* (PRDP), an approach that identifies the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30710<END_URL>

Progressive Mixed-Precision Decoding for Efficient LLM Inference\nIn spite of the great potential of large language models (LLMs) across various tasks, their deployment on resource-constrained devices remains challenging due to their excessive computational and memory demands. Quantization has emerged as an effective solution by storing weights in reduced precision. However, utilizing low precisions (i.e.~2/3-bit) to substantially alleviate the memory-boundedness of LLM decoding, still suffers f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29823<END_URL>

Progressive Parameter Efficient Transfer Learning for Semantic Segmentation\nParameter Efficient Transfer Learning (PETL) excels in downstream classification fine-tuning with minimal computational overhead, demonstrating its potential within the pre-train and fine-tune paradigm. However, recent PETL methods consistently struggle when fine-tuning for semantic segmentation tasks, limiting their broader applicability. In this paper, we identify that fine-tuning for semantic segmentation requires la<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29265<END_URL>

Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation\nA powerful architecture for universal segmentation relies on transformers that encode multi-scale image features and decode object queries into mask predictions. With efficiency being a high priority for scaling such models, we observed that the state-of-the-art method Mask2Former uses \~50% of its compute only on the transformer encoder. This is due to the retention of a full-length token-level repres<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28965<END_URL>

Progress or Regress? Self-Improvement Reversal in Post-training\nSelf-improvement through post-training methods such as iterative preference learning has been acclaimed for enhancing the problem-solving capabilities (e.g., mathematical reasoning) of Large Language Models (LLMs) without human intervention. However, as our exploration deepens, it is crucial to critically assess whether these enhancements indeed signify comprehensive progress or if they could lead to unintended regressions. Through<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29657<END_URL>

Projection Head is Secretly an Information Bottleneck\nRecently, contrastive learning has risen to be a promising paradigm for extracting meaningful data representations. Among various special designs, adding a projection head on top of the encoder during training and removing it for downstream tasks has proven to significantly enhance the performance of contrastive learning. However, despite its empirical success, the underlying mechanism of the projection head remains under-explored. In this p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30021<END_URL>

Prompt as Knowledge Bank: Boost Vision-language model via Structural Representation for  zero-shot medical detection\nZero-shot medical detection can further improve detection performance without relying on annotated medical images even upon the fine-tuned model, showing great clinical value. Recent studies leverage grounded vision-language models (GLIP) to achieve this by using detailed disease descriptions as prompts for the target disease name during the inference phase.  However, these metho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28543<END_URL>

Prompting Fairness: Integrating Causality to Debias Large Language Models\nLarge language models (LLMs), despite their remarkable capabilities, are susceptible to generating biased and discriminatory responses. As LLMs increasingly influence high-stakes decision-making (e.g., hiring and healthcare), mitigating these biases becomes critical. In this work, we propose a causality-guided debiasing framework to tackle social biases, aiming to reduce the harmful dependence between LLMs' decisions and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30833<END_URL>

Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models\nInstruction-tuned language models (LM) are able to respond to imperative commands, providing a more natural user interface compared to their base counterparts. In this work, we present Promptriever, the first retrieval model able to be prompted like an LM. To train Promptriever, we curate and release a new instance-level instruction training set from MS MARCO, spanning nearly 500k instances. Promptriever not only <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28345<END_URL>

Proposal: ICLR 2025 Workshop on Building Trust in Language Models and Applications\nAs Large Language Models (LLMs) are rapidly adopted across diverse industries, concerns around their trustworthiness, safety, and ethical implications increasingly motivate academic research, industrial development, and legal innovation. LLMs are increasingly integrated into complex applications, where they must navigate challenges related to data privacy, regulatory compliance, and dynamic user interactions. The<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23984<END_URL>

ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids\nWe develop ProtComposer to generate protein structures conditioned on spatial protein layouts that are specified via a set of 3D ellipsoids capturing substructure shapes and semantics. At inference time, we condition on ellipsoids that are hand-constructed, extracted from existing proteins, or from a statistical model, with each option unlocking new capabilities. Hand-specifying ellipsoids enables users to control the l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31248<END_URL>

Protecting against simultaneous data poisoning attacks\nCurrent backdoor defense methods are evaluated against a single attack at a time. This is unrealistic, as powerful machine learning systems are trained on large datasets scraped from the internet, which may be attacked multiple times by one or more attackers. We demonstrate that multiple backdoors can be simultaneously installed in a single model through parallel data poisoning attacks without substantially degrading clean accuracy. Further<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28204<END_URL>

Proteina: Scaling Flow-based Protein Structure Generative Models\nRecently, diffusion- and flow-based generative models of protein structures have emerged as a powerful tool for de novo protein design. Here, we develop Proteina, a new large-scale flow-based protein backbone generator that utilizes hierarchical fold class labels for conditioning and relies on a tailored scalable transformer architecture with up to $5\times$ as many parameters as previous models. To meaningfully quantify performan<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29538<END_URL>

ProteinBench: A Holistic Evaluation of Protein Foundation Models\nRecent years have witnessed a surge in the development of protein foundation models, significantly improving performance in protein prediction and generative tasks ranging from 3D structure prediction and protein design to conformational dynamics. However, the capabilities and limitations associated with these models remain poorly understood due to the absence of a unified evaluation framework. To fill this gap, we introduce Prote<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30553<END_URL>

Protein Language Model Fitness is a Matter of Preference\nLeveraging billions of years of evolution, scientists have trained protein language models (pLMs) to understand the sequence and structure space of proteins aiding in the design of more functional proteins. Although they have shown ability to improve efficiency in engineering, it remains unclear under what conditions they will succeed or fail. We aim to predict the circumstances in which pLMs can successfully perform zero-shot fitness est<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29443<END_URL>

ProtoSnap: Prototype Alignment For Cuneiform Signs\nThe cuneiform writing system served as the medium for transmitting knowledgein the ancient Near East for a period of over three thousand years. Cuneiformsigns have a complex internal structure which is the subject of expert paleographicanalysis, as variations in sign shapes bear witness to historical developments andtransmission of writing and culture over time. However, prior automated techniquesmostly treat sign types as categorical and do no<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29313<END_URL>

Prototype antithesis for biological few-shot class-incremental learning\nDeep learning has become essential in the biological species recognition task. However, a significant challenge is the ability to continuously learn new or mutated species with limited annotated samples. Since species within the same family typically share similar traits, distinguishing between new and existing (old) species during incremental learning often faces the issue of species confusion. This can result in "catastro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29109<END_URL>

ProtPainter: Draw or Drag Protein via Topology-guided Diffusion\nRecent advances in protein backbone generation have achieved promising results under structural, functional, or physical constraints. However, existing methods lack the flexibility for precise topology control, limiting navigation of the backbone space. We present $\textbf{ProtPainter}$, a diffusion-based approach for generating protein backbones conditioned on 3D curves. ProtPainter follows a two-stage process: curve-based sketchi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29857<END_URL>

Provable Benefit of Annealed Langevin Monte Carlo for Non-log-concave Sampling\nWe consider the outstanding problem of sampling from an unnormalized density that may be non-log-concave and multimodal. To enhance the performance of simple Markov chain Monte Carlo (MCMC) methods, techniques of annealing type have been widely used. However, quantitative theoretical guarantees of these techniques are under-explored. This study takes a first step toward providing a non-asymptotic analysis of annealed<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29777<END_URL>

Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics\nGeometric tempering is a popular approach to sampling from challenging multi-modal probability distributions by instead sampling from a sequence of distributions which interpolate, using the geometric mean, between an easier proposal distribution and the target distribution. In this paper, we theoretically investigate the soundness of this approach when the sampling algorithm is Langevin dynamics, proving both upp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30453<END_URL>

Provable Convergence Bounds for Hybrid Dynamical Sampling and Optimization\nAnalog dynamical accelerators (DXs) are a growing sub-field in computer architecture research, offering order-of-magnitude gains in power efficiency and latency over traditional digital methods in several machine learning, optimization, and sampling tasks. However, limited-capacity accelerators require hybrid analog/digital algorithms to solve real-world problems, commonly using large-neighborhood local search (LNLS) fra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30347<END_URL>

Provable Uncertainty Decomposition via Higher-Order Calibration\nWe give a principled method for decomposing the predictive uncertainty of a model into aleatoric and epistemic components with explicit semantics relating them to the real-world data distribution. While many works in the literature have proposed such decompositions, they lack the type of formal guarantees we provide. Our method is based on the new notion of higher-order calibration, which generalizes ordinary calibration to the set<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29551<END_URL>

Provable unlearning in topic modeling and downstream tasks\nMachine unlearning algorithms are increasingly important as legal concerns arise around the provenance of training data, but verifying the success of unlearning is often difficult. Provable guarantees for unlearning are often limited to supervised learning settings. In this paper, we provide the first theoretical guarantees for unlearning in the pre-training and fine-tuning paradigm by studying topic models, simple bag-of-words language<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28973<END_URL>

Provable weak-to-strong generalization via benign overfitting\nThe classic teacher-student model in machine learning posits that a strong teacher supervises a weak student to improve the student's capabilities.    We instead consider the inverted situation, where a weak teacher supervises a strong student with imperfect pseudolabels.     This paradigm was recently brought forth by \citet{burns2023weak} and termed \emph{weak-to-strong generalization}.     We theoretically investigate weak-to-stro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30984<END_URL>

Provably Accurate Shapley Value Estimation via Leverage Score Sampling\nOriginally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to attribute model predictions to specific input features. However, computing Shapley values exactly is expensive: for a model with $n$ features, $O(2^n)$ model evaluations are necessary. To address this issue, approximation algorithms are widely used. One of the most popular is the Kernel <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27831<END_URL>

Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning\nConformal prediction provides model-agnostic and distribution-free uncertainty quantification through prediction sets that are guaranteed to include the ground truth with any user-specified probability. Yet, conformal prediction is not reliable under poisoning attacks where adversaries manipulate both training and calibration data, which can significantly alter prediction sets in practice. As a solution, we propose re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28340<END_URL>

Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks\nExplaining Graph Neural Network (XGNN) has gained growing attention to facilitate the trust of using GNNs, which is the mainstream method to learn graph data. Despite their growing attention, Existing XGNNs focus on improving the explanation performance, and its robustness under attacks is largely unexplored. We noticed that an adversary can slightly perturb the graph structure such that the explanation result <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28705<END_URL>

Provably Safeguarding a Classifier from OOD and Adversarial Samples: an Extreme Value Theory Approach\nThis paper introduces a novel method, Sample-efficient Probabilistic Detection using Extreme Value Theory (SPADE),  which transforms a classifier into an abstaining classifier, offering provable protection against out-of-distribution and adversarial samples. The approach is based on a Generalized Extreme Value (GEV) model of the training distribution in the classifier's latent space, enabling t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28554<END_URL>

Provence: efficient and robust context pruning for retrieval-augmented generation\nRetrieval-Augmented Generation improves various aspects of large language models (LLMs) generation,  but suffers from computational overhead caused by long contexts, and the propagation of irrelevant retrieved information into generated responses. Context pruning deals with both aspects, by removing irrelevant parts of retrieved contexts before LLM generation. Existing context pruning approaches are limited, and d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29557<END_URL>

Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning\nLarge language models (LLMs) can prove mathematical theorems formally by generating proof steps (\textit{a.k.a.} tactics) within a proof system. However, the space of possible tactics is vast and complex, while the available training data for formal proofs is limited, posing a significant challenge to LLM-based tactic generation. To address this, we introduce a neuro-symbolic tactic generator that synergizes the mathematic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30331<END_URL>

Proximal Mapping Loss: Understanding Loss Functions in Crowd Counting & Localization\nCrowd counting and localization involves extracting the number and distribution of crowds from images or videos using computer vision techniques. Most counting methods are based on density regression and are based on an ``intersection'' hypothesis, *i.e.*, one pixel is influenced by multiple points in ground truth, which is inconsistent with reality since one pixel would not contain two objects. This paper prop<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30798<END_URL>

Proxy Denoising for Source-Free Domain Adaptation\nSource-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain with no access to the source data. Inspired by the success of large Vision-Language (ViL) models in many applications, the latest research has validated ViL's benefit for SFDA by using their predictions as pseudo supervision. However, we observe that ViL's supervision could be noisy and inaccurate at an unknown rate, potentially introducin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30349<END_URL>

PseDet: Revisiting the Power of Pseudo Label in Incremental Object Detection\nIncremental Objection Detection (IOD) facilitates the expansion of the usage scope of object detectors without forgetting previously acquired knowledge. Current approaches mostly adopt response-level knowledge distillation to overcome forgetting issues, by conducting implicit memory replay from the teacher model on new training data. However, this indirect learning paradigm does not fully leverage the knowledge generat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30145<END_URL>

P-SPIKESSM: HARNESSING PROBABILISTIC SPIKING STATE SPACE MODELS FOR LONG-RANGE DEPENDENCY TASKS\nSpiking neural networks (SNNs) are posited as a computationally efficient and biologically plausible alternative to conventional neural architectures, with their core computational framework primarily using the leaky integrate-and-fire (LIF) neuron model. However, the limited hidden state representation of LIF neurons, characterized by a scalar membrane potential, and sequential spike generation proc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29587<END_URL>

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount\nIn object detection, the number of instances is commonly used to determine whether a dataset follows a long-tailed distribution, implicitly assuming that the model will perform poorly on categories with fewer instances. This assumption has led to extensive research on category bias in datasets with imbalanced instance distributions. However, even in datasets with relatively balanced instance cou<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29994<END_URL>

Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection\nIn the open world, detecting out-of-distribution (OOD) data, whose labels are disjoint with those of in-distribution (ID) samples, is important for reliable deep neural networks (DNNs). To achieve better detection performance, one type of approach proposes to fine-tune the model with auxiliary OOD datasets to amplify the difference between ID and OOD data through a separation loss defined on model outputs. Ho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28462<END_URL>

Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling, and Zero-Shot Transfer\nConstructing transferable descriptors for conformation representation of molecular and biological systems has been a long-standing challenge in drug discovery, learning-based molecular dynamics, and protein mechanism analysis. Geometric graph neural networks (Geom-GNNs) with all-atom information have transformed atomistic simulations by serving as a general learnable geometric descripto<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31010<END_URL>

PuzzleFusion++: Auto-agglomerative 3D Fracture Assembly by Denoise and Verify\nThis paper proposes a novel “auto-agglomerative” 3D fracture assembly method, PuzzleFusion++, resembling how humans solve challenging spatial puzzles. Starting from individual fragments, the approach 1) aligns and merges fragments into larger groups akin to agglomerative clustering and 2) repeats the process iteratively in completing the assembly akin to auto-regressive methods. Concretely, a diffusion model denoises <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30838<END_URL>

PvNeXt: Rethinking Network Design and Temporal Motion for Point Cloud Video Recognition\nPoint cloud video perception has become an essential task for the realm of 3D vision. Current 4D representation learning techniques typically engage in iterative processing coupled with dense query operations. Although effective in capturing temporal features, this approach leads to substantial computational redundancy. In this work, we propose a framework, named as PvNeXt, for effective yet efficient point <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29195<END_URL>

PWM: Policy Learning with Multi-Task World Models\nReinforcement Learning (RL) has made significant strides in complex tasks but struggles in multi-task settings with different embodiments. World models methods offer scalability by learning a simulation of the environment, but often rely on inefficient gradient-free optimization methods for policy extraction. In contrast, gradient-based methods exhibit lower variance but fail to handle discontinuities. Our work reveals that well-regularized worl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28766<END_URL>

Pyramidal Flow Matching for Efficient Video Generative Modeling\nVideo generation requires modeling a vast spatiotemporal space, which demands significant computational resources and data usage. To reduce the complexity, the prevailing approaches employ a cascaded architecture to avoid direct training with full resolution latent. Despite reducing computational demands, the separate optimization of each sub-stage hinders knowledge sharing and sacrifices flexibility. This work introduces a unified<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30909<END_URL>

Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting Mitigation\nLarge Language Models (LLMs), trained on a large amount of corpus, have demonstrated remarkable abilities. However, it may not be sufficient to directly apply open-source LLMs like Llama to certain real-world scenarios, since most of them are trained for \emph{general} purposes. Thus, the demands for customizing publicly available LLMs emerge, but are currently under-studied. In this work, we consider customiz<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29368<END_URL>

QERA: an Analytical Framework for Quantization Error Reconstruction\nThe growing number of parameters and computational demands of large language models (LLMs) present significant challenges for their efficient deployment.Recently, there is an increasing interest in quantizing weights to extremely low precision while offsetting the resulting error with low-rank, high-precision error reconstruction terms.The combination of quantization and low-rank approximation is now popular in both adapter-bas<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30014<END_URL>

Qihoo-T2X: An Efficient Proxy-Tokenized Diffusion Transformer for Text-to-Any-Task\nThe global self-attention mechanism in diffusion transformers involves redundant computation due to the sparse and redundant nature of visual information, and the attention map of tokens within a spatial window shows significant similarity. To address this redundancy, we propose the Proxy-Tokenized Diffusion Transformer (PT-DiT), which employs sparse representative token attention (where the number of representat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28517<END_URL>

Qinco2: Vector Compression and Search with Improved  Implicit Neural Codebooks\nVector quantization is a fundamental technique for compression and large-scale nearest neighbor search. For high-accuracy operating points, multi-codebook quantization associates  data vectors with one element from each of multiple codebooks. An example is residual quantization (RQ), which iteratively quantizes the residual error of previous steps. Dependencies between the different parts of the code are, however, ig<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31099<END_URL>

QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing\nMulti-task reinforcement learning (MTRL) aims to learn several tasks simultaneously for better sample efficiency than learning them separately. Traditional methods achieve this by sharing parameters or relabeling data between tasks.  In this work, we introduce a new framework for sharing behavioral policies across tasks, which can be used in addition to existing MTRL methods. The key idea is to improve each task's off-policy data<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29165<END_URL>

qNBO: quasi-Newton Meets Bilevel Optimization\nBilevel optimization, which addresses challenges in hierarchical learning tasks, has gained significant interest in machine learning. Implementing gradient descent for bilevel optimization presents computational hurdles, notably the need to compute the exact lower-level solution and the inverse Hessian of the lower-level objective. While these two aspects are inherently connected, existing methods typically handle them separately by solving the lowe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30570<END_URL>

QPM: Discrete Optimization for Globally Interpretable Image Classification\nUnderstanding the classifications of deep neural networks, e.g. used in safety-critical situations, is becoming increasingly important. While recent models can locally explain a single decision, to provide a faithful global explanation about an accurate model’s general behavior is a more challenging open task. Towards that goal, we introduce the Quadratic Programming Enhanced Model (QPM), which learns globally interpreta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30260<END_URL>

QP-SNN: Quantized and Pruned Spiking Neural Networks\nBrain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to encode information and operate in an asynchronous event-driven manner, offering a highly energy-efficient paradigm for machine intelligence. However, the current SNN community focuses primarily on performance improvement by developing large-scale models, which limits the applicability of SNNs in resource-limited edge devices. In this paper, we propose a hardware-friendly <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29921<END_URL>

Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning\nValue-based reinforcement learning (RL) can in principle learn effective policies for a wide range of multi-turn problems, from games to dialogue to robotic control, including via offline RL from static previously collected datasets. However, despite the widespread use of policy gradient methods to train large language models for single turn tasks (e.g., question answering), value-based methods for multi-turn RL in an off-policy o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27935<END_URL>

QuaDiM: A Conditional Diffusion Model For Quantum State Property Estimation\nQuantum state property estimation (QPE) is a fundamental challenge in quantum many-body problems in physics and chemistry, involving the prediction of characteristics such as correlation and entanglement entropy through statistical analysis of quantum measurement data. Recent advances in deep learning have provided powerful solutions, predominantly using auto-regressive models. These models generally assume an intrinsic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29772<END_URL>

Quality Measures for Dynamic Graph Generative Models\nDeep generative models have recently achieved significant success in modeling graph data, including dynamic graphs, where topology and features evolve over time. However, unlike in vision and language domains, evaluating generative models for dynamic graphs is challenging due to the difficulty of visualizing their output, making quantitative metrics essential. In this work, we develop a new quality metric specifically for evaluating generativ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30758<END_URL>

Quamba: A Post-Training Quantization Recipe for Selective State Space Models\nState Space Models (SSMs) have emerged as an appealing alternative to Transformers for large language models, achieving state-of-the-art accuracy with constant memory complexity which allows for holding longer context lengths than attention-based networks. The superior computational efficiency of SSMs in long sequence modeling positions them favorably over Transformers in many scenarios. However, improving the efficien<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28449<END_URL>

Quantifying Generalization Complexity for Large Language Models\nWhile large language models (LLMs) have shown exceptional capabilities in understanding complex queries and performing sophisticated tasks, their generalization abilities are often deeply entangled with  memorization, necessitating more precise evaluation. To address this challenge, we introduce Scylla, a dynamic evaluation framework that quantitatively measures the generalization abilities of LLMs. Scylla disentangles generalizati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28616<END_URL>

Quantifying Past Error Matters: Conformal Inference for Time Series\nUncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data. Conformal prediction provides a pivotal and flexible instrument for assessing the uncertainty of machine learning models through prediction sets. Recently, a series of online conformal inference methods updated thresholds of prediction sets by performing online gradient descent on a seque<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29659<END_URL>

Quantify Uncertainty and Hallucination in Foundation Models: The Next Frontier in Reliable AI\n*How can we trust large language models (LLMs) when they generate text with confidence, but sometimes hallucinate or fail to recognize their own limitations*? As foundation models like LLMs and multimodal systems become pervasive across high-stakes domains—from healthcare and law to autonomous systems—the need for uncertainty quantification (UQ) is more critical than ever. Uncertainty quantification pr<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23965<END_URL>

Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations\nNeural operators serve as universal approximators for general continuous operators. In this paper, we derive the approximation rate of solution operators for the nonlinear parabolic partial differential equations (PDEs), contributing to the quantitative approximation theorem for solution operators of nonlinear PDEs. Our results show that neural operators can efficiently approximate these solution operators without <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27724<END_URL>

Quantitative Certification of Bias in Large Language Models\nLarge Language Models (LLMs) can produce biased responses that can cause representational harms. However, conventional studies are insufficient to thoroughlyevaluate LLM biases across multiple generations for different demographic groups(a.k.a. counterfactual bias), as they do not scale to large number of inputs and do notprovide guarantees. Therefore, we propose the first framework, QCB (QuantitativeCertification of Bias) that certifi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30226<END_URL>

Quantized Spike-driven Transformer\nSpiking neural networks (SNNs) are emerging as a promising energy-efficient alternative to traditional artificial neural networks (ANNs) due to their spike-driven paradigm.However, recent research in the SNN domain has mainly focused on enhancing accuracy by designing large-scale Transformer structures, which typically rely on substantial computational resources, limiting their deployment on resource-constrained devices.To overcome this challenge, we propose a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30954<END_URL>

Quantum (Inspired)  $D^2$-sampling with Applications\n$D^2$-sampling is a fundamental component of sampling-based clustering algorithms such as $k$-means++. Given a dataset  $V \subset \mathbb{R}^d$ with $N$ points and a center set $C \subset \mathbb{R}^d$, $D^2$-sampling refers to picking a point from $V$ where the sampling probability of a point is proportional to its squared distance from the nearest center in $C$.The popular $k$-means++ algorithm is simply a $k$-round $D^2$-sampling process,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28068<END_URL>

Quantum-PEFT: Ultra parameter-efficient fine-tuning\nThis paper introduces Quantum-PEFT that leverages quantum computations for parameter-efficient fine-tuning (PEFT). Unlike other additive PEFT methods, such as low-rank adaptation (LoRA), Quantum-PEFT exploits an underlying full-rank yet surprisingly parameter efficient _quantum unitary parameterization_. With the use of Pauli parameterization, the number of trainable parameters grows only logarithmically with the ambient dimension, as opposed <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28975<END_URL>

Query-based Knowledge Transfer for Heterogeneous Learning Environments\nDecentralized collaborative learning under data heterogeneity and privacy constraints has rapidly advanced. However, existing solutions like federated learning,  ensembles, and transfer learning,  often fail to adequately serve the unique needs of clients, especially when local data representation is limited.  To address this issue, we propose a novel framework called Query-based Knowledge Transfer (QKT) that enables tailore<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29312<END_URL>

Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Model\nRecent advancements in large language models (LLMs) have highlighted the importance of extending context lengths for handling complex tasks. While traditional methods for training on long contexts often use filtered long documents, these approaches lead to domain imbalances, limiting model performance. To address this, techniques like random document concatenation (Standard) and similarity-based method<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28141<END_URL>

R2Det: Exploring Relaxed Rotation Equivariance in 2D Object Detection\nGroup Equivariant Convolution (GConv) empowers models to capture equivariant features and explore underlying symmetries in data, improving performance. However, real-world scenarios often deviate from ideal symmetric systems caused by physical permutation, characterized by non-trivial actions of a symmetry group, resulting in asymmetries that affect the outputs, a phenomenon known as symmetry breaking. Traditional GConv-based<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30402<END_URL>

Radar: Fast Long-Context Decoding for Any Transformer\nTransformer models have demonstrated exceptional performance across a wide range of applications. Though forming the foundation of Transformer models, the dot-product attention does not scale well to long-context data since its time requirement grows quadratically with context length. In this work, we propose Radar, a training-free approach that accelerates inference by dynamically searching for the most important context tokens. For any pre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29218<END_URL>

RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards\nRetrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) by retrieving knowledge from external resources. To adapt LLMs for RAG pipelines, current approaches use instruction tuning to optimize LLMs, improving their ability to utilize retrieved knowledge. This supervised fine-tuning (SFT) approach focuses on equipping LLMs to handle diverse RA<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29729<END_URL>

RAG-SR: Retrieval-Augmented Generation for Neural Symbolic Regression\nSymbolic regression is a key task in machine learning, aiming to discover mathematical expressions that best describe a dataset. While deep learning has increased interest in using neural networks for symbolic regression, many existing approaches rely on pre-trained models. These models require significant computational resources and struggle with regression tasks involving unseen functions and variables. A pre-training-free <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29868<END_URL>

RainbowPO: A Unified Framework for Combining Improvements in Preference Optimization\nRecently, numerous preference optimization algorithms have been introduced as extensions to the Direct Preference Optimization (DPO) family. While these methods have successfully aligned models with human preferences, there is a lack of understanding regarding the contributions of their additional components. Moreover, fair and consistent comparisons are scarce, making it difficult to discern which components g<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28018<END_URL>

RandLoRA: Full rank parameter-efficient fine-tuning of large models\nLow-Rank Adaptation (LoRA) and its variants have shown impressive results in reducing the number of trainable parameters and memory requirements of large transformer networks while maintaining fine-tuning performance. However, the low-rank nature of the weight update inherently limits the representation power of the fine-tuned model, potentially compromising performance on complex tasks.This raises a critical question: when a p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30212<END_URL>

Random Is All You Need: Random Noise Injection on Feature Statistics for Generalizable Deep Image Denoising\nRecent advancements in generalizable deep image denoising have catalyzed the development of robust noise-handling models. The current state-of-the-art, Masked Training (MT), constructs a masked swinir model which is trained exclusively on Gaussian noise ($\sigma$=15) but can achieve commendable denoising performance across various noise types (*i.e.* speckle noise, poisson noise). However<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27690<END_URL>

Random-Set Neural Networks\nMachine learning is increasingly deployed in safety-critical domains where erroneous predictions may lead to potentially catastrophic consequences, highlighting the need for learning systems to be aware of how confident they are in their own predictions: in other words, 'to know when they do not know’. In this paper, we propose a novel Random-Set Neural Network (RS-NN) approach to classification which predicts *belief functions* (rather than classical probability vect<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28286<END_URL>

Ranking-aware adapter for text-driven image ordering with CLIP\nRecent advances in vision-language models (VLMs) have made significant progress in downstream tasks that require quantitative concepts such as facial age estimation and image quality assessment, enabling VLMs to explore applications like image ranking and retrieval. However, existing studies typically focus on the reasoning based on a single image and heavily depend on text prompting, limiting their ability to learn comprehensive un<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30043<END_URL>

RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank\nNumerous works propose post-hoc, model-agnostic explanations for learning to rank, focusing on ordering entities by their relevance to a query through feature attribution methods. However, these attributions often weakly correlate or contradict each other, confusing end users.  We adopt an axiomatic game-theoretic approach, popular in the feature attribution community, to identify a set of fundamental axioms that every rank<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31045<END_URL>

Rapidly Adapting Policies to the Real-World via Simulation-Guided Fine-Tuning\nRobot learning requires a considerable amount of data to realize the promise of generalization. However, it can be challenging to actually collect the magnitude of high-quality data necessary for generalization entirely in the real world. Simulation can serve as a source of plentiful data, wherein techniques such as reinforcement learning can obtain broad coverage over states and actions.  However, high-fidelity physi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29282<END_URL>

RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models\nDifferentially private diffusion models (DPDMs) harness the remarkable generative capabilities of diffusion models while enforcing differential privacy (DP) for sensitive data. However, existing DPDM training approaches often suffer from significant utility loss, large memory footprint, and expensive inference cost, impeding their practical uses.  To overcome such limitations, we present RAPID: Retrieval Augmented Pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28006<END_URL>

Rapid Selection and Ordering of In-Context Demonstrations via Prompt Embedding Clustering\nWhile Large Language Models (LLMs) excel at in-context learning (ICL) using just a few demonstrations, their performances are sensitive to demonstration orders. The reasons behind this sensitivity remain poorly understood. In this paper, we investigate the prompt embedding space to bridge the gap between the order sensitivity of ICL with inner workings of decoder-only LLMs, uncovering the clustering proper<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31211<END_URL>

Rare event modeling with self-regularized normalizing flows: what can we learn from a single failure?\nIncreased deployment of autonomous systems in fields like transportation and robotics have seen a corresponding increase in safety-critical failures. These failures can be difficult to model and debug due to the relative lack of data: compared to tens of thousands of examples from normal operations, we may have only seconds of data leading up to the failure. This scarcity makes it challenging t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28821<END_URL>

Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance\nState-of-the-art text-to-image (T2I) diffusion models often struggle to generate rare compositions of concepts, e.g., objects with unusual attributes. In this paper, we show that the compositional generation power of diffusion models on such rare concepts can be significantly enhanced by the Large Language Model (LLM) guidance. We start with empirical and theoretical analysis, demon<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30557<END_URL>

RaSA: Rank-Sharing Low-Rank Adaptation\nLow-rank adaptation (LoRA) has been prominently employed for parameter-efficient fine-tuning of large language models (LLMs). However, the limited expressive capacity of LoRA, stemming from the low-rank constraint, has been recognized as a bottleneck, particularly in rigorous tasks like code generation and mathematical reasoning. To address this limitation, we introduce Rank-Sharing Low-Rank Adaptation (RaSA), an innovative extension that enhances the expr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30271<END_URL>

Rational Decision-Making Agent with Learning Internal Utility Judgment\nWith remarkable advancements, large language models (LLMs) have attracted significant efforts to develop LLM-based agents capable of executing intricate multi-step decision-making tasks. Existing approaches predominantly build upon the external performance measure to guide the decision-making process but the reliance on the external performance measure as prior is problematic in real-world scenarios, where such prior may be <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30292<END_URL>

Rationalizing and Augmenting Dynamic Graph Neural Networks\nGraph data augmentation (GDA) has shown significant promise in enhancing the performance, generalization, and robustness of graph neural networks (GNNs). However, contemporary methodologies are often limited to static graphs, whose applicability on dynamic graphs—more prevalent in real-world applications—remains unexamined. In this paper, we empirically highlight the challenges faced by static GDA methods when applied to dynamic graphs,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28032<END_URL>

RA-TTA: Retrieval-Augmented Test-Time Adaptation for Vision-Language Models\nVision-language models (VLMs) are known to be susceptible to distribution shifts between pre-training data and test data, and test-time adaptation (TTA) methods for VLMs have been proposed to mitigate the detrimental impact of the distribution shifts. However, the existing methods solely rely on the internal knowledge encoded within the parameters, which are constrained to pre-training data. To complement the limitation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29434<END_URL>

RazorAttention: Efficient KV Cache Compression Through Retrieval Heads\nThe memory and computational demands of Key-Value (KV) cache present significant challenges for deploying long-context language models. Previous approaches attempt to mitigate this issue by selectively dropping tokens, which irreversibly erases critical information that might be needed for future queries. In this paper, we propose a novel compression technique for KV cache that preserves all token information. Our investigat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28028<END_URL>

RB-Modulation: Training-Free Personalization using Stochastic Optimal Control\nWe propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models.Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style images, and (c) effective composition of style and content. RB-Modu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29091<END_URL>

RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation\nBimanual manipulation is essential in robotics, yet developing foundation models is extremely challenging due to the inherent complexity of coordinating two robot arms (leading to multi-modal action distributions) and the scarcity of training data. In this paper, we present the Robotics Diffusion Transformer (RDT), a pioneering diffusion foundation model for bimanual manipulation. It is built on scalable Diffusion Transformers (DiTs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27746<END_URL>

Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model\nElectrocardiogram (ECG) is essential for the clinical diagnosis of arrhythmias and other heart diseases, but deep learning methods based on ECG often face limitations due to the need for high-quality annotations. Although previous ECG self-supervised learning (eSSL) methods have made significant progress in representation learning from unannotated ECG data, they typically treat ECG signals as ordinary time-<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30897<END_URL>

Ready-to-React: Online Reaction Policy for Two-Character Interaction Generation\nThis paper addresses the task of generating two-character online interactions. Previously, two main settings existed for two-character interaction generation: (1) generating one's motions based on the counterpart's complete motion sequence, and (2) jointly generating two-character motions based on specific conditions. We argue that these settings fail to model the process of real-life two-character interactions, whe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28451<END_URL>

Real2Code: Reconstruct Articulated Objects via Code Generation\nWe present Real2Code, a novel approach to reconstructing articulated objects via code generation. Given visual observations of an object, we first reconstruct its part geometry using image segmentation and shape completion. We represent these object parts with oriented bounding boxes, from which a fine-tuned large language model (LLM) predicts joint articulation as code. By leveraging pre-trained vision and language models, our appr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30528<END_URL>

Re-Aligning Language to Visual Objects with an Agentic Workflow\nLanguage-based object detection (LOD) aims to align visual objects with language expressions. A large amount of paired data is utilized to improve LOD model generalizations. During the training process, recent studies leverage vision-language models (VLMs) to automatically generate human-like expressions for visual objects, facilitating training data scaling up. In this process, we observe that VLM hallucinations bring inaccurate o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29934<END_URL>

Real-time design of architectural structures with differentiable mechanics and neural networks\nDesigning mechanically efficient geometry for architectural structures like shells, towers, and bridges is an expensive iterative process.Existing techniques for solving such inverse mechanical problems rely on traditional direct optimization methods, which are slow and computationally expensive, limiting iteration speed and design exploration.Neural networks would seem to offer a solution, via data-d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29515<END_URL>

Realtime Reinforcement Learning: Towards Rapid Asynchronous Deployment of Large Models\nRealtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectively minimize long-term regret. However, recent advances in machine learning involve larger neural networks with longer inference times, raising questions about their applicability in realtime systems where quick reactions are crucial. We present an analysis of lower bounds <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28874<END_URL>

Real-Time Video Generation with Pyramid Attention Broadcast\nWe present Pyramid Attention Broadcast (PAB), a real-time, high quality and training-free approach for DiT-based video generation. Our method is founded on the observation that attention difference in the diffusion process exhibits a U-shaped pattern, indicating significant redundancy. We mitigate this by broadcasting attention outputs to subsequent steps in a pyramid style. It applies different broadcast strategies to each attention b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28773<END_URL>

Reasoning Elicitation in Language Models via Counterfactual Feedback\nDespite the increasing effectiveness of language models, their reasoning capabilities remain underdeveloped. In particular, causal reasoning through counterfactual question answering is lacking. This work aims to bridge this gap. We first derive novel metrics that balance accuracy in factual and counterfactual questions, capturing a more complete view of the reasoning abilities of language models than traditional factual-only <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29410<END_URL>

Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval\nLarge language models (LLMs) have demonstrated significant potential in clinical decision support. Yet LLMs still suffer from hallucinations and lack fine-grained contextual medical knowledge, limiting their high-stake healthcare applications such as clinical diagnosis. Traditional retrieval-augmented generation (RAG) methods attempt to address these limitations but frequently retrieve sparse or irrelevant inform<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30752<END_URL>

Reasoning of Large Language Models over Knowledge Graphs with Super-Relations\nWhile large language models (LLMs) have made significant progress in processing and reasoning over knowledge graphs, current methods suffer from a high non-retrieval rate. This limitation reduces the accuracy of answering questions based on these graphs. Our analysis reveals that the combination of greedy search and forward reasoning is a major contributor to this issue. To overcome these challenges, we introduce the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28196<END_URL>

Reassessing EMNLP 2024’s Best Paper: Does Divergence-Based Calibration for MIAs Hold Up?\nAt EMNLP 2024, the [Best Paper Award](https://x.com/emnlpmeeting/status/1857176180128198695/photo/1) was given to **"Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method"**. The paper addresses Membership Inference Attacks (MIAs), a key issue in machine learning related to privacy. The authors propose a new calibration method and introduce **PatentMIA**, a benchmark ut<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31327<END_URL>

Reassessing How to Compare and Improve the Calibration of Machine Learning Models\nA machine learning model is calibrated if its predicted probability for an outcome matches the observed frequency for that outcome conditional on the model prediction. This property has become increasingly important as the impact of machine learning models has continued to spread to various domains. As a result, there are now a dizzying number of recent papers on measuring and improving the calibration of (specifi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29330<END_URL>

ReAttention: Training-Free Infinite Context with Finite Attention Scope\nThe long-context capability of the Large Language Models (LLM) has made significant breakthroughs, but the maximum supported context length remains a critical bottleneck limiting their practical applications. The constraint of context length in LLMs arises from the self-attention mechanism, which cannot effectively and efficiently capture the semantic relationships within infinitely long contexts via the limited pre-trained<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30071<END_URL>

REBIND: Enhancing Ground-state Molecular Conformation Prediction via Force-Based Graph Rewiring\nPredicting the ground-state 3D molecular conformations from 2D molecular graphs is critical in computational chemistry due to its profound impact on molecular properties. Deep learning (DL) approaches have recently emerged as promising alternatives to computationally-heavy classical methods such as density functional theory (DFT). However, we discover that existing DL methods inadequately model inter<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29367<END_URL>

RECAST: Reparameterized, Compact weight Adaptation for Sequential Tasks\nIncremental learning aims to adapt to new sets of categories over time with minimal computational overhead. Prior work often addresses this task by training efficient task-specific adaptors that modify frozen layer weights or features to capture relevant information without affecting predictions on any previously learned categories. While these adaptors are generally more efficient than finetuning the entire network, they s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30133<END_URL>

RecDreamer: Consistent Text-to-3D Generation via Uniform Score Distillation\nCurrent text-to-3D generation methods based on score distillation often suffer from geometric inconsistencies, leading to repeated patterns across different poses of 3D assets. This issue, known as the Multi-Face Janus problem, arises because existing methods struggle to maintain consistency across varying poses and are biased toward a canonical pose. While recent work has improved pose control and approximation, these <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29139<END_URL>

RecFlow: An Industrial Full Flow Recommendation Dataset\nIndustrial recommendation systems (RS) rely on the multi-stage pipeline to balance effectiveness and efficiency when delivering items from a vast corpus to users. Existing RS benchmark datasets primarily focus on the exposure space, where novel RS algorithms are trained and evaluated. However, when these algorithms transition to real-world industrial RS, they face a critical challenge: handling unexposed items—a significantly larger space <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27909<END_URL>

Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon\nMemorization in language models is typically treated as a homogenous phenomenon, neglecting the specifics of the memorized data. We instead model memorization as the effect of a set of complex factors that describe each sample and relate it to the model and corpus. To build intuition around these factors, we break memorization down into a taxonomy: recitation of highly duplicated sequences, reconstruction of inhere<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31090<END_URL>

ReCogLab: a framework testing relational reasoning, cognitive hypotheses on LLMs\nA fundamental part of human cognition is the ability to not only recall memories, but to reason and manipulate information from them. In cognitive science and psychology, this is termed relational reasoning or relational memory and a number of cognitive effects and biases have been observed and proposed. Some of these effects include \textit{congruence}, \textit{the symbolic distance effect} and \textit{transitive <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27731<END_URL>

Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data\nWe present RASO, a foundation model designed to Recognize Any Surgical Object, offering robust open-set recognition capabilities across a broad range of surgical procedures and object classes, in both surgical images and videos. RASO leverages a novel weakly-supervised learning framework that generates tag-image-text pairs automatically from large-scale unannotated surgical lecture videos, significantly reducing the n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28666<END_URL>

Reconciling Model Multiplicity for Downstream Decision Making\nWe consider the problem of \emph{model multiplicity} in downstream decision-making, a setting where two predictive models of equivalent accuracy cannot agree on what action to take for a downstream decision-making problem. Prior work attempts to address model multiplicity by resolving prediction disagreement between models. However, we show that even when the two predictive models approximately agree on their individual predictions a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27942<END_URL>

Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs\nAs Graph Neural Networks (GNNs) become more pervasive, it becomes paramount to build reliable tools for explaining their predictions.A core desideratum is that explanations are *faithful*, i.e., that they portray an accurate picture of the GNN's reasoning process.However, a number of different faithfulness metrics exist, begging the question of what is faithfulness exactly and how to achieve it.We make three key c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28564<END_URL>

Reconstruction-Guided Policy: Enhancing Decision-Making through Agent-Wise State Consistency\nAn important challenge in multi-agent reinforcement learning is partial observability, where agents cannot access the global state of the environment during execution and can only receive observations within their field of view. To address this issue, previous works typically use the dimensional-wise state, which is obtained by applying MLP or dimensional-based attention on the global state, for decisio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29270<END_URL>

Reconstructive Visual Instruction Tuning\nThis paper introduces reconstructive visual instruction tuning (ROSS), a family of Large Multimodal Models (LMMs) that exploit vision-centric supervision signals. In contrast to conventional visual instruction tuning approaches that exclusively supervise text outputs, ROSS prompts LMMs to supervise visual outputs via reconstructing input images. By doing so, it capitalizes on the inherent richness and detail present within input images themselves, which <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30741<END_URL>

Recovering Manifold Structure Using Ollivier Ricci Curvature\nWe introduce ORC-ManL, a new algorithm to prune spurious edges from nearest neighbor graphs using a criterion based on Ollivier-Ricci curvature and estimated metric distortion. Our motivation comes from manifold learning: we show that when the data generating the nearest-neighbor graph consists of noisy samples from a low-dimensional manifold, edges that shortcut through the ambient space have more negative Ollivier-Ricci curvature th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29161<END_URL>

Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates\nCausal discovery with latent variables is an important and challenging problem. To identify latent variables and infer their causal relations, most existing works rely on the assumption that latent variables have pure children. Considering that this assumption is potentially restrictive in practice and not strictly necessary in theory, by introducing the concept of homologous surrogate, this paper eliminates the need <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28885<END_URL>

Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow\nDiffusion models have greatly improved visual generation but are hindered by slow generation speed due to the computationally intensive nature of solving generative ODEs. Rectified flow, a widely recognized solution, improves generation speed by straightening the ODE path. Its key components include: 1) using the diffusion form of flow-matching, 2) employing $\boldsymbol v$-prediction, and 3) performing rectification (a.k.a. r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28415<END_URL>

ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability\nRetrieval-Augmented Generation (RAG) models are designed to incorporate external knowledge, reducing hallucinations caused by insufficient parametric (internal) knowledge. However, even with accurate and relevant retrieved content, RAG models can still produce hallucinations by generating outputs that conflict with the retrieved information. Detecting such hallucinations requires disentangling how<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27644<END_URL>

Redefining the task of Bioactivity Prediction\nSmall molecules are vital to modern medicine, and accurately predicting their bioactivity against protein targets is crucial for therapeutic discovery and development. However, current machine learning models often rely on spurious features, leading to biased outcomes. Notably, a simple pocket-only baseline can achieve results comparable to, and sometimes better than, more complex models that incorporate both the protein pockets and the small molecu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29625<END_URL>

Reducing Hallucinations in Large Vision-Language Models via Latent Space Steering\nHallucination poses a challenge to the deployment of large vision-language models (LVLMs) in applications. Unlike in large language models (LLMs), hallucination in LVLMs often arises from misalignments between visual inputs and textual outputs. This paper investigates the underlying mechanisms of hallucination, focusing on the unique structure of LVLMs that distinguishes them from LLMs. We identify that hallucinat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30013<END_URL>

REEF: Representation Encoding Fingerprints for Large Language Models\nProtecting the intellectual property of open-source Large Language Models (LLMs) is very important, because training LLMs costs extensive computational resources and data. Therefore, model owners and third parties need to identify whether a suspect model is a subsequent development of the victim model. To this end, we propose a training-free REEF to identify the relationship between the suspect and victim models from the persp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29580<END_URL>

Re-evaluating Open-ended Evaluation of Large Language Models\nEvaluation has traditionally focused on ranking candidates for a specific skill. Modern generalist models, such as Large Language Models (LLMs), decidedly outpace this paradigm. Open-ended evaluation systems, where candidate models are compared on user-submitted prompts, have emerged as a popular solution. Despite their many advantages, we show that the current Elo-based rating systems can be susceptible to and even reinforce biases i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28571<END_URL>

Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model\nSemi-supervised learning (SSL) effectively leverages unlabeled data and has been proven successful across various fields. Current safe SSL methods believe that unseen classes in unlabeled data harm the performance of SSL models. However, previous methods for assessing the impact of unseen classes on SSL model performance are flawed. They fix the size of the unlabeled dataset and adjust the proportion of un<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29363<END_URL>

Reexamining the Aleatoric and Epistemic Uncertainty Dichotomy\nWhen discussing uncertainty estimates for the safe deployment of AI agents in the real world, the field typically distinguishes between aleatoric and epistemic uncertainty. This dichotomy may seem intuitive and well-defined at first glance, but this blog post reviews examples, quantitative findings, and theoretical arguments that reveal that popular definitions of aleatoric and epistemic uncertainties directly contradict each other a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31334<END_URL>

RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code\nRecent advances in language model (LM) agents and function calling have enabled autonomous, feedback-driven systems to solve problems across various digital domains. To better understand the unique limitations of LM agents, we introduce RefactorBench, a benchmark consisting of 100 large handcrafted multi-file refactoring tasks in popular open-source repositories. Solving tasks within RefactorBench requires thorough exp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29864<END_URL>

Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment\nPersonalized image generation has emerged from the recent advancements in generative models. However, these generated personalized images often suffer from localized artifacts such as incorrect logos, reducing fidelity and fine-grained identity details of the generated results. Furthermore, there is little prior work tackling this problem. To help improve these identity details in the personalized image generation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30475<END_URL>

REFINE: Inversion-Free Backdoor Defense via Model Reprogramming\nBackdoor attacks on deep neural networks (DNNs) have emerged as a significant security threat, allowing adversaries to implant hidden malicious behaviors during the model training phase. Pre-processing-based defense, which is one of the most important defense paradigms, typically focuses on input transformations or backdoor trigger inversion (BTI) to deactivate or eliminate embedded backdoor triggers during the inference process. H<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31020<END_URL>

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning\nHow to alleviate the hallucinations of Large Language Models (LLMs) has always been the fundamental goal pursued by the LLMs research community. Looking through numerous hallucination-related studies, a mainstream category of methods is to reduce hallucinations by optimizing the knowledge representation of LLMs to change their output. Considering that the core focus of these works is the knowledge acquired by models, an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30207<END_URL>

Refining CLIP's Spatial Awareness: A Visual-Centric Perspective\nContrastive Language-Image Pre-training (CLIP) excels in global alignment with language but exhibits limited sensitivity to spatial information, leading to strong performance in zero-shot classification tasks but underperformance in tasks requiring precise spatial understanding. Recent approaches have introduced Region-Language Alignment (RLA) to enhance CLIP's performance in dense multimodal tasks by aligning regional visual repre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31093<END_URL>

Reflective Gaussian Splatting\nNovel view synthesis has experienced significant advancements owing to increasingly capable NeRF- and 3DGS-based methods. However, reflective object reconstruction remains challenging, lacking a proper solution to achieve real-time, high-quality rendering while accommodating inter-reflection. To fill this gap, we introduce a Reflective Gaussian splatting (Ref-Gaussian) framework characterized with two components: (I) Physically based deferred rendering that empower<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27783<END_URL>

Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation\nWith the recent emergence of foundation models trained on internet-scale data and demonstrating remarkable generalization capabilities, such foundation models have become more widely adopted, leading to an expanding range of application domains. Despite this rapid proliferation, the trustworthiness of foundation models remains underexplored. Specifically, the out-of-distribution detect<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29663<END_URL>

Reframing Structure-Based Drug Design Model Evaluation via Metrics Correlated to Practical Needs\nRecent advances in structure-based drug design (SBDD) have produced surprising results, with models often generating molecules that achieve better Vina docking scores than actual ligands. However, these results are frequently overly optimistic due to the limitations of docking score accuracy and the challenges of wet-lab validation. While generated molecules may demonstrate high QED (drug-likeness) <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29635<END_URL>

ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement\nPost-training Large Language Models (LLMs) with explicit reasoning trajectories can enhance their reasoning abilities. However, acquiring such high-quality trajectory data typically demands meticulous supervision from humans or superior models, which can be either expensive or license-constrained. In this paper, we explore how far an LLM can improve its reasoning by self-synthesizing reasoning paths as training data withou<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29261<END_URL>

ReGen: Generative Robot Simulation via Inverse Design\nSimulation plays a key role in scaling robot learning and validating policies, but constructing simulations remains labor-intensive. In this paper, we introduce ReGen, a generative simulation framework that automates this process using inverse design. Given an agent's behavior (such as a motion trajectory or objective function) and its textual description, we infer the underlying scenarios and environments that could have caused the behavior<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30399<END_URL>

REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments\nDo generalist agents only require large models pre-trained on massive amounts of data to rapidly adapt to new environments? We propose a novel approach to pre-train relatively small models and adapt them to unseen environments via in-context learning, without any finetuning. Our key idea is that retrieval offers a powerful bias for fast adaptation. Indeed, we demonstrate that even a simple retrieval-based<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29847<END_URL>

RegMix: Data Mixture as Regression for Language Model Pre-training\nThe data mixture for large language model pre-training significantly impacts performance, yet how to determine an effective mixture remains unclear. We propose RegMix to automatically identify a high-performing data mixture by formulating it as a regression task. RegMix involves training a set of small models with diverse data mixtures and fitting a regression model to predict their performance given their respective mixtures. W<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30960<END_URL>

Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF\nLarge Language Models (LLMs) have achieved remarkable success at tasks like summarization that involve a *single turn* of interaction. However, they can still struggle with *multi-turn* tasks like dialogue that require long-term planning. Previous works on multi-turn dialogue extend single-turn reinforcement learning from human feedback (RLHF) methods to the multi-turn setting by treating all prior dialogue turns <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29044<END_URL>

Regret Bounds for Episodic Risk-Sensitive Linear Quadratic Regulator\nRisk-sensitive linear quadratic regulator is one of the most fundamental problems in risk-sensitive optimal control. In this paper, we study online adaptive control of risk-sensitive linear quadratic regulator in the finite horizon episodic setting. We propose a simple least-squares greedy algorithm and show that it achieves $\widetilde{\mathcal{O}}(\log N)$ regret under a specific identifiability assumption, where $N$ is the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29427<END_URL>

Regretful Decisions under Label Noise\nMachine learning models are routinely used to support decisions that affect individuals -- be it to screen a patient for a serious illness or to gauge their response to treatment. In these tasks, we are limited to learning models from datasets where the labels are subject to noise. In this work, we study the impact of learning under label noise at the instance level. We introduce a notion of *regret* for this regime, which measures the number of unforeseen <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30842<END_URL>

Regret-Optimal List Replicable Bandit Learning: Matching Upper and Lower Bounds\nThis paper investigates *list replicability* [Dixon et al., 2023] in the context of multi-armed (also linear) bandits (MAB). We define an algorithm $A$ for MAB to be $(\ell,\delta)$-list replicable if with probability at least $1-\delta$, $A$ has at most $\ell$ traces in independent executions even with different random bits, where a trace means sequence of arms played during an execution. For $k$-armed bandits, alt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31254<END_URL>

Regularization by Texts for Latent Diffusion Inverse Solvers\nThe recent development of diffusion models has led to significant progress in solving inverse problems by leveraging these models as powerful generative priors. However, challenges persist due to the ill-posed nature of such problems, often arising from ambiguities in measurements or intrinsic system symmetries. To address this, we introduce a novel latent diffusion inverse solver, regularization by text (TReg), inspired by the human <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29505<END_URL>

Regularizing Energy among Training Samples for Out-of-Distribution Generalization\nThe energy-based model provides a unified framework for various learning models where an energy value is assigned to each configuration of random variables based on probability. Recently, different methods have been proposed to derive an energy value out of the logits of a classifier for out-of-distribution (OOD) detection or OOD generalization. However, these methods mainly focus on the energy difference between <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29985<END_URL>

Regulatory DNA Sequence Design with Reinforcement Learning\nCis-regulatory elements (CREs), such as promoters and enhancers, are relatively short DNA sequences that directly regulate the expression of specific genes. The fitness of CREs, i.e., their functionality to enhance gene expression, highly depend on its nucleotide sequence, especially the composition of some special motifs known as transcription factor binding sites (TFBSs). Designing CREs to optimize their fitness is crucial for therape<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30369<END_URL>

Re-Imagining Multimodal Instruction Tuning: A Representation View\nMultimodal instruction tuning has proven to be an effective strategy for achieving zero-shot generalization by fine-tuning pre-trained Large Multimodal Models (LMMs) with instruction-following data. However, as the scale of LMMs continues to grow, fully fine-tuning these models has become highly parameter-intensive. Although Parameter-Efficient Fine-Tuning (PEFT) methods have been introduced to reduce the number of tunable parame<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27640<END_URL>

Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics\nMany organisms and cell types, from bacteria to cancer cells, exhibit a remarkable ability to adapt to fluctuating environments. Additionally, cells can leverage memory of past environments to better survive previously-encountered stressors. From a control perspective, this adaptability poses significant challenges in driving cell populations toward extinction, and is thus an open question with great clinical signi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28960<END_URL>

Reinforcement Learning from Imperfect Corrective Actions and Proxy Rewards\nIn practice, reinforcement learning (RL) agents are often trained with a possibly imperfect proxy reward function, which may lead to a human-agent alignment issue (i.e., the learned policy either converges to non-optimal performance with low cumulative rewards, or achieves high cumulative rewards but in an undesired manner). To tackle this issue, we consider a framework where a human labeler can provide additional feedba<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30110<END_URL>

Reinforcement learning with combinatorial actions for coupled restless bandits\nReinforcement learning (RL) has increasingly been applied to solve real-world planning problems, with progress in handling large state spaces and time horizons. However, a key bottleneck in many domains is that RL methods cannot accommodate large, combinatorially structured action spaces. In such settings, even representing the set of feasible actions at a single step may require a complex discrete optimization formu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30448<END_URL>

Relation-Aware Diffusion for Heterogeneous Graphs with Partially Observed Features\nDiffusion-based imputation methods, which impute missing features through the iterative propagation of observed features, have shown impressive performance in homogeneous graphs. However, these methods are not directly applicable to heterogeneous graphs, which have multiple types of nodes and edges, due to two key issues: (1) the presence of nodes with undefined features hinders diffusion-based imputation; (2) tr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29544<END_URL>

Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems\nThe fairness of clustering algorithms has gained widespread attention across various areas, including machine learning, In this paper, we study fair $k$-means clustering in Euclidean space.   Given a dataset comprising several groups, the fairness constraint requires that each cluster should contain a proportion of points from each group within specified lower and upper bo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28424<END_URL>

Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA\nLarge language models (LLMs) are expensive to deploy. Parameter sharing offers a possible path towards reducing their size and cost, but its effectiveness in modern LLMs remains fairly limited. In this work, we revisit "layer tying" as form of parameter sharing in Transformers, and introduce novel methods for converting existing LLMs into smaller "Recursive Transformers" that share parameters across layers, with mi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29334<END_URL>

RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data\nWe present RelCon, a novel self-supervised Relative Contrastive learning approach that uses a learnable distance measure in combination with a softened contrastive loss for training an motion foundation model from wearable sensors. The learnable distance measure captures motif similarity and domain-specific semantic information such as rotation invariance. The learned distance provides a measurement of semanti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28603<END_URL>

Release the Powers of Prompt Tuning: Cross-Modality Prompt Transfer\nPrompt Tuning adapts frozen models to new tasks by prepending a few learnable embeddings to the input.However, it struggles with tasks that suffer from data scarcity.To address this, we explore Cross-Modality Prompt Transfer, leveraging prompts pretrained on a data-rich modality to improve performance on data-scarce tasks in another modality.As a pioneering study, we first verify the feasibility of cross-modality prompt transfe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29594<END_URL>

Reliable and Diverse Evaluation of LLM Medical Knowledge Mastery\nMastering medical knowledge is crucial for medical-specific LLMs. However, despite the existence of medical benchmarks like MedQA, a unified framework that fully leverages existing knowledge bases to evaluate LLMs' mastery of medical knowledge is still lacking. In the study, we propose a novel framework PretexEval that dynamically generates reliable and diverse test samples to evaluate LLMs for any given medical knowledge base. We<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29535<END_URL>

RelitLRM: Generative Relightable Radiance for Large Reconstruction Models\nWe propose RelitLRM, a Large Reconstruction Model (LRM) for generating high-quality Gaussian splatting representations of 3D objects under novel illuminations from sparse (4-8) posed images captured under unknown static lighting. Unlike prior inverse rendering methods requiring dense captures and slow optimization, often causing artifacts like incorrect highlights or shadow baking, RelitLRM adopts a feed-forward transform<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31081<END_URL>

ReMatching Dynamic Reconstruction Flow\nReconstructing dynamic scenes from image inputs is a fundamental computervision task with many downstream applications. Despite recent advancements, existingapproaches still struggle to achieve high-quality reconstructions from unseenviewpoints and timestamps. This work introduces the ReMatching framework,designed to improve generalization quality by incorporating deformation priors intodynamic reconstruction models. Our approach advocates for velocity-fie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29079<END_URL>

REMEDY: Recipe Merging Dynamics in Large Vision-Language Models\nModel merging has emerged as a powerful technique for combining task-specific vision models into a unified and multi-functional model. Previous methods represented by task arithmetic, have demonstrated effectiveness and scalability in this domain. When large vision-language models (LVLMs) arise with model size scaling up, this design becomes challenging to fuse different instruction-tuned LVLMs for generalization enhancement. The l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28692<END_URL>

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing\nSparsely activated Mixture-of-Experts (MoE) models are widely adopted to scale up model capacity without increasing the computation budget. However, vanilla TopK routers are trained in a discontinuous, non-differentiable way, limiting their performance and scalability. To address this issue, we propose ReMoE, a fully differentiable MoE architecture that offers a simple yet effective drop-in replacement for the conventional TopK+So<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31028<END_URL>

Remove Symmetries to Control Model Expressivity\nWhen symmetry is present in the loss function, the model is likely to be trapped in a low-capacity state that is sometimes known as a ``collapse." Being trapped in these low-capacity states can be a major obstacle to training across many scenarios where deep learning technology is applied. We first prove two concrete mechanisms through which symmetries lead to reduced capacities and ignored features during training. We then propose a simple and th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30250<END_URL>

ReNovo: Retrieval-Based \emph{De Novo} Mass Spectrometry Peptide Sequencing\nProteomics is the large-scale study of proteins. Tandem mass spectrometry, as the only high-throughput technique for protein sequence identification, plays a pivotal role in proteomics research. One of the long-standing challenges in this field is peptide identification, which entails determining the specific peptide (sequence of amino acids) that corresponds to each observed mass spectrum. The conventional approach inv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27978<END_URL>

Repetition Improves Language Model Embeddings\nBidirectional models are considered essential for strong text embeddings. Recent approaches to adapt autoregressive language models (LMs) into strong text embedding models have largely had the requirement to modify the LM architecture to be bidirectional. We challenge this premise by introducing "echo embeddings" which converts autoregressive LMs into high quality text embedding models without changing the architecture or requiring fine-tuning. By r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30621<END_URL>

RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph\nLarge Language Models (LLMs) excel in code generation yet struggle with modern AI software engineering tasks. Unlike traditional function-level or file-level coding tasks, AI software engineering requires not only basic coding proficiency but also advanced skills in managing and interacting with code repositories. However, existing methods often overlook the need for repository-level code understanding, which is cruci<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28957<END_URL>

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think\nRecent studies have shown that the denoising process in (generative) diffusion models can induce meaningful (discriminative) representations inside the model, though the quality of these representations still lags behind those learned through recent self-supervised learning methods. We argue that one main bottleneck in training large-scale diffusion models for generation lies in effectively learnin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30467<END_URL>

Representational Similarity via Interpretable Visual Concepts\nHow do two deep neural networks differ in how they arrive at a decision? Measuring deep network similarity has been a long-standing open question. Most existing metrics provide a single number to measure the similarity of two networks at a given layer, but give no insight into what makes them similar or dissimilar. We introduce an interpretable representational similarity method (RSVC) to compare two networks. We use RSVC to discover<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28681<END_URL>

Representation Learning for Long Tail Recognition via Feature Space Re-Construction\nDeep learning has achieved significant success on balanced datasets. However, real-world data often exhibit a long-tailed distribution. Empirical results show that long-tailed data skews representations where head classes dominate the feature space. Many methods have been proposed to empirically correct the skewed representations. However, a clear theoretical understanding of the underlying causes and extent of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30248<END_URL>

Representative Guidance: Diffusion Model Sampling with Consistency\nThe diffusion sampling process faces a persistent challenge stemming from its incoherence, attributable to varying noise directions across different time steps. Our Representative Guidance (RepG) offers a new perspective to handle this issue by reformulating the sampling process with a coherent direction towards a representative target.In this formulation, while the classic classifier guidance improves feature discernment by ste<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28815<END_URL>

Repulsive Latent Score Distillation for Solving Inverse Problems\nScore Distillation Sampling (SDS) has been pivotal for leveraging pre-trained diffusion models in downstream tasks such as inverse problems, but it faces two major challenges: $(i)$ mode collapse and $(ii)$ latent space inversion, which become more pronounced in high-dimensional data. To address mode collapse, we introduce a novel variational framework for posterior sampling. Utilizing the Wasserstein gradient flow interpretation <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29081<END_URL>

Repurposing in AI: A Distinct Approach or an Extension of Creative Problem Solving?\nCreativity is defined as the ability to produce novel, useful, and surprising ideas. A sub area of creativity is creative problem solving, the capacity of an agent to discover novel and previously unseen ways to accomplish a task, according to its perspective. While creative problem solving has been extensively studied in AI, the related concept of repurposing - identifying and utilizing existing resources in in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31336<END_URL>

ReSi: A Comprehensive Benchmark for Representational Similarity Measures\nMeasuring the similarity of different representations of neural architectures is a fundamental task and an open research challenge for the machine learning community. This paper presents the first comprehensive benchmark for evaluating representational similarity measures based on well-defined groundings of similarity. The representational similarity (ReSi) benchmark consists of (i) six carefully designed tests for similar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29754<END_URL>

Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs\nResidual connections and normalization layers have become standard design choices for graph neural networks (GNNs), and were proposed as solutions to the mitigate the oversmoothing problem in GNNs. However, how exactly these methods help alleviate the oversmoothing problem from a theoretical perspective is not well understood. In this work, we provide a formal and precise characterization of (linearized) GNNs with<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28713<END_URL>

Residual Deep Gaussian Processes on Manifolds\nWe propose practical deep Gaussian process models on Riemannian manifolds, similar in spirit to residual neural networks.With manifold-to-manifold hidden layers and an arbitrary last layer, they can model manifold- and scalar-valued functions, as well as vector fields.We target data inherently supported on manifolds, which is too complex for shallow Gaussian processes thereon.For example, while the latter perform well on high-altitude wind data, the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30105<END_URL>

Residual Kernel Policy Network: Enhancing Stability and Robustness in RKHS-Based Reinforcement Learning\nAchieving optimal performance in reinforcement learning requires robust policies supported by training processes that ensure both sample efficiency and stability. Modeling the policy in reproducing kernel Hilbert space (RKHS) enables efficient exploration of local optimal solutions. However, the stability of existing RKHS-based methods is hindered by significant variance in gradients, while t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31103<END_URL>

Residual-MPPI: Online Policy Customization for Continuous Control\nPolicies developed through Reinforcement Learning (RL) and Imitation Learning (IL) have shown great potential in continuous control tasks, but real-world applications often require adapting trained policies to unforeseen requirements. While fine-tuning can address such needs, it typically requires additional data and access to the original training metrics and parameters.In contrast, an online planning algorithm, if capable of me<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28816<END_URL>

Residual Stream Analysis with Multi-Layer SAEs\nSparse autoencoders (SAEs) are a promising approach to interpreting the internal representations of transformer language models. However, SAEs are usually trained separately on each transformer layer, making it difficult to use them to study how information flows across layers. To solve this problem, we introduce the multi-layer SAE (MLSAE): a single SAE trained on the residual stream activation vectors from every transformer layer. Given that the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29317<END_URL>

Restating the Proof of Linear Convergence for Linear GNNs\nWe lead the readers through the core proof of a pioneering paper that studies the training dynamics of linear GNNs. First, we reorganize the proof and provide a more concise and reader-friendly version, highlighting several key components. In doing so, we identify a hidden error and correct it, demonstrating that it has no impact on the main result. Additionally, we offer a dialectical discussion on the strengths and an overlooked aspect<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31357<END_URL>

Restructuring Vector Quantization with the Rotation Trick\nVector Quantized Variational AutoEncoders (VQ-VAEs) are designed to compress a continuous input to a discrete latent space and reconstruct it with minimal distortion. They operate by maintaining a set of vectors---often referred to as the codebook---and quantizing each encoder output to the nearest vector in the codebook. However, as vector quantization is non-differentiable, the gradient to the encoder flows _around_ the vector quantiza<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30284<END_URL>

Restyling Unsupervised Concept Based Interpretable Networks with Generative Models\nDeveloping inherently interpretable models for prediction has gained prominence in recent years. A subclass of these models, wherein the interpretable network relies on learning high-level concepts, are valued because of closeness of concept representations to human communication. However, the visualization and understanding of the learnt unsupervised dictionary of concepts encounters major limitations, especiall<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30505<END_URL>

RESuM: A Rare Event Surrogate Model for  Physics Detector Design\nThe experimental discovery of neutrinoless double-beta decay (NLDBD) would answer one of the most important questions in physics: Why is there more matter than antimatter in our universe? To maximize the chances of discovery, NLDBD experiments must optimize their detector designs to minimize the probability of background events contaminating the detector. Given that this probability is inherently low, design optimization either re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28496<END_URL>

Rethinking and improving autoformalization: towards a faithful metric and a Dependency Retrieval-based approach\nAs a central component in formal verification, statement autoformalization has been widely studied including the recent efforts from machine learning community, but still remains a widely-recognized difficult and open problem. In this paper, we delve into two critical yet under-explored gaps: 1) absence of faithful and universal automated evaluation for autoformalization results; 2) a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28759<END_URL>

Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives\nWhile audio-visual learning equips models with a richer understanding of the real world by leveraging multiple sensory modalities, this integration also introduces new vulnerabilities to adversarial attacks. In this paper, we present a comprehensive study of the adversarial robustness of audio-visual models, considering both temporal and modality-specific vulnerabilities. We propose two powerful adversaria<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28927<END_URL>

Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple Logits Retargeting Approach\nIn the field of long-tailed recognition, the Decoupled Training paradigm has shown exceptional promise by dividing training into two stages: representation learning and classifier re-training.  While previous work has tried to improve both stages simultaneously, this complicates isolating the effect of classifier re-training.  Recent studies reveal that simple regularization can produce strong fea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29813<END_URL>

Rethinking Copyright Infringements In the Era Of Text-to-Image Generative Models\nThe advent of text-to-image generative models has led artists to worry that their individual styles may be copied, creating a pressing need to reconsider the lack of protection for artistic styles under copyright law. This requires answering challenging questions, like what defines style and what constitutes style infringment. In this work, we build on prior legal scholarship to develop an automatic and interpretab<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31257<END_URL>

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior\nRecent advancements in diffusion models have been leveraged to address inverse problems without additional training, and Diffusion Posterior Sampling (DPS) (Chung et al., 2022a) is among the most popular approaches. Previous analyses suggest that DPS accomplishes posterior sampling by approximating the conditional score. While in this paper, we demonstrate that the conditional score approximation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30272<END_URL>

Rethinking Efficient 3D Equivariant Graph Neural Networks\nUnderstanding complex three-dimensional (3D) structures of graphs is essential for accurately modeling various properties, yet many existing approaches struggle with fully capturing the intricate spatial relationships and symmetries inherent in such systems, especially in large-scale, dynamic molecular datasets. These methods often must balance trade-offs between expressiveness and computational efficiency, limiting their scalability. To<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30925<END_URL>

Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words\nSparse autoencoders (SAEs) have gained a lot of attention as a promising tool to improve the interpretability of large language models (LLMs) by mapping the complex superposition of *polysemantic* neurons into *monosemantic* features and composing a sparse dictionary of words.However, traditional performance metrics like Mean Squared Error and $\mathrm{L}_{0}$ sparsity ignore the evaluation of the semant<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30209<END_URL>

Rethinking Fair Representation Learning for Performance-Sensitive Tasks\nWe investigate the prominent class of fair representation learning methods for bias mitigation. Using causal reasoning to define and formalise different sources of dataset bias, we reveal important implicit assumptions inherent to these methods. We prove fundamental limitations on fair representation learning when evaluation data is drawn from the same distribution as training data and run experiments across a range of medi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28312<END_URL>

Rethinking Graph Neural Networks From A Geometric Perspective Of Node Features\nMany works on graph neural networks (GNNs) focus on graph topologies and analyze graph-related operations to enhance performance on tasks such as node classification. In this paper, we propose to understand GNNs based on a feature-centric approach. Our main idea is to treat the features of nodes from each label class as a whole, from which we can identify the centroid. The convex hull of these centroids forms a simpl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28532<END_URL>

Rethinking Graph Prompts: Unraveling the Power of Data Manipulation in Graph Neural Networks\nGraph Neural Networks (GNNs) have transformed graph learning but face challenges like distribution shifts, data anomalies, and adversarial vulnerabilities. Graph prompt emerges as a novel solution, enabling data transformation to align graph data with pre-trained models without altering model parameters. This paradigm addresses negative transfer, enhances adaptability, and bridges modality gaps. Unlike <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31337<END_URL>

Rethinking Invariance in In-context Learning\nIn-Context Learning (ICL) has emerged as a pivotal capability of auto-regressive large language models, yet it is hindered by a notable sensitivity to the ordering of context examples regardless of their mutual independence. To address this issue, recent studies have introduced several variant algorithms of ICL that achieve permutation invariance. However, many of these do not exhibit comparable performance with the standard auto-regressive ICL algor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28271<END_URL>

Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off\nAdversarial training often suffers from a robustness-accuracy trade-off, where achieving high robustness comes at the cost of accuracy.One approach to mitigate this trade-off is leveraging invariance regularization, which encourages model invariance under adversarial perturbations; however, it still leads to accuracy loss.In this work, we closely analyze the challenges of using invariance regul<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29949<END_URL>

Rethinking Light Decoder-based Solvers for Vehicle Routing Problems\nLight decoder-based solvers have gained popularity for solving vehicle routing problems (VRPs) due to their efficiency and ease of integration with reinforcement learning algorithms. However, they often struggle with generalization to larger problem instances or different VRP variants. This paper revisits light decoder-based approaches, analyzing the implications of their reliance on static embeddings and the inherent challenge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30992<END_URL>

Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond\nLarge language models (LLMs) should undergo rigorous audits to identify potential risks, such as copyright and privacy infringements. Once these risks emerge, timely updates are crucial to remove undesirable responses, ensuring legal and safe model usage. It has spurred recent research into LLM unlearning, focusing on erasing targeted undesirable knowledge without compromising the integrity of other, non-targeted respons<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28734<END_URL>

Rethinking Multiple-Instance Learning From Feature Space to Probability Space\nMultiple-instance learning (MIL) was initially proposed to identify key instances within a set (bag) of instances when only one bag-level label is provided. Current deep MIL models mostly solve multi-instance aggregation problem in feature space. Nevertheless, with the increasing complexity of data, this paradigm faces significant risks in representation learning stage, which could lead to algorithm degradation in dee<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28025<END_URL>

Rethinking Neural Multi-Objective Combinatorial Optimization via Neat Weight Embedding\nRecent decomposition-based neural multi-objective combinatorial optimization (MOCO) methods struggle to achieve desirable performance. Even equipped with complex learning techniques, they often suffer from significant optimality gaps in weight-specific subproblems. To address this challenge, we propose a neat weight embedding method to learn weight-specific representations, which captures weight-instance inte<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30285<END_URL>

Rethinking Reward Model Evaluation: Are We Barking up the Wrong Tree?\nReward Models (RMs) are crucial for aligning language models with human preferences. Currently, the evaluation of RMs depends on measuring accuracy against a validation set of manually annotated preference data.Although this method is straightforward and widely adopted, the relationship between RM accuracy and downstream policy performance remains under-explored.In this work, we conduct experiments in a synthetic setting to i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30496<END_URL>

Rethinking Reward Modeling in Preference-based Large Language Model Alignment\nThe Bradley-Terry (BT) model is a common and successful practice in reward modeling for Large Language Model (LLM) alignment. However, it remains unclear *why* this model --- originally developed for multi-player stochastic game matching --- can be adopted to convert pairwise response comparisons to reward values and make predictions. Especially given the fact that only a limited number of prompt-response pairs are sp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28181<END_URL>

Rethinking Self-Distillation: Label Averaging and Enhanced Soft Label Refinement with Partial Labels\nWe investigate the mechanisms of self-distillation in multi-class classification, particularly in the context of linear probing with fixed feature extractors where traditional feature learning explanations do not apply. Our theoretical analysis reveals that multi-round self-distillation effectively performs label averaging among instances with high feature correlations, governed by the eigenvect<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30413<END_URL>

Rethinking Shapley Value for Negative Interactions in Non-convex Games\nWe study causal interaction for payoff allocation in cooperative game theory, including quantifying feature attribution for deep learning models. Most feature attribution methods mainly stem from the criteria from the Shapley value, which provides a unique payoff vector for players by marginalizing contributions in a cooperative game. However, interactions between players in the game do not exactly appear in the original for<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29127<END_URL>

Rethinking Sparse Scaling through the Lens of Average Active Parameter Count\nParameter pruning has emerged as a promising technique to address the growing computational demand of large language models (LLMs). While many studies focus on post-training pruning of LLMs, sparse pre-training offers a compelling alternative: sparsifying during pre-training reduces both training and inference costs. In this work, we conduct the first comprehensive study on optimal sparse pre-training configurations fo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27967<END_URL>

Rethinking Spiking Neural Networks from an Ensemble Learning Perspective\nSpiking neural networks (SNNs) have gained widespread attention for their low power consumption and spatio-temporal dynamics. In this paper, we consider an SNN as an ensemble of multiple temporal subnetworks that share architecture and weights, but produce different outputs due to differences in initial states (neuron membrane potentials). We identify a key factor influencing ensemble performance: excessive differences in <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29191<END_URL>

Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation\nDrug-target binding affinity prediction is a fundamental task for drug discovery. It has been extensively explored in literature and promising results are reported. However, in this paper, we demonstrate that the results may be misleading and cannot be well generalized to real practice. The core observation is that the canonical randomized split of a test set in conventional evaluation le<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28654<END_URL>

Rethinking the role of frames for SE(3)-invariant crystal structure modeling\nCrystal structure modeling using geometric graph neural networks is important in various machine learning applications in materials science. In these applications, capturing SE(3)-invariant geometric features in crystal structures is a fundamental requirement for these networks. One approach is to model with orientation-standardized structures through structure-aligned coordinate systems called `frames.' However, unlik<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28789<END_URL>

Rethinking Visual Counterfactual Explanations Through Region Constraint\nVisual counterfactual explanations (VCEs) have recently gained immense popularity as a tool for clarifying the decision-making process of image classifiers. This trend is largely motivated by what these explanations promise to deliver -- indicate semantically meaningful factors that change the classifier's decision. However, we argue that current state-of-the-art approaches lack a crucial component -- the region constraint <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28798<END_URL>

Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model\nIllumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among these algorithms, diffusion-based models (DM) have shown promising performance but are often burdened by heavy computational demands and pixel misalignment issues when predicting the image-level distribution. To tackle these<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28550<END_URL>

Retri3D: 3D Neural Graphics Representation Retrieval\nLearnable 3D Neural Graphics Representations (3DNGR) have emerged as promising 3D representations for reconstructing 3D scenes from 2D images. Numerous works, including Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and their variants, have significantly enhanced the quality of these representations. The ease of construction from 2D images, suitability for online viewing/sharing, and applications in game/art design downstream ta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28267<END_URL>

Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization\nAntibodies are essential proteins responsible for immune responses in organisms, capable of specifically recognizing antigen molecules of pathogens. Recent advances in generative models have significantly enhanced rational antibody design. However, existing methods mainly create antibodies from scratch without template constraints, leading to model optimization challenges and unnatural sequences. To addr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29186<END_URL>

Retrieval Head Mechanistically Explains Long-Context Factuality\nDespite the recent progress in long-context language models, it remains elusive how transformer-based models exhibit the capability to retrieve relevant information from arbitrary locations within the long context. This paper aims to address this question. Our systematic investigation across a wide spectrum of models reveals that a special type of attention heads are largely responsible for retrieving information, which we dub retr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30373<END_URL>

Retrieval or Reasoning: The Roles of Graphs and Large Language Models in Efficient Knowledge-Graph-Based Retrieval-Augmented Generation\nLarge Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) addresses these issues by grounding LLM outputs in structured external knowledge from KGs. However, current KG-based RAG frameworks still struggle to optimize the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30084<END_URL>

RetroInText: A Multimodal Large Language Model Enhanced Framework for Retrosynthetic Planning via In-Context Representation Learning\nDevelopment of robust and effective strategies for retrosynthetic planning requires a deep understanding of the synthesis process. A critical step in achieving this goal is accurately identifying synthetic intermediates. Current machine learning-based methods often overlook the valuable context from the overall route, focusing only on predicting reactants from the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30129<END_URL>

Revamping Diffusion Guidance for Conditional and Unconditional Generation\nClassifier-free guidance (CFG) has become the standard method for enhancing the quality of conditional diffusion models. However, employing CFG requires either training an unconditional model alongside the main diffusion model or modifying the training procedure by periodically inserting a null condition.  There is also no clear extension of CFG to unconditional models. In this paper, we revisit the core principles of CFG<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29126<END_URL>

Revealing and Mitigating Over-Attention in Knowledge Editing\nLarge Language Models~(LLMs) have demonstrated superior performance across a wide range of tasks, but they still exhibit undesirable errors due to incorrect knowledge learned from the training data. To avoid this, knowledge editing methods emerged to precisely edit the specific model knowledge via efficiently modifying a very small percentage of parameters. However, those methods can lead to the problem of **Specificity Failure**, whe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30995<END_URL>

Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)\nPre-trained large language models (LLMs) have been reliably integrated with visual input for multimodal tasks. The widespread adoption of instruction-tuned image-to-text vision-language assistants (VLAs) like LLaVA and InternVL necessitates evaluating gender biases. We study gender bias in 22 popular open-source VLAs with respect to personality traits, skills, and occupations. Our results show that VLAs replicate huma<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28356<END_URL>

Revealing the 3D Cosmic Web through Gravitationally Constrained Neural Fields\nWeak gravitational lensing is the slight distortion of galaxy shapes caused primarily by the gravitational effects of dark matter in the universe. In our work, we seek to invert the weak lensing signal from 2D telescope images to reconstruct a 3D map of the universe’s dark matter field. While inversion typically yeilds a 2D projection of the dark matter field, accurate 3D maps of the dark matter distribution are essen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30603<END_URL>

Reveal Object in Lensless Photography via Region Gaze and Amplification\nDetecting concealed objects, such as in vivo lesions or camouflage, requires customized imaging systems. Lensless cameras, being compact and flexible, offer a promising alternative to bulky lens systems. However, the absence of lenses leads to measurements lacking visual semantics, posing significant challenges for concealed object detection (COD). To tackle this issue, we propose a region gaze-amplification network (RGANet<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30401<END_URL>

RevisEval: Improving LLM-as-a-Judge via Response-Adapted References\nWith significant efforts in recent studies, LLM-as-a-Judge has become a cost-effective alternative to human evaluation for assessing the text generation quality in a wide range of tasks. However, there still remains a reliability gap between LLM-as-a-Judge and human evaluation. One important reason is the lack of guided oracles in the evaluation process. Motivated by the role of reference pervasively used in classic text evalua<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31178<END_URL>

Revisiting a Design Choice in Gradient Temporal Difference Learning\nOff-policy learning enables a reinforcement learning (RL) agent to reason counterfactually about policies that are not executed and is one of the most important ideas in RL. It, however, can lead to instability when combined with function approximation and bootstrapping, two arguably indispensable ingredients for large-scale reinforcement learning. This is the notorious deadly triad. The seminal work Sutton et al. (2008) pionee<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31094<END_URL>

Revisiting Convolution Architecture in the Realm of DNA Foundation Models\nIn recent years, A variety of methods based on Transformer and state space model (SSM) architectures have been proposed, advancing foundational DNA language models. However, there is a lack of comparison between these recent approaches and the classical architecture—convolutional networks (CNNs)—on foundation model benchmarks.This raises the question: are CNNs truly being surpassed by these recent approaches based on tran<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30599<END_URL>

Revisiting Delta-Parameter Pruning For Fine-Tuned Models\nStoring open-source fine-tuned models separately introduces redundancy and  increases response times in applications utilizing multiple models. Delta-parameter pruning (DPP), particularly the random drop and rescale (DARE) method proposed by Yu et al., addresses this by pruning the majority of delta parameters—the differences between fine-tuned and pre-trained model weights—while typically maintaining minimal performance loss. However, DA<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29137<END_URL>

Revisiting DNN Training for Intermittently-Powered Energy-Harvesting Micro-Computers\nThe deployment of Deep Neural Networks (DNNs) in energy-constrained environments, such as Energy Harvesting Wireless Sensor Networks (EH-WSNs), introduces significant challenges due to the intermittent nature of power availability. This study introduces NExUME, a novel training methodology designed specifically for DNNs operating under such constraints. We propose a dynamic adjustment of training parameters—dro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29619<END_URL>

Revisiting In-context Learning Inference Circuit in Large Language Models\nIn-context Learning (ICL) is an emerging few-shot learning paradigm on Language Models (LMs) with inner mechanisms un-explored. There are already existing works describing the inner processing of ICL, while they struggle to capture all the inference phenomena in large language models. Therefore, this paper proposes a comprehensive circuit to model the inference dynamics and try to explain the observed phenomena of ICL. In<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27767<END_URL>

Revisiting Large-Scale Non-convex Distributionally Robust Optimization\nDistributionally robust optimization (DRO) is a powerful technique to train robust machine learning models that perform well under distribution shifts. Compared with empirical risk minimization (ERM), DRO optimizes the expected loss under the worst-case distribution inan uncertainty set of distributions. This paper revisits the important problem of DRO with non-convex smooth loss functions. For this problem, Jin et al. (2021<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30100<END_URL>

Revisiting Mode Connectivity in Neural Networks with Bezier Surface\nUnderstanding the loss landscapes of neural networks (NNs) is critical for optimizing model performance. Previous research has identified the phenomenon of mode connectivity on curves, where two well-trained NNs can be connected by a continuous path in parameter space where the path maintains nearly constant loss. In this work, we extend the concept of mode connectivity to explore connectivity on surfaces, significantly broaden<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31207<END_URL>

REVISITING MULTI-PERMUTATION EQUIVARIANCE THROUGH THE LENS OF IRREDUCIBLE REPRESENTATIONS\nThis paper explores the characterization of equivariant linear layers for representations of permutations and related groups. Unlike traditional approaches,which address these problems using parameter-sharing, we consider an alternativemethodology based on irreducible representations and Schur’s lemma. Using thismethodology, we obtain an alternative derivation for existing models like DeepSets,2-IGN graph <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30985<END_URL>

Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later\nThe widespread enthusiasm for deep learning has recently expanded into the domain of tabular data. Recognizing that the advancement in deep tabular methods is often inspired by classical methods, e.g., integration of nearest neighbors into neural networks, we investigate whether these classical methods can be revitalized with modern techniques.We revisit a differentiable version of $K$-nearest neighbors (KNN<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30081<END_URL>

Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts\nPrompt-based techniques, such as prompt-tuning and prefix-tuning, have gained prominence for their efficiency in fine-tuning large pre-trained models. Despite their widespread adoption, the theoretical foundations of these methods remain limited. For instance, in prefix-tuning, we observe that a key factor in achieving performance parity with full fine-tuning lies in the reparameterization strategy. However, the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29676<END_URL>

Revisiting Random Walks for Learning on Graphs\nWe revisit a recent model class for machine learning on graphs, where a random walk on a graph produces a machine-readable record, and this record is processed by a deep neural network to directly make vertex-level or graph-level predictions. We refer to these stochastic machines as random walk neural networks (RWNNs), and through principled analysis, show that we can design them to be isomorphism invariant while capable of universal approximation <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29618<END_URL>

Revisiting Source-Free Domain Adaptation: a New Perspective via Uncertainty Control\nSource-Free Domain Adaptation (SFDA) seeks to adapt a pre-trained source model to the target domain using only unlabeled target data, without access to the original source data. While current state-of-the-art (SOTA) methods rely on leveraging weak supervision from the source model to extract reliable information for self-supervised adaptation, they often overlook the uncertainty that arises during the transfer p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28386<END_URL>

Revisiting Zeroth-Order Optimization:  Minimum-Variance Two-Point Estimators and  Directionally Aligned Perturbations\nIn this paper, we explore the two-point zeroth-order gradient estimator and identify the optimal distribution of random perturbations that minimizes the estimator's variance. We formulate it as a constrained functional optimization problem over the space of perturbation distributions. Our findings reveal that optimal perturbations either maintain a fixed length or align directio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27697<END_URL>

Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models\nRecent advancements in multimodal models highlight the value of rewritten captions for improving performance, yet key challenges remain. For example, while synthetic captions often provide superior quality and image-text alignment, it is not clear whether they can fully replace AltTexts: the role of synthetic captions and their interaction with original web-crawled AltTexts in pre-training is still not well unde<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29536<END_URL>

Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation\nMicro-batch clipping, a gradient clipping method, has recently shown potential in enhancing auto-speech recognition (ASR) model performance. However, the underlying mechanism behind this improvement remains mysterious, particularly the observation that only certain micro-batch sizes are beneficial. In this paper, we make the first attempt to explain this phenomenon. Inspired by recent data pruning research, we assume <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28314<END_URL>

Revisit the open nature of open vocabulary segmentation\nIn Open-Vocabulary Segmentation (OVS), we observe a consistent drop in modelperformance as the query vocabulary set expands, especially when it includes se-mantically similar and ambiguous vocabularies, such as ‘sofa’ and ‘couch’. Theprevious OVS evaluation protocol, however, does not account for such ambiguity,as any mismatch between predicted and human-annotated pairs is simply treatedas incorrect on a pixel-wise basis. This contradicts <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31104<END_URL>

Revolutionizing EMCCD Denoising through a Novel Physics-Based Learning Framework for Noise Modeling\nElectron-multiplying charge-coupled device (EMCCD) has been instrumental in sensitive observations under low-light situations including astronomy, material science, and biology. Despite its ingenious designs to enhance target signals overcoming read-out circuit noises, produced images are not completely noise free, which could still cast a cloud on desired experiment outcomes, especially in fluor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27890<END_URL>

REvolve: Reward Evolution with Large Language Models using Human Feedback\nDesigning effective reward functions is crucial to training reinforcement learning (RL) algorithms. However, this design is non-trivial, even for domain experts, due to the subjective nature of certain tasks that are hard to quantify explicitly. In recent works, large language models (LLMs) have been used for reward generation from natural language task descriptions, leveraging their extensive instruction tuning and commo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29060<END_URL>

Reward Dimension Reduction for Scalable Multi-Objective Reinforcement Learning\nIn this paper, we introduce a simple yet effective reward dimension reduction method to tackle the scalability challenges of multi-objective reinforcement learning algorithms. While most existing approaches focus on optimizing two to four objectives, their abilities to scale to environments with more objectives remain uncertain. Our method uses a dimension reduction approach to enhance learning efficiency and policy <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28095<END_URL>

Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning\nA promising approach for improving reasoning in large language models is to use process reward models (PRMs). PRMs provide feedback at each step of a multi-step reasoning trace, improving credit assignment over outcome reward models (ORMs) that only provide feedback at the final step. However, collecting dense, per-step human labels is not scalable, and training PRMs from automatically-labeled data has thus far led to lim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30649<END_URL>

Reward Learning from Multiple Feedback Types\nLearning rewards from preference feedback has become an important tool in the alignment of agentic models. Preference-based feedback, often implemented as a binary comparison between multiple completions, is an established method to acquire large-scale human feedback. However, human feedback in other contexts is often much more diverse. Such diverse feedback can better support the goals of a human annotator, and the simultaneous use of multiple sourc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30703<END_URL>

RFMamba: Frequency-Aware State Space Model for RF-Based Human-Centric Perception\nHuman-centric perception with radio frequency (RF) signals has recently entered a new era of end-to-end processing with Transformers. Considering the long-sequence nature of RF signals, the State Space Model (SSM) has emerged as a superior alternative due to its effective long-sequence modeling and linear complexity. However, integrating SSM into RF-based sensing presents unique challenges including the fundamental<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28529<END_URL>

RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction\nRecent advancements in generative modeling have significantly enhanced the reconstruction of audio waveforms from various representations. While diffusion models are adept at this task, they are hindered by latency issues due to their operation at the individual sample point level and the need for numerous sampling steps. In this study, we introduce RFWave, a cutting-edge multi-band Rectified Flow approach designed to reconstru<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28820<END_URL>

RGB-Event ISP: The Dataset and Benchmark\nEvent-guided imaging has received significant attention due to its potential to revolutionize instant imaging systems. However, the prior methods primarily focus on enhancing RGB images in a post-processing manner, neglecting the challenges of image signal processor (ISP) dealing with event sensor and the benefits events provide for reforming the ISP process. To achieve this, we conduct the first research on event-guided ISP. First, we present a new even<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30541<END_URL>

Risk Informed Policy Learning for Safer Exploration\nReinforcement learning algorithms typically necessitate extensive exploration of the state space to find optimal policies. However, in safety-critical applications, the risks associated with such exploration can lead to catastrophic consequences. Existing safe exploration methods attempt to mitigate this by imposing constraints, which often result in overly conservative behaviours and inefficient learning. Heavy penalties for early constraint <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28826<END_URL>

Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples\nDiffusion models are mainly studied on image data. However, non-image data (e.g., tabular data) are also prevalent in real applications and tend to be noisy due to some inevitable factors in the stage of data collection, degrading the generation quality of diffusion models. In this paper, we consider a novel problem setting where every collected sample is paired with a vector indicating the data quality: risk vect<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29132<END_URL>

Risk-Sensitive Variational Actor-Critic: A Model-Based Approach\nRisk-sensitive reinforcement learning (RL) with an entropic risk measure typically requires knowledge of the transition kernel or performs unstable updates w.r.t. exponential Bellman equations. As a consequence, algorithms that optimize this objective have been restricted to tabular or low-dimensional continuous environments. In this work we leverage the connection between the entropic risk measure and the RL-as-inference framework<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28669<END_URL>

RMB: Comprehensively benchmarking reward models in LLM alignment\nReward models (RMs) guide the alignment of large language models (LLMs), steering them toward behaviors preferred by humans. Evaluating RMs is the key to better aligning LLMs. However, the current evaluation of RMs may not directly correspond to their alignment performance due to the limited distribution of evaluation data and evaluation methods that are not closely related to alignment objectives. To address these limitations, we<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28561<END_URL>

RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style\nReward models are critical in techniques like Reinforcement Learning from Human Feedback (RLHF) and Inference Scaling Laws, where they guide language model alignment and select optimal responses. Despite their importance, existing reward model benchmarks often evaluate models by asking them to distinguish between responses generated by models of varying power. However, this approach fails to assess reward models on <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29702<END_URL>

RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything\nRecent segmentation methods, which adopt large-scale data training and transformer architecture, aim to create one foundation model that can perform multiple tasks.    However, most of these methods rely on heavy encoder and decoder frameworks, hindering their performance in real-time scenarios.    To explore real-time segmentation, recent advancements primarily focus on semantic segmentation within specific environments, such as autonom<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31184<END_URL>

RNNs are not Transformers (Yet):  The Key Bottleneck on In-Context Retrieval\nThis paper investigates the gap in representation powers of Transformers and Recurrent Neural Networks (RNNs), which are more memory efficient than Transformers. We aim to understand whether RNNs can match the performance of Transformers, particularly when enhanced with Chain-of-Thought (CoT) prompting. Our theoretical analysis reveals that CoT improves RNNs but is insufficient to close the gap with Transformers. A key<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28783<END_URL>

Robotouille: An Asynchronous Planning Benchmark for LLM Agents\nEffective asynchronous planning, or the ability to efficiently reason and plan over states and actions that must happen in parallel or sequentially, is essential for agents that must account for time delays, reason over diverse long-horizon tasks, and collaborate with other agents. While large language model (LLM) agents show promise in high-level task planning, current benchmarks focus primarily on short-horizon tasks and do not ev<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29809<END_URL>

Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Dataset\nThe pre-training of visual representations has enhanced the efficiency of robot learning. Due to the lack of large-scale in-domain robotic datasets, prior works utilize in-the-wild human videos to pre-train robotic visual representation. Despite their promising results, representations from human videos are inevitably subject to distribution shifts and lack the dynamics information crucial for ta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27726<END_URL>

RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection\nWhile recent low-cost radar-camera approaches have shown promising results inmulti-modal 3D object detection, both sensors face challenges from environmen-tal and intrinsic disturbances. Poor lighting or adverse weather conditions de-grade camera performance, while radar suffers from noise and positional ambigu-ity. Achieving robust radar-camera 3D object detection requires consistent perfor-mance <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30658<END_URL>

Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport\nAggregating data from multiple sources can be formalized as an *Optimal Transport* (OT) barycenter problem, which seeks to compute the average of probability distributions with respect to OT discrepancies. However, in real-world scenarios, the presence of outliers and noise in the data measures can significantly hinder the performance of traditional statistical methods for estimating OT barycenters. To address this issu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30524<END_URL>

Robust Conformal Prediction with a Single Binary Certificate\nConformal prediction (CP) converts any model's output to prediction sets with a guarantee to cover the true label with (adjustable) high probability. Robust CP extends this guarantee to worst-case (adversarial) inputs. Existing baselines achieve robustness by bounding randomly smoothed conformity scores. In practice, they need expensive Monte-Carlo (MC) sampling ($\sim10^4$ samples per point) to maintain an acceptable set size. We pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28494<END_URL>

Robust Deep Equivariant Structure from Motion\nMultiview Structure from Motion is a fundamental and challenging computer vision problem. A recent deep-based approach utilized matrix equivariant architectures for simultaneous recovery of camera pose and 3D scene structure from large image collections. That work, however, made the unrealistic assumption that the point tracks given as input are almost clean of outliers. Here, we propose an architecture suited to dealing with outliers by adding a mu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27825<END_URL>

Robust Feature Learning for Multi-Index Models in High Dimensions\nRecently, there have been numerous studies on feature learning with neural networks, specifically on learning single- and multi-index models where the target is a function of a low-dimensional projection of the input. Prior works have shown that in high dimensions, the majority of the compute and data resources are spent on recovering the low-dimensional projection; once this subspace is recovered, the remainder of the target can<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29175<END_URL>

Robust Function-Calling for On-Device Language Model via Function Masking\nLarge language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls. Nonetheless, effectively harnessing their potential for executing complex tasks crucially relies on enhancements in their function-calling capabilities. This paper identifies a critical gap in existing function-calling models, where performance varies significantly across benchmarks,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27722<END_URL>

Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning\nDriven by inherent uncertainty and the sim-to-real gap, robust reinforcement learning (RL) seeks to improve resilience against the complexity and variability in agent-environment sequential interactions. Despite the existence of a large number of RL benchmarks, there is a lack of standardized benchmarks for robust RL. Current robust RL policies often focus on a specific type of uncertainty and are evaluated in disti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31105<END_URL>

RobustKV: Defending Large Language Models against Jailbreak Attacks via KV Eviction\nJailbreak attacks circumvent LLMs' built-in safeguards by concealing harmful queries within adversarial prompts. While most existing defenses attempt to mitigate the effects of adversarial prompts, they often prove inadequate as adversarial prompts can take arbitrary, adaptive forms. This paper introduces RobustKV, a novel jailbreak defense that takes a fundamentally different approach by selectively removing cr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30018<END_URL>

Robust Learning in Bayesian Parallel Branching Graph Neural Networks: The Narrow Width Limit\nThe infinite width limit of random neural networks is known to result in Neural Networks as Gaussian Process (NNGP) (Lee et al. (2018)), characterized by task-independent kernels. It is widely accepted that larger network widths contribute to improved generalization (Park et al. (2019)). However, this work challenges this notion by investigating the narrow width limit of the Bayesian Parallel Branching <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30498<END_URL>

Robust LLM safeguarding via refusal feature adversarial training\nLarge language models (LLMs) are vulnerable to adversarial attacks that can elicit harmful responses. Defending against such attacks remains challenging due to the opacity of jailbreaking mechanisms and the high computational cost of training LLMs robustly. We demonstrate that adversarial attacks share a universal mechanism for circumventing LLM safeguards that works by ablating a dimension in the residual stream embedding space c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28144<END_URL>

Robustness Auditing for Linear Regression: To Singularity and Beyond\nIt has recently been discovered that the conclusions of many highly influential econometrics studies can be overturned by removing a very small fraction of their samples (often less than $0.5\%$). These conclusions are typically based on the results of one or more Ordinary Least Squares (OLS) regressions, raising the question: given a dataset, can we certify the robustness of an OLS fit on this dataset to the removal of a give<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29431<END_URL>

Robustness Inspired Graph Backdoor Defense\nGraph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable to backdoor attacks, posing a significant threat to their real-world adoption. Despite initial efforts to defend against specific graph backdoor attacks, there is no work on defending against various types of backdoor attacks where generated triggers have different properties<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28019<END_URL>

Robustness of Quantum Algorithms for Nonconvex Optimization\nIn this paper, we systematically study quantum algorithms for finding an $\epsilon$-approximate second-order stationary point ($\epsilon$-SOSP) of a $d$-dimensional nonconvex function, a fundamental problem in nonconvex optimization, with noisy zeroth- or first-order oracles as inputs. We first prove that, up to noise of $O(\epsilon^{10}/d^5)$, perturbed accelerated gradient descent equipped with quantum gradient estimation takes $O(\l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30083<END_URL>

Robustness Reprogramming for Representation Learning\nThis work tackles an intriguing and fundamental open challenge in representation learning: Given a well-trained deep learning model, can it be reprogrammed to enhance its robustness against adversarial or noisy input perturbations without altering its parameters?To explore this, we revisit the core feature transformation mechanism in representation learning and propose a novel non-linear robust pattern matching technique as a robust alternati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29574<END_URL>

Robust-PIFu: Robust Pixel-aligned Implicit Function for 3D Human Digitalization from a Single Image\nExisting methods for 3D clothed human digitalization perform well when the input image is captured in ideal conditions that assume the lack of any occlusion. However, in reality, images may often have occlusion problems such as incomplete observation of the human subject's full body, self-occlusion by the human subject, and non-frontal body pose. When given such input images, these existing metho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28845<END_URL>

Robust Representation Consistency Model via Contrastive Denoising\nRobustness is essential for deep neural networks, especially in security-sensitive applications. To this end, randomized smoothing provides theoretical guarantees for certifying robustness against adversarial perturbations. Recently, diffusion models have been successfully employed for randomized smoothing to purify noise-perturbed samples before making predictions with a standard classifier. While these methods excel at small pe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29144<END_URL>

Robust Root Cause Diagnosis using In-Distribution Interventions\nDiagnosing the root cause of an anomaly in a complex interconnected system is a pressing problem in today's cloud services and industrial operations. Effective root cause diagnosis calls for identifying nodes whose disrupted local mechanisms cause anomalous behavior at a target node. We propose In-Distribution Interventions (IDI), a novel algorithm that predicts root cause as nodes that meet two criteria: 1) **Anomaly:** root cause<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28541<END_URL>

Robust Simulation-Based Inference under Missing Data\nSimulation-based inference (SBI) methods typically require fully observed data to infer parameters of models with intractable likelihood functions. However, datasets often contain missing values due to incomplete observations, data corruptions (common in astrophysics), or instrument limitations (e.g., in high-energy physics applications). In such scenarios, missing data must be imputed before applying any SBI method. This work formalizes the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30254<END_URL>

Robust System Identification: Finite-sample Guarantees and Connection to Regularization\nWe address the problem of learning nonlinear dynamical systems from a single sample trajectory. While the least squares estimate (LSE) is commonly used for this task, it suffers from poor identification errors when the sample size is small or the model fails to capture the system's true dynamics. To overcome these limitations, we propose a robust LSE framework, which incorporates robust optimization techniqu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29222<END_URL>

Robust Transfer of Safety-Constrained Reinforcement Learning Agents\nReinforcement learning (RL) often relies on trial and error, which may cause undesirable outcomes. As a result, standard RL is inappropriate for safety-critical applications. To address this issue, one may train a safe agent in a controlled environment (where safety violations are allowed) and then transfer it to the real world (where safety violations may have disastrous consequences). Prior work has made this transfer safe as<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28164<END_URL>

Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances\nCurrent image watermarking methods are vulnerable to advanced image editing techniques enabled by large-scale text-to-image models. These models can distort embedded watermarks during editing, posing significant challenges to copyright protection. In this work, we introduce W-Bench, the first comprehensive benchmark designed to evaluate the robustness of watermarking methods against a wide range of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31223<END_URL>

Robust Weight Initialization for Tanh Neural Networks with Fixed Point Analysis\nAs a neural network's depth increases, it can achieve high generalization performance. However, training deep networks is challenging due to gradient and signal propagation issues. To address these challenges, extensive theoretical research and various methods have been introduced. Despite these advances, effective weight initialization methods for tanh neural networks remain underexplored. This paper presents a nov<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30383<END_URL>

RocketEval: Efficient automated LLM evaluation via grading checklist\nEvaluating large language models (LLMs) in diverse and challenging scenarios is essential to align them with human preferences. To mitigate the prohibitive costs associated with human evaluations, utilizing a powerful LLM as a judge has emerged as a favored approach. Nevertheless, this methodology encounters several challenges, including substantial expenses, concerns regarding privacy and security, and reproducibility. In thi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27674<END_URL>

Rodimus*: Breaking the Accuracy-Efficiency Trade-Off with Efficient Attentions\nRecent advancements in Transformer-based large language models (LLMs) have set new standards in natural language processing. However, the classical softmax attention incurs significant computational costs, leading to a $O(T)$ complexity for per-token generation, where $T$ represents the context length. This work explores reducing LLMs' complexity while maintaining performance by introducing Rodimus and its enhanced v<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30177<END_URL>

Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery\nIdentifying the root causes of anomalies in multivariate time series is challenging due to the complex dependencies among the series. In this paper, we propose a comprehensive approach called AERCA that inherently integrates Granger causal discovery with root cause analysis. By defining anomalies as interventions on the exogenous variables of time series, AERCA not only learns the Granger causality amo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28602<END_URL>

Rotated Runtime Smooth: Training-Free Activation Smoother for accurate INT4 inference\nLarge language models have demonstrated promising capabilities upon scaling up parameters. However, serving large language models incurs substantial computation and memory movement costs due to their large scale. Quantization methods have been employed to reduce service costs and latency. Nevertheless, outliers in activations hinder the development of INT4 weight-activation quantization. Existing approaches se<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29374<END_URL>

Round and Round We Go! What makes Rotary Positional Encodings useful?\nPositional Encodings (PEs) are a critical component of Transformer-based Large Language Models (LLMs), providing the attention mechanism with important sequence-position information. One of the most popular types of encoding used today in LLMs are Rotary Positional Encodings (RoPE), that rotate the queries and keys based on their relative distance. A common belief is that RoPE is useful because it helps to decay token depende<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30251<END_URL>

RouteLLM: Learning to Route LLMs from Preference Data\nLarge language models (LLMs) excel at a wide range of tasks, but choosing the right model often involves balancing performance and cost. Powerful models offer better results but are expensive, while smaller models are more cost-effective but less capable. To address this trade-off, we introduce a training framework for learning efficient router models that dynamically select between a stronger and weaker LLM during inference. Our framework l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30737<END_URL>

ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL\nDespite the significant advancements in Text-to-SQL (Text2SQL) facilitated by large language models (LLMs), the latest state-of-the-art techniques are still trapped in the in-context learning of closed-source LLMs (e.g., GPT-4), which limits their applicability in open scenarios. To address this challenge, we propose a novel RObust mUltitask Tuning and collaboration mEthod (ROUTE) to improve the comprehensive capabilities of open-<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30587<END_URL>

Routing Experts: Learning to Route Dynamic Experts in Existing Multi-modal Large Language Models\nRecently, mixture of experts (MoE) has become a popular paradigm for achieving the trade-off between modal capacity and efficiency of multimodal large language models (MLLMs). Different from previous efforts, we are dedicated to exploring the dynamic experts in existing MLLMs and showing that a standard MLLM can also be a mixture of experts. However, achieving this target is still notoriously challe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27884<END_URL>

RRM:  Robust Reward Model Training Mitigates Reward Hacking\nReward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. However, traditional RM training, which relies on response pairs tied to specific prompts, struggles to disentangle prompt-driven preferences from prompt-independent artifacts, such as response length and format. In this work, we expose a fundamental limitation of current RM training methods, where RMs fail to effectively distinguis<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30783<END_URL>

R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference\nLarge Language Models (LLMs), while demonstrating remarkable capabilities across various applications, present significant challenges during inference due to their substantial model size, especially when deployed on edge devices. Activation sparsity offers a promising solution to reduce computation and memory movement, enabling more efficient inference, particularly for small-batch on-device applications. However, current appr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30686<END_URL>

RTDiff: Reverse Trajectory Synthesis via Diffusion for Offline Reinforcement Learning\nIn offline reinforcement learning (RL), managing the distribution shift between the learned policy and the static offline dataset is a persistent challenge that can result in overestimated values and suboptimal policies. Traditional offline RL methods address this by introducing conservative biases that limit exploration to well-understood regions, but they often overly restrict the agent's generalization capa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31264<END_URL>

RTop-K: Ultra-Fast Row-Wise Top-K Selection for Neural Network Acceleration on GPUs\nTop-k selection algorithms are fundamental in a wide range of applications, from high-performance computing and information retrieval to big data processing and neural network model training. In this paper, we present RTop-K, a highly efficient parallel row-wise top-k selection algorithm specifically designed for GPUs. RTop-K leverages a binary search-based approach to optimize row-wise top-k selection, providin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29763<END_URL>

RuAG: Learned-rule-augmented Generation for Large Language Models\nIn-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer from limited contextual window size, leading to insufficient information injection. To this end, we propose a novel framework to automatically distill large volumes of offline data into interpretable first-order logic rules, which are injected into LLM<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30545<END_URL>

S4M: S4 for multivariate time series forecasting with Missing values\nMultivariate time series data are integral to numerous real-world applications, including finance, healthcare, and meteorology, where accurate forecasting is paramount for informed decision-making and proactive measures. However, the presence of missing data poses significant challenges, often undermining the performance of predictive models. Traditional two-step approaches that first impute missing values and then perform for<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30554<END_URL>

SafeDiffuser: Safe Planning with Diffusion Probabilistic Models\nDiffusion models have shown promise in data-driven planning. While these planners are commonly employed in applications where decisions are critical, they still lack established safety guarantees. In this paper, we address this limitation by introducing SafeDiffuser, a method to equip diffusion models with safety guarantees via control barrier functions. The key idea of our approach is to embed finite-time diffusion invariance, i.e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28682<END_URL>

Safety Alignment Should be Made More Than Just a Few Tokens Deep\nThe safety alignment of current Large Language Models (LLMs) is vulnerable. Simple attacks, or even benign fine-tuning, can jailbreak aligned models. We note that many of these vulnerabilities are related to a shared underlying issue: safety alignment can take shortcuts, wherein the alignment adapts a model's generative distribution primarily over only its very first few output tokens. We unifiedly refer to this issue as shallow s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30893<END_URL>

Safety Layers in Aligned Large Language Models: The Key to LLM Security\nAligned LLMs are secure, capable of recognizing and refusing to answer malicious questions. However, the role of internal parameters in maintaining such security is not well understood yet, further these models can be vulnerable to security degradation when subjected to fine-tuning attacks. To address these challenges, our work uncovers the mechanism behind security in aligned LLMs at the parameter level, identifying a smal<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28578<END_URL>

Safety-Prioritizing Curricula for Constrained Reinforcement Learning\nCurriculum learning aims to accelerate reinforcement learning (RL) by generating curricula, i.e., sequences of tasks of increasing difficulty. Although existing curriculum generation approaches provide benefits in sample efficiency, they overlook safety-critical settings where an RL agent must adhere to safety constraints.Thus, these approaches may generate tasks that cause RL agents to violate safety constraints during traini<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28899<END_URL>

SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generation\nRecent advances in diffusion models have significantly enhanced their ability to generate high-quality images and videos, but they have also increased the risk of producing unsafe content. Existing unlearning/editing-based methods for safe generation remove harmful concepts from the models but face several challenges: (1) They cannot instantly remove harmful or undesirable concepts (e.g., artist styles) without<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28752<END_URL>

SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration\nThe transformer architecture predominates across various models. As the heart of the transformer, attention has a computational complexity of $O(N^2)$, compared to $O(N)$ for linear transformations. When handling large sequence lengths, attention becomes the primary time-consuming component. Although quantization has proven to be an effective method for accelerating model inference, existing quantization methods pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29829<END_URL>

SAGEPhos: Sage Bio-Coupled and Augmented Fusion for Phosphorylation Site Detection\nPhosphorylation site prediction based on kinase-substrate interaction plays a vital role in understanding cellular signaling pathways and disease mechanisms. Computational methods for this task can be categorized into kinase-family-focused and individual kinase-targeted approaches. Individual kinase-targeted methods have gained prominence for their ability to explore a broader protein space and provide more preci<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28768<END_URL>

Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking\nAligning AI systems with human preferences typically suffers from the infamous *reward hacking* problem, where optimization of an imperfect reward model leads to undesired behaviors. In this paper, we investigate reward hacking in offline preference optimization, which aims to improve an initial model using a preference dataset. We identify two types of reward hacking stemming from statistical fluctua<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30189<END_URL>

SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation\nAs advancements in large language models (LLMs) continue and the demand for personalized models increases, parameter-efficient fine-tuning (PEFT) methods (e.g., LoRA) will become essential due to their efficiency in reducing computation costs.However, recent studies have raised alarming concerns that LoRA fine-tuning could potentially compromise the safety alignment in LLMs, posing significant risks for the model owner.In this paper, we fir<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30283<END_URL>

Salvage: Shapley-distribution Approximation Learning Via Attribution Guided Exploration for Explainable Image Classification\nThe integration of deep learning into critical vision application areas has given rise to a necessity for techniques that can explain the rationale behind predictions. In this paper, we address this need by introducing Salvage, a novel removal-based explainability method for image classification. Our approach involves training an explainer model that learns the prediction<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29378<END_URL>

SAM 2: Segment Anything in Images and Videos\nWe present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos. We build a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. Our model is a simple transformer architecture with streaming memory for real-time video processing. SAM 2 trained on our data provides strong performance across a wide range of tasks. I<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30219<END_URL>

Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling\nEfficiently modeling sequences with infinite context length has long been a challenging problem. Previous approaches have either suffered from quadratic computational complexity or limited extrapolation ability in length generalization. In thiswork, we present Samba, a simple hybrid architecture that layer-wise combinesMamba, a selective State Space Model (SSM), with Sliding Window Attention(SWA). Samba se<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29115<END_URL>

Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking\nMultiple object tracking in complex scenarios - such as coordinated dance performances, team sports, or dynamic animal groups - presents unique challenges. In these settings, objects frequently move in coordinated patterns, occlude each other, and exhibit long-term dependencies in their trajectories. However, it remains a key open research question on how to model long-range dependencies within tracklets, interdependenci<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29814<END_URL>

SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation\nThe Segment Anything model (SAM) has shown a generalized ability to group image pixels into patches, but applying it to semantic-aware segmentation still faces major challenges. This paper presents SAM-CP, a simple approach that establishes two types of composable prompts beyond SAM and composes them for versatile segmentation. Specifically, given a set of classes (in texts) and a set of SAM patches, the Type-I prompt judge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29459<END_URL>

SaMer: A Scenario-aware Multi-dimensional Evaluator for Large Language Models\nEvaluating the response quality of large language models (LLMs) for open-ended questions poses a significant challenge, especially given the subjectivity and multi-dimensionality of "quality" in natural language generation. Existing LLM evaluators often neglect that different scenarios require distinct evaluation criteria. In this work, we propose **SaMer**, a scenario-aware multi-dimensional evaluator designed to pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29184<END_URL>

Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models\nMultimodal Large Language Models (MLLMs) exhibit promising advancements across various tasks, yet they still encounter significant trustworthiness issues. Prior studies apply Split Conformal Prediction (SCP) in language modeling to construct prediction sets with statistical guarantees. However, these methods typically rely on internal model logits or are restricted to multiple-choice se<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30685<END_URL>

SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement\nIn this paper, we explore a principal way to enhance the quality of widely pre-existing coarse masks, enabling them to serve as reliable training data for segmentation models to reduce the annotation cost. In contrast to prior refinement techniques that are tailored to specific models or tasks in a close-world manner, we propose SAMRefiner, a universal and efficient approach by adapting SAM to the mask refinement task. The <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30090<END_URL>

SANA: Efficient High-Resolution Text-to-Image Synthesis with Linear Diffusion Transformers\nWe introduce Sana, a text-to-image framework that can efficiently generate images up to 4096$\times$4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU. Core designs include: (1) Deep compression autoencoder: unlike traditional AEs, which compress images only 8$\times$, we trained an AE that can com<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29897<END_URL>

SANER: Annotation-free Societal Attribute Neutralizer for Debiasing CLIP\nLarge-scale vision-language models, such as CLIP, are known to contain societal bias regarding protected attributes (e.g., gender, age). This paper aims to address the problems of societal bias in CLIP. Although previous studies have proposed to debias societal bias through adversarial learning or test-time projecting, our comprehensive study of these works identifies two critical limitations: 1) loss of attribute informat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27802<END_URL>

SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation\nThe development of diffusion models has led to significant progress in image and video generation tasks, with pre-trained models like the Stable Diffusion series playing a crucial role.However, a key challenge remains in downstream task applications: how to effectively and efficiently adapt pre-trained diffusion models to new tasks.Inspired by model pruning which lightens large pre-trained models by rem<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27860<END_URL>

Satisficing Exploration in Bandit Optimization\nMotivated by the concept of satisficing in decision-making, we consider the problem of satisficing exploration in bandit optimization. In this setting, the learner aims at finding a satisficing arm whose mean reward exceeds a certain threshold. The performance is measured by satisficing regret, which is the cumulative deficit of the chosen arm's mean reward compared to the threshold. We propose $\texttt{SELECT}$, a general algorithmic template for <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30941<END_URL>

SAVA: Scalable Learning-Agnostic Data Valuation\nSelecting data for training machine learning models is crucial since large, web-scraped, real datasets contain noisy artifacts that affect the quality and relevance of individual data points. These noisy artifacts will impact model performance. We formulate this problem as a data valuation task, assigning a value to data points in the training set according to how similar or dissimilar they are to a clean and curated validation set. Recently, LAVA<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31253<END_URL>

SBSC: Step-by-Step Coding for Improving Mathematical Olympiad Performance\nWe propose Step-by-Step Coding (SBSC): a multi-turn math reasoning framework that enables Large Language Models (LLMs) to generate sequence of programs for solving Olympiad level math problems. After each turn/step, by leveraging the code execution outputs and programs of previous steps, the model generates the next sub-task and the corresponding program to complete it. This way, SBSC, sequentially navigates to reach the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27845<END_URL>

Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier\nGraph unlearning has emerged as a pivotal research area for ensuring privacy protection, given the widespread adoption of Graph Neural Networks (GNNs) in applications involving sensitive user data. Among existing studies, certified graph unlearning is distinguished by providing robust privacy guarantees. However, current certified graph unlearning methods are impractical for large-scale graphs because they nec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28301<END_URL>

Scalable Bayesian Learning with posteriors\nAlthough theoretically compelling, Bayesian learning with modern machine learning models is computationally challenging since it requires approximating a high dimensional posterior distribution. In this work, we (i) introduce posteriors, an easily extensible PyTorch library hosting general-purpose implementations making Bayesian learning accessible and scalable to large data and parameter regimes; (ii) present a tempered framing of stochastic gradient <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28863<END_URL>

Scalable Benchmarking and Robust Learning for Noise-Free Ego-Motion and 3D Reconstruction from Noisy Video\nWe aim to redefine robust ego-motion estimation and photorealistic 3D reconstruction by addressing a critical limitation: the reliance on noise-free data in existing models. While such sanitized conditions simplify evaluation, they fail to capture the unpredictable, noisy complexities of real-world environments. Dynamic motion, sensor imperfections, and synchronization perturbations lead t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29719<END_URL>

Scalable Decentralized Learning with Teleportation\nDecentralized SGD can run with low communication costs, but its sparse communication characteristics deteriorate the convergence rate, especially when the number of nodes is large. In decentralized learning settings, communication is assumed to occur on only a given topology, while in many practical cases, the topology merely represents a preferred communication pattern, and connecting to arbitrary nodes is still possible. Previous studies have<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30604<END_URL>

Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction\nSequential decision-making in high-dimensional continuous action spaces, particularly in stochastic environments, faces significant computational challenges. We explore this challenge in the traditional offline RL setting, where an agent must learn how to make decisions based on data collected through a stochastic behavior policy. We present \textit{Latent Macro Action Planner} (L-MAP), which addresses this<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28299<END_URL>

Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics\nLearning to sample from complex unnormalized distributions over discrete domains emerged as a promising research direction with applications in statistical physics, variational inference, and combinatorial optimization. Recent work has demonstrated the potential of diffusion models in this domain. However, existing methods face limitations in memory scaling and thus the number of attainable diffusion steps <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28285<END_URL>

Scalable Extraction of Training Data from Aligned, Production Language Models\nWe show that *alignment*---a standard process that tunes LLMs to follow instructions in a harmless manner---seems to prevent existing data extraction attacks. We develop two novel attacks that undo a model's alignment and recover thousands of training examples from the popular proprietary model, OpenAI's ChatGPT.  Our most potent attack causes ChatGPT to emit training data in over 23% of conversations, and enables tar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27894<END_URL>

Scalable Influence and Fact Tracing for Large Language Model Pretraining\nTraining data attribution (TDA) methods aim to attribute model outputs back to specific training examples, and the application of these methods to large language model (LLM) outputs could significantly advance model transparency and data curation. However, it has been challenging to date to apply these methods to the full scale of LLM pretraining. In this paper, we introduce a gradient-based method that works effectively a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28824<END_URL>

Scalable Lifelong Multimodal Instruction Tuning via Dynamic Data Selection\nVisual instruction datasets from various distributors are released at different times and often contain a significant number of redundant text-image pairs, depending on their task compositions (i.e., skills) or reference sources. This redundancy greatly limits the efficient deployment of lifelong-adaptable Multimodal Large Language Models (MLLMs), hindering their ability to refine existing skills and acquire new competen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30379<END_URL>

Scalable Mechanistic Neural Networks\nWe propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) (Pervez et al., 2024), we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. This significant improvement enables efficient mode<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29818<END_URL>

Scalable Universal T-Cell Receptor Embeddings from Adaptive Immune Repertoires\nT cells are a key component of the adaptive immune system, targeting infections, cancers, and allergens with specificity encoded by their T cell receptors (TCRs), and retaining a memory of their targets. High-throughput TCR repertoire sequencing captures a cross-section of TCRs that encode the immune history of any subject, though the data are heterogeneous, high dimensional, sparse, and mostly unlabeled. Sets of TCR<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27808<END_URL>

Scale-Aware Contrastive Reverse Distillation for Unsupervised Anomaly Detection\nUnsupervised anomaly detection using deep learning has garnered significant research attention due to its broad applicability, particularly in medical imaging where labeled anomalous data are scarce. While earlier approaches leverage generative models like autoencoders and generative adversarial networks (GANs), they often fall short due to overgeneralization. Recent methods explore various strategies, including mem<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30228<END_URL>

Scale-aware Recognition in Satellite Images under Resource Constraints\nRecognition of features in satellite imagery (forests, swimming pools, etc.) depends strongly on the spatial scale of the concept and therefore the resolution of the images. This poses two challenges:Which resolution is best suited for recognizing a given concept, and where and when should the costlier higher-resolution (HR) imagery be acquired? We present a novel scheme to address these challenges by introducing three compo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29697<END_URL>

Scale-Free Graph-Language Models\nGraph-language models (GLMs) have shown great potential in graph-based semi-supervised learning. In GLMs, graph generation and text embedding are two key stages, which are typically achieved by inferring a latent graph and finetuning a language model, respectively. However, the former relies on artificial assumptions about the underlying edge distribution, while the latter requires sufficient data annotations. This paper introduces a novel GLM that addresses the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28413<END_URL>

Scaling and evaluating sparse autoencoders\nSparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing activations from a sparse bottleneck layer. Since language models learn many concepts, autoencoders need to be very large to recover all relevant features. However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28040<END_URL>

Scaling Diffusion Language Models via Adaptation from Autoregressive Models\nDiffusion Language Models (DLMs) have emerged as a promising new paradigm for text generative modeling, potentially addressing limitations of autoregressive (AR) models. However, current DLMs have been studied at a smaller scale compared to their AR counterparts and lack fair comparison on language modeling benchmarks. Additionally, training diffusion models from scratch at scale remains challenging. Given the prevalenc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28659<END_URL>

Scaling FP8 training to trillion-token LLMs\nWe train, for the first time, large language models using FP8 precision on datasets up to 2 trillion tokens --- a 20-fold increase over previous limits. Through these extended training runs, we uncover critical instabilities in FP8 training that were not observable in earlier works with shorter durations. We trace these instabilities to outlier amplification by the SwiGLU activation function. Interestingly, we show, both analytically and empirically, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30430<END_URL>

Scaling Instruction-tuned LLMs to Million-token Contexts via Hierarchical Synthetic Data Generation\nLarge Language Models (LLMs) struggle with long-context reasoning, not only due to the quadratic scaling of computational complexity with sequence length but also because of the scarcity and expense of annotating long-context data. There has been barely any open-source work that systematically ablates long-context data, nor is there any openly available instruction tuning dataset with contexts su<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30552<END_URL>

Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport\nDiffusion-based image generators are becoming unique methods for illumination harmonization and editing. The current bottleneck in scaling up the training of diffusion-based illumination editing models is mainly in the difficulty of preserving the underlying image details and maintaining intrinsic properties, such as albedos, unchanged. Without appropriate constraints, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28002<END_URL>

Scaling Large Language Model-based Multi-Agent Collaboration\nRecent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning. Inspired by the neural scaling law—increasing neurons enhances performance, this study explores whether the continuous addition of collaborative agents can yield similar benefits. Technically, we utilize directed acyclic graphs to organize agents into a multi-a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30077<END_URL>

Scaling Laws for Adversarial Attacks on Language Model Activations and Tokens\nWe explore a class of adversarial attacks targeting the activations of language models to derive upper-bound scaling laws on their attack susceptibility. By manipulating a relatively small subset of model activations, $a$, we demonstrate the ability to control the exact prediction of a significant number (in some cases up to 1000) of subsequent tokens $t$. We empirically verify a scaling law where the maximum number o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29238<END_URL>

Scaling Laws for Downstream Task Performance in Machine Translation\nScaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on studying scaling laws for pretraining (upstream) loss. However, in transfer learning settings, in which LLMs are pretrained on an unsupervised dataset and then finetuned on a downstream task, we often also care about the downstream performance. In this work, we study the scaling behavior in a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27915<END_URL>

Scaling Laws for Precision\nLow precision training and inference affect both the quality and cost of language models, but current scaling laws do not account for this. In this work, we devise "precision-aware" scaling laws for both training and inference. We propose that training in lower precision reduces the model's "effective parameter count," allowing us to predict the additional loss incurred from training in low precision and post-train quantization. For inference, we find that the degrada<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27832<END_URL>

Scaling Long Context Training Data by Long-Distance Referrals\nTraining large language models for long context understanding faces the challenge of data shortage.Previous data engineering approaches mechanically concatenate short documents, which may create many pseudo long documents but raise concerns about data quality.In this paper, we study the core attribute of high quality data for long context training, and provide a data pipeline, LongPack, to scalesuch data.We found that long distance r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28036<END_URL>

Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining\nA significant aspiration of offline reinforcement learning (RL) is to develop a generalist agent with high capabilities from large and heterogeneous datasets. However, prior approaches that scale offline RL either rely heavily on expert trajectories or struggle to generalize to diverse unseen tasks. Inspired by the excellent generalization of world model in conditional video generation, we explore the potential <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29567<END_URL>

Scaling Optimal LR Across Token Horizons\nState-of-the-art LLMs are powered by scaling -- scaling model size, dataset size, and cluster size. It is economically infeasible to extensively tune hyperparameters for the largest runs. Instead, approximately optimal hyperparameters must be inferred or transferred from smaller experiments. Hyperparameter transfer across model sizes has been studied in Yang et. al. However, hyperparameter transfer across dataset size -- or token horizon -- has not been <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29358<END_URL>

Scaling Test-Time Compute Optimally Can be More Effective than Scaling LLM Parameters\nEnabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we scale up inference-time computation in LLMs, with a focus on answering: if an LLM is allowed to use a fixed but non-trivial amount of inference-time compute, how much can it improve its performance on a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31024<END_URL>

Scaling Transformers for Low-Bitrate High-Quality Speech Coding\nThe tokenization of audio with neural audio codec models is a vital part of modern AI pipelines for the generation or understanding of speech, alone or in a multimodal context. Traditionally such tokenization models have concentrated on low parameter-count architectures using only components with strong inductive biases. In this work we show that by applying a transformer architecture with large parameter count to this problem, and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31006<END_URL>

Scaling up Masked Diffusion Models on Text\nMasked diffusion models (MDMs) have shown promise in language modeling, yet their scalability and effectiveness in core language tasks, such as conditional generation and language understanding, remain underexplored. This paper establishes the first scaling law for MDMs, demonstrating a scaling rate comparable to autoregressive models (ARMs) and a relatively small compute gap. Motivated by their scalability, we train a family of MDMs with up to 1.1 bil<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29366<END_URL>

Scaling up the Banded Matrix Factorization Mechanism for Large Scale Differentially Private ML\nCorrelated noise mechanisms such as DP Matrix Factorization (DP-MF) have proven to be effective alternatives to DP-SGD in large-epsilon few-epoch training regimes.  Significant work has been done to find the best correlated noise strategies, and the current state-of-the-art approach is DP-BandMF , which optimally balances the benefits of privacy amplification and noise correlation.  Despite it's utili<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30906<END_URL>

Scaling Wearable Foundation Models\nWearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large volumes of data; however, making sense of these observations for scientific and actionable insights is non-trivial. Inspired by the empirical success of generative modeling, where large neural networks learn powerful representations from vast amounts of text, image, video, or audio dat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27713<END_URL>

Scattered Forest Search: Smarter Code Space Exploration with LLMs\nWe propose a novel approach to scaling LLM inference for code generation. Weframe code generation as a black box optimization problem within the code space,and employ optimization-inspired techniques to enhance exploration. Specifically,we introduce SCATTERED FOREST SEARCH to enhance solution diversity whilesearching for solutions. Our theoretical analysis illustrates how these methods avoidlocal optima during optimization. Exten<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29947<END_URL>

Scene Flow as a Partial Differential Equation\nWe reframe scene flow as the task of estimating a continuous space-time ordinary differential equation (ODE) that describes motion for an entire observation sequence, represented with a neural prior. Our method, _EulerFlow_, optimizes this neural prior estimate against several multi-observation reconstruction objectives, enabling high quality scene flow estimation via self-supervision on real-world data. EulerFlow works out-of-the-box without tuning<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31266<END_URL>

Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure\nEstimating matrices in the symmetric positive-definite (SPD) cone is of interest for many applications ranging from computer vision to graph learning. While there exist various convex optimization-based estimators, they remain limited in expressivity due to their model-based approach. The success of deep learning motivates the use of learning-based approaches to estimate SPD matrices with neural networks in a data-d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27939<END_URL>

SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding\nScientific literature understanding is crucial for extracting targeted information and garnering insights, thereby significantly advancing scientific discovery.Despite the remarkable success of Large Language Models (LLMs), they face challenges in scientific literature understanding, primarily due to (1) a lack of scientific knowledge and (2) unfamiliarity with specialized scientific tasks.To develop an LLM specialized in scie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30757<END_URL>

ScImage: How good are multimodal large language models at scientific text-to-image generation?\nMultimodal large language models (LLMs) have demonstrated impressive capabilities in generating high-quality images from textual instructions. However, their performance in generating scientific images—a critical application for accelerating scientific progress—remains underexplored. In this work, we address this gap by introducing ScImage, a benchmark designed to evaluate the multimodal capabilities <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27964<END_URL>

SC-OmniGS: Self-Calibrating Omnidirectional Gaussian Splatting\n360-degree cameras streamline data collection for radiance field 3D reconstruction by capturing comprehensive scene data. However, traditional radiance field methods do not address the specific challenges inherent to 360-degree images. We present SC-OmniGS, a novel self-calibrating omnidirectional Gaussian splatting system for fast and accurate omnidirectional radiance field reconstruction using 360-degree images. Rather than conver<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30809<END_URL>

SCOPE: A Self-supervised framework for Improving Faithfulness in Conditional Text Generation\nLarge Language Models (LLMs), when used for conditional text generation, often produce hallucinations, i.e., information that is unfaithful or not grounded in the input context. This issue arises in typical conditional text generation tasks, such as text summarization and data-to-text generation, where the goal is to produce fluent text based on contextual input. When fine-tuned on specific domains, LLM<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28981<END_URL>

SCOPE: SCALABLE OPTIMIZATION FOR EFFICIENT AND ADPATIVE FOUNDATION MODELS\nIn the rapidly evolving landscape of AI, the development of scalable optimization methods to yield efficient and adaptive foundation models has significant demand in the space of their inference service. In specific, enabling model efficiency while allowing them to be adaptable to various new down-stream tasks has multifold challenges. Firstly, the model’s ability to quickly learn adaptive and efficient sub-model selectio<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23988<END_URL>

Score-based free-form architectures for high-dimensional Fokker-Planck equations\nDeep learning methods, which incorporate the PDE residual as loss function, have recently emerged to solve Fokker-Planck equations. Without reference solutions, proper normalization condition is required to avoid a trivial solution. However, soft constraints require careful balancing of multi-objective loss function, and specific network architectures may limit representation capacity. In this paper, we propose a n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30928<END_URL>

Score-based Self-supervised MRI Denoising\nMagnetic resonance imaging (MRI) is a powerful noninvasive diagnostic imaging tool that provides unparalleled soft tissue contrast and anatomical detail. Noise contamination, especially in accelerated and/or low-field acquisitions, can significantly degrade image quality and diagnostic accuracy. Supervised learning based denoising approaches have achieved impressive performance but require high signal-to-noise ratio (SNR) labels, which are often unavail<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27982<END_URL>

Score Forgetting Distillation: A Swift, Data-Free Method for Machine Unlearning in Diffusion Models\nThe machine learning community is increasingly recognizing the importance of fostering trust and safety in modern generative AI (GenAI) models. We posit machine unlearning (MU) as a crucial foundation for developing safe, secure, and trustworthy GenAI models. Traditional MU methods often rely on stringent assumptions and require access to real data. This paper introduces Score Forgetting Distilla<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28804<END_URL>

Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning\nOffline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance by leveraging pre-collected data and meta-learning techniques. Previous context-based approaches predominantly rely on the intuition that alternating optimization between the context encoder and the policy can lead to performance improveme<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30493<END_URL>

SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection\nFine-tuning on task-specific data to boost downstream performance is a crucial step for leveraging Large Language Models (LLMs). However, though fine-tuning enhances the model performance for specialized applications, previous studies have demonstrated that fine-tuning the models on several adversarial samples or even benign data can greatly comprise the model's pre-equipped alignment and safety capabilities. In this work,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29422<END_URL>

Searching for Optimal Solutions with LLMs via Bayesian Optimization\nScaling test-time compute to search for optimal solutions is an important step towards building generally-capable language models that can reason. Recent work, however, shows that tasks of varying complexity require distinct search strategies to solve optimally, thus making it challenging to design a one-size-fits-all approach. Prior solutions either attempt to predict task difficulty to select the optimal search strategy, ofte<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29164<END_URL>

SEBRA : Debiasing through Self-Guided Bias Ranking\nRanking samples by fine-grained estimates of spuriosity (the degree to which spurious cues are present) has recently been shown to significantly benefit bias mitigation, over the traditional binary biased-vs-unbiased partitioning of train sets. However, this spuriousity ranking comes with the requirement of human supervision. In this paper, we propose a debiasing framework based on our novel Self-Guided Bias Ranking (Sebra), that mitigates bias<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29907<END_URL>

SeCom: On Memory Construction and Retrieval for Personalized Conversational Agents\nTo deliver coherent and personalized experiences in long-term conversations, existing approaches typically perform retrieval augmented response generation by constructing memory banks from conversation history at either the turn-level, session-level, or through summarization techniques.In this paper, we explore the impact of different memory granularities and present two key findings: (1) Both turn-level and sess<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27790<END_URL>

Second Order Bounds for Contextual Bandits with Function Approximation\nMany works have developed algorithms no-regret algorithms for contextual bandits with function approximation, where the mean rewards over context-action pairs belongs to a function class $\mathcal{F}$. Although there are many approaches to this problem, one that has gained in importance is the use of algorithms based on the optimism principle such as optimistic least squares. It can be shown the regret of this algorithm scal<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28780<END_URL>

Second-Order Fine-Tuning without Pain for LLMs: A Hessian Informed Zeroth-Order Optimizer\nFine-tuning large language models (LLMs) is necessary for specific downstream tasks, but classic first-order optimizer entails prohibitive GPU memory because of the back propagation. Recent works such as MeZO have turned to zeroth-order optimizers for fine-tuning, which reduce substantial memory by using two forward passes. However, heterogeneous curvatures across different parameter dimensions in LLMs oft<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29118<END_URL>

Second-Order Min-Max Optimization with Lazy Hessians\nThis paper studies second-order methods for convex-concave minimax optimization.  Monteiro & Svaiter (2012)  proposed a method to solve the problem with an optimal iteration complexity of $\mathcal{O}(\epsilon^{-3/2})$ to find an $\epsilon$-saddle point.  However, it is unclear whether thecomputational complexity, $\mathcal{O}((N+ d^2) d \epsilon^{-2/3})$, can be improved. In the above, we follow  Doikov et al. (2023) and assume the complexit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28680<END_URL>

Second Workshop on Representational Alignment (Re$^2$-Align)\nBoth natural and artificial intelligences form representations of the world that they use to reason, make decisions, and communicate. Despite extensive research across machine learning, neuroscience, and cognitive science, it remains unclear what the most appropriate ways are to compare and align the representations of intelligence systems. In the second edition of the Workshop on Representational Alignment (Re$$^2$$-Align), we bring <BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23963<END_URL>

SecureGS: Boosting the Security and Fidelity of 3D Gaussian Splatting Steganography\n3D Gaussian splatting (3DGS) has emerged as a premier method for 3D representation due to its real-time rendering and high-quality outputs, underscoring the critical need to protect the copyright and privacy of 3D assets. Traditional NeRF steganography methods fail to address the explicit nature of 3DGS since its point cloud files are publicly accessible. Existing GS steganography solutions mitigate some issues <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30245<END_URL>

SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators\nLarge Language Models (LLMs) have transformed natural language processing, but face significant challenges in widespread deployment due to their high runtime cost. In this paper, we introduce SeedLM, a novel post-training compression method that uses seeds of a pseudo-random generator to encode and compress model weights. Specifically, for each block of weights, we find a seed that is fed into a Linear Feedback Shift Registe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28000<END_URL>

Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models\nAdvancements in Natural Language Processing (NLP), have led to the emergence of Large Language Models (LLMs) such as GPT, Llama, Claude, and Gemini, which excel across a range of tasks but require extensive fine-tuning to align their outputs with human expectations. A widely used method for achieving this alignment is Reinforcement Learning from Human Feedback (RLHF), which, despite its success, faces ch<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27970<END_URL>

See It from My Perspective: How Language Affects Cultural Bias in Image Understanding\nVision-language models (VLMs) can respond to queries about images in many languages. However, beyond language, culture affects how we see things.  For example, individuals from Western cultures focus more on the central figure in an image while individuals from East Asian cultures attend more to scene context (Nisbett 2001).  In this work, we characterize the Western bias of VLMs in image understanding and inv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29299<END_URL>

See What You Are Told: Visual Attention Sink in Large Multimodal Models\nLarge multimodal models (LMMs) "see" images by leveraging the attention mechanism between text and visual tokens in the transformer decoder. Ideally, these models should focus on key visual information relevant to the text token. However, recent findings indicate that LMMs have an extraordinary tendency to consistently allocate high attention weights to specific visual tokens, even when these tokens are irrelevant to the co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30795<END_URL>

SegLLM: Multi-round Reasoning Segmentation with Large Language Model\nWe present SegLLM, a novel multi-round interactive reasoning segmentation model that enhances LLM-based segmentation by exploiting conversational memory of both visual and textual outputs. By leveraging a mask-aware multimodal LLM, SegLLM re-integrates previous segmentation results into its input stream, enabling it to reason about complex user intentions and segment objects in relation to previously identified entities, inclu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29732<END_URL>

Segment Any 3D Object with Language\nIn this paper, we investigate Open-Vocabulary 3D Instance Segmentation (OV-3DIS) with free-form language instructions. Earlier works mainly rely on annotated base categories for training which leads to limited generalization to unseen novel categories. To mitigate the poor generalizability to novel categories, recent works generate class-agnostic masks or projecting generalized masks from 2D to 3D, subsequently classifying them with the assistance of 2D found<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30409<END_URL>

Select before Act: Spatially Decoupled Action Repetition for Continuous Control\nReinforcement Learning (RL) has achieved remarkable success in various continuous control tasks, such as robot manipulation and locomotion.Different to mainstream RL which makes decisions at individual steps, recent studies have incorporated action repetition into RL, achieving enhanced action persistence with improved sample efficiency and superior performance.However, existing methods treat all action dimensions a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29767<END_URL>

SELECTFORMER: PRIVATE AND PRACTICAL DATA SE- LECTION FOR TRANSFORMERS\nCritical to a free data market is $ \textit{private data selection}$, i.e. the model owner selects and then appraises training data from the data owner before both parties commit to a transaction. To keep the data and model private, this process shall evaluate the target model to be trained over Multi-Party Computation (MPC). While prior work suggests that evaluating Transformer-based models over MPC is prohibitively expensiv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31130<END_URL>

Selective Aggregation for Low-Rank Adaptation in Federated Learning\nWe investigate LoRA in federated learning through the lens of the asymmetry analysis of the learned $A$ and $B$ matrices. In doing so, we uncover that $A$ matrices are responsible for learning general knowledge, while $B$ matrices focus on capturing client-specific knowledge. Based on this finding, we introduce Federated Share-A Low-Rank Adaptation (FedSA-LoRA), which employs two low-rank trainable matrices $A$ and $B$ to model<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28693<END_URL>

Selective Attention Improves Transformer\nUnneeded elements in the attention's context degrade performance. We introduce Selective Attention, a simple parameter-free change to the standard attention mechanism which reduces attention to unneeded elements. Selective attention consistently improves language modeling performance across model sizes and context lengths. For example, a range of transformers trained with the language modeling objective on C4 with selective attention perform equivalently<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27940<END_URL>

Selective induction Heads: How Transformers Select Causal Structures in Context\nTransformers have exhibited exceptional capabilities in sequence modelling tasks, leveraging self-attention and in-context learning. Critical to this success are induction heads, attention circuits that enable copying tokens based on their previous occurrences. In this work, we introduce a novel synthetic framework designed to enable the theoretical analysis of transformers’ ability to dynamically handle causal stru<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29090<END_URL>

Selective Label Enhancement Learning for Test-Time Adaptation\nTest-time adaptation (TTA) aims to adapt a pre-trained model to the target domain using only unlabeled test samples. Most existing TTA approaches rely on definite pseudo-labels, inevitably introducing false labels and failing to capture uncertainty for each test sample. This prevents pseudo-labels from being flexibly refined as the model adapts during training, limiting their potential for performance improvement. To address this, we<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31070<END_URL>

Selective Task Group Updates for Multi-Task Optimization\nMulti-task learning enables the acquisition of task-generic knowledge by training multiple tasks within a unified architecture. However, training all tasks together in a single architecture can lead to performance degradation, known as negative transfer, which is a main concern in multi-task learning. Previous works have addressed this issue by optimizing the multi-task network through gradient manipulation or weighted loss adjustments. H<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30394<END_URL>

Selective Unlearning via Representation Erasure Using Adversarial Training\nWhen deploying machine learning models in the real world,  we often face the challenge of “unlearning” specific data points or subsets after training. Inspired by Domain-Adversarial Training of Neural Networks (DANN), we propose a novel algorithm, SURE, for targeted unlearning. SURE treats the process as a domain adaptation problem, where the “forget set” (data to be removed) and a validation set from the same distributi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30023<END_URL>

Self-Attention-Based Contextual Modulation Improves Neural System Identification\nConvolutional neural networks (CNNs) have been shown to be state-of-the-art models for visual cortical neurons. Cortical neurons in the primary visual cortex are sensitive to contextual information mediated by extensive horizontal and feedback connections. Standard CNNs integrate global contextual information to model contextual modulation via two mechanisms: successive convolutions and a fully connected readout la<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30094<END_URL>

Self-Boosting Large Language Models with  Synthetic Preference Data\nThrough alignment with human preferences, Large Language Models (LLMs) have advanced significantly in generating honest, harmless, and helpful responses. However, collecting high-quality preference data is a resource-intensive and creativity-demanding process, especially for the continual improvement of LLMs. We introduce SynPO, a self-boosting paradigm that leverages synthetic preference data for model alignment. SynPO employs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30794<END_URL>

Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models\nWhile recent Large Vision-Language Models (LVLMs) have shown remarkable performance in multi-modal tasks, they are prone to generating hallucinatory text responses that do not align with the given visual input, which restricts their practical applicability in real-world scenarios. In this work, inspired by the observation that the text-to-image generation process is the inverse of ima<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28052<END_URL>

SELF-EVOLVED REWARD LEARNING FOR LLMS\nReinforcement Learning from Human Feedback (RLHF) is a crucial technique for aligning language models with human preferences and is a key factor in the success of modern conversational models like GPT-4, ChatGPT, and Llama 2. A significant challenge in employing RLHF lies in training a reliable RM, which relies on high-quality labels. Typically, these labels are provided by human experts or a stronger AI, both of which can be costly and introduce bias that <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29197<END_URL>

Self-Evolving Multi-Agent Networks for Software Development\nLLM-driven multi-agent collaboration (MAC) systems have demonstrated impressive capabilities in automatic software development at the function level. However, their heavy reliance on human design limits their adaptability to the diverse demands of real-world software development.To address this limitation, we introduce EvoMAC, a novel self-evolving paradigm for MAC networks. Inspired by traditional neural network training, EvoMAC obtai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31011<END_URL>

Self-Improvement in Language Models: The Sharpening Mechanism\nRecent work in language modeling has raised the possibility of “self-improvement,” where an LLM evaluates and refines its own generations to achieve higher performance without external feedback. It is impossible for this self-improvement to create information that is not already in the model, so why should we expect that this will lead to improved capabilities? We offer a new theoretical perspective on the capabilities of self-improv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29372<END_URL>

Self-Improving Foundation Models Without Human Supervision\nAs foundation models (FMs) scale, they face a data bottleneck, where the growth of high-quality internet data unable to keep pace with their training needs. This is most apparent with text data already, has been a consistent problem in domains such as embodied intelligence, and is expected to soon inflict other modalities as well. ***Self-improvement***, a paradigm where models generate and train on synthetic data generated from the sam<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23971<END_URL>

Self-Improving Robust Preference Optimization\nBoth online and offline RLHF methods such as PPO and DPO have been extremely successful in aligning AI with human preferences. Despite their success, the existing methods suffer from some fundamental limitations: prominent among those limitations are (a) models trained with RLHF  can learn from mistakes  or negative examples through RL mechanism or contrastive loss at the time of training. However at the time of inference they are not equipped with <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29219<END_URL>

Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models\nHallucination remains a significant challenge in Large Vision-Language Models (LVLMs). To alleviate this issue, some methods, known as contrastive decoding, induce hallucinations by manually disturbing the raw vision or instruction inputs and then mitigate them by contrasting the outputs of the original and disturbed LVLMs. However, these holistic input disturbances sometimes induce potential noise and also<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28166<END_URL>

Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts\nWe present Self-MoE, an approach that transforms a monolithic LLM into a compositional, modular system of self-specialized experts, named MiXSE (MiXture of Self-specialized Experts). Our approach leverages self-specialization, which constructs expert modules using self-generated synthetic data, each equipping a shared base LLM with distinct domain-specific capabilities, activated via self-optimized routing. This<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30184<END_URL>

Self-Normalized Resets for Plasticity in Continual Learning\nPlasticity Loss is an increasingly important phenomenon that refers to the empirical observation that as a neural network is continually trained on a sequence of changing tasks, its ability to adapt to a new task diminishes over time. We introduce Self-Normalized Resets (SNR), a simple adaptive algorithm that mitigates plasticity loss by resetting a neuron’s weights when evidence suggests its firing rate has effectively dropped to zero<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30297<END_URL>

Self-Play Preference Optimization for Language Model Alignment\nStandard reinforcement learning from human feedback (RLHF) approaches relying on parametric models like the Bradley-Terry model fall short in capturing the intransitivity and irrationality in human preferences. Recent advancements suggest that directly working with preference probabilities can yield a more accurate reflection of human preferences, enabling more flexible and accurate language model alignment. In this paper, we propos<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29189<END_URL>

Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models\nOne core capability of large language models~(LLMs) is to follow natural language instructions. However, the issue of automatically constructing high-quality training data to enhance the complex instruction-following abilities of LLMs without manual annotation remains unresolved. In this paper, we introduce AutoIF, the first scalable and reliable method for automatically generating instructi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29049<END_URL>

Self-supervised contrastive learning performs non-linear system identification\nSelf-supervised learning (SSL) approaches have brought tremendous success across many tasks and domains. It has been argued that these successes can be attributed to a link between SSL and identifiable representation learning: Temporal structure and auxiliary variables ensure that latent representations are related to the true underlying generative factors of the data. Here, we deepen this connection and show that SS<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29828<END_URL>

Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement\nMagnetic Resonance Imaging (MRI), including diffusion MRI (dMRI), serves as a ``microscope'' for anatomical structures and routinely mitigates the influence of low signal-to-noise ratio scans by compromising temporal or spatial resolution. However, these compromises fail to meet clinical demands for both efficiency and precision. Consequently, denoising is a vital preprocessing step, particularly for dMRI, where clean d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27810<END_URL>

Self-Supervised Diffusion Processes for Electron-Aware Molecular Representation Learning\nPhysical properties derived from electronic distributions are essential information determining molecular properties. However, the electron-level information is not accessible in most real-world complex molecules due to extensive computational costs of determining uncertain electronic distributions. For this reason, existing machine learning methods for molecular property prediction have remained in regress<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29476<END_URL>

Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining\nSelf-supervised monocular depth estimation (SSMDE) aims to predict the dense depth map of a monocular image, by learning depth from RGB image sequences, eliminating the need for ground-truth depth labels.Although this approach simplifies data acquisition compared to supervised methods, it struggles with reflective surfaces, as they violate the assumptions of Lambertian reflectance, leading to ina<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29298<END_URL>

Self-Updatable Large Language Models with Parameter Integration\nDespite significant advancements in large language models (LLMs), the rapid and frequent integration of small-scale experiences, such as interactions with sur- rounding objects, remains a substantial challenge. Two critical factors in assimilating these experiences are (1) **Efficacy**: the ability to accurately remember recent events; (2) **Retention**: the capacity to recall long-past experiences. Current methods either embed exp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29183<END_URL>

SelKD: Selective Knowledge Distillation via Optimal Transport Perspective\nKnowledge Distillation (KD) has been a popular paradigm for training a (smaller) student model from its teacher model. However, little research has been done on the practical scenario where only a subset of the teacher's knowledge needs to be distilled, which we term selective KD (SelKD). This demand is especially pronounced in the era of foundation models, where the teacher model can be significantly larger than the stud<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30244<END_URL>

Semantic Aware Representation Learning for Lifelong Learning\nThe human brain excels at lifelong learning by not only encoding information in sparse activation codes but also leveraging rich semantic structures and relationships between newly encountered and previously learned objects. This ability to utilize semantic similarities is crucial for efficient learning and knowledge consolidation, yet is often underutilized in current continual learning approaches. To bridge this gap, we propose Sema<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29333<END_URL>

Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations\nGenerative models transform random noise into images; their inversion aims to transform images back to structured noise for recovery and editing. This paper addresses two key tasks: (i) *inversion* and (ii) *editing* of a real image using stochastic equivalents of rectified flow models (such as Flux). Although Diffusion Models (DMs) have recently dominated the field of generative modeling for images, their in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30203<END_URL>

Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe Responses in LLMs\nLarge Language Models (LLMs) generating unsafe responses to toxic prompts is a significant issue in their applications. While various efforts aim to address this safety concern, previous approaches often demand substantial human data collection or rely on the less dependable option of using another LLM to generate corrective data. In this paper, we aim to take this problem and overcome limitations of requiring<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28590<END_URL>

Semantics-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors\nLarge language models (LLMs) have achieved remarkable performance across many tasks, yet aligning them with desired behaviors remains challenging. Activation intervention has emerged as an effective and economical method to modify the behavior of LLMs. Despite considerable interest in this area, current intervention methods exclusively employ a fixed steering vector to modify model activations, lacking adaptability<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30762<END_URL>

Semantic Skill Extraction via Vision-Language Model Guidance for Efficient Reinforcement Learning\nExtracting temporally extended skills can significantly improve the efficiency of reinforcement learning (RL) by breaking down complex decision-making problems with sparse rewards into simpler subtasks and enabling more effective credit assignment. However, existing abstraction methods either discover skills in an unsupervised manner, which often lacks semantic information and leads to erroneous or<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27665<END_URL>

Semantix: An Energy-guided Sampler for Semantic Style Transfer\nRecent advances in style and appearance transfer are impressive, but most methods isolate global style and local appearance transfer, neglecting semantic correspondence. Additionally, image and video tasks are typically handled in isolation, with little focus on integrating them for video transfer. To address these limitations, we introduce a novel task, *Semantic Style Transfer*, which involves transferring style and appearance fea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28103<END_URL>

SEMDICE: Off-policy State Entropy Maximization via Stationary Distribution Correction Estimation\nIn the unsupervised pre-training for reinforcement learning, the agent aims to learn a prior policy for downstream tasks without relying on task-specific reward functions. We focus on state entropy maximization (SEM), where the goal is to learn a policy that maximizes the entropy of the state's stationary distribution. In this paper, we introduce SEMDICE, a principled off-policy algorithm that compu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28205<END_URL>

Semialgebraic Neural Networks: From roots to representations\nMany numerical algorithms in scientific computing—particularly in areas like numerical linear algebra, PDE simulation, and inverse problems—produce outputs that can be represented by semialgebraic functions; that is, the graph of the computed function can be described by finitely many polynomial equalities and inequalities. In this work, we introduce Semialgebraic Neural Networks (SANNs), a neural network architecture capable of repre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27662<END_URL>

Semi-Parametric Retrieval via Binary Bag-of-Tokens Index\nInformation retrieval has transitioned from standalone systems into essential components across broader applications, with indexing efficiency, cost-effectiveness, and freshness becoming increasingly critical yet often overlooked. In this paper, we introduce SemI-parametric Disentangled Retrieval (SiDR), a bi-encoder retrieval framework that decouples retrieval index from neural parameters to enable efficient, low-cost, and parameter-agno<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28545<END_URL>

Semi-Supervised CLIP Training by Enforcing Semantic and Trapezoidal Consistency\nVision-language pre-training models, such as CLIP, have demonstrated strong capability in rapidly adapting to downstream tasks through fine-tuning, and have been widely applied across various tasks. However, when the downstream tasks are constrained by limited image-text paired data, CLIP struggles to effectively address the domain gap between the pre-training and the target tasks. To address this limitation, we pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30720<END_URL>

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving\nUnderstanding world dynamics is crucial for planning in autonomous driving. Recent methods attempt to achieve this by learning a 3D occupancy world model that forecasts future surrounding scenes based on current observation. However, 3D occupancy labels are still required to produce promising results. Considering the high annotation cost for 3D outdoor scenes, we propose a semi-supervised vision-centric 3D occupancy <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28213<END_URL>

Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations\nParametric differential equations of the form $\partial{\textbf{u}}/\partial{t} = f(\textbf{u}, \textbf{x}, t, \mathbf{p})$, where $\mathbf{p}$ represents physical system parameters, are fundamental across scientific and engineering disciplines. Recent advances in surrogate modeling, particularly deep learning frameworks like the Fourier Neural Operator (FNO), have demonstrated<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30461<END_URL>

Sensitivity Verification for Decision Tree Ensembles\nTree ensemble models, such as Gradient Boosted Decision Trees (GBDTs) andrandom forests, are widely popular models for a variety of machine learning tasks.The power of these models comes from the ensemble of decision trees, whichmakes analysis of such models significantly harder than for single trees. As aresult, recent work has focused on developing exact and approximate techniquesfor questions such as robustness verification, fairness and e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28785<END_URL>

Sensor-Invariant Tactile Representation\nHigh-resolution tactile sensors have become critical for embodied perception and robotic manipulation. However, a key challenge in the field is the lack of transferability between sensors due to design and manufacturing variations, which result in significant differences in tactile signals. This limitation hinders the ability to transfer models or knowledge learned from one sensor to another. To address this, we introduce a novel method for extracting Sen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29640<END_URL>

SEPARATE: A Simple Low-rank Projection for Gradient Compression in Modern Large-scale Model Training Process\nTraining Large Language Models (LLMs) presents a significant communication bottleneck, predominantly due to the growing scale of the gradient to communicate across multi-device clusters. However, how to mitigate communication overhead in practice remains a formidable challenge due to the weakness of the methodology of the existing compression methods, especially the neglect of the charac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30774<END_URL>

Separation Power of Equivariant Neural Networks\nThe separation power of a machine learning model refers to its ability to distinguish between different inputs and is often used as a proxy for its expressivity. Indeed, knowing the separation power of a family of models is a necessary condition to obtain fine-grained universality results. In this paper, we analyze the separation power of equivariant neural networks, such as convolutional and permutation-invariant networks.We first present a compl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29661<END_URL>

SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction\nLarge Language Models (LLMs) have demonstrated improved generation performance by incorporating externally retrieved knowledge, a process known as retrieval-augmented generation (RAG). Despite the potential of this approach, existing studies evaluate RAG effectiveness by 1) assessing retrieval and generation components jointly, which obscures retrieval's distinct contribution, or 2) examining retrievers using tra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28663<END_URL>

Sequential Controlled Langevin Diffusions\nAn effective approach for sampling from unnormalized densities is based on the idea of gradually transporting samples from an easy prior to the complicated target distribution. Two popular methods are (1) Sequential Monte Carlo (SMC), where the transport is performed through successive annealed densities via prescribed Markov chains and resampling steps, and (2) recently developed diffusion-basedsampling methods, where a learned dynamical transport is u<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28991<END_URL>

Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning\nReinforcement learning (RL) has emerged as a promising tool for combinatorial optimization (CO) problems due to its ability to learn fast, effective, and generalizable solutions. Nonetheless, existing works mostly focus on one-shot deterministic CO, while sequential stochastic CO (SSCO) has rarely been studied despite its broad applications such as adaptive influence maximization (IM) and infectious disease<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30618<END_URL>

Seq-VCR: Preventing  Collapse in Intermediate Transformer Representations for Enhanced Reasoning\nDecoder-only Transformers often struggle with complex reasoning tasks, particularly arithmetic reasoning requiring multiple sequential operations. In this work, we identify representation collapse in the model’s intermediate layers as a key factor limiting their reasoning capabilities. To address this, we propose Sequential Variance-Covariance Regularization (Seq-VCR), which enhances the entropy of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31097<END_URL>

SeRA: Self-Reviewing and Alignment of LLMs using Implicit Reward Margins\nDirect alignment algorithms (DAAs), such as direct preference optimization (DPO), have become popular alternatives to Reinforcement Learning from Human Feedback (RLHF) due to their simplicity, efficiency, and stability. However, the preferences used by DAAs are usually collected before alignment training begins and remain unchanged (off-policy). This design leads to two problems where the policy model (1) picks up on spuri<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27987<END_URL>

Severing Spurious Correlations with Data Pruning\nDeep neural networks have been shown to learn and rely on spurious correlations present in the data that they are trained on. Reliance on such correlations can cause these networks to malfunction when deployed in the real world, where these correlations may no longer hold. To overcome the formation of such correlations, recent studies propose approaches that yield promising results. These works, however, study settings where the strength of the s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30555<END_URL>

SFESS: Score Function Estimators for $k$-Subset Sampling\nAre score function estimators a viable approach to learning with $k$-subset sampling? Sampling $k$-subsets is a fundamental operation in machine learning that is not amenable to differentiable parametrization, impeding gradient-based optimization. Prior work has focused on relaxed sampling or approximate pathwise gradients but dismissed score function estimators due to their high variance. Inspired by the success of score function estimat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28261<END_URL>

SGD with memory: fundamental properties and stochastic acceleration\nAn important open problem is the theoretically feasible acceleration of mini-batch SGD-type algorithms on quadratic problems with power-law spectrum. In the non-stochastic setting, the optimal exponent $\xi$ in the loss convergence $L_t\sim C_Lt^{-\xi}$ is double that in plain GD and is achievable using Heavy Ball (HB) with a suitable schedule; this no longer works in the presence of mini-batch noise. We address this challenge <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29667<END_URL>

SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation\nMethods for image-to-video generation have achieved impressive, photo-realistic quality. However, adjusting specific elements in generated videos, such as object motion or camera movement, is often a tedious process of trial and error, e.g., involving re-generating videos with different random seeds. Recent techniques address this issue by fine-tuning a pre-trained model to follow conditioning signals, such as bounding boxes or<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27979<END_URL>

Shallow diffusion networks provably learn hidden low-dimensional structure\nDiffusion-based generative models provide a powerful framework for learning to sample from a complex target distribution. The remarkable empirical success of these models applied to high-dimensional signals, including images and video, stands in stark contrast to classical results highlighting the curse of dimensionality for distribution recovery. In this work, we take a step towards understanding this gap through a care<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30033<END_URL>

Shape as Line Segments: Accurate and Flexible Implicit Surface Representation\nDistance field-based implicit representations like signed/unsigned distance fields have recently gained prominence in geometry modeling and analysis. However, these distance fields are reliant on the closest distance of points to the surface, introducing inaccuracies when interpolating along cube edges during surface extraction. Additionally, their gradients are ill-defined at certain locations, causing distortions in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29644<END_URL>

Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation\nGraph Neural Networks (GNNs) have demonstrated remarkable performance in various graph-based machine learning tasks, yet evaluating the importance of neighbors of testing nodes remains largely unexplored due to the challenge of assessing data importance without test labels. To address this gap, we propose Shapley-Guided Utility Learning (SGUL), a novel framework for graph inference data valuation. SGUL innovatively com<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30761<END_URL>

Shared-AE: Unsupervised Identification of Shared Subspaces in High-dimensional Neural and Behavioral Activity\nUnderstanding the relationship between behavior and neural activity is crucial for understanding brain function. One effective method is to learn embeddings for interconnected modalities. For simple behavioral tasks, neural features can be learned based on labels. However, complex behavioral tasks and social behaviors require joint extraction of both behavioral and neural features. In t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27666<END_URL>

SharedContextBench: How Lossy are Long-context Methods in KV Cache Reuse\nLong-context Large Language Models (LLMs) have unlocked numerous possibilities for downstream applications, many of which involve multiple requests sharing the same input context. Recent inference frameworks like vLLM and SGLang, as well as LLMs providers such as OpenAI, Gemini and Claude, have employed prefix caching techniques to accelerate multi-requests with shared context. However, existing long-context methods are pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28803<END_URL>

Sharper Guarantees for Learning Neural Network Classifiers with Gradient Methods\nIn this paper, we study the data-dependent convergence and generalization behavior of gradient methods for neural networks with smooth activation. Our first result is a novel bound on the excess risk of deep networks trained by the logistic loss via an alogirthmic stability analysis. Compared to previous works, our results improve upon the shortcomings of the well-established Rademacher complexity-based bounds. Imp<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28778<END_URL>

Sharpness-Aware Black-Box Optimization\nBlack-box optimization algorithms have been widely used in various machine learning problems, including reinforcement learning and prompt fine-tuning. However, directly optimizing the training loss value, as commonly done in existing black-box optimization methods, could lead to suboptimal model quality and generalization performance.To address those problems in black-box optimization, we propose a novel Sharpness-Aware Black-box Optimization (SABO) algori<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28779<END_URL>

Sharpness-Aware Minimization Efficiently Selects Flatter Minima Late In Training\nSharpness-Aware Minimization (SAM) has substantially improved the generalization of neural networks under various settings.Despite the success, its effectiveness remains poorly understood.In this work, we discover an intriguing phenomenon in the training dynamics of SAM, shedding lights on understanding its implicit bias towards flatter minima over Stochastic Gradient Descent (SGD).Specifically, we find that *SAM e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29182<END_URL>

Sharpness Aware Minimization: General Analysis and Improved Rates\nSharpness-Aware Minimization (SAM) has emerged as a powerful method for improving generalization in machine learning models by minimizing the sharpness of the loss landscape. However, despite its success, several important questions regarding the convergence properties of SAM in non-convex settings are still open, including the benefits of using normalization in the update rule, the dependence of the analysis on the restrictive b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30738<END_URL>

Shedding Light on Time Series Classification using Interpretability Gated Networks\nIn time-series classification, interpretable models can bring additional insights but be outperformed by deep models since human-understandable features have limited expressivity and flexibility. In this work, we present InterpGN, a framework that integrates an interpretable model and a deep neural network. Within this framework, we introduce a novel gating function design based on the confidence of the interpret<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28432<END_URL>

ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design\nEngineering molecules to exhibit precise 3D intermolecular interactions with their environment forms the basis of chemical design. In ligand-based drug design, bioisosteric analogues of known bioactive hits are often identified by virtually screening chemical libraries with shape, electrostatic, and pharmacophore similarity scoring functions. We instead hypothesize that a generative model which learns the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30057<END_URL>

Shh, don't say that! Domain Certification in LLMs\nLarge language models (LLMs) are often deployed to do constrained tasks, with narrow domains. For example, customer support bots can be built on top of LLMs, relying on their broad language understanding and capabilities to enhance performance. However, these LLMs are adversarially susceptible, potentially generating outputs outside the intended domain. To formalize, assess and mitigate this risk, we introduce \emph{domain certification}; a guar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30364<END_URL>

Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learning\nDeep learning models often lack shift invariance, making them sensitive to input shifts that cause changes in output. While recent techniques seek to address this for images, our findings show that these approaches fail to provide shift-invariance in time series, where the data generation mechanism is more challenging due to the interaction of low and high frequencies. Wors<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28398<END_URL>

ShortcutsBench: A Large-Scale Real-world Benchmark for API-based Agents\nRecent advancements in integrating large language models (LLMs) with application programming interfaces (APIs) have gained significant interest in both academia and industry. Recent work demonstrates that these API-based agents exhibit relatively strong autonomy and planning capabilities. However, their ability to handle multi-dimensional difficulty levels, diverse task types, and real-world demands remains unknown. In this<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28594<END_URL>

Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos\nA short clip of video may contain progression of multiple events and an interesting story line. A human need to capture both the event in every shot and associate them together to understand the story behind it. In this work, we present a new multi-shot video understanding benchmark \dataset with detailed shot-level captions, comprehensive video summaries and question-answering pairs. To facilitate better semantic <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30335<END_URL>

Should VLMs be Pre-trained with Image Data?\nPre-trained LLMs that are further trained with image data perform well on vision-language tasks. While adding images during a second training phase effectively unlocks this capability, it is unclear how much of a gain or loss this two-step pipeline gives over VLMs which integrate images earlier into the training process. To investigate this, we train models spanning various datasets, scales, image-text ratios, and amount of pre-training done before in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29734<END_URL>

Show-o: One Single Transformer to Unify Multimodal Understanding and Generation\nWe present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies autoregressive and (discrete) diffusion modeling to adaptively handle inputs and outputs of various and mixed modalities. The unified model flexibly supports a wide range of vision-language tasks including visual question-answering, text-to-image generation, text-gu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28376<END_URL>

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes\nInferring the causal structure underlying stochastic dynamical systems from observational data holds great promise in domains ranging from science and health to finance. Such processes can often be accurately modeled via stochastic differential equations (SDEs), which naturally imply causal relationships via `which variables enter the differential of which other variables'. In this paper, we develop con<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29848<END_URL>

SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning\nRecent advances in CV and NLP have been largely driven by scaling up the number of network parameters, despite traditional theories suggesting that larger networks are prone to overfitting.These large networks avoid overfitting by integrating components that induce a simplicity bias, guiding models toward simple and generalizable solutions. However, in deep RL, designing and scaling up networks have been less explor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28635<END_URL>

SimPER: Simple Preference Fine-Tuning without Hyperparameters by Perplexity Optimization\nPreference optimization has made significant advances in aligning large language models with preference data. However, existing preference optimization objectives require additional hyperparameters that must be extensively manually adjusted to achieve optimal performance, increasing the complexity and time required for fine-tuning large language models. In this paper, we propose a simplehyperparameter-free <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28626<END_URL>

Simple and Controllable Uniform Discrete Diffusion Language Models\nDiffusion models for continuous data gained widespread adoption owing to their high quality generation and control mechanisms. However, controllable diffusion on discrete data faces challenges: continuous diffusion guidance methods are not applicable and recent discrete diffusion models are not well-suited to control or exhibit a quality gap. Here, we provide a straightforward derivation of classifier-free and classifier-based g<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28719<END_URL>

Simple, Good, Fast: Self-Supervised World Models Free of Baggage\nWhat are the essential components of world models? How far do we get with world models that are not employing RNNs, transformers, discrete representations, and image reconstructions? This paper introduces SGF, a Simple, Good, and Fast world model that uses self-supervised representation learning, captures short-time dependencies through frame and action stacking, and enhances robustness against model errors through data augmentati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27740<END_URL>

Simple ReFlow: Improved Techniques for Fast Flow Models\nDiffusion and flow-matching models achieve remarkable generative performance but at the cost of many neural function evaluations (NFE), which slows inference and limits applicability to time-critical tasks. The ReFlow procedure can accelerate sampling by straightening generation trajectories. But it is an iterative procedure, typically requiring training on simulated data, and results in reduced sample quality. To mitigate sample deteriora<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28855<END_URL>

Simple yet Effective Incomplete Multi-view Clustering: Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction\nMost of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct  bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\textbf{all}$ views.   To eliminate these drawbacks, we present a simple yet effective IMVC approa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30038<END_URL>

Simplifying Deep Temporal Difference Learning\n$Q$-learning played a foundational role in the field reinforcement learning (RL).However, TD algorithms with off-policy data, such as $Q$-learning, or nonlinear function approximation like deep neural networks require several additional tricks to stabilise training, primarily a large replay buffer and target networks. Unfortunately, the delayed updating of frozen network parameters in the target network harms the sample efficiency and, similarly, th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30831<END_URL>

Simplifying, Stabilizing and Scaling Continuous-time Consistency Models\nConsistency models (CMs) are a powerful class of diffusion-based generative models optimized for fast sampling. Most existing CMs are trained using discretized timesteps, which introduce additional hyperparameters and are prone to discretization errors. While continuous-time formulations can mitigate these issues, their success has been limited by training instability. To address this, we propose a simplified theoretical fr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29963<END_URL>

SIMPL: Scalable and hassle-free optimisation of neural representations from behaviour\nHigh-dimensional neural activity in the brain is known to encode low-dimensional, time-evolving, behaviour-related variables. A fundamental goal of neural data analysis consists of identifying such variables and their mapping to neural activity. The canonical approach is to assume the latent variables are behaviour and visualize the subsequent tuning curves. However, significant mismatches between behaviour an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30670<END_URL>

SIM: Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding from Movie-Watching Experiments\nCurrent AI frameworks for brain decoding and encoding, typically train and test models within the same datasets. This limits their utility for brain computer interfaces (BCI) or neurofeedback, for which it would be useful to pool experiences across individuals to better simulate stimuli not sampled during training. A key obstacle to model generalisation is the degree of variability of inter-su<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29830<END_URL>

Simulating Human-like Daily Activities with Desire-driven Autonomy\nExisting task-oriented AI agents often depend on explicit instructions or external rewards, limiting their ability to be driven by intrinsic motivations like humans. In this paper, we present a desire-driven autonomy framework to guide a Large Language Model based (LLM-based) agent to simulate human-like daily activities. In contrast to previous agents, our Desire-driven Autonomous Agent (D2A) operates on the principle of intrin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31056<END_URL>

Simulating Training Dynamics to Reconstruct Training Data from Deep Neural Networks\nWhether deep neural networks (DNNs) memorize the training data is a fundamental open question in understanding deep learning. A direct way to verify the memorization of DNNs is to reconstruct training data from DNNs' parameters. Since parameters are gradually determined by data throughout training, characterizing training dynamics is important for reconstruction. Pioneering works rely on the linear training dyna<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29225<END_URL>

SimulPL: Aligning Human Preferences in Simultaneous Machine Translation\nSimultaneous Machine Translation (SiMT) generates translations while receiving streaming source inputs. This requires the SiMT model to learn a read/write policy, deciding when to translate and when to wait for more source input. Numerous linguistic studies indicate that audiences in SiMT scenarios have distinct preferences, such as accurate translations, simpler syntax, and no unnecessary latency. Aligning SiMT models with<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29316<END_URL>

SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark\nPowder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established benchmarks. To address this, we introduce SimXRD, the largest open-source simulated XRD pattern <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28452<END_URL>

SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects\nWe address the challenge of creating 3D assets for household articulated objects from a single image.Prior work on articulated object creation either requires multi-view multi-state input, or only allows coarse control over the generation process.These limitations hinder the scalability and practicality for articulated object modeling.In this work, we propose a method to generate articulated objects from a single image.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29817<END_URL>

SINGER: Stochastic Network Graph Evolving Operator for High Dimensional PDEs\nWe present a novel framework, StochastIc Network Graph Evolving operatoR (SINGER), for learning the evolution operator of high-dimensional partial differential equations (PDEs). The framework uses a sub-network to approximate the solution at the initial time step and stochastically evolves the sub-network parameters over time by a graph neural network to approximate the solution at later time steps. The framework is de<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27840<END_URL>

Single-agent Poisoning Attacks Suffice to Ruin Multi-Agent Learning\nWe investigate the robustness of multi-agent learning in strongly monotone games with bandit feedback. While previous research has developed learning algorithms that achieve last-iterate convergence to the unique Nash equilibrium (NE) at a polynomial rate, we demonstrate that all such algorithms are vulnerable to adversaries capable of poisoning even a single agent's utility observations. Specifically, we propose an attacking s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31035<END_URL>

Single Teacher, Multiple Perspectives: Teacher Knowledge Augmentation for Enhanced Knowledge Distillation\nDo diverse perspectives help students learn better? Multi-teacher knowledge distillation, which is a more effective technique than traditional single-teacher methods, supervises the student from different perspectives (i.e., teacher). While effective, multi-teacher, teacher ensemble, or teaching assistant-based approaches are computationally expensive and resource-intensive, as they require<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30441<END_URL>

Singular Subspace Perturbation Bounds via Rectangular Random Matrix Diffusions\nGiven a matrix $A \in \mathbb{R}^{m\times d}$ with singular values $\sigma_1\geq \cdots \geq \sigma_d$, and a random matrix $G \in \mathbb{R}^{m\times d}$ with iid $N(0,T)$ entries for some $T>0$, we derive  new bounds on the Frobenius distance between subspaces spanned by the top-$k$ (right) singular vectors of $A$ and $A+G$. This problem arises in numerous applications in statistics where a data matrix may be corru<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30296<END_URL>

SiReRAG: Indexing Similar and Related Information for Multihop Reasoning\nIndexing is an important step towards strong performance in retrieval-augmented generation (RAG) systems. However, existing methods organize data based on either semantic similarity (similarity) or related information (relatedness), but do not cover both perspectives comprehensively. Our analysis reveals that modeling only one perspective results in insufficient knowledge synthesis, leading to suboptimal performance on com<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27699<END_URL>

Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes\nRecent advancements in human motion synthesis have focused on specific types of motions, such as human-scene interaction, locomotion or human-human interaction, however, there is a lack of a unified system capable of generating a diverse combination of motion types. In response, we introduce *Sitcom-Crafter*, a comprehensive and extendable system for human motion generation in 3D space, which can be guided by extensive pl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30316<END_URL>

Size-Generalizable RNA Structure Evaluation by Exploring Hierarchical Geometries\nUnderstanding the 3D structure of RNA is essential for deciphering its function and developing RNA-based therapeutics. Geometric Graph Neural Networks (GeoGNNs) that conform to the $\mathrm{E}(3)$-symmetry have advanced RNA structure evaluation, a crucial step toward RNA structure prediction. However, existing GeoGNNs are still defective in two aspects: 1. inefficient or incapable of capturing the full geometries o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29684<END_URL>

Sketch2Diagram: Generating Vector Diagrams from Hand-Drawn Sketches\nWe address the challenge of automatically generating high-quality vector diagrams from hand-drawn sketches. Vector diagrams are essential for communicating complex ideas across various fields, offering flexibility and scalability. While recent research has progressed in generating diagrams from text descriptions, converting hand-drawn sketches into vector diagrams remains largely unexplored, primarily due to the lack of suitabl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30027<END_URL>

Sketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees\nRandomized algorithms are important for solving large-scale optimization problems efficiently. In this paper, we propose a fast sketching algorithm for least square problems regularized by convex or nonconvex regularization functions, Sketching for Regularized Optimization, or SRO. SRO first generates a sketch of the original data matrix, then solves the sketched problem. We prove minimax rates for sparse signal <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30805<END_URL>

Skill Expansion and Composition in Parameter Space\nHumans excel at reusing prior knowledge to address new challenges and developing skills while solving problems. This paradigm becomes increasingly popular in the development of autonomous agents, as it develops systems that can self-evolve in response to new challenges like human beings. However, previous methods suffer from limited training efficiency when expanding new skills and fail to fully leverage prior knowledge to facilitate new task l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30286<END_URL>

SleepSMC: Ubiquitous Sleep Staging via Supervised Multimodal Coordination\nSleep staging is critical for assessing sleep quality and tracking health. Polysomnography (PSG) provides comprehensive multimodal sleep-related information, but its complexity and impracticality limit its practical use in daily and ubiquitous monitoring. Conversely, unimodal devices offer more convenience but less accuracy. Existing multimodal learning paradigms typically assume that the data types remain consistent betw<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30593<END_URL>

SLMRec: Empowering Small Language Models for Sequential Recommendation\nSequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions. The SR models examine the sequence of a user's actions to discern more complex behavioral patterns and temporal dynamics. Recent research demonstrates the great impact of LLMs on sequential recommendation systems, either viewing sequential recommendation as language modeling or serving as th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30304<END_URL>

SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs\nWe propose SLoPe, a Double-Pruned **S**parse Plus **L**azy L**o**w-rank Adapter **P**r**e**training method for LLMs that improves the accuracy of sparse LLMs while accelerating their pretraining and inference and reducing their memory footprint. Sparse pretraining of LLMs reduces the accuracy of the model, to overcome this, prior work uses dense models during fine-tuning. SLoPe improves the accuracy of sparsely pretraine<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28497<END_URL>

S-LoRA: Scalable Low-Rank Adaptation for Class Incremental Learning\nContinual Learning (CL) with foundation models has recently emerged as a promising approach to harnessing the power of pre-trained models for sequential tasks. Existing prompt-based methods generally use a gating mechanism to select relevant prompts aligned with the test query for further processing. However, the success of these methods largely depends on the precision of the gating mechanism, which becomes less scalable with <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30945<END_URL>

Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generation\nWe present SlotAdapt, an object-centric learning method that combines slot attention with pretrained diffusion models by introducing adapters for slot-based conditioning. Our method preserves the generative power of pretrained diffusion models, while avoiding their text-centric conditioning bias. We also incorporate an additional guidance loss into our architecture  to align cross-att<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28572<END_URL>

SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation\nHuman beings are endowed with a complementary learning system, which bridges the slow learning of general world dynamics with fast storage of episodic memory from a new experience. Previous video generation models, however, primarily focus on slow learning by pre-training on vast amounts of data, overlooking the fast learning phase crucial for episodic memory storage. This oversight leads to inconsistencies across tempora<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29480<END_URL>

Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling\nTraining on high-quality synthetic data from strong language models (LMs) is a common strategy to improve the reasoning performance of LMs. In this work, we revisit whether this strategy is compute-optimal under a fixed inference budget (e.g., FLOPs). To do so, we investigate the trade-offs between generating synthetic data using a stronger but more expensive (SE) model versus a weaker but cheaper (WC) model. We ev<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31080<END_URL>

Small-to-Large Generalization: Training Data Influences Models Consistently Across Scale\nChoice of training data distribution greatly affects model behavior. Yet, inlarge-scale settings, precisely characterizing *how* changes in trainingdata influence predictions is often difficult due to model training costs.Current practice is to instead extrapolate from scaled down,inexpensive-to-train proxy models. However, changes in data do not influencesmaller and larger models identically. Therefore, un<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30845<END_URL>

SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction\nPredicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. However, the scarcity of large-scale driving datasets has hindered the development of robust and generalizable motion prediction models, limiting their ability to capture complex interactions and road geometries. Inspired by recent advances in na<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30548<END_URL>

SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback\nRAG systems consist of multiple modules to work together. However, these modules are usually separately trained. We argue that a system like RAG that incorporates multiple modules should be jointly optimized to achieve optimal performance. To demonstrate this, we design a specific pipeline called SmartRAG that includes a policy network and a retriever. The policy network can serve as 1) a decision maker that decides when to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29838<END_URL>

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision\nSMILES, as a crucial textual representation of molecular information, is increasingly drawing interest for its pre-trained language models. However, most existing pre-trained SMILES language models (LMs) only provide supervision at the single-token level during pre-training and fail to fully leverage substructural information of molecules. This limitation results in the pre-training task being overly simplistic and fur<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29960<END_URL>

SMITE: Segment Me In TimE\nSegmenting an object in a video presents significant challenges. Each pixel must be accurately labeled, and these labels must remain consistent across frames. The difficulty increases when the segmentation is with arbitrary granularity, meaning the number of segments can vary arbitrarily, and masks are defined based on only one or a few sample images. In this paper, we address this issue by employing a pre-trained text to image diffusion model supplemented with an addi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30055<END_URL>

Smoothing the Shift: Towards Stable Test-time Adaptation under Complex Multimodal Noises\nTest-time adaptation (TTA) aims to tackle distribution shifts using unlabeled test data without access to the source data. In the context of multimodal data, there are more complex noise patterns than unimodal data such as simultaneous corruptions for multiple modalities and missing modalities. Besides, in real-world applications, corruptions from different distribution shifts are always mixed. Existing TTA<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28200<END_URL>

SOAP: Improving and Stabilizing Shampoo using Adam\nThere is growing evidence of the effectiveness of Shampoo, a higher-order preconditioning method, over Adam in deep learning optimization tasks. However, Shampoo's drawbacks include additional hyperparameters and computational overhead when compared to Adam, which only updates running averages of first- and second-moment quantities. This work establishes a formal connection between Shampoo (implemented with the 1/2 power) and Adafactor --- a me<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30183<END_URL>

SoftCVI: Contrastive variational inference with self-generated soft labels\nEstimating a distribution given access to its unnormalized density is pivotal in Bayesian inference, where the posterior is generally known only up to an unknown normalizing constant. Variational inference and Markov chain Monte Carlo methods are the predominant tools for this task; however, both are often challenging to apply reliably, particularly when the posterior has complex geometry. Here, we introduce Soft Contras<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29737<END_URL>

Solving Differential Equations with Constrained Learning\n(Partial) differential equations (PDEs) are fundamental tools for describing natural phenomena, making their solution crucial in science and engineering. While traditional methods, such as the finite element method, provide reliable solutions, their accuracy is often tied to the use of computationally intensive fine meshes. Moreover, they do not naturally account for measurements or prior solutions, and any change in the problem parameter<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30951<END_URL>

Solving hidden monotone variational inequalities with surrogate losses\nDeep learning has proven to be effective in a wide variety of loss minimization problems.However, many applications of interest, like minimizing projected Bellman error and min-max optimization, cannot be modelled as minimizing a scalar loss function but instead correspond to solving a variational inequality (VI) problem.This difference in setting has caused many practical challenges as naive gradient-based approaches from s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31004<END_URL>

Solving Multiplayer Partially Observable Stochastic Games by Divergence-Regularized Discounted Aggregation\nThis paper presents Divergence-Regularized Discounted Aggregation (DRDA), a multi-round learning system for solving partially observable stochastic games (POSGs), which unify normal-form games (NFGs), extensive-form games (EFGs) with perfect recall, and Markov games (MGs). In each single round, DRDA can be viewed as a discounted variant of Follow the Regularized Leader (FTRL) under a gener<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30072<END_URL>

Solving New Tasks by Adapting Internet Video Knowledge\nVideo generative models, beyond enabling the production of astounding visual creations, offer a promising pathway for unlocking novel, text-conditioned robotic behaviors, whether utilized as a video planner or as a policy supervisor.  When pretrained on internet-scale datasets, such video models intimately understand alignment with natural language, and can thus facilitate novel text-conditioned behavior generalization.  At the same time, h<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28326<END_URL>

Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model\nThe Mixture-of-Experts (MoE) has gained increasing attention in studying Large Vision-Language Models (LVLMs). It uses a sparse model to replace the dense model, achieving comparable performance while activating fewer parameters during inference, thus significantly reducing the inference cost. Existing MoE methods in LVLM encourage different experts to specialize in different tokens, and they usually employ a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29390<END_URL>

Solving Video Inverse Problems Using Image Diffusion Models\nRecently, diffusion model-based inverse problem solvers (DIS) have emerged as state-of-the-art approaches for addressing inverse problems, including image super-resolution, deblurring, inpainting, etc. However, their application to video inverse problems arising from spatio-temporal degradation remains largely unexplored due to the challenges in training video diffusion models.To address this issue, here we introduce an innovative vide<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29542<END_URL>

SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios\nThe systematic evaluation of speech separation and enhancement models under moving sound source conditions typically requires extensive data comprising diverse scenarios. However, real-world datasets often contain insufficient data to meet the training and evaluation requirements of models. Although synthetic datasets offer a larger volume of data, their acoustic simulations lack realism. Consequ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30201<END_URL>

SOO-Bench: Benchmarks for Evaluating the Stability of Offline Black-Box Optimization\nBlack-box optimization aims to find the optima through building a model close to the black-box objective function based on function value evaluation. However, in many real-world tasks, such as design of molecular formulas and mechanical structures, it is perilous, costly, or even infeasible to evaluate the objective function value of an actively sampled solution. In this situation, optimization can only be cond<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29089<END_URL>

SOREL: A Stochastic Algorithm for Spectral Risks Minimization\nThe spectral risk has wide applications in machine learning, especially in real-world decision-making, where people are concerned with more than just average model performance. By assigning different weights to the losses of different sample points, rather than the same weights as in the empirical risk, it allows the model's performance to lie between the average performance and the worst-case performance. In this paper, we propose S<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28287<END_URL>

SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal\nEvaluating aligned large language models' (LLMs) ability to recognize and reject unsafe user requests is crucial for safe, policy-compliant deployments. Existing evaluation efforts, however, face three limitations that we address with **SORRY-Bench**, our proposed benchmark. **First**, existing methods often use coarse-grained taxonomies of unsafe topics, and are over-representing some fine-grained topics. For example, a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29252<END_URL>

Sort-free Gaussian Splatting via Weighted Sum Rendering\nRecently, 3D Gaussian Splatting (3DGS) has emerged as a significant advancement in 3D scene reconstruction, attracting considerable attention due to its ability to recover high-fidelity details while maintaining low complexity. Despite the promising results achieved by 3DGS, its rendering performance is constrained by its dependence on costly non-commutative alpha-blending operations. These operations mandate complex view dependent sorting<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27749<END_URL>

SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound Generation\nSound content creation, essential for multimedia works such as video games and films, often involves extensive trial-and-error, enabling creators to semantically reflect their artistic ideas and inspirations, which evolve throughout the creation process, into the sound.Recent high-quality diffusion-based Text-to-Sound (T2S) generative models provide valuable tools for creators. However, these models oft<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30028<END_URL>

SPA: 3D Spatial-Awareness Enables Effective Embodied Representation\nIn this paper, we introduce SPA, a novel representation learning framework that emphasizes the importance of 3D spatial awareness in embodied AI. Our approach leverages differentiable neural rendering on multi-view images to endow a vanilla Vision Transformer (ViT) with intrinsic spatial understanding. We present the most comprehensive evaluation of embodied representation learning to date, covering 268 tasks across 8 simulator<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30883<END_URL>

SPA-BENCH: A COMPREHENSIVE BENCHMARK FOR SMARTPHONE AGENT EVALUATION\nSmartphone agents are increasingly important for helping users control devices efficiently, with (Multimodal) Large Language Model (MLLM)-based approaches emerging as key contenders. Fairly comparing these agents is essential but challenging, requiring a varied task scope, the integration of agents with different implementations, and a generalisable evaluation pipeline to assess their strengths and weaknesses. In this paper, w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29820<END_URL>

SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels\nNode Anomaly Detection (NAD) has gained significant attention in the deep learning community due to its diverse applications in real-world scenarios. Existing NAD methods primarily embed graphs within a single Euclidean space, while overlooking the potential of non-Euclidean spaces. Besides, to address the prevalent issue of limited supervision in real NAD tasks, previous methods tend to leverage<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29569<END_URL>

SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training\nLarge Language Models (LLMs) have demonstrated exceptional performance across diverse tasks, yet their training remains highly resource intensive and susceptible to critical challenges such as training instability. A predominant source of this instability stems from gradient and loss spikes, which disrupt the learning process, often leading to costly interventions like checkpoint recovery and experiment restarts, further amplify<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30015<END_URL>

Sparse Autoencoders Do Not Find Canonical Units of Analysis\nA common goal of mechanistic interpretability is to decompose the activations of neural networks into features: interpretable properties of the input computed by the model. Sparse autoencoders (SAEs) are a popular method for finding these features in LLMs, and it has been postulated that they can be used to find a \textit{canonical} set of units: a unique and complete list of atomic features. We cast doubt on this belief using two nove<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30676<END_URL>

Sparse autoencoders reveal selective remapping of visual concepts during adaptation\nAdapting foundation models for specific purposes has become a standard approach in machine learning systems, yet it is an open question which mechanisms take place during the adaptation. Here we develop PatchSAE to discover interpretable candidate concepts from vision encoders with spatially localized attributions. We explore how these concepts influence the model behavior and extend it to investigate how recent<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28675<END_URL>

Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models\nIn-context learning, the ability to adapt based on a few examples in the input prompt, is a ubiquitous feature of large language models (LLMs). However, as LLMs' in-context learning abilities continue to improve, understanding this phenomenon mechanistically becomes increasingly important. In particular, it is not well-understood how LLMs learn to solve specific classes of problems, such as reinforcement learning (<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31107<END_URL>

Sparse components distinguish visual pathways & their alignment to neural networks\nThe ventral, dorsal, and lateral streams in high-level human visual cortex are implicated in distinct functional processes. Yet, deep neural networks (DNNs) trained on a single task model the entire visual system surprisingly well, hinting at common computational principles across these pathways. To explore this inconsistency, we applied a novel sparse decomposition approach to identify the dominant components of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30147<END_URL>

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models\nWe introduce methods for discovering and applying **sparse feature circuits**. These are causally implicated subnetworks of human-interpretable features for explaining language model behaviors. Circuits identified in prior work consist of polysemantic and difficult-to-interpret units like attention heads or neurons, rendering them unsuitable for many downstream applications. In contrast, sparse featu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30192<END_URL>

Sparse Learning for State Space Models on Mobile\nTransformer models have been widely investigated in different domains by providing long-range dependency handling and global contextual awareness, driving the development of popular AI applications such as ChatGPT, Gemini, and Alexa.State Space Models (SSMs) have emerged as strong contenders in the field of sequential modeling, challenging the dominance of Transformers. SSMs incorporate a selective mechanism that allows for dynamic parameter adju<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28076<END_URL>

SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models\nInstruction-following is a fundamental capability of language models, requiring the model to recognize even the most subtle requirements in the instructions and accurately reflect them in its output.Such an ability is well-suited for and often optimized by preference learning.However, existing methods often directly sample multiple independent responses from the model when creating preference p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30675<END_URL>

Sparse Matrix in Large Language Model Fine-tuning\nVarious parameter-efficient fine-tuning (PEFT) methods, including LoRA and its variants, have gained popularity for reducing computational costs. However, there is often an accuracy gap between PEFT approaches and full fine-tuning (FT), and this discrepancy has not yet been systematically explored. In this work, we introduce a method for selecting sparse sub-matrices that aim to minimize the performance gap between PEFT vs. full fine-tuning (FT)<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30274<END_URL>

SparsyFed: Sparse Adaptive Federated Learning\nSparse training is often adopted in cross-device federated learning (FL) environments where constrained devices collaboratively train a machine learning model on private data by exchanging pseudo-gradients across heterogeneous networks. Although sparse training methods can reduce communication overhead and computational burden in FL, they are often not used in practice for the following key reasons: (1) data heterogeneity impacts more clients’ conse<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29840<END_URL>

SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Model\nIntegrating the 3D world into large language models (3D-based LLMs) has been a promising research direction for 3D scene understanding. However, current 3D-based LLMs fall short in situated understanding due to two key limitations: 1) existing 3D datasets are constructed from a global perspective of the 3D scenes and lack situated context.2) the architectures of the current 3D-based LLMs lack an explicit mechanism for<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30351<END_URL>

Spatial-Mamba: Effective Visual State Space Models via Structure-Aware State Fusion\nSelective state space models (SSMs), such as Mamba, highly excel at capturing long-range dependencies in 1D sequential data, while their applications to 2D vision tasks still face challenges. Current visual SSMs often convert images into 1D sequences and employ various scanning patterns to incorporate local spatial dependencies. However, these methods are limited in effectively capturing the complex image spatia<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28708<END_URL>

SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG\nThe non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neurotechnology generalization.Without labeled calibration data for target domains, the problem is a source-free unsupervised domain adaptation (SFUDA) problem.For scenarios with constant label distribution, Riemannian geometry-aware statistical <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30495<END_URL>

Specialized Foundation Models struggle to beat Supervised Baselines\nFollowing its success for vision and text, the “foundation model” (FM) paradigm—pretraining large models on massive data, then fine-tuning on target tasks—has rapidly expanded to domains in the sciences, engineering, healthcare, and beyond. Has this achieved what the original FMs accomplished, i.e. the supplanting of traditional supervised learning in their domains? To answer we look at three modalities—genomics, satellite data<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30102<END_URL>

Spectral Compressive Imaging via Unmixing-driven Subspace Diffusion Refinement\nSpectral Compressive Imaging (SCI) reconstruction is inherently ill-posed, offering multiple plausible solutions from a single observation. Traditional deterministic methods typically struggle to effectively recover high-frequency details. Although diffusion models offer promising solutions to this challenge, their application is constrained by the limited training data and high computational demands associated with <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29715<END_URL>

Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows\nRecent advancements in operator-type neural networks have shown promising results in approximating the solutions of spatiotemporal Partial Differential Equations (PDEs). However, these neural networks often entail considerable training expenses, and may not always achieve the desired accuracy required in many scientific and engineering disciplines. In this paper, we propose a new learning framew<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29939<END_URL>

Spectro-Riemannian Graph Neural Networks\nCan integrating spectral and curvature signals unlock new potential in graph representation learning? Non-Euclidean geometries, particularly Riemannian manifolds such as hyperbolic (negative curvature) and spherical (positive curvature), offer powerful inductive biases for embedding complex graph structures like scale-free, hierarchical, and cyclic patterns. Meanwhile, spectral filtering excels at processing signal variations across graphs, making it eff<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31145<END_URL>

Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling\nRecent advances in knowledge distillation (KD) have enabled smaller student models to approach the performance of larger teacher models. However, popular methods such as supervised KD and on-policy KD, are adversely impacted by the knowledge gaps between teacher-student in practical scenarios. Supervised KD suffers from a distribution mismatch between training with a static dataset and inference ov<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30392<END_URL>

Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting\nRetrieval augmented generation (RAG) combines the generative abilities of large language models (LLMs) with external knowledge sources to provide more accurate and up-to-date responses. Recent RAG advancements focus on improving retrieval outcomes through iterative LLM refinement or self-critique capabilities acquired through additional instruction tuning of LLMs. In this work, we introduce Speculative RAG - a framework <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27773<END_URL>

Speech Robust Bench: A Robustness Benchmark For Speech Recognition\nAs Automatic Speech Recognition (ASR) models become ever more pervasive, it is important to ensure that they make reliable predictions under corruptions present in the physical and digital world. We propose  Speech Robust Bench (SRB), a comprehensive benchmark for evaluating the robustness of ASR models to diverse corruptions. SRB is composed of 114 input perturbations which simulate an heterogeneous range of corruptions that AS<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30483<END_URL>

Spherical Tree-Sliced Wasserstein Distance\nSliced Optimal Transport (OT) simplifies the OT problem in high-dimensional spaces by projecting supports of input measures onto one-dimensional lines, then exploiting the closed-form expression of the univariate OT to reduce the computational burden of OT. Recently, the Tree-Sliced method has been introduced to replace these lines with more intricate structures, known as tree systems. This approach enhances the ability to capture topological informati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30344<END_URL>

Spider 2.0: Can Language Models Resolve Real-World Enterprise Text-to-SQL Workflows?\nReal-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics.We introduce Spider 2.0, an evaluation framework comprising $595$ real-world text-to-SQL workflow problems derived from enterprise-level database use cases. The databases in Spider 2.0 are sou<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29294<END_URL>

SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking\nThe recent advancements in large language models (LLMs) with billions of parameters have significantly boosted their performance across various real-world applications. However, the inference processes for these models require substantial energy and computational resources, presenting considerable deployment challenges. In contrast, human brains, which contain approximately 86 billion biological neur<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29210<END_URL>

Spiking Vision Transformer with Saccadic Attention\nThe combination of Spiking Neural Networks (SNNs) and Vision Transformers (ViTs) holds potential for achieving both energy efficiency and high performance, particularly suitable for edge vision applications. However, a significant performance gap still exists between SNN-based ViTs and their ANN counterparts. Here, we first analyze why SNN-based ViTs suffer from limited performance and identify a mismatch between the vanilla self-attention mech<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28227<END_URL>

SpinQuant: LLM Quantization with Learned Rotations\nPost-training quantization (PTQ) techniques applied to weights, activations, and the KV cache greatly reduce memory usage, latency, and power consumption of Large Language Models (LLMs), but may lead to large quantization errors when outliers are present. Rotating activation or weight matrices helps remove outliers and benefits quantization. In this work, we identify a collection of applicable rotation parameterizations that lead to identical o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28338<END_URL>

SplatFormer: Point Transformer for Robust 3D Gaussian Splatting\n3D Gaussian Splatting (3DGS) has recently transformed photorealistic reconstruction, achieving high visual fidelity and real-time performance. However, rendering quality significantly deteriorates when test views deviate from the camera angles used during training, posing a major challenge for applications in immersive free-viewpoint rendering and navigation. In this work, we conduct a comprehensive evaluation of 3DGS and related n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30697<END_URL>

SplineGS: Learning Smooth Trajectories in Gaussian Splatting for Dynamic Scene Reconstruction\nReconstructing complex scenes with deforming objects for novel view synthesis is a challenging task. Recent works have addressed this with 3D Gaussian Splatting, which effectively reconstructs static scenes with high quality in short training time, by adding specialized modules for the deformations of Gaussian blobs. However, designing an effective deformation module that incorporates appropriate spati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28059<END_URL>

Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports\nUnderstanding multi-agent movement is critical across various fields. The conventional approaches typically focus on separate tasks such as trajectory prediction, imputation, or spatial-temporal recovery. Considering the unique formulation and constraint of each task, most existing methods are tailored for only one, limiting the ability to handle multiple tasks simultaneously, which is a common requirement in <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30682<END_URL>

SportU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models\nMultimodal Large Language Models (MLLMs) are advancing the ability to reason about complex sports scenarios by integrating textual and visual information. To comprehensively evaluate their capabilities, we introduce SPORTU, a benchmark designed to assess MLLMs across multi-level sports reasoning tasks. SPORTU comprises two key components: SPORTU-text, featuring 900 multiple-choice questions with human-an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27805<END_URL>

Spreading Out-of-Distribution Detection on Graphs\nNode-level out-of-distribution (OOD) detection on graphs has received significant attention from the machine learning community. However, previous approaches are evaluated using unrealistic benchmarks that consider only randomly selected OOD nodes, failing to reflect the interactions among nodes. In this paper, we introduce a new challenging task to model the interactions of OOD nodes in a graph, termed spreading OOD detection, where a newly eme<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28324<END_URL>

Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment\nAligning large language models (LLMs) with human preferences becomes a key component to obtaining state-of-the-art performance, but it yields a huge cost to construct a large human-annotated preference dataset. To tackle this problem, we propose a new framework, Spread Preference Annotation with direct preference judgment (SPA), that boosts the alignment of LLMs using only a very small amount of human-annotated<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30572<END_URL>

Spurious Forgetting in Continual Learning of Language Models\nRecent advancements in large language models (LLMs) reveal a perplexing phenomenon in continual learning: despite extensive training, models experience significant performance declines, raising questions about task alignment and underlying knowledge retention. This study first explores the concept of "spurious forgetting", proposing that such performance drops often reflect a decline in task alignment rather than true knowledge loss. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29593<END_URL>

SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget\nOptimizing the Key-Value (KV) cache of the Large Language Model (LLM) has been considered critical to saving the cost of inference. Most of the existing KV-cache compression algorithms attempted to sparsify the sequence of tokens by taking advantage of the different importance of tokens. However, most of these methods treat all layers equally, allocating the same KV budget to each layer. This approach is <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30707<END_URL>

SRSA: Skill Retrieval and Adaptation for Robotic Assembly Tasks\nEnabling robots to learn novel tasks in a data-efficient manner is a long-standing challenge. Common strategies involve carefully leveraging prior experiences, especially transition data collected on related tasks. Although much progress has been made in developing such strategies for general pick-and-place manipulation, far fewer studies have investigated contact-rich assembly tasks, where precise control is essential. In this wor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29656<END_URL>

SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic Soundscapes\nSelf-supervised pre-trained audio networks have seen widespread adoption in real-world systems, particularly in multi-modal large language models. These networks are often employed in a frozen state, under the assumption that the self-supervised pre-training has sufficiently equipped them to handle real-world audio. However, a critical question remains: how well do these models actually perform in real-world <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28347<END_URL>

SSOLE: Rethinking Orthogonal Low-rank Embedding for Self-Supervised Learning\nSelf-supervised learning (SSL) aims to learn meaningful representations from unlabeled data. Orthogonal Low-rank Embedding (OLE) shows promise for SSL by enhancing intra-class similarity in a low-rank subspace and promoting inter-class dissimilarity in a high-rank subspace, making it particularly suitable for multi-view learning tasks. However, directly applying OLE to SSL poses significant challenges: (1) the virtuall<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27685<END_URL>

Stabilized Neural Prediction of Potential Outcomes in Continuous Time\nPatient trajectories from electronic health records are widely used to predict potential outcomes of treatments over time, which then allows for personalizing care. Yet, existing neural methods for this purpose have a key limitation: while some adjust for time-varying confounders, these methods assume that the time series are recorded in discrete time. In other words, they are constrained to settings where measurements and tr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29172<END_URL>

Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation\nRecent advances in GPU-based parallel simulation have enabled practitioners to collect large amounts of data and train complex control policies using deep reinforcement learning (RL), on commodity GPUs. However, such successes for RL in robotics have been limited to tasks sufficiently simulated by fast rigid-body dynamics. Simulation techniques for soft bodies are comparatively several orders of magnitude slower, there<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30460<END_URL>

Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning\nEffective decision-making in partially observable environments demands robust memory management. Despite their success in supervised learning, current deep-learning memory models struggle in reinforcement learning environments that are partially observable and long-term. They fail to efficiently capture relevant past information, adapt flexibly to changing observations, and maintain stable updates over long <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29355<END_URL>

Stable Segment Anything Model\nThe Segment Anything Model (SAM) achieves remarkable promptable segmentation given high-quality prompts which, however, often require good skills to specify. To make SAM robust to casual prompts, this paper presents the first comprehensive analysis on SAM’s segmentation stability across a diverse spectrum of prompt qualities, notably imprecise bounding boxes and insufficient points. Our key finding reveals that given such low-quality prompts, SAM’s mask decoder ten<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28332<END_URL>

STAFF: Speculative Coreset Selection for Task-Specific Fine-tuning\nTask-specific fine-tuning is essential for the deployment of large language models (LLMs), but it requires significant computational resources and time. Existing solutions have proposed coreset selection methods to improve data efficiency and reduce model training overhead, but they still have limitations: ❶ Overlooking valuable samples at high pruning rates, which degrades the coreset’s performance.❷ Requiring high time overhea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30360<END_URL>

STAMP: Scalable Task- And Model-agnostic Collaborative Perception\nPerception is a crucial component of autonomous driving systems. However, single-agent setups often face limitations due to sensor constraints, especially under challenging conditions like severe occlusion, adverse weather, and long-range object detection. Multi-agent collaborative perception (CP) offers a promising solution that enables communication and information sharing between connected vehicles. Yet, the heterogeneity amon<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30771<END_URL>

Standard Gaussian Process Can Be Excellent for High-Dimensional Bayesian Optimization\nA long-standing belief holds that Bayesian Optimization (BO) with standard Gaussian processes (GP) --- referred to as standard BO --- underperforms in high-dimensional optimization problems. While this belief seems plausible, it lacks both robust empirical evidence and theoretical justification. To address this gap, we present a systematic investigation. First, through a comprehensive evaluation across eleven <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28574<END_URL>

Standardizing Structural Causal Models\nSynthetic datasets generated by structural causal models (SCMs) are commonly used for benchmarking causal structure learning algorithms. However, the variances and pairwise correlations in SCM data tend to increase along the causal ordering. Several popular algorithms exploit these artifacts, possibly leading to conclusions that do not generalize to real-world settings. Existing metrics like $\operatorname{Var}$-sortability and $\operatorname{R^2}$-sortabi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29160<END_URL>

STAR: Stability-Inducing Weight Perturbation for Continual Learning\nHumans can naturally learn new and varying tasks in a sequential manner.   Continual learning is a class of learning algorithms that updates its learned model as it sees new data (on potentially new tasks) in a sequence.  A key challenge in continual learning is that as the model is updated to learn new tasks, it becomes susceptible to \textit{catastrophic forgetting}, where knowledge of previously learned tasks is lost. A popu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30891<END_URL>

STAR: Synthesis of Tailored Architectures\nIterative improvement of model architectures is fundamental to deep learning: Transformers first enabled scaling, and recent advances in model hybridization have pushed the quality-efficiency frontier. However, optimizing architectures remains challenging and expensive, with a variety of automated or manual approaches that fall short, due to limited progress in the design of search spaces and due to the simplicity of resulting patterns and heuristics. I<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30205<END_URL>

Start Smart: Leveraging Gradients For Enhancing Mask-based XAI Methods\nMask-based explanation methods offer a powerful framework for interpreting deep learning model predictions across diverse data modalities, such as images and time series, in which the central idea is to identify an instance-dependent mask that minimizes the performance drop from the resulting masked input. Different objectives for learning such masks have been proposed, all of which, in our view, can be unified under an info<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30158<END_URL>

State Space Model Meets Transformer: A New Paradigm for 3D Object Detection\nDETR-based methods, which use multi-layer transformer decoders to refine object queries iteratively, have shown promising performance in 3D indoor object detection. However, the scene point features in the transformer decoder remain fixed, leading to minimal contributions from later decoder layers, thereby limiting performance improvement. Recently, State Space Models (SSM) have shown efficient context modeling ability <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29524<END_URL>

State Space Models are Provably Comparable to Transformers in Dynamic Token Selection\nDeep neural networks based on state space models (SSMs) are attracting significant attention in sequence modeling since their computational cost is significantly smaller than that of Transformers. While the capabilities of SSMs have been demonstrated through experiments in various tasks, theoretical understanding of SSMs is still limited. In particular, most theoretical studies discuss the capabilities of SSM <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29700<END_URL>

Statistical Advantages of Perturbing Cosine Router in Mixture of Experts\nThe cosine router in Mixture of Experts (MoE) has recently emerged as an attractive alternative to the conventional linear router. Indeed, the cosine router demonstrates favorable performance in image and language tasks and exhibits better ability to mitigate the representation collapse issue, which often leads to parameter redundancy and limited representation potentials. Despite its empirical success, a comprehensive ana<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28871<END_URL>

Statistical Tractability of Off-policy Evaluation of History-dependent Policies in POMDPs\nWe investigate off-policy evaluation (OPE), a central and fundamental problemin reinforcement learning (RL), in the challenging setting of Partially ObservableMarkov Decision Processes (POMDPs) with large observation spaces. Recentworks of Uehara et al. (2023a); Zhang & Jiang (2024) developed a model-freeframework and identified important coverage assumptions (called belief and outcome coverage) that enabl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29675<END_URL>

STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs\nIn this paper, we present the first structural binarization method for LLM compression to less than 1-bit precision. Although LLMs have achieved remarkable performance, their memory-bound nature during the inference stage hinders the adoption of resource-constrained devices. Reducing weights to 1-bit precision through binarization substantially enhances computational efficiency. We observe that some weights in binarized LLMs can be <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30878<END_URL>

Stealthy Shield Defense: A Conditional Mutual Information-Based Approach against Black-Box Model Inversion Attacks\nModel inversion attacks (MIA) aim to uncover private training data by accessing public models, raising  increasing concerns about privacy breaches. Black-box MIA, where attackers can generate inputs and obtain the model's outputs arbitrarily, has gained more attention due to its closer alignment with real-world scenarios and greater potential threats. Existing defenses primarily fo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28325<END_URL>

Steering Large Language Models between Code Execution and Textual Reasoning\nWhile a lot of recent research focuses on enhancing the textual reasoning capabilities of Large Language Models (LLMs) by optimizing the multi-agent framework or reasoning chains, several benchmark tasks can be solved with 100\% success through direct coding, which is more scalable and avoids the computational overhead associated with textual iterating and searching. Textual reasoning has inherent limitations in solving<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30940<END_URL>

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction\nGenerative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very building blocks of life in protein sequences. However, application domains need to exert control over the generated data by steering the generative process—typically via RLHF—to satisfy a specified property, reward, or affinity metric. In this paper, we study the problem of st<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29801<END_URL>

Steering Protein Family Design through Profile Bayesian Flow\nProtein family design emerges as a promising alternative by combining the advantages of de novo protein design and mutation-based directed evolution.In this paper, we propose ProfileBFN, the Profile Bayesian Flow Networks, for specifically generative modeling of protein families. ProfileBFN extends the discrete Bayesian Flow Network from an MSA profile perspective, which can be trained on single protein sequences by regarding it as a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29753<END_URL>

Stem-OB: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion Inversion\nVisual imitation learning methods demonstrate strong performance, yet they lack generalization when faced with visual input perturbations like variations in lighting and textures. This limitation hampers their practical application in real-world settings. To address this, we propose ***Stem-OB*** that leverages the inversion process of pretrained image diffusion models to suppress <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27776<END_URL>

Step-by-Step Reasoning for Math Problems  via Twisted Sequential Monte Carlo\nAugmenting the multi-step reasoning abilities of Large Language Models (LLMs) has been a persistent challenge. Recently, verification has shown promise in improving solution consistency by evaluating generated outputs. However, current verification approaches suffer from sampling inefficiencies, requiring a large number of samples to achieve satisfactory performance. Additionally, training an effective verifier often d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29208<END_URL>

ST-GCond: Self-supervised and Transferable Graph Dataset Condensation\nThe increasing scale of graph datasets significantly enhances deep learning models but also presents substantial training challenges. Graph dataset condensation has emerged to condense large datasets into smaller yet informative ones that maintain similar test performance. However, these methods require downstream usage to match the original dataset and task, which is impractical in real-world scenarios. Our empirical studies<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27835<END_URL>

Stick-breaking Attention\nThe self-attention mechanism traditionally relies on the softmax operator, necessitating positional embeddings like RoPE, or position biases to account for token order.But current methods using still face length generalisation challenges.We investigate an alternative attention mechanism based on the stick-breaking process in larger scale settings.The method works as follows: For each token before the current, we determine a break point, which represents the proportion o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28218<END_URL>

Stiefel Flow Matching for Moment-Constrained Structure Elucidation\nMolecular structure elucidation is a critical step in understanding chemical phenomena, with applications to identifying molecules in natural products, lab syntheses, forensic samples, and the interstellar medium.We consider the task of elucidating a molecule's 3D structure from only its molecular formula and moments of inertia, motivated by the ability of rotational spectroscopy to precisely measure these moments.While existing<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30787<END_URL>

ST-Modulator: Modulating Space-Time Attention for Multi-Grained Video Editing\nRecent advancements in diffusion models have significantly improved video generation and editing capabilities. However, multi-grained video editing, which encompasses class-level, instance-level, and part-level modifications, remains a formidable challenge. The major difficulties in multi-grained editing include semantic misalignment of text-to-region control and feature coupling within the diffusion model. To address<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29603<END_URL>

Stochastic Bandits Robust to Adversarial Attacks\nThis paper investigates stochastic multi-armed bandit algorithms that are robust to adversarial attacks, where an attacker can first observe the learner's action and *then* alter their reward observation.We study two cases of this model, with or without the knowledge of an attack budget $C$, defined as an upper bound of the summation of the difference between the actual and altered rewards. For both cases, we devise two types of algorithms with r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27916<END_URL>

Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance\nStochastic gradient descent with momentum, also known as Stochastic Heavy Ball method (SHB), is one of the most popular algorithms for solving large-scale stochastic optimization problems in various machine learning tasks. In practical scenarios, tuning the step-size and momentum parameters of the method is a prohibitively expensive and time-consuming process. In this work, inspired by the recent advanta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28388<END_URL>

Stochastic Semi-Gradient Descent for Learning Mean Field Games with Population-Aware Function Approximation\nMean field games (MFGs) model interactions in large-population multi-agent systems through population distributions. Traditional learning methods for MFGs are based on fixed-point iteration (FPI), where policy updates and induced population distributions are computed separately and sequentially. However, FPI-type methods may suffer from inefficiency and instability due to potential oscill<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28035<END_URL>

Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold\nOptimization in the Bures-Wasserstein space has been gaining popularity in the machine learning community since it draws connections between variational inference and Wasserstein gradient flows. The variational inference objective function of Kullback–Leibler divergence can be written as the sum of the negative entropy and the potential energy, making forward-backward Euler the method of choice. Notably<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28700<END_URL>

StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces\nWe propose a zero-shot method for generating images in arbitrary spaces (e.g., a sphere for 360◦ panoramas and a mesh surface for texture) using a pretrained image diffusion model. The zero-shot generation of various visual content using a pretrained image diffusion model has been explored mainly in two directions. First, Diffusion Synchronization–performing reverse diffusion processes jointly across differ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29307<END_URL>

STORM: Spatio-TempOral Reconstruction Model For Large-Scale Outdoor Scenes\nWe present STORM, a spatio-temporal reconstruction model designed to reconstruct in-the-wild dynamic outdoor scenes from sparse observations. Existing dynamic reconstruction methods rely heavily on dense observations across space and time and strong motion supervision, therefore suffering from lengthy optimization time, limited generalizability to novel views or scenes, and degenerated quality caused by noisy pseudo-labe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29959<END_URL>

Storybooth: Training-Free Multi-Subject Consistency for Improved Visual Storytelling\nConsistent text-to-image generation depicting the *same* subjects across different images has gained significant recent attention due to its widespread applications in the fields of visual-storytelling and multiple-shot video generation.  While remarkable, existing methods often require costly finetuning for each subject and struggle to maintain consistency across multiple characters. In this work, we first ana<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30098<END_URL>

Straightness of Rectified Flow: A Theoretical Insight into Wasserstein Convergence\nDiffusion models have emerged as a powerful tool for image generation and denoising. Typically, generative models learn a trajectory between the starting noise distribution and the target data distribution. Recently Liu et al. (2023b) designed a novel alternative generative model Rectified Flow(RF), which aims to learn straight flow trajectories from noise to data using a sequence of convex optimization problems <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29078<END_URL>

Straight to Zero: Why Linearly Decaying the Learning Rate to Zero Works Best for LLMs\nLLMs are commonly trained with a learning rate (LR) warmup, followed by cosine decay to 10% of the maximum (10x decay). In a large-scale empirical study, we show that under an optimal max LR, a simple linear decay-to-zero (D2Z) schedule consistently outperforms other schedules when training at compute-optimal dataset sizes. Benefits increase further with more training tokens; e.g., a 617M-parameter model train<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28738<END_URL>

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning\nRobot learning is witnessing a significant increase in the size, diversity, and complexity of pre-collected datasets, mirroring trends in domains such as natural language processing and computer vision. Many robot learning methods treat such datasets as multi-task expert data and learn a multi-task, generalist policy by training broadly across them. Notably, while these generalist policies can improve the average performance ac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31008<END_URL>

Strategic Classification With Externalities\nWe propose a new variant of the strategic classification problem: a principal reveals a classifier, and $n$ agents report their (possibly manipulated) features to be classified. Motivated by real-world applications, our model crucially allows the manipulation of one agent to affect another; that is, it explicitly captures inter-agent externalities. The principal-agent interactions are formally modeled as a Stackelberg game, with the resulting agent ma<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28377<END_URL>

Strategist: Self-improvement of LLM Decision Making via Bi-Level Tree Search\nTraditional reinforcement learning (RL) typically requires vast amounts of trainingdata to develop effective policies. In contrast, large language models (LLMs)exhibit strong generalization and zero-shot capabilities, but struggle with plan-ning and understanding complex action policies. In this work, we introduceSTRATEGIST, a novel approach that integrates the strengths of both methods. Ourapproach leverages LLMs to l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28807<END_URL>

Streaming Algorithms For $\ell_p$ Flows and $\ell_p$ Regression\nWe initiate the study of one-pass streaming algorithms for underdetermined $\ell_p$ linear regression problems of the form  $$      \min_{\mathbf A\mathbf x = \mathbf b} \lVert\mathbf x\rVert_p \,, \qquad       \text{where } \mathbf A \in \mathbb R^{n \times d} \text{ with } n \ll d \,,  $$  which generalizes basis pursuit ($p = 1$) and least squares solutions to  underdetermined linear systems ($p = 2$). We study the column-arriva<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30029<END_URL>

Streaming Video Question-Answering with In-context Video KV-Cache Retrieval\nWe propose ReKV, a novel, training-free approach that integrates seamlessly with existing Video Large Language Models (Video-LLMs) to enable efficient streaming video question-answering (StreamingVQA). Traditional VideoQA systems struggle with long videos, as they must process the entire video before responding to queries, and repeat this process for each new question. In contrast, our approach analyzes long videos in a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30750<END_URL>

Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge\nRecent advances in Large Language Models (LLMs) have enabled the development of Video-LLMs, advancing multimodal learning by bridging video data with language tasks. However, current video understanding models struggle with processing long video sequences, supporting multi-turn dialogues, and adapting to real-world dynamic scenarios. To address these issues, we propose StreamChat, a training-free framework <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30097<END_URL>

Streamlining Bayesian Deep Learning\nThe rising interest in Bayesian deep learning (BDL) has led to a plethora of methods for estimating the posterior distribution. However, efficient computation of inferences, such as predictions, has been largely overlooked with Monte Carlo integration remaining the standard. In this work we examine streamlining prediction in BDL through a single forward pass without sampling. For this we use local linearisation on activation functions and local Gaussian appro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28294<END_URL>

Streamlining Redundant Layers to Compress Large Language Models\nThis paper introduces LLM-Streamline, a pioneer work on layer pruning for large language models (LLMs). It is based on the observation that different layers have varying impacts on hidden states, enabling the identification of less important layers to be pruned. LLM-Streamline comprises two parts: layer pruning, which removes consecutive layers with the lowest importance based on target sparsity, and layer replacement, a novel modu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30185<END_URL>

Streamlining the Design Space of ML4TSP Suggests Principles for Learning and Search\nDespite the rich works on machine learning (ML) for combinatorial optimization (CO), a unified, principled framework remains lacking. This study utilizes the Travelling Salesman Problem (TSP) as a major case study, with adaptations demonstrated for other CO problems, dissecting established mainstream learning-based solvers to outline a comprehensive design space. It advances a unified modular streamline incorpor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28796<END_URL>

Strength Estimation and Human-Like Strength Adjustment in Games\nStrength estimation and adjustment are crucial in designing human-AI interactions, particularly in games where AI surpasses human players.This paper introduces a novel strength system, including a strength estimator (SE) and an SE-based Monte Carlo tree search, denoted as SE-MCTS, which predicts strengths from games and offers different playing strengths with human styles.The strength estimator calculates strength scores and predic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30488<END_URL>

StringLLM: Understanding the String Processing Capability of Large Language Models\nString processing, which mainly involves the analysis and manipulation of strings, is a fundamental component of modern computing. Despite the significant advancements of large language models (LLMs) in various natural language processing (NLP) tasks, their capability in string processing remains underexplored and underdeveloped. To bridge this gap, we present a comprehensive study of LLMs' string processing capa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28579<END_URL>

Strong Preferences Affect the Robustness of Value Alignment\nValue alignment, which aims to ensure that large language models (LLMs) and other AI agents behave in accordance with human values, is critical for ensuring safety and trustworthiness of these systems. A key component of value alignment is the modeling of human preferences as a representation of human values. In this paper, we investigate the robustness of value alignment by examining the sensitivity of preference models. Specifically,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29453<END_URL>

StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization\nRetrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) in many knowledge-based tasks. However, existing RAG methods struggle with knowledge-intensive reasoning tasks, because useful information required to these tasks are badly scattered. This characteristic makes it difficult for existing RAG methods to accurately identify key informat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30265<END_URL>

Structural-Entropy-Based Sample Selection for Efficient and Effective Learning\nSample selection improves the efficiency and effectiveness of machine learning models by providing informative and representative samples. Typically, samples can be modeled as a sample graph, where nodes are samples and edges represent their similarities. Most existing methods are based on local information, such as the training difficulty of samples, thereby overlooking global information, such as connectivity patte<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27779<END_URL>

Structure Language Models for Protein Conformation Generation\nProteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is crucial for advancing drug discovery. Traditional physics-based simulation methods often struggle with sampling equilibrium conformations and are computationally expensive. Recently, deep generative models have shown promise in generating protein conformations as a more efficient alternative. Howeve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29785<END_URL>

Student-Informed Teacher Training\nImitation learning with a privileged teacher has proven effective for learning complex control behaviors from high-dimensional inputs, such as images. In this framework, a teacher is trained with privileged task information, while a student tries to predict the actions of the teacher with more limited observations, e.g., in a robot navigation task, the teacher might have access to distances to nearby obstacles, while the student only receives visual observation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30431<END_URL>

Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning\nExtracting relevant information from a stream of high-dimensional observations is a central challenge for deep reinforcement learning agents. Actor-critic algorithms add further complexity to this challenge, as it is often unclear whether the same information will be relevant to both the actor and the critic. To this end, we here explore the principles that underlie effective representations for an act<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28067<END_URL>

Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking\nThe release of ChatGPT in November 2022 sparked an explosion of interest in post-training and an avalanche of new preference optimization (PO) methods. These methods claim superior alignment by virtue of better correspondence with human pairwise preferences, often measured by LLM-judges. In this work, we attempt to answer the following question -- do LLM-judge preferences translate to progress on other, more concre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29906<END_URL>

Subgraph Federated Learning for Local Generalization\nFederated Learning (FL) on graphs enables collaborative model training to enhance performance without compromising the privacy of each client. However, existing methods often overlook the mutable nature of graph data, which frequently introduces new nodes and leads to shifts in label distribution. Since they focus solely on performing well on each client's local data, they are prone to overfitting to their local distributions (i.e., local ove<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29061<END_URL>

Subtask-Aware Visual Reward Learning from Segmented Demonstrations\nReinforcement Learning (RL) agents have demonstrated their potential across various robotic tasks. However, they still heavily rely on human-engineered reward functions, requiring extensive trial-and-error and access to target behavior information, often unavailable in real-world settings. This paper introduces REDS: REward learning from Demonstration with Segmentations, a novel reward learning framework that leverages action-fr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28446<END_URL>

Sufficient Context: A New Lens on Retrieval Augmented Generation Systems\nAugmenting LLMs with context leads to improved performance across many applications. Despite much research on Retrieval Augmented Generation (RAG) systems, an open question is whether errors arise because LLMs fail to utilize the context from retrieval or the context itself is insufficient to answer the query. To shed light on this, we develop a new notion of sufficient context, along with a way to classify instances that <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30092<END_URL>

SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights\nLarge language models (LLMs) like GPT-4, PaLM, and LLaMA have shown significant improvements in various reasoning tasks. However, smaller models such as Llama-3-8B and DeepSeekMath-Base still struggle with complex mathematical reasoning because they fail to effectively identify and correct reasoning errors. Recent reflection-based methods aim to address these issues by enabling self-reflection and self-correctio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29720<END_URL>

Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalization\nSuperalignment, where humans act as weak supervisors for superhuman models, has become a crucial problem with the rapid development of Large Language Models (LLMs). Recent work has preliminarily studied this problem by using weak models to supervise strong models, and discovered that weakly supervised strong students can consistently outperform weak teachers towards the alignment target, leading to a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30200<END_URL>

Supervised and Semi-Supervised Diffusion Maps with Label-Driven Diffusion\nIn this paper, we introduce Supervised Diffusion Maps (SDM) and Semi-Supervised Diffusion Maps (SSDM), which transform the well-known unsupervised dimensionality reduction algorithm, Diffusion Maps, into supervised and semi-supervised learning tools. The proposed methods, SDM and SSDM, are based on our new approach that treats the labels as a second view of the data. This unique framework allows us to incorporate ideas fr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30307<END_URL>

Support is All You Need for Certified VAE Training\nVariational Autoencoders (VAEs) have become increasingly popular and deployed in safety-critical applications. In such applications, we want to give certified probabilistic guarantees on performance under adversarial attacks. We propose a novel method, CIVET, for certified training of VAEs. CIVET depends on the key insight that we can bound worst-case VAE error by bounding the error on carefully chosen support sets at the latent layer. We show <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28349<END_URL>

SurFhead: Affine Rig Blending for Geometrically Accurate 2D Gaussian Surfel Head Avatars\nRecent advancements in head avatar rendering using Gaussian primitives have achieved significantly high-fidelity results. Although precise head geometry is crucial for applications like mesh reconstruction and relighting, current methods struggle to capture intricate geometric details and render unseen poses due to their reliance on similarity transformations, which cannot handle stretch and shear transform<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31173<END_URL>

Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation\nTraining a language model to be both helpful and harmless requires careful calibration of refusal behaviours: Models should refuse to follow malicious instructions or give harmful advice (e.g. “how do I kill someone?”), but they should not refuse safe requests, even if they superficially resemble unsafe ones (e.g. “how do I kill a Python process?”). Avoiding such false refusal, as prior work ha<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29621<END_URL>

Surprising Effectiveness of pretraining Ternary  Language Model at Scale\nRapid advancements in GPU computational power has outpaced memory capacity and bandwidth growth, creating bottlenecks in Large Language Model (LLM) inference. Post-training quantization is the leading method for addressing memory-related bottlenecks in LLM inference, but it suffers from significant performance degradation below 4-bit precision. This paper addresses these challenges by investigating the pretraining of low-b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29550<END_URL>

SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency\nWe present Stable Video 4D (SV4D) — a latent video diffusion model for multi-frame and multi-view consistent dynamic 3D content generation. Unlike previous methods that rely on separately trained generative models for video generation and novel view synthesis, we design a unified diffusion model to generate novel view videos of dynamic 3D objects.  Specifically, given a monocular reference video, SV4D generates nove<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28061<END_URL>

SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding\nDespite the significant advancements of Large Vision-Language Models (LVLMs) on established benchmarks, there remains a notable gap in suitable evaluation regarding their applicability in the emerging domain of long-context streaming video understanding. Current benchmarks for video understanding typically emphasize isolated single-instance text inputs and fail to evaluate the capacity to sustain temporal <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30198<END_URL>

SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression\nThe advancements in Large Language Models (LLMs) have been hindered by their substantial sizes, which necessitate LLM compression methods for practical deployment. Singular Value Decomposition (SVD) offers a promising solution for LLM compression. However, state-of-the-art SVD-based LLM compression methods have two key limitations: truncating smaller singular values may lead to higher compression loss, a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30003<END_URL>

SVDQuant: Absorbing Outliers by Low-Rank Component for 4-Bit Diffusion Models\nDiffusion models have been proven highly effective at generating high-quality images. However, as these models grow larger, they require significantly more memory and suffer from higher latency, posing substantial challenges for deployment. In this work, we aim to accelerate diffusion models by quantizing their weights and activations to 4 bits. At such an aggressive level, both weights and activations are highly sens<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27906<END_URL>

SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix\nVideo generation models have demonstrated great capability of producing impressive monocular videos, however, the generation of 3D stereoscopic video remains under-explored. We propose a pose-free and training-free approach for generating 3D stereoscopic videos using an off-the-shelf monocular video generation model. Our method warps a generated monocular video into camera views on stereoscopic baseline using estimated video depth<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28086<END_URL>

SWE-bench Multimodal: Do Autonomous Programming Systems Generalize to New Software Domains?\nAutonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual elements such as images. Th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28177<END_URL>

Swift4D: Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene\nNovel view synthesis has long been a practical but challenging task, although the introduction of numerous methods to solve this problem, even combining advanced representations like 3D Gaussian Splatting, they still struggle to recover high-quality results and often consume too much storage memory and training time. In this paper we propose Swift4D, a divide-and-conquer 3D Gaussian<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29075<END_URL>

Swift Hydra:  Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models\nDespite a plethora of anomaly detection models developed over the years, their ability to generalize to unseen anomalies remains an issue, particularly in critical systems. This paper aims to address this challenge by introducing Swift Hydra, a new framework for training an anomaly detection method based on generative AI and reinforcement learning (RL). Through featuring an RL policy that operat<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29771<END_URL>

SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration\nSpeculative decoding (SD) has emerged as a widely used paradigm to accelerate the inference of large language models (LLMs) without compromising generation quality. It works by first employing a compact model to draft multiple tokens efficiently and then using the target LLM to verify them in parallel. While this technique has achieved notable speedups, most existing approaches necessitate either additional parameters or<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30412<END_URL>

Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning\nVision Foundation Models (VFMs) have demonstrated outstanding performance on numerous downstream tasks. However, due to their inherent representation biases originating from different training paradigms, VFMs exhibit advantages and disadvantages across distinct vision tasks. Although amalgamating the strengths of multiple VFMs for downstream tasks is an intuitive strategy, effectively exploit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28912<END_URL>

Sylber: Syllabic Embedding Representation of Speech from Raw Audio\nSyllables are compositional units of spoken language that play a crucial role in human speech perception and production. However, current neural speech representations lack structure, resulting in dense token sequences that are costly to process. To bridge this gap, we propose a new model, Sylber, that produces speech representations with clean and robust syllabic structure. Specifically, we propose a self-supervised model that <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30312<END_URL>

SyllableLM: Learning Coarse Semantic Units for Speech Language Models\nLanguage models require tokenized inputs. However, tokenization strategies for continuous data like audio and vision are often based on simple heuristics such as fixed sized convolutions or discrete clustering, which do not necessarily align with the semantic structure of the data. For speech in particular, the high resolution of waveforms (16,000 samples/second or more) presents a significant challenge as speech-based langua<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28995<END_URL>

Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length\nSymbolic regression, a task discovering the formula best fitting the given data, is typically based on the heuristical search. These methods usually update candidate formulas to obtain new ones with lower prediction errors iteratively. However, since formulas with similar function shapes may have completely different symbolic forms, the prediction error does not decrease monotonous<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28504<END_URL>

SymDiff: Equivariant Diffusion via Stochastic Symmetrisation\nWe propose SYMDIFF, a novel method for constructing equivariant diffusionmodels using the recently introduced framework of stochastic symmetrisation.SYMDIFF resembles a learned data augmentation that is deployed at samplingtime, and is lightweight, computationally efficient, and easy to implement ontop of arbitrary off-the-shelf models. Notably, in contrast to previous work,SYMDIFF typically does not require any neural network compone<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28724<END_URL>

SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models\nGenerating novel crystalline materials has potential to lead to advancements in fields such as electronics, energy storage, and catalysis. The defining characteristic of crystals is their symmetry, which plays a central role in determining their physical properties. However, existing crystal generation methods either fail to generate materials that display the symmetries of real-world crystals, or simply replicate the symmetry<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27763<END_URL>

SYMPOL: Symbolic Tree-Based On-Policy Reinforcement Learning\nReinforcement learning (RL) has seen significant success across various domains, but its adoption is often limited by the black-box nature of neural network policies, making them difficult to interpret. In contrast, symbolic policies allow representing decision-making strategies in a compact and interpretable way. However, learning symbolic policies directly within on-policy methods remains challenging.In this paper, we introduce SYMP<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28234<END_URL>

SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints\nRecent advancements in video diffusion models demonstrate remarkable capabilities in simulating real-world dynamics and 3D consistency. This progress motivates us to explore the potential of these models to maintain dynamic consistency across diverse viewpoints, a feature highly sought after in applications like virtual filming. Unlike existing methods focused on multi-view generation of single objects for 4D reco<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28481<END_URL>

Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling\nContrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various architectures, from vision transformers~(ViTs) to convolutional networks (ResNets) have been trained with CLIP to serve as general solutions to diverse vision tasks.This paper explores the differences across various CLIP-trained vision backbones.Despite using the same data and training <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29198<END_URL>

Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning\nDisentangled representation learning aims to uncover the latent variables underlying observed data, yet identifying these variables under mild assumptions remains challenging. Some methods rely on sufficient changes in the distribution of latent variables indicated by auxiliary variables, such as domain indices, but acquiring enough domains is often impractical. Alternative approaches exploit<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30309<END_URL>

Synergy Learning with Small Models promotes LLM Zero-Shot Tabular Prediction\nRecent development in large language models (LLMs) has demonstrated impressive zero-shot proficiency on unstructured textual or multi-modal tasks across various domains. However, despite with inherent world knowledge, their application on structured tabular data prediction still lags behind, primarily due to the numerical insensitivity and modality discrepancy that brings a gap between LLM reasoning and statistical mac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29343<END_URL>

SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints\nGenerative models see increasing use in computer-aided drug design. However, while performing well at capturing distributions of molecular motifs, they often produce synthetically inaccessible molecules. To address this, we introduce SynFlowNet, a GFlowNet model whose action space uses chemical reactions and buyable reactants to sequentially build new molecules. By incorporating forward synthesis as an explicit constra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27946<END_URL>

SynQ: Accurate Zero-shot Quantization by Synthesis-aware Fine-tuning\nHow can we accurately quantize a pre-trained model without any data?Quantization algorithms are widely used for deploying neural networks on resource-constrained edge devices.Zero-shot Quantization (ZSQ) addresses the crucial and practical scenario where training data are inaccessible for privacy or security reasons.However, three significant challenges hinder the performance of existing ZSQ methods: 1) noise in the synthetic <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31109<END_URL>

Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo\nA wide range of LLM applications require generating text that conforms to syntactic or semantic constraints. Imposing such constraints nontrivially alters the distribution over sequences, usually making exact sampling intractable. In this work, building on the Language Model Probabilistic Programming framework of Lew et al. (2023), we develop an approach to approximate inference for controlled LLM generation base<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27761<END_URL>

Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search\nProgrammatic reinforcement learning (PRL) has been explored for representing policies through programs as a means to achieve interpretability and generalization. Despite promising outcomes, current state-of-the-art PRL methods are hindered by sample inefficiency, necessitating tens of millions of program-environment interactions. To tackle this challenge, we introduce a novel LLM-guided search fram<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30780<END_URL>

Synthesizing Realistic fMRI: A Physiological Dynamics-Driven Hierarchical Diffusion Model for Efficient fMRI Acquisition\nFunctional magnetic resonance imaging (fMRI) is essential for mapping brain activity but faces challenges like lengthy acquisition time and sensitivity to patient movement, limiting its clinical and machine learning applications. While generative models such as diffusion models can synthesize fMRI signals to alleviate these issues, they often underperform due to neglecting th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27664<END_URL>

Synthetic continued pretraining\nPretraining on large-scale, unstructured internet text enables language models to acquire a significant amount of world knowledge.However, this knowledge acquisition is data-inefficient---to learn a fact, models must be trained on hundreds to thousands of diverse representations of it.This poses a challenge when adapting a pretrained model to a small corpus of domain-specific documents, where each fact may appear rarely or only once.We propose to bridge this gap <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31270<END_URL>

Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data\nWe present Synthio, a novel approach for augmenting small-scale audio classification datasets with synthetic data. Our goal is to improve audio classification accuracy with limited labeled data. Traditional data augmentation techniques, which apply artificial transformations (e.g., adding random noise or masking segments), struggle to create data that captures the true diversity present in real-world audios. To ad<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29111<END_URL>

SysBench: Can LLMs Follow System Message?\nLarge Language Models (LLMs) have become instrumental across various applications, with the customization of these models to specific scenarios becoming increasingly critical. System message, a fundamental component of LLMs, is consist of carefully crafted instructions that guide the behavior of model to meet intended goals. Despite the recognized potential of system messages to optimize AI-driven solutions,  there is a notable absence of a comprehensiv<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30048<END_URL>

SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems\nSurrogate models are used to predict the behavior of complex energy systems that are too expensive to simulate with traditional numerical methods. Our work introduces the use of language descriptions, which we call "system captions" or SysCaps, to interface with such surrogates. We argue that interacting with surrogates through text, particularly natural language, makes these models more accessible for both experts and no<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31201<END_URL>

System 1.x: Learning to Balance Fast and Slow Planning with Language Models\nLanguage models can be used to solve long-horizon planning problems in two distinct modes. In a fast 'System-1' mode, models directly generate plans without any explicit search or backtracking, and in a slow 'System-2' mode, they plan step-by-step by explicitly searching over possible actions. System-2 planning, while typically more effective, is also computationally more expensive and often infeasible for long plans or<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27660<END_URL>

Systematic Outliers in Large Language Models\nOutliers have been widely observed in Large Language Models (LLMs), significantly impacting model performance and posing challenges for model compression. Understanding the functionality and formation mechanisms of these outliers is critically important. Existing works, however, largely focus on reducing the impact of outliers from an algorithmic perspective, lacking an in-depth investigation into their causes and roles. In this work, we provide a de<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28203<END_URL>

Systematic Relational Reasoning With Epistemic Graph Neural Networks\nDeveloping models that can learn to reason is a notoriously challenging problem. We focus on reasoning in relational domains, where the use of Graph Neural Networks (GNNs) seems like a natural choice. However, previous work has shown that regular GNNs lack the ability to systematically generalize from training examples on test graphs requiring longer inference chains, which fundamentally limits their reasoning abilities. A com<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28252<END_URL>

Systems with Switching Causal Relations: A Meta-Causal Perspective\nMost works on causality in machine learning assume that causal relationships are governed by a constant underlying process. However, the flexibility of agents' actions or tipping point behavior in the environmental process can change the qualitative dynamics of the system. As a result, new causal relationships may emerge, while existing ones change or disappear, resulting in an altered causal graph. To analyze these qualitative <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30125<END_URL>

T2V2: A Unified Non-Autoregressive Model for Speech Recognition and Synthesis via Multitask Learning\nWe introduce T2V2 (**T**ext to **V**oice and **V**oice to **T**ext), a unified non-autoregressive model capable of performing both automatic speech recognition (ASR) and text-to-speech (TTS) synthesis within the same framework. T2V2 uses a shared Conformer backbone with rotary positional embeddings to efficiently handle these core tasks, with ASR trained using Connectionist Temporal Classificati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29506<END_URL>

T2V-Turbo-v2: Enhancing Video Model Post-Training through Data, Reward, and Conditional Guidance Design\nIn this paper, we focus on enhancing a diffusion-based text-to-video (T2V) model during the post-training phase by distilling a highly capable consistency model from a pretrained T2V model. Our proposed method, T2V-Turbo-v2, introduces a significant advancement by integrating various supervision signals, including high-quality training data, reward model feedback, and conditional guidance, in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30564<END_URL>

TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation\nSynthesizing high-quality tabular data is an important topic in many data science tasks, ranging from dataset augmentation to privacy protection. However, developing expressive generative models for tabular data is challenging due to its inherent heterogeneous data types, complex inter-correlations, and intricate column-wise distributions. In this paper, we introduce TabDiff, a joint diffusion framework that models all multi-mod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28087<END_URL>

TabM: Advancing tabular deep learning with parameter-efficient ensembling\nDeep learning architectures for supervised learning on tabular data range from simple multilayer perceptrons (MLP) to sophisticated Transformers and retrieval-augmented methods.This study highlights a major, yet so far overlooked opportunity for substantially improving tabular MLPs; namely, parameter-efficient ensembling -- a paradigm for imitating an ensemble of models with just one model.We start by describing TabM -- a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29590<END_URL>

TabWak: A Watermark for Tabular Diffusion Models\nSynthetic data offers alternatives for data augmentation and sharing. Till date, it remains unknown how to use watermarking techniques to trace and audit synthetic tables generated by tabular diffusion models to mitigate potential misuses. In this paper, we design TabWak, the first watermarking method to embed invisible signatures that control the sampling of Gaussian latent codes used to synthesize table rows via the diffusion backbone. TabWak h<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30853<END_URL>

Tackling Data Corruption in Offline Reinforcement Learning via Sequence Modeling\nLearning policy from offline datasets through offline reinforcement learning (RL) holds promise for scaling data-driven decision-making while avoiding unsafe and costly online interactions. However, real-world data collected from sensors or humans often contains noise and errors, posing a significant challenge for existing offline RL methods, particularly when the real-world data is limited. Our study reveals that <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28283<END_URL>

TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models\nCausal language models have demonstrated remarkable capabilities, but their size poses significant challenges for deployment in resource-constrained environments. Knowledge distillation, a widely-used technique for transferring knowledge from a large teacher model to a small student model, presents a promising approach for model compression.A significant remaining issue lies in the major diff<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29025<END_URL>

Tailoring Mixup to Data for Calibration\nAmong all data augmentation techniques proposed so far, linear interpolation of training samples, also called Mixup, has found to be effective for a large panel of applications.   Along with improved predictive performance, Mixup is also a good technique for improving calibration.  However, mixing data carelessly can lead to manifold mismatch, i.e., synthetic data lying outside original  class manifolds, which can deteriorate calibration.  In this work, w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31046<END_URL>

Talking Turns: Benchmarking Audio Foundation Models on Turn-Taking Dynamics\nThe recent wave of audio foundation models (FMs) could provide new capabilities for conversational modeling. However, there have been limited efforts to evaluate these audio FMs comprehensively on their ability to have natural and interactive conversations. To engage in meaningful conversation with the end user, we would want the FMs to additionally perform a fluent succession of turns without too much overlapping speec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31129<END_URL>

Taming Overconfidence in LLMs: Reward Calibration in RLHF\nLanguage model calibration refers to the alignment between the confidence of the model and the actual performance of its responses.While previous studies point out the overconfidence phenomenon in Large Language Models (LLMs) and show that LLMs trained with Reinforcement Learning from Human Feedback (RLHF) are overconfident with a more sharpened output probability, in this study, we reveal that RLHF tends to lead models to express verbal<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28542<END_URL>

Taming Transformer Without Using Learning Rate Warmup\nScaling Transformer to a large scale without using some technical tricks  such as learning rate warump and an obviously lower learning rate, is an extremely challenging task, and is increasingly gaining more attention. In this paper, we provide a theoretical analysis for training Transformer and reveal a key problem behind the model crash phenomenon in the training, \ie, the spectral energy concentration of $W_q^{\top} W_k$ (where $W_q$ and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30268<END_URL>

TAPE3D: Tracking All Pixels Efficiently in 3D\nTracking dense 3D motion from monocular video remains challenging, particularly when aiming for pixel-level precision over long sequences. We introduce TAPE3D, a novel method that efficiently tracks every pixel in 3D space, enabling accurate motion estimation across entire videos. Our approach leverages a joint global-local attention mechanism for reduced-resolution tracking, followed by a transformer-based upsampler to achieve high-resolution predi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29002<END_URL>

Targeted Attack Improves Protection against Unauthorized Diffusion Customization\nDiffusion models build a new milestone for image generation yet raising public concerns, for they can be fine-tuned on unauthorized images for customization. Protection based on adversarial attacks rises to encounter this unauthorized diffusion customization, by adding protective watermarks to images and poisoning diffusion models. However, current protection, leveraging untargeted attacks, does not appear to be ef<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29155<END_URL>

Targeted Manipulation and Deception Emerge in LLMs Trained on User* Feedback\nAs LLMs become more widely deployed, there is increasing interest in directly optimizing for feedback from end users (e.g. thumbs up) in addition to feedback from paid annotators. However, training to maximize human feedback creates a perverse incentive structure for the AI to resort to manipulative or deceptive tactics to obtain positive feedback from users who are vulnerable to such strategies. We study this phenomen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29353<END_URL>

TASAR: Transfer-based Attack on Skeletal Action Recognition\nSkeletal sequence data, as a widely employed representation of human actions, are crucial in Human Activity Recognition (HAR). Recently, adversarial attacks have been proposed in this area, which exposes potential security concerns, and more importantly provides a good tool for model robustness test. Within this research, transfer-based attack is an important tool as it mimics the real-world scenario where an attacker has no knowledge <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30195<END_URL>

Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling\nSpecialist language models (LMs) focus on a specific task or domain on which they often outperform generalist LMs of the same size. However, the specialist data needed to pretrain these models is only available in limited amount for most tasks. In this work, we build specialist models from large generalist training sets instead. We adjust the training distribution of the generalist data with guidance from the limited dom<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28318<END_URL>

Task Descriptors Help Transformers Learn Linear Models In-Context\nLarge language models (LLMs) exhibit strong in-context learning (ICL) ability, which allows the model to make predictions on new examples based on the given prompt. Recently, a line of research (Von Oswald et al., 2023; Aky¨urek et al., 2023; Ahn et al., 2023; Mahankali et al., 2023; Zhang et al., 2024) considered ICL for a simple linear regression setting and showed that the forward pass of Transformers is simulating some varian<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28511<END_URL>

TC-MoE: Augmenting Mixture of Experts with Ternary Expert Choice\nThe Mixture of Experts (MoE) architecture has emerged as a promising solution for reducing computational overhead by selectively activating subsets of model parameters. The effectiveness of MoE models is primarily dependent on their routing mechanisms, with the widely adopted Top-K routing scheme used to activate experts. However, the Top-K scheme has notable limitations, including unnecessary activations and underutilization of e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28959<END_URL>

TDDBench: A Benchmark for Training data detection\nTraining Data Detection (TDD) is a task aimed at determining whether a specific data instance is used to train a  machine learning model. In the computer security literature, TDD is also referred to as Membership Inference Attack (MIA). Given its potential to assess the risks of training data breaches, ensure copyright authentication, and verify model unlearning, TDD has garnered significant attention in recent years, leading to the development <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28739<END_URL>

TD-Paint: Faster Diffusion Inpainting Through Time Aware Pixel Conditioning\nDiffusion models have emerged as highly effective techniques for inpainting, however, they remain constrained by slow sampling rates. While recent advances have enhanced generation quality, they have also increased sampling time, thereby limiting scalability in real-world applications. We investigate the generative sampling process of diffusion-based inpainting models and observe that these models make minimal use of th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28905<END_URL>

Teaching Human Behavior Improves Content Understanding Abilities Of LLMs\nCommunication is defined as ``Who says what to whom with what effect.'' A message from a communicator generates downstream receiver effects, also known as behavior. Receiver behavior, being a downstream effect of the message, carries rich signals about it. Even after carrying signals about the message, the behavior data is often ignored while training large language models. We show that training LLMs on receiver behavior c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28866<END_URL>

Teaching LLMs How To Learn with Contextual Fine-Tuning\nPrompting Large Language Models (LLMs), or providing context on the expected model of operation, is an effective way to steer the outputs of such models to satisfy human desiderata after they have been trained. But in rapidly evolving domains, there is often need to fine-tune LLMs to improve either the kind of knowledge in their memory or their abilities to perform open ended reasoning in new domains. When human's learn new concepts, we oft<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30340<END_URL>

TEASER: Token Enhanced Spatial Modeling for Expressions Reconstruction\n3D facial reconstruction from a single in-the-wild image is a crucial task in human-centered computer vision tasks. While existing methods can recover accurate facial shapes, there remains significant space for improvement in fine-grained expression capture.  Current approaches struggle with irregular mouth shapes, exaggerated expressions, and asymmetrical facial movements. We present TEASER (Token EnhAnced Spatial modeling <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28296<END_URL>

TempMe: Video Temporal Token Merging for Efficient Text-Video Retrieval\nMost text-video retrieval methods utilize the text-image pre-trained models like CLIP as a backbone. These methods process each sampled frame independently by the image encoder, resulting in high computational overhead and limiting practical deployment. Addressing this, we focus on efficient text-video retrieval by tackling two key challenges: 1. From the perspective of trainable parameters, current parameter-efficient fine<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28516<END_URL>

Temporal Difference Learning: Why It Can Be Fast and How It Will Be Faster\nTemporal difference (TD) learning represents a fascinating paradox: It is the prime example of a divergent algorithm that has not vanished after its instability was proven. On the contrary, TD continues to thrive in reinforcement learning (RL), suggesting that it provides significant compensatory benefits. Empirical evidence supports this, as many RL tasks require substantial computational resources, and TD delivers a cr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28658<END_URL>

Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness\nSpiking Neural Networks (SNNs), models inspired by neural mechanisms in the brain, allow for energy-efficient implementation on neuromorphic hardware. However, SNNs trained with current direct training approaches are constrained to a specific time step. This "temporal inflexibility" 1) hinders SNNs' deployment on time-step-free fully event-driven chips and 2) prevents energy-per<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30705<END_URL>

Temporal Heterogeneous Graph Generation with Privacy, Utility, and Efficiency\nNowadays, Temporal Heterogeneous Graphs attract much research and industrial attention for building the next-generation Relational Deep Learning Models and Applications, due to their informative structures and features. While providing timely and precise services like personalized recommendations and question answering, this rich information also introduces extra exposure risk for each node in the graph, because the d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28030<END_URL>

Temporal Reasoning Transfer from Text to Video\nVideo Large Language Models (Video LLMs) have shown promising capabilities in video comprehension, yet they struggle with tracking temporal changes and reasoning about temporal relationships.While previous research attributed this limitation to the ineffective temporal encoding of visual inputs, our diagnostic study reveals that video representations contain sufficient information for even small probing classifiers to achieve perfect accuracy.Surpr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28136<END_URL>

TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data\nLarge vision and language assistants have enabled new capabilities for interpreting natural images. These approaches have recently been adapted to earth observation data, but they are only able to handle single image inputs, limiting their use for many real-world tasks. In this work, we develop a new vision and language assistant called TEOChat that can engage in conversations about temporal sequences of earth observ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28289<END_URL>

TestGenEval: A Real World Unit Test Generation and Test Completion Benchmark\nCode generation models can help improve many common software tasks ranging from code completion to defect prediction. Most of the existing benchmarks for code generation LLMs focus on code authoring or code completion. Surprisingly, there has been far less effort dedicated to benchmarking software testing, despite the strong correlation between well-tested software and effective bug detection. To address this gap, we c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30800<END_URL>

Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning\nLarge language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex temporal logic. Existing research has explored LLM performance on temporal reasoning using diverse datasets and benchmarks. However, these studies often rely on real-world data that LLMs may have encountered during pre-training or employ anonymization te<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31040<END_URL>

Test-Time Adaptation for Combating Missing Modalities in Egocentric Videos\nUnderstanding videos that contain multiple modalities is crucial, especially in egocentric videos, where combining various sensory inputs significantly improves tasks like action recognition and moment localization. However, real-world applications often face challenges with incomplete modalities due to privacy concerns, efficiency needs, or hardware issues. Current methods, while effective, often necessitate retraining <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31208<END_URL>

Test-time Adaptation for Cross-modal Retrieval with Query Shift\nThe success of most existing cross-modal retrieval methods heavily relies on the assumption that the given queries follow the same distribution of the source domain. However, such an assumption is easily violated in real-world scenarios due to the complexity and diversity of queries, thus leading to the query shift problem.Specifically, query shift refers to the online query stream originating from the domain that follows a differe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30549<END_URL>

Test-time Adaptation for Image Compression with Distribution Regularization\nCurrent test- or compression-time adaptation image compression (TTA-IC) approaches, which leverage both latent and decoder refinements as a two-step adaptation scheme, have potentially enhanced the rate-distortion (R-D) performance of learned image compression models on cross-domain compression tasks, \textit{e.g.,} from natural to screen content images.  However, compared with the emergence of various decoder refinemen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29083<END_URL>

Test-time Adaptation for Regression by Subspace Alignment\nThis paper investigates test-time adaptation (TTA) for regression, where a regression model pre-trained in a source domain is adapted to an unknown target distribution with unlabeled target data.Although regression is one of the fundamental tasks in machine learning, most of the existing TTA methods have classification-specific designs, which assume that models output class-categorical predictions, whereas regression models typically out<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29596<END_URL>

Test-Time Ensemble via Linear Mode Connectivity: A Path to Better Adaptation\nTest-time adaptation is a valuable approach for online adjustment of pretrained models to handle distribution shifts in test data. While existing research has focused primarily on optimizing stability during adaptation with dynamic data streams, less attention has been given to enhancing model representations for improved adaptation capability. This paper addresses this gap by introducing Test-Time Ensemble (TTE), whic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30982<END_URL>

TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes\nWe introduce TetSphere Splatting, a Lagrangian geometry representation designed for high-quality 3D shape modeling. TetSphere splatting leverages an underused yet powerful geometric primitive -- volumetric tetrahedral meshes. It represents 3D shapes by deforming a collection of tetrahedral spheres, with geometric regularizations and constraints that effectively resolve common mesh issues such as irregular <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30753<END_URL>

Text2PDE: Latent Diffusion Models for Accessible Physics Simulation\nRecent advances in deep learning have inspired numerous works on data-driven solutions to partial differential equation (PDE) problems. These neural PDE solvers can often be much faster than their numerical counterparts; however, each presents its unique limitations and generally balances training cost, numerical accuracy, and ease of applicability to different problem setups. To address these limitations, we introduce several <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29869<END_URL>

TexTailor: Customized Text-aligned Texturing via Effective Resampling\nWe present TexTailor, a novel method for generating consistent object textures from textual descriptions. Existing text-to-texture synthesis approaches utilize depth-aware diffusion models to progressively generate images and synthesize textures across predefined multiple viewpoints. However, these approaches lead to a gradual shift in texture properties across viewpoints due to (1) insufficient integration of previously synt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31205<END_URL>

TextSeg: Reimagining Image Segmentation as Text Generation\nMultimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks; however, effectively integrating image segmentation into these models remains a significant challenge. In this paper, we introduce TextSeg, a novel text-as-mask paradigm that casts image segmentation as a text generation problem, eliminating the need for additional decoders and significantly simplifying the segmentation process. Our ke<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27892<END_URL>

Text-to-Image Rectified Flow as Plug-and-Play Priors\nLarge-scale diffusion models have achieved remarkable performance in generative tasks. Beyond their initial training applications, these models have proven their ability to function as versatile plug-and-play priors. For instance, 2D diffusion models can serve as loss functions to optimize 3D implicit models. Rectified Flow, a novel class of generative models, has demonstrated superior performance across various domains. Compared to diffusion<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29568<END_URL>

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics\nFuture link prediction is a fundamental challenge in various real-world dynamic systems. To address this, numerous temporal graph neural networks (temporal GNNs) and benchmark datasets have been developed. However, these datasets often feature excessive repeated edges and lack complex sequential dynamics, a key characteristic inherent in many real-world applications such as recommender systems and ``Who-To-Follow'' on<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30756<END_URL>

The 2nd Workshop on Foundation Models in the Wild\nIn the era of AI-driven transformations, foundation models (FMs) have become pivotal in various applications, from natural language processing to computer vision. These models, with their immense capabilities,reshape the future of scientific research and the broader human society, but also introduce challenges intheir in-the-wild/real-world deployments. The 2nd Workshop on FMs in the Wild delves into the urgent need forthese models to be useful <BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23989<END_URL>

The 3D-PC: a benchmark for visual perspective taking in humans and machines\nVisual perspective taking (VPT) is the ability to perceive and reason about the perspectives of others. It is an essential feature of human intelligence, which develops over the first decade of life and requires an ability to process the 3D structure of visual scenes. A growing number of reports have indicated that deep neural networks (DNNs) become capable of analyzing 3D scenes after training on large image datasets. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29481<END_URL>

The adaptive complexity of log-concave sampling\nIn large-data applications, such as the inference process of diffusion models, it is desirable to design sampling algorithms with a high degree of parallelization. In this work, we study the adaptive complexity of sampling, which is the minimum number of sequential rounds required to achieve sampling given polynomially many queries executed in parallel at each round. For unconstrained sampling, we examine distributions that are log-smooth or log-L<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30393<END_URL>

The AdEMAMix Optimizer: Better, Faster, Older\nMomentum based optimizers are central to a wide range of machine learning applications. These typically rely on an Exponential Moving Average (EMA) of gradients, which decays exponentially the present contribution of older gradients. This accounts for gradients being local linear approximations which lose their relevance as the iterate moves along the loss landscape. This work questions the use of a single EMA to accumulate past gradients and empiri<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28625<END_URL>

The Breakdown of Gaussian Universality in Classification of High-dimensional Mixtures\nThe assumption of Gaussian or Gaussian mixture data has been extensively exploited in a long series of precise performance analyses of machine learning (ML) methods, on large datasets having comparably numerous samples and features. To relax this restrictive assumption, subsequent efforts have been devoted to establish "Gaussian equivalent principles" by studying scenarios of Gaussian universality where the as<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29450<END_URL>

The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG\nAll data modalities are not created equal, even when the signal they measure comes from the same source. In the case of the brain, two of the most important data modalities are the scalp electroencephalogram (EEG), and the intracranial electroencephalogram (iEEG). iEEG benefits from a higher signal-to-noise ratio (SNR), as it measures the electrical activity directly in the brain, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29124<END_URL>

The Complexity of Two-Team Polymatrix Games with Independent Adversaries\nAdversarial multiplayer games are an important object of study in multiagent learning. In particular, polymatrix zero-sum games are a multiplayer setting where Nash equilibria are known to be efficiently computable. Towards understanding the limits of tractability in polymatrix games, we study the computation of Nash equilibria in such games where each pair of players plays either a zero-sum or a coordination game. We are <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30687<END_URL>

The Computational Complexity of Circuit Discovery for Inner Interpretability\nMany proposed applications of neural networks in machine learning, cognitive/brain science, and society hinge on the feasibility of inner interpretability via circuit discovery. This calls for empirical and theoretical explorations of viable algorithmic options. Despite advances in the design and testing of heuristics, there are concerns about their scalability and faithfulness at a time when we lack understanding of t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29673<END_URL>

The Computational Complexity of Positive Non-Clashing Teaching in Graphs\nWe study the classical and parameterized complexity of computing the positive non-clashing teaching dimension of a set of concepts, that is, the smallest number of examples per concept required to successfully teach an intelligent learner under the considered, previously established model. For any class of concepts, it is known that this problem can be effortlessly transferred to the setting of balls in a graph $G$. We est<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30095<END_URL>

The Crucial Role of Samplers in Online Direct Preference Optimization\nDirect Preference Optimization (DPO) has emerged as a stable, scalable, and efficient solution for language model alignment.Despite its empirical success, the *optimization* properties, particularly the impact of samplers on its convergence rates, remain underexplored. In this paper, we provide a rigorous analysis of DPO's *convergence rates* with different sampling strategies under the exact gradient setting, revealing a sur<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30363<END_URL>

The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise\nDiffusion models have achieved remarkable success in text-to-image generation tasks, yet the influence of initial noise remains largely unexplored. In this study, we identify specific regions within the initial noise image, termed trigger patches, that play a key role in inducing object generation in the resulting images. Notably, these patches are **universal** and can be generalized across variou<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30257<END_URL>

The Directionality of Optimization Trajectories in Neural Networks\nThe regularity or implicit bias in neural network optimization has been typically studied via the parameter norms or the landscape curvature, often overlooking the trajectory leading to these parameters. However, properties of the trajectory --- particularly its directionality --- capture critical aspects of how gradient descent navigates the landscape to converge to a solution. In this work, we introduce the notion of a Traject<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30103<END_URL>

The Effectiveness of Curvature-Based Rewiring and the Role of Hyperparameters in GNNs Revisited\nMessage passing is the dominant paradigm in Graph Neural Networks (GNNs). The efficiency of message passing, however, can be limited by the topology of the graph. This happens when information is lost during propagation due to being oversquashed when travelling through bottlenecks. To remedy this, recent efforts have focused on graph rewiring techniques, which disconnect the input graph originating f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30396<END_URL>

The Foundations of Tokenization: Statistical and Computational Concerns\nTokenization--the practice of converting strings of characters from an alphabet into sequences of tokens over a vocabulary--is a critical step in the NLP pipeline. The use of token representations is widely credited with increased model performance but is also the source of many undesirable behaviors, such as spurious ambiguity or inconsistency. Despite its recognized importance as a standard representation method in NLP, t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30592<END_URL>

The Future of Machine Learning Data Practices and Repositories\nDatasets are a central pillar of machine learning (ML) research—from pretraining to evaluation and benchmarking. However, a growing body of work highlights serious issues throughout the ML data ecosystem, including the under-valuing of data work, ethical issues in datasets that go undiscovered, a lack of standardized dataset deprecation procedures, the (mis)use of datasets out-of-context, an overemphasis on single metrics rather tha<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23970<END_URL>

The Hidden Cost of Waiting for Accurate Predictions\nAlgorithmic predictions are increasingly informing societal resource allocations by identifying individuals for targeting. Policymakers often build these systems with the assumption that by gathering more observations on individuals, they can improve predictive accuracy and, consequently, allocation efficiency. An overlooked yet consequential aspect of prediction-driven allocations is that of timing. The planner has to trade off relying on ear<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30653<END_URL>

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation\nThis paper introduces the counter-intuitive generalization results of overfitting pre-trained large language models (LLMs) on very small datasets. In the setting of open-ended text generation, it is well-documented that LLMs tend to generate repetitive and dull sequences, a phenomenon that is especially apparent when generating using greedy decoding. This issue persists even with state-of-the-art LLMs co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30156<END_URL>

The Illustrated AlphaFold\nWe present the Illustrated AlphaFold, a visual walkthrough of the architecture and information flow of AlphaFold 3. We explain every model component and training detail, with particular focus on the advances since AlphaFold 2 – including the unified tokenization scheme that extends to DNA, RNA, and small molecules, as well as the novel diffusion-based structural module. Finally, we include some musings on the ML lessons learned from studying AlphaFold 3.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31362<END_URL>

The impact of allocation strategies in subset learning on the expressive power of neural networks\nIn traditional machine learning, models are defined by a set of parameters, which are optimized to perform specific tasks. In neural networks, these parameters correspond to the synaptic weights. However, in reality, it is often infeasible to control or update all weights. This challenge is not limited to artificial networks but extends to biological networks, such as the brain, where the extent of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27957<END_URL>

The KoLMogorov Test: Compression by Code Generation\nCompression is at the heart of intelligence. A theoretically optimal way to compress any sequence of data is to find the shortest program that outputs that sequence and then halts. However, such Kolmogorov compression is uncomputable, and code generating LLMs struggle to approximate this theoretical ideal, as it requires reasoning, planning and search capabilities beyond those of current models. In this work, we introduce the *KoLMogorov-Test*<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30532<END_URL>

The Labyrinth of Links: Navigating the Associative Maze of Multi-modal LLMs\nMulti-modal Large Language Models (MLLMs) have exhibited impressive capability. However, recently many deficiencies of MLLMs have been found compared to human intelligence, $\textit{e.g.}$, hallucination. To drive the MLLMs study, the community dedicated efforts to building larger benchmarks with complex tasks. In this paper, we propose benchmarking an essential but usually overlooked intelligence: $\textbf{association}<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27924<END_URL>

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD\nWe propose a simple heuristic privacy analysis of noisy clipped stochastic gradient descent (DP-SGD) in the setting where only the last iterate is released and the intermediate iterates remain hidden. Namely, our heuristic assumes a linear structure for the model.We show experimentally that our heuristic is predictive of the outcome of privacy auditing applied to various training proce<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30434<END_URL>

The "Law'' of the Unconscious Contrastive Learner: Probabilistic Alignment of Unpaired Modalities\nWhile internet-scale data often come in pairs (e.g., audio+image, image+text), we often want to perform inferences over modalities unseen together in the training data (e.g., audio+text). Prior work has addressed this issue by learning multiple contrastive embedding spaces between existing modality pairs, implicitly hoping that unseen modality pairs will end up being aligned. This theoretical paper<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30439<END_URL>

The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?\nMotivated by reducing the computational and storage costs of LLMs, model compression and KV cache compression have attracted much attention of researchers. However, Current methodologies predominantly emphasize maintaining the performance of compressed LLMs, as measured by perplexity or simple accuracy, on tasks involving common sense knowledge question answering and basic arithmetic reasoning. In this blog, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31348<END_URL>

The Low-Rank Bottleneck in Attention\nAttention-based mechanisms are widely used in machine learning, most prominently in transformers. However, hyperparameters such as the rank of the attention matrices and the number of attention heads are scaled nearly the same way in all realizations of this architecture, without theoretical justification. In this paper, we prove that the rank can have a dramatic effect on the representational capacity of attention. This effect persists even when the number <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27747<END_URL>

The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling\nBiological language model performance depends heavily on pretraining data quality, diversity, and size. While metagenomic datasets feature enormous biological diversity, their utilization as pretraining data has been limited due to challenges in data accessibility, quality filtering and deduplication. Here, we present the Open MetaGenomic (OMG) corpus, a genomic pretraining dataset totalling 3.1T base pairs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28618<END_URL>

The Optimization Landscape of SGD Across the Feature Learning Strength\nWe consider neural networks (NNs) where the final layer is down-scaled by a fixed hyperparameter $\gamma$. Recent work has identified $\gamma$ as controlling the strength of feature learning.As $\gamma$ increases, network evolution changes from "lazy" kernel dynamics to "rich" feature-learning dynamics, with a host of associated benefits including improved performance on common tasks.In this work, we conduct a thorough empir<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28707<END_URL>

Theory, Analysis, and Best Practices for Sigmoid Self-Attention\nAttention is a key part of the transformer architecture. It is a sequence-to-sequence mapping that transforms each sequence element into a weighted sum of values. The weights are typically obtained as the softmax of dot products between keys and queries. Recent work has explored alternatives to softmax attention in transformers, such as ReLU and sigmoid activations. In this work, we revisit sigmoid attention and conduct an in-depth<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29205<END_URL>

Theory on Mixture-of-Experts in Continual Learning\nContinual learning (CL) has garnered significant attention because of its ability to adapt to new tasks that arrive over time. Catastrophic forgetting (of old tasks) has been identified as a major issue in CL, as the model adapts to new tasks. The Mixture-of-Experts (MoE) model has recently been shown to effectively mitigate catastrophic forgetting in CL, by employing a gating network to sparsify and distribute diverse tasks among multiple expe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30816<END_URL>

Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers\nThe denoising diffusion model has recently emerged as a powerful generative technique, capable of transforming noise into meaningful data. While theoretical convergence guarantees for diffusion models are well established when the target distribution aligns with the training distribution, practical scenarios often present mismatches. One common case is in zero-shot conditional diffusion sampling, where the target con<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28407<END_URL>

The Pitfalls of Memorization: When Memorization Hurts Generalization\nNeural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explanations. This leads to poor generalization if the learned explanations are spurious. In this work, we formalize $\textit{the interplay between memorization and generalization}$, showing that spurious correlations would particularly lead to poor generalization when are combined with memoriza<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27908<END_URL>

The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions\nStance detection holds great potential to improve online political discussions through its deployment in discussion platforms for purposes such as content moderation, topic summarisation or to facilitate more balanced discussions. Typically, transformer-based models are employed directly for stance detection, requiring vast amounts of data. However, the wide variety of debate topics in online politica<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27816<END_URL>

The Ramanujan Library - Automated Discovery on the Hypergraph of Integer Relations\nFundamental mathematical constants appear in nearly every field of science, from physics to biology. Formulas that connect different constants often bring great insight by hinting at connections between previously disparate fields.Discoveries of such relations, however, have remained scarce events, relying on sporadic strokes of creativity by human mathematicians.Recent developments of algorithms for automated co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30374<END_URL>

The Representation Geometry of Features and Hierarchy in Large Language Models\nThe linear representation hypothesis is the informal idea that semantic concepts are encoded as linear directions in the representation spaces of large language models (LLMs). Previous work has shown how to make this notion precise for representing binary concepts that have natural contrasts (e.g., {male, female}) as _directions_ in representation space. However, many natural concepts do not have natural contrasts (e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29106<END_URL>

The Rise and Down of Babel Tower: Investigating the Evolution Process of Multilingual Code Large Language Model\nLarge language models (LLMs) have shown significant multilingual capabilities. However, the mechanisms underlying the development of these capabilities during pre-training are not well understood. In this paper, we use code LLMs as an experimental platform to explore the evolution of multilingual capabilities in LLMs during the pre-training process. Based on our observations, we propo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28901<END_URL>

ThermalGaussian: Thermal 3D Gaussian Splatting\nThermography is especially valuable for the military and other users of surveillance cameras. Some recent methods based on Neural Radiance Fields (NeRF) are proposed to reconstruct the thermal scenes in 3D from a set of thermal and RGB images. However, unlike NeRF, 3D Gaussian splatting (3DGS) prevails due to its rapid training and real-time rendering. In this work, we propose ThermalGaussian, the first thermal 3DGS approach capable of rendering hi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27712<END_URL>

THE ROBUSTNESS OF DIFFERENTIABLE CAUSAL DISCOVERY IN MISSPECIFIED SCENARIOS\nCausal discovery aims to learn causal relationships between variables from targeted data, making it a fundamental task in machine learning. However, causal discovery algorithms often rely on unverifiable causal assumptions, which are usually difficult to satisfy in real-world data, thereby limiting the broad application of causal discovery in practical scenarios. Inspired by these considerations, this work extensively b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28686<END_URL>

The Same but Different: Structural Similarities and Differences in Multilingual Language Modeling\nWe employ new tools from mechanistic interpretability in order to ask whether the internal structure of large language models (LLMs) shows correspondence to the linguistic structures which underlie the languages on which they are trained. In particular, we ask (1) when two languages employ the same morphosyntactic processes, do LLMs handle them using shared internal circuitry? and (2) when two lang<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29893<END_URL>

The Superposition of Diffusion Models\nThe Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurring the significant computational burden of re-training a larger combined model. In this paper, we cast the problem of combining multiple pre-trained diffusion models at the generation stage under a novel proposed framework termed superposition. Theoretically, we derive superpositio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31117<END_URL>

The Unreasonable Ineffectiveness of the Deeper Layers\nUnderstanding *where* and *how* knowledge is stored in LLMs is an active and important area of research. In this work, we take a model pruning approach: if removing certain parameters does not affect model output in *question-answering knowledge benchmarks*, then those parameters are likely are not useful for storing knowledge. To find these parameters, we identify the optimal block of layers to prune by considering similarity across layers;<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28400<END_URL>

The Utility and Complexity of In- and Out-of-Distribution Machine Unlearning\nMachine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowledge gaps post-deployment. Despite this importance, existing approaches are often heuristic and lack formal guarantees. In this paper, we analyze the fundamental utility, time, and space complexity trade-offs of approximate unlearning, providing rigorous certification analog<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30223<END_URL>

The Value of Sensory Information to a Robot\nA decision-making agent, such as a robot, must observe and react to any new task-relevant information that becomes available from its environment. We seek to study a fundamental scientific question: what value does sensory information hold to an agent at various moments in time during the execution of a task? Towards this, we empirically study agents of varying architectures, generated with varying policy synthesis approaches (imitation, RL, model-bas<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28678<END_URL>

The Vital Role of Gradient Clipping in Byzantine-Resilient Distributed Learning\nByzantine-resilient distributed machine learning seeks to achieve robust learning performance in the presence of misbehaving or adversarial workers.While state-of-the-art (SOTA) robust distributed gradient descent (Robust-DGD) methods wereproven theoretically optimal, their empirical success has often relied on pre-aggregation gradient clipping.However, the currently considered staticclipping strategy exhibits mixed<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31272<END_URL>

ThinkBot: Embodied Instruction Following with Thought Chain Reasoning\nEmbodied Instruction Following (EIF) requires agents to complete human instruction by interacting objects in complicated surrounding environments. Conventional methods directly consider the sparse human instruction to generate action plans for agents, which usually fail to achieve human goals because of the instruction incoherence in action descriptions. On the contrary, we propose ThinkBot that reasons the thought chain in h<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28066<END_URL>

Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation\nRetrieval-augmented generation (RAG) has improved large language models (LLMs) by using knowledge retrieval to overcome knowledge deficiencies. However, current RAG methods often fall short of ensuring the depth and completeness of retrieved information, which is necessary for complex reasoning tasks. In this work, we introduce Think-on-Graph 2.0 (ToG-2), a hybrid RAG framew<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28367<END_URL>

Think Then React: Towards Unconstrained Action-to-Reaction Motion Generation\nModeling human-like action-to-reaction generation has significant real-world applications, like human-robot interaction and games.Despite recent advancements in single-person motion generation, it is still challenging to well handle action-to-reaction generation, due to the difficulty of directly predicting reaction from action sequence without prompts, and the absence of a unified representation that effectively encod<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29437<END_URL>

ThinK: Thinner Key Cache by Query-Driven Pruning\nLarge Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications. However, their increased computational and memory demands present significant challenges, especially when handling long sequences.This paper focuses on the long-context scenario, addressing the inefficiencies in KV cache memory consumption during inference. Unlike existing approaches that<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28435<END_URL>

Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models\nRecent advancements in large language models (LLMs) have demonstrated that progressive refinement, rather than providing a single answer, results in more accurate and thoughtful outputs. However, existing methods often rely heavily on supervision signals to evaluate previous responses, making it difficult to effectively assess output quality in more open-ended scenarios. Additionally, these methods are typicall<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28295<END_URL>

Think Twice Before Claiming Your Optimization Algorithm Outperformance - Review and Beyond\nIn this blog, we revisit the convergence analysis of first-order algorithms in minimization and minimax optimization problems. Within the classical oracle model framework, we review the state-of-the-art upper and lower bound results in various settings, aiming to identify gaps in existing research. With the rapid development of applications like machine learning and operation research, we further identify<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31341<END_URL>

Think while You Generate: Discrete Diffusion with Planned Denoising\nDiscrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce *Discrete Diffusion with Planned Denoising* (DDPD), a novel framework that separates the generation process into two models: a planner and a denoiser. At inference time, the planner selects which positions to denoise next by identifying the most corrupted positions i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29942<END_URL>

Three-in-One: Fast and Accurate Transducer for Hybrid-Autoregressive Speech Recognition\nWe present Hybrid-Autoregressive Inference Transducers (HAI-T), a novel architecture for speech recognition that extends the Token-and-Duration Transducer (TDT) model. Trained with randomly masked predictor network outputs, HAI-T supports both autoregressive inference with all network components and non-autoregressive inference without the predictor. Additionally, we propose a novel semi-autoregressive infer<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29973<END_URL>

Three Mechanisms of Feature Learning in a Linear Network\nUnderstanding the dynamics of neural networks in different width regimes is crucial for improving their training and performance. We present an exact solution for the learning dynamics of a one-hidden-layer linear network, with one-dimensional data, across any finite width, uniquely exhibiting both kernel and feature learning phases. This study marks a technical advancement by enabling the analysis of the training trajectory from any init<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29349<END_URL>

ThunderKittens: Simple, Fast, and $\textit{Adorable}$ Kernels\nThe challenge of mapping AI architectures to GPU hardware is creating a critical bottleneck in AI progress. Despite substantial efforts, hand-written custom kernels fail to meet their theoretical performance thresholds, even on well-established operations like linear attention.The diverse hardware capabilities of GPUs might suggest that we need a wide variety of techniques to achieve high performance. However, our work explores wheth<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31243<END_URL>

TidalDecode: Fast and Accurate LLM Decoding with Position Persistent Sparse Attention\nLarge language models (LLMs) have driven significant advancements across diverse NLP tasks, with long-context models gaining prominence for handling extended inputs. However, the expanding key-value (KV) cache size required by Transformer architectures intensifies the memory constraints, particularly during the decoding phase, creating a significant bottleneck. Existing sparse attention mechanisms designed to <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30389<END_URL>

TIGER: Time-frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation\nIn recent years, much speech separation research has focused primarily on improving model performance. However, for low-latency speech processing systems, high efficiency is equally important. Therefore, we propose a speech separation model with significantly reduced parameters and computational costs: ***Time-frequency Interleaved Gain Extraction and Reconstruction network (TIGER)***. TIGER lev<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28155<END_URL>

TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models\nHow humans can effectively and efficiently acquire images has always been a perennial question. A classic solution is *text-to-image retrieval* from an existing database; however, the limited database typically lacks creativity. By contrast, recent breakthroughs in *text-to-image generation* have made it possible to produce attractive and counterfactual visual content, but it faces challenges in synthesizing kno<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28444<END_URL>

Tight Clusters Make Specialized Experts\nSparse Mixture-of-Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core of the MoE model is the router, which learns the underlying clustering structure of the input distribution in order to send input tokens to appropriate experts. However, latent clusters may be unidentifiable in high dimension, which causes slow convergence, susceptibility to data contamination, and overall de<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29725<END_URL>

Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model\nMachine learning models can be trained with formal privacy guarantees via differentially private optimizers such as DP-SGD. In this work, we focus on a threat model where the adversary has access only to the final model, with no visibility into intermediate updates. In the literature, this ``hidden state'' threat model exhibits a significant gap between the lower bound from empirical privacy auditing and the theoretical upper b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27756<END_URL>

Tight Lower Bounds under Asymmetric High-Order Hölder Smoothness and Uniform Convexity\nIn this paper, we provide tight lower bounds for the oracle complexity of minimizing high-order Hölder smooth and uniformly convex functions. Specifically, for a function whose $p^{th}$-order derivatives are Hölder continuous with degree $\nu$ and parameter $H$, and that is uniformly convex with degree $q$ and parameter $\sigma$, we focus on two asymmetric cases: (1) $q > p + \nu$, and (2) $q < p+\nu$. Given <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28882<END_URL>

Tight Time Complexities in Parallel Stochastic Optimization with Arbitrary Computation Dynamics\nIn distributed stochastic optimization, where parallel and asynchronous methods are employed, we establish optimal time complexities under virtually any computation behavior of workers/devices/CPUs/GPUs, capturing potential disconnections due to hardware and network delays, time-varying computation powers, and any possible fluctuations and trends of computation speeds. These real-world scenarios are <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29045<END_URL>

Time After Time: Scalable Effect Estimation for Interventions on When and What to do\nDecision support in fields such as healthcare and finance requires reasoning about treatment timing. Artificial Intelligence holds great potential for supporting such decisions by estimating the causal effect of policies such as medication regimens, or resource allocation schedules. However, existing methods for effect estimation are limited in their ability to handle \emph{irregular time}. While treatments and<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30918<END_URL>

TimeInf: Time Series Data Contribution via Influence Functions\nEvaluating the contribution of individual data points to a model's prediction is critical for interpreting model predictions and improving model performance. Existing data contribution methods have been applied to various data types, including tabular data, images, and text; however, their primary focus has been on i.i.d. settings. Despite the pressing need for principled approaches tailored to time series datasets, the problem of e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29389<END_URL>

TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting\nReal-world time series often have multiple frequency components that are intertwined with each other, making accurate time series forecasting challenging. Decomposing the mixed frequency components into multiple single frequency components is a natural choice. However, the information density of patterns varies across different frequencies, and employing a uniform modeling approach for differe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27844<END_URL>

TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis\nTime series analysis plays a critical role in numerous applications, supporting tasks such as forecasting, classification, anomaly detection, and imputation. In this work, we present the time series pattern machine (TSPM), a model designed to excel in a broad range of time series tasks through powerful representation and pattern extraction capabilities. Traditional time series models often struggle to capture u<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31219<END_URL>

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts\nDeep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications. In response, we introduce Time-MoE, a scalable and unified <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28954<END_URL>

Timer-XL: Long-Context Transformers for Unified Time Series Forecasting\nWe present Timer-XL, a generative Transformer for unified time series forecasting. To uniformly predict 1D and 2D time series, we generalize next token prediction, predominantly adopted for causal generation of 1D sequences, to multivariate next token prediction. The proposed paradigm uniformly formulates various forecasting scenarios as a long-context generation problem. We opt for the generative Transformer, which can cap<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30062<END_URL>

Time, Space and Streaming Efficient Algorithm for Heavy Attentions\nA central problem related to transformers can be stated as follows: given two $n \times d$ matrices $Q$ and $K$, and a non-negative function $f$, define the matrix $A$ as follows: (1) apply the function $f$ to each entry of the $n \times n$ matrix $Q K^T$, and then (2) normalize each of the row sums of $A$ to be equal to $1$. The matrix $A$ can be computed in $O(n^2 d)$ time assuming $f$ can be applied to a number in constant ti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30728<END_URL>

TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning\nMultimodal Large Language Models (MLLMs) have demonstrated impressive performance in short video understanding. However, understanding long-form videos still remains challenging for MLLMs. This paper proposes TimeSuite, a collection of new designs to adapt the existing short-form video MLLMs for long video understanding, including a simple yet efficient framework to process long video sequence, a high-quality video data<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28421<END_URL>

TIPS: Text-Image Pretraining with Spatial awareness\nWhile image-text representation learning has become very popular in recent years, existing models tend to lack spatial awareness and have limited direct applicability for dense understanding tasks. For this reason, self-supervised pretraining is still the go-to method for many dense vision applications (e.g. depth estimation, semantic segmentation), despite the lack of explicit supervisory signals. In this paper, we close this gap between imag<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30452<END_URL>

TIS-DPO: Token-level Importance Sampling for Direct Preference Optimization With Estimated Weights\nDirect Preference Optimization (DPO) has been widely adopted for preference alignment of Large Language Models (LLMs) due to its simplicity and effectiveness. However, DPO is derived as a bandit problem in which the whole response is treated as a single arm, ignoring the importance differences between tokens, which may affect optimization efficiency and make it difficult to achieve optimal results<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28368<END_URL>

T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data\nSelf-supervision is often used for pre-training to foster performance on a downstream task by constructing meaningful representations of samples. Self-supervised learning (SSL) generally involves generating different views of the same sample and thus requires data augmentations that are challenging to construct for tabular data. This constitutes one of the main challenges of self-supervision for structured data. In the present <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28793<END_URL>

TLDR: Token-Level Detective Reward Model for Large Vision Language Models\nAlthough reward models have been successful in improving multimodal large language models, the reward models themselves remain brutal and contain minimal information. Notably, existing reward models only mimic human annotations by assigning only one feedback to any text, no matter how long the text is. In the realm of multimodal language models, where models are required to process both images and texts, a naive reward mo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29193<END_URL>

To Clip or not to Clip: the Dynamics of SGD with Gradient Clipping in High-Dimensions\nThe success of modern machine learning is due in part to the adaptive optimization methods that have been developed to deal with the difficulties of training large models over complex datasets. One such method is gradient clipping: a practical procedure with limited theoretical underpinnings. In this work, we study clipping in a least squares problem under streaming SGD. We develop a theoretical analysis of th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28617<END_URL>

To Code or Not To Code? Exploring Impact of Code in Pre-training\nIncluding code in the pre-training data mixture, even for models not specifically designed for code, has become a common practice in LLMs pre-training. While there has been anecdotal consensus among practitioners that code data plays a vital role in general LLMs' performance, there is only limited work analyzing the precise impact of code on non-code tasks. In this work, we systematically investigate the impact of code data on gen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27667<END_URL>

To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning\nChain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs). But for what kinds of tasks is this extra "thinking" really helpful? To analyze this, we conducted a quantitative meta-analysis covering over 100 papers using CoT and ran our own evaluations of 20 datasets across 14 models. Our results show that CoT gives strong performance benefits <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27873<END_URL>

ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge\nDiffusion models break down the challenging task of generating data from high-dimensional distributions into a series of easier denoising steps. Inspired by this paradigm, we propose a novel approach that extends the diffusion framework into modality space, decomposing the complex task of RGB image generation into simpler, interpretable stages. Our method, termed {\papernameAbbrev}, cascades modality-spec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30087<END_URL>

TODO: Enhancing LLM Alignment with Ternary Preferences\nAligning large language models (LLMs) with human intent is critical for enhancing their performance across a variety of tasks. Standard alignment techniques, such as Direct Preference Optimization (DPO), often rely on the binary Bradley-Terry (BT) model, which can struggle to capture the complexities of human preferences—particularly in the presence of noisy or inconsistent labels and frequent ties. To address these limitations, we introduc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27951<END_URL>

TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters\nTransformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these models remains a significant concern. This problem arises primarily from their dependence on a fixed number of parameters within linear projections. When architectural modifications (e.g., channel dimensions) are introduced, the entire model <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28360<END_URL>

Token Pruning Meets Audio: Investigating Unique Behaviors in Vision Transformer-Based Audio Classification\nVision Transformers (ViTs) have achieved state-of-the-art performance across various computer vision tasks. To reduce the high computational cost of ViTs, token pruning has been proposed to selectively remove tokens that are not crucial. While effective in vision tasks by discarding non-object regions, applying this technique to audio tasks presents unique challenges. In audio processing, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29571<END_URL>

Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction\nThe attention operator is arguably the key distinguishing factor of transformer architectures, which have demonstrated state-of-the-art performance on a variety of tasks. However, transformer attention operators often impose a significant computational burden, with the computational complexity scaling quadratically with the number of tokens. In this work, we propose a novel transformer attention operator whose co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28513<END_URL>

Token-Supervised Value Models for Enhancing Mathematical Problem-Solving Capabilities of Large Language Models\nWith the rapid advancement of test-time compute search strategies to improve the mathematical problem-solving capabilities of large language models (LLMs), the need for building robust verifiers has become increasingly important. However, all these inference strategies rely on existing verifiers originally designed for Best-of-N search, which makes them sub-optimal for tree search tech<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30898<END_URL>

ToolACE: Enhancing Function Calling with Accuracy, Complexity, and Diversity\nFunction calling significantly extends the application boundary of large language models (LLMs), where high-quality and diverse training data is critical for unlocking this capability. However, collecting and annotating real function-calling data is challenging, while synthetic data from existing pipelines often lack coverage and accuracy. In this paper, we present ToolACE, an automatic agentic pipeline designed to gen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30779<END_URL>

Tool Decoding: A Plug-and-Play Approach to Enhancing Language Models for Tool Usage\nDespite the significant advancements in large language models (LLMs), their tool-use capabilities remain limited. This limitation stems from the fact that existing approaches often merely adapt strategies designed for basic natural language tasks, overlooking the specific challenges inherent in tool usage, such as precise tool selection, strict predefined formats, and accurate parameter assignment.To bridge this<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30936<END_URL>

ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models\nTool-Augmented Language Models (TALMs) leverage external APIs to answer user queries across various domains. However, existing benchmark datasets for TALM research often feature simplistic dialogues that do not reflect real-world scenarios, such as the need for models to ask clarifying questions or proactively call additional APIs when essential information is missing. To address these limitations, we construct a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30135<END_URL>

ToolGen: Unified Tool Retrieval and Calling via Generation\nAs large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation. Traditional methods rely on inputting tool descriptions as context, which is constrained by context length and requires separate, often inefficient, retrieval mechanisms. We introduce ToolGen, a paradigm shift that integrates tool knowledge directly into the LLM’s parameters by<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29311<END_URL>

Tool-Planner: Task Planning with Clusters across Multiple Tools\nLarge language models (LLMs) have demonstrated exceptional reasoning capabilities, enabling them to solve various complex problems. Recently, this ability has been applied to the paradigm of tool learning. Tool learning involves providing examples of tool usage and their corresponding functions, allowing LLMs to formulate plans and demonstrate the process of invoking and executing each tool. LLMs can address tasks that they cannot <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28985<END_URL>

TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning\nThis work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as  Movement Primitives (MP), allowing for smooth and efficient <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29902<END_URL>

Top-m Data Values Identification\nData valuation has found many real-world applications, e.g., data pricing and data selection. However, the most adopted approach -- Shapley value (SV) -- is computationally expensive due to the large number of model trainings required. Fortunately, most applications (e.g., data selection) require only knowing the $m$ data points with the highest data values (i.e., top-$m$ data values), which implies the potential for fewer model trainings as exact data values ar<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28522<END_URL>

TopoDiffusionNet: A Topology-aware Diffusion Model\nDiffusion models excel at creating visually impressive images but often struggle to generate images with a specified topology. The Betti number, which represents the number of structures in an image, is a fundamental measure in topology. Yet, diffusion models fail to satisfy even this basic constraint. This limitation restricts their utility in applications requiring exact control, like robotics and environmental modeling. To address this, we p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29223<END_URL>

TopoGaussian: Inferring Internal Topology Structures from Visual Clues\nWe present TopoGaussian, a holistic, particle-based pipeline for inferring the interior structure of an opaque object from easily accessible photos and videos as input. Traditional mesh-based approaches require tedious and error-prone mesh filling and fixing process, while typically output rough boundary surface. Our pipeline combines Gaussian Splatting with a novel, versatile particle-based differentiable simulator that sim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30595<END_URL>

Topograph: An Efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation\nTopological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy. Existing topology-aware methods often lack robust topological guarantees, are limited to specific use cases, or impose high computational costs. In this work, we propose a novel, graph-based framework for topolo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29716<END_URL>

TopoLM: brain-like spatio-functional organization in a topographic language model\nNeurons in the brain are spatially organized such that neighbors on tissue often exhibit similar response profiles. In the human language system, experimental studies have observed clusters for syntactic and semantic categories, but the mechanisms underlying this functional organization remain unclear. Here, building on work from the vision literature, we develop TopoLM, a transformer language model with an explic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29162<END_URL>

Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity\nTopological deep learning (TDL) is a rapidly growing field that seeks to leverage topological structure in data and facilitate learning from data supported on topological objects, ranging from molecules to 3D shapes. Most TDL architectures can be unified under the framework of higher-order message-passing (HOMP), which generalizes graph message-passing to higher-order domains. In the f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30372<END_URL>

Topological Schrödinger Bridge Matching\nGiven two boundary distributions, the \emph{Schrödinger Bridge} (SB) problem seeks the “most likely” random evolution between them with respect to a reference process. It has revealed rich connections to recent machine learning methods for generative modeling and distribution matching. While these methods perform well in Euclidean domains, they are not directly applicable to topological domains such as graphs and simplicial complexes, which are crucial fo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29332<END_URL>

Topological Zigzag Spaghetti for Diffusion-based Generation and Prediction on Graphs\nDiffusion models have recently emerged as a new powerful machinery for generative artificial intelligence on graphs, with applications ranging from drug design to knowledge discovery. However, despite their high potential, most, if not all, currently existing graph diffusion models are limited in their ability to holistically describe the intrinsic {\it higher-order} topological graph properties, which obstruct<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28459<END_URL>

TopoNets: High performing vision and language models with brain-like topography\nNeurons in the brain are organized such that nearby cells tend to share similar functions. AI models lack this organization, and past efforts to introduce topography have often led to trade-offs between topography and task performance. In this work, we present *TopoLoss*, a new loss function that promotes spatially organized topographic representations in AI models without significantly sacrificing task performance.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29552<END_URL>

To Tackle Adversarial Transferability: A Novel Ensemble Training Method with Fourier Transformation\nEnsemble methods are commonly used for enhancing robustness in machine learning. However, due to the ''transferability'' of adversarial examples, the performance of an ensemble model can be seriously affected even it contains a set of independently trained sub-models. To address this issue, we propose an efficient data transformation method based on a cute  ''weakness allocation'' strategy, to di<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30054<END_URL>

ToVE: Efficient Vision-Language Learning via Knowledge Transfer from Vision Experts\nVision-language (VL) learning requires extensive visual perception capabilities, such as fine-grained object recognition and spatial perception. Recent works typically rely on training huge models on massive datasets to develop these capabilities. As a more efficient alternative, this paper proposes a new framework that Transfers the knowledge from a hub of Vision Experts (ToVE) for efficient VL learning, levera<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30410<END_URL>

Toward Efficient Multi-Agent Exploration With Trajectory Entropy Maximization\nRecent works have increasingly focused on learning decentralized policies for agents as a solution to the scalability challenges in Multi-Agent Reinforcement Learning (MARL), where agents typically share the parameters of a policy network to make action decisions. However, this parameter sharing can impede efficient exploration, as it may lead to similar behaviors among agents. Different from previous mutual informati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29242<END_URL>

Toward Exploratory Inverse Constraint Inference with Generative Diffusion Verifiers\nAn important prerequisite for safe control is aligning the policy with the underlying constraints in the environment. In many real-world applications, due to the difficulty of manually specifying these constraints, existing works have proposed recovering constraints from expert demonstrations by solving the Inverse Constraint Learning (ICL) problem. However, ICL is inherently ill-posed, as multiple constraints c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31251<END_URL>

Toward Generalizing Visual Brain Decoding to Unseen Subjects\nVisual brain decoding aims to decode visual information from human brain activities. Despite the great progress, one critical limitation of current brain decoding research lies in the lack of generalization capability to unseen subjects. Prior works typically focus on decoding brain activity of individuals based on the observation that different subjects exhibit different brain activities, while it remains unclear whether brain decodi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30608<END_URL>

Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment\nClassifier-Free Guidance (CFG) is a critical technique for enhancing the sample quality of visual generative models. However, in autoregressive (AR) multi-modal generation, CFG introduces design inconsistencies between language and visual content, contradicting the design philosophy of unifying different modalities for visual AR. Motivated by language model alignment methods, we propose Condition Contrastive Alignment<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28596<END_URL>

Toward Robust Defenses Against LLM Weight Tampering Attacks\nRapid advances in the capabilities of large language models (LLMs) have raised widespread concerns regarding their potential for malicious use. Open-weight LLMs present unique challenges, as existing safeguards lack robustness to tampering attacks that modify model weights. For example, recent works have demonstrated that refusal and unlearning safeguards can be trivially removed with a few steps of fine-tuning. These vulnerabilities n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31026<END_URL>

Towards a Complete Logical Framework for GNN Expressiveness\nDesigning expressive Graph neural networks (GNNs) is an important topic in graph machine learning fields. Traditionally, the Weisfeiler-Lehman (WL) test has been the primary measure for evaluating GNN expressiveness. However, high-order WL tests can be obscure, making it challenging to discern the specific graph patterns captured by them. Given the connection between WL tests and first-order logic, some have explored the logical expres<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28279<END_URL>

Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders\nTime series anomaly detection plays a vital role in a wide range of applications. Existing methods require training one specific model for each dataset, which exhibits limited generalization capability across different target datasets, hindering anomaly detection performance in various scenarios with scarce training data. Aiming at this problem, we propose constructing a general time series an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29176<END_URL>

Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation\nAI models for science have the potential to harness large datasets, accelerate scientific discoveries and transform numerous fields. Through this workshop, our mission is to foster interdisciplinary collaboration to develop fully autonomous AI systems, addressing challenges like benchmark datasets, human-AI collaboration, robust tools and methods for validating AI outputs, and trustworthiness. B<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23991<END_URL>

Towards a  learning theory of representation alignment\nIt has recently been argued that AI models' representations are becoming aligned as their scale and performance increase. Empirical analyses have been designed to support this idea and conjecture the possible alignment of different representations toward a shared statistical model of reality. In this paper, we propose a learning-theoretic perspective to representation alignment. First, we review and connect different notions of alignment ba<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30459<END_URL>

Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective\nSynthetic data has become a pivotal resource in post-training tasks for large language models (LLMs) due to the scarcity of high-quality, specific data. While various methods have been developed to generate synthetic data, there remains a discernible gap between the practical effects of synthetic data and our theoretical comprehension. To address this challenge, we commence by presenting<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29438<END_URL>

Towards Automated Knowledge Integration From Human-Interpretable Representations\nIn noisy and low-data environments, a significant challenge in machine learning lies in effectively incorporating inductive biases that enhance data efficiency and robustness. Despite many success of informed machine learning methods, designing algorithms with explicit inductive biases based on prior expert knowledge remains largely a manual process. In this work, we explore how prior knowledge represented in its n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29873<END_URL>

Towards Auto-Regressive Next-Token Prediction: In-context Learning Emerges from Generalization\nLarge language models (LLMs) have demonstrated remarkable in-context learning (ICL) abilities. However, existing theoretical analysis of ICL primarily exhibits two limitations: \textbf{(a) Limited \textit{i.i.d.} Setting.} Most studies focus on supervised function learning tasks where prompts are constructed with \textit{i.i.d.} input-label pairs. This \textit{i.i.d.} assumption diverges significantly<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28825<END_URL>

Towards Bridging Generalization and Expressivity of Graph Neural Networks\nExpressivity and generalization are two critical aspects of graph neural networks (GNNs). While significant progress has been made in studying the expressivity of GNNs, much less is known about their generalization capabilities, particularly when dealing with the inherent complexity of graph-structured data.In this work, we address the intricate relationship between expressivity and generalization in GNNs. Theoretical stu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30574<END_URL>

Towards Calibrated Deep Clustering Network\nDeep clustering has exhibited remarkable performance; however, the over-confidence problem, i.e., the estimated confidence for a sample belonging to a particular cluster greatly exceeds its actual prediction accuracy, has been overlooked in prior research. To tackle this critical issue, we pioneer the development of a calibrated deep clustering framework. Specifically, we propose a novel dual-head (calibration head and clustering head) deep clustering <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30085<END_URL>

Towards Certification of Uncertainty Calibration under Adversarial Attacks\nSince neural classifiers are known to be sensitive to adversarial perturbations that alter their accuracy, certification methods have been developed to provide provable guarantees on the insensitivity of their predictions to such perturbations. On the other hand, in safety-critical applications, the frequentist interpretation of the confidence of a classifier (also known as model calibration) can be of utmost importance.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27949<END_URL>

Towards Continuous Reuse of Graph Models via Holistic Memory Diversification\nThis paper addresses the challenge of incremental learning in growing graphs with increasingly complex tasks. The goal is to continually train a graph model to handle new tasks while retaining its inference ability on previous tasks. Existing methods usually neglect the importance of memory diversity, limiting in effectively selecting high-quality memory from previous tasks and remembering broad previous knowledge with<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29744<END_URL>

Towards counterfactual fairness thorough auxiliary variables\nThe challenge of balancing fairness and predictive accuracy in machine learning models, especially when sensitive attributes such as race, gender, or age are considered, has motivated substantial research in recent years. Counterfactual fairness ensures that predictions remain consistent across counterfactual variations of sensitive attributes, which is a crucial concept in addressing societal biases. However, existing counterfactual <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30258<END_URL>

Towards Domain Adaptive Neural Contextual Bandits\nContextual bandit algorithms are essential for solving real-world decision making problems. In practice, collecting a contextual bandit's feedback from different domains may involve different costs. For example, measuring drug reaction from mice (as a source domain) and humans (as a target domain). Unfortunately, adapting a contextual bandit algorithm from a source domain to a target domain with distribution shift still remains a major challenge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30002<END_URL>

Towards Effective Evaluations and Comparison for LLM Unlearning Methods\nThe imperative to eliminate undesirable data memorization underscores the significance of machine unlearning for large language models (LLMs). Recent research has introduced a series of promising unlearning methods, notably boosting the practical significance of the field. Nevertheless, adopting a proper evaluation framework to reflect the true unlearning efficacy is also essential yet has not received adequate attention. T<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27842<END_URL>

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL\nIn Model-Based Reinforcement Learning (MBRL), incorporating causal structures into dynamics models provides agents with a structured understanding of the environments, enabling efficient decision. Empowerment as an intrinsic motivation enhances the ability of agents to actively control their environments by maximizing the mutual information between future states and actions. We posit that empowerment coupled with causa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27900<END_URL>

Towards Explaining the Power of Constant-depth Graph Neural Networks for Linear Programming\nGraph neural networks (GNNs) have recently emerged as powerful tools for solving complex optimization problems, often being employed to approximate solution mappings. Empirical evidence shows that even *shallow* GNNs (with fewer than ten layers) can achieve strong performance in predicting optimal solutions to linear programming (LP) problems. This finding is somewhat counter-intuitive, as LPs are *globa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30174<END_URL>

Towards Faster Decentralized Stochastic Optimization with Communication Compression\nCommunication efficiency has garnered significant attention as it is considered the main bottleneck for large-scale decentralized Machine Learning applications in distributed and federated settings. In this regime, clients are restricted to transmitting small amounts of compressed information to their neighbors over a communication graph. Numerous endeavors have been made to address this challenging problem by d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30517<END_URL>

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians\nThe foundation model paradigm is transforming Machine Learning Force Fields (MLFFs), leveraging general-purpose representations to perform a variety of computational chemistry tasks. Although MLFF foundation models have begun to close the accuracy gap relative to first-principles methods, there is still a strong need for faster inference speed. Additionally, while model development is incre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31192<END_URL>

Towards Federated RLHF with Aggregated Client Preference for LLMs\nReinforcement learning with human feedback (RLHF) fine-tunes a pretrained large language model (LLM) using user preference data, enabling it to generate content aligned with human preferences. However, due to privacy concerns, users may be reluctant to share sensitive preference data. To address this, we propose utilizing Federated Learning (FL) techniques, allowing large-scale preference collection from diverse real-world users <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28445<END_URL>

Towards Foundation Models for Mixed Integer Linear Programming\nMixed Integer Linear Programming (MILP) is essential for modeling complex decision-making problems but faces challenges in computational tractability and interpretability. Current deep learning approaches for MILP focus on specific problem classes and do not generalize to unseen classes. To address this shortcoming, we take a foundation model training approach, where we train a single deep learning model on a diverse set of MILP pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30856<END_URL>

Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations\nGeneral intelligence requires quick adaptation across tasks. While existing reinforcement learning (RL) methods have made progress in generalization, they typically assume only distribution changes between source and target domains. In this paper, we explore a wider range of scenarios where not only the distribution but also the environment spaces may change. For example, in the CoinRun environment, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29113<END_URL>

Towards Generalization Bounds of GCNs for Adversarially Robust Node Classification\nAdversarially robust generalization of Graph Convolutional Networks (GCNs) has garnered significant attention in various security-sensitive application areas, driven by intrinsic adversarial vulnerability. Albeit remarkable empirical advancement, theoretical understanding of the generalization behavior of GCNs subjected to adversarial attacks remains elusive. To make progress on the mystery, we establish unified <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29027<END_URL>

Towards General-Purpose Model-Free Reinforcement Learning\nReinforcement learning (RL) promises a framework for near-universal problem-solving. In practice however, RL algorithms are often tailored to specific benchmarks, relying on carefully tuned hyperparameters and algorithmic choices. Recently, powerful model-based RL methods have shown impressive generalist results across benchmarks but come at the cost of increased complexity and slow run times, limiting their broader applicability. In thi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29666<END_URL>

Towards Hierarchical Rectified Flow\nWe formulate a hierarchical rectified flow to model data distributions. It hierarchically couples multiple ordinary differential equations (ODEs) and defines a time-differentiable stochastic process that generates a data distribution from a known source distribution. Each ODE resembles the ODE that is solved in a classic rectified flow, but differs in its domain, i.e., location, velocity, acceleration, etc. Unlike the classic rectified flow formulation, which<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30901<END_URL>

Towards hyperparameter-free optimization with differential privacy\nDifferential privacy (DP) is a privacy-preserving paradigm that protects the training data when training deep learning models. Critically, the performance of models is determined by the training hyperparameters, especially those of the learning rate schedule, thus requiring fine-grained hyperparameter tuning on the data. In practice, it is common to tune the learning rate hyperparameters through the grid search that (1) is compu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31119<END_URL>

Towards Improving Exploration through Sibling Augmented GFlowNets\nExploration is a key factor for the success of an active learning agent, especially when dealing with sparse extrinsic terminal rewards and long trajectories. We introduce Sibling Augmented Generative Flow Networks (SA-GFN), a novel framework designed to enhance exploration and training efficiency of Generative Flow Networks (GFlowNets). SA-GFN uses a decoupled dual network architecture, comprising of a main Behavior Network and <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30233<END_URL>

Towards Interpreting Visual Information Processing in Vision-Language Models\nVision-Language Models (VLMs) are powerful tools for processing and understanding text and images. We study the processing of visual tokens in the language model component of LLaVA, a prominent VLM. Our approach focuses on analyzing the localization of object information, the evolution of visual token representations across layers, and the mechanism of integrating visual information for predictions. Through ablation st<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29034<END_URL>

Towards Learning High-Precision Least Squares Algorithms with Sequence Models\nThis paper investigates whether sequence models can learn to perform numerical algorithms, e.g. gradient descent, on the fundamental problem of least squares. Our goal is to inherit two properties of standard algorithms from numerical analysis: (1) machine precision, i.e. we want to obtain solutions that are accurate to near floating point error, and (2) numerical generality, i.e. we want them to apply broadly across <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28098<END_URL>

Towards Marginal Fairness Sliced Wasserstein Barycenter\nThe Sliced Wasserstein barycenter (SWB) is a widely acknowledged method for efficiently generalizing the averaging operation within probability measure spaces. However, achieving marginal fairness SWB, ensuring approximately equal distances from the barycenter to marginals, remains unexplored. The uniform weighted SWB is not necessarily the optimal choice to obtain the desired marginal fairness barycenter due to the heterogeneous structure<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29877<END_URL>

Towards more rigorous evaluations of language models\nAs language models (LMs) become increasingly sophisticated and existing benchmarks approach saturation, the need for rigorous evaluation methods grows more pressing. Many evaluations lack the statistical rigour needed to draw meaningful conclusions, leading to a potential over-confidence in results that might not hold up under scrutiny or replication. This post advocates for bringing fundamental statistical principles to language model evalua<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31354<END_URL>

Towards Multiple Character Image Animation Through Enhancing Implicit Decoupling\nControllable character image animation has a wide range of applications. Although existing studies have consistently improved performance, challenges persist in the field of character image animation, particularly concerning stability in complex backgrounds and tasks involving multiple characters. To address these challenges, we propose a novel multi-condition guided framework for character image animation, employi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29146<END_URL>

Towards Neural Scaling Laws for Time Series Foundation Models\nScaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-distribution (ID) data, leaving their out-of-distribution (OOD) scaling behavior and the influence of model architectures less explored. In this work, we examine two common TSFM architectures—encoder-only and decoder-only Transformers—and investigate their sc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27992<END_URL>

Towards Optimal Multi-draft Speculative Decoding\nLarge Language Models (LLMs) have become an indispensable part of natural language processing tasks. However, autoregressive sampling has become an efficiency bottleneck. Multi-Draft Speculative Decoding (MDSD) is a recent approach where, when generating each token, a small draft model generates multiple drafts, and the target LLM verifies them in parallel, ensuring that the final output conforms to the target model distribution. The two main des<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30699<END_URL>

Towards Out-of-Modal Generalization without Instance-level Modal Correspondence\nThe world is understood from various modalities, such as appearance, sound, language, etc. Since each modality only partially represents objects in a certain physical meaning, leveraging additional ones is beneficial in both theory and practice. However, exploiting novel modalities normally requires cross-modal pairs corresponding to the same instance, which is extremely resource-consuming and sometimes even impossi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29970<END_URL>

Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control\nDisentangling model activations into human-interpretable features is a centralproblem in interpretability. Sparse autoencoders (SAEs) have recently attractedmuch attention as a scalable unsupervised approach to this problem. However, ourimprecise understanding of ground-truth features in realistic scenarios makes itdifficult to measure the success of SAEs. To address this challenge, we proposeto evaluate SAEs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31206<END_URL>

Towards Realistic Data Generation for Real-World Super-Resolution\nExisting image super-resolution (SR) techniques often fail to generalize effectively in complex real-world settings due to the significant divergence between training data and practical scenarios. To address this challenge, previous efforts have either manually simulated intricate physical-based degradations or utilized learning-based techniques, yet these approaches remain inadequate for producing large-scale, realistic, and div<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30091<END_URL>

Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology\nDeveloping agents capable of navigating to a target location based on language instructions and visual information, known as vision-language navigation (VLN), has attracted widespread interest. Most research has focused on ground-based agents, while UAV-based VLN remains relatively underexplored. Recent efforts in UAV vision-language navigation predominantly adopt ground-based VLN settings, relying on predefi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28193<END_URL>

Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization\nThis study addresses the challenge of noise in training datasets for Direct Preference Optimization (DPO), a method for aligning Large Language Models (LLMs) with human preferences. We categorize noise into pointwise noise, which includes low-quality data points, and pairwise noise, which encompasses erroneous data pair associations that affect preference rankings. Utilizing Distributionall<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30508<END_URL>

Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models\nLarge Language Models (LLMs) have demonstrated strong reasoning and memorization capabilities via pretraining on massive textual corpora. However, this poses risk of privacy and copyright violations, highlighting the need for efficient machine unlearning methods that remove sensitive data without retraining from scratch. While Gradient Ascent (GA) is commonly used to unlearn by reducing the likelihood of generating<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31216<END_URL>

Towards Robust Multimodal Open-set Test-time Adaptation via Adaptive Entropy-aware Optimization\nTest-time adaptation (TTA) has demonstrated significant potential in addressing distribution shifts between training and testing data. Open-set test-time adaptation (OSTTA) aims to adapt a source pre-trained model online to an unlabeled target domain that contains unknown classes. This task becomes more challenging when multiple modalities are involved. Existing methods have primarily focused on unim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28748<END_URL>

Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning\nMachine unlearning is the process of efficiently removing the influence of a training data instance from a trained machine learning model without retraining it from scratch. A popular subclass of unlearning approaches is exact machine unlearning, which focuses on techniques that explicitly guarantee the removal of the influence of a data instance from a model.  Exact unlearning approaches use a machine learning mode<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28344<END_URL>

Towards Scalable Topological Regularizers\nLatent space matching, which consists of matching distributions of features in latent space, is a crucial component for tasks such as adversarial attacks and defenses, domain adaptation, and generative modelling.    Metrics for probability measures, such as Wasserstein and maximum mean discrepancy, are commonly used to quantify the differences between such distributions.    However, these are often costly to compute, or do not appropriately take the geo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30329<END_URL>

Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression\nDeep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that the covariance is sample dependent and is often unknown. Consequently, recent methods learn the covariance through unsupervised frameworks, which unfortunately yield a trade-off between computational complexity and accuracy. While this tr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29712<END_URL>

Towards Semantic Equivalence of Tokenization in Multimodal LLM\nMultimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in processing vision-language tasks. One of the crux of MLLMs lies in vision tokenization, which involves efficiently transforming input visual signals into feature representations that are most beneficial for LLMs. However, existing vision tokenizers, essential for semantic alignment between vision and language, remain problematic. Existing methods <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28428<END_URL>

Towards Synergistic Path-based Explanations for Knowledge Graph Completion: Exploration and Evaluation\nKnowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), a crucial task for numerous applications such as recommendation systems and drug repurposing. The success of knowledge graph embedding (KGE) models provokes the question about the explainability: ``\textit{Which the patterns of the input KG are most determinant to the prediction}?'' Partic<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29360<END_URL>

Towards Unbiased Learning in Semi-Supervised Semantic Segmentation\nSemi-supervised semantic segmentation aims to learn from a limited amount of labeled data and a large volume of unlabeled data, which has witnessed impressive progress with the recent advancement of deep neural networks. However, existing methods tend to neglect the fact of class imbalance issues, leading to the Matthew effect, that is, the poorly calibrated model’s predictions can be biased to- wards the majority classes and aw<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30785<END_URL>

Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias\nScore-based diffusion models have achieved incredible performance in generating realistic images, audio, and video data. While these models produce high-quality samples with impressive details, they often introduce unrealistic artifacts, such as distorted fingers or hallucinated texts with no meaning. This paper focuses on textual hallucinations, where diffusion models correctly generate individual symbols bu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29614<END_URL>

Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective\nDiffusion-Based Purification (DBP) has emerged as an effective defense mechanism against adversarial attacks. The efficacy of DBP has been attributed to the forward diffusion process, which narrows the distribution gap between clean and adversarial images through the addition of Gaussian noise. Although this explanation has some theoretical support, the significance of its contribution to robustness r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28104<END_URL>

Towards Understanding the Universality of Transformers for Next-Token Prediction\nCausal Transformers are trained to predict the next token for a given context. While it is widely accepted that self-attention is crucial for encoding the causal structure of sequences, the precise underlying mechanism behind this in-context autoregressive learning ability remains unclear. In this paper, we take a step towards understanding this phenomenon by studying the approximation ability of Transformers for n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27720<END_URL>

Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning\nSemi-supervised learning (SSL), exemplified by FixMatch (Sohn et al., 2020), has shown significant generalization advantages over supervised learning (SL), particularly in the context of deep neural networks (DNNs). However, it is still unclear, from a theoretical standpoint, why FixMatch-like SSL algorithms generalize  better than SL on DNNs. In this work, we present the first theoretical justification for the enhan<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31162<END_URL>

Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It\nLabel smoothing (LS) is a popular regularisation method for training neural networks as it is effective in improving test accuracy and is simple to implement. ''Hard'' one-hot labels are ''smoothed'' by uniformly distributing probability mass to other classes, reducing overfitting. Prior work has shown that in some cases *LS can degrade selective classification (SC)* -- where the aim is to reject miscl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30864<END_URL>

Towards Unified Human Motion-Language Understanding via Sparse Interpretable Characterization\nRecently, the comprehensive understanding of human motion has been a prominent area of research due to its critical importance in many fields. However, existing methods often prioritize specific downstream tasks and roughly align text and motion features within a CLIP-like framework. This results in a lack of rich semantic information which restricts a more profound comprehension of human motions, ulti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29810<END_URL>

Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures\nThe hypothesis of \textit{Universality} in interpretability suggests that different neural networks may converge toimplement similar algorithms on similar tasks. In this work, we investigate two mainstream architecturesfor language modeling, namely Transformers and Mambas, to explore the extent of their mechanistic similarity.We propose to use Sparse Autoencoders (SAEs) to isolate interpretable features fr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31148<END_URL>

Toward Understanding In-context vs. In-weight Learning\nIt has recently been demonstrated empirically that in-context learningemerges in transformers when certain distributional properties are present in the training data, but this ability can also diminish upon further training.We provide a new theoretical understanding of these phenomena by identifying simplified distributional properties that give rise to the emergence and eventual disappearance of in-context learning.We do so by first analyz<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29178<END_URL>

TPO: Aligning Large Language Models with Multi-branch & Multi-step Preference Trees\nIn the domain of complex reasoning tasks, such as mathematical reasoning, recent advancements have proposed the use of Direct Preference Optimization (DPO) to suppress output of dispreferred responses, thereby enhancing the long-chain reasoning capabilities of large language models (LLMs). To this end, these studies employed LLMs to generate preference trees via Tree-of-thoughts (ToT) and sample the paired prefe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29846<END_URL>

TRACE: Temporal Grounding Video LLM  via Causal Event Modeling\nVideo Temporal Grounding (VTG) is a crucial capability for video understanding models and plays a vital role in downstream tasks such as video browsing and editing. To effectively handle various tasks simultaneously and enable zero-shot prediction, there is a growing trend in employing video LLMs for VTG tasks. However, current video LLM-based methods rely exclusively on natural language generation, lacking the ability to model the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31224<END_URL>

TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies\nAlthough large vision-language-action (VLA) models pretrained on extensive robot datasets offer promising generalist policies for robotic learning, they still struggle with spatial-temporal dynamics in interactive robotics, making them less effective in handling complex tasks, such as manipulation. In this work, we introduce visual trace prompting, a simple yet effective approach to facilitate V<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29130<END_URL>

Tracing Representation Progression: Analyzing and Enhancing Layer-Wise Similarity\nAnalyzing the similarity of internal representations within and across different models has been an important technique for understanding the behavior of deep neural networks. Most existing methods for analyzing the similarity between representations of high dimensions, such as those based on Centered Kernel Alignment (CKA), rely on statistical properties of the representations for a set of data points. In this pa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27907<END_URL>

Tracking objects that change in appearance with phase synchrony\nObjects we encounter often change appearance as we interact with them. Changes in illumination (shadows), object pose, or movement of nonrigid objects can drastically alter available image features. How do biological visual systems track objects as they change? It may involve specific attentional mechanisms for reasoning about the locations of objects independently of their appearances — a capability that prominent neuroscientific <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28488<END_URL>

Tracking the Copyright of Large Vision-Language Models through Parameter Learning Adversarial Attacks\nLarge vision-language models (LVLMs) have demonstrated remarkable image understanding and dialogue capabilities, allowing them to handle a variety of visual question answering tasks. However, their widespread availability raises concerns about unauthorized usage and copyright infringement, where users or individuals can develop their own LVLMs by fine-tuning published models. In this paper, we <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30074<END_URL>

Track-On: Transformer-based Online Point Tracking with Memory\nIn this paper, we consider the problem of long-term point tracking, which requires consistent identification of points across multiple frames in a video, despite changes in appearance, lighting, perspective, and occlusions. We target online tracking on a frame-by-frame basis, making it suitable for real-world, streaming scenarios. Specifically, we introduce Track-On, a simple transformer-based model designed for online long-term poin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28358<END_URL>

TrackTheMind: program-guided adversarial data generation for theory of mind reasoning\nDo large language models (LLMs) have theory of mind? A plethora of papers and benchmarks have been introduced to evaluate if current models have been able to develop this key ability of social intelligence. However, all rely on limited datasets with simple patterns that can potentially lead to problematic blind spots in evaluation and an overestimation of model capabilities. We introduce TrackTheMind, the firs<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31166<END_URL>

Tractable Multi-Agent Reinforcement Learning through Behavioral Economics\nA significant roadblock to the development of principled multi-agent reinforcement learning is the fact that desired solution concepts like Nash equilibria may be intractable to compute. To overcome this obstacle, we take inspiration from behavioral economics and show that---by imbuing agents with important features of human decision-making like risk aversion and bounded rationality---a class of risk-averse quantal respon<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28091<END_URL>

Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context\nTransformers have the capacity to act as supervised learning algorithms: by properly encoding a set of labeled training (''in-context'') examples and an unlabeled test example into an input sequence of vectors of the same dimension, the forward pass of the transformer can produce predictions for that unlabeled test example.  A line of recent work has shown that when linear transformers are pre-trained on random<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28608<END_URL>

Training-Free Activation Sparsity in Large Language Models\nActivation sparsity can enable practical inference speedups in large language models (LLMs) by reducing the compute and memory-movement required for  matrix multiplications during the forward pass. However, existing methods face limitations that inhibit widespread adoption. Some approaches are tailored towards older models with ReLU-based sparsity, while others require extensive continued pre-training on up to hundreds of billions of to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28994<END_URL>

Training-free Camera Control for Video Generation\nWe propose a training-free and robust solution to offer camera movement control for off-the-shelf video diffusion models. Unlike previous work, our method does not require any supervised finetuning on camera-annotated datasets or self-supervised training via data augmentation. Instead, it can be plugged and played with most pretrained video diffusion models and generate camera controllable videos with a single image or text prompt as input. The <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30066<END_URL>

Training-Free Dataset Pruning for Instance Segmentation\nExisting dataset pruning techniques primarily focus on classification tasks, limiting their applicability to more complex and practical tasks like instance segmentation. Instance segmentation presents three key challenges: pixel-level annotations, instance area variations, and class imbalances, which significantly complicate dataset pruning efforts. Directly adapting existing classification-based pruning methods proves ineffective due to t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28161<END_URL>

Training-Free Diffusion Model Alignment with Sampling Demons\nAligning diffusion models with user preferences has been a key challenge.Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model retraining.Our approach works by controlling<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28034<END_URL>

Training Free Exponential Context Extension via Cascading KV Cache\nThe transformer's context window is vital for tasks such as few-shot learning and conditional generation as it preserves previous tokens for active memory. However, as the context lengths increase, the computational costs grow quadratically, hindering the deployment of large language models (LLMs) in real-world, long sequence scenarios. Although some recent key-value caching (KV Cache) methods offer linear inference complexity, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28984<END_URL>

Training-free Guidance in Multi-modal Generative Flow for Inverse Molecular Design\nGiven an unconditional generative model and a predictor for a target property (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training. As a highly efficient technique for steering generative models toward flexible outcomes, training-free guidance has gained increasing attention in diffusion models. However, existing methods only <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30288<END_URL>

Training Free Guided Flow-Matching with Optimal Control\nControlled generation with pre-trained Diffusion and Flow Matching models has vast applications. One strategy for guiding ODE-based generative models is through optimizing a target loss $R(x_1)$ while staying close to the prior distribution. Along this line, some recent work showed the effectiveness of guiding flow model by differentiating through its ODE sampling process. Despite the superior performance, the theoretical understanding of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30912<END_URL>

Training-free LLM-generated Text Detection by Mining Token Probability Sequences\nLarge language models (LLMs) have demonstrated remarkable capabilities in generating high-quality texts across diverse domains. However, the potential misuse of LLMs has raised significant concerns, underscoring the urgent need for reliable detection of LLM-generated texts. Conventional training-based detectors often struggle with generalization, particularly in cross-domain and cross-model scenarios. In contrast, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27889<END_URL>

Training-Free Message Passing for Learning on Hypergraphs\nHypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to generate informative node features for various downstream tasks like node classification. However, the message passing module in existing HNNs typically requires a computationally intensive training process, which limits their practical use. To tackle this challen<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31030<END_URL>

Training Language Models on Synthetic Edit Sequences Improves Code Synthesis\nSoftware engineers mainly write code by editing existing programs. In contrast, language models (LMs)  autoregressively synthesize programs in a single pass. One explanation for this is the scarcity of sequential edit data. While high-quality instruction data for code synthesis is already scarce, sequential edit data is even scarcer. To fill this gap, we develop a synthetic data generation algorithm called LintSeq. Thi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30611<END_URL>

Training Language Models to Self-Correct via Reinforcement Learning\nSelf-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs.  Current methods for training self-correction typically depend on either multiple models, a more advanced model, or additional forms of supervision. To address these shortcomings, we develop a multi-turn online reinforcement learning (RL) approach, SCoRe, that significant<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30501<END_URL>

Training Large Language Models for Retrieval-Augmented Question Answering through Backtracking Correction\nDespite recent progress in Retrieval-Augmented Generation (RAG) achieved by large language models (LLMs), retrievers often recall uncorrelated documents, regarded as "noise" during subsequent text generation. To address this, some methods train LLMs to distinguish between relevant and irrelevant documents using labeled data, enabling them to select the most likely relevant ones as context. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30171<END_URL>

Training Mice to Compete with Elephants: A Guide for Customizing Small-Sized LLMs on Knowledge and Skills Data\nCustomizing large language models (LLMs) is increasingly in demand by enterprises and individual developers. It allows LLMs to be tailored for domain expertise, aligned with organizational guidelines, and enhanced for user experience. Effective customization hinges on three core elements: a small-size model, large-scale domain-specific datasets, and an effective training strategy to he<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28940<END_URL>

Training Neural Networks as Recognizers of Formal Languages\nCharacterizing the computational power of neural network architectures in terms of formal language theory remains a crucial line of research, as it describes lower and upper bounds on the reasoning capabilities of modern AI. However, when empirically testing these bounds, existing work often leaves a discrepancy between experiments and the formal claims they are meant to support. The problem is that formal language theory pertains spec<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29163<END_URL>

Training Nonlinear Transformers for Chain-of-Thought Inference:  A Theoretical Generalization Analysis\nChain-of-Thought (CoT) is an efficient prompting method that enables the reasoning ability of large language models by augmenting the query using multiple examples with multiple intermediate steps. Despite the empirical success, the theoretical understanding of how to train a Transformer to achieve the CoT ability remains less explored. This is primarily due to the technical challenges involve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28427<END_URL>

Training One-Dimensional Graph Neural Networks is NP-Hard\nWe initiate the study of the computational complexity of training graph neural networks (GNNs). We consider the classical node classification setting; there, the intractability of training multidimensonal GNNs immediately follows from known lower bounds for training classical neural networks (and holds even for trivial GNNs). However, one-dimensional GNNs form a crucial case of interest: the computational complexity of training such netw<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30841<END_URL>

Training on the Test Task Confounds Evaluation and Emergence\nWe study a fundamental problem in the evaluation of large language models that we call training on the test task. Unlike wrongful practices like training on the test data, leakage, or data contamination, training on the test task is not a malpractice.  Rather, the term describes a growing set of techniques to include task-relevant data in the pretraining stage of a language model. We demonstrate that training on the test task confound<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28639<END_URL>

Training Robust Ensembles Requires Rethinking Lipschitz Continuity\nTransferability of adversarial examples is a well-known property that endangers all classification models, even those that are only accessible through black-box queries. Prior work has shown that an ensemble of models is more resilient to transferability: the probability that an adversarial example is effective against most models of the ensemble is low. Thus, most ongoing research focuses on improving ensemble diversity. Anothe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29370<END_URL>

Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models\nLarge Language Models (LLMs) have significantly advanced natural language processing with exceptional task generalization capabilities. Low-Rank Adaption (LoRA) offers a cost-effective fine-tuning solution, freezing the original model parameters and training only lightweight, low-rank adapter matrices. However, the memory footprint of LoRA is largely dominated by the original model parameters. To mitigate this, w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28143<END_URL>

Trajectory attention for fine-grained video motion control\nRecent advancements in video generation have been greatly driven by video diffusion models, with camera motion control emerging as a crucial challenge in creating view-customized visual content. This paper introduces trajectory attention, a novel approach that performs attention along available pixel trajectories for fine-grained camera motion control. Unlike existing methods that often yield imprecise outputs or neglect temporal correl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31100<END_URL>

Trajectory-Class-Aware Multi-Agent Reinforcement Learning\nIn the context of multi-agent reinforcement learning, *generalization* is a challenge to solve various tasks that may require different joint policies or coordination without relying on policies specialized for each task. We refer to this type of problem as a *multi-task*, and we train agents to be versatile in this multi-task setting through a single training process. To address this challenge, we introduce TRajectory-class-Aware Multi-<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27954<END_URL>

Trajectory-LLM: A Language-based Data Generator for Trajectory Prediction in Autonomous Driving\nVehicle trajectory prediction is a crucial aspect of autonomous driving, which requires extensive trajectory data to train prediction models to understand the complex, varied, and unpredictable patterns of vehicular interactions. However, acquiring real-world data is expensive, so we advocate using Large Language Models (LLMs) to generate abundant and realistic trajectories of interacting vehicles ef<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29466<END_URL>

Transformer Block Coupling and its Correlation with Generalization in LLMs\nLarge Language Models (LLMs) have made significant strides in natural languageprocessing, and a precise understanding of the internal mechanisms driving theirsuccess is essential. In this work, we trace the trajectories of individual tokens as they pass through transformer blocks, and linearize the system along these trajectories through their Jacobian matrices. By examining the relationships between these Jacobians, we <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28555<END_URL>

Transformer Encoder Satisfiability: Complexity and Impact on Formal Reasoning\nWe analyse the complexity of the satisfiability problem, or similarly feasibility problem, (trSAT) for transformer encoders (TE), which naturally occurs in formal verification or interpretation, collectively referred to as formal reasoning. We find that trSAT is undecidable when considering TE as they are commonly studied in the expressiveness community. Furthermore, we identify practical scenarios where trSAT is deci<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29412<END_URL>

Transformer Learns Optimal Variable Selection in Group-Sparse Classification\nTransformers have demonstrated remarkable success across various applications. However, the success of transformers have not been understood in theory. In this work, we give a case study of how transformers can be trained to learn a classic statistical model with "group sparsity", where the input variables form multiple groups, and the label only depends on the variables from one of the groups. We theoretically demonst<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28844<END_URL>

Transformer Meets Twicing: Harnessing Unattended Residual Information\nTransformer-based deep learning models have achieved state-of-the-art performance across numerous language and vision tasks. While the self-attention mechanism, a core component of transformers, has proven capable of handling complex data patterns, it has been observed that the representational capacity of the attention matrix degrades significantly across transformer layers, thereby hurting its overall performance. In this w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31222<END_URL>

Transformers are Universal In-context Learners\nTransformers are deep architectures that define ``in-context mappings'' which enable predicting new tokens based on a given set of tokens (such as a prompt in NLP applications or a set of patches for a vision transformer). In this work, we study in particular the ability of these architectures to handle an arbitrarily large number of context tokens. To mathematically, uniformly address their expressivity, we consider the case that the mappings are <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30885<END_URL>

Transformers Handle Endogeneity in In-Context Linear Regression\nWe explore the capability of transformers to address endogeneity in in-context linear regression. Our main finding is that transformers inherently possess a mechanism to handle endogeneity effectively using instrumental variables (IV). First, we demonstrate that the transformer architecture can emulate a gradient-based bi-level optimization procedure that converges to the widely used two-stage least squares (2SLS) solution at an ex<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29683<END_URL>

Transformers Learn Low Sensitivity Functions: Investigations and Implications\nTransformers achieve state-of-the-art accuracy and robustness across many tasks, but an understanding of their inductive biases and how those biases differ from other neural network architectures remains elusive. In this work, we identify the sensitivity of the model to token-wise random perturbations in the input as a unified metric which explains the inductive bias of transformers across different data modalities an<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30997<END_URL>

Transformers Learn Temporal Difference Methods for In-Context Reinforcement Learning\nTraditionally, reinforcement learning (RL) agents learn to solve new tasks by updating their parameters through interactions with the task environment. However, recent works have demonstrated that transformer-based RL agents, after certain pretraining procedures, can learn to solve new out-of-distribution tasks without parameter updates, a phenomenon known as in-context reinforcement learning (ICRL). The empiri<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29736<END_URL>

Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought\nChain of Thought (CoT) prompting has been shown to significantly improve the performance of large language models (LLMs), particularly in arithmetic and reasoning tasks, by instructing the model to produce intermediate reasoning steps. Despite the remarkable empirical success of CoT and its theoretical advantages in enhancing expressivity, the mechanisms underlying CoT training remain largely unexplored. In this p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28223<END_URL>

Transformers Provably Learn Two-Mixture of Linear Classification via Gradient Flow\nUnderstanding how transformers learn and utilize hidden connections between tokens is crucial to understand the behavior of large language models.To understand this mechanism, we consider the task of two-mixture of linear classification which possesses a hidden correspondence structure among tokens, and study the training dynamics of a symmetric two-headed transformer with ReLU neurons.Motivated by the stage-wise<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30607<END_URL>

Transformers Provably Solve Parity Efficiently with Chain of Thought\nThis work provides the first theoretical analysis of training transformers to solve complex problems by recursively generating intermediate states, analogous to fine-tuning for chain-of-thought (CoT) reasoning. We consider training a one-layer transformer to solve the fundamental $k$-parity problem, extending the work on RNNs by \citet{Wies23}. We establish three key results: (1) any finite-precision gradient-based algorithm, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28433<END_URL>

Transformers Struggle to Learn to Search Without In-context Exploration\nSearch is an ability fundamental in many important tasks, and recent studies have shown that large language models (LLMs) struggle to perform search robustly. It is unknown whether this inability is due to a lack of data, insufficient model parameters, or fundamental limitations of the transformer architecture. In this work, we use the fundamental graph connectivity problem as a testbed to generate effectively limitless hig<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30677<END_URL>

Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model\nWe introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data.Transfusion combines the language modeling loss function (next token prediction) with diffusion to train a single transformer over mixed-modality sequences.We pretrain multiple Transfusion models up to 7B parameters from scratch on a mixture of text and image data, establishing scaling laws with respect to a varie<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29617<END_URL>

Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers\nUnderstanding transition pathways between meta-stable states in molecular systems is crucial to advance material design and drug discovery. However, unbiased molecular dynamics simulations are computationally infeasible due to the high energy barriers separating these states. Although recent machine learning techniques offer potential solutions, they are often limited to simple systems or rely on collective va<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29361<END_URL>

Tree of Attributes Prompt Learning for Vision-Language Models\nPrompt learning has proven effective in adapting vision language models for downstream tasks. However, existing methods usually append learnable prompt tokens solely with the category names to obtain textual features, which fails to fully leverage the rich context indicated in the category name. To address this issue, we propose the Tree of Attributes Prompt learning (TAP), which first instructs LLMs to generate a tree of attributes <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27861<END_URL>

Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy\nFinding meaningful distances between high-dimensional data samples is an important scientific task. To this end, we propose a new tree-Wasserstein distance (TWD) for high-dimensional data with two key aspects. First, our TWD is specifically designed for data with a latent feature hierarchy, i.e., the features lie in a hierarchical space, in contrast to the usual focus on embedding samples in hyperbolic space. Se<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28404<END_URL>

TRENDy: Temporal Regression of Effective Nonlinear Dynamics\nSpatiotemporal dynamics pervade the natural sciences, from the morphogen dynamics underlying patterning in animal pigmentation to the protein waves controlling cell division. A central challenge lies in understanding how controllable parameters induce qualitative changes in system behavior called bifurcations.  This endeavor is made particularly difficult in realistic settings where governing partial differential equations (PDEs) are u<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29850<END_URL>

Triples as the Key: Structuring Makes Decomposition and Verification Easier in LLM-based TableQA\nAs the mainstream approach, LLMs have been widely applied and researched in TableQA tasks. Currently, the core of LLM-based TableQA methods typically include three phases: question decomposition, sub-question TableQA reasoning, and answer verification. However, several challenges remain in this process: i) Sub-questions generated by these methods often exhibit significant gaps with the original ques<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29439<END_URL>

Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups\nThe generative modeling of data on manifold is an important task, for which diffusion models in flat spaces typically need nontrivial adaptations. This article demonstrates how a technique called `trivialization' can transfer the effectiveness of diffusion models in Euclidean spaces to Lie groups. In particular, an auxiliary momentum variable was algorithmically introduced to help transport the position variable betwee<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30456<END_URL>

True Counterfactual Generation from Language Models\nUnderstanding and manipulating the causal generation mechanisms in language models is essential for controlling their behavior. Previous work has primarily relied on techniques such as representation surgery---e.g., model ablations or manipulation of linear subspaces tied to specific concepts---to \emph{intervene} on these models. To understand the impact of interventions precisely, it is useful to examine \emph{counterfactuals}---e.g., how a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29541<END_URL>

Truncated Consistency Models\nConsistency models have recently been introduced to accelerate the generation speed of diffusion models by directly predicting the solution (data) of the probability flow ODE (PF ODE) from initial noise.However, the training of consistency models requires learning to map all intermediate points along PF ODE trajectories to their corresponding endpoints. This task is much more challenging than the ultimate objective of one-step generation, which only concerns the PF <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29213<END_URL>

Truncation Is All You Need: Improved Sampling Of Diffusion Models For Physics-Based Simulations\nState-of-the-art Denoising Diffusion Probabilistic Models (DDPMs) rely on an expensive sampling process with a large Number of Function Evaluations (NFEs) to provide high-fidelity predictions. This computational bottleneck renders diffusion models less appealing as surrogates for the spatio-temporal prediction of physics-based problems with long rollout horizons. We propose Truncated Sampling Models,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31263<END_URL>

Trusted Multi-View Classification via Evolutionary Multi-View Fusion\nMulti-view classification methodologies grounded in the Dempster-Shafer theory, renowned for their reliability in decision-making, have garnered significant application across various safety-critical domains due to their capacity to provide a degree of trustworthiness for each view. However, the adoption of a late fusion strategy by these methodologies constrains the interaction of information among views, thereby leading to s<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29957<END_URL>

Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement\nWe present a principled approach to provide LLM-based evaluation with a rigorous guarantee of human agreement. We first propose that a reliable evaluation method should not uncritically rely on model preferences for pairwise evaluation, but rather assess the confidence of judge models and selectively decide when to trust its judgement. We then show that under this *selective evaluation* framework, human agreement can be <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29482<END_URL>

TSC-Net: Predict Pedestrian Trajectory by Trajectory-Scene-Cell Classification\nTo predict future trajectories of pedestrians, scene is as important as the history trajectory since i) scene reflects the position of possible goals of the pedestrian ii) trajectories are affected by the semantic information of the scene. It requires the model to capture scene information and learn the relation between scenes and trajectories. However, existing methods either apply Convolutional Neural Networks (CNN<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29293<END_URL>

TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting\nSpiking Neural Networks (SNNs) offer a promising, biologically inspired approach for processing spatiotemporal data, particularly for time series forecasting.However, conventional neuron models like the Leaky Integrate-and-Fire (LIF) struggle to capture long-term dependencies and effectively process multi-scale temporal dynamics.To overcome these limitations, we introduce the Temporal Segment Leaky Integrate-and-Fire <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28210<END_URL>

T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching\nSampling from diffusion probabilistic models (DPMs) is often expensive for high-quality image generation and typically requires many steps with a large model. In this paper, we introduce sampling Trajectory Stitching (T-Stitch), a simple yet efficient technique to improve the sampling efficiency with little or no generation degradation. Instead of solely using a large DPM for the entire sampling trajectory<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31118<END_URL>

TTVD: Towards a Geometric Framework for Test-Time Adaptation Based on Voronoi Diagram\nDeep learning models often struggle with generalization when deploying on real-world data, due to the common distributional shift to the training data. Test-time adaptation (TTA) is an emerging scheme used at inference time to address this issue. In TTA, models are adapted online at the same time when making predictions to test data. Neighbor-based approaches have gained attention recently, where prototype emb<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30927<END_URL>

TULIP: Token-length Upgraded CLIP\nWe address the challenge of representing long captions in vision-language models, such as CLIP. By design these models are limited by fixed, absolute positional encodings, restricting inputs to a maximum of 77 tokens and hindering performance on tasks requiring longer descriptions. Although recent work has attempted to overcome this limit, their proposed approaches struggle to model token relationships over longer distances and simply extend to a fixed new toke<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28217<END_URL>

Tuning-Free Bilevel Optimization: New Algorithms and Convergence Analysis\nBilevel optimization has recently attracted considerable attention due to its abundant applications in machine learning problems. However, existing methods rely on prior knowledge of problem parameters to determine stepsizes, resulting in significant effort in tuning stepsizes when these parameters are unknown. In this paper, we propose two novel tuning-free algorithms, D-TFBO and S-TFBO. D-TFBO employs a double-loop stru<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30652<END_URL>

Tuning Frequency Bias of State Space Models\nState space models (SSMs) leverage linear, time-invariant (LTI) systems to effectively learn sequences with long-range dependencies. By analyzing the transfer functions of LTI systems, we find that SSMs exhibit an implicit bias toward capturing low-frequency components more effectively than high-frequency ones. This behavior aligns with the broader notion of frequency bias in deep learning model training. We show that the initialization of an SSM assi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27827<END_URL>

Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization\nRecent advancements in timestep-distilled diffusion models have enabled high-quality image generation that rivals non-distilled multi-step models, but with significantly fewer inference steps. While such models are attractive for applications due to the low inference cost and latency, fine-tuning them with a naive diffusion objective would result in degraded and blurry outputs. An intuitive alternative is to repeat the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28873<END_URL>

Turing completeness of prompting\nSince the success of GPT, large language models (LLMs) have revolutionized machine learning and have initiated the so-called *LLM prompting* paradigm. In the era of LLMs, people train a single general-purpose LLM and provide the LLM with different *prompts* to perform different tasks. However, such empirical success largely lacks theoretical understanding. Here, we present the first theoretical study on the LLM prompting paradigm to the best of our knowledge. In<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30632<END_URL>

Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs\nLarge Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. However, popular sampling methods like top-p  (nucleus sampling) often struggle to balance quality and diversity, especially at higher temperatures, leading to incoherent or repetitive outputs. To address this challenge, we propose min-p sampling, a dynamic truncation method that<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30358<END_URL>

TVNet: A Novel Time Series Analysis Method Based on Dynamic Convolution and 3D-Variation\nWith the recent development and advancement of Transformer and MLP architectures, significant strides have been made in time series analysis. Conversely, the performance of Convolutional Neural Networks (CNNs) in time series analysis has fallen short of expectations, diminishing their potential for future applications. Our research aims to enhance the representational capacity of Convolutional Neural Networ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29927<END_URL>

TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation\nDespite significant advancements in customizing text-to-image and video generation models, generating images and videos that effectively integrate multiple personalized concepts remains challenging. To address this, we present TweedieMix, a novel method for composing customized diffusion models during the inference phase. By analyzing the properties of reverse diffusion sampling, our approach divides the sampl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28914<END_URL>

Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models\nContrastive vision-language models (VLMs), like CLIP, have gained popularity for their versatile applicability to various downstream tasks. Despite their successes in some tasks, like zero-shot object recognition, they perform surprisingly poor on other tasks, like attribute recognition. Previous work has attributed these challenges to the modality gap, a separation of ima<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27997<END_URL>

Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization\nNeural networks are often challenging to work with due to their large size and complexity. To address this, various methods aim to reduce model size by sparsifying or decomposing weight matrices, such as magnitude pruning and low-rank or block-diagonal factorization. In this work, we present Double Sparse Factorization (DSF), where we factorize each weight matrix into two sparse matrices. Altho<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30435<END_URL>

TypedThinker: Typed Thinking Improves Large Language Model Reasoning\nDespite significant advancements in the reasoning capabilities of Large Language Models (LLMs), the exploration of diverse reasoning solutions remains understudied. In this paper, we propose TypedThinker, a novel framework that enhances LLMs’ problem-solving abilities by incorporating multiple reasoning types (deductive, inductive, abductive, and analogical). Our analysis across four benchmarks reveals that different reasoning<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29421<END_URL>

u-$\mu$P: The Unit-Scaled Maximal Update Parametrization\nThe Maximal Update Parametrization ($\mu$P) aims to make the optimal hyperparameters (HPs) of a model independent of its size, allowing them to be swept using a cheap proxy model rather than the full-size target model. We present a new scheme, u-$\mu$P, which improves upon $\mu$P by combining it with Unit Scaling, a method for designing models that makes them easy to train in low-precision. The two techniques have a natural affinity: $\mu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29774<END_URL>

UGMathBench: A Diverse and Dynamic Benchmark for Undergraduate-Level Mathematical Reasoning with Large Language Models\nLarge Language Models (LLMs) have made significant strides in mathematical reasoning, underscoring the need for a comprehensive and fair evaluation of their capabilities. However, existing benchmarks often fall short, either lacking extensive coverage of undergraduate-level mathematical problems or probably suffering from test-set contamination. To address these issues, we intr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28857<END_URL>

UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition\nFace recognition (FR) stands as one of the most crucial applications in computer vision. The accuracy of FR models has significantly improved in recent years due to the availability of large-scale human face datasets. However, directly using these datasets can inevitably lead to privacy and legal problems. Generating synthetic data to train FR models is a feasible solution to circumvent<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28176<END_URL>

Ultra-Sparse Memory Network\nIt is widely acknowledged that the performance of Transformer models is exponentially related to their number of parameters and computational complexity. While approaches like Mixture of Experts (MoE) decouple parameter count from computational complexity, they still face challenges in inference due to high memory access costs. This work introduces UltraMem, incorporating large-scale, ultra-sparse memory layer to address these limitations. Our approach significantly <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27653<END_URL>

Unbounded: A Generative Infinite Game of Character Life Simulation\nWe introduce the concept of a generative infinite game, a video game that transcends the traditional boundaries of finite, hard-coded systems by using generative models. Inspired by James P. Carse's distinction between finite and infinite games, we leverage recent advances in generative AI to create Unbounded: a game of character life simulation that is fully encapsulated in generative models. Specifically, Unbounded draws inspi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27943<END_URL>

Uncertainty-Aware Decoding with Minimum Bayes' Risk\nDespite their outstanding performance in the majority of scenarios, contemporarylanguage models still occasionally produce undesirable outputs, for example, hallucinated text. While such behaviors have previously been linked to uncertainty,there is a notable lack of methods that actively consider uncertainty during textgeneration. In this work, we show how Minimum Bayes’ Risk (MBR) decoding, amethod that was originally designed to account for <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28763<END_URL>

Uncertainty Herding: One Active Learning Method for All Label Budgets\nMost active learning research has focused on methods which perform well when many labels are available, but can be dramatically worse than random selection when label budgets are small.Other methods have focused on the low-budget regime, but do poorly as label budgets increase.As the line between "low" and "high" budgets varies by problem,this is a serious issue in practice.We propose *uncertainty coverage*,an objective which<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29463<END_URL>

Uncertainty modeling for fine-tuned implicit functions\nImplicit functions such as Neural Radiance Fields (NeRFs), occupancy networks, and signed distance functions (SDFs) have become pivotal in computer vision for reconstructing detailed object shapes from sparse views. Achieving optimal performance with these models can be challenging due to the extreme sparsity of inputs and distribution shifts induced by data corruptions. To this end, large, noise-free synthetic datasets can serve as shape p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28687<END_URL>

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations\nWe propose a novel Stochastic Differential Equation (SDE) framework to address the problem of learning uncertainty-aware representations for graph-structured data. While Graph Neural Ordinary Differential Equations (GNODEs) have shown promise in learning node representations, they lack the ability to quantify uncertainty. To address this, we introduce Latent Graph Neural Stochastic Differential Equations (LGNSDE<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29534<END_URL>

Uncovering Gaps in How Humans and LLMs Interpret Subjective Language\nHumans often rely on subjective natural language to direct language models (LLMs); for example, users might instruct the LLM to write an *enthusiastic* blogpost, while developers might train models to be *helpful* and *harmless* using LLM-based edits. The LLM's *operational semantics* of such subjective phrases---how it adjusts its behavior when each phrase is included in the prompt---thus dictates how aligned it is with human<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28791<END_URL>

Uncovering Latent Memories in Large Language Models\nFrontier AI systems are making transformative impacts across society, but such benefits are not without costs: models trained on web-scale datasets containing personal and private data raise profound concerns about data privacy and security. Language models are trained on extensive corpora including potentially sensitive or proprietary information, and the risk of data leakage, where the model response reveals pieces of such information, remai<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30058<END_URL>

Uncovering Overfitting in Large Language Model Editing\nKnowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs). However, existing editing methods often struggle with complex tasks, such as multi-hop reasoning. In this paper, we identify and investigate the phenomenon of **Editing Overfit**, where edited models assign disproportionately high probabilities to the edit target, hindering the generalization of new <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28074<END_URL>

Underdamped Diffusion Bridges with Applications to Sampling\nWe provide a general framework for learning diffusion bridges that transport prior to target distributions. It includes existing diffusion models for generative modeling, but also underdamped versions with degenerate diffusion matrices, where the noise only acts in certain dimensions. Extending previous findings, our framework allows to rigorously show that score-matching in the underdamped case is indeed equivalent to maximizing a low<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29713<END_URL>

Understanding and Enhancing the Transferability of Jailbreaking Attacks\nJailbreaking attacks can effectively manipulate open-source large language models (LLMs) to produce harmful responses. Nevertheless, these attacks exhibit limited transferability, failing to consistently disrupt proprietary LLMs. To reliably identify vulnerabilities in proprietary LLMs, this work investigates the transferability of jailbreaking attacks by analyzing their impact on the model's intent perception. By incorpora<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29142<END_URL>

Understanding Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing\nStructured State Space Models (SSMs) have emerged as alternatives to transformers, addressing the challenges of processing long sequences.While SSMs are often regarded as effective in capturing long-term dependencies, we theoretically demonstrate that they suffer from a strong recency bias.Our empirical findings reveal that this bias impairs the models' ability to recall distant information and introd<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28272<END_URL>

Understanding Constraint Inference in Safety-Critical Inverse Reinforcement Learning\nIn practical applications, the underlying constraint knowledge is often unknown and difficult to specify. To address this issue, recent advances in Inverse Constrained Reinforcement Learning (ICRL) have focused on inferring these constraints from expert demonstrations. However, the ICRL approach typically characterizes constraint learning as a tri-level optimization problem, which is inherently complex due to i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30596<END_URL>

Understanding Factual Recall in Transformers via Associative Memories\nLarge language models have demonstrated an impressive ability to perform factual recall. Prior work has found that transformers trained on factual recall tasks can store information at a rate proportional to their parameter count. In our work, we show that shallow transformers can use a combination of associative memories to obtain such near optimal storage capacity. We begin by proving that the storage capacities of both lin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28731<END_URL>

Understanding Long Videos with Multimodal Language Models\nLarge Language Models (LLMs) have allowed recent LLM-based approaches to achieve excellent performance on long-video understanding benchmarks. We investigate how extensive world knowledge and strong reasoning skills of LLMs influence evaluations on standard long video benchmarks. Surprisingly, we discover that LLM-based models yield surprisingly good accuracy on long-video tasks with limited video information, sometimes even with no vide<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29788<END_URL>

Understanding Matrix Function Normalizations in Covariance Pooling from the Lens of Riemannian Geometry\nGlobal Covariance Pooling (GCP) has been demonstrated to improve the performance of Deep Neural Networks (DNNs) by exploiting second-order statistics of high-level representations. GCP typically performs classification of the covariance matrices by applying matrix function normalization, such as matrix logarithm or power, followed by a Euclidean classifier. However, covariance matrices inhere<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28270<END_URL>

Understanding Methods for Scalable MCTS\nMonte Carlo Tree Search (MCTS) is pivotal for decision-making in environments with large state spaces, such as strategic games and real-world optimization. This blog post explores scalable methods for parallelizing MCTS through various forms of parallelism and distributed approaches. By analyzing techniques such as root, leaf, and tree parallelism, as well as advanced strategies like lock-free concurrency, virtual loss, and distributed depth-first methods<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31350<END_URL>

Understanding Model Calibration - A gentle introduction and visual exploration of calibration and the expected calibration error (ECE)\nTo be considered reliable, a model must be calibrated so that its confidence in each decision closely reflects its true outcome. In this blogpost we'll take a look at the most commonly used definition for calibration and then dive into a frequently used evaluation measure for model calibration. We'll then cover some of the drawbacks of this measure and how these<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31360<END_URL>

Understanding Optimization in Deep Learning with Central Flows\nOptimization in deep learning remains poorly understood, even in the simple setting of deterministic (i.e. full-batch) training. A key difficulty is that much of an optimizer's behavior is *implicitly* determined by complex oscillatory dynamics, referred to as the "edge of stability." The main contribution of this paper is to show that an optimizer's implicit behavior can be explicitly captured by a *central flow*: a differential eq<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28135<END_URL>

Understanding Reasoning with Looped Models\nLarge language models have shown promising abilities in reasoning problems and scaling laws suggest that parameter count is a key driver. Recent works (Chen & Zou, 2024; Ye et al., 2024) argue that for reasoning, depth plays a very important role in addition to parameter count. In this work, we make a more fine-grained claim — many reasoning problems require large depth but not necessarily many parameters, in the sense that they can be solved via loope<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28971<END_URL>

Understanding the Impacts of GenAI Requires Understanding the Impact of Anthropomorphic AI\nMany state-of-the-art generative AI (GenAI) systems are increasingly prone to anthropomorphic behaviors, i.e., to generating outputs that are perceived to be human-like. While this has led to scholars increasingly raising concerns about possible negative impacts such anthropomorphic AI systems can give rise to, anthropomorphism in AI development, deployment, and use remains vastly overlooked, understudied<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31358<END_URL>

Understanding the Stability-based Generalization of Personalized Federated Learning\nDespite great achievements in algorithm design for Personalized Federated Learning (PFL), research on the theoretical analysis of generalization is still in its early stages. Some recent theoretical results have investigated the generalization performance of personalized models under the problem setting and hypothesis in the convex condition, which do not consider the real iteration performance during the non-co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27649<END_URL>

Understanding Virtual Nodes: Oversquashing and Node Heterogeneity\nWhile message passing neural networks (MPNNs) have convincing success in a range of applications, they exhibit limitations such as the oversquashing problem and their inability to capture long-range interactions. Augmenting MPNNs with a virtual node (VN) removes the locality constraint of the layer aggregation and has been found to improve performance on a range of benchmarks. We provide a comprehensive theoretical analysis of th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29859<END_URL>

Understanding Visual Concepts Across Models\nLarge multimodal models such as Stable Diffusion can generate, detect, and classify new visual concepts after optimizing just the prompt. How are prompt embeddings for visual concepts found by prompt tuning methods different from typical discrete prompts? We conduct a large-scale analysis on three state-of-the-art models in text-to-image generation, open-set object detection, and zero-shot classification, and find that prompts optimized to represent n<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30851<END_URL>

Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape View\nTraining language models currently requires pre-determining a fixed compute budget because the typical cosine learning rate schedule depends on the total number of steps. In contrast, the Warmup-Stable-Decay (WSD) schedule uses a constant learning rate to produce a main branch of iterates that can in principle continue indefinitely without a pre-specified compute budget. Then, given any compute budget, one can <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28484<END_URL>

Unearthing Skill-level Insights for Understanding Trade-offs of Foundation Models\nWith models getting stronger, evaluations have grown more complex, testing multiple skills in one benchmark and even in the same instance at once. However, skill-wise performance is obscured when inspecting aggregate accuracy, under-utilizing the rich signal modern benchmarks contain. We propose an automatic approach to recover the underlying skills relevant for any evaluation instance, by way of inspecting model-<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28591<END_URL>

U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models\nU-Nets are among the most widely used architectures in computer vision, renowned for their exceptional performance in applications such as image segmentation, denoising, and diffusion modeling. However, a theoretical explanation of the U-Net architecture design has not yet been fully established. This paper introduces a novel interpretation of the U-Net architecture by studying cer<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28085<END_URL>

Unhackable Temporal Reward for Scalable Video MLLMs\nIn the pursuit of superior video-processing MLLMs, we have encountered a perplexing paradox: the “anti-scaling law”, where more data and larger models lead to worse performance. This study unmasks the culprit: “temporal hacking”, a phenomenon where models shortcut by fixating on select frames, missing the full video narrative. In this work, we systematically establish a comprehensive theory of temporal hacking, defining it from a reinforcement<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30267<END_URL>

Uni$^2$Det: Unified and Universal Framework for Prompt-Guided Multi-dataset 3D Detection\nWe present Uni$^2$Det, a brand new framework for unified and universal multi-dataset training on 3D detection, enabling robust performance across diverse domains and generalization to unseen domains. Due to substantial disparities in data distribution and variations in taxonomy across diverse domains, training such a detector by simply merging datasets poses a significant challenge. Motivated by this observ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30625<END_URL>

UniCBE: An Uniformity-driven Comparing Based Evaluation Framework with Unified Multi-Objective Optimization\nHuman preference plays a significant role in measuring large language models and guiding them to align with human values. Unfortunately, current comparing-based evaluation (CBE) methods typically focus on a single optimization objective, failing to effectively utilize scarce yet valuable preference signals. To address this, we delve into key factors that can enhance the accuracy, converge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28168<END_URL>

UniCon: Unidirectional Information Flow for Effective Control of Large-Scale Diffusion Models\nWe introduce UniCon, a novel architecture designed to enhance control and efficiency in training adapters for large-scale diffusion models like the Diffusion transformer. Unlike existing methods that rely on bidirectional interaction between the diffusion model and control adapter, UniCon implements a unidirectional flow from the diffusion network to the adapter, allowing the adapter alone to generate <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27986<END_URL>

UniCoTT: A Unified Framework for Structural Chain-of-Thought Distillation\nChains of thought (CoTs) have achieved success in enhancing the reasoning capabilities of large language models (LLMs), while their effectiveness is predominantly observed in LLMs.     Existing solutions methods adopt distillation to inject chain-of-thought capabilities into small models (SLMs).    However, they:     (1) can not guarantee the rationality of the generated explanation due to hallucinations;     (2) ignore d<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31068<END_URL>

UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation\nWe present UniDetox, a universally applicable method designed to mitigate toxicity across various large language models (LLMs).Previous detoxification methods are typically model-specific, addressing only individual models or model families, and require careful hyperparameter tuning due to the trade-off between detoxification efficacy and language modeling performance. In contrast, UniDetox provides a detoxific<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28932<END_URL>

UniDrive: Towards Universal Driving Perception Across Camera Configurations\nVision-centric autonomous driving has demonstrated excellent performance with economical sensors. As the fundamental step, 3D perception aims to infer 3D information from 2D images based on 3D-2D projection. This makes driving perception models susceptible to sensor configuration (e.g., camera intrinsics and extrinsics) variations. However, generalizing across camera configurations is important for deploying autonomous <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28636<END_URL>

Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers\nScore-based diffusion models have emerged as powerful techniques for generating samples from high-dimensional data distributions. These models involve a two-phase process: first, injecting noise to transform the data distribution into a known prior distribution, and second, sampling to recover the original data distribution from noise. Among the various sampling methods, deterministic samplers stand out fo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30206<END_URL>

Unified Neural Solvers for General TSP and Multiple Combinatorial Optimization Tasks via Problem Reduction and Matrix Encoding\nVarious neural solvers have been devised for combinatorial optimization (CO), which are often tailored for specific problem types, ranging from TSP, CVRP to SAT, etc. Yet, it remains an open question how to achieve universality regarding problem representing and learning with a general  framework. This paper first proposes RedCO, to unify a set of CO problems by reducin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27742<END_URL>

Unified Parameter-Efficient Unlearning for LLMs\nThe advent of Large Language Models (LLMs) has revolutionized natural language processing, enabling advanced understanding and reasoning capabilities across a variety of tasks. Fine-tuning these models for specific domains, particularly through Parameter-Efficient Fine-Tuning (PEFT) strategies like LoRA, has become a prevalent practice due to its efficiency. However, this raises significant privacy and security concerns, as models may inadvertentl<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27670<END_URL>

Unifying and Verifying Mechanistic Interpretations: A Case Study with Group Operations\nA recent line of work in mechanistic interpretability has focused on reverse-engineering the computation performed by neural networks trained on the binary operation of finite groups.We investigate the internals of one-hidden-layer neural networks trained on this task, revealing previously unidentified structureand producing a more complete description of such models in a step towards unifying the explanation<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30732<END_URL>

Unifying Causal Representation Learning with the Invariance Principle\nCausal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect of new interventions or more robust classification. A plethora of methods have been developed, each tackling carefully crafted problem settings that lead to different types of identifiability. The folklore is that these different settings are important, as<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28503<END_URL>

Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark\nTo build safe and reliable graph machine learning systems, unsupervised graph-level anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection (GLOD) have received significant attention in recent years. Though these two lines of research share the same objective, they have been studied independently in the community due to distinct evaluation setups, creating a gap t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28832<END_URL>

UniGEM: A Unified Approach to Generation and Property Prediction for Molecules\nMolecular generation and molecular property prediction are both crucial for drug discovery, but they are often developed independently. Inspired by recent studies, which demonstrate that diffusion model, a prominent generative approach, can learn meaningful data representations that enhance predictive tasks, we explore the potential for developing a unified generative model in the molecular domain that effectively ad<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29987<END_URL>

UniGS: Unified Language-Image-3D Pretraining with Gaussian Splatting\nRecent advancements in multi-modal 3D pre-training methods have shown promising efficacy in learning joint representations of text, images, and point clouds. However, adopting point clouds as 3D representation fails to fully capture the intricacies of the 3D world and exhibits a noticeable gap between the discrete points and the dense 2D pixels of images. To tackle this issue, we propose UniGS, integrating 3D Gaussian Splattin<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30882<END_URL>

uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs\nIn this paper, we present a novel algorithm, `uniINF`, for the Heavy-Tailed Multi-Armed Bandits (HTMAB) problem, demonstrating robustness and adaptability in both stochastic and adversarial environments. Unlike the stochastic MAB setting where loss distributions are stationary with time, our study extends to the adversarial setup, where losses are generated from heavy-tailed distributions that depend on both arms and tim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31113<END_URL>

UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery\nDrug discovery is crucial for identifying candidate drugs for various diseases. However, its low success rate often results in a scarcity of annotations, posing a few-shot learning problem. Existing methods primarily focus on single-scale features, overlooking the hierarchical molecular structures that determine different molecular properties. To address these issues, we introduce Universal Matching Networks (UniMatch), <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27929<END_URL>

Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization\nDirect Preference Optimization (DPO), and its numerous variants, are increasingly used for aligning language models. Although they are designed to teach a model to generate preferred responses more frequently relative to dispreferred responses, prior work has observed that the likelihood of preferred responses often decreases during training. The current work sheds light on the causes and implications of this c<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27968<END_URL>

Union-over-Intersections: Object Detection beyond Winner-Takes-All\nThis paper revisits the problem of predicting box locations in object detection architectures. Typically, each box proposal or box query aims to directly maximize the intersection-over-union score with the ground truth, followed by a winner-takes-all non-maximum suppression where only the highest scoring box in each region is retained. We observe that both steps are sub-optimal: the first involves regressing proposals to the ent<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30208<END_URL>

UniRestore3D: A Scalable Framework For General Shape Restoration\nShape restoration aims to recover intact 3D shapes from defective ones, such as those that are incomplete, noisy, and low-resolution. Previous works have achieved impressive results in shape restoration subtasks thanks to advanced generative models. While effective for specific shape defects, they are less applicable in real-world scenarios involving multiple defect types simultaneously. Additionally, training on limited subsets o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27785<END_URL>

Uni-Sign: Toward Unified Sign Language Understanding at Scale\nSign language pre-training has gained increasing attention for its ability to enhance performance across various sign language understanding (SLU) tasks. However, existing methods often suffer from a gap between pre-training and fine-tuning, leading to suboptimal results. To address this, we propose Uni-Sign, a unified pre-training framework that eliminates the gap between pre-training and downstream SLU tasks through a large-scale g<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31250<END_URL>

Universal generalization guarantees for Wasserstein distributionally robust models\nDistributionally robust optimization has emerged as an attractive way to train robust machine learning models, capturing data uncertainty and distribution shifts. Recent statistical analyses have proved that generalization guarantees of robust models based on the Wasserstein distance have generalization guarantees that do not suffer from the curse of dimensionality. However, these results are either approximate, <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31241<END_URL>

Universal Image Restoration Pre-training via Degradation Classification\nThis paper proposes the Degradation Classification Pre-Training (DCPT), which enables models to learn how to classify the degradation type of input images for universal image restoration pre-training. Unlike the existing self-supervised pre-training methods, DCPT utilizes the degradation type of the input image as an extremely weak supervision, which can be effortlessly obtained, even intrinsic in all image restoration data<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29746<END_URL>

Universal Multimodal Retrieval with Multimodal LLMs\nState-of-the-art retrieval models typically address a straightforward search scenario, where retrieval tasks are fixed (e.g., finding a passage to answer a specific question) and only a single modality is supported for both queries and retrieved results. This paper introduces techniques for advancing information retrieval with multimodal large language models (MLLMs), enabling a broader search scenario, termed universal multimodal retrieval, w<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28720<END_URL>

Universal Sharpness Dynamics in Neural Network Training: Fixed Point Analysis, Edge of Stability, and Route to Chaos\nIn gradient descent dynamics of neural networks, the top eigenvalue of the Hessian of the loss (sharpness) displays a variety of robust phenomena throughout training. This includes early time regimes where the sharpness may decrease during early periods of training (sharpness reduction), and later time behavior such as progressive sharpening and edge of stability. We demonstrate <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29406<END_URL>

UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation\nPre-training and representation learning have been playing an increasingly important role in modern speech processing.  Nevertheless, different applications have been relying on different foundation models, since predominant pre-training techniques are either designed for discriminative tasks or generative tasks. In this work, we make the first attempt at building a unified pre-training framework for both typ<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27705<END_URL>

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy\nMachine unlearning algorithms, designed for selective removal of training data from models, have emerged as a promising approach to growing privacy concerns. In this work, we expose a critical yet underexplored vulnerability in the deployment of unlearning systems: the assumption that the data requested for removal is always part of the original training set. We present a threat model where an attacker can degrade <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30919<END_URL>

Unlearning-based Neural Interpretations\nGradient-based interpretations often require an anchor point of comparison to avoid saturation in computing feature importance. We show that current baselines defined using static functions —constant mapping, averaging or blurring — inject harmful colour, texture or frequency assumptions that deviate from model behaviour. This leads to accumulation of irregular gradients, resulting in attribution maps that are biased, fragile and manipulable. Departing fr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29768<END_URL>

Unleashing the Potential of ConvNets for Query-based Detection and Segmentation\nTransformer and its variants have shown great potential for various vision tasks in recent years, including image classification, object detection and segmentation. Meanwhile, recent studies also reveal that with proper architecture design, convolution networks (ConvNets) also achieve competitive performance with transformers, \eg, ConvNeXt. However, no prior methods have explored to utilize pure convolution to buil<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29537<END_URL>

Unleashing the Potential of Diffusion Models for Incomplete Data Imputation\nGenerative models play an important role in missing data imputation in that they aim to learn the joint distribution of full data. However, applying advanced deep generative models (such as Diffusion models) to missing data imputation is challenging due to 1) the inherent incompleteness of the training data and 2) the difficulty in performing conditional inference from unconditional generative models. To deal with these<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31061<END_URL>

Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning\nLarge language models (LLMs) have achieved remarkable performance on vari-ous natural language tasks. However, they are trained on static corpora and theirknowledge can become outdated quickly in the fast-changing world. This moti-vates the development of knowledge editing methods designed to update certainknowledge in LLMs without changing unrelated others. To make selective edits,previou<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29762<END_URL>

Unlocking Global Optimality in Bilevel Optimization: A Pilot Study\nBilevel optimization has witnessed a resurgence of interest, driven by its critical role in advanced machine learning applications such as hyperparameter optimization, meta-learning, and reinforcement learning. Recent research has focused on proposing efficient methods with provable convergence guarantees.  However, while many prior works have established convergence to stationary points or local minima, obtaining the global opt<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31102<END_URL>

Unlocking Guidance for Discrete State-Space Diffusion and Flow Models\nGenerative models on discrete state-spaces have a wide range of potential applications, particularly in the domain of natural sciences. In continuous state-spaces, controllable and flexible generation of samples with desired properties has been realized using guidance on diffusion and flow models. However, these guidance approaches are not readily amenable to discrete state-space models. Consequently, we introduce a general a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29286<END_URL>

Unlocking Point Processes through Point Set Diffusion\nPoint processes model the distribution of random point sets in mathematical spaces, such as spatial and temporal domains, with applications in fields like seismology, neuroscience, and economics. Existing statistical and machine learning models for point processes are predominantly constrained by their reliance on the characteristic intensity function, introducing an inherent trade-off between efficiency and flexibility. In this paper, we in<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31003<END_URL>

Unlocking Reasoning Potential in Large Language Models by Scaling Code-form Planning\nDespite the remarkable success of large language models (LLMs) on traditional natural language processing tasks, their planning ability remains a critical bottleneck in tackling complex multi-step reasoning tasks. Existing approaches mainly rely on prompting or task-specific fine-tuning, often suffering from weak robustness and cross-task generalization. To address the limitation, we introduce CodePlan, a scala<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29000<END_URL>

Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues\nLinear Recurrent Neural Networks (LRNNs) such as Mamba, RWKV, GLA, mLSTM, and DeltaNet have emerged as efficient alternatives to Transformers in large language modeling, offering linear scaling with sequence length and improved training efficiency. However, LRNNs struggle to perform state-tracking which may impair performance in tasks such as code evaluation or tracking a chess game. Even parity, the simplest state-tracking ta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29442<END_URL>

Unlocking the Potential of Model Calibration in Federated Learning\nOver the past several years, various federated learning (FL) methodologies have been developed to improve model accuracy, a primary performance metric in machine learning. However, to utilize FL in practical decision-making scenarios, beyond considering accuracy, the trained model must also have a reliable confidence in each of its predictions, an aspect that has been largely overlooked in existing FL research. Motivated by this<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29797<END_URL>

Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning\nCatastrophic forgetting (CF) poses a significant challenge in machine learning, where a model forgets previously learned information upon learning new tasks. Despite the advanced capabilities of Large Language Models (LLMs), they continue to face challenges with CF during continual learning. The majority of existing research focuses on analyzing forgetting patterns t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28810<END_URL>

Unposed Sparse Views Room Layout Reconstruction in the Age of Pretrain Model\nMultiple-perspective images room layout reconstruction is poorly investigated due to the tedious and cumbersome steps that emerge from multi-view geometry, e.g camera intrinsic/extrinsic estimation, image matching, and triangulation. However, with the advancement of the current 3D foundation model DUSt3R, a paradigm shift has occurred in the 3D reconstruction realm, moving from a multi-step Structure-from-motion approa<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30437<END_URL>

Unsupervised Disentanglement of Content and Style via Variance-Invariance Constraints\nWe contribute an unsupervised method that effectively learns disentangled content and style representations from sequences of observations. Unlike most disentanglement algorithms that rely on domain-specific labels or knowledge, our method is based on the insight of domain-general statistical differences between content and style --- content varies more among different fragments within a sample but maintains a<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29968<END_URL>

Unsupervised Meta-Learning via In-Context Learning\nUnsupervised meta-learning aims to learn feature representations from unsupervised datasets that can transfer to downstream tasks with limited labeled data.In this paper, we propose a novel approach to unsupervised meta-learning that leverages the generalization abilities of in-context learning observed in transformer architectures. Our method reframes meta-learning as a sequence modeling problem, enabling the transformer encoder to learn task <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30089<END_URL>

Unsupervised Model Tree Heritage Recovery\nThe number of models shared online has recently skyrocketed, with over one million public models available on Hugging Face. Sharing models allows other users to build on existing models, using them as initialization for fine-tuning, improving accuracy and saving compute and energy. However, it also raises important intellectual property issues, as fine-tuning may violate the license terms of the original model or that of its training data. A Model Tree,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29687<END_URL>

Unsupervised Multiple Kernel Learning for Graphs via Ordinality Preservation\nLearning effective graph similarities is crucial for tasks like clustering, yet selecting the optimal kernel to evaluate such similarities in unsupervised settings remains a major challenge. Despite the development of various graph kernels, determining the most appropriate one for a specific task is particularly difficult in the absence of labeled data. Existing methods often struggle to handle the complex structure of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30865<END_URL>

Unsupervised Zero-Shot Reinforcement Learning via Dual-Value Forward-Backward Representation\nOnline unsupervised reinforcement learning (URL) can discover diverse skills via reward-free pre-training and exhibits impressive downstream task adaptation abilities through further fine-tuning.However, online URL methods face challenges in achieving zero-shot generalization, i.e., directly applying pre-trained policies to downstream tasks without additional planning or learning.In this paper, we propo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31255<END_URL>

UNSURE: Unknown Noise level Stein's Unbiased Risk Estimator\nRecently, many self-supervised learning methods for image reconstruction have been proposed that can learn from noisy data alone, bypassing the need for ground-truth references.  Most existing methods cluster around two classes: i) Stein's Unbiased Risk Estimate (SURE) and similar approaches that assume full knowledge of the distribution, and ii) Noise2Self and similar cross-validation methods that require very mild knowledge about the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29592<END_URL>

Unveiling the Magic of Code Reasoning through Hypothesis Decomposition and Amendment\nThe reasoning abilities are one of the most enigmatic and captivating aspects of large language models (LLMs). Numerous studies are dedicated to exploring and expanding the boundaries of this reasoning capability. However, tasks that embody both reasoning and recall characteristics are often overlooked. In this paper, we introduce such a novel task, code reasoning, to provide a new perspective for the reasoning<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28593<END_URL>

URLOST: Unsupervised Representation Learning without Stationarity or Topology\nUnsupervised representation learning has seen tremendous progress. However, it is constrained by its reliance on domain specific stationarity and topology, a limitation not found in biological intelligence systems. For instance, unlike computer vision, human vision can process visual signals sampled from highly irregular and non-stationary sensors. We introduce a novel framework that learns from high-dimensional data <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29948<END_URL>

U-shaped and Inverted-U Scaling behind \\Emergent Abilities of Large Language Models\nLarge language models (LLMs) have been shown to exhibit emergent abilities in some downstream tasks, where performance seems to stagnate at first and then improve sharply and unpredictably with scale beyond a threshold. By dividing questions in the datasets according to difficulty level by average performance, we observe U-shaped scaling for hard questions, and inverted-U scaling followed by steady improvement <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28623<END_URL>

Utilizing Explainable Reinforcement Learning to Improve Reinforcement Learning: A Theoretical and Systematic Framework\nReinforcement learning (RL) faces two challenges: (1) The RL agent lacks explainability. (2) The trained RL agent is, in many cases, non-optimal and even far from optimal. To address the first challenge, explainable reinforcement learning (XRL) is proposed to explain the decision-making of the RL agent. In this paper, we demonstrate that XRL can also be used to address the seco<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29522<END_URL>

UV-Attack: Physical-World Adversarial Attacks for Person Detection via Dynamic-NeRF-based UV Mapping\nRecent works have attacked person detectors using adversarial patches or static-3D-model-based texture modifications. However, these methods suffer from low attack success rates when faced with significant human movements. The primary challenge stems from the highly non-rigid nature of the human body and clothing. Current attacks fail to model these 3D non-rigid deformations caused by varied act<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28278<END_URL>

VAE-Var: Variational Autoencoder-Enhanced Variational Methods for Data Assimilation in Meteorology\nData assimilation (DA) is an essential statistical technique for generating accurate estimates of a physical system's states by combining prior model predictions with observational data, especially in the realm of weather forecasting. Effectively modeling the prior distribution while adapting to diverse observational sources presents significant challenges for both traditional and neural network-b<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27950<END_URL>

Valid Conformal Prediction for Dynamic GNNs\nDynamic graphs provide a flexible data abstraction for modelling many sorts of real-world systems, such as transport, trade, and social networks. Graph neural networks (GNNs) are powerful tools allowing for different kinds of prediction and inference on these systems, but getting a handle on uncertainty, especially in dynamic settings, is a challenging problem.In this work we propose to use a dynamic graph representation known in the tensor literature<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28722<END_URL>

Value-aligned Behavior Cloning for Offline Reinforcement Learning via Bi-level Optimization\nOffline reinforcement learning (RL) aims to optimize policies under pre-collected data, without requiring any further interactions with the environment. Derived from imitation learning, Behavior cloning (BC) is extensively utilized in offline RL for its simplicity and effectiveness. Although BC inherently avoids out-of-distribution deviations, it lacks the ability to discern between high and low-quality <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28908<END_URL>

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF\nReinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of preference data, both online and offline RLHF are active areas of investigation. A key bottleneck is understanding how to incorporate uncertainty estimation in the reward function learned from the preference data for RLHF, regardle<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29608<END_URL>

Variance-Reducing Couplings for Random Features\nRandom features (RFs) are a popular technique to scale up kernel methods in machine learning, replacing exact kernel evaluations with stochastic Monte Carlo estimates. They underpin models as diverse as efficient transformers (by approximating attention) to sparse spectrum Gaussian processes (by approximating the covariance function). Efficiency can be further improved by speeding up the convergence of these estimates: a variance reduction problem<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28364<END_URL>

Variational Bayesian Pseudo-Coreset\nThe success of deep learning requires large datasets and extensive training, which can create significant computational challenges. To address these challenges, pseudo-coresets, small learnable datasets that mimic the entire data, have been proposed. Bayesian Neural Networks, which offer predictive uncertainty and probabilistic interpretation for deep neural networks, also face issues with large-scale datasets due to their high-dimensional parameter space. Pr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31258<END_URL>

Variational Best-of-N Alignment\nBest-of-N (BoN) is a popular and effective algorithm for aligning language models to human preferences. The algorithm works as follows: at inference time, N samples are drawn from the language model, and the sample with the highest reward, as judged by a reward model, is returned as the output. Despite its effectiveness, BoN is computationally expensive; it reduces sampling throughput by a factor of N. To make BoN more efficient at inference time, one strategy is<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29381<END_URL>

Variational Diffusion Posterior Sampling with Midpoint Guidance\nDiffusion models have recently shown considerable potential in solving Bayesian inverse problems when used as priors. However, sampling from the resulting denoising posterior distributions remains a challenge as it involves intractable terms. To tackle this issue, state-of-the-art approaches formulate the problem as that of sampling from a surrogate diffusion model targeting the posterior and decompose its scores into two terms: th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30902<END_URL>

Variational Search Distributions\nWe develop variational search distributions (VSD), a method for finding and generating discrete, combinatorial designs of a rare desired class in a batch sequential manner with a fixed experimental budget. We formalize the requirements and desiderata for active generation and formulate a solution via variational inference. In particular, VSD uses off-the-shelf gradient based optimization routines, can learn powerful generative models for designs, and can take ad<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31174<END_URL>

Varying Shades of Wrong: Aligning LLMs with Wrong Answers Only\nIn the absence of abundant reliable annotations for challenging tasks and contexts, how can we expand the frontier of LLM capabilities with potentially wrong answers? We focus on two research questions: (1) Can LLMs generate reliable preferences among wrong options? And if so, (2) Would alignment with such wrong-over-wrong preferences be helpful? We employ methods based on self-consistency, token probabilities, and LLM-as-a-judge to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28317<END_URL>

VCR: Visual Caption Restoration\nWe introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28154<END_URL>

Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors\nThis paper studies how to synthesize face images of non-existent persons, to create a dataset that allows effective training of face recognition (FR) models. Besides generating realistic face images, two other important goals are: 1) the ability to generate a large number of distinct identities (inter-class separation), and 2) a proper variation in appearance of the images for each identity (intra-class variation).Howeve<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29637<END_URL>

Vector-ICL: In-context Learning with Continuous Vector Representations\nLarge language models (LLMs) have shown remarkable in-context learning (ICL) capabilities on textual data. We explore whether these capabilities can be extended to continuous vectors from diverse domains, obtained from black-box pretrained encoders. By aligning input data with an LLM's embedding space through lightweight projectors, we observe that LLMs can effectively process and learn from these projected vectors, which we<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27769<END_URL>

VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning\nProcedural video representation learning is an active research area where the objective is to learn an agent which can anticipate and forecast the future given the present video input, typically in conjunction with textual annotations. Prior works often rely on large-scale pretraining of visual encoders and prediction models with language supervision. However, the necessity and effectiveness of extending compute <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30010<END_URL>

VerifAI: AI Verification in the Wild\nThis workshop explores the intersection of scale-driven generative artificial intelligence (AI) and the correctness-focused principles of verification. Formal analysis tools such as theorem provers, satisfiability solvers, and execution monitoring have demonstrated success in ensuring properties of interest across a range of tasks in software development and mathematics where precise reasoning is necessary. However, these methods face scaling challenges. Rec<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23973<END_URL>

Verifying Properties of Binary Neural Networks Using Sparse Polynomial Optimization\nThis paper explores methods for verifying the properties of Binary Neural Networks (BNNs), focusing on robustness against adversarial attacks. Despite their lower computational and memory needs, BNNs, like their full-precision counterparts, are also sensitive to input perturbations. Established methods for solving this problem are predominantly based on Satisfiability Modulo Theories and Mixed-Integer Linear Pro<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30678<END_URL>

Vertical Federated Learning with Missing Features During Training and Inference\nVertical federated learning trains models from feature-partitioned datasets across multiple clients, who collaborate without sharing their local data. Standard approaches assume that all feature partitions are available during both training and inference. Yet, in practice, this assumption rarely holds, as for many samples only a subset of the clients observe their partition. However, not utilizing incomplete samples<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29822<END_URL>

Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement\nThe imitation of voice, targeted on specific speech attributes such as timbre and speaking style, is crucial in speech generation. However, existing methods rely heavily on annotated data, and struggle with effectively disentangling timbre and style, leading to challenges in achieving controllable generation, especially in zero-shot scenarios. To address these issues, we propose Vevo, a versatile zero-shot voice i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29148<END_URL>

VibeCheck: Discover and Quantify Qualitative Differences in Large Language Models\nLarge language models (LLMs) often exhibit subtle yet distinctive characteristics in their outputs that users intuitively recognize, but struggle to quantify. These "vibes" -- such as tone, formatting, or writing style -- influence user preferences, yet traditional evaluations focus primarily on the singular vibe of correctness.We introduce $\textbf{VibeCheck}$, a system for automatically comparing a pair of LLMs <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29156<END_URL>

ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler\nRecent progress in large-scale text-to-video (T2V) and image-to-video (I2V) diffusion models has greatly enhanced video generation, especially in terms of keyframe interpolation. However, current image-to-video diffusion models, while powerful in generating videos from a single conditioning frame, need adaptation for two-frame (start \& end) conditioned generation, which is essential for effective bounded interpol<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28410<END_URL>

VICtoR: Learning Hierarchical Vision-Instruction Correlation Rewards for Long-horizon Manipulation\nWe study reward models for long-horizon manipulation by learning from action-free videos and language instructions, which we term the visual-instruction correlation (VIC) problem. Existing VIC methods face challenges in learning rewards for long-horizon tasks due to their lack of sub-stage awareness, difficulty in modeling task complexities, and inadequate object state estimation. To address these<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29454<END_URL>

Video Action Differencing\nHow do two individuals differ when performing the same action? In this work, we introduce Video Action Differencing, the novel task of identifying subtle differences between videos of the same action, which has numerous applications, such as coaching and skill acquisition. To enable development on this new task, we first create VidDiffBench, a benchmark dataset containing 557 video pairs, with human annotations of 4,719 fine-grained action differences and 2,075 timesta<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31067<END_URL>

VideoPhy: Evaluating Physical Commonsense for Video Generation\nRecent advances in internet-scale video data pretraining have led to the development of text-to-video generative models that can create high-quality videos across a broad range of visual concepts, synthesize realistic motions and render complex objects. Hence, these generative models have the potential to become general-purpose simulators of the physical world. However, it is unclear how far we are from this goal with the existing t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30714<END_URL>

VideoShield: Regulating Diffusion-based Video Generation Models via Watermarking\nArtificial Intelligence Generated Content (AIGC) has advanced significantly, particularly with the development of video generation models such as text-to-video (T2V) models and image-to-video (I2V) models. However, like other AIGC types, video generation requires robust content control. A common approach is to embed watermarks, but most research has focused on images, with limited attention given to videos. Traditi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27941<END_URL>

Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision\nThe performance and reasoning capabilities of Large Multi-modal Models (LMMs) is dependent on the size and quality of their training datasets. However, collecting datasets that support chain-of-thought instruction tuning is highly challenging. Existing video instruction tuning datasets are often derived by prompting large language models with video captions to generate question-answer pairs, which makes them predomi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30101<END_URL>

VideoWebArena:  Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks\nVideos are often used to learn or extract the necessary information to completetasks in ways different than what text or static imagery can provide. However, manyexisting agent benchmarks neglect long-context video understanding, instead focus-ing on text or static image inputs. To bridge this gap, we introduce VideoWebArena(VideoWA), a benchmark for evaluating the capabilities of long-context multimoda<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27960<END_URL>

ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation\nDiffusion transformers have demonstrated remarkable performance in visual generation tasks, such as generating realistic images or videos based on textual instructions. However, larger model sizes and multi-frame processing for video generation lead to increased computational and memory costs, posing challenges for practical deployment on edge devices. Post-Training Quantization (PTQ) is an eff<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30429<END_URL>

VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation\nVILA-U is a Unified foundation model that integrates Video, Image, Language understanding and generation. Traditional visual language models (VLMs) use separate modules for understanding and generating visual content, which can lead to misalignment and increased complexity. In contrast, VILA-U employs a single autoregressive next-token prediction framework for both tasks, eliminating the need for additional compo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31274<END_URL>

ViSAGe: Video-to-Spatial Audio Generation\nSpatial audio is essential for enhancing the immersiveness of audio-visual experiences, yet its production typically demands complex recording systems and specialized expertise. In this work, we address a novel problem of generating first-order ambisonics, a widely used spatial audio format, directly from silent videos. To support this task, we introduce YT-Ambigen, a dataset comprising 102K 5-second YouTube video clips paired with corresponding first-o<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30759<END_URL>

Vision and Language Synergy for Rehearsal Free Continual Learning\nThe prompt-based approach has demonstrated its success for continual learning problems. However, it still suffers from catastrophic forgetting due to inter-task vector similarity and unfitted new components of previously learned tasks. On the other hand, the language-guided approach falls short of its full potential due to minimum utilized knowledge and participation in the prompt tuning process. To correct this problem, we propo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30681<END_URL>

Vision Language Models are In-Context Value Learners\nPredicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress estimator, or temporal value function, across different tasks and domains requires both a large amount of diverse data and methods which can scale and generalize. To address these challenges, we present Generative Value Learning (GVL), a universal value function estimator that leverages <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28853<END_URL>

Vision-LSTM: xLSTM as Generic Vision Backbone\nTransformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has been extended to a scalable and performant architecture - the xLSTM - which overcomes long-standing LSTM limitations via exponential gating and parallelizable matrix memory structure. In this paper, we introduce Vision-LSTM (ViL), an adaption of the xLSTM building blocks t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29584<END_URL>

Vision models trained to estimate spatial latents learned similar ventral-stream-aligned representations\nStudies of the functional role of the primate ventral visual stream have traditionally focused on object categorization, often ignoring -- despite much prior evidence -- its role in estimating "spatial" latents such as object position and pose. Most leading ventral stream models are derived by optimizing networks for object categorization, which seems to imply that the ventral stream is also<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28907<END_URL>

Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures\nTransformers have revolutionized computer vision and natural language processing, but their high computational complexity limits their application in high-resolution image processing and long-context analysis. This paper introduces Vision-RWKV (VRWKV), a model that builds upon the RWKV architecture from the NLP field with key modifications tailored specifically for vision tasks. Similar to the Vision Transformer <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28412<END_URL>

VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents\nRetrieval-augmented generation (RAG) is an effective technique that enables large language models (LLMs) to utilize external knowledge sources for generation. However, current RAG systems are solely based on text, rendering it impossible to utilize vision information like layout and images that play crucial roles in real-world multi-modality documents. In this paper, we introduce VisRAG, which tackles this issue by <BEGIN_URL>https://iclr.cc//virtual/2025/poster/27679<END_URL>

VisualAgentBench: Towards Large Multimodal Models as Visual Agents\nLarge Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to form highly capable visual agents that are postulated to excel across a myriad of tasks.  However, existing benchmarks fail to sufficiently challenge or showcase the full potential of LMMs as agents in complex, real-world environments.   To address this gap, we introduce VisualAgentBench (VAB),<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31108<END_URL>

Visual Agents as Fast and Slow Thinkers\nAchieving human-level intelligence requires refining cognitive distinctions between \textit{System 1} and \textit{System 2} thinking. While contemporary AI, driven by large language models, demonstrates human-like traits, it falls short of genuine cognition. Transitioning from structured benchmarks to real-world scenarios presents challenges for visual agents, often leading to inaccurate and overly confident responses. To address the challenge, we introdu<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28402<END_URL>

Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs\nLarge Vision-Language Models (LVLMs) often produce responses that misalign with factual information, a phenomenon known as hallucinations. While hallucinations are well-studied, the exact causes behind them remain underexplored. In this paper, we first investigate the root causes of hallucinations in LVLMs. Our findings reveal that existing mitigation techniques primarily reduce hallucinations for visual recogniti<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31078<END_URL>

Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark\nLarge Multimodal Models (LMMs) have made significant strides in visual question-answering for single images. Recent advancements like long-context LMMs have allowed them to ingest larger, or even multiple, images. However, the ability to process a large number of visual tokens does not guarantee effective retrieval and reasoning for multi-image question answering (MIQA), especially in real-world applications like photo album sear<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30702<END_URL>

Visually Consistent Hierarchical Image Classification\nHierarchical classification  requires predicting an entire taxonomy tree rather than a single flat level, which demands both  accurate predictions at each level and consistency across levels.  However, solving hierarchical classification often compromises fine-grained accuracy compared to flat classification because each level requires distinct features, making it a multi-task problem.For example, the fine-grained classification of "Green He<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30832<END_URL>

Visually Guided Decoding: Gradient-Free Hard Prompt Inversion with Language Models\nText-to-image generative models like DALL-E and Stable Diffusion have revolutionized visual content creation across various applications, including advertising, personalized media, and design prototyping. However, crafting effective textual prompts to guide these models remains challenging, often requiring extensive trial and error. Existing prompt inversion methods, such as soft and hard prompt techniques, suffe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28466<END_URL>

Visual-O1: Understanding Ambiguous Instructions via Multi-modal Multi-turn Chain-of-thoughts Reasoning\nAs large-scale models evolve, language instructions are increasingly utilized in multi-modal tasks. Due to human language habits, these instructions often contain ambiguities in real-world scenarios, necessitating the integration of visual context or common sense for accurate interpretation. However, even highly intelligent large models exhibit observable performance limitations on ambiguous i<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27930<END_URL>

VLAS: Vision-Language-Action Model with Speech Instructions for Customized Robot Manipulation\nVision-language-action models (VLAs) have recently become highly prevalent in robot manipulation due to its end-to-end architecture and impressive performance. However, current VLAs are limited to processing human instructions in textual form, neglecting the more natural speech modality for human interaction. A typical approach of incorporating speech modality into VLA necessitates a separate speech re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30076<END_URL>

VL-Cache: Sparsity and Modality-Aware KV Cache Compression for Vision-Language Model Inference Acceleration\nVision-Language Models (VLMs) have demonstrated impressive performance across a versatile set of tasks. A key challenge in accelerating VLMs is storing and accessing the large Key-Value (KV) cache that encodes long visual contexts, such as images or videos. While existing KV cache compression methods are effective for Large Language Models (LLMs), directly migrating them to VLMs yields su<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30231<END_URL>

VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning\nLarge language models (LLMs) famously exhibit emergent in-context learning (ICL) - the ability to rapidly adapt to new tasks using few-shot examples provided as a prompt, without updating the model's weights. Built on top of LLMs, vision large language models (VLLMs) have advanced significantly in areas such as recognition, reasoning, and grounding. However, investigations into multimodal ICL have predominantly focused on <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29026<END_URL>

VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks\nEmbedding models have been crucial in enabling various downstream tasks such as semantic similarity, information retrieval, and clustering. Recently, there has been a surge of interest in developing universal text embedding models that can generalize across tasks (e.g., MTEB). However, progress in learning universal multimodal embedding models has been relatively slow despite their importance. In this work, we aim t<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29555<END_URL>

VLMaterial: Procedural Material Generation with Large Vision-Language Models\nProcedural materials, represented as functional node graphs, are ubiquitous in computer graphics for photorealistic material appearance design. They allow users to perform intuitive and precise editing to achieve desired visual appearances. However, creating a procedural material given an input image requires professional knowledge and significant effort. In this work, we leverage the ability to convert procedural mate<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27856<END_URL>

Voila: Evaluation of MLLMs For Perceptual Understanding and Analogical Reasoning\nMultimodal Large Language Models (MLLMs) have become a powerful tool forintegrating visual and textual information. Despite their exceptional performanceon visual understanding benchmarks, measuring their ability to reason abstractlyacross multiple images remains a significant challenge. To address this, we introduce VOILA , a large-scale, open-ended, dynamic benchmark designed to evaluate MLLMs’ perceptual underst<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28264<END_URL>

VoxDialogue: Can Spoken Dialogue Systems Understand Information Beyond Words?\nWith the rapid advancement of large models, voice assistants are gradually acquiring the ability to engage in open-ended daily conversations with humans. However, current spoken dialogue systems often overlook multi-modal information in audio beyond text, such as speech rate, volume, emphasis, and background sounds. Relying solely on Automatic Speech Recognition (ASR) can lead to the loss of valuable auditory cues, th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27903<END_URL>

VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis\nDespite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content. They tend to synthesize quasi-static videos, ignoring the necessary visual change-over-time implied in the text prompt. Meanwhile, scaling these models to enable longer, more dynamic video synthesis often remains computationally intractab<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30044<END_URL>

VTDexManip: A Dataset and Benchmark for Visual-tactile Pretraining and Dexterous Manipulation with Reinforcement Learning\nVision and touch are the most commonly used senses in human manipulation. While leveraging human manipulation videos for robotic task pretraining has shown promise in prior works, it is limited to image and language modalities and deployment to simple parallel grippers. In this paper, aiming to address the limitations, we collect a vision-tactile dataset by humans manipulati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28627<END_URL>

VVC-Gym: A Fixed-Wing UAV Reinforcement Learning Environment for Multi-Goal Long-Horizon Problems\nMulti-goal long-horizon problems are prevalent in real-world applications. The additional goal space introduced by multi-goal problems intensifies the spatial complexity of exploration; meanwhile, the long interaction sequences in long-horizon problems exacerbate the temporal complexity of exploration. Addressing the great exploration challenge posed by multi-goal long-horizon problems depends not <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30922<END_URL>

Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations\nLarge language models (LLMs) are capable of generating *plausible* explanations of how they arrived at an answer to a question. However, these explanations can misrepresent the model's "reasoning" process, i.e., they can be *unfaithful*. This, in turn, can lead to over-trust and misuse. We introduce a new approach for measuring the faithfulness of LLM explanations. First, we provide a rigorous definition of faithfuln<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30987<END_URL>

Ward: Provable RAG Dataset Inference via LLM Watermarks\nRetrieval-Augmented Generation (RAG) improves LLMs by enabling them to incorporate external data during generation. This raises concerns for data owners regarding unauthorized use of their content in RAG systems. Despite its importance, the challenge of detecting such unauthorized usage remains underexplored, with existing datasets and methodologies from adjacent fields being ill-suited for its study. In this work, we take several steps to<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28576<END_URL>

WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning\nWhen optimizing transportation systems, anticipating traffic flows is a central element. Yet, computing such traffic equilibria remains computationally expensive. Against this background, we introduce a novel combinatorial optimization augmented neural network architecture that allows for fast and accurate traffic flow predictions. We propose WardropNet, a neural network that combines classical layers with a subsequent equi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30834<END_URL>

Warm Diffusion: Recipe for Blur-Noise Mixture Diffusion Models\nDiffusion probabilistic models have achieved remarkable success in generative tasks across diverse data types. While recent studies have explored alternative degradation processes beyond Gaussian noise, this paper bridges two key diffusion paradigms: hot diffusion, which relies entirely on noise, and cold diffusion, which uses only blurring without noise. We argue that hot diffusion fails to exploit the strong correlation between hi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28184<END_URL>

Wasserstein Distances, Neuronal Entanglement, and Sparsity\nDisentangling polysemantic neurons is at the core of many current approaches to interpretability of large language models. Here we attempt to study how disentanglement can be used to understand performance, in particular under weight sparsity, one of today's leading post-training optimization techniques. We suggest a novel measure for estimating neuronal entanglement: the Wasserstein distance of a neuron’s output distribution to a Gauss<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29028<END_URL>

Wasserstein-Regularized Conformal Prediction under General Distribution Shift\nConformal prediction yields a prediction set with guaranteed $1-\alpha$ coverage of the true target under the i.i.d. assumption, which can fail and lead to a gap between $1-\alpha$ and the actual coverage. Prior studies bound the gap using total variation distance, which cannot identify the gap changes under distribution shift at different $\alpha$, thus serving as a weak indicator of prediction set validity. Besides,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29180<END_URL>

Watch Less, Do More: Implicit Skill Discovery for Video-Conditioned Policy\nIn this paper, we study the problem of video-conditioned policy learning. While previous works mostly focus on learning policies that perform a single skill specified by the given video, we take a step further and aim to learn a policy that can perform multiple skills according to the given video, and generalize to unseen videos by recombining these skills. To solve this problem, we propose our algorithm, Watch-Less-Do-M<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28751<END_URL>

Watermark Anything With Localized Messages\nImage watermarking methods are not tailored to handle small watermarked areas.This restricts applications in real-world scenarios where parts of the image may come from different sources or have been edited.We introduce a deep-learning model for localized image watermarking, dubbed the Watermark Anything Model (WAM). The WAM embedder imperceptibly modifies the input image, while the extractor segments the received image into watermarked and non-waterma<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30152<END_URL>

Wavelet-based Positional Representation for Long Context\nIn the realm of large-scale language models, a significant challenge arises when extrapolating sequences beyond the maximum allowable length. This is because the model's position embedding mechanisms are limited to positions encountered during training, thus preventing effective representation of positions in longer sequences.We analyzed conventional position encoding methods for long contexts and found the following characteristics.(1) W<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29808<END_URL>

Wavelet Diffusion Neural Operator\nSimulating and controlling physical systems described by partial differential equations (PDEs) are crucial tasks across science and engineering. Recently, diffusion generative models have emerged as a competitive class of methods for these tasks due to their ability to capture long-term dependencies and model high-dimensional states. However, diffusion models typically struggle with handling system states with abrupt changes and generalizing to higher resolutio<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30342<END_URL>

WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling\nLanguage models have been effectively applied to modeling natural signals, such as images, video, speech, and audio. A crucial component of these models is the codec tokenizer, which compresses high-dimensional natural signals into lower-dimensional discrete tokens. In this paper, we introduce WavTokenizer, which offers several advantages over previous SOTA acoustic codec models in the audio domain: 1) extr<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27745<END_URL>

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors\nIn this work, we focus on the task of weakly supervised affordance grounding, where a model is trained to identify affordance regions on objects using human-object interaction images and egocentric object images without dense labels. Previous works are mostly built upon class activation maps, which are effective for semantic segmentation but may not be suitable for locating actions and functions. Leveraging recent advan<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31269<END_URL>

Weakly Supervised Video Scene Graph Generation via Natural Language Supervision\nExisting Video Scene Graph Generation (VidSGG) studies are trained in a fully supervised manner, which requires all frames in a video to be annotated, thereby incurring high annotation cost compared to Image Scene Graph Generation (ImgSGG). Although the annotation cost of VidSGG can be alleviated by adopting a weakly supervised approach commonly used for ImgSGG (WS-ImgSGG) that uses image captions, there are two key<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30280<END_URL>

Weak to Strong Generalization for Large Language Models with Multi-capabilities\nAs large language models (LLMs) grow in sophistication, some of their capabilities surpass human abilities, making it essential to ensure their alignment with human values and intentions, i.e., Superalignment. This superalignment challenge is particularly critical for complex tasks, as annotations provided by humans, as weak supervisors, may be overly simplistic, incomplete, or incorrect. Previous work has demonstra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29903<END_URL>

Weak-to-Strong Generalization Through the Data-Centric Lens\nThe weak-to-strong generalization phenomenon is the driver for important machine learning applications including highly data-efficient learning and, most recently, performing superalignment. While decades of research have resulted in numerous algorithms that produce strong empirical performance, understanding what aspects of data enable weak-to-strong generalization has been understudied. We propose a simple data-centric mechanism that<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27959<END_URL>

Weak-to-Strong Preference Optimization: Stealing Reward from Weak Aligned Model\nAligning language models (LMs) with human preferences has become a key area of research, enabling these models to meet diverse user needs better. Inspired by weak-to-strong generalization, where a strong LM fine-tuned on labels generated by a weaker model can consistently outperform its weak supervisor, we extend this idea to model alignment. In this work, we observe that the alignment behavior in weaker models can <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28894<END_URL>

WeatherGFM: Learning a Weather Generalist Foundation Model via In-context Learning\nThe Earth's weather system involves intricate weather data modalities and diverse weather understanding tasks, which hold significant value to human life. Existing data-driven models focus on single weather understanding tasks (e.g., weather forecasting). While these models have achieved promising results, they fail to tackle various complex tasks within a single and unified model. Moreover, the paradigm that rel<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28660<END_URL>

Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation\nLarge language models (LLMs) have recently gained much attention in building autonomous agents. However, performance of current LLM-based web agents in long-horizon tasks is far from optimal, often yielding errors such as repeatedly buying a non-refundable flight ticket. By contrast, humans can avoid such an irreversible mistake, as we have an awareness of the potential outcomes (e.g., losing money) of <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28448<END_URL>

WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning\nLarge language models (LLMs) have shown remarkable potential as autonomous agents, particularly in web-based tasks. However, existing LLM web agents face significant limitations: high-performing agents rely on expensive proprietary LLM APIs, while open LLMs lack the necessary decision-making capabilities. This paper introduces WebRL, a novel self-evolving online curriculum reinforcement learning framework <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28354<END_URL>

Weighted Multi-Prompt Learning with Description-free Large Language Model Distillation\nRecent advances in pre-trained Vision Language Models (VLMs) have shown promising potential through \textit{prompt learning} in effectively adapting to downstream tasks without requiring additional annotated paired datasets.To supplement text information in VLMs dependently trained on correlation with vision data, new approaches leveraging Large Language Models (LLM) in prompts have been proposed, enhancing r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29892<END_URL>

Weighted Point Cloud Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric\nIn typical multimodal contrastive learning, such as CLIP, encoders produce onepoint in the latent representation space for each input. However, one-point representation has difficulty in capturing the relationship and the similarity structure of a huge amount of instances in the real world. For richer classes of the similarity, we propose the use of weighted point clouds, namely, sets of pairs of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27976<END_URL>

Weighted-Reward Preference Optimization for Implicit Model Fusion\nWhile fusing heterogeneous open-source LLMs with varying architectures and sizes can potentially integrate the strengths of different models, existing fusion methods face significant challenges, such as vocabulary alignment and merging distribution matrices. These procedures are not only complex but also prone to introducing noise and errors. In this paper, we propose an implicit fusion method, Weighted-Reward Preference Optimiza<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28854<END_URL>

What Are Good Positional Encodings for Directed Graphs?\nPositional encodings (PEs) are essential for building powerful and expressive graph neural networks and graph transformers, as they effectively capture the relative spatial relationships between nodes. Although extensive research has been devoted to PEs in undirected graphs, PEs for directed graphs remain relatively unexplored. This work seeks to address this gap. We first introduce the notion of *Walk Profile*, a generalization of walk-co<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28146<END_URL>

What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis\nThe Transformer architecture has inarguably revolutionized deep learning, overtaking classical architectures like multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs). At its core, the attention block differs in form and functionality from most other architectural components in deep learning - to the extent that Transformers are often accompanied by adaptive optimizers, layer normalization, le<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31064<END_URL>

What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits\nA grand challenge in biology is to discover evolutionary traits---features of organisms common to a group of species with a shared ancestor in the tree of life (also referred to as phylogenetic tree). With the growing availability of image repositories in biology, there is a tremendous opportunity to discover evolutionary traits directly from images in the form of a hierarchy of prototy<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30990<END_URL>

What is Wrong with Perplexity for Long-context Language Modeling?\nHandling long-context inputs is crucial for large language models (LLMs) in tasks such as extended conversations, document summarization, and many-shot in-context learning. While recent approaches have extended the context windows of LLMs and employed perplexity (PPL) as a standard evaluation metric, PPL has proven unreliable for assessing long-context capabilities. The underlying cause of this limitation has remained unclear. In<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28884<END_URL>

What Kind of Pretraining Data Do Large Language Models Rely on When Doing Reasoning?\nThe capabilities and limitations of Large Language Models (LLMs) have been sketched out in great detail in recent years, providing an intriguing yet conflicting picture. On the one hand, LLMs demonstrate a general ability to solve problems. On the other hand, they show surprising reasoning gaps when compared to humans, casting doubt on the robustness of their generalisation strategies. The sheer volume of data <BEGIN_URL>https://iclr.cc//virtual/2025/poster/31190<END_URL>

What Makes a Good Diffusion Planner for Decision Making?\nDiffusion models have recently shown significant potential in solving decision-making problems, particularly in generating behavior plans -- also known as diffusion planning. While numerous studies have demonstrated the impressive performance of diffusion planning, the mechanisms behind the key components of a good diffusion planner remain unclear and the design choices are highly inconsistent in existing studies. In this work, we address<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30839<END_URL>

What Makes a Maze Look Like a Maze?\nA unique aspect of human visual understanding is the ability to flexibly interpret abstract concepts: acquiring lifted rules explaining what they symbolize, grounding them across familiar and unfamiliar contexts, and making predictions or reasoning about them. While off-the-shelf vision-language models excel at making literal interpretations of images (e.g., recognizing object categories such as tree branches), they still struggle to make sense of such visual<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30138<END_URL>

What Makes Large Language Models Reason in (Multi-Turn) Code Generation?\nPrompting techniques such as chain-of-thought have established themselves as a popular vehicle for improving the outputs of large language models (LLMs). For code generation, however, their exact mechanics and efficacy are under-explored using unified metrics and benchmarks. We thus investigate the effects of a wide range of prompting strategies with a focus on automatic re-prompting over multiple turns and computational r<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29201<END_URL>

What Matters in Learning from Large-Scale Datasets for Robot Manipulation\nImitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent on building such large-scale datasets around the globe. Despite the continuous growth of such efforts, we still lack a systematic understanding of what data should be collected to improve the utility of a robotics dataset and facilitate downstrea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29974<END_URL>

What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?\nExtensive pre-training with large data is indispensable for downstream geometry and semantic visual perception tasks. Thanks to large-scale text-to-image (T2I) pretraining, recent works show promising results by simply fine-tuning T2I diffusion models for a few dense perception tasks. However, several crucial design decisions in this process still lack comprehensive justification, encompassing the necessity of th<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30558<END_URL>

What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models\nDeep Generative Models are frequently used to learn continuous representations of complex data distributions using a finite number of samples. For any generative model, including pre-trained foundation models with GAN, Transformer or Diffusion architectures, generation performance can vary significantly based on which part of the learned data manifold is sampled. In this paper we study the post-training l<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28903<END_URL>

What should a neuron aim for? Designing local objective functions based on information theory\nIn modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological systems build on self-organized, local learning, achieving robustness and efficiency with limited global information. We here show how self-organization between individual artificial neurons can be achieved by designing abstract bi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30518<END_URL>

What's New in My Data? Novelty Exploration via Contrastive Generation\nFine-tuning is widely used to adapt language models for specific goals, often leveraging real-world data such as patient records, customer-service interactions, or web content in languages not covered in pre-training.These datasets are typically massive, noisy, and often confidential, making their direct inspection challenging.However, understanding them is essential for guiding model deployment and informing decisions about <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30165<END_URL>

What's the Move? Hybrid Imitation Learning via Salient Points\nWhile imitation learning (IL) offers a promising framework for teaching robots various behaviors, learning complex tasks remains challenging. Existing IL policies struggle to generalize effectively across visual and spatial variations even for simple tasks. In this work, we introduce **SPHINX**: **S**alient **P**oint-based **H**ybrid **I**mitatio**N** and e**X**ecution, a flexible IL policy that leverages multimodal observations (poi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28225<END_URL>

What to align in multimodal contrastive learning?\nHumans perceive the world through multisensory integration, blending the information of different modalities to adapt their behavior.Contrastive learning offers an appealing solution for multimodal self-supervised learning. Indeed, by considering each modality as a different view of the same entity, it learns to align features of different modalities in a shared representation space. However, this approach is intrinsically limited as it only lea<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29742<END_URL>

When Attention Sink Emerges in Language Models: An Empirical View\nLanguage Models (LMs) assign significant attention to the first token, even if it is not semantically important, which is known as **attention sink**. This phenomenon has been widely adopted in applications such as streaming/long context generation, KV cache optimization, inference acceleration, model quantization, and others.  Despite its widespread use, a deep understanding of attention sink in LMs is still lacking. In this wor<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30847<END_URL>

When does compositional structure yield compositional generalization? A kernel theory.\nCompositional generalization (the ability to respond correctly to novel combinations of familiar components) is thought to be a cornerstone of intelligent behavior. Compositionally structured (e.g. disentangled) representations are essential for this; however, the conditions under which they yield compositional generalization remain unclear. To address this gap, we present a general theory of compositional ge<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30345<END_URL>

When do GFlowNets learn the right distribution?\nGenerative Flow Networks (GFlowNets) are an emerging class of sampling methods for distributions over discrete and compositional objects, e.g., graphs. In spite of their remarkable success in problems such as drug discovery and phylogenetic inference, the question of when and whether GFlowNets learn to sample from the target distribution remains underexplored. To tackle this issue, we first assess the extent to which a violation of the detailed ba<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30708<END_URL>

When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach\nA common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to be permuted without altering the underlying problem structure. Recently, GNNs have emerged as a promising approach for solving ILPs. However, a significant challenge arises when applying GNNs to ILPs with symmetry: classic GNN architectures struggle to differentiate between symmetric variables, which limits their predictive ac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27839<END_URL>

When Graph Neural Networks Meet Dynamic Mode Decomposition\nGraph Neural Networks (GNNs) have emerged as fundamental tools for a wide range of prediction tasks on graph-structured data. Recent studies have drawn analogies between GNN feature propagation and diffusion processes, which can be interpreted as dynamical systems. In this paper, we delve deeper into this perspective by connecting the dynamics in GNNs to modern Koopman theory and its numerical method, Dynamic Mode Decomposition (DMD). W<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28958<END_URL>

When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers\nweighted sum of task vectors, each of which is the weight update from the pre-trained model to fine-tuned models for certain tasks. This approach recently gained attention as a computationally efficient inference method for model editing, e.g., multi-task learning, forgetting, and out-of-domain generalization capabilities. However, the theoretical understanding of why task vectors can e<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27911<END_URL>

When LLMs Play the Telephone Game: Cumulative Changes and Attractors in Iterated Cultural Transmissions\nAs large language models (LLMs) start interacting with each other and generating an increasing amount of text online, it becomes crucial to better understand how information is transformed as it passes from one LLM to the next. While significant research has examined individual LLM behaviors, existing studies have largely overlooked the collective behaviors and information distortions arising<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28880<END_URL>

When Prompt Engineering Meets Software Engineering: CNL-P as Natural and Robust "APIs'' for Human-AI Interaction\nAs large language models (LLMs) gain increasing capabilities, they are being widely applied in areas such as intelligent customer service, code generation, and knowledge management. Prompts written in natural language (NL) serve as the "APIs'' for human-LLM interaction. To enhance prompt quality, best practices for prompt engineering (PE) have been established, along with writing gui<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29577<END_URL>

When Selection meets Intervention: Additional Complexities in Causal Discovery\nWe address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug trial are usually patients of the relevant disease; A/B tests on mobile applications target existing users only, and gene perturbation studies typically focus on specific cell types, such as cancer cells. Ignoring this bias leads to incorre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27799<END_URL>

Where Am I and What Will I See: An Auto-Regressive Model for Spatial Localization and View Prediction\nSpatial intelligence is the ability of a machine to perceive, reason, and act in three dimensions within space and time.Recent advancements in large-scale auto-regressive models have demonstrated remarkable capabilities across various reasoning tasks. However, these models often struggle with fundamental aspects of spatial reasoning, particularly in answering questions like "Where am I?" and "W<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29853<END_URL>

Which Tasks Should Be Compressed Together? A Causal Discovery Approach for Efficient Multi-Task Representation Compression\nConventional image compression methods are inadequate for intelligent analysis, as they overemphasize pixel-level precision while neglecting semantic significance and the interaction among multiple tasks.  This paper introduces a Taskonomy-Aware Multi-Task Compression framework comprising (1) inter-coherent task grouping, which organizes synergistic tasks into shared repres<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27804<END_URL>

White-Box Text Detectors Using Proprietary LLMs: A Probability Distribution Estimation Approach\nLarge language models (LLMs) can generate text almost indistinguishable from human-written one, highlighting the importance of machine-generated text detection. However, current zero-shot techniques face challenges as white-box methods are restricted to use weaker open-source LLMs, and black-box methods are limited by partial observations from stronger proprietary LLMs. It seems impossible to enable <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29149<END_URL>

Why Does the Effective Context Length of LLMs Fall Short?\nAdvancements in distributed training and efficient attention mechanisms have significantly expanded the context window sizes of large language models (LLMs). However, recent work reveals that the effective context lengths of open-source LLMs often fall short, typically not exceeding half of their training lengths. In this work, we attribute this limitation to the left-skewed frequency distribution of relative positions formed in LLMs pre<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28906<END_URL>

Why RoPE Struggles to Maintain Long-Term Decay in Long Sequences?\nRotary Position Embedding (RoPE) improves upon traditional positional encodings but struggles with long-term decay in contexts exceeding its training length, limiting the model's generalization to longer sequences. Our experiments suggest that this issue may stem from a high proportion of obtuse angles on the complex plane between the linear transformations of query and key embeddings.<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31353<END_URL>

Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks\nDeep neural networks are vulnerable to backdoor attacks, a  type of adversarial attack that poisons the training data to manipulate the behavior of models trained on such data. Clean-label attacks are a more stealthy form of backdoor attacks that can perform the attack without changing the labels of poisoned data.Early works on clean-label attacks added triggers to a random subset of the training set, ignoring the<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31195<END_URL>

Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse\nDeep neural networks (DNNs) at convergence consistently represent the training data in the last layer via a geometric structure referred to as neural collapse. This empirical evidence has spurred a line of theoretical research aimed at proving the emergence of neural collapse, mostly focusing on the unconstrained features model. Here, the features of the penultimate layer are free variables, which makes the model da<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31212<END_URL>

WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild\nWe introduce WildBench, an automated evaluation framework designed to benchmark large language models (LLMs) using challenging, real-world user queries. WildBench consists of 1,024 tasks carefully selected from over one million human-chatbot conversation logs. For automated evaluation with WildBench, we have developed two metrics, WB-Reward and WB-Score, which are computable using advanced LLMs such as GPT-4-turbo. <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29940<END_URL>

Will Synthetic Data Finally Solve the Data Access Problem?\nAccessing large scale and high quality data has been shown to be one of the most important factors to the performance of machine learning models. Recent works show that large (language) models can greatly benefit from training with massive data from diverse (domain specific) sources and aligning with user intention. However, the use of certain data sources can trigger privacy, fairness, copyright, and safety concerns. The impressive per<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/24001<END_URL>

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct\nLarge language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning. However, most existing open-source models are only pre-trained on large-scale internet data and without math-related optimization. In this paper, we present WizardMath, which enhances the mathematical reasoning abilities of LLMs,<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28471<END_URL>

Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers\nTransformer-based models generate hidden states that are difficult to interpret. In this work, we aim to interpret these hidden states and control them at inference, with a focus on motion forecasting. We leverage the phenomenon of neural collapse and use linear probes to measure interpretable features in hidden states. Our experiments reveal meaningful directions and distances between hidden states of opposing fe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30124<END_URL>

WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models\nRecent advancements in large language models (LLMs) have driven a revolutionary paradigm shift in process automation from Robotic Process Automation to Agentic Process Automation by automating the workflow orchestration procedure based on LLMs. However, existing LLMs (even the advanced OpenAI GPT-4o) are confined to achieving satisfactory capability in workflow orchestration. To address this limitation, we present<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31085<END_URL>

Workshop on AI for Children: Healthcare, Psychology, Education\nCurrent AI research and applications often prioritize adult-focused solutions, while progress in AI designed specifically for children's development, health, and education has lagged behind. Our workshop aims to spotlight this issue and bring together researchers from diverse fields to discuss the future of AI design and its applications for children.In the era of AI, developing bespoke AI systems for children holds special signific<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23985<END_URL>

Workshop on Embodied Intelligence with Large Language Models In Open City Environment\nThis workshop is motivated by a fact: human beings have strong embodied intelligence in an open environment, but it is still challenging for large language models and LLM agents. Despite some progress on embodied AI in static and indoor environments, the LLM agents still struggle with tasks in large-scale outdoor environments, such as navigation, search, spatial reasoning, task planning, etc. Therefore, we pro<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23964<END_URL>

Workshop on Reasoning and Planning for Large Language Models\nThis workshop explores the growing capabilities of large language models (LLMs), such as OpenAI's o1 model, in reasoning, planning, and decision-making, highlighting recent advances and challenges. We aim to examine how reinforcement learning methods, post-training optimization, and efficient inference techniques can further enhance LLMs' reasoning capabilities. Topics include training approach for enhancing reasoning and planning abi<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23968<END_URL>

Workshop on Sparsity in LLMs (SLLM): Deep Dive into Mixture of Experts, Quantization, Hardware, and Inference\nLarge Language Models (LLMs) have emerged as transformative tools in both research and industry, excelling across a wide array of tasks. However, their growing computational demands especially during inference—raise significant concerns about accessibility, environmental sustainability, and deployment feasibility. At the same time, sparsity-based techniques are proving critical not just<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23996<END_URL>

Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions\nDespite the remarkable advancements towards generalizability and autonomy in AI systems, persistent challenges such as spurious correlations and shortcut learning continue to hinder the robustness, reliability, and ethical deployment of machine learning systems. These challenges arise from the statistical nature of machine learning algorithms and their implicit or inductive biases at all stages, including data pre<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23995<END_URL>

World Model on Million-Length Video And Language With Blockwise RingAttention\nEnabling long-context understanding remains a key challenge in scaling existing sequence models -- a crucial component in developing generally intelligent models that can process and operate over long temporal horizons that potentially consist of millions of tokens. In this paper, we seek to make strides towards addressing these challenges by providing a comprehensive exploration into the full development process to p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30229<END_URL>

World Models: Understanding, Modelling and Scaling\nOur workshop covers the widest range of topics related to World Models, including understanding, modelling, and closely aligning with cutting-edge generative AI and broader applications such as robotics and embodied AI. We are glad to announce that nine confirmed top-tier researchers including the founder of world models have confirmed to attend in person as speakers and panelists. The workshop widely targets AI researchers, industry profession<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/24000<END_URL>

W-PCA Based Gradient-Free Proxy for Efficient Search of Lightweight Language Models\nThe demand for efficient natural language processing (NLP) systems has led to the development of lightweight language models. Previous work in this area has primarily focused on manual design or training-based neural architecture search (NAS) methods. Recently, zero-shot NAS methods have been proposed for evaluating language models without the need for training. However, prevailing approaches to zero-shot NAS of<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29249<END_URL>

XAI4Science: From Understanding Model Behavior to Discovering New Scientific Knowledge\nMachine learning (ML) models are impressive when they work but they can also show unreliable, untrustworthy, and harmful dangerous behavior. Such behavior is even more common in the era of large models, such as chatGPT, which are quickly being adopted even though we do not understand why they work so well and fail miserably at times. Unfortunately, such rapid dissemination encourages irresponsible use, for ex<BEGIN_URL>https://iclr.cc//virtual/2025/workshop/23980<END_URL>

XAIguiFormer: explainable artificial intelligence guided transformer for brain disorder identification\nEEG-based connectomes offer a low-cost and portable method to identify brain disorders using deep learning. With the growing interest in model interpretability and transparency, explainable artificial intelligence (XAI) is widely applied to understand the decision of deep learning models. However, most research focuses solely on interpretability analysis based on the insights from XAI, overloo<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30644<END_URL>

X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale\nLarge language models (LLMs) have achieved remarkable success across various NLP tasks, yet their focus has predominantly been on English due to English-centric pre-training and limited multilingual data. While some multilingual LLMs claim to support for hundreds of languages, models often fail to provide high-quality response for mid- and low-resource languages, leading to imbalanced performance heavily skewed <BEGIN_URL>https://iclr.cc//virtual/2025/poster/29024<END_URL>

X-Drive: Cross-modality Consistent Multi-Sensor Data Synthesis for Driving Scenarios\nRecent advancements have exploited diffusion models for the synthesis of either LiDAR point clouds or camera image data in driving scenarios. Despite their success in modeling single-modality data marginal distribution, there is an under- exploration in the mutual reliance between different modalities to describe com- plex driving scenes. To fill in this gap, we propose a novel framework, X-DRIVE, to model the <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30182<END_URL>

X-Fi: A Modality-Invariant Foundation Model for Multimodal Human Sensing\nHuman sensing, which employs various sensors and advanced deep learning technologies to accurately capture and interpret human body information, has significantly impacted fields like public security and robotics. However, current human sensing primarily depends on modalities such as cameras and LiDAR, each of which has its own strengths and limitations. Furthermore, existing multi-modal fusion solutions are typically desi<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29125<END_URL>

xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation\nThe continuous advancement of large language models (LLMs) has brought increasing attention to the critical issue of developing fair and reliable methods for evaluating their performance. Particularly, the emergence of cheating phenomena, such as test set leakage and prompt format overfitting, poses significant challenges to the reliable evaluation of LLMs. As evaluation frameworks commonly use Regular Expression (Re<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30821<END_URL>

X-Gen: Ego-centric Video Prediction by Watching Exo-centric Videos\nGenerating videos in the first-person perspective has broad application prospects in the field of augmented reality and embodied intelligence.In this work, we explore the cross-view video prediction task, where given an exo-centric video, the first frame of the corresponding ego-centric video, and textual instructions, the goal is to generate future frames of the ego-centric video. Inspired by the notion that hand-object interac<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30773<END_URL>

XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning\nFollowing the success of the in-context learning paradigm in large-scale language and computer vision models, the recently emerging field of in-context reinforcement learning is experiencing a rapid growth. However, its development has been held back by the lack of challenging benchmarks, as all the experiments have been carried out in simple environments and on small-scale datasets. We present **XLand-100B**, a <BEGIN_URL>https://iclr.cc//virtual/2025/poster/28316<END_URL>

X-NeMo: Expressive Neural Motion Reenactment via Disentangled Latent Attention\nWe propose X-NeMo, a novel zero-shot diffusion-based portrait animation pipeline that animates a static portrait using facial movements from a driving video of a different individual. Our work first identifies the root causes of the limitations in prior approaches, such as identity leakage and difficulty in capturing subtle and extreme expressions. To address these challenges, we introduce a fully end-to-end training<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29937<END_URL>

YOLO-RD: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary\nIdentifying and localizing objects within images is a fundamental challenge, and numerous efforts have been made to enhance model accuracy by experimenting with diverse architectures and refining training strategies. Nevertheless, a prevalent limitation in existing models is overemphasizing the current input while ignoring the information from the entire dataset. We introduce an innovative $\textbf{R}et<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30052<END_URL>

Youku Dense Caption: A Large-scale Chinese Video Dense Caption Dataset and Benchmarks\nWith the explosive growth of video content, video captions have emerged as a crucial tool for video comprehension, significantly enhancing the ability to understand and retrieve information from videos. However, most publicly available dense video captioning datasets are in English, resulting in a scarcity of large-scale and high-quality Chinese dense video captioning datasets. To address this gap within the C<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27881<END_URL>

You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning\nThe ever-increasing size of large language models (LLMs) presents significant challenges for deployment due to their heavy computational and memory requirements. Current model pruning techniques attempt to alleviate these issues by relying heavily on external calibration datasets to determine which parameters to prune or compress, thus limiting their flexibility and scalability across different compression ra<BEGIN_URL>https://iclr.cc//virtual/2025/poster/30946<END_URL>

You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs\nRecently, some works have tried to combine diffusion and Generative Adversarial Networks (GANs) to alleviate the computational cost of the iterative denoising inference in Diffusion Models (DMs). However, existing works in this line suffer from either training instability and mode collapse or subpar one-step generation learning efficiency. To address these issues, we introduce YOSO, a novel generati<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29561<END_URL>

Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data\nDiscrete diffusion models with absorbing processes have shown promise in language modeling. The key quantities to be estimated are the ratios between the marginal probabilities of two transitive states at all timesteps, called the concrete score. In this paper, we reveal that the concrete score in absorbing diffusion can be expressed as conditional probabilities of clean data, multiplied by a time-depe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28131<END_URL>

Your Mixture-of-Experts LLM Is Secretly an Embedding Model for Free\nWhile large language models (LLMs) excel on generation tasks, their decoder-only architecture often limits their potential as embedding models if no further representation finetuning is applied. Does this contradict their claim of generalists? To answer the question, we take a closer look at Mixture-of-Experts (MoE) LLMs. Our study shows that the expert routers in MoE LLMs can serve as an off-the-shelf embedding model with prom<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28939<END_URL>

Your Weak LLM is Secretly a Strong Teacher for Alignment\nThe burgeoning capabilities of large language models (LLMs) have underscored the need for alignment to ensure these models act in accordance with human values and intentions. Existing alignment frameworks present constraints either in the form of expensive human effort or high computational costs. This paper explores a promising middle ground, where we employ a weak LLM that is significantly less resource-intensive than top-tier models, y<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28137<END_URL>

YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus\nEven for better-studied sign languages like American Sign Language (ASL), data is the bottleneck for machine learning research. The situation is worse yet for the many other sign languages used by Deaf/Hard of Hearing communities around the world. In this paper, we present YouTube-SL-25, a large-scale, open-domain multilingual corpus of sign language videos with seemingly well-aligned captions drawn from YouTub<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28414<END_URL>

ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish\nData-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we introduce the Zebrafish Activity Prediction Benchmark (ZAPBench) to measure progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain. The benchmark is based on a novel dataset containing 4d light-sheet microscopy recordings of over<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28372<END_URL>

Zero-cost Proxy for Adversarial Robustness Evaluation\nDeep neural networks (DNNs) easily cause security issues due to the lack of adversarial robustness. An emerging research topic for this problem is to design adversarially robust architectures via neural architecture search (NAS), i.e., robust NAS. However, robust NAS needs to train numerous DNNs for robustness estimation, making the search process prohibitively expensive. In this paper, we propose a zero-cost proxy to evaluate the adversaria<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27675<END_URL>

ZeroDiff: Solidified Visual-semantic Correlation in Zero-Shot Learning\nZero-shot Learning (ZSL) aims to enable classifiers to identify unseen classes. This is typically achieved by generating visual features for unseen classes based on learned visual-semantic correlations from seen classes. However, most current generative approaches heavily rely on having a sufficient number of samples from seen classes. Our study reveals that a scarcity of seen class samples results in a marked decrease in pe<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27809<END_URL>

Zero-shot Imputation with Foundation Inference Models for Dynamical Systems\nDynamical systems governed by ordinary differential equations (ODEs) serve as models for a vast number of natural and social phenomena. In this work, we offer a fresh perspective on the classical problem of imputing missing time series data, whose underlying dynamics are assumed to be determined by ODEs. Specifically, we revisit ideas from amortized inference and neural operators, and propose a novel supervised learning<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29879<END_URL>

Zero-shot Model-based Reinforcement Learning using Large Language Models\nThe emerging zero-shot capabilities of Large Language Models (LLMs) have led to their applications in areas extending well beyond natural language processing tasks. In reinforcement learning, while LLMs have been extensively used in text-based environments, their integration with continuous state spaces remains understudied. In this paper, we investigate how pre-trained LLMs can be leveraged to predict in-context the dynam<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27971<END_URL>

Zero-shot Novel View Synthesis via Adaptive Modulating Video Diffusion Process\nBy harnessing the potent generative capabilities of pre-trained large video diffusion models, we propose a new novel view synthesis paradigm that operates \textit{without} the need for training. The proposed method adaptively modulates the diffusion sampling process with the given views to enable the creation of visually pleasing results from single or multiple views of static scenes or monocular videos of dynamic sc<BEGIN_URL>https://iclr.cc//virtual/2025/poster/27681<END_URL>

Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models\nUnsupervised reinforcement learning (RL) aims at pre-training models that can solve a wide range of downstream tasks in complex environments. Despite recent advancements, existing approaches suffer from several limitations: they may require running an RL process on each task to achieve a satisfactory performance, they may need access to datasets with good coverage or well-curated task-specific samples, or they may pre-train <BEGIN_URL>https://iclr.cc//virtual/2025/poster/30661<END_URL>

Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity\nZeroth-order optimization (ZO) is a memory-efficient strategy for fine-tuning Large Language Models using only forward passes. However, applying ZO fine-tuning in memory-constrained settings such as mobile phones and laptops remains challenging since these settings often involve weight quantization, while ZO requires full-precision perturbation and update. In this study, we address this limitation by combining static sparse ZO f<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28438<END_URL>

Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference\nReward inference (learning a reward model from human preferences) is a critical intermediate step in Reinforcement Learning from Human Feedback (RLHF) for fine-tuning Large Language Models (LLMs) such as ChatGPT. In practice, reward inference faces several fundamental challenges, including double problem misspecification, reward model evaluation without ground truth, distribution shift, and over<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29030<END_URL>

ZETA: Leveraging $Z$-order Curves for Efficient Top-$k$ Attention\nOver recent years, the Transformer has become a fundamental building block for sequence modeling architectures. Yet at its core is the use of self-attention, whose memory and computational cost grow quadratically with the sequence length $N$, rendering it prohibitively expensive for long sequences. A promising approach is top-$k$ attention, which selects only the $k$ most relevant tokens and achieves performance comparable to van<BEGIN_URL>https://iclr.cc//virtual/2025/poster/28649<END_URL>

Zigzag Diffusion Sampling: The Path to Success ls Zigzag\nDiffusion models, the most popular generative paradigm so far, can inject conditional information into the generation path to guide the latent towards desired directions. However, existing text-to-image diffusion models often fail to maintain high image quality and high prompt-image alignment for those challenging prompts. To mitigate this issue and enhance existing pretrained diffusion models, we mainly made three contributions in this p<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29938<END_URL>

ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box Vision-Language Models\nRecent research has introduced various approaches for prompt-tuning black-box vision-language models, referred to as black-box prompt-tuning (BBPT). While BBPT has demonstrated considerable potential, it is often found that many existing methods require an excessive number of queries (i.e., function evaluations), which poses a significant challenge in real-world scenarios where the number of allowed queries is lim<BEGIN_URL>https://iclr.cc//virtual/2025/poster/31142<END_URL>

ZooProbe: A Data Engine for Evaluating, Exploring, and Evolving Large-scale Training Data for Multimodal LLMs\nMultimodal Large Language Models (MLLMs) are thriving through continuous fine-tuning by LLMs. Driven by the law that "scale is everything", MLLMs expand their training sets during version iterations. In this paper, we propose a large-scale training data engine built around an evaluating-exploring-evolving (E3) loop. Evaluating the data provides insights into its characteristics. Explori<BEGIN_URL>https://iclr.cc//virtual/2025/poster/29564<END_URL>