Datasets:
Tasks:
Text Generation
Formats:
csv
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
multimodal
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explanation | Why is the performance of your method better on paraphrased datasets than on the Normal Dataset? | Regarding the occasionally better performance of Profiler (and also other baselines) on paraphrased datasets in Table 1 and Table 2, it is important to note that these are in-distribution results, where the training and test data distributions are the same. When detectors are tested in an out-of-distribution setting—where the detector is trained on the original dataset and tested on the paraphrased dataset—all detectors exhibit a performance degradation, as shown in Figure 4. The improved performance on paraphrased datasets under the in-distribution setting suggests that paraphrased data is more separable in this context. We attribute this to two main reasons: (1) paraphrasing may inadvertently expose more model-specific characteristics, and (2) different LLMs may interpret and encode patterns of human-written texts differently, thereby reducing detection complexity. However, the performance drop observed in the out-of-distribution setting indicates that paraphrasing remains an effective evasion technique in real-world deployments. | ['Table 1', 'Table 2', 'Figure 4'] | ['images/28a3fb2eb860b2336250a168e4be3a619b992036b649ac8490cf8856010977aa.jpg', 'images/66b65d6ef25661f61096bcb5ba493ecd43565a459b1d58ac7031289ca040692c.jpg', 'images/ee2afb98560f1235c1389cdfa71d968a022447de87c1ce4638ab8d2f07577b6b.jpg'] | ['mixed'] | 3 | 2 | 5 | {'where V is the vocabulary of the surrogate model M, and P˜k ∈R||V ||×1 is the one-hot encoded vector of input token xk over the vocabulary list V . The calculated context losses L = [L1, · · · , LW ] are then used in the next stage to extract the inference pattern. ': '1'} | {'1': 'where V is the vocabulary of the surrogate model M, and P˜k ∈R||V ||×1 is the one-hot encoded vector of input token xk over the vocabulary list V . The calculated context losses L = [L1, · · · , LW ] are then used in the next stage to extract the inference pattern. '} | {'images/6aab4e92c975eda2500335b4f5cd9ae1c46b56dc872b79f30e841caf88d27a56.jpg': '1', 'images/ee2afb98560f1235c1389cdfa71d968a022447de87c1ce4638ab8d2f07577b6b.jpg': '4'} | {'1': 'images/6aab4e92c975eda2500335b4f5cd9ae1c46b56dc872b79f30e841caf88d27a56.jpg', '4': 'images/ee2afb98560f1235c1389cdfa71d968a022447de87c1ce4638ab8d2f07577b6b.jpg'} | {'images/28a3fb2eb860b2336250a168e4be3a619b992036b649ac8490cf8856010977aa.jpg': '1', 'images/66b65d6ef25661f61096bcb5ba493ecd43565a459b1d58ac7031289ca040692c.jpg': '2'} | {'1': 'images/28a3fb2eb860b2336250a168e4be3a619b992036b649ac8490cf8856010977aa.jpg', '2': 'images/66b65d6ef25661f61096bcb5ba493ecd43565a459b1d58ac7031289ca040692c.jpg'} | {} | ['images/6aab4e92c975eda2500335b4f5cd9ae1c46b56dc872b79f30e841caf88d27a56.jpg', 'where V is the vocabulary of the surrogate model M, and P˜k ∈R||V ||×1 is the one-hot encoded vector of input token xk over the vocabulary list V . The calculated context losses L = [L1, · · · , LW ] are then used in the next stage to extract the inference pattern. '] | a8a6339a943fa79ae72382fb9f1d022d8409510904d542963b580682babf239b | d969953a0cbdd7fa8485cf1555a32f7b3d62a7a4 |
explanation | What improvements does FacLens provide over existing methods? | Our work has clear improvements over existing works in practical applications (efficiency beyond performance) due to the following reasons. In Figure 2, we compare the ante-hoc method (FacLens) with post-hoc methods (SAPLMA and INSIDE). Unlike post-hoc methods, which rely on costly answer generation, the ante-hoc method avoids inference costs and controls risks in advance. As shown in Figure 2, despite post-hoc methods having more information (i.e., generated answers), FacLens still performs better. Table 1 shows that FacLens achieves clear performance gains over most baselines. While the performance gains over LoRA and Self-Evaluation are slightly smaller, FacLens significantly outperforms both baselines in terms of training efficiency (see Table 2), which is a crucial factor for practical application. | ['Figure 2', 'Table 1', 'Table 2'] | ['images/3a032e8ef66ebf1569cb7a5f5b30d2f997352c8737325ac4a352e836ccc0b46b.jpg', 'images/b864240b4fe713dbd59ab6cd0219dfd87ab698d5d8661c3167be66f1532aa911.jpg', 'images/169180e0dc9b1431032dd782a66b4967dd3b610e1bdbbe40e7daf0b1a0519c10.jpg'] | ['mixed'] | 3 | 2 | 5 | {'Unsupervised domain adaptation performs well for cross-LLM FacLens. Given an LLM, we train FacLens on the training data of the corresponding domain and directly test the FacLens on the test data of another domain. The results in the upper part of Figure 6 are unsatisfactory. After unsupervised domain adaptation, the cross-LLM FacLens can work well in the target domain, as depicted in the the lower part of Figure 6. We also discuss the choice of the kernel function in Appendix G, and find that linear kernel performs well, indicating that the NFP features derived by genc are inherently discriminative. Furthermore, we observe that FacLens demonstrates better transferability between LLMs of similar scales. In future work, we will explore more effective methods to enhance FacLens’s transferability between LLMs of significantly different scales. ': '1', 'NFP Dataset Construction. Given an LLM m and a QA dataset, for each question q ∈Q, we assign a binary label y to the (m, q) pair, where y = 1 if m fails to generate the golden answer for q, and y = 0 otherwise. The goal of NFP is to predict the labels prior to answer generation. Specifically, we follow previous work (Mallen et al., 2023) to adopt QA datasets with short answers like entity mentions, and mark an LLM’s response as non-factual (i.e., y = 1) if no sub-string of the response matches any of the gold answers.2 To ensure the experimental reproducibility, we set the LLM’s decoding strategy to greedy search rather than top-p or top-k sampling. We have also run the sampling-based decoding for response generation, and find that all the experimental conclusions in this paper still hold true. In this work, we consider four LLMs and three QA datasets, which results in 4 × 3 = 12 NFP datasets. In each NFP dataset, consisting of samples in the form of ((m, q), y), we randomly sample 20% samples for training, 10% samples for validation, and use the remaining samples for testing. ': '2'} | {'1': 'Unsupervised domain adaptation performs well for cross-LLM FacLens. Given an LLM, we train FacLens on the training data of the corresponding domain and directly test the FacLens on the test data of another domain. The results in the upper part of Figure 6 are unsatisfactory. After unsupervised domain adaptation, the cross-LLM FacLens can work well in the target domain, as depicted in the the lower part of Figure 6. We also discuss the choice of the kernel function in Appendix G, and find that linear kernel performs well, indicating that the NFP features derived by genc are inherently discriminative. Furthermore, we observe that FacLens demonstrates better transferability between LLMs of similar scales. In future work, we will explore more effective methods to enhance FacLens’s transferability between LLMs of significantly different scales. ', '2': 'NFP Dataset Construction. Given an LLM m and a QA dataset, for each question q ∈Q, we assign a binary label y to the (m, q) pair, where y = 1 if m fails to generate the golden answer for q, and y = 0 otherwise. The goal of NFP is to predict the labels prior to answer generation. Specifically, we follow previous work (Mallen et al., 2023) to adopt QA datasets with short answers like entity mentions, and mark an LLM’s response as non-factual (i.e., y = 1) if no sub-string of the response matches any of the gold answers.2 To ensure the experimental reproducibility, we set the LLM’s decoding strategy to greedy search rather than top-p or top-k sampling. We have also run the sampling-based decoding for response generation, and find that all the experimental conclusions in this paper still hold true. In this work, we consider four LLMs and three QA datasets, which results in 4 × 3 = 12 NFP datasets. In each NFP dataset, consisting of samples in the form of ((m, q), y), we randomly sample 20% samples for training, 10% samples for validation, and use the remaining samples for testing. '} | {'images/3a032e8ef66ebf1569cb7a5f5b30d2f997352c8737325ac4a352e836ccc0b46b.jpg': '2'} | {'2': 'images/3a032e8ef66ebf1569cb7a5f5b30d2f997352c8737325ac4a352e836ccc0b46b.jpg'} | {'images/169180e0dc9b1431032dd782a66b4967dd3b610e1bdbbe40e7daf0b1a0519c10.jpg': '2', 'images/b864240b4fe713dbd59ab6cd0219dfd87ab698d5d8661c3167be66f1532aa911.jpg': '1'} | {'2': 'images/169180e0dc9b1431032dd782a66b4967dd3b610e1bdbbe40e7daf0b1a0519c10.jpg', '1': 'images/b864240b4fe713dbd59ab6cd0219dfd87ab698d5d8661c3167be66f1532aa911.jpg'} | {} | ['NFP Dataset Construction. Given an LLM m and a QA dataset, for each question q ∈Q, we assign a binary label y to the (m, q) pair, where y = 1 if m fails to generate the golden answer for q, and y = 0 otherwise. The goal of NFP is to predict the labels prior to answer generation. Specifically, we follow previous work (Mallen et al., 2023) to adopt QA datasets with short answers like entity mentions, and mark an LLM’s response as non-factual (i.e., y = 1) if no sub-string of the response matches any of the gold answers.2 To ensure the experimental reproducibility, we set the LLM’s decoding strategy to greedy search rather than top-p or top-k sampling. We have also run the sampling-based decoding for response generation, and find that all the experimental conclusions in this paper still hold true. In this work, we consider four LLMs and three QA datasets, which results in 4 × 3 = 12 NFP datasets. In each NFP dataset, consisting of samples in the form of ((m, q), y), we randomly sample 20% samples for training, 10% samples for validation, and use the remaining samples for testing. ', 'Unsupervised domain adaptation performs well for cross-LLM FacLens. Given an LLM, we train FacLens on the training data of the corresponding domain and directly test the FacLens on the test data of another domain. The results in the upper part of Figure 6 are unsatisfactory. After unsupervised domain adaptation, the cross-LLM FacLens can work well in the target domain, as depicted in the the lower part of Figure 6. We also discuss the choice of the kernel function in Appendix G, and find that linear kernel performs well, indicating that the NFP features derived by genc are inherently discriminative. Furthermore, we observe that FacLens demonstrates better transferability between LLMs of similar scales. In future work, we will explore more effective methods to enhance FacLens’s transferability between LLMs of significantly different scales. '] | 4ab6d6d8dcdf8b7a45b9b9c864dc3959193bbda43c25d024ee44e0234248444d | e2297ed06ca065d361ec3f28961b352c3377db10 |
explanation | How does FacLens compare to previous methods in terms of performance? | Table 1 shows that FacLens achieves clear performance gains over most baselines. While the performance gains over LoRA and Self-Evaluation are slightly smaller, FacLens significantly outperforms both of them in terms of training efficiency (see Table 2), which is crucial for practical applications. Moreover, as shown in Figure 2, we compared FacLens with post-hoc methods. Despite post-hoc methods having access to additional information (i.e., the generated answers), FacLens still performs better. | ['Table 1', 'Table 2', 'Figure 2'] | ['images/b864240b4fe713dbd59ab6cd0219dfd87ab698d5d8661c3167be66f1532aa911.jpg', 'images/169180e0dc9b1431032dd782a66b4967dd3b610e1bdbbe40e7daf0b1a0519c10.jpg', 'images/3a032e8ef66ebf1569cb7a5f5b30d2f997352c8737325ac4a352e836ccc0b46b.jpg'] | ['mixed'] | 3 | 2 | 5 | {'where zS,i = genc (xS,i) , zT,j = genc (xT,j), NS = NT = |Qtrain| is the number of questions for training, and k (·) denotes a kernel function. We discuss the choice of kernel function in Appendix G. The hidden question representations are taken from the middle layer of the LLM. ': '1'} | {'1': 'where zS,i = genc (xS,i) , zT,j = genc (xT,j), NS = NT = |Qtrain| is the number of questions for training, and k (·) denotes a kernel function. We discuss the choice of kernel function in Appendix G. The hidden question representations are taken from the middle layer of the LLM. '} | {'images/add180d7870c649480bd2826bf4b5b054bf92dd72510bcbfde99e0efaf2a9972.jpg': '7', 'images/3a032e8ef66ebf1569cb7a5f5b30d2f997352c8737325ac4a352e836ccc0b46b.jpg': '2'} | {'7': 'images/add180d7870c649480bd2826bf4b5b054bf92dd72510bcbfde99e0efaf2a9972.jpg', '2': 'images/3a032e8ef66ebf1569cb7a5f5b30d2f997352c8737325ac4a352e836ccc0b46b.jpg'} | {'images/169180e0dc9b1431032dd782a66b4967dd3b610e1bdbbe40e7daf0b1a0519c10.jpg': '2', 'images/b864240b4fe713dbd59ab6cd0219dfd87ab698d5d8661c3167be66f1532aa911.jpg': '1'} | {'2': 'images/169180e0dc9b1431032dd782a66b4967dd3b610e1bdbbe40e7daf0b1a0519c10.jpg', '1': 'images/b864240b4fe713dbd59ab6cd0219dfd87ab698d5d8661c3167be66f1532aa911.jpg'} | {} | ['images/add180d7870c649480bd2826bf4b5b054bf92dd72510bcbfde99e0efaf2a9972.jpg', 'where zS,i = genc (xS,i) , zT,j = genc (xT,j), NS = NT = |Qtrain| is the number of questions for training, and k (·) denotes a kernel function. We discuss the choice of kernel function in Appendix G. The hidden question representations are taken from the middle layer of the LLM. '] | 670d6826b93a707dab76d21a73b5c691457ec286bcc186606cd4c02327464670 | e2297ed06ca065d361ec3f28961b352c3377db10 |
explanation | What analyses have the authors done on how properties of the dataset affect the performance of MLLMs? | In Figure 5 of the paper, we present the relationship between the number of images and the accuracy of image association in the IITC task. From the figure, we can see the following: 1. The image association accuracy of the VEGA-base-4k model decreases as the number of images increases. 2. For the other closed-source models, there is also a general negative correlation between the number of images and image association accuracy. The increase in the number of images makes image selection in the IITC task more challenging. We have supplemented the analysis with the relationship between token length and image accuracy. Details can be found in the table above: 1) Statistically, there is a general negative correlation between image accuracy and token length. 2) Due to the uneven distribution of token lengths in the test set (see Figure 4 of the paper), there is a limited amount of test data in the 0-1k and 7-8k ranges (with only 9 and 28 samples, respectively), which may lead to some margin of error in these intervals. 3) As shown in Table 2 of the paper, for all models, the image accuracy in IITC 4k is higher than in IITC 8k, further supporting the negative correlation between accuracy and token length. The increase in context length introduces more redundant information, making image selection more challenging. | ['Figure 5', 'Figure 4', 'Table 2'] | ['images/e4de0bd64fbf86f5f6d26dd1d132f25e1b4ab75f15d00969dc8b95148cc06e38.jpg', 'images/804f68c55932623c3d9dfb50941f9e1f5b2d9f67de4f5c63abcea211bb0f685a.jpg', 'images/c253055b0b2a2668877b56f92b77d8e7edba2c56e1e1558d781fe91e3e938c77.jpg'] | ['mixed'] | 3 | 2 | 5 | {'Ultimately, we have developed a novel dataset, designated as VEGA. It is comprised of two subsets, one tailored for the IITC task and another for the ITA task. The longest interleaved image-text content in VEGA reaches up to 8 images and 8k tokens. We design the instruction of the IITC task to be a question about only one of the images, requiring the model to specify the image it refers to in its answer. We assess the model’s interleaved image-text reading comprehension ability by both the correct rate of associated images, and the text quality of the answer by ROUGELin (2004) and BLEU Papineni et al. (2002). We have evaluated several state-of-the-art MLLMs on our dataset, validating the challenge of our tasks. Furthermore, we have fine-tuned the Qwen-VL-Chat model Bai et al. (2023) on the VEGA dataset to set a robust baseline for the IITC task. ': '1'} | {'1': 'Ultimately, we have developed a novel dataset, designated as VEGA. It is comprised of two subsets, one tailored for the IITC task and another for the ITA task. The longest interleaved image-text content in VEGA reaches up to 8 images and 8k tokens. We design the instruction of the IITC task to be a question about only one of the images, requiring the model to specify the image it refers to in its answer. We assess the model’s interleaved image-text reading comprehension ability by both the correct rate of associated images, and the text quality of the answer by ROUGELin (2004) and BLEU Papineni et al. (2002). We have evaluated several state-of-the-art MLLMs on our dataset, validating the challenge of our tasks. Furthermore, we have fine-tuned the Qwen-VL-Chat model Bai et al. (2023) on the VEGA dataset to set a robust baseline for the IITC task. '} | {'images/804f68c55932623c3d9dfb50941f9e1f5b2d9f67de4f5c63abcea211bb0f685a.jpg': '4', 'images/31070a6e7c3b53f02d80b85f2a2fffaeba361f9c85891c05adfcb10e733faf05.jpg': '1', 'images/e4de0bd64fbf86f5f6d26dd1d132f25e1b4ab75f15d00969dc8b95148cc06e38.jpg': '5'} | {'4': 'images/804f68c55932623c3d9dfb50941f9e1f5b2d9f67de4f5c63abcea211bb0f685a.jpg', '1': 'images/31070a6e7c3b53f02d80b85f2a2fffaeba361f9c85891c05adfcb10e733faf05.jpg', '5': 'images/e4de0bd64fbf86f5f6d26dd1d132f25e1b4ab75f15d00969dc8b95148cc06e38.jpg'} | {'images/c253055b0b2a2668877b56f92b77d8e7edba2c56e1e1558d781fe91e3e938c77.jpg': '2'} | {'2': 'images/c253055b0b2a2668877b56f92b77d8e7edba2c56e1e1558d781fe91e3e938c77.jpg'} | {} | ['images/31070a6e7c3b53f02d80b85f2a2fffaeba361f9c85891c05adfcb10e733faf05.jpg', 'Ultimately, we have developed a novel dataset, designated as VEGA. It is comprised of two subsets, one tailored for the IITC task and another for the ITA task. The longest interleaved image-text content in VEGA reaches up to 8 images and 8k tokens. We design the instruction of the IITC task to be a question about only one of the images, requiring the model to specify the image it refers to in its answer. We assess the model’s interleaved image-text reading comprehension ability by both the correct rate of associated images, and the text quality of the answer by ROUGELin (2004) and BLEU Papineni et al. (2002). We have evaluated several state-of-the-art MLLMs on our dataset, validating the challenge of our tasks. Furthermore, we have fine-tuned the Qwen-VL-Chat model Bai et al. (2023) on the VEGA dataset to set a robust baseline for the IITC task. '] | 8caf5a4e8ea45a9c61b2a596fe76417f7aa5a3d875406f1784a388872e17ead8 | ff04147bfeb3ecdb49c1ad6b729c8776be9205bc |
explanation | How does the paper address the marginal improvements observed in the experimental results? | Notice that spectral regularization is always amongst the best-performing methods in all experiments. Moreover, in several experiments, spectral regularization was significantly better than any other baseline: Figure 1 (left), Figure 2 (right), Figure 3. | ['Figure 1', 'Figure 2', 'Figure 3'] | ['images/cc3e375bd58a5db5d5bfb40f3e0e6e18698bfc87b9e91e9c19f39f395a094447.jpg', 'images/eae1f91f604a2b20a638a94f3ad6b7ae424d1a59ccfeab1575bd54f87cd3f353.jpg', 'images/99156fcd57bbe834cdf486d0f7684362f9231d5f09b81a4e5686dc70589ef3c9.jpg'] | ['figure'] | 3 | 2 | 5 | {'Neural network initialization is key to trainability (He et al., 2015; Hinton and Salakhutdinov, 2006). One property of the initialization thought to be important is that the layerwise mapping, hl+1 = ReLU(θlhl), has a Jacobian with singular values that are close to or exactly one (Glorot and Bengio, 2010; Pennington et al., 2017; Saxe et al., 2014; Xiao et al., 2018). Writing this Jacobian explicitly, we have that Jl = ∂∂hlh+1 = Dlθl where Dl = Diag(ReLU′([θlhl]1), . . . , ReLU′([θlhl]d)). 2 We can obtain upper and lower bounds on the singular values of the layerwise Jacobian in terms of the singular values of the weight matrix. Denoting the ordered singular values of θl and Dl by σd(θl) ≤· · · ≤σ1(θl) and σd(Dl) ≤· · · ≤σ1(Dl), respectively, we have σd(Dl)σi(θl) < σi(Jl) < σ1(Dl)σi(θl) for all i ∈{1, . . . , d} (Zhang, 2011, Theorem 8.13). In particular, if the spectral norm (largest singular value) of the weight matrix θl increases, then the spectral norm of the Jacobian Dl increases as well, potentially impacting trainability. Furthermore, the condition number κ(Jl) = σ1(Jl)/σd(Jl) can be bounded with the product of the condition numbers of θl and Dl, κ(θl) and κ(Dl) as κ(θl)/κ(Dl) ≤κ(Jl) ≤κ(θl)κ(Dl). Thus, if our goal is to keep the singular values of the Jacobian close to one by controlling the singular values of the weight matrix, we should ensure that the condition number of the latter is not too large. ': '1'} | {'1': 'Neural network initialization is key to trainability (He et al., 2015; Hinton and Salakhutdinov, 2006). One property of the initialization thought to be important is that the layerwise mapping, hl+1 = ReLU(θlhl), has a Jacobian with singular values that are close to or exactly one (Glorot and Bengio, 2010; Pennington et al., 2017; Saxe et al., 2014; Xiao et al., 2018). Writing this Jacobian explicitly, we have that Jl = ∂∂hlh+1 = Dlθl where Dl = Diag(ReLU′([θlhl]1), . . . , ReLU′([θlhl]d)). 2 We can obtain upper and lower bounds on the singular values of the layerwise Jacobian in terms of the singular values of the weight matrix. Denoting the ordered singular values of θl and Dl by σd(θl) ≤· · · ≤σ1(θl) and σd(Dl) ≤· · · ≤σ1(Dl), respectively, we have σd(Dl)σi(θl) < σi(Jl) < σ1(Dl)σi(θl) for all i ∈{1, . . . , d} (Zhang, 2011, Theorem 8.13). In particular, if the spectral norm (largest singular value) of the weight matrix θl increases, then the spectral norm of the Jacobian Dl increases as well, potentially impacting trainability. Furthermore, the condition number κ(Jl) = σ1(Jl)/σd(Jl) can be bounded with the product of the condition numbers of θl and Dl, κ(θl) and κ(Dl) as κ(θl)/κ(Dl) ≤κ(Jl) ≤κ(θl)κ(Dl). Thus, if our goal is to keep the singular values of the Jacobian close to one by controlling the singular values of the weight matrix, we should ensure that the condition number of the latter is not too large. '} | {'images/99156fcd57bbe834cdf486d0f7684362f9231d5f09b81a4e5686dc70589ef3c9.jpg': '3', 'images/cc3e375bd58a5db5d5bfb40f3e0e6e18698bfc87b9e91e9c19f39f395a094447.jpg': '1', 'images/a3485f80f366e7de3691aaad423ab16f973943002252e73580726f373f1bd657.jpg': '5', 'images/eae1f91f604a2b20a638a94f3ad6b7ae424d1a59ccfeab1575bd54f87cd3f353.jpg': '2'} | {'3': 'images/99156fcd57bbe834cdf486d0f7684362f9231d5f09b81a4e5686dc70589ef3c9.jpg', '1': 'images/cc3e375bd58a5db5d5bfb40f3e0e6e18698bfc87b9e91e9c19f39f395a094447.jpg', '5': 'images/a3485f80f366e7de3691aaad423ab16f973943002252e73580726f373f1bd657.jpg', '2': 'images/eae1f91f604a2b20a638a94f3ad6b7ae424d1a59ccfeab1575bd54f87cd3f353.jpg'} | {} | {} | {} | ['Neural network initialization is key to trainability (He et al., 2015; Hinton and Salakhutdinov, 2006). One property of the initialization thought to be important is that the layerwise mapping, hl+1 = ReLU(θlhl), has a Jacobian with singular values that are close to or exactly one (Glorot and Bengio, 2010; Pennington et al., 2017; Saxe et al., 2014; Xiao et al., 2018). Writing this Jacobian explicitly, we have that Jl = ∂∂hlh+1 = Dlθl where Dl = Diag(ReLU′([θlhl]1), . . . , ReLU′([θlhl]d)). 2 We can obtain upper and lower bounds on the singular values of the layerwise Jacobian in terms of the singular values of the weight matrix. Denoting the ordered singular values of θl and Dl by σd(θl) ≤· · · ≤σ1(θl) and σd(Dl) ≤· · · ≤σ1(Dl), respectively, we have σd(Dl)σi(θl) < σi(Jl) < σ1(Dl)σi(θl) for all i ∈{1, . . . , d} (Zhang, 2011, Theorem 8.13). In particular, if the spectral norm (largest singular value) of the weight matrix θl increases, then the spectral norm of the Jacobian Dl increases as well, potentially impacting trainability. Furthermore, the condition number κ(Jl) = σ1(Jl)/σd(Jl) can be bounded with the product of the condition numbers of θl and Dl, κ(θl) and κ(Dl) as κ(θl)/κ(Dl) ≤κ(Jl) ≤κ(θl)κ(Dl). Thus, if our goal is to keep the singular values of the Jacobian close to one by controlling the singular values of the weight matrix, we should ensure that the condition number of the latter is not too large. ', 'images/a3485f80f366e7de3691aaad423ab16f973943002252e73580726f373f1bd657.jpg'] | e103290df88fe0eeb1f60aaf6df31d7daf51b5ff817ce6d7c06e4f19ca381e1f | 05fe05b0399402d34686a7b695820eaf3b6b5eca |
explanation | What improvements does spectral regularization provide over L2 regularization? | Empirically, spectral regularization is a large improvement over L2 regularization in several of our experiments, e.g. Figure 1 (left), Figure 2 (right), and Figure 3. Moreover, spectral regularization is more robust to its hyperparameter and always among the 1 or 2 best-performing methods in all of our experiments. | ['Figure 1', 'Figure 2', 'Figure 3'] | ['images/cc3e375bd58a5db5d5bfb40f3e0e6e18698bfc87b9e91e9c19f39f395a094447.jpg', 'images/eae1f91f604a2b20a638a94f3ad6b7ae424d1a59ccfeab1575bd54f87cd3f353.jpg', 'images/99156fcd57bbe834cdf486d0f7684362f9231d5f09b81a4e5686dc70589ef3c9.jpg'] | ['figure'] | 3 | 2 | 5 | {'Loss of Trainability Mitigators In our main results, we compare spectral regularization against L2 regularization towards zero, shrink and perturb (Ash and Adams, 2020), L2 regularization towards the initialization (Kumar et al., 2023), recycling dormant neurons (ReDO, Sokar et al., 2023), concatenated ReLU (Abbas et al., 2023; Shang et al., 2016), and Wasserstein regularization (Lewandowski et al., 2023). Several regularizers in the continual learning without forgetting literature rely on privileged task information, which is not applicable to the task-agnostic setting that we consider. We use the streaming conversion (Elsayed and Mahmood, 2024) to transform elastic weight consolidation (Kirkpatrick et al., 2017; Zenke et al., 2017), so that it no longer requires task boundary information, and include it as a baseline. Additional experiment details can be found in Appendix B. ': '1', 'Loss of plasticity in the continual learning literature can refer to either loss of trainability (Dohare et al., 2021; Lyle et al., 2023) or to loss of generalization (Ash and Adams, 2020). Because trainability is a requirement for learning and generalization, we focus primarily on loss of trainability. Specifically, we use loss of trainability to refer to the phenomenon that the objective value, Jτ(θ(τT )), increases as a function of the task τ. Equivalently, the performance measures, such as accuracy, decrease with new tasks. Under the assumption that the tasks are sampled independently and identically, this would suggest that the neural network’s trainability diminishes on new tasks. ': '2'} | {'1': 'Loss of Trainability Mitigators In our main results, we compare spectral regularization against L2 regularization towards zero, shrink and perturb (Ash and Adams, 2020), L2 regularization towards the initialization (Kumar et al., 2023), recycling dormant neurons (ReDO, Sokar et al., 2023), concatenated ReLU (Abbas et al., 2023; Shang et al., 2016), and Wasserstein regularization (Lewandowski et al., 2023). Several regularizers in the continual learning without forgetting literature rely on privileged task information, which is not applicable to the task-agnostic setting that we consider. We use the streaming conversion (Elsayed and Mahmood, 2024) to transform elastic weight consolidation (Kirkpatrick et al., 2017; Zenke et al., 2017), so that it no longer requires task boundary information, and include it as a baseline. Additional experiment details can be found in Appendix B. ', '2': 'Loss of plasticity in the continual learning literature can refer to either loss of trainability (Dohare et al., 2021; Lyle et al., 2023) or to loss of generalization (Ash and Adams, 2020). Because trainability is a requirement for learning and generalization, we focus primarily on loss of trainability. Specifically, we use loss of trainability to refer to the phenomenon that the objective value, Jτ(θ(τT )), increases as a function of the task τ. Equivalently, the performance measures, such as accuracy, decrease with new tasks. Under the assumption that the tasks are sampled independently and identically, this would suggest that the neural network’s trainability diminishes on new tasks. '} | {'images/99156fcd57bbe834cdf486d0f7684362f9231d5f09b81a4e5686dc70589ef3c9.jpg': '3', 'images/cc3e375bd58a5db5d5bfb40f3e0e6e18698bfc87b9e91e9c19f39f395a094447.jpg': '1', 'images/eae1f91f604a2b20a638a94f3ad6b7ae424d1a59ccfeab1575bd54f87cd3f353.jpg': '2'} | {'3': 'images/99156fcd57bbe834cdf486d0f7684362f9231d5f09b81a4e5686dc70589ef3c9.jpg', '1': 'images/cc3e375bd58a5db5d5bfb40f3e0e6e18698bfc87b9e91e9c19f39f395a094447.jpg', '2': 'images/eae1f91f604a2b20a638a94f3ad6b7ae424d1a59ccfeab1575bd54f87cd3f353.jpg'} | {} | {} | {} | ['Loss of Trainability Mitigators In our main results, we compare spectral regularization against L2 regularization towards zero, shrink and perturb (Ash and Adams, 2020), L2 regularization towards the initialization (Kumar et al., 2023), recycling dormant neurons (ReDO, Sokar et al., 2023), concatenated ReLU (Abbas et al., 2023; Shang et al., 2016), and Wasserstein regularization (Lewandowski et al., 2023). Several regularizers in the continual learning without forgetting literature rely on privileged task information, which is not applicable to the task-agnostic setting that we consider. We use the streaming conversion (Elsayed and Mahmood, 2024) to transform elastic weight consolidation (Kirkpatrick et al., 2017; Zenke et al., 2017), so that it no longer requires task boundary information, and include it as a baseline. Additional experiment details can be found in Appendix B. ', 'Loss of plasticity in the continual learning literature can refer to either loss of trainability (Dohare et al., 2021; Lyle et al., 2023) or to loss of generalization (Ash and Adams, 2020). Because trainability is a requirement for learning and generalization, we focus primarily on loss of trainability. Specifically, we use loss of trainability to refer to the phenomenon that the objective value, Jτ(θ(τT )), increases as a function of the task τ. Equivalently, the performance measures, such as accuracy, decrease with new tasks. Under the assumption that the tasks are sampled independently and identically, this would suggest that the neural network’s trainability diminishes on new tasks. '] | 22132795dd4d718836bcea76aa5a9ee27154f136067d4d67d1e043271a66c6a1 | 05fe05b0399402d34686a7b695820eaf3b6b5eca |
explanation | How are passenger profiles integrated into the origin-destination matrix at the regional or stop level? | As shown in Figure 1(d), a walking distance is deemed acceptable if it is limited to 1.1 km. Concerning the average velocity, Figure 1(e), and the trip time, Figure 1(f), all registers with values greater than 80 km/h and 2 hours are unconsidered. These values were estimated by local specialists based on the passengers’ usage patterns and the transportation infrastructure in Salvador. We emphasize that the reader can modify these values according to their needs once both raw and processed data are shared. | ['Figure 1'] | ['images/c5a31d9a40f25518ecd6eaeca1e01c4b3acbd744bcdc450d17a22bfd197dd041.jpg'] | ['figure'] | 1 | 4 | 5 | {'In the subsequent phase, Figure 1(c), we analyzed user types to determine the feasibility of estimating their alighting points. In Salvador, there is no device to validate the passengers’ alighting; therefore, the main challenge is to estimate it by analyzing the following boarding. Moreover, it is impossible to track older people because they are not individually identified. According to local policies, the fares for such passengers are recorded as general users without identification. Consequently, we are unable to estimate their alighting points. Another particular case that prevents us from identifying users’ alighting points occurs when there is only a single trip registration on a given day. In such cases, we can only determine the boarding point, with no information available about the alighting point. Therefore, we cannot consider such situations in our analyses. ': '1', 'In our context, spatial data do not depend on time t, i.e., their information is time-invariant. Specifically, in every vertex vi ∈V , we store the following features: geographical position, number of boarding and alighting per vehicle, and passenger load. The features specifically concerning edges (vi, vj) ∈E include the distance between stops and stations, the trip duration, the mean velocity, and the Renovation Factor (RF). The RF is a well-known metric used in transportation research to assess the total demand in a line, i.e., it is computed on a set of edges that belong to the line [ITDP, 2016]. Formally, this metric is the ratio of the total demand of a line to the load on its critical link. Higher renovation factors occur when there are many short trips along the line. Corridors with very high renovation factor rates are more proftiable because they handle the same number of paying customers with fewer vehicles [ITDP, 2016]. Besides the individual features, there is relevant information shared by both vertices and edges, such as the number of passengers per vehicle, lines and directions, vehicle characteristics, altitude, and trips. ': '2', 'All information shared by SUNT was collected from March 2024 to July 2024 and aggregated into 5-minute intervals. This interval allows the data to be represented as a temporal graph, in addition to the spatial information. However, we emphasize that this interval can be adjusted according to the readers’ requirements. It is possible to work with a static graph using a single interval or to summarize all days using, for example, a mean function. Additional details about all the data comprising SUNT are available in Apendix B. ': '3'} | {'1': 'In the subsequent phase, Figure 1(c), we analyzed user types to determine the feasibility of estimating their alighting points. In Salvador, there is no device to validate the passengers’ alighting; therefore, the main challenge is to estimate it by analyzing the following boarding. Moreover, it is impossible to track older people because they are not individually identified. According to local policies, the fares for such passengers are recorded as general users without identification. Consequently, we are unable to estimate their alighting points. Another particular case that prevents us from identifying users’ alighting points occurs when there is only a single trip registration on a given day. In such cases, we can only determine the boarding point, with no information available about the alighting point. Therefore, we cannot consider such situations in our analyses. ', '2': 'In our context, spatial data do not depend on time t, i.e., their information is time-invariant. Specifically, in every vertex vi ∈V , we store the following features: geographical position, number of boarding and alighting per vehicle, and passenger load. The features specifically concerning edges (vi, vj) ∈E include the distance between stops and stations, the trip duration, the mean velocity, and the Renovation Factor (RF). The RF is a well-known metric used in transportation research to assess the total demand in a line, i.e., it is computed on a set of edges that belong to the line [ITDP, 2016]. Formally, this metric is the ratio of the total demand of a line to the load on its critical link. Higher renovation factors occur when there are many short trips along the line. Corridors with very high renovation factor rates are more proftiable because they handle the same number of paying customers with fewer vehicles [ITDP, 2016]. Besides the individual features, there is relevant information shared by both vertices and edges, such as the number of passengers per vehicle, lines and directions, vehicle characteristics, altitude, and trips. ', '3': 'All information shared by SUNT was collected from March 2024 to July 2024 and aggregated into 5-minute intervals. This interval allows the data to be represented as a temporal graph, in addition to the spatial information. However, we emphasize that this interval can be adjusted according to the readers’ requirements. It is possible to work with a static graph using a single interval or to summarize all days using, for example, a mean function. Additional details about all the data comprising SUNT are available in Apendix B. '} | {'images/c5a31d9a40f25518ecd6eaeca1e01c4b3acbd744bcdc450d17a22bfd197dd041.jpg': '1', 'images/23f1fdc539186d67330f172d2edf9ee702c9e85cdedb350bee9c37ac4c5cfed4.jpg': '3'} | {'1': 'images/c5a31d9a40f25518ecd6eaeca1e01c4b3acbd744bcdc450d17a22bfd197dd041.jpg', '3': 'images/23f1fdc539186d67330f172d2edf9ee702c9e85cdedb350bee9c37ac4c5cfed4.jpg'} | {} | {} | {} | ['images/23f1fdc539186d67330f172d2edf9ee702c9e85cdedb350bee9c37ac4c5cfed4.jpg', 'In the subsequent phase, Figure 1(c), we analyzed user types to determine the feasibility of estimating their alighting points. In Salvador, there is no device to validate the passengers’ alighting; therefore, the main challenge is to estimate it by analyzing the following boarding. Moreover, it is impossible to track older people because they are not individually identified. According to local policies, the fares for such passengers are recorded as general users without identification. Consequently, we are unable to estimate their alighting points. Another particular case that prevents us from identifying users’ alighting points occurs when there is only a single trip registration on a given day. In such cases, we can only determine the boarding point, with no information available about the alighting point. Therefore, we cannot consider such situations in our analyses. ', 'All information shared by SUNT was collected from March 2024 to July 2024 and aggregated into 5-minute intervals. This interval allows the data to be represented as a temporal graph, in addition to the spatial information. However, we emphasize that this interval can be adjusted according to the readers’ requirements. It is possible to work with a static graph using a single interval or to summarize all days using, for example, a mean function. Additional details about all the data comprising SUNT are available in Apendix B. ', 'In our context, spatial data do not depend on time t, i.e., their information is time-invariant. Specifically, in every vertex vi ∈V , we store the following features: geographical position, number of boarding and alighting per vehicle, and passenger load. The features specifically concerning edges (vi, vj) ∈E include the distance between stops and stations, the trip duration, the mean velocity, and the Renovation Factor (RF). The RF is a well-known metric used in transportation research to assess the total demand in a line, i.e., it is computed on a set of edges that belong to the line [ITDP, 2016]. Formally, this metric is the ratio of the total demand of a line to the load on its critical link. Higher renovation factors occur when there are many short trips along the line. Corridors with very high renovation factor rates are more proftiable because they handle the same number of paying customers with fewer vehicles [ITDP, 2016]. Besides the individual features, there is relevant information shared by both vertices and edges, such as the number of passengers per vehicle, lines and directions, vehicle characteristics, altitude, and trips. '] | c8f71f59ce47e86848347df22d37552cc7e4d12d8bf81a5447d0338086cffd33 | 5aa218287d89432e6fc34652ca252cfe99d92e21 |
explanation | What is the rationale for the experimental configurations chosen in the study? | Figure 4 (MAD): This figure focuses on a case study demonstrating a counter-intuitive phenomenon where introducing errors can improve performance—a rare observation in multi-agent systems. MAD was selected specifically for its relevance to this unique insight. Figure 7a (Exclusion of MAD): MAD was excluded from Figure 7a because this experiment involves scenarios with malicious instruction-sending agents, which are not present in the MAD system configuration. Figure 8 (Self-collab and Camel): Only Self-collab and Camel are included in Figure 8 because they represent the weaker systems within the Linear and Flat structures, respectively. Our objective in this experiment is to illustrate how our proposed defense method enhances resilience in weaker systems. To provide greater clarity on our multi-agent system settings, we have added a comprehensive table summarizing the experimental configurations. | ['Figure 4', 'Figure 7', 'Figure 8'] | ['images/0b55b386169f13d50b1b7ff47bfa61c9126516d2fca0fe9057685662016c9e22.jpg', 'images/c0b3ca79844ea127d86dcdd3fcf3e95955edba6f7897f1c1e732e26c9b917a50.jpg', 'images/28fe1291bf019109651723940887ed6ff1e1b4a60028a15b648aaa959d5b622c.jpg'] | ['figure'] | 3 | 2 | 5 | {'Current LLMs prioritize natural language over code. Fig. 6b illustrates that distraction comments can mislead LLMs into accepting incorrect code as correct across all six systems studied. This indicates that the systems tend to prioritize comments over the actual code. In the example, the system detects an error in the code when no comments are present. However, when a comment stating “the bug had been corrected” is added, the system overlooks the error and proceeds with the next task. AUTOTRANSFORM exploits this characteristic of LLMs to execute successful attacks. ': '1'} | {'1': 'Current LLMs prioritize natural language over code. Fig. 6b illustrates that distraction comments can mislead LLMs into accepting incorrect code as correct across all six systems studied. This indicates that the systems tend to prioritize comments over the actual code. In the example, the system detects an error in the code when no comments are present. However, when a comment stating “the bug had been corrected” is added, the system overlooks the error and proceeds with the next task. AUTOTRANSFORM exploits this characteristic of LLMs to execute successful attacks. '} | {'images/0b55b386169f13d50b1b7ff47bfa61c9126516d2fca0fe9057685662016c9e22.jpg': '4', 'images/c0b3ca79844ea127d86dcdd3fcf3e95955edba6f7897f1c1e732e26c9b917a50.jpg': '7', 'images/28fe1291bf019109651723940887ed6ff1e1b4a60028a15b648aaa959d5b622c.jpg': '8', 'images/18cebe9c02b6160708591815b62a39d65034f0db42e6f9495510e4a203c2c009.jpg': '1'} | {'4': 'images/0b55b386169f13d50b1b7ff47bfa61c9126516d2fca0fe9057685662016c9e22.jpg', '7': 'images/c0b3ca79844ea127d86dcdd3fcf3e95955edba6f7897f1c1e732e26c9b917a50.jpg', '8': 'images/28fe1291bf019109651723940887ed6ff1e1b4a60028a15b648aaa959d5b622c.jpg', '1': 'images/18cebe9c02b6160708591815b62a39d65034f0db42e6f9495510e4a203c2c009.jpg'} | {} | {} | {} | ['Current LLMs prioritize natural language over code. Fig. 6b illustrates that distraction comments can mislead LLMs into accepting incorrect code as correct across all six systems studied. This indicates that the systems tend to prioritize comments over the actual code. In the example, the system detects an error in the code when no comments are present. However, when a comment stating “the bug had been corrected” is added, the system overlooks the error and proceeds with the next task. AUTOTRANSFORM exploits this characteristic of LLMs to execute successful attacks. ', 'images/18cebe9c02b6160708591815b62a39d65034f0db42e6f9495510e4a203c2c009.jpg'] | 28348747626e6364ef8ed1d3cf3ae2a27e837a31c9e72c81cf34fc34a077ec92 | 5f4382c8b4eb16e5bc379f3c02f21f53318dbacb |
explanation | What evidence supports the claim of improved zero-shot generalization? | We respectfully disagree with the reviewer’s assertion that the paper does not demonstrate improved zero-shot generalization, as we show this in Procgen (see aggregate performance added to Table 3). Additionally, we present the FDD approach (Table 2), where we observe improvement in the generalization gap for the DMC environments. That said, we understand that the improved performance in the original environment in Table 1 (not necessarily a bad thing!) could lead to confusion. We are happy to rephrase the title if you have a recommendation. One proposal could be 'Synthetic Data Enables Training Robust Agents from Offline Data,' as our agents perform well across a wide range of settings. We also updated Tables 2 and 3 to include $Test/Train$ and $Train-Test$ results for both environments, aligning with the metrics suggested by the reviewer. | ['Table 2', 'Table 3', 'Table 1'] | ['images/5569c6a3bfa524742be607e0b74ea0c979027562b132ff607589531c164f2e8b.jpg', 'images/dcb7d6d85125826affdf8c728bcb27be4cf1096384a051e10fe381534f2d375b.jpg', 'images/4700ceaef17c3682b2201018d66e8a2e1c59985dcb94ee0a1293d3d2c28e41f8.jpg'] | ['table'] | 3 | 2 | 5 | {} | {} | {'images/d65db754ee4fe5be255f66580d4f92aa03a1107db5b556c3b4ea7d63b56fec34.jpg': '5', 'images/8759ff84202e49665b7630122fce5a4391e5fd728dd26bcf8bd79ba128b548f2.jpg': '2'} | {'5': 'images/d65db754ee4fe5be255f66580d4f92aa03a1107db5b556c3b4ea7d63b56fec34.jpg', '2': 'images/8759ff84202e49665b7630122fce5a4391e5fd728dd26bcf8bd79ba128b548f2.jpg'} | {'images/4700ceaef17c3682b2201018d66e8a2e1c59985dcb94ee0a1293d3d2c28e41f8.jpg': '1', 'images/5569c6a3bfa524742be607e0b74ea0c979027562b132ff607589531c164f2e8b.jpg': '2', 'images/dcb7d6d85125826affdf8c728bcb27be4cf1096384a051e10fe381534f2d375b.jpg': '3'} | {'1': 'images/4700ceaef17c3682b2201018d66e8a2e1c59985dcb94ee0a1293d3d2c28e41f8.jpg', '2': 'images/5569c6a3bfa524742be607e0b74ea0c979027562b132ff607589531c164f2e8b.jpg', '3': 'images/dcb7d6d85125826affdf8c728bcb27be4cf1096384a051e10fe381534f2d375b.jpg'} | {} | ['images/8759ff84202e49665b7630122fce5a4391e5fd728dd26bcf8bd79ba128b548f2.jpg', 'images/d65db754ee4fe5be255f66580d4f92aa03a1107db5b556c3b4ea7d63b56fec34.jpg'] | 5357a51d9c1a64e442ce83018c4e81ed44c53e736a443bd65b61b021ea85c150 | 67ffaaf503d82d0615454baf237f5e5a9ff7bb19 |
explanation | Do you have a proof that PolyReLU and PolyNorm have equivalent expressivity? | Thank you for pointing out the less precise expression. We have rephrased the sentence as follows: 'From Figure 1, one can see that the expressivity of PolyNorm is greater than or equal to that of PolyReLU.' The claim is primarily supported through the empirical evidence provided in the paper. As can be observed in Figure 1, Figure 6 and Figure 7, both PolyReLU and PolyNorm exhibit superior expressivity in comparison to other activation functions, with PolyNorm demonstrating equal or greater expressive capacity than PolyReLU. | ['Figure 1', 'Figure 6', 'Figure 7'] | ['images/a4c46101de0b0f13b987de572c9324742705fcb26f894ba6c6254c285adddf1a.jpg', 'images/046ab70a2b4e254b1ece36680859bb7ab5fac1877cc75d8c41d950778c8f1046.jpg', 'images/b14e1904e8ec3fdee265107c7746e771c19d93de74f082fb6f52c4f54678406b.jpg'] | ['figure'] | 3 | 2 | 5 | {'Hyperparameters. Unless otherwise specified, we use a third-order PolyCom by default and initialize the coefficients as ai = 1/3 for i = 1, 2, 3 and set a0 = 0. Model weights are randomly initialized. For optimization, we apply the AdamW optimizer with β1 = 0.9 and β2 = 0.95. All models are trained on sequences of 4096 tokens. For the dense model, we set the initial learning rate to 3e-4, decaying to 1.5e-5 using a cosine scheduler. The MoE model starts with a learning rate of 4e-4, also decaying according to a cosine schedule. We summary the hyperparameters in Table 7. ': '1'} | {'1': 'Hyperparameters. Unless otherwise specified, we use a third-order PolyCom by default and initialize the coefficients as ai = 1/3 for i = 1, 2, 3 and set a0 = 0. Model weights are randomly initialized. For optimization, we apply the AdamW optimizer with β1 = 0.9 and β2 = 0.95. All models are trained on sequences of 4096 tokens. For the dense model, we set the initial learning rate to 3e-4, decaying to 1.5e-5 using a cosine scheduler. The MoE model starts with a learning rate of 4e-4, also decaying according to a cosine schedule. We summary the hyperparameters in Table 7. '} | {'images/b14e1904e8ec3fdee265107c7746e771c19d93de74f082fb6f52c4f54678406b.jpg': '7', 'images/046ab70a2b4e254b1ece36680859bb7ab5fac1877cc75d8c41d950778c8f1046.jpg': '6', 'images/824414a9a148b783330a35bbd312329fc253390c4f58a7711bcb9a1d90809da1.jpg': '2', 'images/a4c46101de0b0f13b987de572c9324742705fcb26f894ba6c6254c285adddf1a.jpg': '1'} | {'7': 'images/b14e1904e8ec3fdee265107c7746e771c19d93de74f082fb6f52c4f54678406b.jpg', '6': 'images/046ab70a2b4e254b1ece36680859bb7ab5fac1877cc75d8c41d950778c8f1046.jpg', '2': 'images/824414a9a148b783330a35bbd312329fc253390c4f58a7711bcb9a1d90809da1.jpg', '1': 'images/a4c46101de0b0f13b987de572c9324742705fcb26f894ba6c6254c285adddf1a.jpg'} | {} | {} | {} | ['images/824414a9a148b783330a35bbd312329fc253390c4f58a7711bcb9a1d90809da1.jpg', 'Hyperparameters. Unless otherwise specified, we use a third-order PolyCom by default and initialize the coefficients as ai = 1/3 for i = 1, 2, 3 and set a0 = 0. Model weights are randomly initialized. For optimization, we apply the AdamW optimizer with β1 = 0.9 and β2 = 0.95. All models are trained on sequences of 4096 tokens. For the dense model, we set the initial learning rate to 3e-4, decaying to 1.5e-5 using a cosine scheduler. The MoE model starts with a learning rate of 4e-4, also decaying according to a cosine schedule. We summary the hyperparameters in Table 7. '] | c1bc3c66ef0dee68fef185813dcc321a868969e1fce058e8db05d4896e37025c | 8b6c738aadc6b44e6ec8736d7e10c499122c0609 |
explanation | Include aforementioned key benchmarks to facilitate a more comprehensive comparison. | We provide additional performance comparisons with distillation sampling variant on CIFAR-10 (Table 1) and with direct consistency training variant on ImageNet 64 × 64 (Table 2). We have now included the key baselines [2], [3], [4] in Table 3 and Table 4. | ['Table 1', 'Table 2', 'Table 3', 'Table 4'] | ['images/4584bc1cab2b3666269c237bdbbd1b4df550e3959b8bb76bdede29a12727b351.jpg', 'images/a6254a44a961174af259338151f5d83522877672f091e94dc159e438aded5ddc.jpg', 'images/75f3909c72e81a021d776ae110c21cbef76c5af19d2a275c98cb87c82056d383.jpg', 'images/0c955ce83df71273494300492571aa486b8447724460975d37e40202ee8c8a1f.jpg'] | ['table'] | 4 | 1 | 5 | {'Instead of directly regressing on the ground truth vector field, Consistency-FM directly defines straight flows with consistent velocity that start from different times to the same endpoint. Specifically, we have the following lemma (prove in Appendix A.1): ': '1'} | {'1': 'Instead of directly regressing on the ground truth vector field, Consistency-FM directly defines straight flows with consistent velocity that start from different times to the same endpoint. Specifically, we have the following lemma (prove in Appendix A.1): '} | {} | {} | {'images/4584bc1cab2b3666269c237bdbbd1b4df550e3959b8bb76bdede29a12727b351.jpg': '1', 'images/75f3909c72e81a021d776ae110c21cbef76c5af19d2a275c98cb87c82056d383.jpg': '3', 'images/a6254a44a961174af259338151f5d83522877672f091e94dc159e438aded5ddc.jpg': '2', 'images/0c955ce83df71273494300492571aa486b8447724460975d37e40202ee8c8a1f.jpg': '4'} | {'1': 'images/4584bc1cab2b3666269c237bdbbd1b4df550e3959b8bb76bdede29a12727b351.jpg', '3': 'images/75f3909c72e81a021d776ae110c21cbef76c5af19d2a275c98cb87c82056d383.jpg', '2': 'images/a6254a44a961174af259338151f5d83522877672f091e94dc159e438aded5ddc.jpg', '4': 'images/0c955ce83df71273494300492571aa486b8447724460975d37e40202ee8c8a1f.jpg'} | {} | ['Instead of directly regressing on the ground truth vector field, Consistency-FM directly defines straight flows with consistent velocity that start from different times to the same endpoint. Specifically, we have the following lemma (prove in Appendix A.1): '] | fd2f46c9e9ce065018261c79e4bf414a71abfae337f3faa2bf15a48fdd911f0c | 8c2ef55eef0d86e9d05bef581f26ff0fb739fa87 |
explanation | What are the reasons for the different performances of the unguided approach across various tasks? | The proposed distillation methods indeed have different effects in different tasks. The Table 2 corresponds to the scenario of zero-shot inference on large language models. In this case, to produce meaningful (not random) inference, the model capacity and training dataset need to be sufficiently large. As we often observe in such large-scale training, the help brought by the enhanced training technique will be reduced compared to the scenario of fine-tuning a smaller model using limited data. This leads to the smaller relative difference in Table 2 compared to Table 1 and Table 3. On the other hand, we did find issues in hyperparameter tuning in certain tasks and thank you for pointing out that. We performed grid search of hyperparameters in all the experiments for fair comparison, and as for the QNLI and QQP tasks the unguided model hyperparameters we identified failed to converge very well, leading to rather low accuracy. However, we performed more complete hyperparameter search on all the tasks after the initial submission and found some configurations with better results on QNLI, QQP, and SST2, given in the revised Table 1. The CALD models still outperform the unguided models by more than 10% in average accuracy. Therefore, the conclusions we drawn from the experiments are not affected. | ['Table 1', 'Table 2', 'Table 3'] | ['images/83b6e86b10024de7152534b36aabdc49122020f75cebe1217ea2380354aff292.jpg', 'images/79766378db8c691eda1c3f51d27ae46996a8e59c5475c93a621738726d28df13.jpg', 'images/bd5358fe10a83b107dacdece7f6c535060a32b18ad102fc6b077d4c6375984b6.jpg'] | ['table'] | 3 | 2 | 5 | {'• Target Guided. We can directly transfer the parameters from the fine-tuned teacher target model and distill from it. More specifically, given the model inputs for training on a target classification task, we denote the hidden states from the student and the teacher target as H(s) and H(t), each with m vectors; outputs (class probabilities) of the student and the teacher target as y(s) and y(t); and one-hot labels as y. Then the loss terms are written as ': '1', 'Although not our focus, we also carry out an extra experiment to convert a widely-used open-source LM of various sizes, namely Pythia (Biderman et al., 2023), into Mamba for language modeling by retraining on a small subset of the pretraining corpus. ': '2'} | {'1': '• Target Guided. We can directly transfer the parameters from the fine-tuned teacher target model and distill from it. More specifically, given the model inputs for training on a target classification task, we denote the hidden states from the student and the teacher target as H(s) and H(t), each with m vectors; outputs (class probabilities) of the student and the teacher target as y(s) and y(t); and one-hot labels as y. Then the loss terms are written as ', '2': 'Although not our focus, we also carry out an extra experiment to convert a widely-used open-source LM of various sizes, namely Pythia (Biderman et al., 2023), into Mamba for language modeling by retraining on a small subset of the pretraining corpus. '} | {} | {} | {'images/83b6e86b10024de7152534b36aabdc49122020f75cebe1217ea2380354aff292.jpg': '1', 'images/79766378db8c691eda1c3f51d27ae46996a8e59c5475c93a621738726d28df13.jpg': '2', 'images/bd5358fe10a83b107dacdece7f6c535060a32b18ad102fc6b077d4c6375984b6.jpg': '3'} | {'1': 'images/83b6e86b10024de7152534b36aabdc49122020f75cebe1217ea2380354aff292.jpg', '2': 'images/79766378db8c691eda1c3f51d27ae46996a8e59c5475c93a621738726d28df13.jpg', '3': 'images/bd5358fe10a83b107dacdece7f6c535060a32b18ad102fc6b077d4c6375984b6.jpg'} | {} | ['• Target Guided. We can directly transfer the parameters from the fine-tuned teacher target model and distill from it. More specifically, given the model inputs for training on a target classification task, we denote the hidden states from the student and the teacher target as H(s) and H(t), each with m vectors; outputs (class probabilities) of the student and the teacher target as y(s) and y(t); and one-hot labels as y. Then the loss terms are written as ', 'Although not our focus, we also carry out an extra experiment to convert a widely-used open-source LM of various sizes, namely Pythia (Biderman et al., 2023), into Mamba for language modeling by retraining on a small subset of the pretraining corpus. '] | 4a4b0196466c6c22db5b60d2b3f0218bd1a1b5721c5c8290e83a3e171768f2c5 | 91bbf564af0c392bf3d0152e8ff6b20e5a1f211f |
explanation | How is the last-demonstration clustering supported by evidence? | While we acknowledge that last-demonstration clustering may appear less pronounced in some visualizations, multiple lines of evidence still support its existence: Figure 3a shows elevated percentage frequencies for last demonstrations compared to middle positions, Figure 3b demonstrates higher partial derivative norms for the last chunk versus middle chunks, and we've added new evidence in Figure 5 showing attention weights to the last token steadily increase across layers. | ['Figure 3', 'Figure 5'] | ['images/fabe8c971816529b4c874def50c0f2e100520e70af95a726acf1450e01eea639.jpg', 'images/fe074d7e6b9aab3e309f6ad1ffdc5778d949aecc4bf0867ae31b1e7e1ffc94eb.jpg'] | ['figure'] | 2 | 3 | 5 | {'We prepare 100 randomized prompts and compute the partial derivative norms similarly to Section 3.2. To ensure the prompts are differently distributed to training ones, we build each prompt as a sequence of 50 to 100 random words, resulting in meaningless sentences. For each prompt, we compute its chunk partial derivative norms, then average over 100 prompts. Figure 6 shows interesting results. It reveals a robust correlation between first-demonstration clustering and the utilization of the causal attention mask. Specifically, the importance of beginning tokens is markedly elevated when, and only when, the causal attention mask is applied, which aligns with the findings presented in Proposition 4.1. On the other hand, the case for last-demonstration is more complex. While the importance of ending tokens remains distinctively high when sinusoidal positional encoding is employed in the absence of a causal attention mask, this phenomenon is not observed for rotary and trainable positional encoding. This suggests that the importance of ending tokens is influenced by the interplay between the causal structure and the choice of positional encoding method. ': '1'} | {'1': 'We prepare 100 randomized prompts and compute the partial derivative norms similarly to Section 3.2. To ensure the prompts are differently distributed to training ones, we build each prompt as a sequence of 50 to 100 random words, resulting in meaningless sentences. For each prompt, we compute its chunk partial derivative norms, then average over 100 prompts. Figure 6 shows interesting results. It reveals a robust correlation between first-demonstration clustering and the utilization of the causal attention mask. Specifically, the importance of beginning tokens is markedly elevated when, and only when, the causal attention mask is applied, which aligns with the findings presented in Proposition 4.1. On the other hand, the case for last-demonstration is more complex. While the importance of ending tokens remains distinctively high when sinusoidal positional encoding is employed in the absence of a causal attention mask, this phenomenon is not observed for rotary and trainable positional encoding. This suggests that the importance of ending tokens is influenced by the interplay between the causal structure and the choice of positional encoding method. '} | {'images/ec3135d1cfef854bb75e4265222eba50ed2b0ed0d52b35742ddda3078c21d394.jpg': '2', 'images/fabe8c971816529b4c874def50c0f2e100520e70af95a726acf1450e01eea639.jpg': '3', 'images/72e917b6bbc2426132b4a78754ac69c62ff71dbba1420f390b68497c5bb6d90e.jpg': '6', 'images/fe074d7e6b9aab3e309f6ad1ffdc5778d949aecc4bf0867ae31b1e7e1ffc94eb.jpg': '5'} | {'2': 'images/ec3135d1cfef854bb75e4265222eba50ed2b0ed0d52b35742ddda3078c21d394.jpg', '3': 'images/fabe8c971816529b4c874def50c0f2e100520e70af95a726acf1450e01eea639.jpg', '6': 'images/72e917b6bbc2426132b4a78754ac69c62ff71dbba1420f390b68497c5bb6d90e.jpg', '5': 'images/fe074d7e6b9aab3e309f6ad1ffdc5778d949aecc4bf0867ae31b1e7e1ffc94eb.jpg'} | {} | {} | {} | ['images/72e917b6bbc2426132b4a78754ac69c62ff71dbba1420f390b68497c5bb6d90e.jpg', 'images/ec3135d1cfef854bb75e4265222eba50ed2b0ed0d52b35742ddda3078c21d394.jpg', 'We prepare 100 randomized prompts and compute the partial derivative norms similarly to Section 3.2. To ensure the prompts are differently distributed to training ones, we build each prompt as a sequence of 50 to 100 random words, resulting in meaningless sentences. For each prompt, we compute its chunk partial derivative norms, then average over 100 prompts. Figure 6 shows interesting results. It reveals a robust correlation between first-demonstration clustering and the utilization of the causal attention mask. Specifically, the importance of beginning tokens is markedly elevated when, and only when, the causal attention mask is applied, which aligns with the findings presented in Proposition 4.1. On the other hand, the case for last-demonstration is more complex. While the importance of ending tokens remains distinctively high when sinusoidal positional encoding is employed in the absence of a causal attention mask, this phenomenon is not observed for rotary and trainable positional encoding. This suggests that the importance of ending tokens is influenced by the interplay between the causal structure and the choice of positional encoding method. '] | 2ce088d4fbe67d6821915184112e5defdd7a3bcfcfe7b9ce6b34a0b27eb435bc | b8fc178ed7dc8207c662d4ba992e64d9a28fc8ee |
explanation | Does the method work with real-world images? | Our work works well with real-world images (see the first two samples of Figure 4, all three samples of Figure 5, first two samples of Figure 6, and all the samples of Figure 7). | ['Figure 4', 'Figure 5', 'Figure 6', 'Figure 7'] | ['images/9b75a55929abeeee0f970442e7358f841aba7019bab5cdac23752b0c2ed34f32.jpg', 'images/ece6ed302a7295cf3813537e94d26a34f41157b56cd355d789ada87b42bac8ea.jpg', 'images/4894814557a8f53513b6310e3ee6de20a59c21ba809ee7c937ed9716f8450cb1.jpg', 'images/4302891effddf7411089361e222e4f6d69e2c5ade47430b9764184817e08b1a6.jpg'] | ['figure'] | 4 | 1 | 5 | {} | {} | {'images/4894814557a8f53513b6310e3ee6de20a59c21ba809ee7c937ed9716f8450cb1.jpg': '6', 'images/c4510071d04ee16399d200e62fee65a7c0007882c555daff9368e23ae77f2c23.jpg': '3', 'images/4302891effddf7411089361e222e4f6d69e2c5ade47430b9764184817e08b1a6.jpg': '7', 'images/ece6ed302a7295cf3813537e94d26a34f41157b56cd355d789ada87b42bac8ea.jpg': '5', 'images/9b75a55929abeeee0f970442e7358f841aba7019bab5cdac23752b0c2ed34f32.jpg': '4'} | {'6': 'images/4894814557a8f53513b6310e3ee6de20a59c21ba809ee7c937ed9716f8450cb1.jpg', '3': 'images/c4510071d04ee16399d200e62fee65a7c0007882c555daff9368e23ae77f2c23.jpg', '7': 'images/4302891effddf7411089361e222e4f6d69e2c5ade47430b9764184817e08b1a6.jpg', '5': 'images/ece6ed302a7295cf3813537e94d26a34f41157b56cd355d789ada87b42bac8ea.jpg', '4': 'images/9b75a55929abeeee0f970442e7358f841aba7019bab5cdac23752b0c2ed34f32.jpg'} | {} | {} | {} | ['images/c4510071d04ee16399d200e62fee65a7c0007882c555daff9368e23ae77f2c23.jpg'] | b607fe6e943eefd103b89382aad8a02304a8098893da7dd0d8060f6c1189ad21 | dc4965f7e90b8b1f74b0f2cf392194fdb07ae1ab |
explanation | What are the reasons for the marginal accuracy improvements observed in the ablation studies? | For the performance improvement of the model, it is important to highlight that many of the baselines we selected are recent and highly competitive models, making accuracy improvements both challenging and meaningful. Regarding the relatively marginal improvements observed in the ablation studies, this is because we conducted experiments in four aspects within the ablation study: The first two aspects focus on analyzing the impact of the number of segments and encoders in PSformer. From Table 3 and Table 4, the observed changes in these areas are indeed minor (with an average MSE variation of 0.002 on the ETTh1 and ETTm1 datasets), demonstrating that PSformer is robust to these two critical hyperparameters. The latter two aspects involve ablation studies on the model's key innovations: parameter sharing and segment attention. From Table 5 and Table 6, it can be observed that the variances in metrics are highly significant. Specifically, parameter sharing achieved an average MSE reduction of 0.016 on ETTm1, while SegAtt achieved an average MSE reduction of 0.017 on ETTh1. These results demonstrate that the two core contributions of PSformer significantly improve its performance, making it more competitive compared to a wide range of baseline models. | ['Table 3', 'Table 4', 'Table 5', 'Table 6'] | ['images/aa351033b8cf75db6e07454ead56e7b665fae04b673800e8f2558e4d54cef916.jpg', 'images/1f1078f753f79ee4153f96eee248fd1500d4c26e7a555df9cf2f32b3f99ab65b.jpg', 'images/9309bdd10a5e15d03da92fbb58615df58674e8dbbcf7250ca71335513ecb2cba.jpg', 'images/51c306a362a485c4852f716f2bfcf255a948e69e522101b7a27c655d91600da9.jpg'] | ['table'] | 4 | 1 | 5 | {'After passing through n layers of the PSformer Encoder, the final output is Xpred = XoutW F , where Xpred ∈RM×F , and W F ∈RL×F is a linear mapping, where F is the prediction length. The Xpred is the final output of the PSformer model. The PSformer structure does not use positional encoding, as the segment attention mixes local spatiotemporal information. We discuss this in more detail in Appendix A.6 and Appendix B.8. ': '1'} | {'1': 'After passing through n layers of the PSformer Encoder, the final output is Xpred = XoutW F , where Xpred ∈RM×F , and W F ∈RL×F is a linear mapping, where F is the prediction length. The Xpred is the final output of the PSformer model. The PSformer structure does not use positional encoding, as the segment attention mixes local spatiotemporal information. We discuss this in more detail in Appendix A.6 and Appendix B.8. '} | {} | {} | {'images/51c306a362a485c4852f716f2bfcf255a948e69e522101b7a27c655d91600da9.jpg': '6', 'images/aa351033b8cf75db6e07454ead56e7b665fae04b673800e8f2558e4d54cef916.jpg': '3', 'images/1f1078f753f79ee4153f96eee248fd1500d4c26e7a555df9cf2f32b3f99ab65b.jpg': '4', 'images/9309bdd10a5e15d03da92fbb58615df58674e8dbbcf7250ca71335513ecb2cba.jpg': '5'} | {'6': 'images/51c306a362a485c4852f716f2bfcf255a948e69e522101b7a27c655d91600da9.jpg', '3': 'images/aa351033b8cf75db6e07454ead56e7b665fae04b673800e8f2558e4d54cef916.jpg', '4': 'images/1f1078f753f79ee4153f96eee248fd1500d4c26e7a555df9cf2f32b3f99ab65b.jpg', '5': 'images/9309bdd10a5e15d03da92fbb58615df58674e8dbbcf7250ca71335513ecb2cba.jpg'} | {} | ['After passing through n layers of the PSformer Encoder, the final output is Xpred = XoutW F , where Xpred ∈RM×F , and W F ∈RL×F is a linear mapping, where F is the prediction length. The Xpred is the final output of the PSformer model. The PSformer structure does not use positional encoding, as the segment attention mixes local spatiotemporal information. We discuss this in more detail in Appendix A.6 and Appendix B.8. '] | 8abcf8cbc84e27faa2a3473349a878f22d2e9d285585994ea6deb78140bf8142 | e69a59c151ec85e9a7265a99a50bc763aa6cf326 |
explanation | What is the motivation for introducing an uncertainty-aware exploration strategy? | We have updated the abstract to clarify the motivation of our work. As further elaborated in the introduction, most existing methods treat recommendation as a static process, which prevents them from effectively accounting for users’ evolving preferences. Sequential recommendation methods address this limitation to some extent by leveraging previously interacted items to capture users’ dynamic behavior. However, prior RL-based recommender system models largely rely on standard exploration strategies, such as ε-greedy, which are less effective in scenarios with a large item space and sparse reward signals due to limited user interactions. As a result, these methods may struggle to learn an optimal policy that adequately captures users’ evolving preferences and achieves the maximum expected reward over the long term. The qualitative results presented in Figure 1 and Table 1 illustrate the limitations of existing approaches and further highlight the need for a systematic, uncertainty-aware exploration strategy. | ['Figure 1', 'Table 1'] | ['images/fa5fccc5987c17ab6cac4b5db3a07f8ec93a37436f6e558577aba21b8cbbcf4f.jpg', 'images/9b094692d407a6efcb89998338d3da6e042001fee571511e0a247c7e41638bc3.jpg'] | ['mixed'] | 2 | 3 | 5 | {'where ratingu,i is the user assigned rating, τ is the threshold to identify if a user provided rating is positive. Evidential reward aggregates the recommended items’ rating as a traditional reward r balanced with their vacuity predictions as a measure of information gain, denoted as an uncertainty regularizer R. During testing, for item i′ not appearing in user u’s interaction history Hu, a neutral rating ratingu,i′ = τ will be assigned to give neutral feedback. ': '1'} | {'1': 'where ratingu,i is the user assigned rating, τ is the threshold to identify if a user provided rating is positive. Evidential reward aggregates the recommended items’ rating as a traditional reward r balanced with their vacuity predictions as a measure of information gain, denoted as an uncertainty regularizer R. During testing, for item i′ not appearing in user u’s interaction history Hu, a neutral rating ratingu,i′ = τ will be assigned to give neutral feedback. '} | {'images/df2c9e1253936ea04152414231d474bf9ca9030048f1ea7acdaa739044d37396.jpg': '2', 'images/fa5fccc5987c17ab6cac4b5db3a07f8ec93a37436f6e558577aba21b8cbbcf4f.jpg': '1'} | {'2': 'images/df2c9e1253936ea04152414231d474bf9ca9030048f1ea7acdaa739044d37396.jpg', '1': 'images/fa5fccc5987c17ab6cac4b5db3a07f8ec93a37436f6e558577aba21b8cbbcf4f.jpg'} | {'images/0d9baf01287057b51e00bb8299a7e6f498abf529debe3d15976263037e9bbbb1.jpg': '3', 'images/9b094692d407a6efcb89998338d3da6e042001fee571511e0a247c7e41638bc3.jpg': '1'} | {'3': 'images/0d9baf01287057b51e00bb8299a7e6f498abf529debe3d15976263037e9bbbb1.jpg', '1': 'images/9b094692d407a6efcb89998338d3da6e042001fee571511e0a247c7e41638bc3.jpg'} | {} | ['images/df2c9e1253936ea04152414231d474bf9ca9030048f1ea7acdaa739044d37396.jpg', 'images/0d9baf01287057b51e00bb8299a7e6f498abf529debe3d15976263037e9bbbb1.jpg', 'where ratingu,i is the user assigned rating, τ is the threshold to identify if a user provided rating is positive. Evidential reward aggregates the recommended items’ rating as a traditional reward r balanced with their vacuity predictions as a measure of information gain, denoted as an uncertainty regularizer R. During testing, for item i′ not appearing in user u’s interaction history Hu, a neutral rating ratingu,i′ = τ will be assigned to give neutral feedback. '] | a833561f0ef7484e750c82f53cfc0766535b7e1c1697d5c3a86b2caa0fa0ec11 | 01bc18d9733b34622eff9efd4422fca8f18b069c |
explanation | On tasks where the model already performs well, does C&P fine-tuning lead to a decline in performance? | According to Table 6, InternVL2-2B and InternVL2-8B show minor declines on a few datasets where they originally performed well. We attribute this to the possibility that both cognitive and perceptual responses may occasionally fail simultaneously while maintaining consistency, as illustrated in Figure 4a. However, our analysis indicates that such cases are rare. Moreover, considering the significant improvement in C&P consistency after fine-tuning, these 'trade-offs' are acceptable. | ['Table 6', 'Figure 4'] | ['images/bcb639e76fd6dc9fbd9ce9c46b15d6202f6959795d950b3d155df1b78c839daf.jpg', 'images/cc3acc6bad6037c01587df84ec064bd17d72732e9e58af6558ab04c50b980ae2.jpg'] | ['mixed'] | 2 | 3 | 5 | {'Table 2 shows the evaluation results. Overall, closed-source models have higher C&P consistency compared to open-source models. Qwen-VL-Max achieves the highest C&P consistency at 79.98%, followed by GPT-4o at 68.60%. Among the open-source models, Qwen-VL-Chat demonstrates the ': '1', 'Notably, OCR annotations are required in Section 2.3. For the DocVQA dataset, the official OCR annotations are used, while the other datasets use OCR annotations produced by Duguang OCR1. ': '2'} | {'1': 'Table 2 shows the evaluation results. Overall, closed-source models have higher C&P consistency compared to open-source models. Qwen-VL-Max achieves the highest C&P consistency at 79.98%, followed by GPT-4o at 68.60%. Among the open-source models, Qwen-VL-Chat demonstrates the ', '2': 'Notably, OCR annotations are required in Section 2.3. For the DocVQA dataset, the official OCR annotations are used, while the other datasets use OCR annotations produced by Duguang OCR1. '} | {'images/cc3acc6bad6037c01587df84ec064bd17d72732e9e58af6558ab04c50b980ae2.jpg': '4'} | {'4': 'images/cc3acc6bad6037c01587df84ec064bd17d72732e9e58af6558ab04c50b980ae2.jpg'} | {'images/bcb639e76fd6dc9fbd9ce9c46b15d6202f6959795d950b3d155df1b78c839daf.jpg': '6', 'images/2947bf84e1016e8842d69621a930d869f093ca5b459b699f7a73c36a6b6fc8f9.jpg': '4'} | {'6': 'images/bcb639e76fd6dc9fbd9ce9c46b15d6202f6959795d950b3d155df1b78c839daf.jpg', '4': 'images/2947bf84e1016e8842d69621a930d869f093ca5b459b699f7a73c36a6b6fc8f9.jpg'} | {} | ['Table 2 shows the evaluation results. Overall, closed-source models have higher C&P consistency compared to open-source models. Qwen-VL-Max achieves the highest C&P consistency at 79.98%, followed by GPT-4o at 68.60%. Among the open-source models, Qwen-VL-Chat demonstrates the ', 'images/2947bf84e1016e8842d69621a930d869f093ca5b459b699f7a73c36a6b6fc8f9.jpg', 'Notably, OCR annotations are required in Section 2.3. For the DocVQA dataset, the official OCR annotations are used, while the other datasets use OCR annotations produced by Duguang OCR1. '] | 20fe7fb2e826aaf4eb4a1389904161ab54b16c90753811caa2ca465b23ab243b | 08af6e3bbee2dba7d63f9faef1d3963bebb02a2c |
explanation | What is the relationship between N, n, and m? | In Table 2, N = m = n, where m and n represent the sample sizes for each of the two distributions being tested. In Figure 5, however, N = m + n, which represents the total sample size received for the experiment. | ['Table 2', 'Figure 5'] | ['images/2950f7b20dfe365b5c28b1ed08c83ceb608b33783477a621623b59347cc6318d.jpg', 'images/692e64130ff3aac0cde8b87c3679d8fedebd43fb50805bb63a333a330a465d76.jpg'] | ['mixed'] | 2 | 3 | 5 | {'Theorem 5.1. (Lopez-Paz & Oquab, 2018b) Let f ′ ∈Cϕ : X →{0, 1} be the SSL-C2ST classifier model. Let H0 : t = 1 and H1 : t = 1 −ϵ(P, Q; f ′), where t is the test accuracy and ϵ(P, Q; f ′) = Pr(zi,li)∼D [f ′(zi) ̸= li] /2 ∈ 0, 21 represents the inability of f ′ to distinguish between P and Q. The test power of tˆ is: ': '1', 'where nte = |Ste| and I is the indicator function. Finally, we compute the p-value to determine if the test statistic is significantly greater than the random guessing accuracy, utilizing the approximate null distribution of C2ST outlined in Appendix D.1 and the permutation test discussed next. ': '2', 'and f ∗(zk) be the estimate of the conditional probability distribution I p(lk = 1|zk) > 1 , the statistic or the accuracy of the classifier f ∗on Ste can be written as: ': '3'} | {'1': 'Theorem 5.1. (Lopez-Paz & Oquab, 2018b) Let f ′ ∈Cϕ : X →{0, 1} be the SSL-C2ST classifier model. Let H0 : t = 1 and H1 : t = 1 −ϵ(P, Q; f ′), where t is the test accuracy and ϵ(P, Q; f ′) = Pr(zi,li)∼D [f ′(zi) ̸= li] /2 ∈ 0, 21 represents the inability of f ′ to distinguish between P and Q. The test power of tˆ is: ', '2': 'where nte = |Ste| and I is the indicator function. Finally, we compute the p-value to determine if the test statistic is significantly greater than the random guessing accuracy, utilizing the approximate null distribution of C2ST outlined in Appendix D.1 and the permutation test discussed next. ', '3': 'and f ∗(zk) be the estimate of the conditional probability distribution I p(lk = 1|zk) > 1 , the statistic or the accuracy of the classifier f ∗on Ste can be written as: '} | {'images/692e64130ff3aac0cde8b87c3679d8fedebd43fb50805bb63a333a330a465d76.jpg': '5'} | {'5': 'images/692e64130ff3aac0cde8b87c3679d8fedebd43fb50805bb63a333a330a465d76.jpg'} | {'images/2950f7b20dfe365b5c28b1ed08c83ceb608b33783477a621623b59347cc6318d.jpg': '2'} | {'2': 'images/2950f7b20dfe365b5c28b1ed08c83ceb608b33783477a621623b59347cc6318d.jpg'} | {} | ['Theorem 5.1. (Lopez-Paz & Oquab, 2018b) Let f ′ ∈Cϕ : X →{0, 1} be the SSL-C2ST classifier model. Let H0 : t = 1 and H1 : t = 1 −ϵ(P, Q; f ′), where t is the test accuracy and ϵ(P, Q; f ′) = Pr(zi,li)∼D [f ′(zi) ̸= li] /2 ∈ 0, 21 represents the inability of f ′ to distinguish between P and Q. The test power of tˆ is: ', 'where nte = |Ste| and I is the indicator function. Finally, we compute the p-value to determine if the test statistic is significantly greater than the random guessing accuracy, utilizing the approximate null distribution of C2ST outlined in Appendix D.1 and the permutation test discussed next. ', 'and f ∗(zk) be the estimate of the conditional probability distribution I p(lk = 1|zk) > 1 , the statistic or the accuracy of the classifier f ∗on Ste can be written as: '] | 57d8aa3edeb2dbd5843784fdc93d50eda986568b887c34b5165e185e1aced37e | 117a7d1efe9b6cebaba614db86e709185420d408 |
explanation | How does the addition of cross-modal data impact the performance of your model? | We have conducted extensive experiments and ablation studies to demonstrate the benefits of adding cross-modal data under the same model structure and token budget. The results are presented in Table 1 and Figure 6. | ['Table 1', 'Figure 6'] | ['images/c33c24982b8e0230e8525a7adde26a5543eaa8420130b670cf13a875e8704f17.jpg', 'images/102bf6a906a8851cfdd1c79ca2ea69ab3c198ac7f4a8e6fd88b5466602dfdeab.jpg'] | ['mixed'] | 2 | 3 | 5 | {'BSM employs a single-nucleotide tokenizer with a vocabulary that includes nucleotides, amino acids, and special tokens. It uses an autoregressive architecture to model biological sequences such as genes and proteins. By learning next-token prediction, the model reasons over sequences causally and captures statistical patterns and dependencies in the training data, enabling effective representation and generation of biological sequences. Furthermore, the autoregressive architecture’s sequential nature effectively handles long-range dependencies, which is crucial in biological sequences like DNA, RNA, and proteins, where long-context information can reveal critical functional relationships or structural interactions. ': '1'} | {'1': 'BSM employs a single-nucleotide tokenizer with a vocabulary that includes nucleotides, amino acids, and special tokens. It uses an autoregressive architecture to model biological sequences such as genes and proteins. By learning next-token prediction, the model reasons over sequences causally and captures statistical patterns and dependencies in the training data, enabling effective representation and generation of biological sequences. Furthermore, the autoregressive architecture’s sequential nature effectively handles long-range dependencies, which is crucial in biological sequences like DNA, RNA, and proteins, where long-context information can reveal critical functional relationships or structural interactions. '} | {'images/2a282d81483ab9546bf759391883e174ba84c87d74eabaa34c26ce02fb1b988f.jpg': '5', 'images/102bf6a906a8851cfdd1c79ca2ea69ab3c198ac7f4a8e6fd88b5466602dfdeab.jpg': '6', 'images/39752a43862b401b88e5097b689efb2d95d26e7802d6f768274cf3b18def9b76.jpg': '3'} | {'5': 'images/2a282d81483ab9546bf759391883e174ba84c87d74eabaa34c26ce02fb1b988f.jpg', '6': 'images/102bf6a906a8851cfdd1c79ca2ea69ab3c198ac7f4a8e6fd88b5466602dfdeab.jpg', '3': 'images/39752a43862b401b88e5097b689efb2d95d26e7802d6f768274cf3b18def9b76.jpg'} | {'images/c33c24982b8e0230e8525a7adde26a5543eaa8420130b670cf13a875e8704f17.jpg': '1'} | {'1': 'images/c33c24982b8e0230e8525a7adde26a5543eaa8420130b670cf13a875e8704f17.jpg'} | {} | ['images/2a282d81483ab9546bf759391883e174ba84c87d74eabaa34c26ce02fb1b988f.jpg', 'images/39752a43862b401b88e5097b689efb2d95d26e7802d6f768274cf3b18def9b76.jpg', 'BSM employs a single-nucleotide tokenizer with a vocabulary that includes nucleotides, amino acids, and special tokens. It uses an autoregressive architecture to model biological sequences such as genes and proteins. By learning next-token prediction, the model reasons over sequences causally and captures statistical patterns and dependencies in the training data, enabling effective representation and generation of biological sequences. Furthermore, the autoregressive architecture’s sequential nature effectively handles long-range dependencies, which is crucial in biological sequences like DNA, RNA, and proteins, where long-context information can reveal critical functional relationships or structural interactions. '] | f7db81ab5514b0fd0666d31b7dfd586921d981fa3d40fec604ea5fb3d76b12be | 24fd5d6b134b0c6def366de2ca6cae4543e39f62 |
explanation | How does the proposed model handle large deformations in medical images? | Our new draft currently extends Table 1 with a full rigid transformation setting including all 3 transformations: rotation, scaling, and translation. However, we would like to point out that across all settings of Experiment 1, we apply Brownian noise deformation at multiple scales to ensure the synthetic transformation is not strictly rigid. The amount this local deformation impacts the tissue structures of Experiment 1 can be seen in the qualitative results of Figure 3 of the manuscript. | ['Table 1', 'Figure 3'] | ['images/bd332f00b08d46c3fe079807993e810711ef42010efacdfaf9ef76c4f2dfb014.jpg', 'images/416fa5fe6a345128a602cf05f287b6bb8b06c438dbb519062a04f760b6c7a49e.jpg'] | ['mixed'] | 2 | 3 | 5 | {'In this section, we first formally establish the limitations imposed on deformable image registration by the grid constraints of Eulerian frameworks. Afterwards, we establish a Lagrangian formulation that does not make any grid assumptions (section 2.1). Within this context, we highlight the advantages offered by geometric deep learning in modeling deformations as interactions between free-floating features (section 2.2). Next, we propose a data-driven form of local interpolation, which facilitates multi-scale deformation modeling by learning to propagate deformations across resolutions (section 2.3). Finally, we combine these ideas to construct an end-to-end trainable neural network capable of learning deformable registration in continuous domains in a coarse-to-fine fashion (section 2.4). ': '1', 'A common necessary preprocessing technique employed to mitigate this issue involves an exhaustive search for an initial affine alignment. This reduces the degrees of freedom in the transformation parameters by guaranteeing that similar features are captured in a consistent spatial context, thus reducing the range of representations experienced by the network. Recent works combat the misalignmentdependent complexity by incorporating transformer layers throughout the network (Chen et al., 2022; 2023; Liu et al., 2022; Meng et al., 2022; Wang et al., 2023; Zhu & Lu, 2022). This enables greater flexibility in the feature extraction process as the transformer layer’s attention mechanism is able to establish non-local spatial relationships at the cost of increased learnable parameters. Similarly, cascading approaches have shown increased accuracy by recovering the misalignment progressively, modeling the transformation as a sequence of deformations (Hu et al., 2022; Sandkühler et al., 2019; Zhao et al., 2019). ': '2'} | {'1': 'In this section, we first formally establish the limitations imposed on deformable image registration by the grid constraints of Eulerian frameworks. Afterwards, we establish a Lagrangian formulation that does not make any grid assumptions (section 2.1). Within this context, we highlight the advantages offered by geometric deep learning in modeling deformations as interactions between free-floating features (section 2.2). Next, we propose a data-driven form of local interpolation, which facilitates multi-scale deformation modeling by learning to propagate deformations across resolutions (section 2.3). Finally, we combine these ideas to construct an end-to-end trainable neural network capable of learning deformable registration in continuous domains in a coarse-to-fine fashion (section 2.4). ', '2': 'A common necessary preprocessing technique employed to mitigate this issue involves an exhaustive search for an initial affine alignment. This reduces the degrees of freedom in the transformation parameters by guaranteeing that similar features are captured in a consistent spatial context, thus reducing the range of representations experienced by the network. Recent works combat the misalignmentdependent complexity by incorporating transformer layers throughout the network (Chen et al., 2022; 2023; Liu et al., 2022; Meng et al., 2022; Wang et al., 2023; Zhu & Lu, 2022). This enables greater flexibility in the feature extraction process as the transformer layer’s attention mechanism is able to establish non-local spatial relationships at the cost of increased learnable parameters. Similarly, cascading approaches have shown increased accuracy by recovering the misalignment progressively, modeling the transformation as a sequence of deformations (Hu et al., 2022; Sandkühler et al., 2019; Zhao et al., 2019). '} | {'images/416fa5fe6a345128a602cf05f287b6bb8b06c438dbb519062a04f760b6c7a49e.jpg': '3'} | {'3': 'images/416fa5fe6a345128a602cf05f287b6bb8b06c438dbb519062a04f760b6c7a49e.jpg'} | {'images/bd332f00b08d46c3fe079807993e810711ef42010efacdfaf9ef76c4f2dfb014.jpg': '1', 'images/72348eec94135ab7a03b205acb09c70e2e7df45331db3948baf5b1e8224a4a18.jpg': '2'} | {'1': 'images/bd332f00b08d46c3fe079807993e810711ef42010efacdfaf9ef76c4f2dfb014.jpg', '2': 'images/72348eec94135ab7a03b205acb09c70e2e7df45331db3948baf5b1e8224a4a18.jpg'} | {} | ['images/72348eec94135ab7a03b205acb09c70e2e7df45331db3948baf5b1e8224a4a18.jpg', 'A common necessary preprocessing technique employed to mitigate this issue involves an exhaustive search for an initial affine alignment. This reduces the degrees of freedom in the transformation parameters by guaranteeing that similar features are captured in a consistent spatial context, thus reducing the range of representations experienced by the network. Recent works combat the misalignmentdependent complexity by incorporating transformer layers throughout the network (Chen et al., 2022; 2023; Liu et al., 2022; Meng et al., 2022; Wang et al., 2023; Zhu & Lu, 2022). This enables greater flexibility in the feature extraction process as the transformer layer’s attention mechanism is able to establish non-local spatial relationships at the cost of increased learnable parameters. Similarly, cascading approaches have shown increased accuracy by recovering the misalignment progressively, modeling the transformation as a sequence of deformations (Hu et al., 2022; Sandkühler et al., 2019; Zhao et al., 2019). ', 'In this section, we first formally establish the limitations imposed on deformable image registration by the grid constraints of Eulerian frameworks. Afterwards, we establish a Lagrangian formulation that does not make any grid assumptions (section 2.1). Within this context, we highlight the advantages offered by geometric deep learning in modeling deformations as interactions between free-floating features (section 2.2). Next, we propose a data-driven form of local interpolation, which facilitates multi-scale deformation modeling by learning to propagate deformations across resolutions (section 2.3). Finally, we combine these ideas to construct an end-to-end trainable neural network capable of learning deformable registration in continuous domains in a coarse-to-fine fashion (section 2.4). '] | a6b72d9a6bc04b0c1dffad81c4bc17a49f27acd5641db79d0e693ac20938121e | 2e71063092065f2b211c52664560426b1e04c5ef |
explanation | How does the CoTFormer model compare to the standard Transformer in terms of performance? | The accuracy of the standard Transformer in Table 1 can indicate the distance between the CoTFormer and the standard Transformer. Therefore, it is necessary to add the standard Transformer to Figure 2. | ['Table 1', 'Figure 2'] | ['images/bd79fb6eebc5aefb653b4b480e9e9c98751350fed8a08da40c192caa576035f5.jpg', 'images/78d36fde1a32e35714e8df05588902cacc90cd989890adaa24267f1cafab50a9.jpg'] | ['mixed'] | 2 | 3 | 5 | {} | {} | {'images/e3201107214ce7a424118b6fd025aea53e7dfe9577b425b4c9c7787ead0069ae.jpg': '4', 'images/5a7031f87b40f338004cf846e40e18025b7e98d528cdd79cdfebd6680fee6792.jpg': '3', 'images/f2bd897de74379c2b125865a2fdc18f79b9187744fe9c4c27dc0fe5afae18fdc.jpg': '5', 'images/78d36fde1a32e35714e8df05588902cacc90cd989890adaa24267f1cafab50a9.jpg': '2'} | {'4': 'images/e3201107214ce7a424118b6fd025aea53e7dfe9577b425b4c9c7787ead0069ae.jpg', '3': 'images/5a7031f87b40f338004cf846e40e18025b7e98d528cdd79cdfebd6680fee6792.jpg', '5': 'images/f2bd897de74379c2b125865a2fdc18f79b9187744fe9c4c27dc0fe5afae18fdc.jpg', '2': 'images/78d36fde1a32e35714e8df05588902cacc90cd989890adaa24267f1cafab50a9.jpg'} | {'images/bd79fb6eebc5aefb653b4b480e9e9c98751350fed8a08da40c192caa576035f5.jpg': '1'} | {'1': 'images/bd79fb6eebc5aefb653b4b480e9e9c98751350fed8a08da40c192caa576035f5.jpg'} | {} | ['images/e3201107214ce7a424118b6fd025aea53e7dfe9577b425b4c9c7787ead0069ae.jpg', 'images/5a7031f87b40f338004cf846e40e18025b7e98d528cdd79cdfebd6680fee6792.jpg', 'images/f2bd897de74379c2b125865a2fdc18f79b9187744fe9c4c27dc0fe5afae18fdc.jpg'] | 6998e59fbcab22b1bc6ee609d88666efa0ea344c3bc37844ea2e058426bcfe0c | 3a439959ac98f4b2f52116ae11b370605e09b606 |
explanation | What are the performance differences between the SSF and MSF strategies? | First, we present the SSF and MSF visualization comparison in Figure 2. The SSF has a single change, while the MSF has a variety of changes. Second, in Table 6 we perform ablation experiments of SSF and MSF on segmentation, and we analyze why MSF is more suitable for segmentation. | ['Figure 2', 'Table 6'] | ['images/260264af09a8f3445bbdd80fdeec2b07693b431df57ccf3eae6333d168781a3a.jpg', 'images/5ae993f2b704b12e16b72c4e9ac2a9756bf3c36653746a6f51959b728caed000.jpg'] | ['mixed'] | 2 | 3 | 5 | {'To simulate the distortion and deformation of an object, we have chosen to use the Sine function as our residual function. The inherent periodic nature of the Sine function allows us to adjust the number of regions that are deformed with precision. Additionally, by manipulating the amplitude of the Sine function, we can precisely control the intensity of the deformation. This displacement field, generated by the Sine function, effectively distorts and deforms specific local regions of the point cloud data without altering the overall topology. As a result, the augmented point cloud data contains more intricate and detailed local features. The standard Sine function is shown below: ': '1'} | {'1': 'To simulate the distortion and deformation of an object, we have chosen to use the Sine function as our residual function. The inherent periodic nature of the Sine function allows us to adjust the number of regions that are deformed with precision. Additionally, by manipulating the amplitude of the Sine function, we can precisely control the intensity of the deformation. This displacement field, generated by the Sine function, effectively distorts and deforms specific local regions of the point cloud data without altering the overall topology. As a result, the augmented point cloud data contains more intricate and detailed local features. The standard Sine function is shown below: '} | {'images/01b46f660d690ae0f356567e49caf8b198e9bc41fea3c545d5a72c54bbc6bcd6.jpg': '4', 'images/260264af09a8f3445bbdd80fdeec2b07693b431df57ccf3eae6333d168781a3a.jpg': '2'} | {'4': 'images/01b46f660d690ae0f356567e49caf8b198e9bc41fea3c545d5a72c54bbc6bcd6.jpg', '2': 'images/260264af09a8f3445bbdd80fdeec2b07693b431df57ccf3eae6333d168781a3a.jpg'} | {'images/5ae993f2b704b12e16b72c4e9ac2a9756bf3c36653746a6f51959b728caed000.jpg': '6', 'images/8cc72a64880c6c21759991d0f88f1ec620fd16727c49450e5f7a67b51eb99754.jpg': '5'} | {'6': 'images/5ae993f2b704b12e16b72c4e9ac2a9756bf3c36653746a6f51959b728caed000.jpg', '5': 'images/8cc72a64880c6c21759991d0f88f1ec620fd16727c49450e5f7a67b51eb99754.jpg'} | {} | ['images/8cc72a64880c6c21759991d0f88f1ec620fd16727c49450e5f7a67b51eb99754.jpg', 'To simulate the distortion and deformation of an object, we have chosen to use the Sine function as our residual function. The inherent periodic nature of the Sine function allows us to adjust the number of regions that are deformed with precision. Additionally, by manipulating the amplitude of the Sine function, we can precisely control the intensity of the deformation. This displacement field, generated by the Sine function, effectively distorts and deforms specific local regions of the point cloud data without altering the overall topology. As a result, the augmented point cloud data contains more intricate and detailed local features. The standard Sine function is shown below: ', 'images/01b46f660d690ae0f356567e49caf8b198e9bc41fea3c545d5a72c54bbc6bcd6.jpg'] | 915d3f9f1702d60c5e98d2340e38873dd76632da2b5d1e3e1d7a9dfb85c2f5fc | 3b7721717f4d4bb039675982f8604ef8379258d5 |
explanation | How does the GSA-R2R dataset address the diversity of real-world environments? | We have made significant efforts to expand the diversity of GSA-R2R to include 20 distinct scene types, compared to just six in R2R. This diversity covers a wide range of daily scenarios and exceeds that of existing embodied navigation datasets, as highlighted in Table 1 of our paper. We already include multiple commercial spaces such as cinemas, shops, and restaurants, as illustrated in Figure 2 of our paper. | ['Table 1', 'Figure 2'] | ['images/52bf352cbd52ddb91e50272965f8dfd54170eea96c743cb3adf62eba877558ce.jpg', 'images/6178eda5ffcafe2b6b73084fd1941e5d713fc12a75f8318278a58a5aedf8cf64.jpg'] | ['mixed'] | 2 | 3 | 5 | {} | {} | {'images/1a425bbe2763a3120894c3389ccec7ee600b5454cb5de1118f2041dea2aabfeb.jpg': '1', 'images/34842d927b45d096db0f8485a57e2098bd1596a0bd7265f2e4fd1f7720206aaa.jpg': '4', 'images/6178eda5ffcafe2b6b73084fd1941e5d713fc12a75f8318278a58a5aedf8cf64.jpg': '2'} | {'1': 'images/1a425bbe2763a3120894c3389ccec7ee600b5454cb5de1118f2041dea2aabfeb.jpg', '4': 'images/34842d927b45d096db0f8485a57e2098bd1596a0bd7265f2e4fd1f7720206aaa.jpg', '2': 'images/6178eda5ffcafe2b6b73084fd1941e5d713fc12a75f8318278a58a5aedf8cf64.jpg'} | {'images/52bf352cbd52ddb91e50272965f8dfd54170eea96c743cb3adf62eba877558ce.jpg': '1', 'images/d88d1a88a655df45e8d41933a6a1b701c4ac4d7240316f9d40b38df2b625399c.jpg': '4'} | {'1': 'images/52bf352cbd52ddb91e50272965f8dfd54170eea96c743cb3adf62eba877558ce.jpg', '4': 'images/d88d1a88a655df45e8d41933a6a1b701c4ac4d7240316f9d40b38df2b625399c.jpg'} | {} | ['images/1a425bbe2763a3120894c3389ccec7ee600b5454cb5de1118f2041dea2aabfeb.jpg', 'images/d88d1a88a655df45e8d41933a6a1b701c4ac4d7240316f9d40b38df2b625399c.jpg', 'images/34842d927b45d096db0f8485a57e2098bd1596a0bd7265f2e4fd1f7720206aaa.jpg'] | 79321511912b2964f578557dbd5b0e3962b310f5fe14ce7b8b3ecb7cee6bd556 | 466366db3c29af46db9db97a71f1c21c2940ea95 |
explanation | What is the exact computational time/cost for the proposed method compared to existing MetaBBO methods? | We have demonstrated in the experiments (Figure 3, zero-shot performance) that the trained NeurELA can be seamlessly integrated into existing MetaBBO methods to provide effective dynamic landscape analysis, without further re-training. We also provide the inference wall time comparison in Table 1 to compare the computational cost required to obtain the landscape feature by our NeurELA and traditional ELA, where the results show that NeurELA requires less processing time than traditional ELA, particularly for the high-dimensional problem. | ['Figure 3', 'Table 1'] | ['images/404469c60be80871de0a0cac273007fcc1f18dfb0d7cdc107fc8c79a31f770b5.jpg', 'images/2860d185ad0551afe2aabb501992df2d0b7f46bca5e5e298a5678d318f671126.jpg'] | ['mixed'] | 2 | 3 | 5 | {'PIE. PIE normalizes observation ot using two min-max normalization operations: first on the candidate solutions {Xit}im=1 against the search range, and second on the objective values {yit}im=1 using the extremum values at time step t. This ensures unified representation and generalization by scaling all values to [0, 1]. For a d-dimensional optimization problem, the normalized observations ot ': '1', 'Model Complexity (RQ6). We discuss the relationship between the model complexity and the zero-shot performance (unseen MetaBBO algorithm & problem sets) of our NeurELA. Concretely, We pre-train NeurELA under 6 different model complexities, with various hidden dimensions, i.e., h = (16, 64), and the number of the Ts-Attn module, i.e., l = (1, 3, 5). We additionally pre-train three MLP baselines, which substitute the Ts-Attn module in NeurELA with a linear feed-forward layer, which holds a shape of h × h, h = (16, 64, 128). We report both the zero-shot performance (yaxis) and the computational efficiency (x-axis, presented as the consumed wall time for computing the landscape features) in Figure 7, where the dashed lines denotes the performance and wall-time of the Original baseline. #para denotes the number of the learnable parameters. The results show that: 1) a significant performance gap is observed between the MLP baselines and our Ts-Attn module (h = 16, l = 1). It validates the effectiveness of our Ts-Attn design, which enhance the feature extraction of our NeurELA by encouraging the information sharing at both the cross-solution and cross-dimension levels; 2) As the model complexity increases, the performance of the Ts-Attn module drops rapidly. It reveals that the increased number of learnable parameters challenges the optimization ability of the backend ES. Given the limited computational resources, it is difficult to identify the optimal parameters θ∗. ': '2'} | {'1': 'PIE. PIE normalizes observation ot using two min-max normalization operations: first on the candidate solutions {Xit}im=1 against the search range, and second on the objective values {yit}im=1 using the extremum values at time step t. This ensures unified representation and generalization by scaling all values to [0, 1]. For a d-dimensional optimization problem, the normalized observations ot ', '2': 'Model Complexity (RQ6). We discuss the relationship between the model complexity and the zero-shot performance (unseen MetaBBO algorithm & problem sets) of our NeurELA. Concretely, We pre-train NeurELA under 6 different model complexities, with various hidden dimensions, i.e., h = (16, 64), and the number of the Ts-Attn module, i.e., l = (1, 3, 5). We additionally pre-train three MLP baselines, which substitute the Ts-Attn module in NeurELA with a linear feed-forward layer, which holds a shape of h × h, h = (16, 64, 128). We report both the zero-shot performance (yaxis) and the computational efficiency (x-axis, presented as the consumed wall time for computing the landscape features) in Figure 7, where the dashed lines denotes the performance and wall-time of the Original baseline. #para denotes the number of the learnable parameters. The results show that: 1) a significant performance gap is observed between the MLP baselines and our Ts-Attn module (h = 16, l = 1). It validates the effectiveness of our Ts-Attn design, which enhance the feature extraction of our NeurELA by encouraging the information sharing at both the cross-solution and cross-dimension levels; 2) As the model complexity increases, the performance of the Ts-Attn module drops rapidly. It reveals that the increased number of learnable parameters challenges the optimization ability of the backend ES. Given the limited computational resources, it is difficult to identify the optimal parameters θ∗. '} | {'images/404469c60be80871de0a0cac273007fcc1f18dfb0d7cdc107fc8c79a31f770b5.jpg': '3', 'images/1ea8a4c9f98bd3c072369dd6b23ed6a0b0386c676b238e2f01bc9429d0b2366e.jpg': '2'} | {'3': 'images/404469c60be80871de0a0cac273007fcc1f18dfb0d7cdc107fc8c79a31f770b5.jpg', '2': 'images/1ea8a4c9f98bd3c072369dd6b23ed6a0b0386c676b238e2f01bc9429d0b2366e.jpg'} | {'images/2860d185ad0551afe2aabb501992df2d0b7f46bca5e5e298a5678d318f671126.jpg': '1'} | {'1': 'images/2860d185ad0551afe2aabb501992df2d0b7f46bca5e5e298a5678d318f671126.jpg'} | {} | ['Model Complexity (RQ6). We discuss the relationship between the model complexity and the zero-shot performance (unseen MetaBBO algorithm & problem sets) of our NeurELA. Concretely, We pre-train NeurELA under 6 different model complexities, with various hidden dimensions, i.e., h = (16, 64), and the number of the Ts-Attn module, i.e., l = (1, 3, 5). We additionally pre-train three MLP baselines, which substitute the Ts-Attn module in NeurELA with a linear feed-forward layer, which holds a shape of h × h, h = (16, 64, 128). We report both the zero-shot performance (yaxis) and the computational efficiency (x-axis, presented as the consumed wall time for computing the landscape features) in Figure 7, where the dashed lines denotes the performance and wall-time of the Original baseline. #para denotes the number of the learnable parameters. The results show that: 1) a significant performance gap is observed between the MLP baselines and our Ts-Attn module (h = 16, l = 1). It validates the effectiveness of our Ts-Attn design, which enhance the feature extraction of our NeurELA by encouraging the information sharing at both the cross-solution and cross-dimension levels; 2) As the model complexity increases, the performance of the Ts-Attn module drops rapidly. It reveals that the increased number of learnable parameters challenges the optimization ability of the backend ES. Given the limited computational resources, it is difficult to identify the optimal parameters θ∗. ', 'PIE. PIE normalizes observation ot using two min-max normalization operations: first on the candidate solutions {Xit}im=1 against the search range, and second on the objective values {yit}im=1 using the extremum values at time step t. This ensures unified representation and generalization by scaling all values to [0, 1]. For a d-dimensional optimization problem, the normalized observations ot ', 'images/1ea8a4c9f98bd3c072369dd6b23ed6a0b0386c676b238e2f01bc9429d0b2366e.jpg'] | 33fa994b4d9460e0a41f23d63db78bbfe1e1a6b0222ca6e28c6ce212fffeef2c | 52338e0fa95ec6a5e01a939a36c8daed3211c494 |
explanation | What MARL settings are presented in the paper? | The MARL settings CooperativePong, PistonBall and Spread are presented in Table 1 and Figure 3. | ['Table 1', 'Figure 3'] | ['images/e326b6cd699b65230781ca064b7dc8e0c74518769469a10020f910ccf56ffa86.jpg', 'images/ff57fad4a640245576a93aca4d413d4fd042bfebf97ab72e198abc6cf0568753.jpg'] | ['mixed'] | 2 | 3 | 5 | {'The training plots for multi-agent environments are shown in Figure 3, following the same methodology. To further compare different scenarios, we allow both agents in CooperativePong to share the same policy. While in PistonBall and Spread, only the controller is centralized, and each of the actors—20 in PistonBall and 3 in Spread—learns its own policy. As in previous experiments, we observe that GRASP and ASC achieve similar performance. ': '1'} | {'1': 'The training plots for multi-agent environments are shown in Figure 3, following the same methodology. To further compare different scenarios, we allow both agents in CooperativePong to share the same policy. While in PistonBall and Spread, only the controller is centralized, and each of the actors—20 in PistonBall and 3 in Spread—learns its own policy. As in previous experiments, we observe that GRASP and ASC achieve similar performance. '} | {'images/d4bc4d7f26d85b616d283efaa11b51d547720393d059af8460c4943bbf79f3b0.jpg': '2', 'images/ff57fad4a640245576a93aca4d413d4fd042bfebf97ab72e198abc6cf0568753.jpg': '3'} | {'2': 'images/d4bc4d7f26d85b616d283efaa11b51d547720393d059af8460c4943bbf79f3b0.jpg', '3': 'images/ff57fad4a640245576a93aca4d413d4fd042bfebf97ab72e198abc6cf0568753.jpg'} | {'images/68745102ec79efac81ef48cbfa782ed2d3970ee106e08ed4f94f4daa3f353f7c.jpg': '2', 'images/e326b6cd699b65230781ca064b7dc8e0c74518769469a10020f910ccf56ffa86.jpg': '1'} | {'2': 'images/68745102ec79efac81ef48cbfa782ed2d3970ee106e08ed4f94f4daa3f353f7c.jpg', '1': 'images/e326b6cd699b65230781ca064b7dc8e0c74518769469a10020f910ccf56ffa86.jpg'} | {} | ['images/d4bc4d7f26d85b616d283efaa11b51d547720393d059af8460c4943bbf79f3b0.jpg', 'images/68745102ec79efac81ef48cbfa782ed2d3970ee106e08ed4f94f4daa3f353f7c.jpg', 'The training plots for multi-agent environments are shown in Figure 3, following the same methodology. To further compare different scenarios, we allow both agents in CooperativePong to share the same policy. While in PistonBall and Spread, only the controller is centralized, and each of the actors—20 in PistonBall and 3 in Spread—learns its own policy. As in previous experiments, we observe that GRASP and ASC achieve similar performance. '] | f48df9d51e3796924fa36c31d59c5ac5c95533c249bddb76dbb0895ec9726c7a | 52654c7bcc7ede0930ec2ee1e88ac24f1c68621d |
explanation | How does the proposed approach compare against non-equivariant policy learning algorithms? | We directly compare our proposed approach against non-equivariant policy learning algorithms. The non-equivariant baselines perform much worse in terms of performance and sample efficiency (see 'Sideview NonEqui' Figure 5 and Table 1). The non-equivariant methods were trained with data augmentation and still underperformed the equivariant versions. These results were also observed in [1]. | ['Figure 5', 'Table 1'] | ['images/533dcc4ba8374a381b12f6e0a58fc2d7cbb9eb7bbeabfd7dd0bd4b95581ab8e3.jpg', 'images/97e8c25df23ebf0bb39ff2c1446d1262167f67bb3b1216035a2576da9c25530f.jpg'] | ['mixed'] | 2 | 3 | 5 | {'Wang et al. (2022b) showed that equivariant networks can still be effective when there is some mismatch between the symmetry group used to constrain the model and the physically accurate task symmetry. Specifically, they found that using image rotations on sideview images to capture O(2) actions on the scene is better than not using equivariance. Nevertheless, there is a noticeable performance gap when compared to the top-down image setting. ': '1', 'Learning Latent or Approximate Symmetry For some learning problems, there could be a mismatch between the symmetry in the ground truth function and the symmetry in the equivariant network because the symmetry cannot be easily described in the input space or the ground truth function is only partially symmetric. Falorsi et al. (2018) and Park et al. (2022) showed that symmetric neural representations can be extracted using traditional networks with a self-supervised loss. These symmetric representations can be further processed with equivariant layers leading to improved generalization (Esteves et al., 2019; Klee et al., 2023). Another solution to combat this problem is to use approximate or relaxed equivariant neural networks (Wang et al., 2022e; 2024b; Huang et al., 2024b) to relax the equivariant constraint in the network to better match the symmetry in the ground truth function. Alternatively, Wang et al. (2022b) showed that even with the symmetry match, a fully equivariant model that enforces symmetry to out-of-distribution data can still outperform non-equivariant baselines, as long as the symmetry in the model does not conflict with the ground truth function (Wang et al., 2024a). A similar finding was shown in De Silva et al. (2023) where training with out-of-distribution data could aid learning. Although the solution of Wang et al. (2022b) is simple and effective, there remains a significant performance gap compared to not having the symmetry mismatch. Our work provides a simple means to close this gap. ': '2'} | {'1': 'Wang et al. (2022b) showed that equivariant networks can still be effective when there is some mismatch between the symmetry group used to constrain the model and the physically accurate task symmetry. Specifically, they found that using image rotations on sideview images to capture O(2) actions on the scene is better than not using equivariance. Nevertheless, there is a noticeable performance gap when compared to the top-down image setting. ', '2': 'Learning Latent or Approximate Symmetry For some learning problems, there could be a mismatch between the symmetry in the ground truth function and the symmetry in the equivariant network because the symmetry cannot be easily described in the input space or the ground truth function is only partially symmetric. Falorsi et al. (2018) and Park et al. (2022) showed that symmetric neural representations can be extracted using traditional networks with a self-supervised loss. These symmetric representations can be further processed with equivariant layers leading to improved generalization (Esteves et al., 2019; Klee et al., 2023). Another solution to combat this problem is to use approximate or relaxed equivariant neural networks (Wang et al., 2022e; 2024b; Huang et al., 2024b) to relax the equivariant constraint in the network to better match the symmetry in the ground truth function. Alternatively, Wang et al. (2022b) showed that even with the symmetry match, a fully equivariant model that enforces symmetry to out-of-distribution data can still outperform non-equivariant baselines, as long as the symmetry in the model does not conflict with the ground truth function (Wang et al., 2024a). A similar finding was shown in De Silva et al. (2023) where training with out-of-distribution data could aid learning. Although the solution of Wang et al. (2022b) is simple and effective, there remains a significant performance gap compared to not having the symmetry mismatch. Our work provides a simple means to close this gap. '} | {'images/76f7ce360706c044c9d50d7488012c66e2d5866297e6a285cf8aa7ed4ec994a1.jpg': '7', 'images/533dcc4ba8374a381b12f6e0a58fc2d7cbb9eb7bbeabfd7dd0bd4b95581ab8e3.jpg': '5'} | {'7': 'images/76f7ce360706c044c9d50d7488012c66e2d5866297e6a285cf8aa7ed4ec994a1.jpg', '5': 'images/533dcc4ba8374a381b12f6e0a58fc2d7cbb9eb7bbeabfd7dd0bd4b95581ab8e3.jpg'} | {'images/97e8c25df23ebf0bb39ff2c1446d1262167f67bb3b1216035a2576da9c25530f.jpg': '1'} | {'1': 'images/97e8c25df23ebf0bb39ff2c1446d1262167f67bb3b1216035a2576da9c25530f.jpg'} | {} | ['images/76f7ce360706c044c9d50d7488012c66e2d5866297e6a285cf8aa7ed4ec994a1.jpg', 'Learning Latent or Approximate Symmetry For some learning problems, there could be a mismatch between the symmetry in the ground truth function and the symmetry in the equivariant network because the symmetry cannot be easily described in the input space or the ground truth function is only partially symmetric. Falorsi et al. (2018) and Park et al. (2022) showed that symmetric neural representations can be extracted using traditional networks with a self-supervised loss. These symmetric representations can be further processed with equivariant layers leading to improved generalization (Esteves et al., 2019; Klee et al., 2023). Another solution to combat this problem is to use approximate or relaxed equivariant neural networks (Wang et al., 2022e; 2024b; Huang et al., 2024b) to relax the equivariant constraint in the network to better match the symmetry in the ground truth function. Alternatively, Wang et al. (2022b) showed that even with the symmetry match, a fully equivariant model that enforces symmetry to out-of-distribution data can still outperform non-equivariant baselines, as long as the symmetry in the model does not conflict with the ground truth function (Wang et al., 2024a). A similar finding was shown in De Silva et al. (2023) where training with out-of-distribution data could aid learning. Although the solution of Wang et al. (2022b) is simple and effective, there remains a significant performance gap compared to not having the symmetry mismatch. Our work provides a simple means to close this gap. ', 'Wang et al. (2022b) showed that equivariant networks can still be effective when there is some mismatch between the symmetry group used to constrain the model and the physically accurate task symmetry. Specifically, they found that using image rotations on sideview images to capture O(2) actions on the scene is better than not using equivariance. Nevertheless, there is a noticeable performance gap when compared to the top-down image setting. '] | cb4f90d46d84bcfa362631838e00cf9d04f56acb8f689fa61189b9993a63f821 | 557f8e7f27e42c5b8fa4a32df0e28d72280ab64b |
explanation | Are there any fundamental differences or novel issues in confidence calibration for Retrieval-Augmented Generation (RAG) compared to calibration in generation models without retrieval augmentation? | In RAG, additional context that the LLM may not know is augmented into the input, which differs from the process where the LLM generates responses solely based on pre-existing knowledge or a given answer. This additional context serves as a hint, creating a different scenario compared to the traditional tasks performed by generation models. For instance, as shown in Table 1-(b), simple confidence calibration methods that do not account for RAG are insufficient to address these challenges. This is because, while RAG can improve performance, it can also lead to overly high confidence. However, existing research has not addressed decision calibration within the RAG framework. Moreover, if the LLM generates responses based solely on the Top-1 document retrieved by the retrieval model, it may fail to provide the optimal information required for decision-making. As illustrated in Figure 1-(a), other documents within the Top-10 may offer more valuable insights or information that contribute to more accurate decisions. This highlights the necessity of not only employing a retrieval-based approach but also integrating LLM-retrieval interactions and calibrating the confidence of the retrieved documents. Therefore, confidence calibration in RAG involves fundamentally different challenges and issues compared to calibration in simple generation models. It requires a comprehensive approach that not only enhances performance through RAG but also addresses the problem of over-confidence and compensates for incomplete or inaccurate information provided by the retrieval model. | ['Table 1', 'Figure 1'] | ['images/1d93a1ae787879849a9853489f77e176cd417c85d200e14f7e189e5daf5e5093.jpg', 'images/ffcff06167847c9f219435a7800054a7da065c26d07942e5a3b2233b9ed79a7a.jpg'] | ['mixed'] | 2 | 3 | 5 | {'Comparison with uncertainty calibration baselines. Table 1 presents a comparison of uncertainty-based baselines across four QA datasets. Our CalibRAG achieves both a lower ‘No Answer’ rate and higher accuracy compared to other baselines, achieving the accuracy of 35.03 and 39.91 on BioASQ and HotpotQA, respectively, representing over a 3% improvement over the bestperforming baseline. Additionally, its confidence level is better calibrated than the other baselines, demonstrating the lowest ECE and BS. CalibRAG†, which regenerates the query for documents that do not exceed the threshold, consistently shows performance improvements. However, while it correctly answers more challenging questions, it also makes accurate decisions with lower confidence, causing some variation in the calibration metrics. ': '1', 'tences, summing over all possible corresponding probabilities would be required—an intractable process due to the exponential number of potential sequences. Consequently, token-level probabilities in current language models often fail to offer reliable confidence estimates for long-form text generation, thereby limiting their application to tasks that extend beyond multiple-choice scenarios. ': '2', 'However, the method proposed by Band et al. (2024) to tackle this calibration problem has three major limitations: 1) it requires supervised fine-tuning for three different LLMs, including the LLM responsible for generating a response z and the forecasting function f parameterized with two LLMs, 2) it further needs proximal policy optimization (PPO; Schulman et al., 2017) for fine-tuning the LLM for response generation, which is known to suffer from training instability (Zhu et al., 2023), and 3) it cannot calibrate the probabilities associated with the user decisions based on the guidance provided by RAG. ': '3'} | {'1': 'Comparison with uncertainty calibration baselines. Table 1 presents a comparison of uncertainty-based baselines across four QA datasets. Our CalibRAG achieves both a lower ‘No Answer’ rate and higher accuracy compared to other baselines, achieving the accuracy of 35.03 and 39.91 on BioASQ and HotpotQA, respectively, representing over a 3% improvement over the bestperforming baseline. Additionally, its confidence level is better calibrated than the other baselines, demonstrating the lowest ECE and BS. CalibRAG†, which regenerates the query for documents that do not exceed the threshold, consistently shows performance improvements. However, while it correctly answers more challenging questions, it also makes accurate decisions with lower confidence, causing some variation in the calibration metrics. ', '2': 'tences, summing over all possible corresponding probabilities would be required—an intractable process due to the exponential number of potential sequences. Consequently, token-level probabilities in current language models often fail to offer reliable confidence estimates for long-form text generation, thereby limiting their application to tasks that extend beyond multiple-choice scenarios. ', '3': 'However, the method proposed by Band et al. (2024) to tackle this calibration problem has three major limitations: 1) it requires supervised fine-tuning for three different LLMs, including the LLM responsible for generating a response z and the forecasting function f parameterized with two LLMs, 2) it further needs proximal policy optimization (PPO; Schulman et al., 2017) for fine-tuning the LLM for response generation, which is known to suffer from training instability (Zhu et al., 2023), and 3) it cannot calibrate the probabilities associated with the user decisions based on the guidance provided by RAG. '} | {'images/ffcff06167847c9f219435a7800054a7da065c26d07942e5a3b2233b9ed79a7a.jpg': '1'} | {'1': 'images/ffcff06167847c9f219435a7800054a7da065c26d07942e5a3b2233b9ed79a7a.jpg'} | {'images/1d93a1ae787879849a9853489f77e176cd417c85d200e14f7e189e5daf5e5093.jpg': '1'} | {'1': 'images/1d93a1ae787879849a9853489f77e176cd417c85d200e14f7e189e5daf5e5093.jpg'} | {} | ['Comparison with uncertainty calibration baselines. Table 1 presents a comparison of uncertainty-based baselines across four QA datasets. Our CalibRAG achieves both a lower ‘No Answer’ rate and higher accuracy compared to other baselines, achieving the accuracy of 35.03 and 39.91 on BioASQ and HotpotQA, respectively, representing over a 3% improvement over the bestperforming baseline. Additionally, its confidence level is better calibrated than the other baselines, demonstrating the lowest ECE and BS. CalibRAG†, which regenerates the query for documents that do not exceed the threshold, consistently shows performance improvements. However, while it correctly answers more challenging questions, it also makes accurate decisions with lower confidence, causing some variation in the calibration metrics. ', 'tences, summing over all possible corresponding probabilities would be required—an intractable process due to the exponential number of potential sequences. Consequently, token-level probabilities in current language models often fail to offer reliable confidence estimates for long-form text generation, thereby limiting their application to tasks that extend beyond multiple-choice scenarios. ', 'However, the method proposed by Band et al. (2024) to tackle this calibration problem has three major limitations: 1) it requires supervised fine-tuning for three different LLMs, including the LLM responsible for generating a response z and the forecasting function f parameterized with two LLMs, 2) it further needs proximal policy optimization (PPO; Schulman et al., 2017) for fine-tuning the LLM for response generation, which is known to suffer from training instability (Zhu et al., 2023), and 3) it cannot calibrate the probabilities associated with the user decisions based on the guidance provided by RAG. '] | 42abe4e25bf7f5872f2e665998243fe7438870187bf54c81839510b58b5fea08 | 65e624095701a1080d5f73fc831b548c8a63296a |
explanation | What are the advantages of the proposed variance-preserving mechanism in the architecture? | Our variance-preserving mechanism embedded in the architecture enables model selection directly from the training loss by preserving prediction variance and consequently preventing the model from overfitting the training set when extreme hyper-parameter configurations are tested and strong distribution shifts happen. This is a clear advantage that enables the predictability of generalization (Table 1). Dealing with overfitting when performing model selection with default architectures without validation data is challenging. See Figure 1 (top) and Table 1. | ['Table 1', 'Figure 1'] | ['images/af8e0d1e88cefbb1b60f3d0310b373ef143a06241764b57cd015fcb81f95376c.jpg', 'images/949446e2d67f0ae6d9110e45b33c6dde0111de219eb62546f0e7c4b43fd47b82.jpg'] | ['mixed'] | 2 | 3 | 5 | {} | {} | {'images/864ea4fde44d7d950cf0a6545208af5190c39403349e790378caf742085537f7.jpg': '4', 'images/835cd1e80907b44c9fd7028ceb4d89d1522cf74150fb28e061c06a499eae8af8.jpg': '5', 'images/949446e2d67f0ae6d9110e45b33c6dde0111de219eb62546f0e7c4b43fd47b82.jpg': '1', 'images/da1e7a460119c378444bfc707f3936ce4167cdd89f28cd6b09351b56e332463a.jpg': '2'} | {'4': 'images/864ea4fde44d7d950cf0a6545208af5190c39403349e790378caf742085537f7.jpg', '5': 'images/835cd1e80907b44c9fd7028ceb4d89d1522cf74150fb28e061c06a499eae8af8.jpg', '1': 'images/949446e2d67f0ae6d9110e45b33c6dde0111de219eb62546f0e7c4b43fd47b82.jpg', '2': 'images/da1e7a460119c378444bfc707f3936ce4167cdd89f28cd6b09351b56e332463a.jpg'} | {'images/af8e0d1e88cefbb1b60f3d0310b373ef143a06241764b57cd015fcb81f95376c.jpg': '1'} | {'1': 'images/af8e0d1e88cefbb1b60f3d0310b373ef143a06241764b57cd015fcb81f95376c.jpg'} | {} | ['images/da1e7a460119c378444bfc707f3936ce4167cdd89f28cd6b09351b56e332463a.jpg', 'images/835cd1e80907b44c9fd7028ceb4d89d1522cf74150fb28e061c06a499eae8af8.jpg', 'images/864ea4fde44d7d950cf0a6545208af5190c39403349e790378caf742085537f7.jpg'] | d376b95e1f8c8b65d07e847649e99383256ba2c9c44c57606267c49c767ceffc | 6b582ea4a5145a03c831aa33976a9f67441057ae |
explanation | Why should SELFEE work? | We first remark that SELFEE begins with an LLM fine-tuned using DPO on the initial seed preference dataset; therefore, depending on the size of the seed dataset and the degree of distribution shift in new prompts for each iteration, the effectiveness of SELFEE can vary. However, our experiments (Table 1) show that it yields significant improvements in alignment performance, demonstrating its suitability for our setup. Additionally, as observed in Table 1 and Figure 4, even online preference learning with an external reward model (Iterative DPO) experiences biased preference features and increased response lengths. This indicates that the increased response length in SELFEE is not merely a result of exacerbating biases from an insufficiently strong starting model but reflects broader challenges inherent in current online preference learning methods. | ['Table 1', 'Figure 4'] | ['images/56ab973f0b003a4464bdc89f222272b0fe685f03571e28cf06274a35639da434.jpg', 'images/95e411fae3db89cb06a06270e17555220fe309c0679484a3fff2cd3fbabeea36.jpg'] | ['mixed'] | 2 | 3 | 5 | {'Fig. 5(a) describes the changes in the response character length throughout the iteration process. From iteration 1 to iteration 4, the response length for Iterative DPO and SELFEE increased significantly (1418 →1709) and (1852 →2412), respectively. In contrast, PFP exhibited only a minimal increase in length (1138 →1187). This highlights that, unlike other iterative improvement algorithms that have a weakness at length bias, PFP learns human preferences well without causing length bias. ': '1', 'To preserve the feature distribution over each iteration of online preference learning, we first map each instruction x ∈Xt used in online learning to the proper preference features. One can expect that the preference feature distribution is preserved by explicitly utilizing the assigned features during response generation and preference judgment. Specifically, this process involves two key components: (a) learning a feature classifier, and (b) assigning a pseudo-label using a relabeling technique. ': '2'} | {'1': 'Fig. 5(a) describes the changes in the response character length throughout the iteration process. From iteration 1 to iteration 4, the response length for Iterative DPO and SELFEE increased significantly (1418 →1709) and (1852 →2412), respectively. In contrast, PFP exhibited only a minimal increase in length (1138 →1187). This highlights that, unlike other iterative improvement algorithms that have a weakness at length bias, PFP learns human preferences well without causing length bias. ', '2': 'To preserve the feature distribution over each iteration of online preference learning, we first map each instruction x ∈Xt used in online learning to the proper preference features. One can expect that the preference feature distribution is preserved by explicitly utilizing the assigned features during response generation and preference judgment. Specifically, this process involves two key components: (a) learning a feature classifier, and (b) assigning a pseudo-label using a relabeling technique. '} | {'images/95e411fae3db89cb06a06270e17555220fe309c0679484a3fff2cd3fbabeea36.jpg': '4'} | {'4': 'images/95e411fae3db89cb06a06270e17555220fe309c0679484a3fff2cd3fbabeea36.jpg'} | {'images/56ab973f0b003a4464bdc89f222272b0fe685f03571e28cf06274a35639da434.jpg': '1', 'images/562f128d30d78018aa05bce14272be8ecf303982a9c1843f66724b6da84d7f54.jpg': '4'} | {'1': 'images/56ab973f0b003a4464bdc89f222272b0fe685f03571e28cf06274a35639da434.jpg', '4': 'images/562f128d30d78018aa05bce14272be8ecf303982a9c1843f66724b6da84d7f54.jpg'} | {} | ['Fig. 5(a) describes the changes in the response character length throughout the iteration process. From iteration 1 to iteration 4, the response length for Iterative DPO and SELFEE increased significantly (1418 →1709) and (1852 →2412), respectively. In contrast, PFP exhibited only a minimal increase in length (1138 →1187). This highlights that, unlike other iterative improvement algorithms that have a weakness at length bias, PFP learns human preferences well without causing length bias. ', 'images/562f128d30d78018aa05bce14272be8ecf303982a9c1843f66724b6da84d7f54.jpg', 'To preserve the feature distribution over each iteration of online preference learning, we first map each instruction x ∈Xt used in online learning to the proper preference features. One can expect that the preference feature distribution is preserved by explicitly utilizing the assigned features during response generation and preference judgment. Specifically, this process involves two key components: (a) learning a feature classifier, and (b) assigning a pseudo-label using a relabeling technique. '] | 13745cddb4137837dc61258323bde9569315729968633a2e6c05f83770e96230 | 729d9ddfbdd5e5b4eaf7653e8b760408d22d4650 |
explanation | What is the key novelty of the paper, particularly regarding the query-adaptive sampler? | Our key contribution lies in the application of query-adaptive frame sampling, which leverages the reasoning ability of the agents. Our approach is particularly focused on improving efficiency and performance when handling long-context videos. As demonstrated in the results (Table 4, Figure 4), our method enhances efficiency by reducing the number of frames accessed, while simultaneously increasing the accuracy of tasks. | ['Table 4', 'Figure 4'] | ['images/73764241d9d6a380ba3f3fef1642353cfbff4f7e9065d255fb34126e54da777d.jpg', 'images/079ec5638365adb75ac75381f5b989af45df1b1819d52fd8b862be90fb25b7ef.jpg'] | ['mixed'] | 2 | 3 | 5 | {'Planning/tool invoking At time step t, the agent L selects an action at and action input xt based on policy π in solving problem D. The actions A are the invokable tools, which are pre-defined and callable functions from the agent. The action input xt is typically the frame number, indicating which frames the tools should access. The input often includes extra arguments, for example the question to query the tools (e.g. Frame index 0, what is happening in the frame?). Once the tools are invoked, it returns a observation O which is the extracted information of the selected frame. The agent L considers the previous observation-action trajectory τt = [a1, o1, . . . , ot−1] : in choosing ': '1'} | {'1': 'Planning/tool invoking At time step t, the agent L selects an action at and action input xt based on policy π in solving problem D. The actions A are the invokable tools, which are pre-defined and callable functions from the agent. The action input xt is typically the frame number, indicating which frames the tools should access. The input often includes extra arguments, for example the question to query the tools (e.g. Frame index 0, what is happening in the frame?). Once the tools are invoked, it returns a observation O which is the extracted information of the selected frame. The agent L considers the previous observation-action trajectory τt = [a1, o1, . . . , ot−1] : in choosing '} | {'images/079ec5638365adb75ac75381f5b989af45df1b1819d52fd8b862be90fb25b7ef.jpg': '4'} | {'4': 'images/079ec5638365adb75ac75381f5b989af45df1b1819d52fd8b862be90fb25b7ef.jpg'} | {'images/69b93f43c76ca6c81d7acf0206b9c0830fef08dc8c2eccdb20f449b0a7e15f75.jpg': '5', 'images/73764241d9d6a380ba3f3fef1642353cfbff4f7e9065d255fb34126e54da777d.jpg': '4', 'images/6a05baf74f5f0377b3a8fd8eb35bd54c29ed1bc563444ee4cfd82c0df906642c.jpg': '8'} | {'5': 'images/69b93f43c76ca6c81d7acf0206b9c0830fef08dc8c2eccdb20f449b0a7e15f75.jpg', '4': 'images/73764241d9d6a380ba3f3fef1642353cfbff4f7e9065d255fb34126e54da777d.jpg', '8': 'images/6a05baf74f5f0377b3a8fd8eb35bd54c29ed1bc563444ee4cfd82c0df906642c.jpg'} | {} | ['images/69b93f43c76ca6c81d7acf0206b9c0830fef08dc8c2eccdb20f449b0a7e15f75.jpg', 'images/6a05baf74f5f0377b3a8fd8eb35bd54c29ed1bc563444ee4cfd82c0df906642c.jpg', 'Planning/tool invoking At time step t, the agent L selects an action at and action input xt based on policy π in solving problem D. The actions A are the invokable tools, which are pre-defined and callable functions from the agent. The action input xt is typically the frame number, indicating which frames the tools should access. The input often includes extra arguments, for example the question to query the tools (e.g. Frame index 0, what is happening in the frame?). Once the tools are invoked, it returns a observation O which is the extracted information of the selected frame. The agent L considers the previous observation-action trajectory τt = [a1, o1, . . . , ot−1] : in choosing '] | 09f828d9a90ed12c038fbf9fbc9635b31b4865415666f51ba283d8c76c6c8b04 | 80917e140b56b5b4d9459329a896fef9e483dacc |
explanation | How does the proposed method compare to DETR in terms of performance and inference speed? | Thanks for the concern. We would like to highlight that our DECO also outperforms DETR with the same settings, *i.e.*, training receipt, architecture etc. The comparisons are shown in Table 2 (as also shown in Figure 1 in supplementary material) and we can see that our DECO obtains better performance than DETR, which justifies the effectiveness and also the main contribution of our proposed method. | ['Table 2', 'Figure 1'] | ['images/5c3bb75a3c6ada6985c4a487688a7f0fa40b6446a0f0dde0be232cc72bfca63d.jpg', 'images/aec1a617d8c4be2ce7b12411ab71b5a77c4e6bc697890acc05b8b8de9c395c34.jpg'] | ['mixed'] | 2 | 3 | 5 | {'DECO Encoder. Similar to DETR, a 1 × 1 convolution is first utilized to reduce the channel dimension of f from C to d and obtain a new feature map z0 ∈ℜd×H×W . In DETR, z0 is fed into stacked transformer encoder layers, which mainly consists of multi-head self-attention (MHSA) and feed-forward network (FFN) to perform spatial and channel information mixing respectively. Recent work such as ConvNeXt (Liu et al., 2022b) has demonstrated that using stacked depthwise and pointwise convolutions could achieve comparable performance with Transformers. Therefore, we use the ConvNeXt blocks to build our DECO encoder. Specifically, each DECO encoder layer is stacked with a 7 × 7 depthwise convolution, a LayerNorm layer, a 1 × 1 convolution, a GELU acitvation and another 1 × 1 convolution. Besides, in DETR, positional encodings are necessary to be added to the input of each transformer encoder layer, since the transformer architecture is permutation-invariant. However, the ConvNet architecture is permutation-variant so that our DECO encoder layers could get rid of any positional encodings. ': '1', 'Meanwhile, some recent work rethinks the strong performance and reveal that the pure ConvNets could also achieve competitive performance via proper architecture design (Liu et al., 2022b; Yu et al., 2022). For example, ConvNeXt (Liu et al., 2022b) competes favorably with vision transformers like Swin Transformer (Liu et al., 2021) in terms of accuracy and computational cost. However, these methods mainly focus on Encoder part of transformer, in which self-attention is utilized and could be replaced by convolution with careful design. These motivate us to explore one important question in this paper: could we obtain an architecture via pure ConvNets but still enjoys the excellent properties similar to attention? ': '2'} | {'1': 'DECO Encoder. Similar to DETR, a 1 × 1 convolution is first utilized to reduce the channel dimension of f from C to d and obtain a new feature map z0 ∈ℜd×H×W . In DETR, z0 is fed into stacked transformer encoder layers, which mainly consists of multi-head self-attention (MHSA) and feed-forward network (FFN) to perform spatial and channel information mixing respectively. Recent work such as ConvNeXt (Liu et al., 2022b) has demonstrated that using stacked depthwise and pointwise convolutions could achieve comparable performance with Transformers. Therefore, we use the ConvNeXt blocks to build our DECO encoder. Specifically, each DECO encoder layer is stacked with a 7 × 7 depthwise convolution, a LayerNorm layer, a 1 × 1 convolution, a GELU acitvation and another 1 × 1 convolution. Besides, in DETR, positional encodings are necessary to be added to the input of each transformer encoder layer, since the transformer architecture is permutation-invariant. However, the ConvNet architecture is permutation-variant so that our DECO encoder layers could get rid of any positional encodings. ', '2': 'Meanwhile, some recent work rethinks the strong performance and reveal that the pure ConvNets could also achieve competitive performance via proper architecture design (Liu et al., 2022b; Yu et al., 2022). For example, ConvNeXt (Liu et al., 2022b) competes favorably with vision transformers like Swin Transformer (Liu et al., 2021) in terms of accuracy and computational cost. However, these methods mainly focus on Encoder part of transformer, in which self-attention is utilized and could be replaced by convolution with careful design. These motivate us to explore one important question in this paper: could we obtain an architecture via pure ConvNets but still enjoys the excellent properties similar to attention? '} | {'images/aec1a617d8c4be2ce7b12411ab71b5a77c4e6bc697890acc05b8b8de9c395c34.jpg': '1'} | {'1': 'images/aec1a617d8c4be2ce7b12411ab71b5a77c4e6bc697890acc05b8b8de9c395c34.jpg'} | {'images/ce86eb6e32ea5f53607d5a4ec12f23ad6d10f8d3cc52aad8d11e95d797c7526a.jpg': '1', 'images/5c3bb75a3c6ada6985c4a487688a7f0fa40b6446a0f0dde0be232cc72bfca63d.jpg': '2'} | {'1': 'images/ce86eb6e32ea5f53607d5a4ec12f23ad6d10f8d3cc52aad8d11e95d797c7526a.jpg', '2': 'images/5c3bb75a3c6ada6985c4a487688a7f0fa40b6446a0f0dde0be232cc72bfca63d.jpg'} | {} | ['Meanwhile, some recent work rethinks the strong performance and reveal that the pure ConvNets could also achieve competitive performance via proper architecture design (Liu et al., 2022b; Yu et al., 2022). For example, ConvNeXt (Liu et al., 2022b) competes favorably with vision transformers like Swin Transformer (Liu et al., 2021) in terms of accuracy and computational cost. However, these methods mainly focus on Encoder part of transformer, in which self-attention is utilized and could be replaced by convolution with careful design. These motivate us to explore one important question in this paper: could we obtain an architecture via pure ConvNets but still enjoys the excellent properties similar to attention? ', 'images/ce86eb6e32ea5f53607d5a4ec12f23ad6d10f8d3cc52aad8d11e95d797c7526a.jpg', 'DECO Encoder. Similar to DETR, a 1 × 1 convolution is first utilized to reduce the channel dimension of f from C to d and obtain a new feature map z0 ∈ℜd×H×W . In DETR, z0 is fed into stacked transformer encoder layers, which mainly consists of multi-head self-attention (MHSA) and feed-forward network (FFN) to perform spatial and channel information mixing respectively. Recent work such as ConvNeXt (Liu et al., 2022b) has demonstrated that using stacked depthwise and pointwise convolutions could achieve comparable performance with Transformers. Therefore, we use the ConvNeXt blocks to build our DECO encoder. Specifically, each DECO encoder layer is stacked with a 7 × 7 depthwise convolution, a LayerNorm layer, a 1 × 1 convolution, a GELU acitvation and another 1 × 1 convolution. Besides, in DETR, positional encodings are necessary to be added to the input of each transformer encoder layer, since the transformer architecture is permutation-invariant. However, the ConvNet architecture is permutation-variant so that our DECO encoder layers could get rid of any positional encodings. '] | a6962b92e1a1db20f165bb3e2736f2e535655de62c44f23849676eec30fdbdc8 | 859cadf9210afc0858163efe25c35e2f15290731 |
explanation | How do you generalize your approach to more complicated and rare compositions? | RareBench already includes the complicated rare composition cases (as the 'complex' case), consisting of three or more concepts, and R2F still exhibits superior performance on such complex cases as shown in Table 6. Specifically, looking at Figure 6, there is an example 'A horned bearded spotted raccoon smiling' from the complex case, and R2F successfully generates the image that accurately follows the prompt. Technically, given examples such as 'adj1 + adj2 + noun', R2F finds a noun that more frequently appears in the context of 'adj1 + adj2', and uses it for frequent concept guidance. | ['Table 6', 'Figure 6'] | ['images/0b5cb04ba6b819219bdae29194748fe720dbd54de165be822a80f0345d14d6b5.jpg', 'images/490f062e589415340fffe20ccdd9705368cc032e994ff34727f920548537a57d.jpg'] | ['mixed'] | 2 | 3 | 5 | {'Efficacy of Alternating Guidance. Figure 8 and Table 6 show the qualitative and quantitative analysis of the R2F’s alternating guidance compared to other possible guidance choices. We apply three guidance choices, (1) Linear interpolation (Interpolate) of latents as in Theorem 3.1, and bring the idea of (2) Composable Diffusion (Liu et al., 2022) and (3) Prompt-to-prompt (P2P) (Hertz et al., 2022). Given a pair of rare-frequent concept prompts, Interpolate linearly interpolates the latants of rare and frequent prompts with α = 0.5 and Composable blends the two prompt embeddings and uses it as the input, until the stop points obtained from LLM. P2P first generates a complete image from the frequent concept prompt and then edits it by the rare concept prompt with attention-control. ': '1', 'Efficacy of Visual-detail-aware Guidance Stop Points. Figure 9 depicts the efficacy of R2F’s adaptive visual-detail-aware stop points compared to when using a fixed stop point on RareBench with single-object case, which has only one stop point. We ablate the fixed stop point in the grid of {5, 10, 20, 30, 40}. With lower stop points such as 5 and 10 (in yellow lines), R2F shows relatively lower performance than those with higher stop points (in green lines) in generating rare concepts for attribute types of property and texture, because these usually require a higher level of visual details to synthesize. This tendency becomes reversed for the attribute type of shape, which tends to require a lower level of visual details. The original R2F, which adaptively determines the guidance stop points based on the appropriate visual detail level for each prompt, naturally leads to the best performance. ': '2'} | {'1': 'Efficacy of Alternating Guidance. Figure 8 and Table 6 show the qualitative and quantitative analysis of the R2F’s alternating guidance compared to other possible guidance choices. We apply three guidance choices, (1) Linear interpolation (Interpolate) of latents as in Theorem 3.1, and bring the idea of (2) Composable Diffusion (Liu et al., 2022) and (3) Prompt-to-prompt (P2P) (Hertz et al., 2022). Given a pair of rare-frequent concept prompts, Interpolate linearly interpolates the latants of rare and frequent prompts with α = 0.5 and Composable blends the two prompt embeddings and uses it as the input, until the stop points obtained from LLM. P2P first generates a complete image from the frequent concept prompt and then edits it by the rare concept prompt with attention-control. ', '2': 'Efficacy of Visual-detail-aware Guidance Stop Points. Figure 9 depicts the efficacy of R2F’s adaptive visual-detail-aware stop points compared to when using a fixed stop point on RareBench with single-object case, which has only one stop point. We ablate the fixed stop point in the grid of {5, 10, 20, 30, 40}. With lower stop points such as 5 and 10 (in yellow lines), R2F shows relatively lower performance than those with higher stop points (in green lines) in generating rare concepts for attribute types of property and texture, because these usually require a higher level of visual details to synthesize. This tendency becomes reversed for the attribute type of shape, which tends to require a lower level of visual details. The original R2F, which adaptively determines the guidance stop points based on the appropriate visual detail level for each prompt, naturally leads to the best performance. '} | {'images/490f062e589415340fffe20ccdd9705368cc032e994ff34727f920548537a57d.jpg': '6'} | {'6': 'images/490f062e589415340fffe20ccdd9705368cc032e994ff34727f920548537a57d.jpg'} | {'images/523c879d828890d674f8c25830a6eb2e9e9f5e1eefe733086146f7153d4b58e4.jpg': '2', 'images/0b5cb04ba6b819219bdae29194748fe720dbd54de165be822a80f0345d14d6b5.jpg': '6'} | {'2': 'images/523c879d828890d674f8c25830a6eb2e9e9f5e1eefe733086146f7153d4b58e4.jpg', '6': 'images/0b5cb04ba6b819219bdae29194748fe720dbd54de165be822a80f0345d14d6b5.jpg'} | {} | ['images/523c879d828890d674f8c25830a6eb2e9e9f5e1eefe733086146f7153d4b58e4.jpg', 'Efficacy of Alternating Guidance. Figure 8 and Table 6 show the qualitative and quantitative analysis of the R2F’s alternating guidance compared to other possible guidance choices. We apply three guidance choices, (1) Linear interpolation (Interpolate) of latents as in Theorem 3.1, and bring the idea of (2) Composable Diffusion (Liu et al., 2022) and (3) Prompt-to-prompt (P2P) (Hertz et al., 2022). Given a pair of rare-frequent concept prompts, Interpolate linearly interpolates the latants of rare and frequent prompts with α = 0.5 and Composable blends the two prompt embeddings and uses it as the input, until the stop points obtained from LLM. P2P first generates a complete image from the frequent concept prompt and then edits it by the rare concept prompt with attention-control. ', 'Efficacy of Visual-detail-aware Guidance Stop Points. Figure 9 depicts the efficacy of R2F’s adaptive visual-detail-aware stop points compared to when using a fixed stop point on RareBench with single-object case, which has only one stop point. We ablate the fixed stop point in the grid of {5, 10, 20, 30, 40}. With lower stop points such as 5 and 10 (in yellow lines), R2F shows relatively lower performance than those with higher stop points (in green lines) in generating rare concepts for attribute types of property and texture, because these usually require a higher level of visual details to synthesize. This tendency becomes reversed for the attribute type of shape, which tends to require a lower level of visual details. The original R2F, which adaptively determines the guidance stop points based on the appropriate visual detail level for each prompt, naturally leads to the best performance. '] | 73f91a08d41dc1714651ac65380475e5d60f0ac5571a09135c97c84643d244fe | 87b23be1436dcbe59f7359a900e9813e81087437 |
explanation | What are the practical uses of crystal symmetry generation in academia or industry? | Our main claim is that SymmCD performs significantly better than prior works at generating crystals with realistic, diverse symmetries, as seen in Figure 4 and Table 1. Many properties of crystals (such as piezoelectricity and optical activity) are determined by symmetry, so when searching for practical crystals, a generative model should be able to generate crystals with desired symmetry. | ['Figure 4', 'Table 1'] | ['images/07b2a3283c639511e149708c5a5a98c631cd92a3b3e9b5ba6167f66e51a8374c.jpg', 'images/7aa13bce6f772da2cea00e5222dff0acc466a227e537bc4e9b71858c5da5e504.jpg'] | ['mixed'] | 2 | 3 | 5 | {'We empirically demonstrate our contributions, particularly in ensuring we generate crystals with desired symmetries while being competitive with existing baselines. In other words, we show that SymmCD generates symmetric, stable, and valid crystals. We compare our proposed method with four recent strong baselines: CDVAE (Xie et al., 2022), DiffCSP (Jiao et al., 2023), DiffCSP++ (Jiao et al., 2024) and FlowMM (Miller et al., 2024). ': '1', 'The main contributions of this work are as follows: I) We demonstrate a novel approach to generating crystals through the unconstrained generation of asymmetric units, along with their symmetry information. II) We introduce a physically-motivated representation for crystallographic site symmetries that generalizes across space groups. (III) We experimentally evaluate our method, finding that it performs on par with previous methods in terms of generating stable structures, while offering significantly improved computational efficiency due to our representation. (IV) We perform an indepth analysis of the symmetry and diversity of crystal structures generated by existing generative models. ': '2'} | {'1': 'We empirically demonstrate our contributions, particularly in ensuring we generate crystals with desired symmetries while being competitive with existing baselines. In other words, we show that SymmCD generates symmetric, stable, and valid crystals. We compare our proposed method with four recent strong baselines: CDVAE (Xie et al., 2022), DiffCSP (Jiao et al., 2023), DiffCSP++ (Jiao et al., 2024) and FlowMM (Miller et al., 2024). ', '2': 'The main contributions of this work are as follows: I) We demonstrate a novel approach to generating crystals through the unconstrained generation of asymmetric units, along with their symmetry information. II) We introduce a physically-motivated representation for crystallographic site symmetries that generalizes across space groups. (III) We experimentally evaluate our method, finding that it performs on par with previous methods in terms of generating stable structures, while offering significantly improved computational efficiency due to our representation. (IV) We perform an indepth analysis of the symmetry and diversity of crystal structures generated by existing generative models. '} | {'images/85d305a053ad51ec1c5ea92d18a72fbc0278174eb6a15776b9fcd72ffa4b5a9f.jpg': '3', 'images/07b2a3283c639511e149708c5a5a98c631cd92a3b3e9b5ba6167f66e51a8374c.jpg': '4'} | {'3': 'images/85d305a053ad51ec1c5ea92d18a72fbc0278174eb6a15776b9fcd72ffa4b5a9f.jpg', '4': 'images/07b2a3283c639511e149708c5a5a98c631cd92a3b3e9b5ba6167f66e51a8374c.jpg'} | {'images/7aa13bce6f772da2cea00e5222dff0acc466a227e537bc4e9b71858c5da5e504.jpg': '1'} | {'1': 'images/7aa13bce6f772da2cea00e5222dff0acc466a227e537bc4e9b71858c5da5e504.jpg'} | {} | ['We empirically demonstrate our contributions, particularly in ensuring we generate crystals with desired symmetries while being competitive with existing baselines. In other words, we show that SymmCD generates symmetric, stable, and valid crystals. We compare our proposed method with four recent strong baselines: CDVAE (Xie et al., 2022), DiffCSP (Jiao et al., 2023), DiffCSP++ (Jiao et al., 2024) and FlowMM (Miller et al., 2024). ', 'images/85d305a053ad51ec1c5ea92d18a72fbc0278174eb6a15776b9fcd72ffa4b5a9f.jpg', 'The main contributions of this work are as follows: I) We demonstrate a novel approach to generating crystals through the unconstrained generation of asymmetric units, along with their symmetry information. II) We introduce a physically-motivated representation for crystallographic site symmetries that generalizes across space groups. (III) We experimentally evaluate our method, finding that it performs on par with previous methods in terms of generating stable structures, while offering significantly improved computational efficiency due to our representation. (IV) We perform an indepth analysis of the symmetry and diversity of crystal structures generated by existing generative models. '] | 84384d9a293ea5d8aa6f43c33e3336541e733d203e9c9bfa96542fd3b5754725 | 999ece922a421954932ad2717fc2f68b13d513cc |
explanation | How does the tokenizer-level decoding method affect the model's performance? | We want to clarify that our token-level graph-constrained decoding would not lead to entities or relationships that do not exist in KGs. During decoding, we use the KG-Trie to restrict the tokens generated by the LLM to those starting with valid prefixes stored in the Trie. This approach has been used by previous methods to limit LLM output within a specific scope, such as all entities in KGs. Our KG-Trie is constructed from paths within KGs. Therefore, under these constraints, only valid entities and relations from KGs can be generated by LLMs to form reasoning paths. We have thoroughly checked the generated results and found zero invalid entities or relations, as shown in Figure 5. Meanwhile, the token-level graph-constrained decoding is more efficient and effective than other LLM-based graph reasoning methods. Due to the unstructured nature of LLMs, they are difficult to apply directly for reasoning on structured knowledge graphs (KGs). Previous LLM-based graph reasoning methods, such as ToG, typically follow an agent paradigm where LLMs iteratively query information from KGs. This approach incurs multiple API calls, resulting in high computational costs and latency. With KG-Trie, we enable LLMs to reason on KGs within a single decoding process, significantly reducing computation overhead and latency. Additionally, incorporating KG-Trie into LLM decoding does not introduce extra computational costs since it only masks out the probabilities of invalid tokens. Furthermore, this integration leverages GPU parallel computation to traverse multiple paths using beam search. Table 2 shows that GCR requires less running time and fewer LLM calls than LLM agent-based methods, such as ToG. | ['Figure 5', 'Table 2'] | ['images/a9c22c25f16dacbfe6afb009ac4154c18ce7d5cd88de363eac9ae889381dc7f6.jpg', 'images/b4366059ed83815dbff2897ba35dcac60bd79f6eff2e3af466c087f34a206fc6.jpg'] | ['mixed'] | 2 | 3 | 5 | {'Large language models (LLMs) have strong reasoning capabilities but still suffer from severe hallucination issues, which undermines the trustworthiness of the reasoning process. To tackle this issue, we propose graph-constrained decoding, which unifies the reasoning ability of LLMs with the structured knowledge in KGs to generate faithful KG-grounded reasoning paths leading to answers. ': '1'} | {'1': 'Large language models (LLMs) have strong reasoning capabilities but still suffer from severe hallucination issues, which undermines the trustworthiness of the reasoning process. To tackle this issue, we propose graph-constrained decoding, which unifies the reasoning ability of LLMs with the structured knowledge in KGs to generate faithful KG-grounded reasoning paths leading to answers. '} | {'images/a9c22c25f16dacbfe6afb009ac4154c18ce7d5cd88de363eac9ae889381dc7f6.jpg': '5', 'images/f2e85e495bbd0facb7ad7758f9e65d318165e81151d0cd3f74811ff9db0793a0.jpg': '2'} | {'5': 'images/a9c22c25f16dacbfe6afb009ac4154c18ce7d5cd88de363eac9ae889381dc7f6.jpg', '2': 'images/f2e85e495bbd0facb7ad7758f9e65d318165e81151d0cd3f74811ff9db0793a0.jpg'} | {'images/b4366059ed83815dbff2897ba35dcac60bd79f6eff2e3af466c087f34a206fc6.jpg': '2', 'images/2b0e6d3dfedbcec23470e74c3999e07ad4ba2415833bd605f829d2d3c8634e86.jpg': '4'} | {'2': 'images/b4366059ed83815dbff2897ba35dcac60bd79f6eff2e3af466c087f34a206fc6.jpg', '4': 'images/2b0e6d3dfedbcec23470e74c3999e07ad4ba2415833bd605f829d2d3c8634e86.jpg'} | {} | ['images/2b0e6d3dfedbcec23470e74c3999e07ad4ba2415833bd605f829d2d3c8634e86.jpg', 'images/f2e85e495bbd0facb7ad7758f9e65d318165e81151d0cd3f74811ff9db0793a0.jpg', 'Large language models (LLMs) have strong reasoning capabilities but still suffer from severe hallucination issues, which undermines the trustworthiness of the reasoning process. To tackle this issue, we propose graph-constrained decoding, which unifies the reasoning ability of LLMs with the structured knowledge in KGs to generate faithful KG-grounded reasoning paths leading to answers. '] | e6a6776b0a81cdcfcd35e1bd0f5e9eb909bbed8679c20eb5d92793430d230f84 | a93a8af29009c03fc1e9cb53ca6471568eb580a5 |
explanation | What evidence supports that the improvement comes from the proposed diffusion policy-constrained iteration rather than the Q-ensemble? | We have to emphasize that the improvement of our proposed method over others is not solely based on high scores in the testing environments, but also on the stability of convergence. To demonstrate that the majority of the improvement stems from the proposed soft Q-guidance rather than the Q-ensemble, we have included an ablation study in Figure 3 in our paper. In this study, all designs and parameters are maintained except that soft Q-guidance is replaced with alternatives, such as denoised guidance (similar to DiffusionQL). As seen in Figure 3, the introduction of Q-ensemble does not enhance the stability of convergence for denoised Q-guidance. Additionally, in Table 1, the reported scores from DiffusionQL utilize *online-model-selection*, which tracks the best models throughout the training process. In contrast, we present the average of the final convergent scores from our method, which demands greater stability from the trained models. To further validate the effectiveness of DAC and to ensure a fair comparison with DiffusionQL, we conduct additional experiments where we replace the Q-ensemble in DAC with the same number of Qs (num of Qs=2) used in DiffusionQL. We also record the scores for DAC using *online-model-selection* (OMS). It can be seen that, under the same protocol (both using OMS), our method without the Q-ensemble significantly outperforms DiffusionQL in most environments, demonstrating the effectiveness of soft Q-guidance. We also observe that using a Q-ensemble of size 5 or larger yields similar performance. | ['Figure 3', 'Table 1'] | ['images/fe9b6e3caf55686bb4d3c144cac0a0668aba9ef004d9cd1d8eb13373c6ef5d3c.jpg', 'images/5ac63d1ac28ead1c5e2e523a3f221a3ebb2bf5d6198835d823817dddb1497d2a.jpg'] | ['mixed'] | 2 | 3 | 5 | {'A natural approach to employing diffusion models in behavior cloning involves replacing the noise predictor with a state-conditional model ϵθ(xt, s, t) that generates actions x0 ∈A based on state s. ': '1', 'In this section, we introduce the Diffusion Actor-Critic (DAC) framework that models the target policy directly as a diffusion model, eliminating the need for density estimation of either the behavior policy or the target policy. Initially, we formulate the KL constraint policy optimization as a diffusion noise regression problem, which yields a soft Q-guidance term for the noise prediction process that enables the learning of the target policy in a supervised manner. Additionally, we introduce Qensemble to stabilize the Q-gradient estimation, which utilizes LCB to mitigate the over-pessimistic estimation associated with taking the ensemble minimum in prior research. ': '2'} | {'1': 'A natural approach to employing diffusion models in behavior cloning involves replacing the noise predictor with a state-conditional model ϵθ(xt, s, t) that generates actions x0 ∈A based on state s. ', '2': 'In this section, we introduce the Diffusion Actor-Critic (DAC) framework that models the target policy directly as a diffusion model, eliminating the need for density estimation of either the behavior policy or the target policy. Initially, we formulate the KL constraint policy optimization as a diffusion noise regression problem, which yields a soft Q-guidance term for the noise prediction process that enables the learning of the target policy in a supervised manner. Additionally, we introduce Qensemble to stabilize the Q-gradient estimation, which utilizes LCB to mitigate the over-pessimistic estimation associated with taking the ensemble minimum in prior research. '} | {'images/f493cfe89e44d120988d5d913ae790d2915d73bde486e42e462583601c2cd850.jpg': '4', 'images/fe9b6e3caf55686bb4d3c144cac0a0668aba9ef004d9cd1d8eb13373c6ef5d3c.jpg': '3'} | {'4': 'images/f493cfe89e44d120988d5d913ae790d2915d73bde486e42e462583601c2cd850.jpg', '3': 'images/fe9b6e3caf55686bb4d3c144cac0a0668aba9ef004d9cd1d8eb13373c6ef5d3c.jpg'} | {'images/5ac63d1ac28ead1c5e2e523a3f221a3ebb2bf5d6198835d823817dddb1497d2a.jpg': '1'} | {'1': 'images/5ac63d1ac28ead1c5e2e523a3f221a3ebb2bf5d6198835d823817dddb1497d2a.jpg'} | {} | ['In this section, we introduce the Diffusion Actor-Critic (DAC) framework that models the target policy directly as a diffusion model, eliminating the need for density estimation of either the behavior policy or the target policy. Initially, we formulate the KL constraint policy optimization as a diffusion noise regression problem, which yields a soft Q-guidance term for the noise prediction process that enables the learning of the target policy in a supervised manner. Additionally, we introduce Qensemble to stabilize the Q-gradient estimation, which utilizes LCB to mitigate the over-pessimistic estimation associated with taking the ensemble minimum in prior research. ', 'A natural approach to employing diffusion models in behavior cloning involves replacing the noise predictor with a state-conditional model ϵθ(xt, s, t) that generates actions x0 ∈A based on state s. ', 'images/f493cfe89e44d120988d5d913ae790d2915d73bde486e42e462583601c2cd850.jpg'] | bff49264fb79cf5c46b980c620440e987355c2465918d80f3400f7ea8b807b5e | b0fbc4860d3a1995a411e7559c6961f48a7cda5e |
explanation | More scrutiny of the physics-informed losses would be beneficial. Some plots of solutions and errors across the poorer performing methods might help understand why they are performing badly. Is it that boundary conditions are not being adhered to? Maybe there are regions of high PDE loss in the resulting solution? Perhaps small changes (e.g. weighing boundary conditions effectively) might lead to improved performance. | We have already plotted the solutions to the poor performance in Figure 4 (c). The figure shows that the boundary condition is strictly obeyed for every network because we use weight=100 for boundary loss and weight=1 for residual loss. Besides, we did experiments on larger weights of boundary conditions to have a more strict boundary condition: we keep the weight of residual loss, and weight=1000 for boundary loss in the poor performance experiments i.e. $
ewline=1000$ in Table 3. | ['Figure 4', 'Table 3'] | ['images/fd9ec57bfd1aa96760031234b763c2614267e736af279f75e746b5661a9956da.jpg', 'images/c5db1317f66009d3c2aba6ceb5a67bfa25ef5402ea5b35c45592df5a2f2b76b3.jpg'] | ['mixed'] | 2 | 3 | 5 | {'Physics-informed neural networks (PINNs) Lagaris et al. (1998); Raissi et al. (2019) are a method used to solve partial differential equations (PDEs) by integrating physical laws with neural networks in machine learning. The use of Kolmogorov-Arnold Networks (KANs) in PINNs has been explored and is referred to as Physics-Informed Kolmogorov-Arnold Networks (PIKANs) Rigas et al. (2024); Wang et al. (2024). Due to the high similarity between KAN and MLP, PIKANs inherit several advantages of PINNs, such as overcoming the curse of dimensionality (CoD) Wojtowytsch & Weinan (2020); Han et al. (2018), handling imperfect data Karniadakis et al. (2021), and performing interpolation Sliwinski & Rigas (2023). PINNs have diverse applications, including fluid dynamics Raissi et al. (2020); Jin et al. (2021); Kashefi & Mukerji (2022), quantum mechanical systems Jin et al. (2022), surface physics Fang & Zhan (2019), electric power systems Nellikkath & Chatzivasileiadis (2022), and biological systems Yazdani et al. (2020). However, they also face challenges such as spectral bias Xu et al. (2019); Wang et al. (2022), error estimation Fanaskov et al. (2024), and scalability issues Yao et al. (2023). ': '1'} | {'1': 'Physics-informed neural networks (PINNs) Lagaris et al. (1998); Raissi et al. (2019) are a method used to solve partial differential equations (PDEs) by integrating physical laws with neural networks in machine learning. The use of Kolmogorov-Arnold Networks (KANs) in PINNs has been explored and is referred to as Physics-Informed Kolmogorov-Arnold Networks (PIKANs) Rigas et al. (2024); Wang et al. (2024). Due to the high similarity between KAN and MLP, PIKANs inherit several advantages of PINNs, such as overcoming the curse of dimensionality (CoD) Wojtowytsch & Weinan (2020); Han et al. (2018), handling imperfect data Karniadakis et al. (2021), and performing interpolation Sliwinski & Rigas (2023). PINNs have diverse applications, including fluid dynamics Raissi et al. (2020); Jin et al. (2021); Kashefi & Mukerji (2022), quantum mechanical systems Jin et al. (2022), surface physics Fang & Zhan (2019), electric power systems Nellikkath & Chatzivasileiadis (2022), and biological systems Yazdani et al. (2020). However, they also face challenges such as spectral bias Xu et al. (2019); Wang et al. (2022), error estimation Fanaskov et al. (2024), and scalability issues Yao et al. (2023). '} | {'images/fd9ec57bfd1aa96760031234b763c2614267e736af279f75e746b5661a9956da.jpg': '4', 'images/9e0c873dd53288bb6b55aa30e6e2ec6ec0df2ab90da0179fa312ded2fd9060d2.jpg': '2'} | {'4': 'images/fd9ec57bfd1aa96760031234b763c2614267e736af279f75e746b5661a9956da.jpg', '2': 'images/9e0c873dd53288bb6b55aa30e6e2ec6ec0df2ab90da0179fa312ded2fd9060d2.jpg'} | {'images/ebae19bc3640cff886b2ec64f7bc1317fc2ee7a4d81adf69998f9b3babd55b96.jpg': '2', 'images/c5db1317f66009d3c2aba6ceb5a67bfa25ef5402ea5b35c45592df5a2f2b76b3.jpg': '3'} | {'2': 'images/ebae19bc3640cff886b2ec64f7bc1317fc2ee7a4d81adf69998f9b3babd55b96.jpg', '3': 'images/c5db1317f66009d3c2aba6ceb5a67bfa25ef5402ea5b35c45592df5a2f2b76b3.jpg'} | {} | ['images/9e0c873dd53288bb6b55aa30e6e2ec6ec0df2ab90da0179fa312ded2fd9060d2.jpg', 'images/ebae19bc3640cff886b2ec64f7bc1317fc2ee7a4d81adf69998f9b3babd55b96.jpg', 'Physics-informed neural networks (PINNs) Lagaris et al. (1998); Raissi et al. (2019) are a method used to solve partial differential equations (PDEs) by integrating physical laws with neural networks in machine learning. The use of Kolmogorov-Arnold Networks (KANs) in PINNs has been explored and is referred to as Physics-Informed Kolmogorov-Arnold Networks (PIKANs) Rigas et al. (2024); Wang et al. (2024). Due to the high similarity between KAN and MLP, PIKANs inherit several advantages of PINNs, such as overcoming the curse of dimensionality (CoD) Wojtowytsch & Weinan (2020); Han et al. (2018), handling imperfect data Karniadakis et al. (2021), and performing interpolation Sliwinski & Rigas (2023). PINNs have diverse applications, including fluid dynamics Raissi et al. (2020); Jin et al. (2021); Kashefi & Mukerji (2022), quantum mechanical systems Jin et al. (2022), surface physics Fang & Zhan (2019), electric power systems Nellikkath & Chatzivasileiadis (2022), and biological systems Yazdani et al. (2020). However, they also face challenges such as spectral bias Xu et al. (2019); Wang et al. (2022), error estimation Fanaskov et al. (2024), and scalability issues Yao et al. (2023). '] | 7b4312c0282f7827977689475824799ec9bcee735d135a31f21774590f086a44 | b2625752041c98c9978af6d3f403718dc2e532ba |
explanation | What verification process is in place for the key insight mentioned in the paper? | Please see 'Response to common comments' above for how this insight is verified through Figure 4. Our key insight states that if a model cannot generate consistently correct responses (sampled with a temperature of 1.0) across k trials, then the same model will struggle to distinguish between these k responses. Table 4, on the other hand, pertains to a different experimental setting in which we study the performance of several models on solving (greedy decoding, one trial) the set of questions identified via our key insight (i.e., these were only questions which GPT-4o struggled to consistently answer correctly). In this context, the solver's accuracy is not indicative of the difficulty in distinguishing between response pairs. Instead, the key takeaway from Table 4 is that identifying a correct response in JudgeBench is highly correlated with, and nearly as difficult as, solving the underlying problem itself. This reinforces the challenging nature of our dataset. | ['Figure 4', 'Table 4'] | ['images/f3451da79021da5c0980e252154f9755a3a290a822227dad6e71ba74ff046351.jpg', 'images/d335b997830106d49063ae1967cce250e8eed91de9515e78bb78496a0dd11ff7.jpg'] | ['mixed'] | 2 | 3 | 5 | {'Figure 1: Comparison of JudgeBench against previous works. Unlike previous works which focus on instruction following or stylistic preferences, the focus of JudgeBench is on evaluating the factual and logical correctness of complex responses to challenging questions. JudgeBench is noticeably more difficult than previous work, containing responses that are impossible for crowdsourced human annotators to evaluate in a reliable and timely manner. ': '1', 'Benchmarks for LLM-based judges and reward models. As LLM-based judges have become a widely adopted method for evaluating and improving large language models (LLMs), several benchmarks have been introduced to assess their effectiveness. Works such as LLMEval (Zhang et al., 2023), MTBench (Zheng et al., 2024), and FairEval (Wang et al., 2023a) focus on evaluating the alignment between LLM-based judges’ responses and human evaluations. As mentioned above, these dataset suffers from the inherent subjectivity of human evaluation, prioritizing stylistic differences over factual and logical correctness. LLMBar (Zeng et al., 2023) instead takes a different approach by assessing LLM-based judges’ ability to follow instructions, using response pairs with clear ground truth preference labels based on adherence to instructions rather than subjective preferences. In contrast, JudgeBench focuses on assessing LLM-based judges’ ability to reason through responses and distinguish between correct and incorrect responses, which is more challenging than instruction following alone. ': '2', 'While instruction following and style are relatively easy for human annotators to judge, factual and logical correctness becomes increasingly challenging with complex problems. In such cases, human evaluators may mistakenly favor responses that seem more plausible or are simply longer, prioritizing style over correctness—thereby violating the hierarchical framework. As a result, human evaluations often become unreliable as the difficulty of the task increases. ': '3'} | {'1': 'Figure 1: Comparison of JudgeBench against previous works. Unlike previous works which focus on instruction following or stylistic preferences, the focus of JudgeBench is on evaluating the factual and logical correctness of complex responses to challenging questions. JudgeBench is noticeably more difficult than previous work, containing responses that are impossible for crowdsourced human annotators to evaluate in a reliable and timely manner. ', '2': 'Benchmarks for LLM-based judges and reward models. As LLM-based judges have become a widely adopted method for evaluating and improving large language models (LLMs), several benchmarks have been introduced to assess their effectiveness. Works such as LLMEval (Zhang et al., 2023), MTBench (Zheng et al., 2024), and FairEval (Wang et al., 2023a) focus on evaluating the alignment between LLM-based judges’ responses and human evaluations. As mentioned above, these dataset suffers from the inherent subjectivity of human evaluation, prioritizing stylistic differences over factual and logical correctness. LLMBar (Zeng et al., 2023) instead takes a different approach by assessing LLM-based judges’ ability to follow instructions, using response pairs with clear ground truth preference labels based on adherence to instructions rather than subjective preferences. In contrast, JudgeBench focuses on assessing LLM-based judges’ ability to reason through responses and distinguish between correct and incorrect responses, which is more challenging than instruction following alone. ', '3': 'While instruction following and style are relatively easy for human annotators to judge, factual and logical correctness becomes increasingly challenging with complex problems. In such cases, human evaluators may mistakenly favor responses that seem more plausible or are simply longer, prioritizing style over correctness—thereby violating the hierarchical framework. As a result, human evaluations often become unreliable as the difficulty of the task increases. '} | {'images/f3451da79021da5c0980e252154f9755a3a290a822227dad6e71ba74ff046351.jpg': '4'} | {'4': 'images/f3451da79021da5c0980e252154f9755a3a290a822227dad6e71ba74ff046351.jpg'} | {'images/d335b997830106d49063ae1967cce250e8eed91de9515e78bb78496a0dd11ff7.jpg': '4'} | {'4': 'images/d335b997830106d49063ae1967cce250e8eed91de9515e78bb78496a0dd11ff7.jpg'} | {} | ['Figure 1: Comparison of JudgeBench against previous works. Unlike previous works which focus on instruction following or stylistic preferences, the focus of JudgeBench is on evaluating the factual and logical correctness of complex responses to challenging questions. JudgeBench is noticeably more difficult than previous work, containing responses that are impossible for crowdsourced human annotators to evaluate in a reliable and timely manner. ', 'While instruction following and style are relatively easy for human annotators to judge, factual and logical correctness becomes increasingly challenging with complex problems. In such cases, human evaluators may mistakenly favor responses that seem more plausible or are simply longer, prioritizing style over correctness—thereby violating the hierarchical framework. As a result, human evaluations often become unreliable as the difficulty of the task increases. ', 'Benchmarks for LLM-based judges and reward models. As LLM-based judges have become a widely adopted method for evaluating and improving large language models (LLMs), several benchmarks have been introduced to assess their effectiveness. Works such as LLMEval (Zhang et al., 2023), MTBench (Zheng et al., 2024), and FairEval (Wang et al., 2023a) focus on evaluating the alignment between LLM-based judges’ responses and human evaluations. As mentioned above, these dataset suffers from the inherent subjectivity of human evaluation, prioritizing stylistic differences over factual and logical correctness. LLMBar (Zeng et al., 2023) instead takes a different approach by assessing LLM-based judges’ ability to follow instructions, using response pairs with clear ground truth preference labels based on adherence to instructions rather than subjective preferences. In contrast, JudgeBench focuses on assessing LLM-based judges’ ability to reason through responses and distinguish between correct and incorrect responses, which is more challenging than instruction following alone. '] | 17c6e6a32c763123494b9c7792e1d9ee15a288f3bad4e98dfd89a1980c2dd308 | c6da374332587e75991c772444d2fe81a84cf9c8 |
explanation | What is the motivation of using vector quantization into spatiotemporal prediction? | Our findings reveal that this belief does not hold true for the majority of state-of-the-art VQ methods, as demonstrated in Table 4 and Figure 5 on page 8 of our paper. We conducted experiments by varying the size of the codebook, from small to large, and found that none led to improved outcomes. Although a larger codebook size results in less deterioration, it does not enhance results. | ['Table 4', 'Figure 5'] | ['images/10719f212eaa31bd5eafbe3a45dce00fef2cc542ff52c8644bc7f47bc5ccf51b.jpg', 'images/a29b3a4f041b6c3762604546a5508ab6d5237cb7f71382ff31c412421c465415.jpg'] | ['mixed'] | 2 | 3 | 5 | {'with probability at least 1 −ε. Therefore, ∥g′−g∥2 ≥(1 + ∆)−1∥Ug −Ug′∥2. Since the s-sparse unit vector covering number is bounded by (Cm/sδ)s, we establish: ': '1'} | {'1': 'with probability at least 1 −ε. Therefore, ∥g′−g∥2 ≥(1 + ∆)−1∥Ug −Ug′∥2. Since the s-sparse unit vector covering number is bounded by (Cm/sδ)s, we establish: '} | {'images/a29b3a4f041b6c3762604546a5508ab6d5237cb7f71382ff31c412421c465415.jpg': '5', 'images/78acd0f2f4d9f888a1ccb28628e112d46ae9bd6922c7ebde2f114e26b75436bd.jpg': '4', 'images/a719b7d7290b4604bfaa233f79551b8f9f68a5eaab0c85e5a3cba9465325c48d.jpg': '1'} | {'5': 'images/a29b3a4f041b6c3762604546a5508ab6d5237cb7f71382ff31c412421c465415.jpg', '4': 'images/78acd0f2f4d9f888a1ccb28628e112d46ae9bd6922c7ebde2f114e26b75436bd.jpg', '1': 'images/a719b7d7290b4604bfaa233f79551b8f9f68a5eaab0c85e5a3cba9465325c48d.jpg'} | {'images/10719f212eaa31bd5eafbe3a45dce00fef2cc542ff52c8644bc7f47bc5ccf51b.jpg': '4'} | {'4': 'images/10719f212eaa31bd5eafbe3a45dce00fef2cc542ff52c8644bc7f47bc5ccf51b.jpg'} | {} | ['images/78acd0f2f4d9f888a1ccb28628e112d46ae9bd6922c7ebde2f114e26b75436bd.jpg', 'images/a719b7d7290b4604bfaa233f79551b8f9f68a5eaab0c85e5a3cba9465325c48d.jpg', 'with probability at least 1 −ε. Therefore, ∥g′−g∥2 ≥(1 + ∆)−1∥Ug −Ug′∥2. Since the s-sparse unit vector covering number is bounded by (Cm/sδ)s, we establish: '] | 08e9c7668c3f06734523fb27dc073e5f4a26b88fb8305a536bbe8c097cc45fd7 | ca6147914709aec09e7b238aac57b2e654fc45c8 |
explanation | Have the authors considered techniques to make the trigger less detectable? | To quantify the visual stealthiness of a trigger, we use a computer vision model as the judge. We trained a benign global model on clean data under the same training settings as the victim FL system, using it as the judge model. We consider a trigger to have good visual stealthiness if its poisoned data can maintain high benign accuracy on the judge model while showing a high attack success rate (ASR) on the victim global model. Based on the ASR results in Table 3 and Figure 3, we selected the trigger size by balancing the trade-off between attack performance and visual stealthiness. | ['Table 3', 'Figure 3'] | ['images/69e33fae2123ee66640c302e2ec75c63c15a58254fc37185ddba49584b88ab55.jpg', 'images/e0f0d3734d440dab10cc79612bf915603d883123df705f906ece46b0c9182f6c.jpg'] | ['mixed'] | 2 | 3 | 5 | {'The capability of malicious clients in our attack is limited to the manipulation of their local training data that are input to their training pipelines. In addition, in line with existing works (Lyu et al., 2023; Zhang et al., 2024; Fang & Chen, 2023; Gong et al., 2022), we do not assume the secrecy of the global model provided by the FL server, as it would typically need to be accessible outside TEEs for use in local inference tasks. As such, in each FL round, clients are granted white-box access to the global model. Originating from initially benign clients that have been compromised, these malicious clients possess some local training data for the FL main task as background knowledge. ': '1', 'Datasets and global models: We evaluated DPOT on four classification datasets with non-IID data distributions: Fashion MNIST, FEMNIST, CIFAR10, and Tiny ImageNet. Table 4 summarizes their basic information and models we used on each dataset. ': '2', 'In this section, we present the performance of DPOT attack against ten defense methods and compare our results with two widely-used data-poisoning attacks. ': '3'} | {'1': 'The capability of malicious clients in our attack is limited to the manipulation of their local training data that are input to their training pipelines. In addition, in line with existing works (Lyu et al., 2023; Zhang et al., 2024; Fang & Chen, 2023; Gong et al., 2022), we do not assume the secrecy of the global model provided by the FL server, as it would typically need to be accessible outside TEEs for use in local inference tasks. As such, in each FL round, clients are granted white-box access to the global model. Originating from initially benign clients that have been compromised, these malicious clients possess some local training data for the FL main task as background knowledge. ', '2': 'Datasets and global models: We evaluated DPOT on four classification datasets with non-IID data distributions: Fashion MNIST, FEMNIST, CIFAR10, and Tiny ImageNet. Table 4 summarizes their basic information and models we used on each dataset. ', '3': 'In this section, we present the performance of DPOT attack against ten defense methods and compare our results with two widely-used data-poisoning attacks. '} | {'images/e0f0d3734d440dab10cc79612bf915603d883123df705f906ece46b0c9182f6c.jpg': '3'} | {'3': 'images/e0f0d3734d440dab10cc79612bf915603d883123df705f906ece46b0c9182f6c.jpg'} | {'images/69e33fae2123ee66640c302e2ec75c63c15a58254fc37185ddba49584b88ab55.jpg': '3'} | {'3': 'images/69e33fae2123ee66640c302e2ec75c63c15a58254fc37185ddba49584b88ab55.jpg'} | {} | ['Datasets and global models: We evaluated DPOT on four classification datasets with non-IID data distributions: Fashion MNIST, FEMNIST, CIFAR10, and Tiny ImageNet. Table 4 summarizes their basic information and models we used on each dataset. ', 'The capability of malicious clients in our attack is limited to the manipulation of their local training data that are input to their training pipelines. In addition, in line with existing works (Lyu et al., 2023; Zhang et al., 2024; Fang & Chen, 2023; Gong et al., 2022), we do not assume the secrecy of the global model provided by the FL server, as it would typically need to be accessible outside TEEs for use in local inference tasks. As such, in each FL round, clients are granted white-box access to the global model. Originating from initially benign clients that have been compromised, these malicious clients possess some local training data for the FL main task as background knowledge. ', 'In this section, we present the performance of DPOT attack against ten defense methods and compare our results with two widely-used data-poisoning attacks. '] | d470c9223d5f671f73f08d91acc25b519cf383bd485a14a39da46ff8742d04c9 | d48240fbd51a9bc4ee932e076defb133e9ee5288 |
explanation | How is the pixel count determined in practice? | Based on the ASR results in Table 3 and Figure 3, we selected the trigger size by balancing the trade-off between attack performance and visual stealthiness—a larger trigger size results in a higher ASR but lower benign accuracy. We set the lower bound for 'Drop' at -30% and the lower bound for 'Final ASR' at 50%, and choose the smallest trigger size that meets both constraints. | ['Table 3', 'Figure 3'] | ['images/69e33fae2123ee66640c302e2ec75c63c15a58254fc37185ddba49584b88ab55.jpg', 'images/e0f0d3734d440dab10cc79612bf915603d883123df705f906ece46b0c9182f6c.jpg'] | ['mixed'] | 2 | 3 | 5 | {'• Trigger size. The number of pixels that a backdoor trigger can alter is specified by the trigger size attribute. Selection of trigger sizes for various datasets are discussed in Appendix D.3. ': '1', 'Existing defenses against backdoor attacks in FL rely on a hypothesis that backdoor attacks will always cause the updating direction of a model to deviate from its original benign objective, because the backdoor objectives defined by backdoored data cannot be achieved within the original direction (Fung et al., 2020; Cao et al., 2021). However, the capabilities of backdoor attacks are not limited to this hypothesis. To counter this hypothesis, adversaries can align the updating directions of a model with respect to backdoor and benign objectives by strategically adjusting the backdoor objective. Applying this idea to FL, if the injection of backdoored data has minimal effect on a client’s model updates, then detecting this client as malicious becomes challenging for defenses based on analyzing clients’ model updates. ': '2'} | {'1': '• Trigger size. The number of pixels that a backdoor trigger can alter is specified by the trigger size attribute. Selection of trigger sizes for various datasets are discussed in Appendix D.3. ', '2': 'Existing defenses against backdoor attacks in FL rely on a hypothesis that backdoor attacks will always cause the updating direction of a model to deviate from its original benign objective, because the backdoor objectives defined by backdoored data cannot be achieved within the original direction (Fung et al., 2020; Cao et al., 2021). However, the capabilities of backdoor attacks are not limited to this hypothesis. To counter this hypothesis, adversaries can align the updating directions of a model with respect to backdoor and benign objectives by strategically adjusting the backdoor objective. Applying this idea to FL, if the injection of backdoored data has minimal effect on a client’s model updates, then detecting this client as malicious becomes challenging for defenses based on analyzing clients’ model updates. '} | {'images/e0f0d3734d440dab10cc79612bf915603d883123df705f906ece46b0c9182f6c.jpg': '3'} | {'3': 'images/e0f0d3734d440dab10cc79612bf915603d883123df705f906ece46b0c9182f6c.jpg'} | {'images/69e33fae2123ee66640c302e2ec75c63c15a58254fc37185ddba49584b88ab55.jpg': '3', 'images/04648f4d66c2bf3ffebc2e5468af1f870aa7099fce095e3ef919fbe9fdde3cff.jpg': '2'} | {'3': 'images/69e33fae2123ee66640c302e2ec75c63c15a58254fc37185ddba49584b88ab55.jpg', '2': 'images/04648f4d66c2bf3ffebc2e5468af1f870aa7099fce095e3ef919fbe9fdde3cff.jpg'} | {} | ['images/04648f4d66c2bf3ffebc2e5468af1f870aa7099fce095e3ef919fbe9fdde3cff.jpg', 'Existing defenses against backdoor attacks in FL rely on a hypothesis that backdoor attacks will always cause the updating direction of a model to deviate from its original benign objective, because the backdoor objectives defined by backdoored data cannot be achieved within the original direction (Fung et al., 2020; Cao et al., 2021). However, the capabilities of backdoor attacks are not limited to this hypothesis. To counter this hypothesis, adversaries can align the updating directions of a model with respect to backdoor and benign objectives by strategically adjusting the backdoor objective. Applying this idea to FL, if the injection of backdoored data has minimal effect on a client’s model updates, then detecting this client as malicious becomes challenging for defenses based on analyzing clients’ model updates. ', '• Trigger size. The number of pixels that a backdoor trigger can alter is specified by the trigger size attribute. Selection of trigger sizes for various datasets are discussed in Appendix D.3. '] | d374c1df2439597a2bc212b3818e23a4b1137f6607d84bbfd435ef62542743bb | d48240fbd51a9bc4ee932e076defb133e9ee5288 |
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MCiteBench Dataset
MCiteBench is a benchmark to evaluate multimodal citation text generation in Multimodal Large Language Models (MLLMs).
- Websites: https://caiyuhu.github.io/MCiteBench
- Paper: https://arxiv.org/abs/2503.02589
- Code: https://github.com/caiyuhu/MCiteBench
Data Download
Please download the MCiteBench_full_dataset.zip. It contains the data.jsonl file and the visual_resources folder.
Data Statistics
Data Format
The data format for data_example.jsonl and data.jsonl is as follows:
question_type: [str] # The type of question, with possible values: "explanation" or "locating"
question: [str] # The text of the question
answer: [str] # The answer to the question, which can be a string, list, float, or integer, depending on the context
evidence_keys: [list] # A list of abstract references or identifiers for evidence, such as "section x", "line y", "figure z", or "table k".
# These are not the actual content but pointers or descriptions indicating where the evidence can be found.
# Example: ["section 2.1", "line 45", "Figure 3"]
evidence_contents: [list] # A list of resolved or actual evidence content corresponding to the `evidence_keys`.
# These can include text excerpts, image file paths, or table file paths that provide the actual evidence for the answer.
# Each item in this list corresponds directly to the same-index item in `evidence_keys`.
# Example: ["This is the content of section 2.1.", "/path/to/figure_3.jpg"]
evidence_modal: [str] # The modality type of the evidence, with possible values: ['figure', 'table', 'text', 'mixed'] indicating the source type of the evidence
evidence_count: [int] # The total count of all evidence related to the question
distractor_count: [int] # The total number of distractor items, meaning information blocks that are irrelevant or misleading for the answer
info_count: [int] # The total number of information blocks in the document, including text, tables, images, etc.
text_2_idx: [dict[str, str]] # A dictionary mapping text information to corresponding indices
idx_2_text: [dict[str, str]] # A reverse dictionary mapping indices back to the corresponding text content
image_2_idx: [dict[str, str]] # A dictionary mapping image paths to corresponding indices
idx_2_image: [dict[str, str]] # A reverse dictionary mapping indices back to image paths
table_2_idx: [dict[str, str]] # A dictionary mapping table paths to corresponding indices
idx_2_table: [dict[str, str]] # A reverse dictionary mapping indices back to table paths
meta_data: [dict] # Additional metadata used during the construction of the data
distractor_contents: [list] # Similar to `evidence_contents`, but contains distractors, which are irrelevant or misleading information
question_id: [str] # The ID of the question
pdf_id: [str] # The ID of the associated PDF document
Citation
If you find MCiteBench useful for your research and applications, please kindly cite using this BibTeX:
@article{hu2025mcitebench,
title={MciteBench: A Benchmark for Multimodal Citation Text Generation in MLLMs},
author={Hu, Caiyu and Zhang, Yikai and Zhu, Tinghui and Ye, Yiwei and Xiao, Yanghua},
journal={arXiv preprint arXiv:2503.02589},
year={2025}
}
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