app.py

import gradio as gr


with gr.Blocks(css="""
#my-img img {
    width: 70% !important;
    display: block;
    margin-left: auto;
    margin-right: auto;
}
#small img {
    width: 40% !important;
    display: block;
    margin-left: auto;
    margin-right: auto;
}
""") as demo:
    gr.HTML("""
<div align="center">

<h1>Efficient Reasoning
<div>
<h3>Part 1:Elastic Reasoning (Scalable Chain of Thoughts via Elastic Reasoning) 🌟
</div>
<br>
</div>
    """)
    gr.HTML("""
<div style="display: flex; justify-content: center; gap: 8px; flex-wrap: wrap;">
  <a href="https://arxiv.org/pdf/2505.05315">
    <img src="https://img.shields.io/badge/paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white" />
  </a>
  <a href="https://huggingface.co/collections/Salesforce/elastic-reasoning-682b4bba108d6ea0a8bab275">
    <img src="https://img.shields.io/badge/E1-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor" />
  </a>
  <a href="https://github.com/SalesforceAIResearch/Elastic-Reasoning">
    <img src="https://img.shields.io/badge/Elastic_Reasoning-000000?style=for-the-badge&logo=github&logoColor=white" />
  </a>
</div>
    """)
    gr.Markdown(
    """

## Introduction
We propose **Elastic Reasoning**, a novel framework for scalable chain of thoughts
that explicitly separates reasoning into two phases—`thinking and solution`—with
independently allocated budgets. At test time, Elastic Reasoning prioritize that
completeness of solution segments, significantly improving reliability under tight
resource constraints. To train models that are robust to truncated thinking, we
introduce a lightweight `budget-constrained rollout` strategy, integrated into GRPO,
which teaches the model to reason adaptively when the thinking process is cut
short and generalizes effectively to unseen budget constraints without additional
training.
    """)
    gr.Image("figs/framework.png", label="Framework", show_label=False, elem_id="my-img")

    gr.Markdown(
    """
**Main Takeaways**
1. ✂️ Thinking + Solution are explicitly separated with independent budgets — boosting reliability under tight compute constraints.
2. 🧠 Budget-Constrained Rollout: We train models to handle truncated reasoning using GRPO.
3. 📈 Flexible scalability: Robust performance across diverse inference budgets on reasoning benchmarks like AIME and LiveCodeBench.
4. ⚙️ Better performance with fewer tokens: Our trained model generates outputs that are 30% shorter while maintaining (or even improving) accuracy.
    """)
    with gr.Row():
        gr.Image("figs/aime.png", label="Framework", show_label=False, elem_id="small")
        gr.Image("figs/livecode.png", label="Framework", show_label=False, elem_id="small")
    gr.Image("figs/codetable.png", label="Framework", show_label=False, elem_id="my-img")
    gr.HTML("""
<div align="center">
<div>
<h3>Part 1:Fractured Chain-of-Thought (Scalable Chain of Thoughts via Elastic Reasoning) 🌟
</div>
<br>
</div>
    """)
    gr.HTML("""
<div style="display: flex; justify-content: center; gap: 8px; flex-wrap: wrap;">
  <a href="https://arxiv.org/pdf/2505.12992">
    <img src="https://img.shields.io/badge/paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white" />
  </a>
  <a href="https://github.com/BaohaoLiao/frac-cot">
    <img src="https://img.shields.io/badge/frac-cot-000000?style=for-the-badge&logo=github&logoColor=white" />
  </a>
</div>
    """)

    gr.Markdown(
    """

## Introduction
Building upon the same core insight as **Elastic Reasoning**—that correct answers can often be derived without waiting for a full chain-of-thought (CoT)—**Fractured Sampling** shifts focus to the **sampling strategy** of reasoning.

Instead of relying on complete, uninterrupted reasoning sequences, Fractured Sampling **breaks the CoT along the temporal dimension**, exploring whether it's possible to "get the right answer without thinking all the way through."

This method introduces sampling control along three key dimensions:

- **Solution Diversity (m) — sampling multiple final outputs from a single reasoning trace.
- **Trajectory Diversity (n) — sampling multiple independent reasoning traces with different seeds (vanilla CoT sampling).
- **Reasoning Depth Diversity (H) — sampling at different intermediate stages of a single reasoning trace.

Among these, the novel **reasoning depth `H`** plays a critical role: by sampling outputs at different depths of partially completed reasoning chains, the model creates multiple sets of "fragmented thoughts + solutions," which are then jointly evaluated to select the most trustworthy outcome.
    """)
    gr.Image("figs/frac-frame.png", label="Framework", show_label=False, elem_id="my-img")
    gr.Image("figs/single.png", label="Framework", show_label=False, elem_id="my-img")
    gr.Image("figs/combine.png", label="Framework", show_label=False, elem_id="my-img")
    gr.Markdown(
    """
## Citation


```bibtex
@article{xu2025scalable,
  title={Scalable Chain of Thoughts via Elastic Reasoning},
  author={Xu, Yuhui and Dong, Hanze and Wang, Lei and Sahoo, Doyen and Li, Junnan and Xiong, Caiming},
  journal={arXiv preprint arXiv:2505.05315},
  year={2025}
}

@misc{liao2025fracturedchainofthoughtreasoning,
      title={Fractured Chain-of-Thought Reasoning}, 
      author={Baohao Liao and Hanze Dong and Yuhui Xu and Doyen Sahoo and Christof Monz and Junnan Li and Caiming Xiong},
      year={2025},
      eprint={2505.12992},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2505.12992}, 
}
```
    """)


if __name__ == "__main__":
    demo.launch()