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[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1) Can you share a plot of accuracy vs. delay for baselines and the variants of your method, including the variant with 1 phase and a dimension 2 MLP? \n\n2) Why is replacing one nonlinearity with a different nonlinearity useful for MPC? \n\n3) Can/should you compare to BOLT in the evaluation?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "-\tThe problem formulation is interesting and new\n-\tThe algorithm appears to cleverly use the available information\n-\tThe empirical performance and experiments are promising\n\nOverall, I think the paper is proposing an interesting new idea (both formulation and algorithm), and hence I gave it a positive score. However, I think the level of polish and writing is rather low, and I think the paper would need a significant clean-up prior to publication." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes SelectFormer, a method for privately selecting data for transformer finetuning using MPC. The setting is that a data holder is trying to sell data to a user, who wants to finetune a transformer on only a subset of the holder’s data. However, the data holder doesn’t want to expose all fo their data, and the data user doesn’t want to expose their model weights to the data holder. The main idea is to iteratively learn a sequence of models, each of which is used to privately rank points in the dataset in terms of their informativeness (entropy of the output distribution of the target model). These models replace nonlinear operations like softmax with an MLP with ReLU activation, where the MLP is trained by collecting input-output pairs from the intermediate model. The authors show that their method significantly reduces the time to select a good training dataset relative to prior private baselines, without sacrificing much in terms of quality (i.e. it selects data points that lead to a good downstream model)." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "-\tThe paper is not polished or easy to read/understand\n-\tIt’s not clear if the paper compares against relevant baselines\n-\tImprecise threat model and privacy guarantees. The threat model does not clearly specify *who* should not learn *what data* \n-\tHow does the MPC handle nonlinearities in the MLP? \n-\tThe level of formalism in the paper is very low \n\nIn your privacy guarantees, please specify *who* learns or does not learn what information. For instance, if the data holder knows the architecture of the model(s) and they know the ranked entropy of their samples, why can’t they train the model locally on the most informative samples to approximate the private model? This leakage was not explored in the paper, to my knowledge. \n\nThe paper’s evaluation considers time-accuracy tradeoffs. However, these are mostly provided implicitly in tables that do not clearly show the tradeoff. It may be helpful to show a plot with selection time on one axis and accuracy on the other, and then see different baselines, including variants of your method. Specifically, based on the results in the Appendix, I think the 1-phase variant with a dimension 2 MLP may do well enough relative to the other hyperparameter settings, especially if you consider its lower delay. But it’s hard to judge from the tables provided. Can you produce this plot and compare it to the suggested hyperparameter settings? \n\nA lot of notation seemed to be undefined or imprecisely defined. This made the paper difficult to read. For instance, in Sec. 4.1, “with a selectivity αi = Si/Si−1” if S_i is a dataset, do you mean the ratio of the *cardinalities* of S_i and S_{i-1}? In Sec 2.1 and 2.2 -- What is the difference between M_t and M_target? Why do you want to query all the samples in D on M_t instead of M_target? I thought M_t was the finetuned model, which is unknown until you finetune with the selected data? The notation $\\hat M_i$ was not defined, and the proxy model was previously defined as $M_p$. And what is $M_g$ in Figure 2? It is not defined until later in the paper, in Section 4.2. But the definition doesn’t match Figure 2 (is it the bottom K or L layers?) What is the difference between $W_i$ and $w_i$? \n\nIn the same vein, the paper does not seem to formally write out the MPC algorithm or prove any guarantees. \n\nThe paper is missing several relevant references, including one potentially important baseline for comparison: \n-\tPang et al, “Bolt: Privacy-Preserving, accurate, and efficient inference for transformers” (S&P 2024)\n\nTable 1 is hard to read. Please enlarge. Also what metric is it providing? Please make the table and caption self-contained. This is true also of the tables in the appendix, many of which don't specify what metric they are listing.\n\n\nMinor comments:\n-\tIntro: “As shown in ??,”\n-\tThe section “Workflow” is very difficult to follow—it’s not clear what you mean by ranking samples by entropy, for instance\n-\tThe model owner has a private, small validation set, on which she wants to maximize the test accuracy.  Do you mean validation accuracy?\n-\t“offline generate a random triple called Beaver triple Beaver (1992)” wrong type of citation typo\n\n* I am not an expert in MPC and may be missing relevant literature and requirements." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Is there a way to make the grid search practical? We probably do not have the luxury of trying MPC with a data vendor multiple times with different parameters in practice. Maybe we can do a grid search to see what parameters perform the best on a non-private test dataset, and then use these values in all actual interactions with data owners?\n\nCan you include a 'baseline' for Figure 1? i.e., show how long the corresponding operations take without using MPC and the memory requirement for each (communication rounds could be omitted, of course)." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The paper studies a problem that has been of interest in past work. It proposes a method that is both very efficient and achieves high utility in the empirical studies. The authors can operate in a more general setting (where examples are unlabelled) than past work. It is clear from the presentation what techniques the authors' method uses to improve upon the past work, and the techniques are made understandable at a high-level even to someone who is not an expert in the area. Furthermore, techniques such as using MLPs for dimension-reduction and fusing multiple operations are themselves sufficiently different from past work and a possibly interesting contribution independent of the problem of data selection." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper considers the private data selection problem, i.e. the problem of a model trainer selecting data to purchase from a data owner without revealing the purchased data to the model trainer. The authors focus on the active learning framework, which targets examples which have the largest prediction entropy, i.e. which the model can learn more from, and does not require labelled examples. Ideally multi-party computation can be used to have the model trainer and data owner compute the examples' entropies (or, if entropies are themselves sensitive, a relative ordering of the examples' entropies) without revealing any other information about the examples themselves. For transformers, MPC is infeasible because transformers use high-dimensional, non-linear operations. Past work got around this issue by approximating the non-linear operations using linear operations. However, using MPC to compute these approximations remains expensive and the approximation comes at a cost in accuracy.\n\nThe authors instead propose a number of techniques to make MPC evaluation of transformers more efficient. First, they fuse multiple non-linear steps. Next, they use multi-layer perceptrons (MLPs) to approximate the fused steps and reduce their dimension (as opposed to past work, which use MLPs to reduce a single non-linear step without dimension reduction). Third, they employ a multi-phase selection protocol, where initially a small model is used to filter the initial dataset $S$ into $S_1$, and in each successive step a larger model is used to filter $S_i$ into $S_{i+1}$ until the final dataset is acquired. To find the MLP approximation and also construct the smaller models, the model trainer purchases a small arbitrary bootstrap dataset up front, and uses the inputs/outputs of the large model on this dataset to train the MLP to approximate layers of the large model. The number of layers and width of the layers, as well as the dimension of the MLPs, can be reduced to construct a smaller model.\n\nThe authors perform an empirical comparison of their data selection protocol to (1) picking data at random (2) an oracle which chooses the highest-entropy examples without MPC, and (3) MPCFormer. At varying dataset sizes, the authors' result is competitive with baseline (2) in terms of accuracy and achieves a ~200x speedup in runtime over (2), and has large accuracy improvements over (1) and (3), on a variety of empirical tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The MLP dimensions, proxy layers, and selectivity per round are chosen via grid search, which might limit the practicality of the method. It would be nice to have either \"standard\" guidance for choosing these parameters or a justification for grid search being practical. See Questions below.\n\nThe paper also needs some editing, there are some major typography errors, though I expect this is easy to handle in a revision. For example: \n* Line 82, citation to ??\n* Line 398 - \"under 3 compute parties\" appears twice\n* Line 498, $d_i$ is not formatted properly" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "- Have you considered alternatives to MPCFormer, such as THE-X (that you cite, Chen et al 2022) or other modern 2PC inference papers for transformers, especially if they use data-independent nonlinearity approximations? Or are there reasons why such baselines don't apply or are already outperformed by MPCFormer in a data selection setting where distillation data is scarce?\n- If I understand correctly, computing ReLUs is still a bottleneck in MPC. This might be why FFNs take most of the computation time in Fig 1. Yet, your method introduces even more ReLUs by adding MLPs. Have you considered other learnable approximations as an alternative to MLPs, such as polynomials with learnable coefficients, which could be more MPC-friendly? Why should we expect MLP approximation to be the best way to obtain MPC-friendly proxy models?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Main strengths:\n- SelectFormer shows strong improvements in delay and accuracy over baselines, which include two naive baselines (Oracle and Random) and one recent MPC inference paper (MPCFormer). \n- Multiphase selection is a nice technique. The key idea is that we can significantly reduce delay by selecting the first datapoints with coarse model proxies, and progressively use more accurate and slower proxies (trained on a now larger collection of purchased datapoints) to better select the following datapoints. It turns out that this technique also improves accuracy a bit.\n- Another useful contribution is MLP approximation of nonlinearities, where the MLPs are trained by generating a large synthetic dataset using metrics coming from the small number of datapoints already purchased.\n- The paper's evaluation is broad and strong, across both vision and NLP tasks. I appreciated the numerous experiments and very granular ablation studies, such as Fig 4 and Fig 6.\n\n\nOther strengths:\n* It is useful to compare the accuracy drop from different MLP approximations.\n- Handling imbalanced and unlabeled data is important.\n- Using computation/communication parallelism sounds intuitive, but it might not be done by other works, so it is valuable to evaluate it. This parallelism offers a nice gain in performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper focuses on private data selection for transformers over MPC. The authors consider a two-party setting, where a model owner wishes to purchase data points from a data owner. The model owner needs this data to fine-tune a target model. To find the most relevant data points without revealing the rest of the dataset, the two parties engage in a multiparty computation. \n\nComputational and communication overhead is a key challenge in making MPC practical for data selection. In an ideal world, the model owner could evaluate its target model on data points with MPC. However, this is not practical for large transformer models, which contain nonlinearities (such as layer norm or softmax) that are prohibitively expensive to compute with MPC. Thus, an alternative approach is to use a cheaper proxy model to approximate the target model efficiently while still selecting useful datapoints. \n\nThis paper proposes a new way of building such proxy models, by replacing costly nonlinearities by more MPC-efficient, trainable, multilayer perceptrons. The authors also introduce a multiphase selection approach where datapoints are selected progressively, thereby using previously selected datapoints to improve the selection of future datapoints. Delay can also be optimized by overlapping computation and communication. \n\nThe paper is evaluated on vision and NLP tasks, and shows significant improvements in both delay and accuracy compared to prior work." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Main weaknesses:\n- I don't know the literature extensively, but I wonder if MPCFormer is really the strongest baseline against which we should evaluate SelectFormer (the other baselines, Oracle and Random, are useful to set the delay/accuracy range but are not real alternatives to SelectFormer). Indeed, as the authors note, \"MPCFORMER’s model distillation approach is ill-suited to data selection\", and I wonder if this is the reason behind MPCFormer's particularly poor accuracy (Table 3). The paper mentions THE-X (Chen et al, 2022), which could be a stronger baseline. Another potentially relevant paper is Bolt, published at S&P 24: https://eprint.iacr.org/2023/1893. I don't know these works in detail, but they might be more amenable to data selection than MPCFormer if they do not rely on data distillation or data-dependent approximations. In short, I am worried that MPCFormer might be a strawman against which SelectFormer shines too easily.\n- Another concern is that the techniques proposed in this paper seem to only apply to a quite specific application, namely MPC data selection. Hence, depending on how widespread MPC data selection is, SelectFormer could have a pretty limited impact. Indeed, the authors note that their \"MLP approximation is specifically suitable for data selection while impractical for model inference directly\", which might limit the applicability of SelectFormer to other problems. \n- Finally, I am not an expert in MPC systems, but I am a bit skeptical of the blanket claim that \"No prior MPC systems exploit such parallelism\". I remember a similar idea being mentioned by Meta in a research whitepaper (https://research.facebook.com/publications/private-computation-framework-2-0/), and a cursory web search returned a preprint showing how \"it is possible to carefully orchestrate the computation and communication steps to overlap\" in ML MPC training and inference (https://arxiv.org/pdf/2209.13643). The paper might still be making valuable contributions in MPC parallelism, but highlighting such contributions might benefit from a more detailed comparison to prior work. \n\nMinor comments:\n* typo: \"Note that we cannot directly compare to PUMA Dong et al. (2023), which is designed under 3 compute parties. and three computing parties.\"\n* I can't find the data showing that MPS reduces total delay by 33% to 61%. Is this from the difference between PM and PMT in Figure 6? Maybe this would be easier to break down if there was a delay column in Table 4?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper addresses the important and challenging problem of efficient private data selection.\n2. The proposed hierarchical algorithm demonstrates thoughtful consideration of multiple components, including multi-phase selection and dimension optimization, to enhance efficiency." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents an efficient approach to private data selection, leveraging multi-party computing (MPC) for data appraisal in complex models such as transformers and ViT. The authors propose using a proxy model that approximates the complex model by emulating non-linear modules with MLP for data appraisal. While experimental results demonstrate improved efficiency compared to random selection and MPCFormer, the paper would benefit from clearer algorithmic descriptions and more comprehensive experimental analysis." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The pipeline of the algorithm is not clear. In multi phase selection, why do you need to query the candidate data? Whether or not to select this subset of data depends on the test data performance. It seems that when you train the MLP, except for the selected data, you also generate an Gaussian distribution for the layer, but how to use this distribution to supervise fine-tuning is not clear. In sum, I hope there will be an algorithm pipeline to clearly show each steps and which parameters are hyper-parameters.\n2. The results in Table 2 is surprising. Does it mean that MLP is enough and we can drop all non-linear layer since some of your results show that all emulations with MLP outperform no emulations. \n3. The gap between MLP and non-linear module is simply shown by the final accuracy of your task, which may contain much randomness. Could you explain the gap in embedding layer? Like how much embedding difference for different layer emulation.\n4. The experimental results are not clear. E.g., in Table 3, did you test the model under the same communication and computation budget? In Figure 5, what does 1 phase mean? How many phased do you use in the \"ours\"? Why not compare your delay with the baseline MPCFormer?\n5. Lack of analysis. As your work focus on improving the efficiency and keeping performance, it is important to specifically analyze the computation and communication costs." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Secure and fast data selection & appraisal over MPC (Multi-Party Computation), for training NLP/CV Transformer models" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024selectformer,\ntitle={{SELECTFORMER}: {PRIVATE} {AND} {PRACTICAL} {DATA} {SE}- {LECTION} {FOR} {TRANSFORMERS}},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2cF3f9t31y},\nnote={under review}\n}" }, "abstract": { "value": "Critical to a free data market is $ \\textit{private data selection}$, i.e. the model owner selects and then appraises training data from the data owner before both parties commit to a transaction. To keep the data and model private, this process shall evaluate the target model to be trained over Multi-Party Computation (MPC). While prior work suggests that evaluating Transformer-based models over MPC is prohibitively expensive, this paper makes it practical for the purpose of data selection. Our contributions are three: (1) a new pipeline for private data selection over MPC; (2) emulating high-dimensional nonlinear operators with low-dimension MLPs, which are trained on a small sample of the data of interest; (3) scheduling MPC in a parallel, multiphase fashion. We evaluate our method on diverse Transformer models and NLP/CV benchmarks. Compared to directly evaluating the target model over MPC, our method reduces the delay from thousands of hours to tens of hours, while only seeing around 0.20% accuracy degradation from training with the selected data." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Secure Multiparty Computation", "Machine Learning", "Efficiency", "Transformer model" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/c40d1c43ba28dc7a9d005bdb42e6c2c8d8a33bf2.pdf" }, "presentation": null, "primary_area": { "value": "infrastructure, software libraries, hardware, systems, etc." }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "SELECTFORMER: PRIVATE AND PRACTICAL DATA SE- LECTION FOR TRANSFORMERS" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I would appreciate if the authors could address the concerns I raised in the weaknesses section." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "This paper presents a well-justified study, especially it clearly identifies current research gap for person identification. Overall it reads very well." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This study introduces a benchmark for gait recognition utilizing a novel structural vibration sensing technique, the geophone. and the new benchmark comprised of in total 100 subjects, collected under indoor or outdoor settings. It invesitgated whether the novel sensing modality can encode identity related information, and what is the limitations or sensitivity of this technique. Although this work addresses an interesting topic, it may not yet provide the technical depth or extensive experimental validation expected for broader applicability. There are also several concerns regarding the experimental settings and presentation clarity." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* [Sample size] Probably for the gait recognition task, we are more interest in how many subjects collected. The subject size, compared with current large dataset, especially the GaitSet, is still not that comparable. \n\n* [Technical contents] I am concerned that the technical content is somewhat limited, even for a benchmark paper, for ICLR. Please consider adding more experiments and tasks to thoroughly validate the usability of this dataset, such as Re-ID, gait event detection, and generalization across subpopulations... I encourage the authors to refer to established works like GaitSet for inspiration. Gait data is highly complex, influenced by factors such as age, gender, emotion, and health conditions. Reflecting on these factors in your experiments would enhance the depth of the study.\n\n* [Applicability] I am also concerned that this kind of ambient sensor can only be applied indoor or with relatively small distances, which might limit its application, compared to wearable data?\n\n* [Experiment setup] In the experiment settings, I noticed that there were no concurrent human activity when recording the data, this may be another issue that limits the usability of this study. Additionally, will the data be sensitive to the perspective of the sensor, as I know it is quite sensitive for vision based person identification. \n\n* [Gait event] gait event detection, is this be validated in terms of accuracy?\n\n* [Dataset details] Further elaboration on the dataset’s composition and subject split for person identification would be valuable, particularly for readers unfamiliar with this topic. \n\n* [Table clarity] Table 5 is not clearly illustrated, what is the performance comparison between structural vibration and camera? Very limited information is given in both the table and the associated texts. Expanding on this comparison would help readers understand the relative strengths of each technique.\n\n* [Minor - clarification] I assume the subjects of A2, A3, A4 are part of A1, correct? Please clarify this. \n\n* [Minor - citation] When citing a work which actually does not play any role in your sentence, please use (X et al., XXXX), rather than X et al. (XXXX)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "Involving human data collection" }, "flag_for_ethics_review": { "value": [ "Yes, Privacy, security and safety" ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Although the authors define the floor in different classes, the hardness might be a more reliable way to classify. Since different carpets' thicknesses may have different responses. And what is the distance range for the geophone, since 4 m is not far for camera sensor" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper is clear and easy to follow, and the tables and figures are easy to understand\nThe proposed question is interesting, trying to build the connection between identity and walking vibration, a fine detail when a human is walking. And authors collect a relatively large dataset in multiple conditions." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors built a dataset with 100 people using geophone to do multiple experiments based on the human's structural vibration. It consists of multiple covariances including floor types, and distances. The work tries to find a connection between the vibration and identities and builds multiple benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "In real-life applications, it is hard to find a good condition to use a geophone to capture a human's gait with little noise.\n\nHow to control the noise in outdoor cases.\n\nCompared to a camera, the vibration-based method is restricted by the sensor and distance. \n\nThe protocol is not clear. How is the train and test set defined? For human identification, the identities appearing in the training set will not be present in the test set. It seems these experiments do not follow this setting" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. What do you mean by events in Table 2?\n2. Line 407 - where is table 10? or did you mean 1?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The description of the data collection protocol is clearly written with sufficient details and clear explanations of the research motivation\n2. The introduction and related work sections contain important background information justifying the motivation for the introduced dataset." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The presented work introduces a new biometric dataset for human gait recognition based on structural vibrations. The dataset is applied to various tasks such as person identification, domain adaptation, and multi-modal scenarios combining vibration and vision-based identification methods. Experimental analysis includes verification of machine-learning and deep learning approaches." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Is there any requirement for using a specific sensor type during the inference if trained on the presented dataset? I'm wondering about the practical implication of the proposed solution. \n2. The work indicates that the concurrent activity was not taken into account, however it's very possible to happen in real-life scenarios. Would the presented dataset be sufficient for handling such scenarios? How should one prepare for additional noise introduced in this way? \n3. It's not clear how filtering of potential noise was performed? Was the assumption that the data collection is performed in an isolated environment without any noise? You mentioned that there was environmental noise present, but how do you quantify its presence? If the assumption is that there is minimal or no noise, it again raises question around the practicality of the solution.\n4. It's not clear how the data was split for training and testing? Were the same subjects present in both subsets or did you ensure no overlap?\n5. One of the motivation behind introducing a new dataset is that other datasets contains a limited number of subjects. It's mentioned that there are 100 subjects in the proposed dataset but then only 30 and 40 subjects are used for floor types and distance measurements. Why not all 100 subjects were used for all of the scenarios?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 1 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. To what extent do walking speed and the carrying of objects impact recognition performance?\n2. Building on question 1, does abnormal gait pose significant challenges for re-identification?\n3. The VIBEID dataset studies an operating distance range of 1.5m to 4m, while vision-based gait recognition typically works at distances over 10m. What is the distance limit for vibration sensors to capture meaningful gait signals?\n4. If there are obstructions between subjects and the sensor, is reliable recognition still possible?\n5. How does the proposed vibration-based gait recognition handle scenarios with multiple pedestrians walking simultaneously?\n6. I recommend replacing Figure 3 with a clearer version.\n7. The evaluation protocol should be more detailed." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Vibration-based gait recognition introduces a novel approach for human identification.\n- The proposed baseline methods are effective.\n- This work establishes the largest-scale vibration-based dataset to date." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a novel dataset termed VIBEID, designed for human gait recognition using structural vibration data. The dataset includes recordings of 100 subjects across various distances, floors, and environments. Experiments demonstrate that structural vibration can serve as a viable biometric trait across different scenarios." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The dataset has a limited number of subjects, although it includes over 88 hours of recorded data.\n- Compared to commonly used vision-based gait recognition, the operating distance remains relatively short.\n- The evaluation setup lacks clarity." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "VIBeID offers a dataset and benchmark for person identification using structural vibrations from walking on three surfaces and three distances from sensor, enabling comparison with video-based methods." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024vibeid,\ntitle={{VIBEID}: A {STRUCTURAL} {VIBRATION}-{BASED} {SOFT} {BIOMETRIC} {DATASET} {FOR} {HUMAN} {GAIT} {RECOGNITION}},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2d734s2WDb},\nnote={under review}\n}" }, "abstract": { "value": "We present VIBeID, a dataset and benchmark designed for advancing non-invasive human gait recognition using structural vibration. Structural vibrations, produced by the rhythmic impact of the toe and heel on the ground, are distinct and can be used as a privacy-preserving and non-cooperative soft-biometric modality. We curated the largest dataset VIBeID consists of footfall generated structural vibrations of 100 subjects. Existing datasets in this field typically include around ten subjects and lack comprehensive exploration of domain adaptation. To thoroughly explore the domain adaptation aspect of this biometric approach, we recorded vibration data on three distinct floor types (wooden, carpet, and cement) and at three distances from the geophone sensor (1.5 m, 2.5 m, and 4.0 m), involving 40\nand 30 subjects, respectively. Additionally, we benchmarked our dataset against video recordings from 15 individuals in an outdoor setting. Beyond providing 88 hours of raw vibration data, VIBeID establishes a comprehensive benchmark for a) person identification: where the aim is to recognize individuals through their unique structural vibrations, b) domain adaptation: assessing model performance across different walking surfaces and sensor positions, and c) multi-modal comparison: comparing vibration-based and vision-based identification methods. Our experiments, using both machine learning and deep learning approaches, establish a baseline for future research in this field, and introduce a large-scale dataset for the broader machine learning community." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Structural vibrations", "Gait Recognition", "Deep learning", "Machine learning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/4dabcff589d64c0ea1808448edfe022eb765c2ca.pdf" }, "presentation": null, "primary_area": { "value": "datasets and benchmarks" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/6d0a082519920bc4a9cf6f87a578e6de8fa5e77f.zip" }, "title": { "value": "VIBEID: A STRUCTURAL VIBRATION-BASED SOFT BIOMETRIC DATASET FOR HUMAN GAIT RECOGNITION" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. The Fisher dataset is a common dataset comparable to Switchboard. What is the performance of the supervised turn-taking prediction model on this dataset?\n2. How can your evaluation protocol be adapted or applied in scenarios where supervised datasets are not available for other languages, such as Chinese?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The originality of the work is commendable. The authors propose a novel evaluation protocol to assess the turn-taking capabilities of spoken dialog systems.\n2. The paper is well-written and provides sufficient experimental details in the Appendix.\n3. The authors plan to open source the evaluation platform." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a new evaluation protocol to assess the spoken dialog system's turn-taking capabilities, i.e., the Moshi and Cascaded model. They use a supervised model as a judge which is trained to predict turn-tasking events in human-human conversation (i.e., Switchboard). The paper presents a comprehensive user study that evaluates the Moshi and Casaded model on their ability to perform turn-taking events, and it finds that they sometimes do not understand when to speak up, can interrupt too aggressively, and rarely backchannel. The main contributions are:\n1. A new evaluation protocol to assess the spoken dialog system's turn-taking capabilities.\n2. Some insight about existing spoken dialogue systems through user study.\n3. Additionally create a test benchmark using Switchboard dataset to evaluate SALMONN, Qwen2-audio-instruct, Qwen-audiochat, Whisper+GPT-4o on their ability to understand and predict turn-taking events." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The evaluation protocol is highly expensive, it needs a supervised dataset to train the judge model. It doesn't work if we do not have a supervised dataset in other languages, such as Chinese. \n2. The filler word set for backchannel detection is heuristic. It may ignore some backchannel case not in filler word set. \n\n1. The evaluation protocol is highly expensive, as it requires a supervised dataset to train the judge model. This approach is not feasible if we lack a supervised dataset in other languages, such as Chinese.\n2. The filler word set for backchannel detection is heuristic and may miss some backchannel cases that are not included in the filler word set." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "NA." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The definition of turn-taking is detailed and clear.\n\n2. The evaluation protocol proposed in this paper contributes to better assessing the performance of audio foundation models in dialogues, providing strong support for the development of voice dialogue systems.\n\n3. This paper reveals many issues existing in current AI dialogue systems when handling turn-taking, offering valuable references for future research." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the challenges of evaluating the turn-taking capabilities of audio foundation models (FMs) in conversational settings.\nIt defines 6 types of Turn-Taking Events and evaluates the performance of end-to-end speech dialogue models as well as cascaded systems.\nThrough the results obtained from this study, the authors discovered numerous issues with existing AI dialogue systems in handling turn-taking, such as sometimes failing to intervene in conversations at appropriate times or excessively interrupting others. Furthermore, the authors conducted tests on multiple open-source and closed-source audio foundation models, revealing their limitations in understanding and predicting turn-taking events, and highlighting areas for improvement." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. This study only tested a few open-source and closed-source audio FM models.\n\n2. There is a lack of comprehensive performance evaluation and summary." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Here are questions for the authors:\n - The thresholds in Sec. 4.4-4.8 seem arbitrary. Is there a specific reason for choosing these values? All units appear to represent likelihoods, yet they range from negative ($threshold_3$ = -0.45) to positive values ($threshold_2$ = 0.1).\n - There are concerns about the reliability of the judge model. Since all results are based on comparisons with this model, is there concrete evidence supporting its credibility? Specifically, the conclusion that Moshi[1] is \"too aggressive\" lacks persuasiveness if it relies solely on comparisons with the judge model.\n\n[1] Defossez et al. Moshi: a speech-text foundation model for real-time dialogue" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The strengths of this paper are as follows:\n 1. This paper provides an automated turn-taking protocol for audio foundation models\n 2. The evaluation platform will be open-sourced." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes an evaluation protocol to measure the turn-taking capabilities of audio foundation models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The weaknesses of this paper are as follows:\n 1. The study aims to measure precise turn-taking, but the thresholds are set to arbitrary values.\n 2. The participants introduced in Sec 3 seem biased, consisting of the authors and related individuals.\n 3. The confidence in some evaluations (Fig. 3(b), (e)) appears high, but no explanation is provided." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "My questions are listed above." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. This paper proposes a comprehensive evaluation protocol and well-designed metrics to assess the turn-taking capabilities of spoken dialogue systems. The evaluation framework and metrics are thoughtfully developed and provide valuable insights.\n2. The paper extends the evaluation of turn-taking capabilities of spoken dialogue systems from corpus-level statistics to a more granular assessment of the timing of turn-taking events. This fine-grained approach enables a more accurate reflection of a spoken dialogue system’s turn-taking capabilities.\n3. The proposed evaluation metrics provide insights into the limitations of current systems in achieving interactive and natural conversations, highlighting areas for potential improvement." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents an evaluation protocol designed to assess the turn-taking capabilities of spoken dialogue systems. It evaluates the exact timing of these events using a supervised model trained to predict them. The experimental results reveal interesting insights about existing spoken dialogue systems and offer valuable suggestions for their future development." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In Metric (E), the judge labels show low consistency with human relevance judgments, indicating that this metric may have limited reliability in assessing the model's ability to handle user interruptions effectively.\n2. My primary concern is the relatively low agreement between the majority of judge labels and human judgments, with most falling below 80%. This raises questions about the strength of the claim that the proposed metrics maintain high consistency with human decisions.\n3. GPT-4o was not evaluated.\n\nIf my above concerns are resolved, I would consider increasing my rating." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. What is the main difference between the proposed evaluation protocol and the previous approach by Ekstedt and Skantze (2022)? Is it impractical to apply the metrics from prior turn-taking evaluation methods to audio FMs?\n2. While the turn-taking prediction model has been evaluated on an out-of-domain task-oriented spoken dialogue corpus, could you evaluate it on additional non-task-oriented spoken dialogue datasets to assess the generalizability of the model?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The evaluation protocol is novel and well-motivated.\n2. The experimental analysis provides valuable insights into turn-taking capabilities of audio foundation models (FMs).\n3. The user study reveals noteworthy observations about current spoken dialogue systems." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a novel evaluation framework for assessing turn-taking capabilities in audio foundation models (FMs). The authors first propose metrics for five core conversational abilities: determining when to speak up, backchannel, interrupt, convey turn-taking cues, and handle interruptions. They develop a supervised model trained on human-human conversations to serve as a judge for evaluating these turn-taking events. Using this framework, they conducted a user study with different spoken dialogue systems (full-duplex E2E spoken dialogue system Moshi and VAD-based cascade dialogue system) and evaluated them. They evaluate several open-source and proprietary audio FMs on their ability to understand and predict turn-taking events." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Turn-taking prediction models used in evaluation protocol require training, which limits scalability and applicability. \n2. The paper does not thoroughly address how its proposed evaluation protocol compares with previous turn-taking approaches, such as Ekstedt and Skantze (2022).\n\nReference\n* Ekstedt, Erik, and Gabriel Skantze. Voice activity projection: Self-supervised learning of turn-taking events. Interspeech 2022" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a novel evaluation protocol using a turn-taking judge model to automatically assess spoken dialog systems, providing valuable insights into their turn-taking capabilities." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024talking,\ntitle={Talking Turns: Benchmarking Audio Foundation Models on Turn-Taking Dynamics},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2e4ECh0ikn},\nnote={under review}\n}" }, "abstract": { "value": "The recent wave of audio foundation models (FMs) could provide new capabilities for conversational modeling. However, there have been limited efforts to evaluate these audio FMs comprehensively on their ability to have natural and interactive conversations. To engage in meaningful conversation with the end user, we would want the FMs to additionally perform a fluent succession of turns without too much overlapping speech or long stretches of silence. Inspired by this, we ask whether the recently proposed audio FMs can understand, predict, and perform turn-taking events? To answer this, we propose a novel evaluation protocol that can assess spoken dialog system's turn-taking capabilities using a supervised model as a judge that has been trained to predict turn-taking events in human-human conversations. Using this protocol, we present the first comprehensive user study that evaluates existing spoken dialogue systems on their ability to perform turn-taking events and reveal many interesting insights, such as they sometimes do not understand when to speak up, can interrupt too aggressively and rarely backchannel. We further evaluate multiple open-source and proprietary audio FMs accessible through APIs on carefully curated test benchmarks from Switchboard to measure their ability to understand and predict turn-taking events and identify significant room for improvement. We will open source our evaluation platform to promote the development of advanced conversational AI systems." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Turn-taking", "Conversation AI", "Audio Foundation Models", "Evaluation Metric", "Evaluation Benchmark" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/baecb289c6c4f3f86f79cf299d51809cc318126f.pdf" }, "presentation": null, "primary_area": { "value": "datasets and benchmarks" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Talking Turns: Benchmarking Audio Foundation Models on Turn-Taking Dynamics" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See weakness." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The paper presents a new, practically meaningful, and challenging setting, and constructs corresponding datasets.\n2. The domain-semantic disentangled design is well-reasoned, clearly aligning the motivation.\n3. The proposed approach demonstrates significant performance improvement on SSB-C.\n4. The writing is clear and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a new challenge for Generalized Category Discovery, which requires model to categorize unlabeled data in the presence of domain shifts. Traditional GCD methods assume all images come from the same domain, which leads to a significant performance drop under domain shifts. The proposed HiLo framework explicitly disentangles semantics and domain, achieving domain adaptation in GCD through patchmix and curriculum learning. Experimental results show performance improvements, validating the effectiveness of the approach." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The performance gain on DomainNet is considerably smaller than on SSB-C, and the improvement over methods like SimGCD, which does not account for domain shift, is modest. This indicates limited robustness on various domain shifts and fails to highlight the advantages of the proposed approach.\n2. The method is sensitive to certain hyperparameters, and $r^{'}$ does not exhibit consistent performance across the original and new domains.\n3. The approach of decoupling domain and semantics is derived from [1], and the use of patchmix for domain adaptation is adapted from [2]. The curriculum learning strategy is also straightforward. Overall, the method seems to be an assembly of prior works, lacking substantial novelty.\n4. There is no analysis of the disentangled domain and semantic features, such as distribution visualizations. This would help illustrate the effectiveness of the disentanglement.\n5. In line 287, same representation loss $L^{rep}_s$ on both domain and semantic features is confusing. This approach may lead domain features to capture information beyond true domain characteristics. It would be valuable to see t-SNE visualizations of domain features, semantic features, and their combination. The author does not provide a corresponding discussion.\n6. Line 313 mentions using pre-trained DINO to obtain $z_d$, but previously $z_d$ is associated with a projection head. If the projection head is discarded, then $z_d$ will always be identical in different time steps. If it is retained, the term “pretrained” is confusing. This needs clarification.\n7. The ablation study is somewhat unclear. For instance, in row (5) where only deep features are used, does this mean all other designs related to the shallow feature $z_d$ are also omitted? This also needs clarification.\n\nReference\n\n[1] Learning deep representations by mutual information estimation and maximization\n\n[2] Patch-mix transformer for unsupervised domain adaptation: A game perspective" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please see the Weaknesses." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The HiLo architecture extracts features from different layers of the vision Transformer and decouples domain and semantic features by minimizing mutual information. This feature processing method, based on the neural network hierarchical structure and information theory, provides a more effective feature representation for category discovery in the presence of domain shifts and avoids the problem of feature confusion in traditional methods.\n\n2. The PatchMix method is introduced into the GCD task and innovatively extended. By adjusting its objective function, it can adaptively utilize labeled and unlabeled data for training. This extension not only combines the advantages of data augmentation but also flexibly adjusts the learning process according to the nature of different data, enhancing the model's ability to learn data from different domains and categories.\n\n3. The curriculum learning method is employed, which dynamically adjusts the sampling probability weights according to the difficulty of samples and the unknown degree of domains. This strategy of gradually introducing samples from easy to difficult conforms to the learning law, enabling the model to better adapt to the challenges brought by domain shifts and improving the model's convergence speed and robustness to complex data distributions.\n\n4. In terms of method design, innovative technical architectures and learning strategies are used, as well as theoretical analyses to verify their effectiveness. From the theoretical derivation of the target error to the analysis of the roles of different components, a solid theoretical foundation is provided for the innovation of the method, demonstrating the advantage of the close integration of theory and practice." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Generalized Category Discovery (GCD) is a challenging task where, given a partially labeled dataset, the model must classify all unlabeled instances. This paper introduces a new task and method to handle the GCD problem when the unlabeled data contains images from different domains. In terms of the method, the HiLo architecture and learning framework involves extracting \"low-level\" (early layers) and \"high-level\" (late layers) features from a vision Transformer and decoupling domain and semantic features by minimizing the mutual information between the two sets of features. The PatchMix contrastive learning method is introduced into the GCD task, with its objective function extended to enable the utilization of both labeled and unlabeled data for training. Curriculum learning is adopted, gradually increasing the sampling probability weight of samples predicted to be from unknown domains to enhance the model's robustness to domain shifts. Experiments are conducted on the DomainNet and the SSB-C benchmark datasets constructed based on the Semantic Shift Benchmark (SSB). The experimental results show that HiLo significantly outperforms existing category discovery models, validating the effectiveness of the method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In HiLo, features are disentangled by assuming that features from different layers represent domain and semantic information, respectively and minimizing the mutual information based on this assumption. However, this assumption may oversimplify the complexity of feature representation in neural networks. In fact, features from different layers may be a mixture of multiple types of information. Simply defining the early layers as domain features and the late layers as semantic features may not be entirely accurate, which may lead to incomplete feature disentanglement in some complex data distributions and affect the performance and generalization ability of the model.\n\n2. The introduction and extension of PatchMix in the GCD task is an innovation, but it also brings some problems. The adjustment of its objective function and its application on different data increases the complexity of the model. When dealing with data with large domain differences, it is a challenge to determine the mixing proportion and application method accurately. If not handled properly, it may introduce too much noise or incorrect information, which may instead interfere with the learning process of the model and reduce the classification performance.\n\n3. In the curriculum learning method, the adjustment parameters of the sampling probability weights need to be selected through the validation set, which increases the dependence of the model on specific datasets. Moreover, for different datasets and tasks, the optimal values of these parameters may vary greatly, and the model cannot adaptively determine these parameters. If these parameters cannot be correctly selected in a new dataset or task, curriculum learning may not be able to play its intended role. It may even have a negative impact on the learning of the model." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. For the experimental results of the ORCA method, what is the backbone used by the authors?\n2. Were any curriculum learning alternatives considered, such as adaptive weighting based on difficulty or dynamic sample weighting? A brief discussion on these choices would clarify why the current approach was favored." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper presents an innovative GCD approach by combining mutual information minimization with domain-specific data augmentation and curriculum learning to handle domain shifts effectively.\n2. Extensive evaluation on both synthetic (SSB-C) and real-world (DomainNet) benchmarks demonstrates the model's robustness and its superiority over baseline GCD models, especially under domain-shifted conditions." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces the HiLo framework, a learning method aimed at tackling Generalized Category Discovery (GCD) under domain shifts. HiLo addresses challenges in categorizing both seen and unseen categories across distinct domains within partially labeled datasets, leveraging a multi-faceted approach: mutual information minimization to separate domain and semantic features, PatchMix for augmented domain adaptation, and a curriculum learning strategy. The proposed method is evaluated on synthetic and real-world domain-shift datasets, showing substantial improvements over existing GCD models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In the \"Problem statement,\" the following sentence is unclear: \"The objective of GCD is ... with singleton cardinalities for the latter.\" The author needs to differentiate between the GCD task setting and the domain shift GCD task setting, so this statement should be revised for clarity and precision.\n2. The font sizes of the tables are not standardized, and the font in table 2 is too small.\n3. I'm curious as to how many runs each of the authors' experimental results were derived from, and given that the differences in the results of the GCD benchmark tests can be very large, the authors should have listed the error lines generated by the three independent runs." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "None" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please clarify the novelty of the proposed method, and include more comparisons with UinOT in the main results." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper proposes a new problem setting and proposes a HiLo, which combines multiple techniques from domain adaption and achieves better results." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a new problem setting: Generalized Category Discovery (GCD) with domain shift. The authors leverage techniques from domain adaptation and curriculum learning to propose a new method called HiLo. Comprehensive experiments on the proposed benchmark demonstrate substantial improvements." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The novelty of the method appears limited, as it seems to combine various techniques from different domains.\n\n2. The comparison with UniOT should be included in the main results. Since the proposed setting is similar to universal domain adaptation, it is essential to compare methods from both domains in the main results.\n\nMinor： \n\nMissing citation for the following important paper\n\n[1] Rastegar et al. Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery. NeurIPS 2023.\n\n[2] Gu et al. Class-relation Knowledge Distillation for Novel Class Discovery. ICCV2023." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "we extracts domain and semantic information independently and minimize their mutual information while incorporating contrastive learning for robust representations with pseudo-labelling strategies" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024hilo,\ntitle={HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2eFq6S35iB},\nnote={under review}\n}" }, "abstract": { "value": "Generalized Category Discovery (GCD) is a challenging task in which, given a partially labelled dataset, models must categorize all unlabelled instances, regardless of whether they come from labelled categories or from new ones. In this paper, we challenge a remaining assumption in this task: that all images share the same \\underline{domain}. Specifically, we introduce a new task and method to handle GCD when the unlabelled data also contains images from different domains to the labelled set. Our proposed `HiLo' networks extract High-level semantic and Low-level domain features, before minimizing the mutual information between the representations. Our intuition is that the clusterings based on domain information and semantic information should be independent. We further extend our method with a specialized domain augmentation tailored for the GCD task, as well as a curriculum learning approach. Finally, we construct a benchmark from corrupted fine-grained datasets as well as a large-scale evaluation on DomainNet with real-world domain shifts, reimplementing a number of GCD baselines in this setting. We demonstrate that HiLo outperforms SoTA category discovery models by a large margin on all evaluations." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Generalized Category Discovery" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/c6c6274e20c9ed743ebf95ea90ca9e108a82e0b3.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Did you conduct any comparative investigations over general conversational preference learning using your reward modelling objective? This would help to verify your intuition that this method is effective due to the unique features of reasoning tasks\n2. Would it be possible to use the Eurus reward model for PPO-based alignment? How would this perform in comparison to the existing finetuning methods" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. With regards to soundness, I feel that the necessary experiments have been run to validate the majority of claims, especially where those claims are with regards to methodological contributions. The authors have also taken pains to remove contaminated data from their work in order to make comparisons fair and meaningful, including when reporting others' work.\n2. The presented language models have strong performance, and the data and reward models are in and of themselves useful contributions to the research community, removing some of the limitations of scale and quality from prior works creating preference datasets and reward models\n3. The investigation surrounding the flaws of existing preference learning models is an original contribution.\n4. In my view the largest contribution is the rather detailed study of creating their ultra-instruct dataset albeit moreso as an engineering challenge.\n5. The experiments are run against meaningful baselines: models of similar scale, trained on similar data in similar ways." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors explore improving large language model reasoning through the curation of high quality training data for that reasoning.\nThis data (UltraInteract) consists in preference trees, with nodes splitting on correct/incorrect responses; critique and refinement of rejected responses; and uses different reasoning schemas/ actor models to increase training data diversity\nThe actor used to generate these trajectories is GPT3.5 Turbo, with GPT4 being used as a critique model with access to an interpreter/tools.\n\nThe authors then use this dataset (alongside others) to finetune 3 language models using the following process:\n1. SFT over the correct actions\n2. Preference learning over correct vs incorrect actions using off the shelf preference learning algorithms\n\nAdditionally the authors also use this to derive a reward model:\n3. Train a reward model, adding in terms for the difference in absolute rewards to the normal Bradley Terrey reward model.\n\nIn my view the key contributions of this paper are:\n* introduction and analysis of preference-tree based instruction following data, which is scalable and effective\n* introduction of improved objectives for training reward models" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. As a minor point the spelling and grammar could be improved; for instance \"Is proprietary models\" (line 470) should be \"Are proprietary models\", and more generally things like \"Perference Learning\" (line 247). More substantially some of the references point to the wrong sections (e.g. the reference to section 5 (replaced with 6) (line 255) -- in this case harming readability (hence my review of the presentation...)\n2. I feel that the modification to the reward model could be better motivated in section 3, for instance by referencing other works that maximise a similar margin loss. At the least it should be explicitly linked to the discussion in section 4.2 that actually seems to motivate it. This might be aided to seperating out the reward modelling section from the finetuning section? Since it seems to follow on more logically from the finetuning investigations\n3. Section 6.1 doesn't really address the section title properly. While the performance itself does suggest that just training on open source data is sufficient (ignoring the instruction following benchmark); the body of the section just talks about mixing in this additional V2 data, and the ensuing performance gains. It would suffice to add a brief comment at the end of line 483 explaining the results of finetuning just on V2\n4. As a general comment I feel that this work feels like three distinct pieces of work rather than a single cohesive one. I.e. the proposal of a new training dataset; a set of models finetuned on this dataset alongside others; and more separetely a reward model trained on a combination of dataset including the one proposed here. One way of mitigating this would be to focus on the contribution of the dataset to the reward modelling phase (using the data from the ablation studies).\n5. Section 2. is a little bit confusing and could be rephrased to make it a little but clearer that it is all just an example." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "If the model is unable to effectively learn from vertical improvements, then it raises the question of why we want to synthesize the dataset with tree structure and why we are providing trajectories to the model." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Authors use a new method to synthesize a dataset for SFT and preference learning, which could potentially enhance model's reasoning abilities. The intuition behind the synthesis method is straightforward and easy to be understood. I think the dataset is cool and it could be a potential approach for model to learn how to improve the response. Plus, the insights on preference learning algorithm is interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors emphasize the performance gap between open-source LLMs and the most advanced models, particularly in reasoning capabilities. They attribute this gap to two primary factors: (1) the lack of high-quality datasets and (2) the under-exploration of preference learning techniques. To address this gap, the authors introduce a novel dataset, ULTRAINTERACT, which features a multi-turn, tree-structured format designed to enhance reasoning abilities. Additionally, they offer new insights into preference algorithms and reward modeling. They argue that effective reward modeling should consider not only the margin between rewards but also the absolute value of the reward itself. Based on this insight, they propose a new reward model that combines two loss functions, L_{BT} and L_{DR}, demonstrating superior performance compared to existing models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1). I agree that providing trajectories to guide model improvements is a potential approach. However, during the training process, I believe that the vertical improvement information, sequential refinement across turns, may not be effectively learned. This is because current preference algorithms primarily focus on horizontal comparisons, assessing responses within the same turn. \n\n2). The reasons behind the better performance of EURES are hard to track and some studies will be necessary if authors want to claim that the proposed dataset is the reason. Because the baselines has different scales and training method, for example, their training dataset could have different size and their preference algorithm could be different, etc.. Plus if EURES can beat some larger model, the claim that the dataset is better will be more convincing.\n\n3). There may be some factors contributing to the value differences observed in reward modeling, especially given the varying formulations of alignment methods. It would be valuable for the authors to offer insights into the potential reasons for these differences in the value of rewards." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- Is the reward model used in training the actor model?\n- L148 “the actor model first decomposes the input problem into several problems” How is this done?\n- L181 “we adopt more diverse reasoning patterns” How exactly is this done?\n- Is python the only tool used?\n- Typo in L263 reward notation\n- What is \"prompt level loose score\" in L282\n- I think the tables have too many numbers in them (tab3 has at least a hundred) and not sure if anyone will look at all of them. Instead, average scores can be put there and the detailed table can move to the appendix. This is only a suggestion though.\n- Which GPT-4 model is used? I think there are multiple versions. \n- How is the reward model performance compared to ArmoRM?\n- How is GPT-4 used as a reward model in tab4?\n- Why does self-consistency drop in fig1 left?\n- How is MCTS decoding done exactly in sec5.2?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper is advancing open science by making the training data and model checkpoints public. Given the significant improvements in reasoning tasks, it is likely that these assets will be helpful to other researchers.\n- The paper also proposes a new way of training reward models that is better suited to reasoning tasks. In addition, the training datasets have multi-step attempts that contain mistakes and tool usage, which is unlike other preference datasets.\n- The experimental section is detailed and provides many interesting results, such as comparing three different preference optimization methods. There are many ablations provided, and evaluations are done on many tasks, which makes the results more convincing." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper has several contributions. First, it builds a dataset on reasoning tasks that contain both correct and wrong steps. Second, it proposed a modified loss function for training a reward model that is better suited for reasoning tasks. Lastly, it trains a set of LLMs using the proposed dataset that have competitive performance on reasoning tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The heavy reliance on GPT responses makes me feel like this is more of distilling GPT. Also, it is not clear what are the usage limitations that will arise from using a proprietary model like GPT4. As shown in tab7, this was crucial for obtaining good performance.\n- The problem of the likelihood of chosen responses going down in reasoning is a known issue and studied prior work [1], which is not cited in the paper (the related work is quite short)\n- The term “multi-turn action” was confusing. It seems that all the tasks require only a single correct response. None of the tasks is truly multi-turn where the model has to do multiple actions. From reading the paper, it seems the term “multi-turn” is used to describe a process where a model can try again if it makes a mistake. Actually, it is not clear how this process works, especially when training the model and evaluating it. Also, the dataset contains observations and judgements, but are they also used when training the actor? What about the python executions? There is very little detail on how the agent is trained on these and evaluated. \n- As mentioned in the previous point, there are certain steps that are not well explained. See the questions for examples. Given that the motivation is to advance open-source LLMs, I think it is important to describe the process of training in more details." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. In line 90-91, the statement is unclear to me. In 'a higher final reward often indicates a better reasoning capability', whose reasoning capability? Can you elaborate a bit more?\n\n2. About the result remove in Table 3 due to data contamination. For some of the model has data contamination issue, the table suggests the TheoryQA is leaked, what about the rest dataset? If the rest doesn't has data contamination issue, should the result be compared? Without TheoryQA number, OpenChat seems like still a strong candidate." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper introduces a novel dataset, ULTRAINTERACT, designed for complex reasoning tasks. It comprises instructions paired with preference trees, featuring reasoning chains, multi-turn interaction trajectories with feedback, and pairwise positive and negative responses. ULTRAINTERACT emphasizes complex and diverse reasoning patterns, encouraging models to break down problems into sub-problems and use tools to solve them. This dataset is a valuable contribution and can be useful for future research on LLM reasoning.\n\n2. The proposed EURUS models achieve state-of-the-art performance on several reasoning benchmarks, demonstrating the effectiveness of ULTRAINTERACT and the proposed training methods. Notably, the smaller EURUS models outperform much larger baselines, showcasing their efficiency.\n\n3. The paper provides valuable insights into preference learning for reasoning tasks. The analysis of reward patterns during training leads to a new reward modeling objective that improves performance, particularly on challenging problems. The authors highlight the importance of the absolute value of rewards in preference learning for reasoning, as opposed to just focusing on relative differences as in general conversation settings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents EURUS, a new collection of large language models (LLMs) and a reward model (RM) designed to enhance reasoning capabilities. The authors develop ULTRAINTERACT, a dataset designed for complex reasoning tasks with 12 datasets spanning math, coding, and logical reasoning problems. ULTRAINTERACT employs preference trees, which pair each instruction with reasoning chains, interaction trajectories with feedback, and pairwise responses for preference learning.\n\nThe authors use ULTRAINTERACT to fine-tune several open-source LLMs, including Mistral-7B, Llama-3, and Mixtral-8x22B. They show that EURUS models achieve top performance on multiple reasoning benchmarks, including LeetCode and TheoremQA. EURUS-7B and LLAMA-3-EURUS-8B even surpass baselines 5 times their size, while EURUX-8X22B outperforms GPT-3.5 Turbo on 12 test sets.\n\nThey also create a reward model, EURUS-RM-7B, that excels on several reward modeling benchmarks and introduce a new reward modeling objective that merges the Bradley-Terry objective with an additional term to directly adjust the reward of chosen and rejected action" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While the authors acknowledge the use of proprietary GPT models in data synthesis, they do not thoroughly analyze the limitations of relying on these models. It would be helpful to discuss the potential biases introduced by GPT models and explore alternative approaches for data generation that rely solely on open-source models. Though, it's worth noting that they attempt to address this by creating ULTRAINTERACT-v2 using only open-source models, which shows promising results.\n\n2. In the paper, a few preference learning algorithms, since the preference pairs are collected in the ULTRAINTERACT, not running RL with the data seems like a big miss." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We present Eurus, state-of-the-art open LLM reasoning generalists and its recipe." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024advancing,\ntitle={Advancing {LLM} Reasoning Generalists with Preference Trees},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2ea5TNVR0c},\nnote={under review}\n}" }, "abstract": { "value": "We introduce EURUS, a suite of large language models (LLMs) optimized for reasoning. Finetuned from Mistral-7B, Llama-3-8B, and Mixtral-8x22B, EURUS models achieve state-of-the-art results among open-source models on a diverse set of benchmarks covering mathematics, code generation, and logical reasoning problems. Notably, EURUX-8X22B outperforms GPT-3.5 Turbo in reasoning through a comprehensive benchmarking across 12 test sets covering five tasks. The strong performance of EURUS can be primarily attributed to ULTRAINTERACT, our newly-curated large-scale, high-quality training data dataset specifically designed for complex reasoning tasks. ULTRAINTERACT can be used in both supervised fine-tuning, preference learning, and reward modeling. It pairs each instruction with a preference tree consisting of (1) reasoning chains with diverse planning strategies in a unified format, (2) multi-turn interaction trajectories with the environment and the critique, and (3) pairwise positive and negative responses to facilitate preference learning. ULTRAINTERACT allows us to conduct an in-depth exploration of preference learning for reasoning tasks. Our investigation reveals that some well-established preference learning algorithms may be less suitable for reasoning tasks compared to their effectiveness in general conversations. The hypothesis is that in reasoning tasks, the space of correct answers is much smaller than that of incorrect ones, so it is necessary to explicitly increase the reward of chosen data. Therefore, in addition to increasing the reward margin as many preference learning algorithms do, the absolute values of positive responses’ rewards should be positive and may serve as a proxy for performance. Inspired by this, we derive a novel reward modeling objective and empirically that it leads to a stable reward modeling curve and better performance. Together with ULTRAINTERACT, we obtain a strong reward model." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Reasoning", "Alignment", "Data" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/ae5353fffc7007a2f336e514219701cd46e7ff4e.pdf" }, "presentation": null, "primary_area": { "value": "alignment, fairness, safety, privacy, and societal considerations" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Advancing LLM Reasoning Generalists with Preference Trees" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "**Q1. Question on the visualization in Figure 3**\n\n- Why don’t the illustrations in the figure contain any “#” (EOS) tokens? Is it due to the pre-processing?\n\n**Q2. Do the trained Looped Transformers simulate the $n$-RASP-L program?**\n\n- Although it might be difficult to reverse-engineer a trained transformer model to figure out what algorithm it actually simulates or implements, it might be interesting if we can observe any kind of similarity between it and the $n$-RASP-L program." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "S1. The paper is written and organized well. Overall, the presentation of the methodology and empirical results is clear and easy to follow.\n\nS2. The idea behind the proposed method is neat and plausible. It is natural to think about adaptively scaling the depth of the model according to the problem length or the problem complexity. This paper successfully implements this idea to solve various interesting algorithmic tasks with the power of Looped Transformers. Also, $n$-RASP-L is an interesting but intuitive generalization of the RASP-L framework by allowing the loops. \n\nS3. The proposed answer-generation framework called FAP is also an interesting component of this work. It might be of separate interest to study.\n\nS4. The paper presents extensive ablation studies on several components of the proposed method. Also, the empirical results (length generalization performances) are impressive enough to convince the readers about the proposed method’s efficacy." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "- This work studies the efficacy of Looped Transformers for Length Generalization of several algorithmic tasks whose computation complexity is known (as a function of the query length).\n- The paper proposes the definition of $n$-RASP-L, a generalization of the RASP-L computation model allowing the loop of RASP-L programs. It is shown, under a general framework called full-answer prediction (FAP), that some tasks (Copying binary sequence (allowing duplicates), Parity, and Binary Addition) have their own $n$-RASP-L program with a linear number of steps in problem length.\n- The authors propose training Looped Transformers (with input injection and curriculum learning) to learn $n$-RASP-L-programmable tasks, where the ground-truth number of steps is known for each task during training. They also propose two variants of inference methods: either we retain the knowledge about the number of steps at inference time (*Oracle*), or we adaptively decide the number of iterations based on the confidence of FAP (*Maximum confidence*).\n- The proposed method is tested on several algorithmic tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**W1. The definition of $n$-RASP-L (Definition 3.1) can be improved.**\n\n- I think the equation “$T(n): \\mathbb{N} \\rightarrow \\mathbb{N}$” should be corrected to “$T: \\mathbb{N} \\rightarrow \\mathbb{N}$” because $T$ (instead of $T(n)$) is a function of input length $n$ representing the number of steps inside a task-solving $n$-RASP-L program.\n- In (2), I guess $P’$ should be a RASP-L program, which is unspecified in the definition.\n- Should $P$ be decomposed to a sequential application of $P’$, i.e., $P = (P’)^{T(n)}$? I don’t think this is exactly true because there are pre-/post-processing parts inside the proposed $n$-RASP-L programs (in Appendix A). Can the same RASP-L program $P’$ handle such parts? (It might be true because of the experimental results, but I cannot fully understand this part.) If not, I guess the definition should be modified to include the pre-/post-processing parts. For example, $P = P_{\\tt pre} \\circ (P’)^{T(n)} \\circ P_{\\tt post}$.\n\n**W2. “Ground truth” number of steps?**\n\n- According to Definition 3.1, a program $P$ suffices to be an $n$-RASP-L if a corresponding $T(n)$ exists. Indeed, Propositions 3.2, 3.3, and 3.4 claim and prove the existence of $T(n)$ for the Parity, Copy (with duplicates), and Binary Addition tasks, respectively.\n- My question is about the uniqueness or optimality of such $T(n)$’s. There might be a clever way to construct another RASP-L program $\\tilde{P}$ so that $P$ can be implemented with $\\tilde{T}(n)$ steps of applying $\\tilde{P}$, where $\\tilde{T}(n)$ is much smaller than the previously known $T(n)$ (e.g., $\\tilde{T}(n) \\in o(T(n))$). It may happen since there is no uniqueness guarantee or lower bound result on $T(n)$.\n - If I venture a guess, I would say it might be possible to implement an $O(\\log n)$-step $n$-RASP-L solution for the Parity task by using the parallelism of the transformer architecture. Please correct me if I am wrong. Also, I understand if it is impossible to show whether this bold guess is true. If you are interested, there are some (probably) useful references about logarithmic-depth transformers [1,2].\n- However, the authors keep using the phrase “ground truth number of steps” throughout the paper, which may lead to misunderstanding that the only way to implement the given $n$-RASP-L program is by using a loop of length $T(n)$.\n- If two different $T(n)$’s can be applied to a single $n$-RASP-L-programmable task, it might be interesting to observe whether the model’s performance changes depending on the choice of $T(n)$.\n- Furthermore, if multiple choices of $T(n)$’s exist for a given task, does knowing only one of them suffice to train reasonably performant Looped Transformers? If we know more than one, how should we choose $T(n)$ when we train the model?\n\n**W3. Shouldn’t we consider the input injection when implementing an $n$-RASP-L program for the given task?**\n\n- The input injection seems to be an important component of their experiments. Since it changes the input vectors of each layer, I guess the task-solving algorithm under input injection might be different from that without it.\n- However, I can’t see that the $n$-RASP-L programs provided in Appendix A reflect the input injection. As I inspect inside the loop of each program, every iteration only reuses the calculation(s) from the previous iteration right before the current one.\n- Shouldn’t we consider the very first input sequence and the result from the previous iteration when implementing the loops? Or is it a valid implementation of input injection? Getting even further, Is there any way to embed the input injection into the $n$-RASP-L programs?\n\n**W4. The proposed training method requires prior knowledge of the task’s structure.**\n\n- The proposed method is limited in that it requires a prior understanding of the structure (e.g., $T(n)$) of the task where we want to train a model. This is because it hinders fully end-to-end training.\n- Are Looped Transformers still useful for achieving length generalization even when we don’t (or cannot) know the exact expression of $T(n)$?\n- Besides, it seems that the depth of the decoder block is determined based on the complexity/difficulty of the subroutine $P’$ at each step inside the loop (Appendix F). How are they actually chosen? Or, how should we decide the size of the repeating decoder block?\n\n**W5. Some experimental details seem missing or wrong.**\n\n- I guess Equation (2) has a typo: shouldn’t it be arg-main instead of arg-max?\n- In Binary Addition, it seems that $T$ is chosen to be $n$ (the length of each operand). However, Proposition 3.4 claims that $T(n)=n+1$ for the same task. Why is there a discrepancy between theory and experiment?\n- In Binary Multiplication, I guess some words are used in a wrong way. In Lines 417-418, I think it should be: “We define the problem length to be the **length** of the second **number**, and set $T$ to be the product of the lengths of two **numbers**.”\n- In Section 6.1.2, are input injections also applied to NTP-based methods? Also, I’m not sure why it is fair to compare their method (based on FAP) to NTP methods with the architectural setting “…with a depth 20 times the depth of the looped block” because such depth might be suboptimal for NTP-based methods.\n- Although the paper indirectly showcases that their adaptive decision of the number of steps works quite well via Figure 5, it would be better to display similar performance plots to Figure 4 (plots based on the “Oracle” inference) but using the adaptive confidence-based method instead, at least in their appendix.\n\n**W6. Minor writing issues**\n\n- Section 4.1, fourth bullet point: I guess $T(n) \\in \\\\{T(1), \\ldots, T(n_{\\rm max})\\\\}$ is correct ($T(1)$ instead of $1$).\n- Equations (1) and (2) have several weird-looking brackets (too many open brackets etc.)\n- Line 510: Use *less* abbreviations like “w.r.t.”\n\n---\n\n**References**\n\n[1] Sanford, Clayton, et al. \"Transformers, parallel computation, and logarithmic depth.\" ICML 2024.\n\n[2] Sanford, Clayton, et al. \"Understanding transformer reasoning capabilities via graph algorithms.\" NeurIPS 2024." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Q1. In Figure 5, why do some tasks perform well even when exceeding the step count, while others degrade immediately? For instance, the performance of the parity task and the binary sum task immediately drops when executed with additional steps, whereas the addition, multiplication, and copy tasks retain accuracy to some extent.\n- Particularly for the copy task, the selected step count is significantly higher than the actual number of steps required, which seems unusual to me.\n\nQ2. Are there any tasks whose T(n) is nonlinear (e.g. sqrt(n), n^2) to the length of the input sequence? It would be interesting to see experimental results for such tasks.\n\nQ3. Why is the output reversed for binary multiplication (but not for binary addition)?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper is well-structured and clearly written.\n- The introduction of Looped Transformers is well-motivated and effectively argued.\n- The results are strong and solid. They do not require the use of a scratchpad. Also, the prediction is conducted using an end-to-end, full-answer prediction setup, which is a more general way than the conventional next-token prediction setup.\n- The paper clearly illustrates that the model can determine the number of steps to take on its own and does not require T(n) in the test time." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates the length generalization problem of Transformer models, which refers to the inability of the model to deal with longer samples than encountered during the training phase. While recent literature has focused on modifying the positional embeddings and the input formats, this paper proposes to use Looped Transformers, which can dynamically adjust their computation steps according to the problem length. The authors define n-RASP-L problems to figure out which problems can be solved by Looped Transformers. Then, they train the models on these tasks (parity, copy, binary addition, binary sum, binary multiplication, unique set) under a full-answer prediction setup. Empirically, the trained models could successfully length-generalize to longer lengths by appropriately adapting the number of loops at inference time." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Weakness 1: Applicability Limited to n-RASP-L Tasks\n\n- The approach is limited to tasks that belong to n-RASP-L categories, as it requires the ground-truth number of steps in the training data.\n\nWeakness 2: Insufficient Experimentation.\n\n- ***Effect of Curriculum Learning.*** How does the model perform without curriculum learning? Is the use of curriculum learning necessary?\n\n- ***Tolerance to Step Counts.*** I am curious whether this method will still perform well with different choices of T(n). For example, for tasks like parity, would the model maintain its performance if T(n) were set to n+1 rather than n? What about 2n instead of n? This question stems from the possibility that there might be more efficient solutions to n-RASP-L problems than human-designed ones, which could work with fewer steps. Testing whether the model is robust under overestimated T(n) values could help verify the robustness of this approach.\n\n- Overall, the paper requires more ablation studies." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I listed my questions in the weaknesses section." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Overall, I really liked the paper, I think that using a looped transformer to achieve length generalization is an interesting idea that was not studied in the past to my knowledge. This paper complements all the other techniques (universal transformers, different types of position emebedding, etc.) that were used in the past for length generalization The paper is well-written and well-explained. This is why I advocate for acceptance of this paper." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper examines how looped transformers perform in terms of length generalization. The focus is on n-RASP-L problems, which are problems that can be tackled using a loop of a single RASP-L program. The concept is that Transformers can learn steps that are independent of length, employing a flexible number of iterations in the looped transformer to achieve length generalization. The authors first demonstrate that n-digit addition, n-bit parity, and copying n symbols can be addressed with n-RASP-L solutions. They then reveal that when utilizing the looped transformer with adaptive stopping time, the results exhibit significantly stronger length generalization compared to next token prediction (NTP) and other methods like using pause tokens or NTP-loop with a fixed stopping time." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I would like to raise the following weaknesses/questions regarding this paper: \n\n- **Lack of other baselines**: What would happen if you have a very deep universal transformer? Universal transformers also have shared parameters and looks equivalent to the loop transformer. The depth may play the role of the number of loops. Would this be equivalent to the fixed loop NTP? It would be interesting to run the same experiments with a universal transformer.\n\n- **Comparison with other methods**: Where would you position the looped transformers in the list of all the tricks for length generalization? Are the effects similar or complementary to change of the input (index hinting, reverse order of operands, etc.) ? Changes of positional encoding? Chain of Thought? It would be interesting to understand this by making combinations of the tricks with looped transformers with other tricks and analyze the performance differences.\n\n- What is the depth of the encoder block in the loop transformer? I think this information is important to put in the main paper. \n\n- **Adaptive inference time**: I think one weak point of the method is actually coming up with an adaptive inference time. The methods that are proposed are nice but may look a bit hacky. Do you think one could learn this adaptive inference time?\n\n- In Figure 2, which adaptive inference time method is used for FAP-Loop-Adaptive?\n\n- Lastly, this is a wild question: have you tried your method on problems where there is no n-RASP-L solutions? Would it still work better than just doing NTP?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Are the quantities reported in Figure 5 indeed for a single training example? When using the maximum confidence criterion, how do the results compare to the ones reported in Figure 4 with access to the ground truth number of steps?\n\n2. In Bansal et al. 2022, they avoid the need for knowing the exact number of steps during training and inference. Have you tried using similar heuristics?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1. The paper is mostly well-written and easy to follow.\n\n2. Demonstrates that, given knowledge of the number of steps required to perform a given task, a certain looped Transformer, which jointly predicts the full output sequence, tends to learn a length-generalizing solution. The length-generalizing capabilities of this looped Transformer are shown to surpass baselines that use next-token prediction." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Empirically explores the ability of looped Transformers, i.e. Transformers that repeatedly apply the same block of layers, to length-generalize on several algorithmic tasks, including copy, parity, and addition. First, the authors manually derive length-generalizing solutions to the considered tasks through a variant of the RASP language, which they term n-RASP-L. Then, based on these ground truth solutions, they show that looped Transformers length-generalize well when trained with access to the true number of steps required to compute the output for a given input." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The main weakness of the current paper is that the significance of the results is somewhat limited. In particular, I find that it falls somewhere in between works that are practically relevant and those that may not be practically relevant, but improve our understanding of certain phenomena. On the one hand, this work shows empirically that, in some algorithmic tasks for which we already know how to write explicitly a length-generalizing solution (in terms of looped Transformer weights), looped Transformers generalize well to longer lengths, if they have access during training to the number of steps required for solving the task for a given input. Consequently, the practical relevance is limited since the proposed method requires that we already know how to manually write a length-generalizing solution, in which case there is arguably no point in learning. On the other hand, this work does not provide much in terms of understanding why or how looped Transformers are able to length-generalize.\n\n Note that one may consider the demonstration of such length generalization to be possible as a main contribution. Yet, the ability to extrapolate through recurrence of layers has been demonstrated in the past, albeit for other architectures (see Bansal et al. 2022 [1], which notably do not require knowing the ground truth number of steps in training).\n\n2. A related issue is the usage of ground truth stopping time during inference. The quantities reported in Figure 5 seem to be for a single training example, yet it is not entirely clear. If so, then how does the maximum confidence stopping criterion fair across the dataset? It would be useful to report results similar to those of Figure 4 but when using the proposed stopping criterion as opposed to the ground truth stopping time, which should be unknown.\n\nOverall, my assessment of the paper tends towards the positive side, yet it is not a clear accept due to the substantial limitations mentioned above. Specifically, the significance of the contributions can be greatly improved if it would be possible to remove the dependence on knowing the ground truth number of steps required to solve the task for a given input during training (and by how it seems from the current results, during test time as well).\n\n\nAdditional (more minor) comments:\n- In Definition 3.1, it seems that the intention is for $P’$ to be some RASP-L program, as opposed to just a program. Otherwise, trivially any program $P$ is an n-RASP-L program by choosing $P’ = P$ and $T(n) = 1$.\n- In Equation (2), I believe that the criterion should be an argmin over the cross entropy loss instead of an argmax.\n\n\n[1] Bansal, A., Schwarzschild, A., Borgnia, E., Emam, Z., Huang, F., Goldblum, M., & Goldstein, T. (2022). End-to-end algorithm synthesis with recurrent networks: Extrapolation without overthinking. Advances in Neural Information Processing Systems, 35, 20232-20242." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024looped,\ntitle={Looped Transformers for Length Generalization},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2edigk8yoU},\nnote={under review}\n}" }, "abstract": { "value": "Recent work has shown that Transformers trained from scratch can successfully solve various arithmetic and algorithmic tasks, such as adding numbers and computing parity. While these Transformers generalize well on unseen inputs of the same length, they struggle with length generalization, i.e., handling inputs of unseen lengths. In this work, we demonstrate that looped Transformers with an adaptive number of steps significantly improve length generalization. We focus on tasks with a known iterative solution, involving multiple iterations of a RASP-L operation—a length-generalizable operation that can be expressed by a finite-sized Transformer. We train looped Transformers using our proposed learning algorithm and observe that they learn highly length-generalizable solutions for various tasks." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Transformers" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/27c2a50c25cc416d848ca4867ff07cb9236e8597.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Looped Transformers for Length Generalization" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- line 41: do you mean “mixing function” instead of “mixture function”?\n- lines 332, 386 and 387: Notation. You are using $\\mathcal{L}$ and $L$ interchangeably. Could you revise this?\n- If I am not mistaken, your identifiability theory does not obtain the causal graph, but a markov equivalence of it (please correct if mistaken). Yet apparently, the synthetic experiments suggest that you estimate the instantaneous causal graph with 100% accuracy (Figure 4, bottom left). Could you provide some explanation for this? For example, is it possible that your assumptions allow for stronger identifiability results that are overlooked in the presented theory?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The manuscript is clear in terms of motivating the problem and introducing the theoretical framework.\n- Incorporation of instantaneous effects into sequential latent variable models is a very significant contribution.\n- The paper discusses limitations of the assumptions in comparison to recent works.\n- The experiments with real-world data motivate the incorporation of instantaneous effects." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes IDOL, a framework for achieving identifiability in sequential latent variable models with instantaneous dependencies. The authors establish identifiability up to permutation of the latent variables and demonstrate that the underlying causal graph can be identified up to its Markov equivalence class (if this interpretation is correct). They thoroughly discuss the limitations of their assumptions in comparison to recent works, which helps underscore the significance of the proposed framework.\n\nAn estimation method is also introduced, with experiments on synthetic data verifying the theoretical results, while real-world experiments highlight the importance of incorporating instantaneous dependencies." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Minor Concerns**\n- **Computational Complexity:** The sparsity constraint introduced in Eq. (11) seems to introduce significant computational complexity to the algorithm. The paper would benefit from a more detailed analysis regarding this. For example, would it be possible to compute wall-clock times (in training) for IDOL in comparison the proposed baselines?\n- **Scalability to High-Dimensional Data:** The authors acknowledge limitations with respect to high-dimensional data, which can restrict the application to real-world scenarios. An experiment to understand how high-dimensional one can go with IDOL would be ideal to support your point to understand how high one could go for IDOL.\n\n**Major Concern: Theory Section Clarity and Limitations**\n\nThe paper’s theoretical claims, particularly around identifiability, would benefit from clarification to avoid potential misunderstandings regarding the nature of identifiability achieved. It appears that IDOL identifies the latent Markov Network rather than the true causal graph for the instantaneous component of the latent dynamics. This is an important distinction, as conditional independence relations allow only for the identification of the Markov equivalence class, not the directed causal structure itself. However, the presentation throughout the paper, especially in the introduction, experiments (such as Figure 4), and conclusions, may lead readers to infer that IDOL identifies the causal graph rather than the Markov network.\n\nTo address this issue, the authors could consider the following changes:\n\n- **Introduction (around line 89):** Indicate that the identifiability of the instantaneous structure in IDOL is only up to a Markov equivalence class, clarifying that IDOL does not identify the directions of edges in the instantaneous part.\n- **Figure 1c Modification:** Consider modifying Figure 1c to remove the arrow pointers from edges, signaling that the result is a Markov network rather than a causal graph when discussing identifiability (this might make sense in terms of theory, but not from a data generation perspective).\n- **Conclusion:** Mention the Markov equivalence class limitation explicitly. This would open a path for further research to extend the identifiability result from Markov equivalence to the full causal structure, especially given the promising empirical results observed in Figure 4.\n\nThe following specific statements in the theory section could be revised to improve clarity and accuracy:\n\n- lines 130-132: “the latent causal relations are also immediately identifiable because conditional independence relations fully characterize instantaneous causal relations in an instantaneous causally sufficient system”. I don’t think this line is correct without any additional assumptions. Conditional independence relations only provide the Markov equivalence class, not the exact causal graph, without further assumptions. Rephrasing this to accurately reflect the distinction between the Markov equivalence class and the causal graph would strengthen the theoretical foundation.\n- lines 171-172: Could you indicate whether $p_{c_t}$ refers to the marginal distribution $p(c_t)$ or the conditional distribution $p(c_t|z_{t-2})$?\nlines 165-188: For better readability, could you indicate $c_t \\in R^{2n}$ in your example? Otherwise, at first glance it reads as $\\{z_{t,i}, z_{t-1,i} \\}$ for $c_{t,i}$ in Theorem 1.\n- line 217: Would it be better to use $\\emptyset$ to refer to $\\Phi$ as an empty set?\n- line 230: Could you define “isomorphic” for Markov networks? A footnote or reference to the Appendix suffices." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "How does IDOL handle cases where the latent process sparsity assumption is only partially met? \n\nCould the authors clarify the computational complexity of IDOL compared to baselines, especially for high-dimensional data? \n\nAre there specific real-world scenarios where IDOL might struggle due to non-invertible mixing processes?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper introduces a novel approach to identifying temporally causal representations in time series data with instantaneous dependencies. This approach addresses a gap by proposing a sparse latent process assumption that is more practical for real-world applications than previous assumptions. \n\nExtensive evaluations are performance to demonstrate the effectiveness of the proposed approach.\n\nThe paper is well-organized. The use of illustrative figures helps clarify the complex concepts." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a framework, IDOL (identification of instantaneous Latent Dynamics), for identifying temporally causal representation with instantaneous dependencies. IDOL employs a sparse latent process assumption, which is more adaptable to real-world data. The framework is validation through extensive experiments on both synthetic and rea-world human motion datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Providing further discussions on the possibility of extending IDOL to handle high-dimensional data can be beneficial. \n\nGiven the limitation due to the dependency on invertible mixing processes, providing guidelines for real-world applicability would add value." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Can you please comment on the performance of your method in noisy environments and low-sample regimes?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The proposed IDOL framework moves beyond traditional methods that often rely on grouping of variables or direct interventions, by introducing a sparse influence assumption to capture the natural sparsity in many real-world datasets. This approach is novel in handling instantaneous dependencies without requiring interventions or grouping. Furthermore, the paper demonstrates rigorous theoretical and empirical quality, supported by a well-founded identifiability proof and a solid mathematical framework. Experimental validation on both synthetic and real-world human motion datasets further underscores the robustness and reliability of the model, showcasing its ability to accurately identify causal relationships and achieve high predictive accuracy is synthetic and real-world datasets. The paper is overall clearly written and easy to follow. Overall, this work is significant for the field, since causal discovery for time series with instantaneous effects is an important open problem." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a framework called IDOL (Identification framework for Instantaneous Latent dynamics) to enhance temporally causal representation learning for time series data with instantaneous dependencies. Traditional approaches for identifying latent causal processes in time series data often assume that the latent causal variables lack instantaneous interactions, limiting real-world applicability. IDOL addresses this limitation by applying a sparsity constraint on causal influences, allowing for both time-delayed and instantaneous dependencies in latent variables. The IDOL frameworkassumes a sparse influence within latent causal processes, allowing both time-delayed and instantaneous relations. Unlike prior methods that require data interventions or predefined groupings to achieve identifiability, IDOL elies on this sparse latent structure alone, making it highly applicable to scenarios where interventions are impractical. The framework’s theoretical foundation is built on leveraging sufficient variability and temporal contextual information, establishing identifiability through a combination of variational inference and sparsity regularization. This enables the model to accurately reconstruct latent variables and the underlying causal relationships without complex external assumptions." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The model assumes an invertible mixing process to reconstruct latent causal structures, which may not always be feasible in real-world data. In some scenarios, particularly in non-linear and noisy environments, this assumption could lead to inaccurate or incomplete latent representations, potentially undermining the model’s performance and causal interpretability. Furthermore, IDOL’s effectiveness heavily depends on the assumption of a sparse latent process. In cases where this sparsity assumption does not hold (i.e., when the causal structure is dense or complex), IDOL’s performance degrades, as demonstrated in the experiments. This sensitivity suggests that the framework may be less robust in scenarios where latent processes are highly interconnected." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024on,\ntitle={On the Identification of Temporal Causal Representation with Instantaneous Dependence},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2efNHgYRvM},\nnote={under review}\n}" }, "abstract": { "value": "Temporally causal representation learning aims to identify the latent causal process from time series observations, but most methods require the assumption that the latent causal processes do not have instantaneous relations. Although some recent methods achieve identifiability in the instantaneous causality case, they require either interventions on the latent variables or grouping of the observations, which are in general difficult to obtain in real-world scenarios. To fill this gap, we propose an \\textbf{ID}entification framework for instantane\\textbf{O}us \\textbf{L}atent dynamics (\\textbf{IDOL}) by imposing a sparse influence constraint that the latent causal processes have sparse time-delayed and instantaneous relations. Specifically, we establish identifiability results of the latent causal process based on sufficient variability and the sparse influence constraint by employing contextual information of time series data. Based on these theories, we incorporate a temporally variational inference architecture to estimate the latent variables and a gradient-based sparsity regularization to identify the latent causal process. Experimental results on simulation datasets illustrate that our method can identify the latent causal process. Furthermore, evaluations on multiple human motion forecasting benchmarks with instantaneous dependencies indicate the effectiveness of our method in real-world settings." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Causal Representation Learning", "Instantaneous Dependency", "Identification" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/badc2e85fc7d35b46f803953bddc9d0e527d1f56.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/a03bf45ce19c829873c3700af4ef80ba4c6a20be.zip" }, "title": { "value": "On the Identification of Temporal Causal Representation with Instantaneous Dependence" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "See weaknesses" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper is extremely well written and enjoyable to read. Moreover, as far I checked the math seems correct and sound. Without being an expert myself on the respective literature, I find the respective very interesting and challenging from a theoretical standpoint." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper revisits the case of random shuffling-type stochastic gradient methods for finite sum minimization problems. More precisely, the paper considers objectives without the traditional structural assumption of Lipschitz smoothness. In doing so, the authors focus on non-convex, strongly convex and convex objectives which satisfy as a smoothness \"surrogate\" the notion of $\\mathcal{l}-$ smoothness.\nTo that end, the authors provide respective convergence rates for each respective case." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "My main concerns consider two main factors:\n\nFirst, the notion of $\\mathcal{l}-$ smoothness introduces additional parameters to be tuned as it becomes apparent from the definitions of the step-sizes in the main theorems. It would be could to include some discussion of how difficult are these to be evaluated both in real life scenarios and in theory. More precisely, do the authors believe that the respective toolbox from the adaptive algorithm like in [1] can be incorporated?\n\nSecondly, the proposed step-size policies seem to rely on a prior knowledge on the iteration horizon $T$. Do the authors believe that an any time convergence rate guarantee can be achieved? \n\n\n[1] Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization, Neurips 2022." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See **Weaknesses**." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "I appreciate the paper tries to tackle more realistic problems (i.e., functions with non-uniform smoothness) and studies the shuffling algorithm, an arguably more common scheme in practice." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the shuffling method under the generalized smooth assumption, which was proposed recently to fit many modern machine learning tasks. The authors proved that, under properly picked parameters, the shuffling method provably converges under the weak smoothness condition for both nonconvex/strongly convex/convex objectives. Numerical experiments are also conducted to support the theory." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Major points**.\n\n1. All convergence results are proved in a high-probability manner. However, the dependence on the margin $\\delta$ is in the order of $\\mathrm{poly}(1/\\delta)$, which makes the results weak. I also suggest the authors explicitly give the dependence on $\\delta$ in the final sample complexity.\n\n2. Some descriptions of the existing works contain wrong facts.\n\n - Lines 90-91, the bounded variance assumption is **not** required to improve the rate $\\mathcal{O}(1/T^2)$ to $\\mathcal{O}(1/(nT^2))$ in Nguyen et al. (2021). Instead, Nguyen et al. (2021) can handle **unbounded** variance.\n\n - Lines 92-93, results in both Mishchenko et al. (2020) and Nguyen et al. (2021) hold under **unbounded** variance condition. The current description is not correct.\n\n3. The conditions on noises, i.e., Assumptions 4.3, 4.4, and 4.7, are strong compared with the existing literature, which significantly reduces the impact of the work. I will elaborate more on this point in the following.\n\n\n2. Nonconvex part. \n\n - Random reshuffling scheme.\n\n - In this case, previous works only consider Assumption 4.7, or even a weaker version, i.e., the non-uniformly bounded variance, to obtain the $\\mathcal{O}(\\sqrt{n}/\\epsilon^{3})$ sample complexity.\n\n - However, to recover the same rate, this work requires much stronger Assumptions 4.3 and 4.4 in Theorems 4.5 and 4.6, respectively. Hence, the results in this paper are not directly comparable to prior literature.\n\n - When only Assumption 4.7 holds, Corollary 4.8 has extra dependence on $n$ as indicated by the authors.\n\n - In addition, I am not sure why Corollary 4.8 is a corollary and cannot find its proof. Did I miss anything?\n\n - Arbitrary shuffling scheme.\n\n - Again, the authors require stronger Assumption 4.3 to make their sample complexity as good as the previous results. However, the latter can hold under non-uniformly smoothness, e.g., see Nguyen et al. (2021). As such, the claim in Lines 63-64 is misleading.\n\n - Moreover, imposing three assumptions on noises may confuse the reader. Especially, the proofs under Assumptions 4.3 and 4.4 are similar as claimed in Lines 860-863. I didn't see a necessary reason for the authors to do so.\n\n4. Strongly convex and convex parts. \n\n - Assumption 4.3 is strong and not assumed in previous best-known results, making the contributions weak.\n\n - As far as I know, the previous best results don't need any assumption on the noises for convex problems (Assumption 4.14). Hence, whichever condition among Assumptions 4.3, 4.4, and 4.7 is used, the result is not an improvement in my opinion.\n\n5. The writing can be improved. Some theorems give a threshold on the stepsize (e.g., Theorem 4.5) but others give an exact choice (e.g., Theorem 4.12). Can the author present a unified statement?\n\n**Minor points**.\n\n1. Line 290, the second $T=\\mathcal{O}(\\frac{1}{\\epsilon^3})$ should be $\\mathcal{O}(\\frac{n}{\\epsilon^3})$.\n\n2. Line 310, $Delta_1$ should be $\\Delta_1$." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "- Definition 2.2 seems missing subscript for $x$ in the right hand side. It is unclear what authors mean. It could be either symmetric (x is any of $x_1$ or $x_2$) either non-symmetric $L_0, L_1$-smoothness (maximization over $x_1$ $x_2$ interval), see [1]. Or something different? Symmetric case is much easier to analyze. I tried to get it through the proofs, and it was very strange to see, that everywhere (e.g. Lemma A.2, Lemma A.4 ) authors used $\\|\\|\\nabla F(\\omega)\\|\\| \\le G$, and then said (line 672) that \"From definition 2.2, we can use Lipschitz smoothness\". The standard smoothness AFAIU. So the question: Can the $G$ be huge? Is it just hidden to the $\\cO$? and is the analisys mainly like for the standard smoothness?\nIt seems to me that it actually is, because in lines 615, 704, 890, 1000, etc authors use standard smoothness inequalities. \nI simply can bound $|\\|\\nabla f(x) \\|\\| = |\\|\\nabla f(x) - \\nabla f(x^*) \\|\\| \\ \\le (L_0 + L_1|\\|\\nabla f(x^*) \\|\\|)R = RL_0$ which could be a G.\nand then my effective smoothness is $L= L_0 + RL_0L_1$.\nIn the non-symmetric we have extra exponents as a multipliers, according to [1].\nIs this what authors effectively did? Of course the one can recover the same rate as for standard smoothness. The problem is that the constant will be huge.\n\n- what is $r := G/L$ in Theorem 4.5, Theorem 4.6, Lemma A.1, Lemma A.2. I couldn't find where authors referr to $r$. What is it for? The results of the mentioned theorems and lemmas do not depend on $r$.\nThen $r$ is appearing in Lemma A.4 and in bounds two subsequent trajectory points difference norm. Which mainly coincides with my above bound on the gradient (if we plug in the step).\n\nI briefly check the proofs and it seems they are to adapting my above bound on the gradient norm to the stochastic case (which is where the weaker variance assumption is used -- no expectation, and the difference between the full gradient and its stochastic counterpart is estimated).\n\nHowever, the correct approach is to allow the step size to increase as the gradient norm approaches zero. E.g [1] suggests clipping with gradient norm in the denominator -- when the norm is large - the stepsize is small and vice-versa.\n\nIf I was mistaken, I would be glad to investigate the proofs more carefully if authors argued that I was wrong. My understating is that gradient norm is just trivially bounded and the contribution is poor.\n\nReferences:\n[1] Z. Chen et al. 2023 Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization" }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Results match the standard Lipschitz smoothness rates" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper considers shuffling-type gradient descent under more general smoothness assumption -- $L_0, L_1$-smoothness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Variance assumptions, which are stronger than the most standard bounded variance assumption" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See the Weaknesses above." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The theoretical analysis is rigorous and considers multiple cases for different function properties.\n2. The authors discuss the limitations of their work and suggest directions for future research." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper examines the convergence rate of shuffling-type gradient methods without assuming Lipschitz smoothness, achieving results that match the current best-known convergence rates. The theoretical analysis covers non-convex, strongly convex, and non-strongly convex cases under both random reshuffling and arbitrary shuffling schemes." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "My main concern is the experimental section, which feels too limited and simple to fully support the theoretical findings.\n1. The data used in the experiments is relatively simple. It would be valuable to see if this method remains effective on more complex datasets, such as image datasets or applications with large language models.\n2. Additionally, since the theoretical analysis includes both random reshuffling and arbitrary shuffling, it would strengthen the paper to show results for both methods compared to the baseline SGD.\n3. Similarly, since the analysis considers three different cases (non-convex, strongly convex, and non-strongly convex), conducting experiments separately under each case would add depth to the findings." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024revisiting,\ntitle={Revisiting Convergence: A Study on Shuffling-Type Gradient Methods},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2ev44Srmt9},\nnote={under review}\n}" }, "abstract": { "value": "Shuffling-type gradient methods are favored in practice for their simplicity and rapid empirical performance. Despite extensive development of convergence guarantees under various assumptions in recent years, most require the Lipschitz smoothness condition, which is often not met in common machine learning models. We highlight this issue with specific counterexamples. To address this gap, we revisit the convergence rates of shuffling-type gradient methods without assuming Lipschitz smoothness. Using our stepsize strategy, the shuffling-type gradient algorithm not only converges under weaker assumptions but also match the current best-known convergence rates, thereby broadening its applicability. We prove the convergence rates for nonconvex, strongly convex, and non-strongly convex cases, each under both random reshuffling and arbitrary shuffling schemes, and under bounded or sub-Gaussian gradient noise. Numerical experiments further validate the performance of our shuffling-type gradient algorithm, underscoring its practical efficacy." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "shuffling-type gradient methods", "convergence analysis", "relaxed smoothness assumptions" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/975ec4dc36967471622efcbb0d684c17bb6d19e6.pdf" }, "presentation": null, "primary_area": { "value": "optimization" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/75a100c42cddfbbf70e03451f8b7371c107e3cd1.zip" }, "title": { "value": "Revisiting Convergence: A Study on Shuffling-Type Gradient Methods" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- The changes in Dynamic Degree seem to exhibit a certain trend; are there any related experimental analyses available?\n- There is a discrepancy between the acceleration results in Table 1 and Table 7. Could you please provide the specific experimental parameter differences (as they seem to be absent in the paper)?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper effectively optimizes DiT using sparse attention and MCD, and the proposed framework demonstrates commendable speed results alongside assured generation quality. Specific strengths include:\n\n- The identification of the Attention Tile phenomenon, accompanied by a detailed analysis, provides a background for the design of sparse attention masks and proposes an algorithm for searching optimal mask sets. Comprehensive evaluation experiments validate the effectiveness of this method.\n- The integration of the consistency distillation method leads to a complete acceleration framework, with rigorous ablation studies confirming the framework's soundness and ensuring generation quality. The FVD metric significantly outperforms the use of the MLCD method alone." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the acceleration of 3D full attention video generation models, focusing on sparsifying 3D attention and reducing sampling steps. The authors propose an algorithm for searching optimal sparse attention masks based on the observed Attention Tile phenomenon and combine this with a consistency distillation method to reduce the number of steps, resulting in an accelerated version of DiT while striving to maintain generation quality." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "While the paper is rich in content, there are still potential issues to consider: According to Table 7, the acceleration benefits from sparse attention masks are not substantial, with noticeable quality degradation occurring beyond a 1.45× acceleration. Although there is some improvement when combined with MLCD (compared to a 5× acceleration), the effectiveness of the design based on the Attention Tile, which is a core contribution of the paper, appears insufficient here." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "N/A" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The paper makes a significant contribution by discovering the \"Attention Tile\" phenomenon in 3D full attention Diffusion Transformers (DiTs) for video data. This insight into the redundancy and repetitive patterns within attention maps is a valuable addition to the understanding of how attention mechanisms function in video generation models.\n2. Building on the Attention Tile observation, the authors propose a new family of sparse 3D attention mechanisms that reduce computational complexity from quadratic to linear concerning the number of video frames. This is a substantial improvement that directly addresses the inefficiency issues in existing models.\n3. The introduction of the EFFICIENT-VDIT framework is a well-thought-out approach that combines multi-step consistency distillation, layer-wise sparse attention mask searching, and knowledge distillation. This pipeline effectively accelerates inference while maintaining high metrics.\n4. Achieving these results using only 0.1% of the pretraining data is notable. It indicates that the method is not only computationally efficient but also data-efficient, which is advantageous when large datasets are not readily available." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper tackles the inefficiency of DiTs used in video diffusion model. The speedup of the presented method comes from two sources: 1) pruning the large full 3D attention of VDM DiTs and 2) distilling the model into a multi-step consistency model.\nThe authors identify a repetitive tile-like pattern, termed \"Attention Tile,\" in the 3D attention maps of video data. Leveraging this pattern, they propose a new family of sparse 3D attention mechanisms that reduce the computational complexity from quadratic to linear with respect to the number of video frames.\nTo further accelerate the inference process, the paper introduces a multi-step consistency distillation (MCD) technique. By dividing the sampling trajectory into segments and performing consistency distillation within each, the number of sampling steps required for video generation is significantly reduced.\nResults show that the method achieves good speedup without suffer much performance, using limited training data." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper could benefit from a more in-depth discussion of the trade-offs involved, such as the balance between sparsity level and video quality or the impact on different types of video content (e.g., fast-moving vs. static scenes). For instance, why don't you directly use the demo videos on OpenSORA's websites and compare the qualitative results? They provided both static scenes with only relative camera poses and more dynamic scenes, e.g. filming of an explosion scene.\n2. The method relies on the observation that the Attention Tile pattern is data-independent. If this assumption does not hold for certain types of video data (e.g., highly dynamic scenes), the efficiency gains might not translate, potentially limiting the method's applicability.\n3. The use of only 0.1% of the pretraining data raises concerns about the generalization capabilities of the accelerated model. While performance loss is minimal on tested datasets, the model may underperform on unseen data or less common video scenarios.\n4. While the paper uses VBench and FVD for evaluation, these metrics may not capture all aspects of video quality, such as temporal coherence in more complex scenes or perceptual quality under different conditions. Including additional metrics or user studies could provide a more comprehensive assessment. This is especially concerning combined with weakness #2, since FVD is commonly known as a weak metric that focuses strongly on independent frames rather than overall video coherence. Overall, the evaluation seems to favor more static videos rather than highly dynamic videos, and I suspect the attention pruning would encourage such results too. A metric that takes motion into account is Content-Debiased FVD [1], but ideally, this is more suitable via a user study (even though I do not think this is necessary for the rebuttal stage, but better prepare it for another iteration of the paper).\n5. Inherit my point in #2 and #4, the paper does not provide any video data, making it challenging to assess the actual quality of the generated contents. From my point of view, a VDM paper should always be accompanied with as many videos as possible within the supplemental material size limit. Again, a good set would be the demo videos on OpenSORA's websites. They provided a wide range of descriptions and all the corresponding text prompts --- supposedly those prompts would work well on OpenSORA.\n\n[1] Ge et al., On the Content Bias in Fréchet Video Distance, in CVPR 2024." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "please refer to the weakness section" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The finding (attention tile) is quite interesting and could be useful for future research in the community in this area.\n - the proposed layer-wise optimal search for sparse attention masks is somewhere novel in the paper." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed an efficient method for DiT based text-to-video generation. They found a unique pattern in the attention map of DiT based video generation diffusion models and proposed a method to exploit this pattern to ignore the computation of attention between many query/key pairs and hence speed up the generation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- This work over-claimed the contribution of the proposed method. Actually, the efficiency improvement is mostly coming from MLCD, which is proposed by another work. The real improvement from the main finding or the proposed 'new' method in this paper is much less than MLCD.\n - Experiment is not thorough. This paper only experimented their method on one DiT based text-to-video generation model.\n - Comparison with other methods is missing. This paper only compared the results from different hyper parameters of the proposed method. Many existing methods that accelerate diffusion models are missing in the paper.\n - The larger diagonal attention is not something new or surprising as the each query token is computing the `correlation` with itself." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "What happens if the stages are switched, i.e., first obtain T_{sparse}, then T_{MCM}​ from T_{sparse}, and finally apply the knowledge distillation step?\n\nTable 4 needs additional quantitative metrics like aesthetic quality, subject consistency, imaging quality, and FVD to provide a complete understanding of the effect of parallelization.\n\nWhen comparing speed-up performance for parallelization, are the baseline models also trained with parallelization (Table 4)?\n\nHow does the proposed model achieve a lower FVD (Table 5) than the main base model, given that the proposed model is ultimately a distilled version of the main model?\n\nHow is the claim (lines 424 to 430) that model performance is within 1% of the base model accurate? It is evident that the numbers for imaging quality and subject class are significantly lower than those of the base model.\n\nAblation studies in Table 6 show that only MLCD can speed up the process by 5 to 8 times compared to the base model without significantly compromising quality. What is the justification, then, for the need for sparse attention maps on top of that?\n\nIt seems the main contribution is the sparse attention part. However, some doubts remain. Therefore, I can increase my rating if my questions and concerns in the weakness section and questions section are answered satisfactorily." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper is well-written.\n\nThe computational complexity of video diffusion models presents a significant challenge, and the authors effectively highlight this issue and provide a good motivation for addressing it.\n\nTo tackle this, the solution provided by the authors of using a sparse attention map is interesting. Although thinking in this direction is not new, the way the authors motivate the solution and compute the attention maps is scientifically sound and has some novelty.\n\nThe computational speed-up achieved by the method looks impressive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a framework to speed up video generation using Video Diffusion Transformers by optimizing attention computation and reducing sampling steps. A repetitive attention tile pattern in 3D attention maps is identified which allows for sparse attention that lowers complexity. The framework uses a three-stage training pipeline: multi-step consistency distillation to reduce sampling steps, a layer-wise search for optimal sparse attention masks, and knowledge distillation to retain performance. This approach claims to achieve up to a 7.8× speedup in video generation with minimal quality loss." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "In the video generation literature, there are models that generate frames sequentially or follow an auto-regressive approach [1,2]. These models may be less computationally expensive than those using full 3D attention heads, yet there is no empirical or theoretical comparison with such models in the paper.\n\nThere should be an ablation study with the separate effects of sparse attention (without the MLCD) to understand each component in more detail.\n\nThe sampling distillation stage (Stage 1) is not really new, either technically or conceptually. There has been a line of work that provides a similar methodology [3,4], etc. It is not clear how different the proposed distillation is from the existing literature. The same can be said for the knowledge distillation in the final stage (Stage 3).\n\nThe paper has only two qualitative video generation results (or at least what I have found), of which only four frames are shown. There should be a lot more generated videos shown side by side to compare the method qualitatively.\n\n[1] Diffusion forcing: Next-token prediction meets full-sequence diffusion. Chen et al. 2024.\n\n[2] Diffusion models are real-time game engines. Valevski et al. 2024.\n\n[3] MLCM: Multistep Consistency Distillation of Latent Diffusion Model. Xie et al. 2024.\n\n[4] SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation. Liu et al. 2024." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024efficientvdit,\ntitle={Efficient-vDiT: Efficient Video Diffusion Transformers With Attention Tile},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2ezRxhlAxJ},\nnote={under review}\n}" }, "abstract": { "value": "Despite the promise of synthesizing high-fidelity videos, Diffusion Transformers (DiTs) with 3D full attention suffer from expensive inference due to the complexity of attention computation and numerous sampling steps. For example, the popular Open-Sora-Plan model consumes more than 9 minutes for generating a single video of 29 frames. This paper addresses the inefficiency issue from two aspects: 1) Prune the 3D full attention based on the redundancy within video data; We identify a prevalent tile-style repetitive pattern in the 3D attention maps for video data, and advocate a new family of sparse 3D attention that holds a linear complexity w.r.t. the number of video frames. 2) Shorten the sampling process based on multi-step consistency distillation; We split the entire sampling trajectory into several segments and perform consistency distillation within each one to activate few-step generation capacities. We further devise a three-stage training pipeline to conjoin the low-complexity attention and few-step generation capacities. Notably, with 0.1% pretraining data, we turn the Open-Sora-Plan-1.2 model into an efficient one that is 7.4x −7.8x faster for 29 and 93 frames 720p video generation with less than 1% performance loss in VBench. In addition, we demonstrate that our approach is amenable to distributed inference, achieving an additional 3.91x speedup when running on 4 GPUs with sequence parallelism." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Efficient inference", "video generation", "diffusion", "Transformer" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/1e84b60ce5ccba47c5a2638140b9a9eedd50673a.pdf" }, "presentation": null, "primary_area": { "value": "infrastructure, software libraries, hardware, systems, etc." }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Efficient-vDiT: Efficient Video Diffusion Transformers With Attention Tile" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1.\tCould the authors clarify how the dependency measure scales with longer time-series datasets? Does the framework handle large datasets efficiently?\n2.\tHave the authors considered extending this approach to other data types beyond time series? If so, how might the framework need to be adapted?\n3.\tIs there a specific reason for not including additional baselines, such as non-latent diffusion models, for comparison in the empirical section?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1.\tThe introduction of dependency measures to diagnose and address posterior collapse is both novel and insightful, providing a fresh perspective on an important issue within latent diffusion models.\n2.\tThe paper offers a solid theoretical foundation for the analysis of posterior collapse, and the proposed framework is well-motivated by both theoretical insights and empirical observations.\n3.\tThe proposed framework demonstrates significant improvements in the performance of time-series generation models, effectively addressing a key limitation in existing approaches." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the problem of posterior collapse in latent diffusion models, specifically when applied to time series data. The authors provide a systematic analysis of this issue, showing that posterior collapse can reduce the expressiveness of latent diffusion to that of a variational autoencoder (VAE). They introduce a novel dependency measure to quantify the impact of latent variables on the generation process and identify a phenomenon called dependency illusion when time series data are shuffled. Building on these insights, the authors propose a new framework that eliminates the KL-divergence regularization, permits an expressive prior distribution, and ensures the decoder remains sensitive to the latent variable. Extensive experiments demonstrate that this framework avoids posterior collapse and significantly improves time series generation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tWhile the paper presents strong results for time-series data, it lacks a detailed discussion on the generalizability of the approach to other data modalities, such as images or text. Including a brief exploration or discussion of potential extensions could further enhance the contribution.\n2.\tThe experimental details, including specific configurations for baselines and the selection of hyperparameters, are not fully elaborated in the main text. Providing more comprehensive explanations in these areas would improve the paper’s clarity and reproducibility.\n3.\tAlthough the results are promising, some of the visualizations could be made more intuitive, particularly for readers unfamiliar with latent diffusion models. Additionally, converting the figures to vector graphics would significantly improve their quality, as several of the current images appear blurry and lack sharpness, which makes interpretation more difficult. Enhancing the clarity of the figures would improve the overall presentation of the paper." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- Could the authors include comparisons with recent state-of-the-art time series models, such as ARIMA, LSTMs, transformers, and TCNs, which are naturally robust against posterior collapse? This would contextualize the proposed method’s advantages relative to stable baselines.\n\n- Could the authors provide clearer definitions or examples for terms like dependency illusion and posterior collapse in the context of latent diffusion models? A simplified explanation would improve accessibility.\n\n- Are there specific real-world applications, such as anomaly detection or real-time forecasting, where this framework would be particularly useful? A discussion of practical use cases would strengthen the framework’s relevance." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The paper addresses a previously underexplored issue in time series diffusion models—posterior collapse—which has primarily been studied in variational autoencoders (VAEs) but not in the context of diffusion models for time series.\n- The dependency measure provides an insightful tool for quantifying the decoder’s reliance on the latent variable. This measure enables detection of both posterior collapse and dependency illusion, offering valuable diagnostic capabilities for latent-variable models.\n- The approach aligns with the paper’s theoretical objectives, yielding meaningful performance improvements." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper investigates the issue of posterior collapse in latent diffusion models for time series data, where the latent variable becomes ineffective in influencing the model’s output. The authors propose a dependency measure to quantify how much the decoder relies on the latent variable, highlighting not only posterior collapse but also a related phenomenon termed dependency illusion. Then the paper introduces a new framework to address these issues by removing KL-divergence regularization and enhancing the decoder’s sensitivity to the latent variable, improving posterior stability. Experiments demonstrate that the proposed method achieves better performance than standard latent diffusion models with posterior collapse mitigation techniques across various time series datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The empirical evaluation lacks comparisons with stable time series models that naturally avoid posterior collapse, such as ARIMA, RNNs, LSTMs, transformers, and temporal convolutional networks. Including these baselines would provide context on whether the proposed framework offers advantages beyond mitigating posterior collapse. The author also did not compare with recent baselines for time series, which are diffusion-based. Please check papers published in NeurIPS/ICLR/ICML in the past two years. \n\n- The paper references Bowman et al. (2016) to support claims about posterior collapse in latent-variable models for time series, which may be outdated. This raises questions about whether latent diffusion models represent the current state of the art in time series modeling. Comparing the approach with recent state-of-the-art time series methods would strengthen the justification for the proposed framework.\n\n- Although the datasets used are realistic, the paper does not discuss broader real-world applications or scenarios where posterior stability is crucial, such as in anomaly detection or real-time forecasting. Adding context on practical use cases would clarify the framework’s relevance." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "It’s unclear to me why negative local dependency is bad. The authors claimed that it’s because the previous timestamp’s data may be from a low density region and therefore an outlier. But in case that the actual next value to be decoded should indeed be an extreme value, why is that problematic?\n\nCan you discuss the stability of the training? In cases where we 1) train the diffusion model and the decoder together 2) we require the decoder to decode the time series regardless of which timestamp’s noised version of the latent variable is selected." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "--The paper tries to focus on the specific issues of time-dependency collapse in the case of time series data and diffusion models.\n\n--The shuffling experiments help illustrate how a latent variable is not being used strongly throughout all time steps" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the issue of posterior collapse in time-series latent diffusion models, a phenomenon that limits model expressivity by reducing it to a simpler VAE model. The authors introduce a dependency-measure regularization aimed at alleviating this collapse specifically within time-series data. Experiment results on three datasets (WARDS, MIMIC, and Earthquakes) demonstrates initial improvements in preventing posterior collapse over shorter timeframes." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "--The problem is not sufficiently well motivated. In particular, the two types of mode collapse which in time series (time-dependent and time-independent) are not discussed. The reduction to a VAE is only about the elimination of the time-dependent influence. The impact of this simplification is not sufficiently discussed.\n\n--Moreover, the less expressivity is not shown explicitly to be a bad thing in the context of time series in general. There are potentially time series which are driven by a static latent process\n\n--Although the dependency measure is well-defined, there is little theoretical analysis exploring its properties and its relationship to the reduction to a VAE model\n\n--There is no analysis of the results showing specifically how the introduced technique solved the problem of mode collapse. Results with good Wasserstein distance do not directly imply that the issue of mode collapse was resolved.\n\n- This paper claims that they are the first to address the posterior collapse problem in latent diffusion for time series, but it really boils down to the old autoencoder problem. And the diffusion model became a redundant module when the input is a standard Gaussian distribution is a simple extension of the problem of autoencoder." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "N/A" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The study of the latent diffusion model when applied in the context of time series is a trended topic and super interesting. The authors approach this by defining a proper dependency measure to quantify the problem of posterior collapse of the latent variable, and propose a new framework inspired by re-thinking the design of VAE and autoencoders. The new framework is equipped with new loss functions and regularizations, free from posterior collapse. The discussion comes together with empirical support. Overall, the paper's content is clear and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new approach to establish a stable posterior for time series within the latent diffusion framework. The new approach circumvents the problematic KL-divergence regularization, prevents the posterior collapse, and maintains the influence of the latent variable in the decoder. The authors provide both theoretical and experimental support to their newly proposed framework." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The experimental results mainly focus on real-world data to demonstrate the sampling benefits of the proposed method. Can the authors conduct synthetic data experiments to interpret and validate the effectiveness of the newly proposed component in the framework (e.g., the introduced loss function or regularization)?\n\n2. The prediction ability of a time series model is critical. Can the authors evaluate the proposed framework in terms of other metrics, such as the predictive MAE, to demonstrate its prediction ability?\n\n3. In addition to point 2, can the author compare with other advanced time-series models? Only compared with the latent diffusion family would not be convincing enough for advertising the model in the time series community." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1.You claim that “when applied to time series data, this framework might suffer from posterior collapse” in the first paragraph. Do you have any evidence to support this claim? Is this phenomenon due to the diminishing influence of the latent variable on the decoder over time steps? How do you justify the decreased dependency correspond to the posterior collapse of latent diffusion for time series data? \n\n2.In section 4.2, you mention that the the variational inference in your framework leads the latent variable to be smooth in its effect on decoder. Is this the reason why your framework can increase the sensitivity of decoder to latent variable? Can your framework be applied to non-time series data? It seems the proposed method is not specific to time series data." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The authors focus on an important issue of latent diffusion, that is posterior collapse, and propose a potential method to quantify the posterior collapse of latent diffusion for time series data." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper aims to address the posterior collapse problem of latent diffusion for time series data. The authors propose a dependency measure method to quantify how posterior collapse happens. And they propose a KL-divergence regularization based method to improve the sensitivity of decoder to the latent variable for time-series latent diffusion." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.Regarding to the dependency illusion, you give an example (upper right subfigure of figure 1) to explain. But it’s unclear from figure 1 how you arrive at the conclusion that \"Even when the time series is randomly shuffled and thus lacks structural dependencies, the decoder of latent diffusion still heavily relies on input observations (instead of the latent variable) for prediction.\" Could you clarify how you determine that the decoder \"heavily\" depends on input observations? Providing a more detailed explanation or additional quantitative evidence would help support this observation.\n\n2.In section 3.1, the definition of posterior collapse seems a general term for all data, not only time series data. In section 3.2, you introduce a dependency measure to demonstrate the occurrence of posterior collapse in time series data. How does this measure specifically address time series data? Would this measure yield the same conclusion if applied to non-time series data?\n\n3.As shown in Figure 4, the dependency of the decoder on the latent variable decreases across both datasets. Although this trend appears improved compared to Figure 2, it would strengthen your findings to compare your method against additional baseline models, rather than only basic latent diffusion.\n\n4.There is a lack of experimental evidence supporting that the proposed dependency measure can accurately assess the impact of the latent variable on the decoder. You should compare your method with other measurement approaches and demonstrate how it outperforms them, providing a more comprehensive validation of its effectiveness.\n\n5.The baselines are not sufficient enough. Only three baselines and all of them are before 2019. Please compare with more the state-of-art works.\n\n6.Reference in this paper seems to be too old. And some of them are repeated. For example, papers in line 573 and line 576 are the same one." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I agree on the importance of posterior collapse issue found by the authors. I am wondering how is the generation performance of time series diffusion model in which latent variables have the same dimension as the observations." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Novelty: It is the first work to discuss the posterior collapse issue for time series latent diffusion. In particular, the paper introduces the novel dependency measure to quantify how the impacts of the latent variables on the generation of the predicting observations, decrease along the time steps. The authors develop a novel framework, which effectively avoid the dependency illusion issue, and outperforms the related time series latent diffusion models in terms of generation quality.\n\nClarity: This work clearly illustrates the posterior collapse and dependency illusion issues by plotting the dependency measures over time steps. The most parts of the analysis are clearly presented, and easy-to-follow.\n\nSignificance: The work demonstrate a significant issue for latent diffusion being applied to capturing time series data. The introduce dependency measure might be used in quantifying the posterior stability of the other related methods, and thus appears to be crucial." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work starts from an analysis on the posterior collapse issue of latent diffusion for capturing time series. In particular, the KL-divergence from the standard Gaussian prior to the latent variable distribution approximated using latent diffusion, may reduce the time series latent diffusion to a VAE model. The authors define a dependency measure, which shows that the influences of the latent variables on decoding the observations, will decrease to zero along the diffusion time steps. In particular, as analyzed in the paper, the decoder built upon recurrent neural nets may decode the current observations only using the past observations, and thus leads to the dependency illusion issues. To address the problems, the paper develops a novel framework, in which the authors remove the KL-divergence regularization that causes the posterior collapse, decode the predicting observations using the latent variable sampled at intermediate diffusion steps, and introduce a novel penalty to avoid dependency illusion issues. The final experiments demonstrate the new framework can effectively avoid posterior collapse, and thus achieves superior generation quality, in comparisions to some SOTA time series latent diffusion methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The final experiments only demonstrate the compared models on only three dataset, using Wasserstein distance to measure between the true and generated time series. Perhaps the experiments could be enhanced by considering more evaluations metrics?" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Conducted a solid impact analysis of posterior collapse for time-series latent diffusion and proposed a new framework that is free from the problem." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024a,\ntitle={A Study of Posterior Stability for Time-Series Latent Diffusion},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2fZ9iOVzpR},\nnote={under review}\n}" }, "abstract": { "value": "Latent diffusion has demonstrated promising results in image generation and permits efficient sampling. However, this framework might suffer from the problem of posterior collapse when applied to time series. In this paper, we first show that posterior collapse will reduce latent diffusion to a variational autoencoder (VAE), making it less expressive. This highlights the importance of addressing this issue. We then introduce a principled method: dependency measure, that quantifies the sensitivity of a recurrent decoder to input variables. Using this tool, we confirm that posterior collapse significantly affects time-series latent diffusion on real datasets, and a phenomenon termed dependency illusion is also discovered in the case of shuffled time series. Finally, building on our theoretical and empirical studies, we introduce a new framework that extends latent diffusion and has a stable posterior. Extensive experiments on multiple real time-series datasets show that our new framework is free from posterior collapse and significantly outperforms previous baselines in time series synthesis." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Latent Diffusion", "Time Series", "Diffusion Models", "Posterior Collapse", "Impact Analysis" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/a907270f26c6262416b21d2cf096b7f589d61e7a.pdf" }, "presentation": null, "primary_area": { "value": "generative models" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "A Study of Posterior Stability for Time-Series Latent Diffusion" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See the weakness section." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper is generally well-written, and the motivation is also clear. It starts with an important problem and proposes a well-motivated solution that requires no finetuning or differential proxy model. \n\n- The paper is clear about how two critical challenges (the soft-value function is both unknown and unnormalized) are addressed by the proposed algorithm." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a method for diffusion models to sample data that is both within target distribution and maximizing some downstream reward function. The problem the paper studies is of great importance, and the method shows empirical effectiveness in some downstream tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The soft value function seems to difficult to approximate in general. Is there any anlysis or justification to quantify the quality of the approximation? How does one know a good approximation is indeed attained? Moreover, how does the approximation quality matter for the generation? More ablation study can improve the paper further.\n\n- Is there any additional computational overhead for the proposed method? Is the approximation to the soft value function costly?\n\n- The performance gain does not seem to be very significant compared to simple baselines, say Best-of-N. From Table 2, Best-of-N baseline is only incrementally worse than the proposed method in molecule-related experiments.\n\n- A minor question: Does the size of the diffusion model affect the performance of SVDD? I will be interested to see how this method works for diffusion models of different size." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. I did not see it mentioned in the manuscript -- how many seeds were used for experiments?\n2. Could the authors provide a discussion of the relation between the consistency of nested SMC and the consequence of using more or less particles in their method?\n3. How expensive is training the soft-value function estimate in SVDD-MC? If it is reasonably long would it be worth adding a fine-tuning based method (e.g., relative trajectory balance https://arxiv.org/abs/2405.20971). On the other hand, if training the soft-value estimate is especially cheap it would be worthwhile to emphasize this more in the manuscript as a benefit of this method compared to direct fine-tuning methods.\n4. Since the goal of SVDD is to sample from the product distribution of reward and pretrained model could they add some metrics evaluating the diversity of their samples or their naturalness (e.g., likelihood under the pre-trained model when available)?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The paper presents a number of strengths, such as\n\n- Presenting a novel application of nested Sequential Monte Carlo to the difficult problem of sampling from the product distribution $p^*(x_0)$ given a pre-trained diffusion model.\n- The method, especially SVDD-PM, provides a particularly efficient method to sample from the product distribution when no differentiable reward is available and the reward function is cheap. The manuscript shows that SVDD can indeed increase the reward over the pre-trained model, offering a compelling option to sample from the target product distribution with little overhead.\n- The problem of cheaply sampling from the product distribution in the presence of non-differentiable rewards is especially significant as existing methods typically require availability of gradients or expensive (typically simulation-based) fine-tuning algorithms. Non-differentiable rewards are often seen in scientific discovery, a target area aptly pointed out by the authors." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces SVDD, a method which aims to sample from the product distribution $p^*(x_0) \\propto p^{pre}_0(x_0) \\exp(R(x_0)/\\alpha)$ for some non-negative reward function $R(x)$, constant $\\alpha \\geq 0$ and pre-trained diffusion model $p^{pre}_t(x_t | x_{t + 1})$.\n\nThe method employs an SMC-inspired procedure for iteratively sampling and reweighting samples according to a soft value function and its corresponding optimal policy. Providing two options to obtain the soft-value function (which is required for the method's importance sampling step), the authors show that SVDD can be used with a cheap approximation based on the diffusion model's denoiser or an amortized version based on regression to a Monte Carlo estimate. The authors evaluate the performance of SVDD on a series of tasks -- images, molecule design, and DNA/RNA design." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Overall I had a some issues regarding clarity of the paper, concerns about sources of bias that are not discussed in the manuscript, and am worried that the experimental section does not paint a fair picture of SVDD's performance relative to baselines. I will discuss each of these in turn\n\n\n\n### Unclear focus of probabilistic inference vs reward maximization\n\nSection 3.2 states that the objective of this paper is to perform probabilistic inference and sample from the target distribution $p^*(x_0) \\propto p^{pre}(x_0)\\exp(R(x_0) / \\alpha)$. However, towards the beginning of the experiment section and throughout the appendix the manuscript begins to say that SVDD is actually meant for reward maximization, not the problem of sampling from $p^*(x_0)$. In particular, the manuscript states that in practice they set $\\alpha = 0$ for all experiments, which corresponds to a constrained reward maximization where $p^*(x_0)$ is a Dirac centered at $x_0^* = \\underset{x_0 \\in Support(p_0^{pre}(x_0))}{\\arg\\max}R(x_0)$. This is quite different from sampling from the $p^*(x_0)$ for any $\\alpha$ and if this is the goal of SVDD it should be compared to baselines which try to do reward maximization.\n\n\n\n### Missing discussion and investigation on bias of soft value function estimates\n\nThe manuscript defines the soft value function as $v_t(x_t) = \\alpha \\log \\mathbb{E}_{x_0 \\sim p^{pre}(x_0 | x_t)}[\\exp(R(x_0) / \\alpha)]$. Next, due to issues with numerical stability for small values of $\\alpha$ the authors make use of an approximation\n\n$v_t(x_t) = \\alpha \\log \\mathbb{E}_{x_0 \\sim p^{pre}(x_0 | x_t)}[\\exp(R(x_0) / \\alpha)]$\n\n$ \\approx \\alpha \\log \\exp(\\mathbb{E}_{x_0 \\sim p^{pre}(x_0 | x_t)}[R(x_0)] / \\alpha)$\n\n$ = \\mathbb{E}_{x_0 \\sim p^{pre}(x_0 | x_t)}[R(x_0)]$\n\nThe second step takes the $\\exp$ function outside of the expectation and as such requires an application of Jensen's inequality, implying that $v_t(x_t) \\geq \\mathbb{E}_{x_0 \\sim p^{pre}(x_0 | x_t)}[R(x_0)]$. This means that the Monte Carlo regression used for SVDD-MC is in fact biased (although consistent), a fact which is not mentioned in the paper.\n\nThe situation is more complicated for SVDD-PM which first applies Jensen's and then another approximation as \n\n$v_t(x_t) \\geq \\mathbb{E}_{x_0 \\sim p^{pre}(x_0 | x_t)}[R(x_0)]$\n\n$ \\approx R(\\mathbb{E}_{x_0 \\sim p^{pre}(x_0 | x_t)}[x_0])$\n\nIt is unclear to me whether the error of the posterior mean estimate can be shown to be bounded as the reward function is potentially non-convex, but would be happy if the authors had some insight into this.\n\nGiven that SVDD requires accurate estimates of the soft-value functions to sample from the target distribution $p^*(x_0)$ I would be more convinced of SVDD's abilities were there a more detailed (potentially including an empirical results) analysis of the bias of the Monte Carlo regression and posterior mean estimates.\n\n\n\n### Issues with inconsistent setting of $\\alpha$ for baselines\n\nThe stated goal of SVDD is to sample from the target distribution $p^*(x_0;\\alpha) \\propto p^{pre}(x_0)\\exp(R(x_0) / \\alpha)$, where the temperature parameter $\\alpha$ controls how peaky $p^*(x_0;\\alpha)$ becomes. As discussed above, as $\\alpha \\rightarrow 0$ the target distribution becomes focused on the maximizer of the reward which is in the support of the pretrained model such that \n\n$\\mathbb{E}_{x \\sim p^* (x;0^+)}[R(x)] = \\underset{x_0 \\in Support(p^{pre}(x_0))}{\\arg\\max}R(x_0)$. \n\nIn general, as the value of $\\alpha$ is decreased the expected reward under the target distribution should increase. As such, comparing the distribution of rewards of generated samples for methods using different values of $\\alpha$ does not paint an accurate picture of each method's performance as one method having a higher quantile reward may simply be a consequence of the setting of $\\alpha$. \n\nUnfortunately, the manuscript's experiments use significantly different values of $\\alpha$ for its method and baselines while using the reward at different quantiles as the main performance metric. This is more problematic as the value of $\\alpha$ for their method is set to $0$ (where the true expected reward is the maximum reward value) and a larger value of $\\alpha$ for baselines. Because the value of $\\alpha$ is not set to be equal for SVDD and all baselines I do not believe that the experimental results in Table 2 paint a fair picture of SVDD's performance.\n\n\n\n### Overall comments\n\nI generally have concerns with the settings of either the number of particles $M$ being too small or the bias of the soft-value function estimates being too large. As far as I understand (and perhaps I am missing something!) by setting $\\alpha=0$ for SVDD in the experiment section the method _should_ be suffering from mode collapse and generating very high reward samples as the target distribution $p^*(x_0)$ is a Dirac centered at $x_0^* = \\underset{x \\in Support(p_0^{pre}(x))}{\\arg\\max}R(x)$. However, samples from SVDD do not exhibit this expected mode collapse, which seems to indicate that either many more particles $M$ need to be used or the bias from the value function estimation is preventing the algorithm from properly sampling from the target distribution.\n\nI note that the main reason for my score mostly due to the issue with inconsistent setting of $\\alpha$ for SVDD and baselines in the experiments section as well as the missing discussion. A missing discussion/analysis of the bias of the value function estimates and their impact on SVDD's ability to sample from the target distribution also contributes significantly to my score." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "N/A" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- Is there a typo under the first equation in Section $4.1$, where the expectation is induced by $p_t^{pre}(\\cdot | x_{t-1}) -$ note the negative instead of positive sign." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The authors provide a widely applicable method that can be applied both to discrete and continuous diffusion settings.\n- The proposed method, unlike previous guidance algorithms, does not rely on an explicitly trained conditional diffusion model (eg. for classifier free guidance), or on differentiable reward terms (eg. classifier based guidance).\n- Results on both image and scientific domains highlight the benefits of the approach towards controlled generation of objects with high downstream reward, as intended.\n- The work also conducts experiments in a wide variety of domains, ranging from images, molecules and DNA." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work provides a unified framework of guidance in diffusion models, both discrete and continuous, with minimal additional training and applicability in domains where a downstream reward might not even be differentiable. The proposed method SVDD (MC and PM) is applicable in discrete diffusion where a continuous gradient of energy cannot be directly added to the discrete state space, as well as cases where the reward is non differentiable which is the case in a lot of scientific domains. The work tackles an important problem in the scientific domain and leads to controllable generation without having to fine-tune large scaled models. Their results show that generations from SVDD lead to higher downstream rewards than the baselines considered." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The authors consider setting $\\alpha=0$ in their experiments. However, prior work highlights that setting $\\alpha=0$ leads to over-optimization and increasingly reduces the diversity and realistic nature of the samples. Could the authors provide clarity on why this is not a problem in their setup?\n- The work relies on two major assumptions (one for SVDD-MC and the other for SVDD-PM), which are neither well motivated theoretically nor are there any details provided about it. \n- **Assumption about SVDD-MC**: The authors replace the logarithm of expectation with the expectation of logarithm in their quantity of interest, which in reality is only a bound on the actual quantity. Could the authors consider experimenting on some synthetic domain to describe the bias and variance caused by this approximation? When is this approximation reasonable and when would it be extremely incorrect?\n- **Assumption about SVDD-PM**: This algorithm combines the above approximation with pushing the expectation inside the reward function $r(\\cdot)$. As with above, could the authors conduct experiments on synthetic domains and highlight when and where such an assumption is reasonable, and when is it violated?\n- While the approach leads to generation of samples with high reward, the authors do not provide any kind of metrics that test for diversity of the samples generated." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "I would appreciate it if the authors could address my theoretical concerns regarding 1.) what is the optimal distribution hit by SVDD 2.) what is the bias introduced in the MC estimate and 3.) the actual computational cost of SVDD-MC.\n\nIn addition, I would appreciate it if the authors could carry out the additional experiments I have suggested with added diversity quantification." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper tackles a timely problem in considering fine-tuning diffusion models. Moreover, the suggested approach of SVDD-PM enjoys being computationally cheap to use as it does not require any further training while both SVDD-MC and SVDD-PM are applicable in settings where the reward function is non-differentiable. This is impactful because this unlocks a lot of potential application domains that have black-box rewards where learning a surrogate reward model is non-trivial. Finally, the paper considers a diverse set of experimental settings to showcase the universality of the proposed approach." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces SVDD which is a new method to fine-tune diffusion models in both continuous and discrete spaces. SVDD-MC learns a soft value function by regression to $x_t$ while SVDD-PM exploits the posterior mean parametrization of masked diffusion models to estimate the soft value function directly. Given a soft value function, SVDD can be applied to a general class of reward functions, including non-differentiable ones, at inference time without further fine-tuning. This can be seen as a variation of the famous Sequential Monte-Carlo algorithm but applied and modified for diffusion models. Experiments are done on images, molecules, and docking and show improvements in fine-tuning performance under a specified reward metric." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "While the paper has some notable strengths there are a few lingering questions that point to potential weaknesses. I will try to list them below.\n\n**Theoretical weaknesses**\n\nTwo main questions arise when looking at the setup. The first one is the actual target distribution and whether SVDD hits it. In the continuous setting, I have severe doubts about whether the correct terminal distribution is reached due to the initial value function bias problem as introduced in Adjoint Matching (Domingo-Enrich et. al 2024). Certainly, nothing in the current theory suggests the process during fine-tuning is memoryless. Moreover, it is unclear what ramifications and bias we introduce in the MC setup when the regression is done using $r(x_0) \\to x_t$ as opposed to the more numerically unstable soft value function. For example, I believe when you remove the $\\exp$ from your regression target, this is a based value function but there is no discussion on this point outside of the fact that it is less numerically stable. As a result, I am dubious about the claims made about hitting the correct target distribution.\n\n\nAnother question is the connection to Sequential Monte Carlo. There is a discussion on this in the paper but I think it's not accurate enough. I disagree with the statement made in the paper. The algorithm you propose is quite literally SMC but adapted to reward maximization, there is even a resampling step which is exactly what is done in SMC. The arguments that SMC is done over a batch are lukewarm. There is nothing wrong with demonstrating that SMC can be effectively applied to sampling from discrete diffusion---like analogously done for an autoregressive model by Zhao et. al 2024---and this is a valuable contribution. I suggest the authors be a bit more forthright with their claims as I would buy it a lot more. In fact, with the right framing, you achieve novelty by showing how SMC applies to a newer more interesting problem domain.\n\n**Additional Technical weaknesses**\n\nOne of the main selling points of SVDD is the fact that it is supposed to be a cheap inference time algorithm. This I believe is not quite true because of the need to estimate the soft value function in SVDD-MC. Indeed, one must estimate the soft value function using rollouts which I believe adds a heavy pre-processing step. I also did not see SVDD-MC in the ablation studies about computational cost---likely because it's significantly more expensive than SVDD-PM. Thus, I believe the main claim for SVDD-MC being a lightweight method is a bit misleading. Of course, if you had the perfect estimated value function then inference scales as indicated in the plot for 3c,d but this is not the full picture.\n\n**Experimental weaknesses**\n\nA glaring missing baseline is Relative Trajectory Balance (Venkataraman et. al 2024) which does fine-tuning exactly like this paper considers for both discrete and continuous diffusion models. I kindly request the authors to consider adding this important baseline. Moreover, it is a bit surprising that there is no text experiment given the heavy emphasis on using Masked Diffusion Models which have primarily been introduced for text. I would be encouraged to see a text experiment---perhaps of a similar scale to Zhao et. al 2024---to highlight that SVDD can be applied in the text setting.\n\nThe current experimental findings in Table 2 are not complete as they do not show other important aspects of the generated sample. They simply show that reward is maximized but this could also happen through gamification of the reward function. For instance, I would appreciate the authors providing sample-based diversity metrics to quantify how bad the drop in diversity is among the baselines. At the very minimum, FID scores for images should be provided and I'll let the authors determine appropriate diversity metrics for the other domains to complement the findings in Table 2.\n\n**Closing remarks**\n\nHaving said all of these weaknesses, I will note that I am open to significantly raising my score if **all of my concerns** are adequately addressed to my level of satisfaction. I will also state that I did not read the appendix so if I have missed something I would appreciate a pointer to the result there.\n\nI encourage the authors in their rebuttal endeavors and I hope they can strengthen the paper which I would like to eventually recommend for acceptance but not in its current state.\n\n\n**References**\n\nVenkatraman, Siddarth, et al. \"Amortizing intractable inference in diffusion models for vision, language, and control.\" arXiv preprint arXiv:2405.20971 (2024).\n\nZhao, Stephen, et al. \"Probabilistic inference in language models via twisted sequential monte carlo.\" arXiv preprint arXiv:2404.17546 (2024)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "I do not have further questions." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "There are several advantages of SVDD over previous methods:\n1. No need for differentiable proxy models\n2. No fine-tuning required\n3. Works with both continuous and discrete spaces\n5. Maintains better sample diversity compared to other approaches\n\nThe writing of this paper is clear." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a new method called Soft Value-based Decoding in Diffusion models (SVDD) for optimizing diffusion models to generate samples with desired properties while maintaining naturalness. The contributions include:\n- SVDD is an inference-time technique that doesn't require fine-tuning the original diffusion model\n- Can work with non-differentiable reward functions, unlike previous methods that required differentiable proxy models\n- Applicable to both continuous and discrete diffusion models in a unified way\n\nThe algorithm works in the following way:\n1. Uses \"soft value functions\" that predict future rewards from intermediate noisy states\n2. At each denoising step:\n - Generates multiple samples using the pre-trained model\n - Selects samples based on their predicted value\nThere are two variants:\n - SVDD-MC: Uses Monte Carlo regression to learn value functions\n - SVDD-PM: Directly uses reward feedback without additional training\n\nExperimental Results span across multiple domains: image generation, molecule generation, DNA/RNA sequence generation. The proposed method consistently outperformed baseline methods while maintaining sample validity. \nThe paper demonstrates that SVDD provides an effective way to guide diffusion models toward desired properties while preserving the natural characteristics learned during pre-training." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The main weakness is about novelty. To be more specific, I can not see significant difference with twisted SMC methods (e.g., the papers mentioned in Sec 6 and App B). In the writing I see two differences claimed by the authors:\n1. In previous works such as Wu et al., the reward is a classifier; while here it is \"reward maximization\" setting. \n\nFirst, I think the setting in this work should not be called \"reward maximization\" but be called \"alignment\" or \"reward sampling\" or similar names, due to the reasons in Sec 3.2 \"HIGH REWARDS WHILE PRESERVING NATURALNESS\". Second, whether the reward function is a classifier is not critical, as even for an usual reward r(x), we can understand it as an unnormalized probability prob(optimality | x).\n\n2. \"SMC methods involve resampling across the “entire” batch, which complicates parallelization. Additionally, when batch sizes are small, as is often the case with recent large diffusion model\"\n\nI do not quite understand this part. I may miss the difference between SVDD and twisted SMC methods. Does the batch size mean the number of particles in SMC? It will be good if there could be a clarification." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Derivative-free, training-free, fine-tuning-free reward optimization algorithm in diffusion models" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024derivativefree,\ntitle={Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2fgzf8u5fP},\nnote={under review}\n}" }, "abstract": { "value": "Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences. However, rather than merely generating designs that are natural, we often aim to optimize downstream reward functions while preserving the naturalness of these design spaces. Existing methods for achieving this goal often require differentiable proxy models (e.g., classifier guidance or DPS) or involve computationally expensive fine-tuning of diffusion models (e.g., classifier-free guidance, RL-based fine-tuning). In our work, we propose a new method to address these challenges. Our algorithm is an iterative sampling method that integrates soft value functions, which looks ahead to how intermediate noisy states lead to high rewards in the future, into the standard inference procedure of pre-trained diffusion models. Notably, our approach avoids fine-tuning generative models and eliminates the need to construct differentiable models. This enables us to (1) directly utilize non-differentiable features/reward feedback, commonly used in many scientific domains, and (2) apply our method to recent discrete diffusion models in a principled way. Finally, we demonstrate the effectiveness of our algorithm across several domains, including image generation, molecule generation, and DNA/RNA sequence generation." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Diffusion models", "Reinforcement learning", "AI for science" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/c64d8504b9afdf5f8263f4ac994b47c05d99e298.pdf" }, "presentation": null, "primary_area": { "value": "generative models" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/9945e26857e3d3afdb7b77aa6d8ab7287d8283fb.pdf" }, "title": { "value": "Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I could not find an ablation study on the number of clusters K vs model performance, have you done these experiments?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper is well written and proposes a justified solution to address the context length issue for in-context learning models such as TabPFN. Authors conduct extensive experiments on many real world dataset to demonstrate the effectiveness of the proposed approach and compare with leading tree-based and deep learning tabular methods." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a mixture of experts approach for in-context learning on tabular data. Each expert in the mixture is a K-means cluster and the model routes the input instance to the closest cluster. This addresses the problem of context size in large datasets and provides a better selection of prompt instance than random sampling. To adapt the model to this type of routing authors also propose fine tuning by selecting a cluster of each training instance and maximizing the likelihood." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "There is a very related previous work \"Retrieval & Fine-Tuning for In-Context Tabular Models\" by Thomas et al, which proposes both nearest neighbor retrieval to improve the prompt and fine tuning with this approach to adapt the model to the target distribution. I think the authors have to compare with this work and highlight what is novel in MixturePFN." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See weaknesses.\n\nCan categorical features simply be encoded as ordinal features? Is that not implying false relationships between unordered elements?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The MICP strategy effectively reduces memory usage, allowing the model to handle larger datasets compared to existing TabPFN\n- CAPFN bootstrapping and finetuning approach appears to be an effective way to mitigate distribution shift ICL for tabular data\n- Extensive benchmarks against 19 strong baselines show good performance in both mean rank and Condorcet ranking across diverse datasets" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes the MixturePFN framework, which extends TabPFN for large tabular datasets by addressing the performance and scalability limitations of the number of table rows. The authors propose:\n1. Mixture of In-Context Prompters (MICP), which optimizes inference by using a sparse mixture of experts to route test samples to specific \"prompters\" that create context-specific prompts to separate large training datasets into manageable clusters. \n2. Context-Aware Finetuning (CAPFN), which addresses distributional shift issues by specializing each prompter on its assigned\ncontext via parameter efficient finetuning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- While MIXTUREPFN improves dataset scalability, it still struggles with feature-rich datasets, potentially limiting its applicability in domains with high-dimensional data, such as patient healthcare data. I realize the authors leave this to future work, but this is an area where simple XGBoost performs quite well, and I would be curious about their thoughts on tackling this issue.\n\n- MICP's reliance on K-Means clustering to segment data into meaningful clusters as the quality of clusters can vary significantly based on dataset properties / distance metric chosen. Poor clustering could lead to suboptimal routing and ineffective prompts for certain test samples. I'd be curious to see some ablations in this area.\n\n- The CAPFN bootstrapping method might introduce biases or overfitting if the sampled subsets are not representative of the entire dataset. Bootstrapping from small clusters may fail to capture enough diversity, especially in cases with imbalanced classes or rare features. I'd be also curious to see how this method works with highly imbalanced labels e.g. 1\\% positive." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Can you provide a comparison with LoCalPFN [1]? If not possible, I think the comparison should be done using k neighbor samples rather than random sampling, at least for TabPFN*.\n\n2. I see that the authors say in the limitations section that they didn't do it on a dataset with a million samples, but I'm somewhat curious about the effectiveness of MixturePFN on a dataset with a million samples, since the paper is aimed at the scale-up aspect.\n\n3. I'm also curious about the effectiveness of MixturePFN on datasets with hundreds or thousands of features, which is very practical in the real world.\n\n----\n[1] Thomas et al., Retrieval & Fine-Tuning for In-Context Tabular Models, NeurIPS 2024" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The idea of Mixture of Experts blending into TabPFN seems novel.\n\n2. The effectiveness of MixturePFN is well evaluated in well-established benchmarks against a variety of baseline methods.\n\n3. Writing is easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors propose MixturePFN, an extension of Sparse Mixture of Experts to TabPFN to alleviate the context size limitations of the existing TabPFN. On the TabZilla benchmark, MixturePFN outperforms state-of-the-art tabular prediction models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The biggest weakness I think is that the paper is missing a comparison with LoCalPFN [1]. Since LoCalPFN also tries to make TabPFN effective even on datasets with many-shots, I think it should be mentioned in the paper.\n\n----\n[1] Thomas et al., Retrieval & Fine-Tuning for In-Context Tabular Models, NeurIPS 2024" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a mixture of prompters technique for tabular in-context learning." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024mixture,\ntitle={Mixture of In-Context Prompters for Tabular {PFN}s},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2fojNANZSv},\nnote={under review}\n}" }, "abstract": { "value": "Recent benchmarks find In-Context Learning (ICL) outperforms both deep learning and tree-based algorithms on small tabular datasets. However, on larger datasets, ICL for tabular learning suffers in both efficiency and effectiveness. In terms of efficiency, transformers incur linear space and quadratic time complexity w.r.t. context size. In terms of effectiveness, contexts at inference encounter distribution shift compared to contexts from pretraining. We propose MixturePFN, which extends Sparse Mixture of Experts to the state-of-the-art ICL for tabular learning model. Specifically, MixturePFN finetunes a specialized ICL expert on each cluster of tabular data and routes new test samples to appropriate experts at inference. MixturePFN supports constant-size contexts by splitting large training datasets into more manageable clusters. MixturePFN addresses distribution shift by finetuning an expert on each training dataset cluster via bootstrapping. Extensive experimental results shows MixturePFN outperforms 19 baselines both in mean rank and as the Condorcet winner across 36 diverse tabular datasets under both accuracy and F1 score with statistical significance." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Prior-Fitted Networks", "Tabular Learning", "Sparse Mixture of Experts." ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/b8bc18fbc85f957a3ad23209a3f6725d57fff7ae.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Mixture of In-Context Prompters for Tabular PFNs" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- Could the authors elaborate on why GIT-STORM occasionally does not surpass STORM and the conditions where improvements are only minor? Understanding this would clarify the contextual efficacy of the MaskGIT prior.\n- Regarding the reported state-of-the-art claim, Table 6 suggests that DrQ-v2 outperforms GIT-STORM in some highlighted environments. Could the authors comment why they claim GIT-STORM provides SOTA results on these? It is not the case, right?\n- What is the rationale for improving STORM over directly utilizing DreamerV3, which appears to perform better in many scenarios? Or put differently: why would one care to improve STORM with the proposed modifications when there is DreamerV3 and I could just use it or improve over DreamerV3? \n\nI am open to increase my score once there is clarity on these questions." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The empirical evaluation spans discrete and continuous action benchmarks, providing a robust assessment of GIT-STORM’s performance. The reported results demonstrate that GIT-STORM not only improves sample efficiency in RL tasks but also enhances video prediction quality, particularly in the Atari 100k benchmark, aligning well with the study's objectives. Moreover, the paper is well-written with a clear structure, providing a good experience as a reader. Extending the transformer-based world models to continuous action tasks also poses a sufficient novelty and broadens the utility of these models in RL and video prediction applications." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper, Masked Generative Priors Improve World Models Sequence Modelling Capabilities, introduces GIT-STORM, an extension of the STORM architecture, incorporating MaskGIT as a dynamics prior to enhance sequence modeling in world models. The authors address two main gaps in previous research: the limitation of transformer-based world models in continuous action environments and the inadequacies of prior methods, like STORM, in capturing effective state representations. Through experiments on Atari 100k (discrete) and DeepMind Control Suite (continuous), GIT-STORM demonstrates improvements in RL and video prediction, suggesting that Masked Generative Priors could be a powerful inductive bias for world models, supporting broader applicability across diverse RL tasks and environments." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "It remains unclear why GIT-STORM does not consistently outperform STORM across all benchmarks or why it fails to close the performance gap with DreamerV3 in environments beyond Atari 100k. The paper does not fully explain the conditions under which GIT-STORM’s improvements are more marginal, suggesting a need for clearer insights into the impact of individual architectural components.\n\nThe paper claims state-of-the-art results for GIT-STORM on select environments, yet Table 6 seems to indicate that DrQ-v2 outperforms GIT-STORM on two environments (where the authors claim they are better?). Clarifying the conditions under which GIT-STORM achieves these results or adjusting the claim would help ensure consistency and accuracy in presenting the model's achievements.\n\nThe proposed approach for handling continuous action spaces is promising, yet lacks a comprehensive empirical analysis. Additional studies on more diverse continuous control tasks could provide stronger validation of the state mixer function's effectiveness and the broader applicability of the model in continuous settings. Most importantly, the modifications from STORM to GIT-STORM are extensive, involving MaskGIT, state mixer, policy adjustments from DreamerV3, and an observation module from STORM. The compounded modifications make it difficult to discern the exact contribution of each component to the reported performance improvements. A more focused ablation study could is required to isolate the impact of each modification." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- Results on the Freeway task have very high variance according to Figure 10. How many out of the five runs does GIT-STORM actually achieve non-zero performance? \n- The most challenging aspect of learning the Freeway task is obtaining the first successful trajectory, which I believe is more related to the exploration strategy than state predictions, given the sparse rewards. How does GIT-STORM assist the agent in exploring more efficiently? Is this strategy stable, or are the successful trajectories obtained by random seeds?\n- Why would the pendulum swingup task fail for both STORM and GIT-STORM? DreamerV2, DreamerV3 and TransDreamer can learn this task fairly easily.\n- The experiment results in Table 5 and Figure 10 appear inconsistent. For instance, the Gopher score reported in Table 5 is 8562, but the last point in Figure 10 shows a performance of around 2500. Do these two results use different metrics?\n- Could you add the learning curves of STORM or DreamerV3 to Figure 10 for a better comparison, considering that you have reproduced these results?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The motivation of incorporating a MaskGIT prior into the STORM architecture is clear.\n- The proposed method is straightforward and easy to reproduce.\n- MaskGIT can effectively improve the video prediction quality of STORM, indicating applicability of GIT-STORM to more complicated tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces GIT-STORM, which incorporates three modifications to the base algorithm STORM: a MaskGIT prior that replaces the MLP dynamics head, a draft-and-revise decoding scheme for enhanced consistency, and a state mixer for continuous action environments. Experimental results demonstrate that GIT-STORM surpasses STORM on both Atari 100K and DMC benchmarks. Video prediction results indicate that this improvement is attributed to more accurate representations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The paper contains a misstatement in its contributions. The authors claim that they \"apply transformer-based world models to continuous action environments for the first time\". This claim is inaccurate, as TransDreamer[1] can also be applied to continuous action environments. The authors are evidently aware of this paper, given that they have cited it in this work.\n- The state-mixer design is not properly addressed. If the authors claim this part of their contribution, they should either elaborate on the design, or provide empirical results to show the superiority of this method. Based on the overlapping tasks, TransDreamer appears to have better performance than GIT-STORM+state-mixer on the continuous control benchmark DMC.\n- The experimental results in Atari 100K only demonstrate marginal improvement. The gain over STORM seems to primarily originate from the gopher task alone, which contains inconsistent results, as detailed in the questions section.\n\n[1] Chen et al. TransDreamer: Reinforcement Learning with Transformer World Models." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. On lines 307-309, I think STORM uses KV caching in both the conditioning phase and the imagination phase, see [here](https://github.com/weipu-zhang/STORM/blob/e0b3fd44320d7e213ec905c673ad3f35b61b89f4/sub_models/world_models.py#L363). The `predict_next()` uses `forward_with_kv_cache()` for decoding.\n\n2. Missing comma on line 214?\n\n3. What's new in the proposed state mixer compared to the STORM's action mixer?\n\n4. `Freeway` is a hard exploration environment, as the agent has to repeat the `up` operation many times to get a first reward, which is a rare event for a random policy. Without the first reward, the value space is all zero and the policy would be further optimized as a uniform random policy. STORM, IRIS, and DIAMOND have different tricks that can mitigate such an issue. But what is the underlying reason for GIT-STORM to reach a non-zero result? I think this is not related to the improved decoding or world modelling quality since DreamerV3 and STORM (w/o traj) could also produce a nearly perfect reconstruction and prediction on `Freeway`.\n\n5. For the `Quadruped Run` in Figure 6, I wonder if it's too small (compared to Figure 4 in [DreamerV3](https://arxiv.org/pdf/2301.04104)).\n\n6. Lines 529-530, \"Replacing...\", the order is reversed." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. This paper clearly distinguishes itself from previous work, with good comparison and illustration.\n\n2. One-hot categorical latent is widely used in recent model-based RL, yet the research on it is insufficient. This paper provides a novel view of it.\n\n3. This paper bridges the gap of the lack of evaluation of transformer-based world models on continuous control tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed to replace the MLP head with MaskGIT prior in STORM to achieve a higher quality of latent generation, and therefore achieve better performance on the Atari100k benchmark.\nThis paper also bridges the gap of the lack of evaluation of transformer-based world models on continuous control tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The motivation and effect of using MaskGIT head in world models are unclear.\nIs there any evidence that the world models would have hallucinations, and how could a MaskGIT head mitigate such issues?\nHow to distinguish if the improved performance (both RL and FVD) comes from more parameters or the MaskGIT prior?\n\n There should be some further investigation into the mechanism of the MaskGIT head. Such as:\n\n (a) What's the difference between the latent variables (or distributions) generated with the MLP head and MaskGIT head?\n\n (b) This MaskGIT head looks like a skip/residual connection from $z_{t}$ to $\\hat{z}_{t+1}$, would this reduce the KL divergence in the training or imagination?\n\n (c) These are sample questions. An investigation like this would improve the soundness and contribution of this paper.\n\n2. Section 2.1 could be more concise, as these are not quite related to the key contributions and are frequently repeated in each of the model-based RL papers." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "There are also some minor questions:\n\n1. In Line 309, why KV cache can improve sample efficiency? Do you mean computational efficiency?\n2. To my knowledge, perplexity is a metric whose lower values mean better. However, in Table 3, higher perplexity is marked as better.\n3. In Figure 6, the quadruped agents are too small in the images. This work seems to have used an unusual camera setting for these tasks.\n\nIf the authors well address my concerns, I am willing to improve my rating." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "To my knowledge, MaskGIT models, with their strong expressiveness, are not yet utilized for world models in the MBRL community." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes GIT-STORM, which utilizes a MaskGIT model instead of an MLP for the prior head in world models (based on STORM). It also makes minor modifications (a state mixer) to support continuous actions. Experiments are done on Atari100k and DMC benchmarks, considering both policy learning and video prediction performance. GIT-STORM outperforms its base method STORM." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The illustration and descriptions of the model are confusing. Can authors provide more insights for their specific designs?\n - In Figure 1 (left), it seems that GIT-STORM uses masked $z_t$ as inputs for reconstructing $z_{t+1}$. This is strange since, in the original MaskGIT, we mask and reconstruct masked target tokens. Similarly, I think it is more reasonable to mask $z_{t+1}$ as inputs. \n - In Figure 1 (left), there is no $\\xi_t$ but only $\\eta_t$. \n - Also, the dot product seems to be a commonly used trick that ties weights for embedding and linear layer before Softmax. If so, relevant literature should be cited.\n - The Draft-and-Revise decoding scheme, if not proposed by this work, should be moved into a preliminary section. \n2. The contribution to supporting continuous actions is overclaimed (as 'for the first time'). In fact, concatenating or summating continuous inputs with hidden states is a too straightforward approach in current VLA models (e.g., OpenVLA for inputting continuous visual representations) and action-conditioned video prediction models (e.g., iVideoGPT for inputting continuous actions).\n3. The performance of GIT-STORM on DMC is outperformed by its base method, DreamerV3." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024masked,\ntitle={Masked Generative Priors Improve World Models Sequence Modelling Capabilities},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2gTEW29qsM},\nnote={under review}\n}" }, "abstract": { "value": "Deep Reinforcement Learning (RL) has become the leading approach for creating artificial agents in complex environments. Model-based approaches, which are RL methods with world models that predict environment dynamics, are among the most promising directions for improving data efficiency, forming a critical step toward bridging the gap between research and real-world deployment. In particular, world models enhance sample efficiency by learning in imagination, which involves training a generative sequence model of the environment in a self-supervised manner.\nRecently, Masked Generative Modelling has emerged as a more efficient and superior inductive bias for modelling and generating token sequences. Building on the Efficient Stochastic Transformer-based World Models (STORM) architecture, we replace the traditional MLP prior with a Masked Generative Prior (e.g., MaskGIT Prior) and introduce GIT-STORM.\nWe evaluate our model on two downstream tasks: reinforcement learning and video prediction. GIT-STORM demonstrates substantial performance gains in RL tasks on the Atari 100k benchmark.\nMoreover, we apply Transformer-based World Models to continuous action environments for the first time, addressing a significant gap in prior research. To achieve this, we employ a state mixer function that integrates latent state representations with actions, enabling our model to handle continuous control tasks. We validate this approach through qualitative and quantitative analyses on the DeepMind Control Suite, showcasing the effectiveness of Transformer-based World Models in this new domain.\nOur results highlight the versatility and efficacy of the MaskGIT dynamics prior, paving the way for more accurate world models and effective RL policies." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "World Modeling", "Model based RL" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/eebe05e57972961f46f1ee28caf39e904ad203f4.pdf" }, "presentation": null, "primary_area": { "value": "reinforcement learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Masked Generative Priors Improve World Models Sequence Modelling Capabilities" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Table 1, the degradation is more severe for Stage 2 is also a factoid dataset. Do you have any explanation? Also, there is big drop when using GSK8k. It will be very insightful to understand the interplays of the datasets.\n\nFor the Replay approach, what if we use a ratio = 1.0?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The papers present problems and solution pretty clear and easy to follows.\nThe authors proposed a simple yet effect way to reduce the interference among the different fine-tuning datasets." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studied the forgetting issues when finetuning a LLM in multi-stage datasets. They focus the setting of continual memorizing of factoid facts - Stage 1 is factoid fact datasets and Stage 2 finetune with fact/non-fact datasets. The authors find non-fact datasets will cause smaller drop. Based on this intuition, the authors proposed a data mixing strategy (introducing some unrelated datasets) in multi-stage fine-tuning to reduce the forgetting." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "There are some other method to reduce the interference among the datasets of stage 1 and stage 2. For example, the method needs to compare with another baseline, i.e. \"mixing of Data A and Data B\"" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "- The authors show that their method causes the model to store factoids in more layers of the model, which presumably means that the factoids overwrite previous data in these shallower layers. It would have been interesting to investigate whether this results in any significant degradation of other model capabilities (e.g. fluency) compared to the basic two-stage training process. I understand that this paper specifically focuses on factoid memorization and contains many experiments already, but this could be mentioned as future work.\n\n- Another interesting experiment would be to vary either the model or the dataset's size, to evaluate the link between model capacity and the efficacy of REMIX/replay techniques. Do the authors have any insight or early intuition regarding this?\n\n- Have the authors considered/tried combining REMIX with classic replay techniques? This seems like a natural next step to know whether the use of both methods leads to even better results." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "- The paper is very well-written and clear. The section order feels natural, and the reasoning and intuition for each idea are given clearly. The tables and charts summarize the data well, and while informative, the text flows well and is not overly complicated. As a result, this is a very pleasant paper to read. In addition, the references are recent and seem relevant.\n\n- The paper touches upon the issue of catastrophic forgetting of factoids in LLMs, which is a relevant and unsolved issue, especially in the current context where many pre-trained LLMs showcase good reasoning capabilities but cannot easily be updated afterward to store novel world knowledge.\n\n- The paper contains a large number of experiments, that give clear motivation for introducing REMIX, and then show its efficacy over many settings.\n\n- The ideas found in this work are not revolutionary per se (which is not to say that they lack originality; see my next point), but the execution is straightforward and good. The authors carefully checked for important details such as dataset overlap.\n\n- The idea of mixing generic/random data with the training dataset is quite creative and original. Despite being counterintuitive, the authors justify this idea mathematically.\n\nAs a result, I recommend this paper for publication with no major point of criticism." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work focuses on the continual memorization setting in LLMs, a subsetting of continual learning in which a model is first fine-tuned on a factoid dataset, then successively fine-tuned on other datasets (multiple training stages) and must retain knowledge learned during the first stage. The authors first demonstrate that catastrophic forgetting occurs in a 2-stage training process, especially if the dataset from the second stage is a factoid one, and that usual replay methods used in continual learning do not satisfactorily mitigate the issue.\n\nThe authors then introduce REMIX, a strategy for preventing forgetting in the multi-stage learning process. In this strategy, additional training data is added to one or both of the training stages. This data takes the form of either generic of random data. The authors show that this new method produces significantly better result than the basic training process on LLaMa-3 and Mistral-7B, which they show to be linked to a more diversified and larger set of layers in which factoids are stored.\n\nFinally, the authors perform a large number of validation experiments, proving that this method is effective with different mixing datasets, and investigate the effect of several other hyperparameters such as the mixing sequence length, the mixing ratio and the number of training stages." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Many of the points of criticism I had while reading this paper were answered later on, or in the appendices. The other points that I have mainly consist of questions (see section below).\n\n- In section 4.2, the word \"Figure\" is used several times instead of \"Table\".\n- Section 3.2 (on replay) is lacking detail in comparison to other sections, especially as it justifies the use of REMIX compared to other replay methods. In particular, I could not find which of the two LLMs was used to measure the effect of replay methods." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Suggestions\n- I would like to suggest the authors the strengthen the motivation of needing to memorize long-tail knowledge through the form of factoids, but showing that it transfers the knowledge itself to downstream NLP tasks that require integrating those long-tail information. Simply getting a high score in the factoid task itself is insufficient to motivate the problem formulation.\n- I would suggest that the authors include more baselines from the continual learning literature that can mitigate the forgetting of previously learned knowledge." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Originality: The paper draws attention to the issue of continual memorization of long-tail information through factoid memorization.\nQuality: The experiments are conducted rigorously, covering a range of datasets and demonstrating REMIX’s impact across several configurations.\nClarity: Explanations are mostly clear, and the figures help illustrate key points. \nSignificance: The method has some practical relevance for fact retention." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper tackles the issue of continual memorization of factoids in large language models (LLMs), focusing on retaining specific, rare knowledge (factoids) as the model undergoes further training on unrelated datasets. Typical replay techniques fail to prevent forgetting of such factoids in LLMs, leading the authors to propose REMIX, a data-mixing approach that interleaves random or generic data during training stages to reduce forgetting. The paper demonstrates that REMIX helps preserve factoid knowledge across various datasets and training scenarios, with results analyzed using tools like Logit Lens." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Unclear Problem Motivation: The paper does not convincingly explain why memorizing long-tail knowledge in the form of factoids is important in practical applications. Without a clear motivation, the relevance of the problem formulation is uncertain, which diminishes the contribution’s significance. If we are only concerned about factoids, why use LLMs in the first place? Why not just use traditional knowledge-based systems? The authors should show how memorizing factoids leads to downstream applications, such as utilizing the information from the factoids on tasks that specifically require LLMs.\nLack of Novelty: REMIX lacks sufficient originality; the idea of mixing generic data into training is not groundbreaking and does not specifically address the unique challenges of factoid memorization. \nLack of Baselines: The authors only explore experience replay as the baseline approaches, whereas there exists other methods in literature that can mitigate forgetting during continued pretraining (parameter expansion-based methods, regularization methods, etc.)" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1 Comparison with Other Forgetting Mitigation Techniques:\nHow does REMIX compare with other established forgetting mitigation methods across different tasks? A systematic comparison would strengthen the case for REMIX’s advantages.\n2 Exploration of Bidirectional Relational Memory with Atomic Fact Datasets:\nThe datasets used appear to consist mainly of isolated factoids or \"atomic\" facts, without directly exploring bidirectional or inverse relational memory. For example, if the model learns that \"Bob’s dad is Sam,\" it would be valuable to evaluate whether the model can infer the inverse relationship, such as \"Who is Sam's son?\" This type of associative memory is essential for comprehensive fact retention, as it reflects a more integrated understanding of relationships. Could the authors clarify whether such tests were conducted, or suggest if REMIX could potentially extend to this type of bidirectional memorization?\n3 Why Forgetting is More Pronounced with Factoid Datasets:\nThe paper reports that models experience significant forgetting when fine-tuned on factoid datasets in the second stage, but not on non-factoid datasets. Could the authors elaborate on why forgetting is more pronounced with factoids compared to non-factoids, as well as any observed differences in how REMIX performs on these types? This could provide further insight into the underlying mechanisms of forgetting and the strengths of REMIX.\n4 Rationale Behind Data Mixing Types:\nThe paper employs various data sources (e.g., Knowledge Pile, random word sequences) as mixed data in REMIX. However, the choice of these sources appears empirical, lacking theoretical justification or detailed explanation. It remains unclear why certain data sources yield better performance on specific tasks, and this potential variation across tasks is not fully explored. There is no clear guideline for selecting mixed data types, nor an analysis of how different types of mixed data impact task performance. A more thorough theoretical or empirical examination of these differences could enhance understanding of REMIX’s applicability and effectiveness across various contexts.\n5 Impact of REMIX on New Task Performance:\nThe paper focuses on preventing forgetting in prior tasks, but it does not discuss the potential impact of REMIX on performance for new tasks introduced in later stages. While REMIX seems effective at preserving knowledge from earlier stages, it remains unclear whether this approach might inadvertently reduce performance on new tasks due to constraints placed on the model’s capacity or flexibility. An analysis of how REMIX affects the model's performance on new tasks would provide a more balanced understanding of its effectiveness in continual learning contexts.\n6 Effectiveness of Random vs. Generic Text Mixing:\nThe paper explores both random word sequence mixing and generic pretraining text mixing in REMIX. However, it is not entirely clear whether these two approaches yield similar or differing effects on knowledge retention. Could the authors provide more details on any observed differences in effectiveness between random and generic data mixing? Understanding how each type impacts forgetting could offer valuable insights into the dynamics of memory retention in large language models.\n7 Combined Mixing Effectiveness:\nThe results indicate that combining random word sequence mixing with generic data mixing produces the best outcomes, but it is not fully explained why this combination is most effective. Is there a theoretical or empirical rationale for why mixing both types of data provides better retention compared to using either one alone? Additional explanation of this combined effect would enhance understanding of REMIX’s underlying mechanisms and may help guide future applications.\n8 100% Accuracy in Table 1:\nIn Table 1, it is stated that all Stage 1 datasets are trained to 100% accuracy before Stage 2 training. Could the authors clarify how this 100% accuracy is achieved and guaranteed across different datasets? Specifically, were there particular training techniques or criteria used to ensure full memorization of Stage 1 data? Additional details on this process would help in understanding the baseline setup for evaluating forgetting.\n9 Suitability Across Task Types:\nHas REMIX been tested on other types of tasks, such as generative or dialogue-based tasks? Additional testing on these tasks would clarify REMIX’s versatility and applicability beyond factoid retention." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Novel Approach to Memory Retention: REMIX introduces a unique approach to mitigate forgetting by mixing random and generic data during training, achieving substantial performance improvement compared to replay-based methods.\nThorough Experimental Analysis: The authors conduct extensive experiments across multiple datasets, providing empirical evidence of REMIX’s effectiveness. They also analyze layer-specific behavior, offering insights into how REMIX modifies the model’s memory dynamics.\nGeneralizable Insight for Continual Learning: By demonstrating the limitations of replay techniques and proposing alternative strategies, this paper offers valuable insights for both continual memory retention and general continual learning in LLMs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper examines the problem of forgetting in large language models (LLMs) during continual learning, particularly when training on a small set of long-tail factoids (subject-relation-object triples). The authors identify two primary challenges in retaining these long-tail facts over successive training stages: the limitations of standard replay techniques and the interference from training on unrelated datasets. To address these challenges, the authors propose REMIX (Random and Generic Data Mixing), which combines unrelated, generic data with the factoid data to prevent forgetting. Through comprehensive experiments, REMIX is shown to outperform replay-based methods and recover performance from severe forgetting. The authors further analyze how REMIX influences the learning process, noting that it shifts the storage of factoids to earlier layers and diversifies the layers used for storing these facts, thus reducing interference from later training stages." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1 Lack of Comparison with Other Forgetting Mitigation Techniques:\nAlthough the authors discuss the limitations of replay-based methods, the paper lacks a systematic comparison with other common forgetting mitigation techniques, such as Elastic Weight Consolidation (EWC) or Knowledge Distillation. For instance, EWC is frequently used in continual learning to reduce interference by regularizing key weights, while Knowledge Distillation selectively retains critical information. Comparing REMIX with these methods would help clarify REMIX’s unique advantages and performance under similar conditions.\n2 Synthetic and Specific Dataset Selection:\nThe datasets used in this paper, such as Key-Value Recall and PopQA, are primarily synthetic and consist of isolated factoids, which may not fully reflect the complexity of real-world data. For example, in practical scenarios, knowledge is often presented in overlapping or nested forms (e.g., “The author of Hamlet is Shakespeare” and “Shakespeare wrote Hamlet”) rather than as isolated facts. Testing REMIX on more commonly used datasets, such as Wikipedia or open-domain QA datasets (e.g., Natural Questions), could provide a more realistic evaluation of its effectiveness and generalizability.\n3 Unclear Justification for Types of Data Mixing:\nThe paper employs both random word sequences and knowledge-rich text (e.g., Knowledge Pile) as mixed data to prevent forgetting, but it does not provide a clear explanation of why these two disparate types would produce similar effects. For example, random word sequences contain no factual content, while Knowledge Pile includes a substantial amount of knowledge and contextual information. The authors could further analyze why both random and knowledge-rich data help prevent forgetting or test the specific impacts of each type in different scenarios.\n4 Impact on Performance in New Tasks:\nWhile REMIX performs well in retaining early-stage knowledge, the paper does not explore its impact on subsequent new tasks. For instance, it would be useful to know whether REMIX might limit the model's ability to learn these new tasks when introduced for fine-tuning. Evaluating REMIX’s impact on new tasks could provide insights into potential trade-offs between memory retention and generalization to new tasks.\n5 Limited Evaluation on Extended Stages:\nThe experiments primarily focus on two-stage continual learning, with limited testing of multi-stage scenarios. In real-world applications, models may undergo multiple updates, such as continual fine-tuning in legal or medical domains. Testing REMIX in a three-stage or four-stage setting could provide better insight into its stability and effectiveness over longer training cycles.\n6 Resource and Scalability Concerns:\nREMIX relies on incorporating additional mixed data during training, which may increase computational costs, especially for large models such as Llama-3-8B. Expanding this method to resource-intensive domains like finance or healthcare could present challenges. If the authors could discuss the trade-offs between added data usage and computational demands or provide a rough estimate of the resources required to implement REMIX in a real-world setting, it would help assess its feasibility and scalability in high-resource environments." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "In continual memorization, the LLM needs to memorize a small set of facts and not forget after further training. Modern LLMs still struggles. We found an effective mitigation strategy by mixing generic data or random word sequences during training." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024continual,\ntitle={Continual Memorization of Factoids in Large Language Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2gW8lTRh9m},\nnote={under review}\n}" }, "abstract": { "value": "Large language models (LLMs) can absorb a massive amount of knowledge through pretraining, but pretraining is inefficient for acquiring long-tailed or specialized facts. Therefore, fine-tuning on specialized or new knowledge that reflects changes in the world has become popular, though it risks disrupting the model’s original capabilities. We study this fragility in the context of continual memorization, where the model is trained on a small set of long-tail factoids (subject-relation-object associations) and must retain these factoids after multiple stages of subsequent training on other datasets. Continual memorization focuses on the specific challenge of retaining long-tail factoids, whereas general continual learning aims to maintain the LLM’s capabilities across a wide range of generic tasks (e.g., reasoning, commonsense knowledge). Through extensive experiments, we show that LLMs suffer from forgetting across a wide range of subsequent tasks, and simple replay techniques do not fully prevent forgetting, especially when the factoid datasets are trained in the later stages. We posit that there are two ways to alleviate forgetting: 1) protect the memorization process as the model learns the factoids, or 2) reduce interference from training in later stages. With this insight, we develop an effective mitigation strategy: REMIX (Random and Generic Data Mixing). REMIX prevents forgetting by mixing generic data sampled from pretraining corpora or even randomly generated word sequences during each stage, despite being unrelated to the memorized factoids in the first stage. REMIX can recover performance from severe forgetting, often outperforming replay-based methods that have access to the factoids from the first stage. We then analyze how REMIX alters the learning process and find that successful forgetting prevention is associated with a pattern: the model stores factoids in earlier layers than usual and diversifies the set of layers that store these factoids. The efficacy of REMIX invites further investigation into the underlying dynamics of memorization and forgetting, opening exciting possibilities for future research." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Continual Learning", "Large Language Model", "Memorization" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/0cdd08e60bf62b2cae91666aae4587bc11f196f5.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Continual Memorization of Factoids in Large Language Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- In line 257 - do you mean top-2 (instead of top-(k-1))?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper is well written and easy to follow\n- The authors clearly state the motivation for the proposed method and its necessity.\n- RORA-VLM introduces a unique two-stage retrieval approach, effectively bridging the gap between visual and textual information for more accurate knowledge retrieval.\n- The paper tackles the common issue of irrelevant or noisy data in retrieval-based methods by implementing noise resilience strategy\n- The paper address a clearly practical application that might be useful for the community and the industry." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces RORA-VLM, a retrieval-augmented framework designed to enhance Vision-Language Models (VLMs) by addressing two main challenges: managing multimodal query discrepancies and filtering out irrelevant, noisy retrievals. RORA-VLM employs a two-stage retrieval process: 1) Image-Anchored Entity Retrieval: This stage retrieves visually similar images based on the query image, anchoring the retrieval with associated entity information 2) Query-Expanded Text Retrieval: Using entity names from the first stage, the method expands the query to retrieve additional textual knowledge from sources like Google Search." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Method:\n\n- Section 3.2: The authors describe in details two stages. For my understanding, stage-1 is just formulation of the K-NN of the query image in the WIT images (within CLIP latent space). This is a well-known concept, especially in this line of work. I think this is well-detailed stage but it should be on the appendix, while the main paper should contain a brief description of the stage.\n- Line 270: “Similarly, the image I is encoded into a sequence of visual embeddings…” - this is not clear. CLIP encodes an image/text intro a shared embeddings space of dimension d. How do you encode the image patches (n) to the same dimension? Do you feed-forward each patch, separately, to the CLIP model? Do you use the N internal CLIP features for each patch? If so, are you sure that their dimension is d, before the last projection layer? Do you project them with the last visual projection layer as the pooled [CLS] token projected? Please elaborate more on this procedure.\n\nSection 5 currently combines results, ablation study, and discussion, which affects the clarity and flow of these findings. Separating these into distinct sections—such as “Results,” “Ablation Study,” and “Discussion”—would make it easier for readers to follow each component and understand the contributions more clearly. Additionally, crucial details and experiments appear to be missing, and some existing experiments do not convincingly support the claims made. Below are specific areas where the section could be strengthened:\n\nEvaluation:\n\n- Main results: Lines 307-316 (Baselines): The authors list a several MLLM backbones for the QA task which is great. However, baselines to compare to should be other RAG methods. If I understand correctly, only RORA-VLM and Wiki-LLaVA* are using Retrieval Augmentations. If so, how is it comparable to other baselines that uses zero-shot?\n- Building on previous point, I am not fully understand the entire training setup: the only model that was tuned (lines 317-345) was RORA-VLM? If so, again, how is it comparable to other baselines? Please clarify these points.\n\nThere are not enough details about the evaluation protocols and datasets in the paper, and some comparisons are missing. For example, what was the training set of each baseline in Table 1? Did the authors fine-tuned each baseline on the same dataset? which one of them use the proposed RAG method? what about other RAG methods and baselines?\n\nAblation Study:\n\n- Lines 365-367 states “we use the widely adopted average pooling (kernel size of 2, stride of 2) to obtain the same number of visual tokens as our refinement approach” What does “widely adopted average pooling” mean on N CLIP vectors? How does it relate to a kernel size of size 2 and stride 2? Did you manipulated the input image/kernel of CLIP to get the same amount of CLIP vectors? The authors should elaborate on the experiment that was done here, it is unclear.\n- Lines 405-419: I am not convinced why this experiment proves that the model ignore “noise” in the retrieval samples. I would be more convinced with an following experiments, for example: providing the model 1 relevant sample with 2 other randomly-sampled ones, will not change the model’s answer, regardless which 2 noise samples were chosen, or by just proving the 1 relevant sample with no other samples. \n- Lines 420-430 describe Figure 4 that supposed to show how the model ignore “noise” samples. However, it seems like the model pays attention to specific words the correlate with the question (e.g., row 1, “how wide…” attend “height” and “width”). These examples does not show any rubsness to “noise” retrieval as intended." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please refer to the above section." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The motivation for this work is clearly presented and easy to follow.\n2. Experiment results demonstrate its effectiveness." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a multimodal version RAG targeting multimodal large language models, such as LLaVA-1.5 for information-seeking VQA. To solve two challenges, the authors propose a 2-stage retrieval process with image-anchored textual query expansion and noise-resilient retrieval augmented generation. Experimental results highlight its effectiveness on OVEN and InfoSeek benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In the Introduction section, providing a figure showing the process of the 2-stage retrieval method would be easier to understand.\n2. Related work discussion and method novelty. How to incorporate multi-modal knowledge into models is not a new problem[1][2]. Some related work is proposed in other multi-modal tasks, such as knowledge-based VQA. Besides, adversarial training is also adopted in existing vision and language training, such as [3]. The authors are encouraged to discuss the existing work and compare the related ones with the proposed method.\n3. The correspondence between the ablation model variants in Table 2 and the proposed module is somewhat unclear. What about the ablation of the two-stage retrieval ?\n4. Figure 2 lacks some of the details of the methodology. The authors are encouraged to refine it.\n\n[1] Gui, Liangke, et al. \"KAT: A Knowledge Augmented Transformer for Vision-and-Language.\" Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.\n[2] Lin, Weizhe, et al. \"Fine-grained late-interaction multi-modal retrieval for retrieval augmented visual question answering.\" Advances in Neural Information Processing Systems 36 (2023): 22820-22840.\n[3] Gan, Zhe, et al. \"Large-scale adversarial training for vision-and-language representation learning.\" Advances in Neural Information Processing Systems 33 (2020): 6616-6628." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. **Differences in Approach and Motivation**: The two-stage retrieval approach proposed in the paper seems to be driven by image entities. Does a retrieval approach that combines images and queries better address pattern differences? Furthermore, how do the authors reconcile the conflicting motivations behind query-oriented visual token refinement (introducing noise for adversarial learning) and adversarial noise injection (focusing on denoising)? Would it be more consistent with adversarial learning principles if the former were used only during inference and the latter only during training?\n2. **Fairness of experimental comparisons**: In Table 1, do the authors plan to conduct more experiments to ensure that all models are evaluated on a level playing field?\n3. **Lack of ablation studies**: Can the authors provide insights on the impact of these parameters (k,l,m) on model performance?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. **Novelty**: The authors address two core challenges in multimodal retrieval-augmented generation (RAG): the retrieval inaccuracies caused by modality discrepancies and the noise often present in retrieved content. To tackle these issues, they propose an innovative solution using a two-stage retrieval process that mitigates modality inconsistency, allowing the system to capture multimodal background information more comprehensively. Combined with an anti-noise strategy, this approach effectively suppresses irrelevant information while enhancing retrieval accuracy and overall performance in multimodal tasks.\n\n2. **Significance**: RORA-VLM offers a valuable method for improving VLMs, especially in knowledge-intensive domains, where retrieval-augmented tasks are often challenged by noise. This framework effectively addresses this key issue, making it particularly suitable for such applications.\n\n3. **Clarity of Presentation**: The paper is well-structured with a clear research motivation, providing thorough explanations of the methodology and experimental results. This clarity aids readers in understanding both the approach and its effectiveness." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces RORA-VLM, a framework aimed at improving Vision-Language Models (VLMs) on knowledge-intensive tasks. The method addresses two challenges: (1) effectively retrieving relevant multimodal information given the inherent discrepancy between vision and language modalities, and (2) managing the noisy and extraneous information in retrieved knowledge. The paper’s contributions include a two-stage retrieval process with image-anchored textual-query expansion and a robust retrieval augmentation method that employs adversarial noise and visual token refinement. Extensive experiments demonstrate that RORA-VLM outperforms current models on benchmarks such as OVEN, InfoSeek, and Enc-VQA." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **Inconsistency in Method and Motivation**: The two-stage retrieval in the paper looks like an image entity-driven retrieval approach, would modal differences be better handled with image+query composite retrieval; Additionally, the motivations behind the designs of Query-oriented Visual Token Refinement and Adversarial Noise Injection for Robust Augmentation seem to conflict. The former introduces noise for adversarial learning, while the latter focuses on denoising. It might align better with the concept of adversarial learning if the former were applied solely during the inference phase and the latter exclusively during training.\n2. **Fairness of Experimental Comparisons**: In the main experiments presented in Table 1, the authors' method has undergone pre-training and fine-tuning on knowledge-intensive datasets, whereas many baseline models may not have been trained on such datasets. This raises questions about the fairness of the experimental comparisons.\n3. **Lack of Ablation Studies**: The paper lacks ablation studies on key parameters such as k, l, and m. Including these analyses would provide valuable insights into the impact of these parameters on the model's performance." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024roravlm,\ntitle={Ro{RA}-{VLM}: Robust Retrieval-Augmented Vision Language Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2h1siDrSMl},\nnote={under review}\n}" }, "abstract": { "value": "Though vision-language models (VLMs) have demonstrated impressive capabilities as general-purpose visual assistants, they still exhibit inferior performance on knowledge-intensive tasks such as information-seeking visual question answering, primarily due to the challenge of accurately encoding all the associations between visual objects and scenes to their corresponding entities and background knowledge. While retrieval augmentation methods offer an efficient way to integrate external knowledge, extending them to vision-language domain presents unique challenges in (1) precisely retrieving relevant information from external sources due to the inherent discrepancy within the multimodal queries, and (2) being resilient to the irrelevant, extraneous and noisy information contained in the retrieved multimodal knowledge snippets. In this work, we introduce RORA-VLM, a novel and robust retrieval augmentation framework specifically tailored for VLMs, with two key innovations: (1) a 2-stage retrieval process with Image-anchored Textual-query Expansion to synergistically combine the visual and textual information in the query and retrieve the most relevant multimodal knowledge snippets; and (2) a robust retrieval augmentation method that strengthens the resilience of VLMs against irrelevant information in the retrieved multimodal knowledge by injecting adversarial noises into the retrieval-augmented training process, and filters out extraneous visual information, such as unrelated entities presented in images, via a query-oriented visual token refinement strategy. We conduct extensive experiments to validate the effectiveness and robustness of our proposed methods on three widely adopted benchmark datasets: OVEN, InfoSeek and Enc-VQA. Our results demonstrate that with a minimal amount of training instance, RORA-VLM enables the LLaVA-v1.5 model to achieve significant performance improvement and constantly outperform state-of-the-art retrieval-augmented VLMs on all benchmarks while also exhibiting a novel zero-shot domain transfer capability." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "retrieval-augmented generation", "vision language model" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/568d83a9236d15299490b7aa52dfee20c3bf546f.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "RoRA-VLM: Robust Retrieval-Augmented Vision Language Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- How does SMF handle sparse data matrices, and what is its performance compared to other methods in such scenarios?\n- Can the authors elaborate on any potential biases that might be introduced by the learned object similarities in SMF?\n- What are the computational requirements for training SMF, and how does it compare to other methods in terms of training time and resource usage?\n- How does SMF perform in dynamic environments where the association data changes over time, and is there any strategy to update the embeddings efficiently?\n- Could the authors provide more insights into the choice of hyperparameters and their impact on the model's performance?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Performance Evaluation: The paper uses a variety of metrics (RMSE, precision at top-K, AUROC, AUPRC) across different datasets to evaluate the model's performance, which provides an assessment of its capabilities.\n- Comparison with State-of-the-Art: SMF is compared against several established methods, which strengthens the paper's claims about the superiority of the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a method, Self-Matrix Factorization (SMF), for learning object representations from association data without prior knowledge of object attributes. The paper claims that SMF outperforms other methods like SLIM, HCCF, and NMF in predicting missing associations and encoding object attributes." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Lack of Theoretical Foundation: The paper could benefit from a deeper theoretical analysis of why SMF works better than existing methods. The underlying assumptions and mathematical properties of SMF need more exploration.\n- Complexity and Scalability: The paper does not discuss the computational complexity of SMF or how it scales with larger datasets, which is crucial for practical applications.\n- Limited Discussion on Hyperparameter Sensitivity: While the paper mentions hyperparameter tuning, there is limited discussion on how sensitive the model's performance is to these hyperparameters, which is important for reproducibility and practical use.\n- Overfitting Concerns: The paper does not address potential overfitting issues, especially given the use of regularization terms in the loss function.\n- Generalization to Other Domains: The paper primarily focuses on association data between two types of objects. It is unclear how well SMF generalizes to other types of data or more complex relationships." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "Please see weaknesses above. My main questions are with respect to the differences of this approach to other MF works. Section 4 kind of wraps this up but doesnt discuss relations and what this method offers.\n\nQ1) What would you say are the contributions of this method compared to the closest ones?\nQ2) could you clarify what specific innovations, if any, have been made in deriving these update rules in Eq3-4 compared to previous work? Could you discuss how the incorporation of the alpha factor contributes to the overall novelty of their approach?\nQ3) could you include more state-of-the-art matrix factorization methods in the comparative analysis? This would help provide a more comprehensive evaluation of SMF's performance." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The paper explores an interesting topic, ie to generate embeddings that capture implicit object attributes by leveraging similarities inferred from associations \n\n- The addition of the term that exploits the fact that objects (amy) lie on multiple linear manifolds, is interesting and seems to provide some gains over NMF." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces Self-Matrix Factorization (SMF), a matrix decomposition method that constrains the nonnegative matrix factorization optimization, among other with a \"Self-Expressivity\" term that aims to preserve the linear manifold information implicit in the original association matrix. \n\nTested on datasets like MovieLens and Drug-SE, SMF outperformed traditional methods in predicting associations and clustering objects based on latent features (e.g., genres or categories). This method shows promise for recommendation systems and unsupervised learning tasks where labeled data is limited." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "W1) There is no related works section, and the contribution and relationships to the closest matrix factorization methods is unclear. Although a popular topic, there is only a handful of matrix factorization works cited. What are the closest matrix factorization works and how does Eq 2 compares? The second term in Eq. 2 allows each row to be reconstructed from others. Is this the first use of this \"self-expressive\" constraint in MF and representation learning, or have similar constraints been applied in other methods?\nI think that authors should consider adding a dedicated related work section comparing SMF to other recent matrix factorization methods, particularly those using similar self-expressive constraints.\n\nW2) The update rule in Eqs 3-4 are derived from Lee & Seung, 2000 and applied to Eq 2. Unclear if there is any substancial contribution there. Same as the addition of factor alpha that is borrowed from related work.\n\n\n\nW3) Figure 1 seems way too generic and fails to adequately illustrate the novel aspects of SMF. Figure 1(a) depicts a generic matrix factorization, which does not highlight SMF’s unique contributions. Figure 1(b) shows linear subspaces, but it lacks clarity on how the method effectively utilizes only points within the same subspace to reconstruct an object. \nThe authors should consider adding a visual representation of how SMF utilizes points within the same subspace for reconstruction, or including a side-by-side comparison with traditional matrix factorization to highlight SMF's unique approach.\n\nW4) The datasets used for evaluating SMF are relatively small, which limits the generalizability of the results, and the comparative analysis is not extensive. The main competitor in Table 2 is NMF, with modest improvements in RMSE observed for SMF. Additionally, SLIM performs significantly worse than NMF, so it may be more insightful to reorder the rows in Table 2 to better highlight SMF's performance against the second-best model." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. The paper relies on the assumption that objects reside on multiple linear low-dimensional manifolds embedded within a high-dimensional space. However, this assumption appears to have already been utilized by numerous prior matrix factorization works, rendering it relatively uninnovative. \n\n2. The paper asserts that object similarities can be derived directly from the data matrix, yet it fails to elucidate the method of learning or the criteria for determining these similarities.\n\n3. The paper compares its proposed SMF to other methods such as SLIM, HCCF, and NMF, but it does not provide a comprehensive analysis of the strengths and weaknesses of each method. \n\n4. The experiments conducted in this paper are relatively simplistic, both in terms of the datasets and tasks employed, and the comparative methods utilized are outdated." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. This paper focuses on the problem of learning object representations from solely association data, and proposes a Self-Matrix Factorization (SMF) method. \n2. The authors performed experiments at recovering missing values on the different association matrices and show that SMF obtains comparable or better predictions than its competitors." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper focuses on the problem of learning object representations from solely association data, and proposes a Self-Matrix Factorization (SMF) method. The innovation of this paper is relatively weak, and the core contributions have not been clearly elaborated.\n\nThere are several concerns that need to be addressed. \n\nFirstly, the paper relies on the assumption that objects reside on multiple linear low-dimensional manifolds embedded within a high-dimensional space. However, this assumption appears to have already been utilized by numerous prior matrix factorization works, rendering it relatively uninnovative. \n\nSecondly, the paper asserts that object similarities can be derived directly from the data matrix, yet it fails to elucidate the method of learning or the criteria for determining these similarities.\n\nThirdly, the paper compares its proposed SMF to other methods such as SLIM, HCCF, and NMF, but it does not provide a comprehensive analysis of the strengths and weaknesses of each method. \n\nForthly, the experiments conducted in this paper are relatively simplistic, both in terms of the datasets and tasks employed, and the comparative methods utilized are outdated." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper relies on the assumption that objects reside on multiple linear low-dimensional manifolds embedded within a high-dimensional space. However, this assumption appears to have already been utilized by numerous prior matrix factorization works, rendering it relatively uninnovative. \n\n2. The paper asserts that object similarities can be derived directly from the data matrix, yet it fails to elucidate the method of learning or the criteria for determining these similarities.\n\n3. The paper compares its proposed SMF to other methods such as SLIM, HCCF, and NMF, but it does not provide a comprehensive analysis of the strengths and weaknesses of each method. \n\n4. The experiments conducted in this paper are relatively simplistic, both in terms of the datasets and tasks employed, and the comparative methods utilized are outdated." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose Self-Matrix Factorization (SMF), a method that learns object representations by constraining them with object similarities that are learned together with the representations from solely association data" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024constraining,\ntitle={Constraining embedding learning with Self-Matrix Factorization},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2hI3o9GHMq},\nnote={under review}\n}" }, "abstract": { "value": "We focus on the problem of learning object representations from solely association data, that is observed associations between objects of two different types, e.g. movies rated by users. We aim to obtain embeddings encoding object attributes that were not part of the learning process, e.g. movie genres. It has been shown that meaningful representations can be obtained by constraining the learning with manually curated object similarities. Here, we assume that objects lie in multiple linear manifolds embedded in high-dimensional space, and we argue that similarities between objects that correspond to sharing manifolds can be learned from the observed associations. We propose Self-Matrix Factorization (SMF), a method that learns object representations by constraining them with object similarities that are learned together with the representations. In our extensive evaluation across three real-world datasets, we compared SMF with SLIM, HCCF and NMF obtaining better performance at predicting missing associations as measured by RMSE and precision at top-K. We also show that SMF outperforms the competitors at encoding object attributes as measured by the embedding distances between objects divided into attribute-driven groups." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "representation learning", "constrained matrix decomposition", "link prediction" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/91b9cdcbd2bf54425c20f058a4b86c7f2388df46.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/5ce77adab0903568b110d3abc140a6089cdc9bc0.zip" }, "title": { "value": "Constraining embedding learning with Self-Matrix Factorization" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "> In the regions of interests selection experiment, the authors only consider 'random,' 'whole,' and 'BPC' as the ROIs...\n\nThe selected BPC area (superior temporal sulcus, angular gyrus, supramarginal gyrus, and opercular, triangular, orbital part of the inferior frontal gyrus) contributes the most to predictive coding, as indicated in some neuroscience studies [1][2].\n\nThe process for selecting \"BPC\": For the Narratives dataset, Destrieux atlas is applied and the above mentioned ROIs are extracted. For LeBel's dataset, since the fMRI signals are not projected to a standardized space, we use the “Auditory” region provided by the authors', containing parietal-temporal-occipital (PTO) area. The BPC area of both datasets cover highly similar area.\n\nThe process for selecting \"random\": For the Narratives dataset, G_and_S_cingul-Ant, G_and_S_subcentral, G_and_S_transv_frontopol, G_orbital, S_front_middle, S_subparieta are selected. For LeBel's dataset, we randomly choose 1000 voxels from brain surface data.\n\nThe process for selecting \"whole\": We use the whole brain surface data as ROIs for both datasets.\n\nWe believe selecting random and whole ROIs as controlled experiments is sufficient for demonstrating the effectiveness of using predictive coding to improve decoding performance: \n\n1. random vs. BPC demonstrates only ROIs related to predictive coding in human language comprehension can improve decoding.\n\n2. whole vs. BPC not only confirms conclusion in 1, but also shows whole brain surface which contains BPC area still can't contribute to better decoding, because some other brain regions contain too much noise. \n\n3. none (PREDFT without SideNet) vs. BPC. PREDFT without SideNet is equivalent to not using any ROIs for predictive coding. This comparison shows predictive coding improves decoding accuracy significantly.\n\n**All the above clarifications are included in sec 4.3 and Appendix A.4. We will add more key information in sec 4.3 in the updated version**\n\n[1]. Evidence of a predictive coding hierarchy in the human brain listening to speech. Nature Human Behavior\n[2]. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature\n\n> Could the authors provide pseudocode for the method...\n\nWe will provide pseudocode in the appendix for edited version of paper. The discussion of time complexity is already in Appendix A.4 (line 845) of original paper.\n\n> The results provided by the authors mostly only include the mean value...\n\nWe guess you indicate the experiment of analyzing the impact of prediction length and distance to model performance (sec 4.4), as we have presented results per subject for other experiments. **The per-subject results of analyzing the impact of prediction length and distance are already presented in Figure 16,17,18 in the appendix of original paper.**\n\n> In the methods section, some symbols are not defined...\n\n**A notation table for symbols is presented in Table 3 in the appendix of original paper.**\n\n### Questions\n\nPlease refer to clarification for weaknesses." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": { "value": "Rebuttal (part 2)" }, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "We sincerely appreciate your effort in reviewing our paper. We will address your concerns point by point.\n\n### Weaknesses\n\n> In Section 3.3, the authors state, 'During the inference stage, as illustrated in Figure 8, the decoder in the side network is abandoned.' However, they do not provide a detailed explanation of why the decoder is discarded or discuss the potential impact of this decision...\n\nWhat we really want is predictive coding representation, which is produced by side network encoder. The side network decoder is designed to help train the side network encoder (we can't find out how to directly obtain predictive coding representation). \nSpecifically, during the training process, the label for side network decoder is predicted words instead of complete sentences (as shown in Figure 3). The side network learns mapping between specific areas of brain (BPC area) and predicted words.\nHowever, the goal of our task is to decode complete sentences. So the side network decoder is useless after training.\n\n**We will add the motivation and reason for discarding the side network decoder in the methodology section in the updated version.**\n\n> As shown in Table 1, PREDFT does not achieve the best performance on ROUGE1-R...\n\nWe think the different lengths of generated content might contribute to this factor, since we don't apply a word rate model to control the number of generated words.\nAlthough PREDFT fails to outperform other models in ROUGE1-R, the gap is narrow.\nJust like recall and precision to f1 score, ROUGE-Recall measures the extent to which a machine-generated content captures the information contained in a reference content, which is a single perspective assessment. ROUGE-Recall and ROUGE-Precision are characterized by a trade-off. When PREDFT gets a relative low ROUGE-R, it gets a high ROUGE-P.\nInstead, ROUGE-F1 is a more comprehensive indicator, combining both ROUGE-P and ROUGE-R. Our model outperforms other models in this metric. \n\n> As shown in Table 1, PREDFT without SideNet performs similarly to other methods. However...\n\nThe SideNet is designed to obtain predictive coding representation.\nPREDFT without SideNet can be viewed as traditional deep learning approach which directly applies Transformer to decode text from brain recordings, while PREDFT with SideNet combines deep learning and neuroscience findings (predictive coding). The SideNet provides predictive coding representation to the decoder in Main network, and the decoder incorporates both current fMRI representation and predictive coding representation for text decoding.\n\nThe idea of PREDFT is motivated by predictive coding theory in Neuroscience, which indicates human can naturally predict upcoming words. Since predictive coding has been verified to contribute to human language comprehension, we seek to investigate whether such predictive information can help language reconstruction. \nThe improvement of incorporating SideNet highlights 1. the effectiveness of our model design 2. predictive coding has potential to improve brain-to-text decoding. **We will provide the illustration of PREDFT without SideNet for better understanding in the updated version.**\n\n> Although the authors provide a detailed description of the hyperparameter selection...\n\nAll the hyperparameters are chosen to minimize the training & validation loss as much as possible.\nWe don't understand which hyperparameter the reviewer is confused about.\nThe influences of ROIs selection, prediction length, prediction distance, $ \\lambda $ to model performance are detailedly discussed in sec 4.3, sec 4.4, Appendix E. The learning rate is set to stabilize training. We don't test the influence of model layers (e.g. Transformer layers) due to limited computational resources." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": { "value": "Rebuttal (part 1)" }, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see weaknesses. I would need to be convinced that majority of the claimed improvements in the model are not merely from a bias towards outputting high-frequency words, and thereby overfitting the chosen test metrics of BLEU and ROGUE, in order to change my score. Right now, I am fairly convinced that this is the case." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The attempt to use hypothesized predictive coding representations to enable better text decoding is interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper describes a decoding method \"PredFT\" that uses a main decoding network and a side network to perform decoding from fMRI recordings of subjects listening to stories to text. The side network is responsible for obtaining predictive coding representations from specific brain regions and integrating them into the main network, enhancing language decoding. The authors claim that this integration leverages brain regions known for predictive functions (like the parietal-temporal-occipital areas) to better align brain signal decoding with anticipated semantic content. This is supported by results that have claimed the brain performs predictive coding during language stimulation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "My main concern is that the metric does not seem to produce even locally coherent text, which substantially damages the authors' claims that this method is an advancement over prior work, such as Tang et al., which uses an LM to guarantee local coherence. Consider the following example from the case study: \"He don’t know my girl you of the eyes but his girl sleep he and he said and he said and the to the and and which I not wrong. But the Guy\". Clearly, this has no meaning, and does not even obey basic local grammatical rules (e.g. \"and and\"). The problem seems to be that the model has merely learned repeat short, high-frequency words like \"the\", \"he\" and \"and\", which improves BLEU/ROGUE score but does not actually move forward towards the goal of better language decoding. I imagine if you just had the model repeatedly and randomly output words sampled from the top 100 most common English words that it would behave fairly similarly. My expectation is that a small percentage of the improvement in BLEU score is genuinely derived from brain signals, with most of the benefit deriving from this output bias. The unreasonably high 5.62 BLEU-3 score when compared to other methods is more of a red flag, because its pretty clear that the model is simply guessing every high frequency trigram in the English language.\n\n The paper is also quite difficult to read for no reason and pointlessly notational, for example when the self-attention equation is repeated three separate times in only slightly different ways." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. In Section 3.3, the authors state that the decoder in the side network is abandoned during the inference stage. Could the authors provide a detailed explanation of why the decoder is discarded and discuss the potential impact of this decision on the overall performance and functionality of the model?\n\n2. As shown in Table 1, PREDFT does not achieve the best performance on ROUGE1-R. Could the authors analyze the potential reasons for this and discuss any factors that may have contributed to the lower performance in this specific model? For instance, how might the model's architecture, training process, or characteristics of the ROUGE1-R metric explain this discrepancy? Did the authors observe any patterns in the types of language constructs where PREDFT underperformed on ROUGE1-R?\n\n3. As shown in Table 1, PREDFT without SideNet performs similarly to other methods, while the inclusion of SideNet leads to a significant performance improvement. Could the authors provide a detailed analysis of this phenomenon to explain how SideNet contributes to the model's enhanced performance?\n\n4. Although the authors provide a detailed description of the hyperparameter selection, could they explain the rationale behind these choices? How do these choices relate to the model's performance or the underlying theory of predictive coding?\n\n5. In the regions of interest selection experiment, the authors only consider 'random,' 'whole,' and 'BPC' as the ROIs. Could the authors clarify whether there are other potential ROIs associated with predictive coding? If so, could they provide supporting neuroscience literature for the selection of BPC? Additionally, can the authors explain the process for selecting these particular ROIs and why they believe these are sufficient to demonstrate the effectiveness of their approach? Did the authors consider any other ROIs, and if so, why were those not included in the study?\n\n6. Could the authors provide pseudocode for the method and an analysis of its time complexity to enhance the reproducibility of the article?\n\n7. The results provided by the authors mostly only include the mean value. Could the authors include the variance and statistical test results in the experimental results?\n\n8. In the methods section, some symbols are not defined. Could the authors compile a list of symbols used in the paper in an appendix to help readers understand better?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Integrating predictive coding theory into the decoding process offers a fresh perspective on reconstructing language from brain signals.\n2. Experimental results demonstrate that PREDFT outperforms other methods across various evaluation metrics, showing significant improvements." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents PREDFT (FMRI-to-Text Decoding with Predictive Coding), a novel framework that utilizes predictive coding to translate fMRI signals into continuous language. This approach combines a primary decoding network with an auxiliary network focused on capturing brain predictive coding, aiming to improve the accuracy of language reconstruction from brain signals. The authors conduct experiments on two established naturalistic language comprehension fMRI datasets, showing that PREDFT achieves state-of-the-art performance across multiple evaluation metrics." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In Section 3.3, the authors state, 'During the inference stage, as illustrated in Figure 8, the decoder in the side network is abandoned.' However, they do not provide a detailed explanation of why the decoder is discarded or discuss the potential impact of this decision. It is recommended to elaborate on the rationale behind this choice and its implications on the overall performance and functionality of the model.\n\n2. As shown in Table 1, PREDFT does not achieve the best performance on ROUGE1-R. The authors should analyze the potential reasons for this and discuss any factors that may have contributed to the lower performance in this specific model. For instance, the model's architecture, training process, or characteristics of the ROUGE1-R metric that might explain the discrepancy. If the authors observed any patterns in the types of language constructs where PREDFT underperformed on ROUGE1-R.\n\n3. As shown in Table 1, PREDFT without SideNet performs similarly to other methods. However, the inclusion of SideNet leads to a significant performance improvement. The authors should provide a detailed analysis of this phenomenon to explain how SideNet contributes to the model's enhanced performance.\n\n4. Although the authors provide a detailed description of the hyperparameter selection, they do not explain the rationale behind these choices. How these choices relate to the model's performance or the underlying theory of predictive coding.\n\n5. \"In the regions of interests selection experiment, the authors only consider 'random,' 'whole,' and 'BPC' as the ROIs, which appears somewhat limited. The paper does not clarify whether there are other potential ROIs associated with predictive coding, nor does it provide supporting neuroscience literature for the selection of BPC. It is recommended to either justify the choice of BPC with relevant references or explore additional ROIs to strengthen the study's validity. If authors can explain the process for selecting these particular ROIs and why authors believe these are sufficient to demonstrate the effectiveness of their approach. Additionally, if authors considered any other ROIs and why those were not included in the study.\n\n6. It is recommended that the authors provide pseudocode for the method and an analysis of its time complexity to enhance the reproducibility of the article.\n\n7. The results provided by the authors mostly only include the meanvalue. The experimental results should provide the mean, variance, and statistical test results.\n\n8. In the methods section, some symbols are not defined. It is recommended that the authors compile a list of symbols used in the paper in an appendix to help readers understand better." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "(1) Why did the author only use BLEU and ROUGE in the experiment? Why doesn't the author use WER, METEOR, and BERTScore which is used in the Tang and MapGuide? BLEU and ROUGE both evaluate the matching degree of n-grams, which can easily lead to surface matching but semantic mismatch. METEOR and BERTScore can better reflect semantic similarity.\n(2) Many of the methods compared by the author incorporate LLM, while the author's model is entirely trained with their own transformer. Does this result in the author's method being inferior to the baseline method in terms of semantic similarity?\n(3) The author's method was inspired by predictive coding and validated it on LLM using a prediction score. But can the author's own model still observe the same phenomenon on the prediction score? I haven't seen the same experiment evaluating the author's own model.\n(4) In some parts of the paper, fMRI is spell as FMRI." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The author provided sufficient experiments to demonstrate the significance of his motivation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In the submission-6263, the authors propose PREDFT (FMRI-to-Text decoding with Predictive coding) , which was inspired by predictive coding theory. This theory suggests that when humans listen to a certain speech, their subconscious brain predicts the words they may hear next. Then the author validated this theory through a prediction score. The verification method is to first calculate the correlation coefficient between the features extracted by LLM at the current location and the brain features, and then add the features of an upcoming text segment to the current location features, calculate the correlation coefficient again, and observe the changes in the correlation coefficient. The experimental results show that incorporating upcoming text features can increase the correlation coefficient between LLM features and brain features. Based on the above experimental results, the author designed their own model, which includes the side network to decode upcoming text. In the decoding of current text, the feature from the side network is used to incorporate the predictive coding theory into the method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Although the author's explanation of motivation is very sufficient, I still have a few major questions about the author's method and list them in the questions part." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. What would be the chance-level performance when reconstructing continuous language? Is there a baseline available for comparison? Additionally, what is the percentage of overlap between random ROIs and whole-brain voxels? Did the authors repeat the selection of random ROIs multiple times to ensure robustness, or did they only select a single set of random ROIs?\n2. What is the rationale for using 4D volume data from the Narratives dataset while using 2D brain data from the Moth Radio Hour dataset? Since the Narratives dataset includes both smoothed and unsmoothed versions, along with brain masks to select activated voxels from the 4D volume, why did the authors make these choices regarding data representation?\n3. There is no interpretation provided for the two encoders used in PREDFT. The authors could project these voxels onto brain maps to verify the quality of their encoders.\n4. Figures 3, 4, 6, and 8 appear redundant. The authors could combine these into a single figure with a comprehensive caption, instead of presenting multiple, repetitive figures.\n5. What does the y-axis represent in Figure 9?\n5. Several major questions are raised in the weaknesses section.\n\nTypos:\n\n1. Line 35: Bhattasali et al. (2019); Wang et al. (2020); Affolter et al. (2020); Zouet al. (2021) - > (Bhattasali et al. 2019; Wang et al. 2020; Affolter et al. 2020; Zouet al. 2021)" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The motivation for using predictive coding in continuous language reconstruction is clear and well-explained.\n2. The proposed approach aims to improve the reconstruction of narrative stories from fMRI brain data. This is a very interesting research area because reconstructing language is challenging due to the slowness of the hemodynamic response.\n3. The authors compared the reconstruction performance using evaluation metrics against recent studies. Additionally, ablation studies were conducted on the proposed approach, with and without the predictive coding component." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Recent brain decoding studies have demonstrated that speech perception can be decoded from fMRI recordings and subsequently reconstructed as continuous language. These studies reconstruct continuous language either from specific regions of interest (ROIs) or from the whole brain, using decoder-based language models like GPT-2. Additionally, recent predictive coding studies reveal that the human brain naturally engages in continuously predicting future words across multiple timescales. Building on recent linguistic brain decoding research and the predictive coding approach, this paper explores predictive coding theory in the context of continuous language reconstruction. To this end, the authors propose PREDFT (fMRI-to-Text decoding with Predictive Coding), which consists of a main decoding network and a side network (the predictive coding component). Experimental results on two naturalistic brain datasets (Moth Radio Hour and Narratives) indicate that PREDFT achieves superior decoding performance when comparing the actual story with the reconstructed story." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. There are several major weaknesses in this work, particularly concerning the evaluation of reconstruction results:\n\t- A major concern is that the current study (PREDFT) does not provide a clear evaluation of reconstruction results compared to the baseline paper by Tang et al. (2023).\n\t- For example, the authors did not evaluate the word rate in the generated narrative story. Since the fMRI data was captured while participants were listening to stories, each word has an onset and offset. Similarly, during decoding, what is the word rate predicted by the proposed model, and does this word rate match the actual word rate of the original stimuli? \n\t- Therefore, comparing the reconstructed stimulus to the ground truth (i.e., the actual transcripts of the stimuli) would provide a good sense of whether the outputs are meaningful, as the dataset includes the ground truth of what words participants heard and when they heard them.\n\n2. Furthermore, the authors performed decoding using either random selections of ROIs, the whole brain, or BPC, which includes language-related ROIs. However, prior studies have focused on specific ROIs, such as the language, prefrontal, and auditory association cortices. Therefore, it is unclear how the proposed method compares with prior methods. Since the authors' main research question revolves around how semantic information is embedded in brain signals to improve decoding, they should consider these ROIs, as they maintain a hierarchy of language processing.\n\t- The random selection of ROIs generally leads to low decoding performance. What are these random ROIs? Do they have any overlap with BPC ROIs?\n\t- Previous studies have conducted both quantitative and qualitative analyses, reporting what the stimulus decoded at each ROI, including language-related regions in both the left and right hemispheres, as well as using four evaluation metrics. However, this paper does not report any reconstructed stimulus in the main content, nor does it include analysis at the ROI level. Additionally, the authors only used two metrics, and throughout the paper, the focus is more on the scores rather than on the main reconstructed language results.\n\n3. Although the authors report some results on predictive length and distance from the current word in Figure 1, there are no qualitative reconstruction results for these different predictive lengths and distances. What type of information is the model forecasting based on brain data? Is it syntactic information, such as nouns and verbs, or semantic content? This analysis is clearly missing from the paper.\n\n4. All the figures lack detailed captions. The results presented in the figures are difficult to understand. For instance, what is the prediction score in each subplot of Figure 1? What does each line in the top plots represent? What does prediction distance \"d\" refer to? Without providing clear details in the figure captions or placing the figures appropriately in the text, it becomes challenging for readers to understand the content and what is being conveyed.\n\n5. Since the authors use two encoders and two decoders in the proposed PREDFT, it is unclear which component is primarily responsible for reconstructing the language and which component provides the theme and narrative structure. It would be interesting if the authors reported the generated stimulus from individual components and from PREDFT as a whole, along with the performance metrics. This would help identify the shared and individual contributions of each component during language reconstruction." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024language,\ntitle={Language Reconstruction with Brain Predictive Coding from f{MRI} Data},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2hKDQ20zDa},\nnote={under review}\n}" }, "abstract": { "value": "Many recent studies have shown that the perception of speech can be decoded from brain signals and subsequently reconstructed as continuous language. However, there is a lack of neurological basis for how the semantic information embedded within brain signals can be used more effectively to guide language reconstruction. Predictive coding theory suggests the human brain naturally engages in continuously predicting future words that span multiple timescales. This implies that the decoding of brain signals could potentially be associated with a predictable future. To explore the predictive coding theory within the context of language reconstruction, this paper proposes PredFT (FMRI-to-Text decoding with Predictive coding). PredFT consists of a main decoding network and a side network. The side network obtains brain predictive coding representation from related brain regions of interest (ROIs) with a self-attention module. This representation is then fused into the main decoding network for continuous language decoding. Experiments are conducted on two popular naturalistic language comprehension fMRI datasets. Results show that PredFT achieves current state-of-the-art decoding performance on several evaluation metrics. Additional observations on the selection of ROIs, along with the length and distance parameters in predictive coding further guide the adoption of predictive coding theory for language reconstruction." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "fMRI-to-text decoding", "predictive coding theory" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/b4151d73d5768790c6f7af1d68357d03c85ba298.pdf" }, "presentation": null, "primary_area": { "value": "applications to neuroscience & cognitive science" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Language Reconstruction with Brain Predictive Coding from fMRI Data" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Have you investigated how the performance of you sampling strategy varies with graph density? Intuitively, in very sparse graphs, walk-based centrality might be less informative.\n2. In the perturbation analysis, could the bound be tightened by considering specific graph properties or structures? For instance, does the bound become tighter for trees or graphs with bounded degree?\n3. Is there any strategies to extend the perturbation analysis to handle edge features or different types of marking strategies?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. the theoretical analysis provide a solid foundation for their proposed method. The utilization of perturbation theory and expressive theory looks correct for me.\n2. This method is neat in design, which only requires minimal computation overhead compared with baselines while maintaining competitive performance.\n3. The experimental validation is comprehensive, besides comparing their method with SOTAs, they conduct synthetic experiments for counting substructures, detailed ablations and experiment time analysis." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a model termed Hybrid Marketing Network (HyMN), which is designed based on two intuitions which may potentially improve the GNN performance. First, marking the nodes included in Subgraph GNNs using walk-based subgraph centrality measures as an efficient strategy. Second, augmenting the node features with the same centrality measures as Structureal Encodings (SEs). The key insight is that walk-based centrality measures serve both as effective indicators for subgraph importance and as infrmative structrual features. The authors theoretically analysed the node marking strategy with graph perturbation theory and demonstrate that their approach effectively balance expressiveness and efficiency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The sampling strategy does not consider interactions between sampled subrgaphs that are already sampled, which can lead to potential redundancy.\n2. The analysis focus primarily on single-node marking. While mentioned in the limitations, extending the analysis to multi-node marking could provide addiitonal insights. Imagine a social network representing employees in a company. In this network, there's a team of three senior managers (say Alice, Bob, and Carol) who work closely together and have very similar connections to other employees. They all interact with the same team members, attend the same meetings, and collaborate on similar projects. According to your approach, since all three managers have high centrality scores due to their positions, the algorithm might select both Alice and Bob for marking. However, because their network positions and connections are so similar, marking both of them provides largely redundant information. It would be more informative to mark Alice (representing the management team's perspective) and then perhaps mark someone from a different department or organizational level, like a developer or a project coordinator, to capture a different aspect of the company's structure.\n3. The approach can be less effective on graphs where walk-based centrality measures don't align with task objectives. Consider a drug discovery task where we're trying to predict whether molecules will bind to a specific protein receptor. The binding activity often depends on specific functional groups located at the periphery of the molecule. Take acetylsalicylic acid (aspirin) as an example. The molecule's binding properties are largely determined by its acetyl group at the edge of the molecule, but the walk-based centrality measure would give more importance to the central benzene ring because it participates in more walks through the molecular graph. In this case, marking nodes based on centrality would emphasize the structurally central but functionally less relevant parts of the molecule, while potentially overlooking the peripheral functional groups that actually determine the binding behavior. This mismatch between structural centrality and functional importance could lead to suboptimal performance on specific prediction tasks." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Could the author provide more evidence to support the validity of the two claims in line 182?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The paper proposes a novel framework that balances the efficiency and expressiveness of subgraph GNNs.\n2. This paper provides a comprehensive theoretical analysis and experimental validation of why the simple subgraph centrality measure is needed for subgraph GNN.\n3. The experiment results demonstrate the effectiveness and efficiency of the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "To balance efficiency and expressiveness in subgraph GNNs, this paper proposes a novel framework that utilizes walk-based centrality measures for subgraph subsampling and structural encoding. The necessity of using centrality measures is demonstrated through theoretical analysis and experimental validation. Experimental results show the effectiveness of HyMN across various tasks and datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The two main components (subsample subgraph using centrality measure, and centrality-based structural encoding) are similar with previous work.[1,2]\n2. A more detailed introduction to the key background of this work would be helpful(i.e. the background and method of the node marking)\n3. More backbone(except GIN) and more baseline should be preferred to be considered by authors.\n\n[1] Sun, Qingyun, et al. \"Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism.\" *Proceedings of the web conference 2021*. 2021.\n\n[2] Rampášek, Ladislav, et al. \"Recipe for a general, powerful, scalable graph transformer.\" *Advances in Neural Information Processing Systems* 35 (2022): 14501-14515." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Refer to Weakness." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The walk centrality-based subgraph selection method achieves efficient subgraph sampling, \nsimplifying the model while ensuring performance, making it suitable for larger subgraphs.\n 2. Experiments on various tasks showcase HyMN's adaptability and performance.\n 3. By ranking nodes based on their centrality values, and mark the top-scoring ones, the model \nreduces computation time, making it applicable to a wider spectrum of downstream tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a novel method called Hybrid Marked Network (HyMN), aimed at balancing \ncomputational efficiency and model expressiveness. The approach combines subgraph GNNs \nwith structure encodings (SEs) based on walk centrality measures. The main innovation lies in \nutilizing subgraph centrality for subgraph sampling and structure encoding, allowing for the \nmaintenance of prediction accuracy while reducing the required number of subgraphs, thus \nlowering computational costs while preserving the model's expressive capacity. Experimental \nvalidation on synthetic and real world tasks demonstrates that HyMN outperforms other state-of\nthe-art GNN methods while reducing computational demands" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Abstract and Background Information:\nThe abstract is incomplete, as it lacks essential background information. This omission leaves me confused about the specific problem the paper aims to address. A well-defined problem statement is crucial for understanding the motivation behind the research and its significance.\n\nExperimental Validation of CSEs:\nI noticed that experiments on the effect of CSEs in the Peptides and Zinc datasets were not conducted. This absence raises questions about the impact of incorporating CSEs (Q4). Clarifying this point is essential for understanding how CSEs contribute to the overall findings of the study. Consider including experimental results or justifications for their omission.\n\nExploration of Selection Strategies:\nThe paper would benefit from exploring more complex selection strategies and different walk-based centrality measures. This exploration could provide deeper insights into the dynamics at play and strengthen the overall analysis.\n\nTypos and Formatting Issues:\nWhile typos and formatting are not the most critical issues, there are several areas that require attention. For instance, the indentation of the abstract does not align with the template requirements. Additionally, line 194 contains a grammatical error with \"is are\" used simultaneously, which should be corrected. Some equations lack punctuation at their end, where it is needed, leading to inconsistencies. Lines 206-210 stray from the scope of Definition 1 and should not be italicized. Furthermore, Algorithm 1 lacks a title, which is necessary for clarity and organization.\n\nSection 2 - Clarification of Content:\nAlthough Section 2 is titled “PRELIMINARIES AND RELATED WORK,” it primarily focuses on related work without adequately presenting the foundational definitions necessary for understanding the subsequent content. It would be beneficial to include a clearer explanation of the definitions and concepts that readers should be familiar with before delving into the related literature." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Could you clarify what new insights are brought by the results in Table 1 compared to those in Figure 6 and Figure 3? The distinction between these results is not immediately clear to me.\n2. In line 247, you mention that the sampling strategy should \"(...) alter the graph representations in a way that is consistent with the target space.\" This aligns with the theoretical concerns in point 1. However, the experimental study performed pertains to counting substructures, a task where the information extracted by SC is expected to be relevant. How do you expect this sampling method to compare to other centralities in tasks where SC may not encode the most relevant information, such as network flow characterization?\n3. The results under OSAN represent 1-OSAN?\n4. Proposition 2 - Appendix B relates MPNNs with SE to DSS-GNN. The complete proposed method shows better results than 1-OSAN (assumed, see above) which is upper-bounded by the 2-WL, and better results than policy-learn which is expected to be more expressive than 1-WL but less than 4-WL. Where is the complete proposed method positioned in the WL framework for expressivity?\n5. In line 424, it is stated that Figure 3 compares random sampling with SC. However, earlier (line 51), you state that random sampling is suboptimal. Why were other sampling strategies not tested to fully validate SC?\n6. What was the method used to select the hyperparameter T? A brief explanation in the text would provide more clarity.\n7. It was not addressed why a higher T, meaning more subgraphs, meaning more information leads to worse results. Is the information not useful? Is it connected to the sampling procedure not taking into account already sampled subgraphs? This is unclear to me.\n8. For proposition 1, Step 2, the block matrix $W^{(j+1)\\_0}$ seems to select the first $k+j$ columns not $k+j+1$? Consider $k=3$ for $j=1$. The block matrix will have dimensions $7 \\times 7$, with only the block in the first spot of the diagonal performing a selection since the second block $I_{j-1}$ is omitted.\n9. For the same proposition, I would like to seek clarification regarding the explanation provided for $AXW^{(j+1)_1}$. Specifically, the identity matrix is described as having dimensions $k - (j - 1)$, yet the reference appears to describe the selection of the last $j$ columns. Additionally, the process by which the iterations from $j = 1$ to $j = k$ contribute to the formation of the final vector is not entirely clear. I would greatly appreciate any further elaboration on these points to enhance my understanding.\n10. Considering choices of T > 1 in the experiments, for theorem 4, what is the impact of k>1 for top-k Subgraph GNN compared to MPNN+CSE?\n\nMinor:\n1. The phrase starting in line 214, \"If instrumental (...)\", is confusing, consider rewriting it.\n2. Quartic is often misspelled as \"quatric\".\n3. Line 890, the expression \"1-hop neighborhood\" is imprecise, I recommend 1-hop induced subgraph.\n4. Line 836, misspelled \"the\" as \"he\".\n5. Missing subscript on line 952?\n6. No identification of what $m$ denotes in equation 15.\n\nSuggestions (Optional):\n1. Line 228, \"(...) untrained 3-layer GIN\". By untrained I assume the weights were initialized at random. If this is the case, for GIN, different random initialization should lead to some variance, even if small, in the results, leading to variance in the perturbations. It would be more robust to report the mean difference in perturbations across multiple random initializations, as this would account for variance in the results.\n2. There is no direct comparison with some relevant network architectures like $I^2$-GNN that are not captured by 1-OSAN. I understand that $I^2$-GNN was used as a comparison point in MAG-GNN, but the results were quite close in the referred work, hence, I believe it would be useful to add such comparison. More importantly, works like ESC-GNN introduce a faster alternative to $I^2$-GNN. Since the presented work also focuses on efficiency, I believe a comparison with ESC-GNN would be interesting.\n3. Since much of the code used is based on GraphGPS, the configs could be more carefully described to be easier to match the experiments in the paper.\n\nThe paper presents contributions that may be of limited novelty while having some inconsistencies. However, I am **very** open to revisiting and potentially increasing my evaluation score, provided the authors effectively address the identified weaknesses and respond to the questions posed. I encourage the authors to consider these aspects to enhance the manuscript's impact and clarity." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. **Relevance and Contextualization**: The proposed work addresses an issue (computational complexity of Subgraph GNNs) that is extremely relevant for the community. It clearly identifies gaps in existing methods and situates the research well within the current state of the art.\n2. **Centrality-based Sampling**: The decision of sampling based on centrality measures has some theoretical backing. The analysis based on perturbations is well founded and creative.\n3. **Structural Encoding**: The decision of incorporating structural encodings with subsampling is well motivated and has sufficient theoretical backing." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The proposed work mainly addresses concerns regarding the temporal complexity of Subgraph GNNs. It proposes a mechanism based on subgraph centrality to sample the graphs that will be part of the bag. Furthermore, it demonstrates that adding centrality encoding to nodes can enhance the discriminative power of sampled Subgraph GNNs while limiting the added computational overhead.\n\nMain Contributions:\n1. Adoption of centrality-based sampling for Subgraph GNNs.\n2. Clear statement and results regarding incorporating structural encoding as means to improve expressivity of sampled Subgraph GNNs without much computational overhead." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **Theoretical Concerns**: The analysis based on perturbation is not sufficient to justify using the highest centrality nodes to guide the sampling procedure. Even though they will tend to produce the highest perturbation in the graph representation, this alone does not fully justify their selection for the sampling procedure. These nodes may cause the highest perturbation in graph representations, but their practical value in enhancing graph learning remains uncertain, there is no guarantee on the usefulness and attainability of such modifications. Additionally, the assumption that high-centrality nodes capture the most relevant information is limited by the specific centrality metric used, which may not capture all essential features.\n2. **Comparison of Centrality Measures**: The comparison between the adopted subgraph centrality, SC, and other centralities is not sufficient to prove SC's superiority. Moreover, centrality measures often complement each other rather than subsumming one another. SC may be more effective for certain tasks but not necessarily for all graph learning tasks. Rather than claiming SC's superiority, it would be better to show in which tasks SC excels and acknowledge that other centrality measures may outperform it in different contexts. Otherwise, stronger results linking the superiority of SC over every other centrality for graph learning, would be necessary.\n3. **RWSE vs CSE**: While the proposed method (CSE) shows some advantages over RWSE, particularly from a sampling perspective as seen in Figure 7, its benefits appear limited. The experiments focus primarily on counting substructures, a relevant task but one that may not fully demonstrate CSE's broader applicability due to its inherent predisposition toward this type of task.\n4. **Inconsistencies in Experiments**: The experimental results lack consistency, as the models used for comparison vary across datasets. It is understandable that in many cases the results for all datasets are not available in the original works. However, this inconsistency can raise confusion and concerns of cherry-picking. It would strengthen the results to ensure uniformity in model comparisons.\n5. **Missing Runtime Comparisons**: The efficiency of the proposed method is emphasized throughout the paper, yet runtime comparisons are not provided for all datasets. Since computational efficiency is a key focus, these comparisons should be included in every experiment to give a more comprehensive view of the method’s benefits.\n6. **Confusing Proof**: The presentation of Theorem 4 lacks clarity. Specifically, in line 910, it is stated that Lemma 1 will be used to demonstrate that the multiset of values in the centrality encoding for each graph will be distinct. However, unless I am overlooking something, Lemma 1 does not seem to establish this. Rather, it appears to indicate that the global node is consistently selected for top-1 sampling, which does not sufficiently ensure distinguishability between the centrality multisets of the two graphs. This interpretation seems supported by the statement in line 963. Moreover, the topic of centrality encoding does not reappear until line 966.\n\nFurthermore, considering that $A$ represents the adjacency matrix and $A^k$ denotes its $k$th power, the assertion in Equation 11 raises concerns regarding its mathematical validity and intuitive clarity. For example, the expression $A^{k+1}\\_{u\\_1,v} = A^{k+1}\\_{u\\_1, ;} \\\\cdot A^{k+1}\\_{;, v}$ appears to be problematic.\n\nFrom a path interpretation perspective, the original statement suggests that the number of paths of length $k+1$ between nodes $u_1$ and $v$ can be derived by aggregating information from $A^{k+1}\\_{u_1, ;}$, which accounts for all paths of length $k+1$ originating from $u_1$, and $A^{k+1}{;, v}$, which encompasses all paths of length $k+1$ terminating at $v$ from any other node. Would it not be more accurate to represent this relationship as $A^{k+1}\\_{u_1,v} = A^{k}\\_{u_1, ;} \\cdot A^{1}\\_{;, v}$?\n\nAdditionally, I would like to point out the presence of redundant statements, such as $A^{k+1}\\_{u_1, v} \\geq A^{k+1}\\_{u_1, v}$ found in line 961, which could benefit from clarification or removal." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a novel framework that uses walk-based centralities as a powerful Structural Encoding and to reduce the computational cost of Subgraph GNNs." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024balancing,\ntitle={Balancing Efficiency and Expressiveness: Subgraph {GNN}s with Walk-Based Centrality},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2hbgKYuao1},\nnote={under review}\n}" }, "abstract": { "value": "We propose an expressive and efficient approach that combines the strengths of two prominent extensions of Graph Neural Networks (GNNs): Subgraph GNNs and Structural Encodings (SEs). Our approach leverages walk-based centrality measures, both as a powerful form of SE and also as a subgraph selection strategy for Subgraph GNNs. By drawing a connection to perturbation analysis, we highlight the effectiveness of centrality-based sampling, and show it significantly reduces the computational burden associated with Subgraph GNNs. Further, we combine our efficient Subgraph GNN with SEs derived from the calculated centrality and demonstrate this hybrid approach, dubbed HyMN, gains in discriminative power. HyMN effectively addresses the expressiveness limitations of Message Passing Neural Networks (MPNNs) while mitigating the computational costs of Subgraph GNNs. Through a series of experiments on synthetic and real-world tasks, we show it outperforms other subgraph sampling approaches while being competitive with full-bag Subgraph GNNs and other state-of-the-art approaches with a notably reduced runtime." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Graph Neural Networks", "Subgraph GNNs", "Subgraphs", "Expressive power" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/7a65c73ac52f0e7707b0c232c03c1d760f621349.pdf" }, "presentation": null, "primary_area": { "value": "learning on graphs and other geometries & topologies" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/c378c8a96651dece3e10c10e00731402737ae554.zip" }, "title": { "value": "Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "None" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "- As a whole, the work seems extensive and relatively careful, from conceptualization to base data collection, human annotation, model training, and evaluation.\n- I am not an expert in EDA, but it seemed like the work was novel from the point of view of such a dataset and model not existing previously.\n- The experimentation is extensive, comparing a fairly large number of models with various evaluation metrics." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a dataset and a model for verilog generation and understanding. It carefully describes the annotation process for the dataset and presents an extensive battery of experimental results. Overall, the paper seems valuable to me, although I should clarify that I am well-versed in code generation, but not in Verilog so I may be missing some context with related work." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- As someone who is not well-versed in Verilog, I would have appreciated an explanation of the basics of the language, what is its basic syntax, characteristics, etc. But there was not very much explanation in the paper.\n- Conceptually, the work was rather straightforward and I did not get many clear research insights from the paper. For this paper I am not extremely concerned about this though, as the work seems valuable nonetheless, and could serve as a base for future research.\n- It was not clear how much of the work will be released for the research community to build on. It seems that some of the data may be released, but presumably the proprietary data will not be? And also it wasn't clear about the model." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "--" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "* Verilog is not a particularly known language. Authors could have explained a bit more its nature, syntax and usage.\n\n* Figure 1, although it helps to understand the flow of data collection, it’s not particularly clear. The fact that the flow goes to the top-left in opposition to the common flow for reading (top to bottom and left to right) makes it unclear. Also, which part is used for training? Only after distil?\n\n* Line 388-392: these lines and Figure 3 describe the progressive training. This explanation is not clear. Are the authors just feeding the model with more to less granular annotations? That could be an example of Curriculum learning. Please clarify and add references if needed.\n\n* why the authors didn’t compared the performance of the new models with Zhang et al., 2024, , Chang et al. 2024b, Liu et al. (2023b); Thakur et al. (2024)," }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Original approach, focusing on both generation and understanding tasks on a low resource code language as Verilog, specifically designed for hardware description.\nThe approach seems reasonable. The field of application is needed and follows the ultimate goal of improving electronic design automation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper deals with the task of code understanding and generation in the context of generation of hardware description language (HDL) code. In particular, this work focuses on the generation of Verilog code.\nThe model is based on an existing CodeLLM (authors used CodeT5+), which was fine tuned with a new augmented dataset created for this purpose. The dataset comprises both open and proprietary verilog codes, which were augmented (commented and summarised) by GPT-4. \nTwo models are trained using a progressive training strategy based on CodeT5+ models. For the understanding benchmark, models are evaluated in terms of BLUE and ROUGE, as well as embedding similarity and GPT score. Results show an improved performance over competitors and baseline models. For the generation part, the models are evaluated on a Verilog generation benchmark introduced by Chang et al. 2024, and compared with GPT series models showing competitive performance against the best, o1-preview and surpassing GPT3.5 and GPT4." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The work lacks clarity. Particularly, the dataset collection and the training regime are not completely clear and their figures do not clarify the issue (see below).\nExperiments seem reasonable but all baselines and competitors weren’t trained specifically on verilog. Since the current work cites other previous approaches, experiments could have compared to them as well (or explain why was not possible)" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "N/A" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "1. The paper introduces a novel task for evaluating LLMs in hardware design, focusing on Verilog understanding—prior work mainly focuses on generation. It introduces new training datasets, evaluation benchmarks, and establishes baselines for this new task.\n\n2. DeepRTL, the model proposed in this paper, uniquely good at both the generation and understanding of Verilog, making it different from other models in the hardware design domain.\n\n3. The methodology for creating a natural language-code parallel corpus via prompt engineering with GPT-4 is innovative and shows promise for broader application in fields where parallel corpora are lacking.\n\n4. The diagrams in this paper describes the proposed methods clearly and intuitively." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper makes a contribution to the field of hardware design automation by addressing both the generation and understanding of Verilog code using large language models (LLMs). While previous studies primarily focused on the generation aspect, this work recognizes the importance of understanding Verilog code and proposes a unified representation model, DeepRTL, built on an enhanced CodeT5+ architecture. This model is trained on a specifically curated dataset that tightly aligns natural language descriptions with Verilog code, aiming to improve the semantic alignment between the two. Additionally, the paper introduces the first benchmark specifically for Verilog understanding and develops two novel metrics, embedding similarity and GPT score, to capture semantic similarities more effectively than traditional n-gram-based metrics like BLEU and ROUGE. In comparative assessments, DeepRTL surpasses GPT-4 in Verilog understanding tasks and matches the performance of OpenAI’s o1-preview model in code generation tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The reason for selecting T5-like models as the base for DeepRTL is not empirically validated. It remains unclear whether the observed performance gains in Verilog understanding are due to T5's encoder-decoder architecture or the synthesized dataset used for fine-tuning. Comparative analysis with a decoder-only model, such as LLaMa-3-1B or DeepSeekCoder-1.3B, using the same dataset for finetuning would provide clearer insights.\n\n2. The paper does not evaluate the impact of varying context window lengths, which is important given that CodeT5+ supports a limited token count (2,048 tokens), while actual Verilog code often exceeds this length. Dropping examples longer than 2,048 tokens will also bias the results in favor of DeepRTL, which is based on CodeT5+. A model accommodating longer context windows could potentially offer superior performance on the general task, but not for this tailored dataset.\n\n3. The evaluation metrics for code understanding—embedding similarity and GPT score—are solely based on GPT models, leading to potential bias, as evidenced by the inflated scores of GPT-3.5, GPT-4, and o1-preview models shown in Table 2. This overlap may make the comparisons bias in favor of GPT-family models.\n\n4. The evaluation of code generation lacks a comprehensive set of baselines. Despite mentioning various Verilog generation models in the related work section, these models are absent from the comparative analysis in Table 3.\n\n5. The fine-tuning dataset includes proprietary code that cannot be released publicly, and the benchmarks used are also developed by the authors. The absence of shared code, data, or models in the publication hinders reproducibility and make it impossible to assess potential data contamination and bias in evaluation." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- Why throw away dataset items that are longer than 2,048 tokens? It is true that this is the maximum input length for CodeT5+; however, why make a choice about the dataset based on the (essentially arbitrary) choice of model used in the specific experiments here?\\\nModern LLMs, including open source ones such as Llama, have context sizes way beyond 2,048 tokens." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "A novel dataset and benchmark for highly specialised programming code (Verilog); this might be interesting as it provides a new and interesting resource for a programming language that does not have as much attention as others such as Python, Jave, or C++." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a novel dataset and model training for Verilog understanding and generation, as well as a new high-quality benchmark for the understanding task.\n\nThe authors provide a large Verilog dataset based on a large quantity of crawled open source code that is processed into code and natural language descriptions via GPT4, as well as a smaller amount of hand-curated code-description items from proprietary sources.\\\nThey also introduce a new benchmark for Verilog understanding, consisting of 100 manually verified, high-quality code-description pairs.\n\nFor experiments, the authors train CodeT5+ -based models of sizes 220M and 16B on their newly introduced dataset, using \"progressive training\" and evaluate model performance in terms of Verilog understanding and generation capabilities.\\\nExperiments show that models trained in this manner outperform strong baselines on various metrics." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Beyond the curation of an interesting new dataset, there is very limited novelty to this work; it seems like authors might be somewhat unfamiliar with the current state of the field of LLMs/Machine Learning, including ML for code:\n- Fine-tuning a CodeT5 model on domain-specific code has been done.\n- The \"progressive training\" is just curriculum learning, which is well-established in the field.\n- Similarity scores based on vector similarity are as old as Word2Vec, if not older.\n- Similarities/evaluations with LMs or LLMs (here \"GPT Score\") are well-established, e.g., see \"LLM as a judge\", BERT Score, etc.\n\nThis seems like it would be a very nice paper for a specialised Verilog/hardware spec conference, but may be of limited value for a venue like ICLR." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024deeprtl,\ntitle={Deep{RTL}: Bridging Verilog Understanding and Generation with a Unified Representation Model},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2hcfoCHKoB},\nnote={under review}\n}" }, "abstract": { "value": "Recent advancements in large language models (LLMs) have demonstrated significant potential in automating the generation of hardware description language (HDL) code from high-level natural language instructions. While fine-tuning has improved these models' performance in hardware design tasks, prior efforts have largely focused on Verilog code generation, overlooking the equally critical task of Verilog understanding. Furthermore, existing models suffer from weak alignment between natural language descriptions and Verilog code, which hampers the generation of high-quality, synthesizable designs. To overcome these limitations, we present DeepRTL, a unified representation model that excels in both Verilog understanding and generation. Based on CodeT5+, DeepRTL is fine-tuned on a comprehensive dataset that aligns Verilog code with rich, multi-level natural language descriptions. We also introduce the first benchmark for Verilog understanding, alongside two novel metrics, embedding similarity and GPT score, that capture semantic similarity more accurately than traditional metrics like BLEU and ROUGE, which are limited to surface-level n-gram overlaps. DeepRTL's progressive training strategy enables it to significantly outperform GPT-4 in Verilog understanding tasks, while achieving performance on par with OpenAI's o1-preview model in Verilog generation tasks." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Large Language Model", "Program Representation Learning", "Verilog Understanding and Generation" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/1b0ad7ee7ee0ae6f8aee3d6c47d7523cb2cf714b.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- unclear sentence line 366: \"Moreover, in an SLDS, only the latent dynamics changes following context switching, hence requiring a non-negligible number of timesteps (depending on the spectral radius of transition operator) for the reflection of context changes in the observation space. in contrast, the compositional nature of factor loading process in the infinite GPFA model allows immediate differential expression of latent processes into neural activities.\"\n\nCan you clarify this a bit? infinite GPFA model seems to also have the factor loading process in latent space, why it allows immediate expression into neural activities than SLDS? \n\n- How is the number of features D selected for svGPFA in the experiments section for synthetic data and real data?\n\n- What's the future research direction for this paper?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper is clearly written. The model formulation and related works are clearly introduced. \n- The authors have done extensive experiments on real neural data and synthetic data, and results seem good." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes the infinite GPFA model, which is Bayesian non-parametric extension of the classic GPFA by combining GPFA with an Indian Buffet Process Prior. This model can potentially infer infinite set of latent factors from data. A variational EM algorithm is proposed to perform the inference. The authors demonstrate the effectiveness of this model through analysis on simulated and real datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The idea of combining GPFA with IBP prior is not revolutionary. \n- I listed some questions in the section below." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "None" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The switching GPFA and switching infinite GPFA models effectively tackle a significant limitation commonly encountered in many latent variable models in neuroscience, particularly within GPFA: the a priori selection of latent dimensionality. Additionally, these models enhance the approach by allowing for unequal contributions of latent variables at different time points, addressing another critical shortcoming of traditional GPFA. This advancement represents a noteworthy contribution to latent variable modeling in neuroscience. The authors also incorporate inducing points for improved scalability, a practical and well-established extension from the existing GP literature." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors present an extension to GPFA, a widely used latent variable model in neuroscience, that uses an Indian Buffet process as a nonparametric extension to automatically select latent dimensions at each time point. This avoids the need for a priori latent dimensionality choice in GPFA, a well-known limitation to the method, and allows for a sparse selection of latent activations at each time point, which can identify transitions in the latent representation, enhancing the models usefulness in the identification of behavioral states. The authors show strong validation on synthetic datasets as well as real spiking data. The theory is clear and model development and implementation is clear and sound." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The weakest part of the manuscript is the lack of evaluations to any competing approach. The authors appear only compare to variants of their own model. In particular, because the authors emphasize the advantage of not needing to pre-select latent dimensionality, some evaluation against the ARD approach in Jensen et al would be appreciated. The authors claim the ARD is inferior due to requiring marginalizing over all of the data to determine latent dimensionality, and this is sound reasoning, however, I am curious as to how exactly different the models fits and latent posteriors would be. It might be possible, for example, for the ARD GPFA model to learn an appropriate number of latent dimensions and have periods of time where different groups of latents are minimally or highly variable. I think it would help a reader get a sense of how svGPFA compares Bayesian GPFA, as the latter is a model that was motivated in a very similar way.\n\nNote also that the manuscript \"Uncovering motifs of concurrent signaling across multiple neuronal populations\", Gokcen et al. also uses an ARD prior in a similar GPFA-style model - might be worth citing\n\nOne small point -- Figure 2 is difficult to render in the browser and this specific page lags. I suspect the figure size is too large, maybe due to panel d. Downsampling this figure before adding it to the latex might help." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "- How does the infinite GPFA handle cases where it identifies overlapping/slightly differing latent factors ?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- **Novelty**: The proposed model, infinite GPFA, has a robust mechanism that allows for estimation of both the number of latent factors and their time-varying activations without requiring manual tuning. In addition, the sparsity allows for learning of more interpretable latent factors, which is helpful for interpreting neural computations.\n- This framework opens up new avenues in neuroscience for exploratory investigations of experimental data.\n- Presentation is clear." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a novel model, an extension to GPFA, that incorporates stochastic activation of latent factors in the loading process via IBP prior. This results in dynamically switching expression of latent factors for each neuron across different timepoints, hence incorporating the dynamic shifts in internal states of the animal. They apply their model, infinite GPFA, to two datasets, one synthetic and one real world neuroscience dataset (hippocampal place cells during spatial navigation)." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- More comparison to other methods could have strengthened the utility and performance of infinite GPFA, specifically, using some of the previously established methods like GPFA with ARD prior. Although GPFA with ARD prior is not designed to capture latent factors across time, it would be useful to show it quantitatively.\n\nMinor points\n- l060 ‘An as example,’ → ‘As an example,’\n- Figure2.a the axis labels are illegible .\n- In general figure 2 gets rendered very slowly, I am not sure the exact cause but it might be worth investigating because if it’s simple like rasterization or high resolution graphics, it can be easy to fix." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "for fig.2c what expected level of masking for Z was used? on that topic, it could also be interesting to show how the gap between gpfa and infinite gpfa inference performance varies with the expected value of alpha. additionally, an inline figure or addition to figure 1 helping to build intuition between numerical values of alpha and the expected number of features could be useful.\n\nis the runtime comparison in Fig.2h for a single EM iteration, or only inference time?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "the paper is very well written. the background section is clear and in my opinion and succesfully takes the reader from the original gpfa to their new generative model that incorporates an indian buffet process prior over the binary masking matrix, Z. since approximate inference in this model is highly non-trivial, the authors developed an approximate variational EM procedure for inference/learning. i appreciated the extensive discussion covering what terms are and are not tractable in the variational bound and how the authors deal with the intractable terms in a practical manner; important details that would clutter the main text were referenced often and helped with further clarifications. their synthetic data example validates their inference approach and reassuringly shows the infinite gpfa model can match standard gpfa inference quality even when there is no encoding variability. in their last experiment, they apply their method to neurophysiological recordings taken from a rat performing a spatial navigation task; they demonstrate how their method can reveal the compositional structure of the latent space by identifying different latent factors being loaded onto the neural population at different locations or trial events." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "the authors introduce switching infinite gpfa — an extension of the classical gpfa model to account for the possibly time varying dependence of observed neural activity on different latent factors. the authors make this possible by using an indian buffet process as the generative model for a (infinite) binary mask that selects the latent features read out to the observation space at each point in time. they outline how to perform tractable variational EM inference/learning for this model class. the authors then validate their model and inference/learning procedure on synthetically generated data, and then show how their method can be used to extract behaviorally meaningful latent features from rats performing a spatial navigation task." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "more comparisons could be helpful. for example, it could have been interesting to see how bGPFA also compares to infinite gpfa with and without model mismatch similar to the synthetic example presented. \n\nfrom fig 2b and fig 3b, it does appear that infinite gpfa takes substantially longer to reach convergence. do the authors expect this difference gets substantially worse with higher latent state dimensionality? it could be helpful to see convergence plots for a dataset that requires higher latent state dimensionalities." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a fully Bayesian nonparametric extension of GPFA that enables discovery of temporally compositional neural manifolds underlying high-dimensional population neuronal activities." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024discovering,\ntitle={Discovering Temporally Compositional Neural Manifolds with Switching Infinite {GPFA}},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2iCIHgE8KG},\nnote={under review}\n}" }, "abstract": { "value": "Gaussian Process Factor Analysis (GPFA) is a powerful latent variable model for extracting low-dimensional manifolds underlying population neural activities. However, one limitation of standard GPFA models is that the number of latent factors needs to be pre-specified or selected through heuristic-based processes, and that all factors contribute at all times. We propose the infinite GPFA model, a fully Bayesian non-parametric extension of the classical GPFA by incorporating an Indian Buffet Process (IBP) prior over the factor loading process, such that it is possible to infer a potentially infinite set of latent factors, and the identity of those factors that contribute to neural firings in a compositional manner at each time point. Learning and inference in the infinite GPFA model is performed through variational expectation-maximisation, and we additionally propose scalable extensions based on sparse variational Gaussian Process methods. We empirically demonstrate that the infinite GPFA model correctly infers dynamically changing activations of latent factors on a synthetic dataset. By fitting the infinite GPFA model to population activities of hippocampal place cells during spatial navigation, we identify non-trivial and behaviourally meaningful dynamics in the neural encoding process." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Computational neuroscience", "neural data analysis", "Bayesian nonparametrics", "latent variable modelling;" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/f9aa50b2ef39d7d517f0d4c89b2bb4d2ccdeea0e.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/06972b22ef78ea30036818ac9922fea1fc71f935.zip" }, "title": { "value": "Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- What are the main differences between the proposed method and SPICE/InfoMetIC? What unique innovations does this paper offer?\n- Why choose THumB instead of MSCOCO for evaluation?\n- The meanings of the numbers should be stated more clearly. For example, what do 5/5 and 80/100 mean？" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The paper highlights the urgent need for developing new metrics, considering the fact that model generations have become so detailed that they often exceed the capability of the automatic evaluation metrics.\n\n- The paper is easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents VisCE2, a vision-language model-based caption evaluation method designed to evaluate captions in a manner that aligns more closely with human preferences. VisCE2 focuses on visual context, which refers to the detailed content of images, including objects, attributes, and relationships. Experiments are conducted on several datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The literature review should be more accurate. For example, SPICE (Anderson et al., 2016) is mainly based on scene graphs rather than n-grams.\n\n- The novelty of this paper is limited. The proposed evaluation method consists two stages: visual context extraction and VLM-based caption evaluation. The first stage analyzes images based on scene graphs, similar to SPICE (Anderson et al., 2016). The second stage evaluates captions with VLMs, which is not new given existing works such as InfoMetIC (Hu et al., 2023) and CLIPScore (Hessel et al., 2021). While the combination of these two stages may be new, it may not meet the innovation standards expected for ICLR submissions." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please see weaknesses.\n\nI will be happy to raise my score if authors address my concerns." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. A novel reference-free image caption evaluation method with VLMs.\n2. This paper is well-written and easy to follow.\n4. This paper proposes a visual context extraction module to describe the image as sentences, which also can be seen as a pseudo reference with abundant details.\n4. The authors conduct comprehensive experiments across multiple datasets." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a reference-free image captioning evaluation metric, called VisCE$^2$. Specifically, VisCE$^2$ leverages pre-trained Vision-Language models (VLMs) to realize two-stage measurements for candidate captions. The first is Visual Context Extraction which uses VLM to obtain detailed descriptions including objects, object attributes and relationships. The second is Vision-LM Caption Evaluation which takes visual context, image and candidate captions as inputs to obtain an evaluation score. Experimental results demonstrate the superiority of this reference-image free method against other metrics." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Figure 1 is not comprehensive. For the left part, RefCLIP-S[1] and RefPAC-S[2] can also accomplish the same measurement. On the other hand, better evaluation performances of VisCE$^2$ than BLEU-4, ROUGE, SPICE and CIDEr are not enough. While for the right part, authors should compare with PAC-S[2] to illustrate the superiority of this work.\n\n2. Line 49 - Line 51 describes the disadvantages about InfoMetIC, but evidence is lacked and can therefore be listed in Figure 1.\n\n3. It is suggested to evaluate the VisCE$^2$ and other reference-free metrics within different *image captioning methods* such as InstructBLIP, LLaVA and even GPT-4, as mentioned in Line 42-Line 44. This is a key step to comprehensively measure the effectiveness of VisCE$^2$. The authors can refer to Table 7 in PAC-S paper[2].\n\n4. Although this paper focuses on reference-free evaluation, it is also recommended to report the results of VisCE$^2$ when the reference captions are provided. \n\n5. An example of visual context given the image should be added into appendix. For instance, authors can list all the objects, object attributes and relationships about the image in Figure 2. \n\n6. In Table 2, it seems that authors only report the values of Kendall’s $\\tau_b$ on Flickr8k-Expert and Composite datasets. Kendall’s $\\tau_c$ should also be included. \n\n7. It is a little bit confusing to read Table 3 about ablation experiments. The first two settings are to prove the effectiveness of each component with the same backbone VLM (LLaVA-v1.5-13B). Then the current model (VisCE$^2$ ours) achieves the best scores across all datasets. But for the last two settings, authors aim to explore the influences of different backbone models or model sizes. From Table 3, GPT-4o can achieve **59.0** score on Composite dataset, higher than VisCE$^2$(**56.0**). THumB and Pascal-50S observe similar phenomenon. Hence, it would be better to split Table 3 into two small tables.\n\n[1] Hessel, Jack, et al. \"Clipscore: A reference-free evaluation metric for image captioning.\" arXiv preprint arXiv:2104.08718 (2021).\n\n[2] Sarto, Sara, et al. \"Positive-augmented contrastive learning for image and video captioning evaluation.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please refer to the questions in the weakness." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The proposed method is easy to understand.\n- The proposed method shows favorable performance compared to existing evaluation methods." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Given the accelerating progress of vision and language modeling, accurate evaluation of machine-generated image captions remains critical. \nIn order to evaluate captions more closely to human preferences, metrics need to discriminate between captions of varying quality and content. \nHowever, conventional metrics fall short of comparing beyond superficial matches of words or embedding similarities; thus, they still need improvement. \nThis paper presents VisCE2, a vision language model-based caption evaluation method. \nThe authors’ method focuses on visual context, which refers to the detailed content of images, including objects, attributes, and relationships. \nBy extracting and organizing them into a structured format, the authors replace the human-written references with visual contexts and help VLMs better understand the image, enhancing evaluation performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The novelty of the proposed method is weak. The only idea in this paper is to use a language model instead of CLIP to evaluate image captioning where the sentence generation performance of the VLMs is imperfect, unlike CLIP’s image-caption alignment performance. The authors suggest using an image captioning model to evaluate image captioning models. How can we evaluate the models that perform better than LLaVA? Using the proposed metric instead of CIDEr or CLIPS scores for future image captioning research is not convincing.\n\n- The discussion on design choice is also weak. In Table 3, the only discussions are on what VLM to use and what kind of visual context to use. However, there are other design choices to be considered. For example, when using language models, a proper prompt is essential. However, the authors didn’t analyze the choice of prompts for the language model. Moreover, whether the visual context extractors (object, attribute, relation) have the best design choice isn't justified. Therefore, it is not clear whether the proposed metric is the best possible method.\n\n- This paper lacks experimental analysis. When suggesting a new evaluation metric, it would be better to evaluate popular image captioning models, such as BLIP2, and analyze the tendency of the performances to understand the unique characteristics of the proposed metric. Also, it would be better to evaluate the proposed metric in different settings, such as FOIL hallucination detection, as CLIPS did." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please see the questions in Weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper proposed a new method to evaluate the generated captions considering the objects, attributes, and relations within the images. And the paper makes great efforts to demonstrate the reliability of the evaluation method by comparing with human judgement. The results indicate this method is better consistent with human rating compared to other metrics." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a method that uses the visual concepts extracted by MLLMs to help evaluate image captions, which makes the evaluation results more consistent with human ratings." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The evaluation process heavily relies on the use of MLLMs in the following ways: 1. It utilizes MLLMs to extract visual concepts from images; 2. It employs MLLMs to generate evaluation scores for these image captions. If the candidate captions are generated by a same MLLM, the evaluation method may fail to provide a fair evaluation.\n\nIt seems that the evaluation time is significantly longer than the time required by other metrics, due to the use of MLLMs in two stages. How long does it take to evaluate one caption based on an image? Please provide concrete timing comparisons between the proposed method and existing metrics. Additionally, why is the image necessary in the Vision-LM Caption Evaluation stage? If the visual concepts are sufficient to represent an image, the evaluation could potentially be conducted without using the image, which might speed up the evaluation process. The paper should include an ablation study comparing performance with and without the image in the Vision-LM Caption Evaluation stage.\n\nAlso, the paper should add ablation studies on the used prompts, particularly regarding the maximum number of objects. According to the prompts shown in Table 4, the maximum number of objects extracted by MLLM is set to 5. How could this choice affect the reliability of the evaluation method?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "N/A" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. When simply constructing the initial prompt (Vanilla) in a more refined way, e.g. by adding a chain-of-thought (CoT) prompt, would better assessment results also be achieved?\n\n2. Can the authors provide a comparison of runtime with existing evaluation methods?\n\n3. The visual context extracted in the first phase will contain some hallucinations, does this have an impact on the evaluation results?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tThe paper is well-organized and clearly written.\n2.\tThe proposed VisCE2 is intuitive. And evaluation experiments on multiple datasets demonstrate that the method outperforms existing evaluation metrics and meets human judgments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a VLM-based image caption evaluation method called VisCE2. The proposed method first obtains structured visual context by prompting the VLM, and then evaluates candidate captions based on the extracted visual context and input image. Extensive evaluation experiments show that VisCE2 outputs scores that have good agreement with human judgment and outperform existing evaluation metrics." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper is somewhat weak on innovation. The method is simply based on two rounds of prompts, which makes the VLM automatically evaluate image captions, and its core is based on the in-context learning ability of the VLM. Assuming that only one round of prompt is used and combined with the chain-of-thought (CoT) method to make the VLM automatically mine the visual context, while setting the last sentence generated by the VLM as the evaluation result, can this also lead to a good image caption evaluation performance?\n\n2. Since two rounds of prompts are required for the VLM to evaluating the image caption, resulting in a high time complexity of this evaluation method, which is not conducive to real-time evaluation. Can the authors provide a comparison of runtime with existing evaluation methods? \n\n3. Based on Table 3 of the ablation experiment, the enhancement brought by visual context does not seem to be particularly significant compared to the original prompt (Vanilla). Can the authors further analyze the reasons for this condition?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024vision,\ntitle={Vision Language Model Based Caption Evaluation Method Leveraging Visual Context Extraction},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2iPvFbjVc3},\nnote={under review}\n}" }, "abstract": { "value": "Given the accelerating progress of vision and language modeling, accurate evaluation of machine-generated image captions remains critical. In order to evaluate captions more closely to human preferences, metrics need to discriminate between captions of varying quality and content. However, conventional metrics fall short of comparing beyond superficial matches of words or embedding similarities; thus, they still need improvement. This paper presents VisCE2, a vision language model-based caption evaluation method. Our method focuses on visual context, which refers to the detailed content of images, including objects, attributes, and relationships. By extracting and organizing them into a structured format, we replace the human-written references with visual contexts and help VLMs better understand the image, enhancing evaluation performance. Through meta-evaluation on multiple datasets, we validated that VisCE2 outperforms the conventional pre-trained metrics in capturing caption quality and demonstrates superior consistency with human judgment." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Image Captioning", "Evaluation", "Vision and Language", "LLM as a judge" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/44f0ab56bf9829e1a7c6bf21e339d2d2f4db6e77.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Vision Language Model Based Caption Evaluation Method Leveraging Visual Context Extraction" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "see the first box" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "see the first box" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work studies a new problem set-up, PREF-RMAB.\nFor me, the problem set-up is very incremental. It is quite similar to duelling bandits. The proposed set-up is more like duelling bandits with state transitions. \n\nThe writing needs to be improved. \n\nThe writing for the intro is too wordy. I wish to see more literature work discussions.\n\nI suggest putting the main theorem (Theorem 1) earlier. I can only see the theoretical result at Page 8. So, the structures for sections 4 and 5 are suggested to re-arrange. \n\nA minor thing: usually, RL or bandit theory works use $\\delta$ to be the failure probability. The greek letter $\\epsilon$ is for something else, like the error rate.\n\nI went through the proofs and some steps are novel, not that typical in bandit/RL literature. But I did not check the correctness of the proofs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "see the first box" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "1.\tEstimating the whole preference matrix $F$ in DOPL algorithm requires large computational cost. Moreover, it would be beneficial to involve a thorough discussion on computational complexity of DOPL.\n2.\tIn experiments, the existing algorithms like MLE_WIBQL, MLE_LP fail to achieve sublinear regret. A detailed discussion on why these algorithms underperform in achieving sublinear regret would provide valuable insights." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tThe paper successfully integrates preference feedback within the RMAB framework, a novel approach that shifts away from traditional scalar reward dependency. Moreover, the presented algorithm DOPL achieves $\\tilde{O}(\\sqrt{T \\ln T})$ regret with theoretical analysis.\n2.\tThe relaxed LP-based direct index policy of DOPL is also given to tackle the limitations of computational intractability.\n3.\tThe writing is clean and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper studies the Restless Multi-Armed Bandits with preference feedback named PREF-RMAB. The authors propose the Direct Online Preference Learning (DOPL) algorithm achieving an $\\tilde{O}(\\sqrt{T \\ln T})$ regret, which is the first to give the theoretical regret upper bound for PREF-RMAB. Moreover, the paper presents numerical experiments which further validate the efficacy of DOPL against various baselines." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tEstimating the whole preference matrix $F$ in DOPL algorithm requires large computational cost. Moreover, it would be beneficial to involve a thorough discussion on computational complexity of DOPL.\n2.\tIn experiments, the existing algorithms like MLE_WIBQL, MLE_LP fail to achieve sublinear regret. A detailed discussion on why these algorithms underperform in achieving sublinear regret would provide valuable insights." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "•\t1. In the 11th step of Algorithm 3 in Section C.3, when inferring \\(\\hat{\\mathbf{F}}_n^{*,k+1,\\text{inf}}(\\sigma_{s_n}, \\sigma_{s_*})\\), only one intermediate column \\( j \\) in \\(\\mathbf{F}\\) is selected to compute \\(\\hat{\\mathbf{F}}_n^{*,k+1,\\text{inf}}(\\sigma_{s_n}, \\sigma_{s_*})\\) based on \\(\\hat{\\mathbf{F}}_n^{*,k}(\\sigma_{s_n}, j)\\) and \\(\\hat{\\mathbf{F}}_n^{*,k}(j, ((*-1)|S|+\\sigma_{s_*}))\\). However, after selecting \\( B \\) arms at each step, the duels are conducted randomly, resulting in many comparison data points beyond \\( j \\) that are not used in the inference process. Could this lead to substantial unutilized information? Could more comparison data, beyond just \\( j \\), be leveraged to infer the preference between \\( s_n \\) and \\( s_* \\)? Alternatively, rather than performing duels randomly, might strategically choosing duel pairs improve performance?\n\n•\t2. In Eq. (3), the authors define \\(\\pi^{opt}\\) as the solution of (1) with scalar rewards, but the footnote states that the regret is with respect to preference feedback, which seems contradictory. This part is unclear to me.\n\n•\t3. In Eq. (46), the suboptimality is accumulated over \\( K \\) episodes. However, since \\( \\omega^k_n \\) is a probability measure and \\( Q_n(s) \\) represents the relative reward of arm \\( n \\) in state \\( s \\), which involves the reward incurred at a specific step \\( h \\) within episode \\( k \\), why doesn’t \\( h \\) appear in this decomposition?\n\n•\t4. In Lemma 6, the authors use Lemma 11 to argue that term0 is negative. However, I find this reasoning unclear, as \\({\\pi^*}\\) does not appear to align with \\(\\tilde{\\pi}\\) as defined in Lemma 6. Specifically, \\(\\mu_{\\pi^*}\\) represents the optimal solution of Eq. (6)-(9), while \\(\\tilde{\\pi}\\) is the index policy developed from \\(\\mu_{\\pi^*}\\) to satisfy the hard constraint. Therefore, I am uncertain that Lemma 11 can indeed be used to prove Lemma 6, and concluding that term0 is negative is not straightforward.\n\n•\t5. In the proof procedure following Eq. (49), from the fourth to the fifth line, the inequality \\(\\sum_{k=1}^K\\sqrt{\\frac{1}{Z^k_n(s,a)}} \\leq \\sqrt{Z^K_n(s,a)}\\) appears incorrect. For instance, if the algorithm visits arm \\( a \\) at state \\( s \\) once at the beginning and never revisits this state, it would hold that \\( Z^1_n(s,a) = \\dots = Z^K_n(s,a) = 1 \\), yielding \\(\\sum_{k=1}^K\\sqrt{\\frac{1}{Z^k_n(s,a)}} = K\\), which is indeed greater than \\( \\sqrt{Z^K_n(s,a)} = 1\\). If I have misunderstood this part, please clarify.\n\n•\t6. In the sentence between lines 1398 and 1399, I think the statement \\(\\sum_{n=1}^N\\sum_{(s,a)}Z^T_n(s,a) \\leq NT\\) should instead be \\(\\sum_{n=1}^N\\sum_{(s,a)}Z^T_n(s,a) = T\\), as the total visits across all arms and states should sum to \\(T\\). In fact, only under this revised statement can the inequality (c) above this sentence be satisfied, otherwise it should be \\(\\sum_{n=1}^N\\sqrt{\\sum_{(s,a)}Z_n^K(s,a)}\\leq N\\sqrt{T}\\). This confusion also appears in the sentence from 1503 to 1505. If I have misunderstood this part, please clarify." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Although RLHF has recently gained significant attention due to its applications in large language models and robotics, this work is the first to consider a preference-based reward model in the restless bandit problem, opening the door for RLHF to be applied much more broadly.\n\n2. By establishing a connection between pairwise preferences and reward values, the authors transform the reward value of each arm and state into a measure based on the preference probability between this state and a reference arm and state, which is intriguing. Additionally, the algorithm can infer the preference between the element j in the preference matrix \\(\\mathbf{F}\\) and the reference elements \\(s_*\\) without directly comparing them, but rather through an intermediate comparator. This clever design reduces the complexity to that of directly observing the reward." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes the PREF-RMAB model, which observes only the preference between two arms rather than the exact reward of each arm in the restless multi-armed bandit problem. By expressing the reward function of any arm n in any state as the sum of a reference reward and a function related to the preference probability between arm n in state s and a reference arm in a reference state, the authors develop a direct index policy based on preference data to choose which arms to activate in the online manner. They establish an O (√(NT|S|ln⁡〖(|S||A|NT)〗 )) regret bound for the proposed algorithm." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "•\t1. A question remains as to whether a preference-based model can outperform direct reward estimation, and whether we really need the preference-base model in RMAB problem. In the first two examples presented in the paper, APP MARKETING and CPAP TREATMENT, while reward data is challenging to estimate accurately and may contain substantial noise, it can still be estimated in some form. Conversely, since preference data inherently provides less information, it is unclear whether incorporating preference data can improve performance over direct reward estimation. Most papers on RLHF use preference feedback to shape the reward for different trajectories in robotics or large language models (LLMs), where trajectory rewards are inherently complex and require function approximation methods to estimate the reward function. However, the RMAB model studied in this paper is a tabular MDP, where rewards can be estimated through multiple sampling.\n\n•\t2. In Algorithm 3 of Section C.3, at each step \\( h \\), the algorithm performs \\( (B-1) \\) random duels to obtain the preference data. Then, when constructing the preference between \\(s_n\\) and the reference \\(s_*\\), only the preference between \\(s_n\\) and \\( j \\) and \\(s_*\\) is used. It appears that \\( s_n \\) could be compared with many other columns \\( i \\) in the \\( F \\) matrix, but the algorithm does not leverage this data. Consequently, Algorithm 3 makes inefficient use of the available information.\n\n•\t3. The MDP considered in the paper is essentially a tabular MDP, and the regret scales with the square root of \\( |S| \\) (the size of the state space), which may be inefficient for large state spaces." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1.\tThe important results Lemmas 4 and 5 are based on Lemma 1. However, it is unclear why there is only a single reference arm * and a single reference state in Lemma 1. In the RMAB setting, DM selects B arms at each time slot, so the use of a single reference arm seems inconsistent.\n2.\tIn Eq. (4), if $\\epsilon=o(1)$, the confidence width becomes arbitrarily large. While if $\\epsilon=\\Theta(1)$, the probability becomes very small. How do the authors balance this trade-off? A more detailed discussion of the setting for $\\epsilon$ would be helpful." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tThe authors present novel approaches to address the PREF-RMAB problem. The results in Lemmas 4 and 5 are particularly interesting and have the potential to inform future algorithmic design.\n2.\tThe propose DOPL algorithm works well on the PREF-RMAB problem.\n3.\tI understand that analyzing the regret bound of the RMAB problem with preference feedback is challenging, so the inclusion of this theoretical bound is commendable." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the restless multi-armed bandit (RMAB) problem with preference feedback (PREF-RMAB) rather than direct reward estimation, motivated by real-world applications like app marketing and CPAP treatment. To address the problem that some arms in some states may not be visited frequently, the authors propose a new method to infer the empirical average of arm preference via the other arms’ empirical preference estimations. Additionally, the authors transform the original reward-based optimization problem into another one in terms of preference feedback directly. Using this transformation, the authors develop a low-complexity index policy for decision making. They also provide theoretical analysis of the regret bound, establishing a regret bound of $\\tilde{\\mathcal{O}}(\\sqrt{T\\ln T})$. Finally, the authors conduct experiments to verify the performance of their proposed DOPL algorithm." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tAlthough the authors make a strong effort to illustrate potential real-world applications of the PREF-RMAB problem, the justification remains unconvincing. For instance, in the app marketing scenario, users’ state transitions do not satisfy the memoryless Markov chain property, as a user currently in state $s_4$ cannot directly transition to $s_1$, and time tracking is ambiguous. Similar concerns apply to the other examples.\n2.\tThe writing can be further improved. For example, \n\n (a) Adding more detail on the composition of the preference matrix $\\mathbf{F}$ would improve clarity.\n\n (b) Eq. (2) needs to be improved, as the notation is confusing. \n\n (c) The objective function (6) is not easy to follow. Please define $\\mu^{\\pi}$ and $\\mu_n$ first. I misunderstood it as a myopic problem until I saw the definition of mu^pi. \n\n (d) I think Lemmas 4 and 5 are more important than Lemmas 1 and 2. The authors can change them into propositions. \n\n (e) Lemma 2 can be improved by defining $Q_n(s)$ first and then present the result in Lemma 2." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024dopl,\ntitle={{DOPL}: Direct Online Preference Learning for Restless Bandits with Preference Feedback},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2iYVBqRHK4},\nnote={under review}\n}" }, "abstract": { "value": "Restless multi-armed bandits (RMAB) has been widely used to model constrained sequential decision making problems, where the state of each restless arm evolves according to a Markov chain and each state transition generates a scalar reward. However, the success of RMAB crucially relies on the availability and quality of reward signals. Unfortunately, specifying an exact reward function in practice can be challenging and even infeasible. In this paper, we introduce Pref-RMAB, a new RMAB model in the presence of preference signals, where the decision maker only observes pairwise preference feedback rather than scalar reward from the activated arms at each decision epoch. Preference feedback, however, arguably contains less information than the scalar reward, which makes Pref-RMAB seemingly more difficult. To address this challenge, we present a direct online preference learning (DOPL) algorithm for Pref-RMAB to efficiently explore the unknown environments, adaptively collect preference data in an online manner, and directly leverage the preference feedback for decision-makings. We prove that DOPL yields a sublinear regret. To our best knowledge, this is the first algorithm to ensure $\\tilde{\\mathcal{O}}(\\sqrt{T\\ln T})$ regret for RMAB with preference feedback. Experimental results further demonstrate the effectiveness of DOPL." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Restless Multi-Armed Bandits", "Preference Feedback", "Online Preference Learning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/7567090398a5bc7e1a5ebaabc9c403025daeb472.pdf" }, "presentation": null, "primary_area": { "value": "reinforcement learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- In Table 7, can you provide clarification on why there is a drop in numbers for MGM-SliME-LLaVA-7B ? Overall, why is the performance gain of 3 Models integration is not much compared to the 2 models ensemble ?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Makes 3 significant observations on VLM ensembles: multi-encoder pays attention different complimentary regions of the image, visual embeddings of the vision encoders are better aligned when trained with same LLM , delta parameter merging of different LLM's help leverage different vision encoders from different MLLM family. These observations help them devise VisionFuse method. \n- Training-Free Fusion Approach: VisionFuse addresses a critical need to enhance MLLMs’ perceptual abilities without incurring additional training costs. This \"training-free\" feature is a significant contribution that helps plugging in diverse models during deployment.\n- The authors conduct exhaustive experiments including inference time/ accuracy to show the effectiveness of multi-encoder, LLM ensemble, token pruning (which model to prune more from) and ablations on the LLM ensemble techniques." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work proposes a new method: \"VisionFuse\" that ensembles different MLLMs by concatenating vision tokens and delta parameters of LLMs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- While the paper shows exhaustive experiments on combining two MLLM families : SLIME and MGM, it is unclear how the method will scale with more than 2 MLLM due to complexity of vision token length as noted in paper. Especially, as shown in Table 8, the VisionFuse will not work when there is huge difference in the delta parameters of the LLMs. This limits the scope of this method to generalize to different MLLMs. Can the authors propose planned solutions to make the fusion more robust for any MLLM ?\n- Novelty: In terms of novelty of the fusion method : the vision token concatenation was from [1], and the delta parameter integration for LLM is from [2]. Hence, the paper does not technically contribute towards the fusion methodology/ algorithm itself. \n- In fig.4, there is an ablation to show the importance of complimentary features of encoders. It is unclear how to choose encoders that have complimentary features ?\n\n\n\n1. Eagle: Exploring the design space for multimodal llms with mixture of encoders. arXiv preprint arXiv:2408.15998, 2024.\n2. Editing models with task arithmetic. In ICLR. OpenReview.net, 2023. URL" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- After fusion, the resulting MLLM processes much longer visual inputs compared to the base models, as it concatenates vision features from multiple vision encoders into a single sequence. A relevant question arises: what if, instead of fusion, we simply increase the length of visual tokens in the base model (e.g., by expanding the input resolution)?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- VisionFuse is a training-free method that can be directly applied to different models within the same MLLM family.\n- The evaluation results in Table 1 demonstrate the effectiveness of the VisionFuse method.\n- The authors also perform extensive experiments and ablation studies to further assess the method's effectiveness." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces VisionFuse, an integration framework designed to enhance the visual perception capabilities of multimodal large language models (MLLMs) by merging different models from the same model family. The approach is built on three key observations: (i) Different MLLMs focus on varying regions of the same visual input for the same query; (ii) The visual feature distributions of encoders within an MLLM family show closer alignment; and (iii) Merging language model parameters helps align the language model with different vision encoders. The authors evaluate VisionFuse on the MGM and SliME models, demonstrating a significant improvement achieved by merging the two models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- As shown in Table 1, the proposed method’s improvements on stronger MLLMs are more limited compared to smaller models. This suggests that the method may not perform as effectively on top-tier models. Additionally, the largest model evaluated in Table 1 is only 8B, which is relatively small compared to current state-of-the-art MLLMs. It would be beneficial for the authors to test the method on larger models with top-tier performance (such as LLaVA-OneVision-Qwen2-72B, LLaVA-NeXTVideo-Qwen2-72B, and Qwen2-VL 72B), as this would help demonstrate the scalability of the proposed approach.\n- The benchmarks chosen in this paper are mostly from general domains and are somewhat outdated. More recent and vision-centric benchmarks, as well as new MLLM benchmarks, are now available. These newer, more challenging benchmarks would better reflect the true capabilities of the proposed method." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "The questions raised in this section are the same as the weaknesses outlined above." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tThe paper introduces a training-free method called VisionFuse, designed to enhance the perception capabilities of MLLMs. This approach enables the utilization of multiple vision encoders from various MLLMs by merging the parameters of their language models. Experiments demonstrate that this method achieves a notable average improvement of over 4% across multiple benchmarks.\n2.\tThe article presents three intriguing insights that could inspire researchers in the development of MLLMs. The visualizations and discussions provided are comprehensive and insightful.\n3.\tThe significance of this work is good. By demonstrating a practical and efficient approach to integrating diverse vision encoders from various MLLMs into a cohesive framework through the merging of language model parameters, the paper not only advances MLLMs in multimodal applications but also enriches the broader field of vision-language integration with valuable insights." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces VisionFuse, a novel training-free method that efficiently utilizes multiple vision encoders from off-the-shelf MLLMs to enhance visual perception. It offers some intriguing insights. For instance, even when given the same query and image, different MLLMs focus on distinct regions. Furthermore, the authors discover that the feature distributions of vision encoders within an MLLM family are highly aligned. Leveraging these insights, they merge the parameters of language models from various MLLMs, enabling a single language model to effortlessly align with multiple vision encoders. Consequently, the proposed method achieves an average performance increase of over 4% when integrating MiniGemini-8B and SLIME-8B. Overall, the proposed VisionFuse method demonstrates the efficiency of merging parameters from multiple MLLMs, thereby harnessing the strengths of various different encoders in a unified approach." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe authors propose that integrating a Multimodal Encoder with VisionFuse enhances the capabilities of MLLMs, as indicated in Equation (4), which suggests the potential to handle more than two MLLMs. However, the primary experiments focus on the integration of two MLLMs, such as MGM-8B and SLIME-8B. Therefore, the question arises: when integrating more than two MLLMs, how should the fusion coefficients be balanced and optimized to ensure effective integration?\n2.\tDoes the paper discuss methods that can support the integration of scaled-up models, such as 65B or 72B models?\n3.\tFrom Figure 3, it appears that the enhancement through mixed parameter fusion is biased towards the selection of visual encoders. If an unsuitable visual encoder is used, it seems that performance could plummet. Are there any guidelines in practical applications for selecting the appropriate MLLMs for fusion enhancement without causing a decline in performance?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. What is the reason for choosing MGM+SliME for most of the experiments instead of using simpler models like LLaVA-1.5?\n2. For the MGM-VILA-7B in Table-8, why do the performances on TextVQA and MME-p increase while others decrease?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The general idea of combining multiple MLLMs with limited additional cost is meaningful.\n2. The observations and discussions could provide some insights for the community.\n3. The overall writing is clear and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a training-free model ensemble method for multimodal LLMs by concatenating the vision tokens and merging the LLM weights. Through exploratory experiments, the paper makes several observations about MLLMs: 1) different MLLMs focus on different image regions; 2) Vision features from MLLMs from the same base LLM exhibit similar distribution; 3) Merging LLM weights is critical to combine vision tokens from different MLLMs. Based on these observations, the paper further proposes the VisionFuse method which combines vision tokens from different MLLMs by concatenation and merging LLM's weights. Experiments of combining MGM and SliME show the effectiveness of the proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The overall positioning of the paper is not very proper. The paper positions itself as combining different vision encoders, so I expect to see combining different types of vision encoders (e.g. CLIP, Siglip, DINOv2 ...) which are shown to be effective in Eagle, BRAVE, and Cambrian-1 by providing more complementary vision information. However, the overall method is more like a general MLLM merging method. The gain of the proposed method comes from different aspects: different vision information due to different preprocess and compression methods; LLM ensemble (different training data & training randomness); and different attention patterns from LLM to vision tokens.\n2. The generalization ability of the proposed method is not well verified, and experiments are mainly conducted based on MGM+SliME. The paper should include more experiments with different MLLM combinations and different numbers of MLLMS in each combination to show the effectiveness of the proposed method.\n3. The paper claims that one big advantage of the proposed method is that it does not require training. However, this relies on the assumption that you already have proper MLLMs with the same base LLM and distinct training data + vision processing structures. However, methods like Eagle only need to train the MLLM with different vision encoders once. \n4. One major disadvantage of the proposed method is the additional token length, which is especially severe when considering combining multiple MLLMs more than 2 or MLLMs with long token lengths. The token pruning method used as a mitigation approach is still not optimal and might hurt the performance in certain tasks (e.g. DocVQA, InfoVQA, ChartQA) or with MLLMs that already have compressed vision tokens with little redundancy." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024enhancing,\ntitle={Enhancing Perception Capabilities of Multimodal {LLM}s with Training-Free Fusions},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2jEiFTLRwX},\nnote={under review}\n}" }, "abstract": { "value": "Multimodal LLMs (MLLMs) equip language models with visual capabilities by aligning vision encoders with language models. \nExisting methods to enhance the visual perception of MLLMs often involve designing more powerful vision encoders, which requires re-aligning these vision modules with the language model, leading to expensive and time-consuming training processes.\nIn this paper, we introduce VisionFuse, a novel integration framework that efficiently utilizes multiple vision encoders from off-the-shelf MLLMs to enhance visual perception without requiring additional training.\nOur approach is motivated by the observation that different MLLMs tend to focus on distinct regions of the same query and image. Moreover, we find that the feature distributions of vision encoders within an MLLM family, a group of MLLMs sharing the same pretrained LLM, are highly aligned.\nBuilding on these insights, VisionFuse enriches the visual context by concatenating the tokens generated by the vision encoders of selected MLLMs within a family. By merging the parameters of language models from different MLLMs, VisionFuse allows a single language model to align with various vision encoders, significantly reducing deployment overhead.\nWe conduct comprehensive evaluations across multiple multimodal benchmarks using various MLLM combinations, \ndemonstrating substantial improvements \nin multimodal tasks. Notably, when integrating MiniGemini-8B and SLIME-8B, VisionFuse achieves an average performance increase of over 4\\%." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Multimodal Large Language Model", "Model Integration" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/211be681f851148ea56f4d78c617366bcef8f389.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusions" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "A more thorough justification for the OCR requirement and a clearer explanation of the new benchmark's significance could enhance the paper’s impact." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Significance: MMMU-Pro addresses critical gaps in existing benchmarks, promoting deeper multimodal understanding over shallow pattern recognition. It sets a higher evaluation standard, likely to drive future research and model development.\n- Quality: The paper rigorously evaluates MMMU-Pro across multiple state-of-the-art models, showing significant performance drops that underscore the benchmark’s challenge. \n- Insights: Experiments with methods like Chain of Thought reasoning and OCR prompts enrich the analysis, verifying the benchmark’s effectiveness in highlighting model limitations." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces MMMU-Pro, a more robust version of the MMMU benchmark. MMMU-Pro aims to more accurately assess multimodal models' true understanding and reasoning capabilities across diverse academic domains by addressing limitations found in the original MMMU. The authors achieve this through three main enhancements: (1) filtering out questions that can be answered using only text, ensuring models rely on multimodal input; (2) expanding the number of answer options to reduce reliance on guessing; and (3) introducing a vision-only input setting where questions are embedded in images, challenging models to integrate visual and textual information. These modifications result in a more rigorous benchmark that better approximates real-world scenarios. Experimental results demonstrate that MMMU-Pro challenges existing models more, revealing performance drops across multiple models and encouraging further exploration in multimodal understanding and reasoning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Unclear Justification for OCR Requirement: One of MMMU-Pro's main contributions is embedding text within images to increase difficulty by requiring OCR. However, this addition may detract from the benchmark’s core goal of evaluating multimodal understanding, as it primarily tests the model’s OCR capabilities rather than its deeper multimodal comprehension. Although it is true that embedding text within images is more realistic, whether the extra difficulty from OCR is significant for LMMs needs more justification, as the extra focus on OCR could potentially obscure the true reasoning ability of models that struggle with OCR but perform well in multimodal integration tasks.\n\nLimited Impact on Model Performance Ranking: While it’s acceptable for a benchmark to yield similar performance scores, MMMU-Pro does not alter the ranking of current models, nor does it reveal new insights into their strengths and weaknesses. This lack of differentiation reduces the benchmark’s ability to provide fresh perspectives on model capabilities, potentially weakening its contribution as an evaluation tool." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please refer to weakness section." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- This paper addresses key limitations in existing benchmarks like MMMU. By introducing a vision-only input mode, MMMU-Pro uniquely challenges models to process visual and textual information in a more realistic, integrated manner. This work also enhances question difficulty and mitigates model reliance on shortcuts, providing an essential tool for testing and advancing multimodal AI.\n\n- The clarity of the paper is strong, with well-organized sections detailing the benchmark's construction and evaluation.\n\n- Additionally, the paper examines the impact of Chain of Thought prompting and OCR on performance within the proposed benchmark, further investigating the limitations present in current MLLMs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces MMMU-Pro, an enhanced multimodal benchmark to rigorously test AI models’ understanding and reasoning by addressing limitations in the original MMMU benchmark. MMMU-Pro utilizes a three-step process to improve robustness: filtering questions answerable by text-only models, increasing candidate options to prevent guesswork, and implementing a vision-only input mode that embeds questions within images, thus requiring integrated visual and textual processing. Experimental results show a significant drop in model performance compared to MMMU, highlighting multimodal challenges. The study further investigates Chain of Thought prompting and OCR effectiveness, identifying areas where current models struggle, and setting directions for future multimodal research​." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- One limitation is that in the vision-only setting, images are manually captured photos and screenshots over a simulated display environment, but only differences in backgrounds, font styles, and font sizes are considered. However, the diversity of real images should also account for factors such as varied lighting conditions and different camera angles (e.g., rotated text in photos).\n\n- While the paper discusses the Chain of Thought (CoT) prompting and OCR’s impact, these evaluations could be expanded to clarify where CoT specifically improves performance. For example, breaking down CoT's impact across different question types or modalities could reveal deeper insights, guiding future model improvements.\n\n- Moreover, the analysis would benefit from more nuanced evaluation metrics that go beyond accuracy, such as tracking misinterpretation rates or identifying where models are most prone to visual-textual integration issues. This additional layer of analysis could provide more actionable insights for researchers looking to address specific multimodal weaknesses." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Regarding the quality of options: Expanding the number of options does indeed reduce model performance. How do you ensure the quality and diversity of these additional options? If there is a method, could you elaborate further on the validation process?\n\nGiven the high construction cost of the benchmark, is it possible to reduce human effort through automated data generation or other technical means? For instance, could models be used to create new visual inputs or options?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Rigorous Evaluation:Using multiple LLMs to vote and filter out questions that can be solved by text-only models does enhance the benchmark’s ability to reflect more accurate visual capabilities. Overall, this benchmark is indeed more complex and better demonstrates the model’s ability to integrate text and visual information.\n\nThe findings and detailed analysis suggest avenues for future improvements, such as better vision-text integration strategies." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper is an extension of MMMU. The evaluation consists of three steps: 1) filtering out questions that can be answered by text-only models, 2) augmenting candidate options to reduce the chances of guessing correctly, and 3) introducing a \"vision-only input\" setting to challenge models to comprehend both visual and textual content simultaneously. \nExperimental results demonstrate a significant drop in model performance on MMMU-Pro" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Expanding the number of options is an obvious way to reduce model performance. The emphasis should be on demonstrating the quality of these expanded options. However, the authors only mention this briefly with a single example.\n2. The work appears straightforward, with most of the effort concentrated on non-technical human labor, making the overall contribution less innovative." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Can the author privode more insight anaysis on the proposed benchmark?\n2. It's good to see the author's effort about how to improve the open-source model's performance on MMMU-Pro." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The proposed benchmark assesses multimodal models’ understanding and reasoning capabilities in a more rigorous method.\n2. The data collection pipeline are confidential due to the engagement of human.\n3. Experiments are comprehensive, assessing current model's performance more accurate." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a new benchmark MMMU-Pro for more accurately and rigorously assessment of a model’s true multimodal understanding and reasoning capabilities by filtering the question that can be answered by LLM dicrectly, adding more options and vision-only input. Experimental results using MMMU-Pro show a significant performance drop in existing model. This paper provides a more rigorous evaluation tool," }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. As the main contribution of this paper is a benchmark. The authors should provide more data analysis on the collected MMMU-Pro and give more insightful conclusion.\n2. Apart from benchmark, the novelty is limited. This paper just tests many models on the proposed benchmark ( I don't think the exploration\n of OCR prompts and COT reasoning can be accounted as a novelty). On the other hand, the dataset collection pipeline is something more like engineering. That's the reason why I think this paper's novelty is limited. Of course, this is not the only criterion for determining whether this paper can be accepted. The proposed benchmark does make a contribution to MLLMs' understanding and reasoning capabilities. My main concern is that the workload and contribution may not be sufficient to be accepted by such a top-tier conference. I may change my rating based on the rebuttal." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please see the above weakness" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1/ The paper is easy to follow\n\n2/ The three upgrades upon MMMU are reasonable and can better examine the MLLM's capability in making sense and reasoning about vision\n\n3/ The paper evaluates a wide range of existing MLLMs and share some insights." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper MMMU-Pro presents an upgraded version of MMMU. MMMU-Pro improves MMMU via (1) filtering out questions answerable by text-only models, (2) augmenting candidate options, and (3) introducing a vision-only input setting where questions are embedded within images. All these are reasonable upgrades upon MMMU, and the paper also presents some nice insights based on existing models' performances. But the upgrades seem to be incremental and not sure whether the efforts are enough for one standalone work." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1/ Fig 1 compares almost 20 models, is it necessary to compare so many models? We can probably derive similar conclusions & insights based on comparing just some representative models. In this era, new MLLMs come out every a few days. I understand from the view of maintaining a benchmark/competition, it is good to be inclusive while seems not helpful to have giant table and figures like Table 1 and Fig 1.\n\n2. Despite the 3 upgrades are reasonable, they seem to be incremental given the wonderful work of MMMU. I am not sure whether the efforts in this pro work are enough for one standalone paper. It could be just a nice technical report; while I guess if people want to pass it, I am also fine.\n\n3. The last upgrade of vision-only input is interesting and the analysis of OCR/COT are good. While it feels to be only just scratching the surface, and if the authors would like to create a more solid work, I would expect some deeper contributions e.g. design new model/algorithm that can better make sense of such purely vision input question, create a training dataset that can power existing MLLM to do much better job on this task." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024mmmupro,\ntitle={{MMMU}-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2jTdHYuguF},\nnote={under review}\n}" }, "abstract": { "value": "This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal models' true understanding and reasoning capabilities through a three-step process based on MMMU: (1) filtering out questions answerable by text-only models, (2) augmenting candidate options, and (3) introducing a vision-only input setting where questions are embedded within images. This setting challenges AI to truly \"see\" and \"read\" simultaneously, testing \\textit{a core human cognitive skill of seamlessly integrating visual and textual information}. Results show that model performance is substantially lower on MMMU-Pro than on MMMU, ranging from 16.8\\% to 26.9\\% across models. \nWe explore the impact of OCR prompts and Chain of Thought (CoT) reasoning, finding that OCR prompts have minimal effect while CoT generally improves performance. MMMU-Pro provides a more rigorous evaluation tool, closely mimicking real-world scenarios and offering valuable directions for future multimodal research." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Evaluation", "Multimodal Understanding", "Multimodal LLMs" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/c87b3772407351456c008b205be546b9684e8fd9.pdf" }, "presentation": null, "primary_area": { "value": "datasets and benchmarks" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See weaknesses." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The method is novel, and the paper is well-written. The methodology is easy to follow, and the experiment section is well-structured and clearly presented. Through dedicated experiments, the authors show that, in nearly all cases, the performance degradation with increasing dependency length cannot be attributed to any of the three phenomena: over-smoothing, over-squashing, or vanishing gradients. This finding opens up two directions for future research: identifying the true causes of this degradation and developing methods to address it." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents an algorithm for generating a synthetic dataset for every dependency length and demonstrates how to use this benchmark to identify, with certain guarantees, the maximum dependency length that a graph learning system can learn. Additionally, the paper illustrates the application of the benchmark in the experiment section." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Overall, the paper is good; however, some improvements are needed in figure presentation. For example, the position of subgraph titles should be consistent across Figures 2, 3, and 4." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "N/A" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Could you clarify why Transformer-based models perform poorly on GLoRa, even at small dependency lengths? Have alternative positional encodings or adaptations been considered?\n2. How does GLoRa handle variations in the number of “holes” within paths, and would testing different numbers of interruptions provide further insight into model performance?\n3. Are there plans to test models that perform well on GLoRa against real-world benchmarks requiring long-range dependencies to validate GLoRa’s practical transferability?\n4. How do various types of implicit GNNs perform on the proposed benchmark?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- **Novelty in Benchmark Design**: GLoRa provides a synthetic benchmark with strict, enforceable dependency-length requirements, filling an important gap in current graph benchmarks.\n- **Theoretical Guarantees**: The benchmark’s theoretical properties, including enforceable dependency lengths, are rigorously proven, making GLoRa a well-grounded tool for long-range dependency evaluation.\n- **Clarity and Structure**: The paper is well-structured, with clear explanations of the benchmark construction process and theoretical foundations." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces GLoRa, a synthetic benchmark designed to evaluate the ability of graph neural networks (GNNs) to capture long-range dependencies. By generating controlled graph examples with enforceable dependency lengths, GLoRa addresses a key gap in current GNN benchmarks. The authors provide theoretical guarantees for GLoRa, showing that models must capture dependencies of exact lengths to perform well. The paper also presents an empirical evaluation of several GNN architectures (including vanilla, over-smoothing-mitigated, over-squashing-mitigated, and Transformer-based GNNs) on GLoRa, revealing that none of the models perform well beyond modest dependency lengths." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **Disconnection Between Theory and Experiment**: The experiments do not fully validate the theoretical properties of GLoRa, such as the enforceable dependency lengths. Testing trained models across a range of dependency lengths or with varied “holes” in paths might provide empirical support for the benchmark’s theoretical claims.\n \n2. **Unexpected Performance of Transformer-Based Models**: Transformer-based GNNs perform poorly on GLoRa, even for small dependency lengths (e.g., \\(d = 3\\)). This contradicts their generally strong performance on other tasks, raising questions about whether GLoRa aligns with their strengths or if implementation details (like positional encodings) limit performance. Further exploration of encoding options or discussing the potential limitations of Transformers on GLoRa would clarify this discrepancy.\n \n3. **Limited Testing of Transferability and Practical Relevance**: GLoRa’s relevance to real-world tasks remains untested, as there are no experiments that transfer GLoRa-trained models to practical benchmarks requiring long-range dependencies. Testing transferability on benchmarks like social networks or molecular graphs would substantiate GLoRa’s practical utility.\n\n4. **Missing Important Evaluations for Implicit GNNs**: While the paper tests many GNN models, most of these GNNs do not claim to capture long-distance dependency. Various types of implicit GNNs have been demonstrated to capture long-distance dependency better, but the paper misses this important category of models. A more comprehensive evaluation on implicit GNNs will be helpful.\n\nWhile GLoRa is a theoretically grounded and novel benchmark for evaluating long-range dependencies in GNNs, the experimental design could be strengthened to better align with its theoretical properties and validate practical relevance. Specifically, testing models across varying dependency lengths, addressing the Transformer performance anomaly, and exploring GLoRa’s transferability to real-world tasks would greatly enhance the impact of this work." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- **Q1**: The experimental setting is not fully clear to me. In L412 the authors state that they “generated 1000 positive and 1000 negative examples by Algorithm 1 for each $d \\in \\{3, \\dots, 15\\}$”. Does that mean that models are trained on $2000 \\cdot 0.8 = 1600$ training samples for each depth $d$ or do you construct a joint training set of size $2000 \\cdot 0.8 \\cdot 13 = 20,800$ training samples and merely evaluate the test accuracy separately for each depth $d$?\n- **Q2**: The authors state in L465-466: “Finally and not surprisingly, all types of graph transformers cannot learn even very short dependencies”. Can the authors provide a more detailed insight into why this result is unsurprising? The GPS model, for example, uses a local message-passing module that should at the very least match the performance of the vanilla GatedGCN. I find that this warrants further analysis. One possible reason could be the possibly low amount of data seen during training; see related **Q1**." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- **S1**: The authors give a clear definition of long-range dependency which is often lacking in prior work.\n- **S2**: The authors benchmark a variety of baselines, from simple GNNs to more involved approaches, as well as transformers.\n- **S3**: The finding that neither over-smoothing, over-squashing or vanishing gradients are the causes for the poor performance at long range is very interesting and deserves more attention in future research.\n- **S4**: The authors have identified a surprisingly simple problem setting that a variety of GNN architectures fail to solve. These findings could lead to interesting follow-up work which aims to understand why the models fail and how these limitation can be overcome." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this work, the authors propose GLoRa, a benchmark generator for long-range dependency tasks on graphs. The authors overcome the limitations of existing work by precisely stating a definition for long-range dependencies and designing a benchmark that guarantees that models cannot solve the generated tasks unless they respect the long-range dependencies in a given graph. An empirical study on a variety of GNN and transformer baselines concludes that no architecture can perform well on the GLoRA tasks for dependencies longer than depth 10. Further, the authors find that neither over-squashing, over-smoothing or vanishing gradients are the cause for this poor performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- **W1**: The authors argue for the need of a synthetic benchmark where long-range dependencies can be guaranteed. The authors argue that such guarantees are not given in real-world benchmarks. While I generally agree with the fact that in real-world benchmarks there may be shortcuts or simpler functions that avoid long-range dependencies but still correctly predict the labels, I am concerned that the present work proposes a benchmark for a problem they cannot identify in the real-world. In particular, the authors argue in the introduction that long-range dependencies are crucial in many applications (recommendation systems, traffic prediction, fake news detection). However, if long-range dependencies are crucial in these applications I would argue that it would be more sensible to derive benchmarks from real-world data in these domains. Further, if the authors are concerned that in real-world data one cannot verify whether long-range dependencies are truly needed to solve a task, I conclude that the authors also cannot guarantee that their proposed notion of long-range dependencies (Definition 1) is actually useful in real-world applications. Hence, I ask the authors to justify the relevance of long-range dependencies in real-world problems or to argue otherwise how progress on GLoRA contributes to solving real-world problems in graph learning.\n- **W2**: The theoretical contributions are formally imprecise and no full proof is given for Theorem 1 (only a proof sketch). First, the authors should clearly state in L385 that Theorem 1 is only supported by a proof sketch. The authors say “proof” in the main paper but “proof sketch” in the appendix. Second, let me expand on what I mean with formally imprecise. The statement of Theorem 1 says “[For every probability $\\mathcal{P}$] there exists a number $K$ such that a set $S$ of $K$ samples […] requires learning dependencies of length $d$ with probability at least $\\mathcal{P}$.” It is not formally clear what it means for a set of samples to require learning dependencies of some length. I could guess that the authors mean that a model cannot separate the set into positive and negative samples unless the model finds the corresponding path of length $d$. However, the statement in its current form is not formally precise. The authors should either formally state their theorem and prove it, or replace the theorem with an informal argument supported by the proof sketch. If the authors decide to formally state their theorem they should carefully define what they mean with the statement above. It is perfectly fine to give an informal (but intuitive) version of the Theorem in the main text and precisely formalize the theorem statement and proof in the appendix. In this case, I recommend to state the theorem in the main text as \"Theorem 1 (informal)\" and then write something like \"For a precise theorem statement and formal proof, see Appendix ...\"." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Figure 3 does not rule out the case of this type of oversmoothing: at the last layer of a GNN, it may be the case that most nodes in one graph have the same embedding. But, this embedding can be different across different graphs that you run a forward pass on.\n2. Why do the Transformers fail, and why do you say this is \"not surprising\"?\n3. Some important details, like how number of layers is chosen, is hidden in Appendix.\n4. What is the way forward for making GNNs that solve GLoRa?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Good criticism of existing benchmarks. Many of them can be solved in a graph independent way, or long-range-dependency independent way.\n2. Interesting that over-squashing is not a problem in directed GLoRa by design (number of paths constant and small).\n3. Experiments on many types of representative GNNs from different families.\n4. In my opinion, good synthetic data experiments were very much needed for this exact question (long-range dependences in graph learning). Doing this well can be quite helpful." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work introduces a new synthetic benchmark, GLoRa, to measure the ability for graph machine learning methods to learn long-range dependencies. For different depths of dependencies and difficulty levels, GLoRa can make synthetic tasks that do not have simple shortcuts that other long-range benchmarks suffer from. Experiments are conducted across many different GNN architectures from different families. It is argued that oversmoothing, oversquashing, and vanishing gradients are not the issues with learning these long-range dependencies." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. For the definition of path-aware dependencies, isn't it easy to satisfy this for node classification functions on complete graphs, even though the connections are very short. In particular, there is no requirement in this definition for non-existence of paths.\n2. Unrigorous proof of Theorem 1\n3. 80% in Figure 2 is a bit arbitrary of a threshold, but this isn't a huge issue.\n4. Algorithm block is a bit hard to understand, but Figure 1 is clear at least." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024glora,\ntitle={{GL}oRa: A Benchmark to Evaluate the Ability to Learn Long-Range Dependencies in Graphs},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2jf5x5XoYk},\nnote={under review}\n}" }, "abstract": { "value": "Learning on graphs is one of the most active research topics in machine learning (ML). Among the key challenges in this field, effectively learning long-range dependencies in graphs has been a particularly difficult problem. It has been observed that, in practice, the performance of many ML approaches, including various types of graph neural networks (GNNs), degrades significantly when the learning task involves long-range dependencies—that is, when the answer is determined by the presence of a certain path of significant length in the graph. This issue has been attributed to several phenomena, including, most prominently, oversmoothing, over-squashing, and vanishing gradient. A number of solutions have been proposed to mitigate these causes. However, evaluation of these solutions is complicated by the fact that existing benchmarks do not really test systems for their ability to learn tasks based on long-range dependencies in a transparent manner. In this paper, we design a synthetic benchmark that provably allows testing systems for this learning ability. We then evaluate state-of-the-art systems against it and conclude that none of them can claim that it can learn long-range dependencies well. We also observe that this weak performance cannot be attributed to any of the three causes, thus indicating that further investigation is necessary." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Graph Learning", "Graph Neural Networks", "Synthetic Benchmarks", "Long-Range Dependencies" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/a2d43a3575f9a825b5747c899f702a79530a162a.pdf" }, "presentation": null, "primary_area": { "value": "learning on graphs and other geometries & topologies" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "GLoRa: A Benchmark to Evaluate the Ability to Learn Long-Range Dependencies in Graphs" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Some questions are included in the weakness section. \n\nThe PCSE approach, described in Algorithm 1, obtains an exploration policy pi_k by solving the optimization problem in Equation 9. In Equation 9, $\\Pi^r$ (rewards, not costs) is defined as \n\n$$\\Pi^r = \\{ \\pi \\in \\Delta: \\inf_{\\mu_0} \\mu_0^T (V^{r, \\pi} - V^{r, \\hat{\\pi}^*}) \\geq \\mathcal{R}_k \\}$$\n\nBecause these are the value function of rewards, I am confused why the difference should not be flipped, such that:\n\n$$\\Pi^r = \\{\\pi \\in \\Delta: \\inf_{\\mu_0} \\mu_0^T (V^{r, \\hat{\\pi}^*} - V^{r, \\pi}) \\geq \\mathcal{R}_k\\}.$$\n\nIn other words, why should the order of the two value functions not be flipped, given it is an infimum." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper presents novel sample complexity guarantees on ICRL problems in the setting where the transition is unknown. While the paper presents substantial notation, Section 4 does a good job of describing the lemmas in understandable terms. Section 5, especially 5.1 and 5.2, would benefit from similar elaboration. The steps in the proofs are mostly explained well." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In applications such as robot learning, it is often the case that the learner (e.g. robot) must abide by certain safety constraints when learning to perform a task. Because such constraints can be hard to specify, methods of learning the constraints from demonstration have been proposed, an approach known as Inverse Constrained Reinforcement Learning (ICRL). Prior work has made one of the following assumptions: access to a known transition model or access to a generative transition model that can be queried at any state-action pair. The existing work that does not impose such assumptions has not examined efficiency and estimation errors. This paper proposes two algorithms for learning a set of feasible constraints that align with the expert preferences. Sample complexity bounds are presented for both algorithms. The algorithms are evaluated on Gridworld and Point Maze tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Several weaknesses are listed below, of varying importance.\n\nI believe the paper would benefit from a broader discussion of the Related Works. More specifically, how does the paper and the setting it considers compare to the following works?\n- Chou et al., “Learning constraints from demonstrations,” 2020\n- Kim and Oh, “Efficient off-policy safe reinforcement learning using trust region conditional value at risk”, 2022.\n- Moskovitz et al., “Reload: Reinforcement learning with optimistic ascent-descent for last-iterate\nconvergence in constrained mdps,” 2023.\n- Lindner et al., “Learning safety constraints from demonstrations with unknown rewards,” 2024\n- Kim et al., “Learning Shared Safety Constraints from Multi-task Demonstrations,” 2024\n\nNearly all of the results in the Empirical Evaluation section (Sec. 6) are visually difficult to parse. For example, the UCB results are almost entirely hidden in Figure 3’s top and middle rows (rewards and costs, respectively). While including numerous baseline comparisons is beneficial, considering including different plots in the appendix to make the comparison more interpretable. In addition to an unclear comparison to the baselines in terms of discounted cumulative rewards and discounted cumulative costs, neither BEAR nor PCSE appear to beat the Random algorithm in terms of WGloU score. Overall, it is unclear to me what the takeaways from the empirical results are.\n\nThe paper assumes finite state and action spaces, as well as an infinite horizon. In the experiments, these assumptions do not always hold (e.g. Point Maze is a continuous environment). There is a brief mention in Appendix D.3 about the density model in the continuous space, but overall, the discussion of how the theoretical assumptions translate into the practical settings considered is lacking.\n\nIn Theorem 5.6, the sample complexity of PCSE is given by the minimum of the sample complexity of BEAR and a term dependent on the minimum cost advantage function. In the proof of Theorem 5.6, the paper states that the sample complexity of BEAR (Theorem 5.5) applies to PCSE because it is optimizing a tighter bound. The justification is Corollary C.6. Examining the proof of Corollary C.6, it is not clear how one is a tighter bound than the other. \n\nThe paper would benefit from further discussion of the optimality criterion. The first constraint, with the Q difference for completeness, “tracks every potential true cost function.” The second constraint, focused on accuracy, expresses that the learned cost function must be close to a true cost function. How does it “[prevent] an unnecessarily large recovered feasible set?” At a higher level, the paper would benefit from more motivation/discussion of why the estimation should be included in the optimization problem. In other words, why can we not naively solve the problem as though we had perfect estimates, and then handle the estimation errors exclusively in the analysis? As discussed above, Section 5 (especially 5.1 and 5.2) would benefit from non-technical elaboration in the style of Section 4.\n\nIn Equation 9, which defines the optimization problem of PCSE, the supremum is over distributions over the state space, rather than the state and action space. More specifically, it is\n\n$\\Pi^r = {\\pi \\in \\Delta: \\inf_{\\mu_0 \\in \\Delta^S} \\mu_0^T (V^{r, \\pi} - V^{r, \\hat{\\pi}^*}) \\geq \\mathcal{R}_k} $\n\nrather than\n\n$\\Pi^r = {\\pi \\in \\Delta: \\inf_{\\mu_0 \\in \\Delta^{S \\times A}} \\mu_0^T (V^{r, \\pi} - V^{r, \\hat{\\pi}^*}) \\geq \\mathcal{R}_k} $" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1) Please discuss challenges that you anticipate in scaling your approach to environments were both state and action spaces are continuous, and potential solutions to the challenges. \n2) Please include a discussion of how you might adapt your theoretical analysis and sample complexity bounds for high-dimensional continuous spaces in your future work." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Concrete theoretical analysis that is well detailed, along with good empirical results for the provided environments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduce two exploratory algorithms, BEAR and PCSE, to solve Inverse Constrained RL problem setting, where constraint signals are learnt from expert policies. The approach recovers a set of feasible constraints that align with expert preferences. A theoretical analyses of these algorithms is provided including sample complexity bounds." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Although some experiments were performed on a continuous setting, it is unknown (not even addressed) how the algorithms scales in both continuous state and action spaces. The current test case is a simple environment with discrete actions." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "I reported some of my questions in the comments above." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The paper tackles an interesting problem setting that may have practical upside for relevant applications;\n- The paper addresses the strategic exploration problem in ICRL, which it has been previously studied in settings with known dynamics or a generative model;\n- The paper provides two algorithmic solutions and corresponding sample complexity results;\n- The paper includes a numerical validation, which is not that common in purely theoretical RL papers." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses inverse constrained reinforcement learning, a problem in which we aim to infer a cost constraint by looking at expert's behaviour only. The specific setting works as follows: We can deploy a policy in the MDP to get a rollout of state transitions and expert's actions, but we cannot see the cost. We aim to infer a set of costs compatible with the expert's actions, which are assumed to be optimal, while minimizing the samples taken from the environment. The paper proposes two algorithmic solutions for this setting, a bonus-based exploration strategy called BEAR and a further refined version called PCSE, together with the analysis of their corresponding sample complexity and a brief empirical evaluation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I summarize below some of my main concerns about the work. More detailed comments are below.\n- The paper dives into a theoretical analysis of a rather peculiar setting without providing proper motivations;\n- The paper seems to have some presentation issues: I understood (most of) the interaction protocol at the start of Section 5, but some details are still obscure, whereas the notation does not look always sharp and detailed. The sample complexity results include some terms for which the dependence with $S, A, \\gamma$ is not obvious;\n- The paper lacks a in-depth discussion of the results, e.g., how they relate with prior works on IRL or ICRL with generative models, the considered assumptions (especially deterministic policies), computational complexity of the algorithms, the technical novelty of the presented analysis;\n- The numerical validation does not seem to be conclusive. Most of the curves are not separated with statistical significance.\n\n**COMMENTS**\n\nMOTIVATION. The formulation of the setting could be more clearly motivated. While it is roughly clear the kind of applications that are target, it is less clear why some choices are made.\n- Is the discounted setting more interesting than finite-horizon for ICRL?\n- In which kind of applications we can expect to have unconstrained access to the MDP online, even though the expert acts under constraints? Do the authors have any example of an application with cost constraints that allows for unconstrained exploration?\n- Also the PAC requirement is somewhat questionable: Under approximation errors of the MDP and expert's policy we are not guaranteed that the optimal policy for the costs in the feasible set is \"safe\" to use in the true MDP (i.e., it would respect the true constraint). This is common in other inverse RL paper, but while some sub-optimality can be acceptable in unconstrained RL, some violations of the constraints are less acceptable in a constrained setting.\n\nPRESENTATION. The presentation is not always sharp in the paper. I am listing below some questions, suggestions on how I think it could be improved.\n- Some broader context could be added to first sentence of the abstract, e.g., that we want to optimize an objective function under cost constraint(s);\n- The equation at l. 143 likely includes a typo. The Definition 3.1 would also benefit from more context and a more formal introduction to the notation (e.g., what do the value functions mean exactly?). It requires quite a lot of time to be processed;\n- I could not fully comprehend Eq. 2. Are $\\zeta$ and $E$ defined somewhere? If that is the case, perhaps it is worth recalling their meaning here;\n- The interaction setting shall be introduced earlier than Sec. 5.1 and some aspects are still not clear then. How is the reward accessed/estimated?\n- Sec. 5.5: \"The above exploration strategy has limitations, as it explores to minimize uncertainty across all policies, which is not aligned with our primary focus of reducing uncertainty for potentially optimal\npolicies.\" This is not fully clear and would benefit from further explanation. I thought the goal was to achieve the PAC requirement with minimal sample complexity. In general, the description of PCSE is not easy to process.\n\nTECHNICAL NOVELTY. BEAR looks like a rather standard bonus-based exploration approach, in which the main novelty seems to come from adapting the bonuses expression to the ICRL setting. Can the authors describe if other uncommon technical challenges arise from the specificity of the setting (especially w.r.t. prior works) and how are they addressed? I am not very familiar with the related literature in solving ICRL with a generative model, but in theoretical RL is sometimes straightforward to get a \"strategic exploration\" result from a \"generative model\" result.\n\nDETERMINISTIC POLICY. Assuming the expert's policy to be deterministic in MDPs is reasonable, a little less in CMDP. Can the authors discuss this point? It looks like they think determinism is necessary. Can they prove that formally?\n\nCOMPARISON WITH PRIOR WORK. The paper shall discuss how the presented sample complexity results compare with prior works in IRL, reward-free exploration, and, especially, ICRL with a generative model. Is the PAC requirement significantly different from prior works? Moreover, the $\\sigma$ terms in the sample complexity may have hidden dependencies in $S, A, \\gamma$...\n\nOTHER COMMENTS\n- ll. 175-181. Those considerations look informal if not incorrect. One can easily imagine optimal trajectories that do not fully overlap with the expert's one in the given example, whereas not all of the sub-optimal trajectories are necessarily satisfying constraints!\n- How can the C_k bonus be computed in BEAR? It seems to include the advantage, but the estimation of the reward is not mentioned anywhere;\n- Are the described approaches computationally tractable?\n- Another alternative yet interesting setting is the one in which the cost is also collected from the environment, but the constraint (threshold) is not known;\n- Some additional related works on misspecification in IRL https://ojs.aaai.org/index.php/AAAI/article/view/26766, https://arxiv.org/pdf/2403.06854 and sample efficient IRL https://arxiv.org/pdf/2402.15392, https://arxiv.org/abs/2409.17355, https://arxiv.org/pdf/2406.03812 could also be mentioned.\n\n**EVALUATION**\n\nThe addressed setting looks technically challenging and of practical interest. I am currently providing a slightly negative evaluation to account for my confusion over some aspects of the paper, which the authors may resolve with their response." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "None" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "None" }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The problem setting of designing provably-efficient algorithms for ICRL is really interesting in my opinion." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper analyses the problem of learning the constraints underlying some expert demonstrations in a provably efficient manner. Given that multiple constraint functions are compatible with the observed behavior, the learning target is the set of cost functions compatible with them." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper is based on Definition 3.1, which defines the notion of feasible cost set. However, because of the typos and of the absence of explanation provided, it is impossible to understand what is the feasible cost set in a formal manner. Without this formal definition, the following results, and in particular Lemma 4.3, cannot be understood. Since all the theorems proved are based on Lemma 4.3, then it is no possible to understand whether the results are correct or not.\n\nTYPOS:\n- 103: what is a space?\n- 107: there is an additional dot . not needed\n- 137: $r$ should not be present in the tuple\n- 138: no need to write \"CMDP without knowing the cost\", because this notation has already been defined earlier\n- 139: the cost should be defined as bounded\n- 139: simbol $\\Pi^*$ never defined\n- 141,143: bad definition of set\n- ...: definitely too many typos. Have the authors read the paper after having written it? Why me and the other reviewers have to waste time in reading your paper if not even you have read it?" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "This paper introduces a strategically efficient exploration framework for Inverse Constrained Reinforcement Learning problems with theoretically tractable sample complexity." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024strategic,\ntitle={Strategic Exploration for Inverse Constraint Inference with Efficiency Guarantee},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2jzhImk4br},\nnote={under review}\n}" }, "abstract": { "value": "In many realistic applications, the constraint is not readily available, and we need to infer the constraints respected by the expert agents from their behaviors. The problem is known as Inverse Constraint Inference (ICI). A common solver, Inverse Constrained Reinforcement Learning (ICRL) seeks to recover the optimal constraints in complex environments in a data-driven manner. Existing ICRL algorithms collect training samples from an interactive environment. However, the efficacy and efficiency of these sampling strategies remain unknown. To bridge this gap, we introduce a strategic exploration framework with guaranteed efficiency. Specifically, we define a feasible constraint set for ICRL problems and investigate how expert policy and environmental dynamics influence the optimality of constraints. Motivated by our findings, we propose two exploratory algorithms to achieve efficient constraint inference via 1) dynamically reducing the bounded aggregate error of cost estimation and 2) strategically constraining the exploration policy. Both algorithms are theoretically grounded with tractable sample complexity. We empirically demonstrate the performance of our algorithms under various environments." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Inverse Constrained Reinforcement Learning", "Exploration Algorithm", "Sample Efficiency" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/c155d5b60e01b9b3d5bb7a1cbb40df06117cd3b4.pdf" }, "presentation": null, "primary_area": { "value": "reinforcement learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/89a6450b4ba947ad8a36f36d69b27786821a195f.zip" }, "title": { "value": "Strategic Exploration for Inverse Constraint Inference with Efficiency Guarantee" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "I have to read GeN Bu et al. (2023) again to understand the proposed method in this paper. And I cannot find Eq (5) of this draft in GeN Bu et al. (2023). What is \\omega in Eq (5) and (6)? Could you write the closed form solution for estimating \\eta? If not, why? \n\nI request the authors to clarify privacy accounting of their proposed method. Starting from the DP definition, e.g., what are their mechanism input and output? How are their “Loss Privatization” and “Gradient Privatization” composed? It looks to me Line 9 in Alg 1 is data dependent, but it is unclear whether it is considered in the DP algorithm or accounting. It is OK to use GDP and/or autoDP library, but I request the authors to detail how GDP/autoDP is used for this specific algorithm.\n\nMinor: the authors might consider the usage difference of \\citep and \\citet in writing." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Setting hyperparameters in DP optimization is an important topic for both modeling and privacy. \n- Experiments demonstrate the advantage of the proposed method compared to naively applying parameter free optimization methods like D-adaptation in DP optimization, and DP-hyper style algorithm by differentially privatizing hyperparameter search." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed a method to estimate hyperparameters (i.e., learning rate) in differentially private optimization with gradient normalization (instead of gradient clipping). As learning rate is the main tuning parameter, the proposed optimizer is hyperparameter free. The proposed additionally differentially privatizes the loss (a scalar) for estimating the learning rate." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The technical part of the paper is generally hard to read. I am not confident the proposed method is technically correct." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "For DP hyper-parameter optimization, have the authors considered using gradients from backpropagation w.r.t learning rate to tune the learning rate privately?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The proposed algorithm works pretty well in the experiment with not much additional cost in computation cost and privacy.\n2. The idea is simple yet effective, factorizing learning rate tuning during training using quadratic approximation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes to tune learning rate privately based on quadratic approximation during DP-SGD training. It is shown that the proposed algorithm can achieve comparable performance with non-DP learning rate search." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The proposed algorithm seems to still require a initial learning rate and the algorithm's sensitivity to the initialization seems missing." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1) The line numbers are missing in the pdf, the authors may want to fix this. In the following I will try my best to describe the location of the typo\n\n\n2) In equation 6, I believe you mean to also use the privatized loss for L(w), currently it is un-privatized and hence the objective is not private. I can see from the algorithm you input to equation 6 privatized losses, so I presume this is a typo in equation 6\n\n3) In the opening paragraph of section 4.1, I do not believe saying Auto Clipping is equivalent to Rg = 0 is correct. This would mean you always clip to 0 gradient norm. I believe you can replace this by saying you always clip to gradient norm 1, which is consistent with equation 1.\n\n4) In algorithm 1 could you write the inputs to algorithm, which I take as initial values for: $\\eta$, $R_l$. This would help with reading/understanding the algorithm\n\n5) In line 8 of algorithm 1, can you replace $(-\\eta,0,\\eta)$ with set notation {$\\eta, 0,\\eta$} to be clearer how equation 6 is instantiated; at first I thought it was run over an interval.\n\n6) In line 9 of algorithm 1 I find it vague as stated; more precisely can you say you minimize the previous fitted quadratic?\n\n7) (Major) In section 5.1 experiment setup paragraph, can you explicitly state the deltas used in the experiments; I could not find this in the appendix either.\n\n8) (Major) In table 2, can you add error bars for the experiments? E.g., report 1 standard deviation\n\n9) (Major) Can you report the grid search range in the appendix?\n\n10) Can you explain what BitFit is briefly in the experimental setup paragraph; I believe this will clarify better the differing results for full finetuning\n\n11) I found the figures hard to read when I printed the paper; consider increasing the size and possibly moving one figure to the appendix." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1) Hyperparameter selection in DP does not inherit the same rules of thumb as non-DP, and hence understanding hyperparameter selection for DP training is a core problem\n2) The results are strong and seemingly near optimal (given the non DP grid search results)\n3) The observation that a specific generic ML hyperparameter selection approach translates well to the DP setting seems important and novel to the DP community" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper discusses how to improve the performance of DP optimization by improving the private selection of hyperparameters. This is done by always scaling the gradient to norm 1 and adjusting the learning rate for each training step privately. The selection of learning rate is done by adapting a rule used in non-DP literature which solves an approximate objective for the best learning rate, but making this private by privatizing the loss evaluation used in this learning rate objective. Extensive experiments shows this improves performance over alternative DP hyperparameter selections, and is often comparable to a non-DP grid search of hyperparameters. Further experiments show the computational overhead is minimal. Certain experimental details are missing in the text, though I believe this can be easily addressed." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) In my opinion the presentation can be improved: in the questions I try and clarify several possible typos. I emphasize this as I originally had more issues with the claims made in the paper but was able to answer these by cross-examining statements elsewhere, which could have been more easily resolved with some changes to writing.\n\n2) Certain details about the experimental setup are missing, such as exact DP $\\delta$ values used, and the range of hyperparameter grid search. I ask questions about these in the questions section (labelled Major), and believe they can be easily addressed, and am willing to increase my score given they are addressed in the rebuttal." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "1. In Theorem 1, when $R_l \\approx L$, the clipping bias is close to zero, where $L = \\frac{1}{B} \\sum_i L_i$ is the public per-sample gradient (right?), which seems to be a CLT-based result (please correct me if I’m wrong). My questions are: \n- (1) Could the authors provide guidance on minimum batch sizes needed (to make CLT works) in practice, based on their empirical observations? Although one always wants large batch sizes but due to limited computational resources, one often can't afford super large batch sizes.\n- (2) I understand why you set $\\tilde{L}\\_{t-1}^{(0)}$ as $R\\_l$ for the next iteration, given that the loss values are similar. However, while $L\\_{t-1}$ might be close to $L\\_{t}$, I worry that $\\tilde{L}\\_{t-1}$ could differ significantly from $\\tilde{L}_{t}$ because of the clipping and noising, which might not give bias $\\approx 0$. Some discussion or empirical results on this would be valuable.\n- (3) What was the rationale for choosing $\\tilde{L}\\_{t-1}^{(0)}$ specifically? Did the authors experiment with other options like $\\tilde{L}\\_{t-1}^{(+1)}$ or $\\tilde{L}\\_{t-1}^{(-1)}$, and if so, what were the results?\n\n2. In the main algorithm, I assumed $\\eta_i \\in \\\\{-1, 0, +1\\\\}$ represents the series of potential lrs. Is there a specific reason for this choice? I understand the need for at least two $\\eta_i$'s, but $\\{-1, +1\\}$ seems more intuitive to me...? Could the authors explain the rationale behind including 0 in the set of potential learning rates? Are there specific benefits for this choice? Also, I’m unclear about how to fit eqn. (6). In Section 4.4, the authors mention that solving this is quite efficient, with the cost \"mainly arising from additional forward passes.\" Could the authors provide more details on the practical implementation of solving equation (6), and specifically, what optimization method was used, and how much computations were typically required to find a solution?\n\n3. Could the authors provide insights into why D-adaption and Prodigy struggle in low-$\\epsilon$ regimes for full finetuning, as seen in the first table of Table 2 and Table 3? Are there specific aspects of these methods that make them less suitable for differentially private optimization? Also, for clarity, could the authors specify the value of $K$ used for HyFreeDP results in Tables 2 and 3? I assumed $K=10$ throughout these experiments, but If it varies, a note explaining the choice for each experiment would be helpful.\n\n4. I noticed in Table 2 that NonDP-GS w/ LS outperforms HyFreeDP, especially on CIFAR-10, and in Table 3, NonDP-GS and HyFreeDP show similar performance. Do authors have any intuitions behind? I’m particularly curious why NonDP-GS w/ LS performs so well on CIFAR-10 dataset - is it because the task is too simple? If I understand correctly, NonDP-GS does not account for privacy loss from hyperparameter tuning, so the $\\epsilon$ values for NonDP-GS might be underestimated. It would be great to include the results for NonDP-GS, considering the privacy cost of tuning. I imagine that HyFreeDP would then strictly outperform it...?\n\n5. It seems to me that this method works for per-batch clipping (since it also ensures dp [1]) as well, except that eqn (7) needs to be modified. It would be particularly useful for differentially privately training models with non-deomposbale loss [1, 2].\n\n[1] Huang, Alyssa, Peihan Liu, Ryumei Nakada, Linjun Zhang, and Wanrong Zhang. \"Safeguarding data in multimodal ai: A differentially private approach to clip training.\" arXiv preprint arXiv:2306.08173 (2023).\n[2] Kong, William, Andrés Muñoz Medina, and Mónica Ribero. \"DP-SGD for non-decomposable objective functions.\" arXiv preprint arXiv:2310.03104 (2023)." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "This is a well-written paper that effectively present its methods. The motivation is clear, the connections to previous work are discussed, and the experimental results are comprehensive and convincing. The method is simple yet effective in terms of efficiency and utility. The theoretical results are presented clearly, avoiding unnecessary complications, and the experiments are solid." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a method for differentially private training that eliminates the need for hyperparameter tuning, addressing a core challenge in DP deep learning. The authors provide clear discussions on the method’s efficiency, privacy guarantees, and utility. Both theoretical and empirical analyses are well-founded and straightforward." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "See below." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We introduce a hyperparameter-free differential privacy training method that automatically adjusts the learning rate, reducing the need for extra tuning efforts in a privatized, efficient, and scalable manner for language and vision tasks." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024towards,\ntitle={Towards hyperparameter-free optimization with differential privacy},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2kGKsyhtvh},\nnote={under review}\n}" }, "abstract": { "value": "Differential privacy (DP) is a privacy-preserving paradigm that protects the training data when training deep learning models. Critically, the performance of models is determined by the training hyperparameters, especially those of the learning rate schedule, thus requiring fine-grained hyperparameter tuning on the data. In practice, it is common to tune the learning rate hyperparameters through the grid search that (1) is computationally expensive as multiple runs are needed, and (2) increases the risk of data leakage as the selection of hyperparameters is data-dependent. In this work, we adapt the automatic learning rate schedule to DP optimization for any models and optimizers, so as to significantly mitigate or even eliminate the cost of hyperparameter tuning when applied together with automatic per-sample gradient clipping. Our hyperparamter-free DP optimization is almost as computationally efficient as the standard non-DP optimization, and achieves state-of-the-art DP performance on various language and vision tasks." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Differential privacy", "optimization", "hyper-parameter tuning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/a78eed81cef4add5960facc7c545357e395ba5ea.pdf" }, "presentation": null, "primary_area": { "value": "alignment, fairness, safety, privacy, and societal considerations" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Towards hyperparameter-free optimization with differential privacy" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See the \"Weaknesses\" section above for specific questions." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "(S1): The paper is highly novel, exploring a quite unusual research direction. The writing is relatively clear and easy to follow. \n\n(S2): Apart from providing the main experiments, the authors also ablate their method quite thoroughly, replacing all the FM-based components with reasonable heuristics." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a method to induce a DSL for building molecules from a given subdomain by casting the DSL construction as a sequence of steps and using a large multimodal pretrained model to make those intermediate choices. The authors then show promising results on a few relevant molecule classes." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "(W1): I am not sure if the main experiments in this work are representative of real-world use. Is being able to simply generate/sample molecules from a given subdomain useful in itself, or would it only be useful if paired with molecular optimization? \n\n(W2): It's not clear to me how the VAE baselines are set up. Are these models pretrained and then fine-tuned on the (small) dataset in question, or trained on the latter directly? Would it make sense to instead use a frozen pretrained VAE and steer it to sample around a given subdomain by inferring the right region of latent space to sample from? Alternatively, for motif-based models such as HierVAE, one could also constrain the set of motifs to those that appear in the given dataset describing the domain. \n\n=== Other comments === \n\nIn the line of VAE-based models there's also MoLeR (from \"Learning to Extend Molecular Scaffolds with Structural Motifs\"), which is a more modern extension of JT-VAE/HierVAE, often shown to perform better than the latter. \n\n \n\n=== Nitpicks === \n\nBelow I list nitpicks (e.g. typos, grammar errors), which did not have a significant impact on my review score, but it would be good to fix those to improve the paper further. \n\n- Top of page 3: \"notations like SMILES or SELFIES are mainly for representation purposes and can lead to issues (…). This may hinder LLMs’ understanding as they lack sufficient pre-training on these notations compared to SMILES\" is confusing \n\n- Lines 189-191: it's not clear how \"u, v share an atom\" should be interpreted given that context suggests u and v are atoms/nodes? \n\n- Line 403: \"We first observe in Tables 1 and that\" - something is missing \n\n- Line 407: the authors refer to \"dimensions\" without explanation of what this means (I assume each dimension is one of the datasets?) \n\n- Line 426: \"surprising considering.\" - something is missing" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see the *Weaknesses* section for my main concerns.\n\nFor now, I’m leaning toward borderline reject, but I’ll be glad to raise the score when all the questions are fully addressed." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Overall, the paper was easy to follow. The writing and the concept figure were clear.\n- An ablation study was conducted for the MMFM module." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Through this paper, the authors propose Foundation Molecular Grammar (FMG), a method that constructs domain-specific languages (DSLs) in a data-efficient manner using multi-modal foundation models (MMFMs). Specifically, FMG eases the MMFM’s task by casting the DSL construction into the problem of constructing a tree decomposition for the molecular graph." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I will combine the *Weaknesses* section and the *Questions* section. My concerns are as follows:\n- Some abbreviations are used without explanation of the full term. For example, the full term for DSL, FM, and MMFM should be provided in Introduction. The full term for the proposed method, FMG, is also only in Abstract and not in the main text.\n- The main weakness of this paper is that the experiments are not extensive and robust. Why only grammar-based and VAE methods were selected as a baseline out of the vast molecular generative methods? Moreover, only small and medium datasets were used in the experiments. It would be great to provide results using more popular and larger datasets such as ZINC250k or MOSES for a broader comparison with previous methods.\n- Interpretability is a major advantage of the proposed method, but this advantage is not properly explained and emphasized in the experiment section. I strongly recommend devoting a few paragraphs to interpretability of FMG with a case study.\n- The authors did not provide the codebase to reproduce the results." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "1. Can you provide more details or examples of the generated molecules and DSL? This would help readers better understand the practical outcomes of your method.\n\n2. The introduction lacks citations for many of the claims made. Could you provide evidence or references to support these statements, particularly regarding the challenges and current state of molecular generation?\n\n3. Regarding your claims about SMILES and SELFIES in Section 2.3, could you address the fact that SELFIES was designed specifically for molecular generation and ensures valid molecules? How does this impact your argument?\n\n4. You mention that alternatives to FMs for molecular generation require extensive training resources. Can you provide evidence or comparisons to support this claim, particularly in relation to your method's computational requirements?\n\n5. Could you clarify the notation used in Tables 1 and 2, particularly the meaning of numbers in parentheses (e.g., Isocyanates (11))? What do these represent?\n\n6. In the results section, can you provide citations for each of the methods listed and clarify what each column in the tables represents?\n\n7. Your analysis refers to dimensions \"2)\" and \"3)\" without clear explanation. Could you elaborate on what these refer to and how they relate to the metrics presented?\n\n8. There seems to be an incomplete sentence in your analysis: \"FMG appears to do exceptionally well for PTC (halides) but poor for HOPV (thiophenes), which is surprising considering.\" Could you complete this thought?\n\n9. Have you considered comparing your method against state-of-the-art language models trained on SMILES for molecular generation? How does your approach compare in terms of efficiency and effectiveness?\n\n10. Can you discuss how your work relates to recent research on improving sample efficiency in molecular generation?\n\n11. Could you elaborate on the details of the method? e.g. what temperature was used for gpt-4o, what image parameters were used (e.g. image resolution, size, etc). Does any of these variables have any influence on the results?\n\n12. For Table 1 and 2, it's not clear how many molecules were generated. Could you please specify this?" }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The work is certainly original in their use of MMFMs for describing molecular substructures and then using them again for proposing how to step-by-step build molecules from those motifs. The idea here is that the MMFM can guide and rationalize the generation of molecules in a given subfield.\n\nThe authors make a good point that LLMs lack abilities to understand chemical objects such as reactions and molecules, especially when these are given in SMILES format which is the most common thing to use, as graphs cannot be directly fed into LLMs. However images depicting the molecules and other things are a good idea to elicit correct chemical analyses from MMFMs, and it seems to work well to describe motifs, molecules, and perform other tasks such as suggesting combinations of motifs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores the potential of multi-modal foundation models (MMFMs) to craft Domain Specific Languages (DSLs) for chemistry. The key argument is that DSLs are very useful, and it's a good idea to build DSLs on specific domains as they facilitate rules for better explaining decisions from models, in this case the decoding process they follow allows them to generate molecules while also providing explainations. This is useful because domain-experts typically trust more something they can rationalize.\nThe authors finally show the performance of their method on some molecular generation benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "### Writing\n\n- It is not very clear what the goal of the paper is. Is it molecular generation, or DSL generation? In either case, very little insight is given into how the generated molecules look like, or how the generated DSL looks like. This is important provided that the paper is so strongly focused on applications.\n\n### Potentially false or misleading claims, lack of evidence/citations.\n- In general the whole introduction section misses a lot of citations. Most of the claims made there are not based on evidence, excepting 3 citations on popular LLM papers, and 1 (Makatura, 2023) that works on LLMs for aid in design. \n- Section 2.3, where the role of FMs for molecular generation is discussed. The authors make several claims that are either false or misleading:\n - \"SMILES or SELFIES are mainly for representation purposes and can lead to issues in the context of generation\". The SELFIES system was specifically designed for molecular generation, one of the advantages being that every SELFIES string represent a valid molecule, tackling any concerns regarding validity [1].\n - The authors state that the alternative to FMs for molecular generation are \"GNNs or million-parameter language models for text\" which \"require extensive training resources\". No evidence or citation is provided for this, and furthermore the current work presents no analysis of the computational resources used by the presented method.\n - The state of the art for molecular generation are indeed language models trained on SMILES [2-4]. Regarding the computational efficiency of these methods, there's a lot of active research focusing on improving the sample efficiency of these methods [5], however none of these works has been considered when making the claims above, nor does the work compare against them in any way.\n\n### Results\n- It is not clear in the results section where each result is coming from, as no citation is linked to each of the methods listed.\n- The notation is very unclear in Table 1 and 2. In particular, the notation Isocyanates (11), does it mean that the dataset of Isocyanates contains 11 samples? This is not clearly stated. Are the results aggregated from the dataset containing Isocyanates, Acrylates and chain extenders? why is this dataset designed like that? \n- It's very unclear what each column represents in these tables. The caption should at least specify this.\n- The analysis is not clear. Example \"...methods do better on 3), but struggle across dimensions 2) and 3).\", what is meant by \"2)\" and \"3)\"? is it refering to Novelty and Diversity? this is not clear and never stated\n- \"However, FMG still leaves some to be desired across 3).\" this sentence is not clear.\n- \"FMG appears to do exceptionally well for PTC (halides) but poor for HOPV (thiophenes), which is surprising considering. As we...\" this sentence is incomplete? \"which is surprising considering...?\"\n\n\n### References\n[1] Krenn, M., Häse, F., Nigam, A., Friederich, P., & Aspuru-Guzik, A. (2019). SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry. arXiv preprint arXiv:1905.13741, 1(3).\n[2] Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., ... & Patronov, A. (2020). REINVENT 2.0: an AI tool for de novo drug design. Journal of chemical information and modeling, 60(12), 5918-5922.\n[3] Öztürk, Hakime et al. “Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery.” Drug discovery today (2020)\n[4] Özçelik, R., de Ruiter, S., Criscuolo, E. et al. Chemical language modeling with structured state space sequence models. Nat Commun 15, 6176 (2024). https://doi.org/10.1038/s41467-024-50469-9\n[5] Guo, J., & Schwaller, P. (2023). Augmented memory: Capitalizing on experience replay to accelerate de novo molecular design. arXiv preprint arXiv:2305.16160." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "In terms of weaknesses, I find it challenging to fully understand or verify the details of the proposed method. The current lack of clarity and the absence of key methodological details make it difficult to assess the approach’s validity and potential for replication. I strongly believe that the paper requires substantial revision to address these gaps. Adding detailed explanations and structural improvements would better support the work’s contributions, as it currently does not seem ready for publication." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Utilizing MLLM as a decision-maker in the molecular tree composition process is a strong approach, and using rendered molecular images as input is a clever choice.\n\n2. The experimental results appear to perform as well as domain expert annotations." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work employs a multimodal language model (MLLM) as a decision-maker in the molecular graph language learning process. In this procedure, each molecule is rendered as an image for MLLM input, and the model outputs decisions and descriptions based on specific prompts. The resulting learned molecular grammar is then applied to generate new molecules within certain classes, demonstrating strong performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Overall, I think the use of MLLM as a decision-maker in the graph grammar learning, or “tree decomposition graph construction” process, is promising. However, the paper’s presentation and writing lack clarity, making it difficult to follow and understand. Additionally, many critical experimental details are missing, which limits the reproducibility and applicability of the method.\n\n1. Lack of Definitions: In the abstract, terms like MMFMs and DSLs are introduced without explanation. I suspect that these abbreviations are under-defined. In the methods section, it would help if the authors included explanations or examples of these terms.\n\n2. Lack of Structure: This method appears aimed at addressing a molecular domain-specific language learning task. However, the introduction section offers no information about molecular language, which surprisingly only appears in the Related Work section. This organization feels unusual and somewhat illogical.\n\n3. Lack of Model and Experimental Details: Both the methods and experiments sections lack fundamental details. For example, which MMFM does this approach employ? What prompts are specifically used? What is the dataset description and training cost? How are the baselines evaluated? I am particularly curious about the training and inference procedures, as the method seems to rely on MLLMs to decide the tree decomposition construction of clique graphs, yet it’s unclear how this process is applied to generate new molecules. Was fine-tuning involved, or was it entirely prompt-based?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024multimodal,\ntitle={Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2kfpkTD5ZE},\nnote={under review}\n}" }, "abstract": { "value": "Recently, domain-specific languages (DSLs) for molecular generation have shown advantages in data-efficiency and interpretability. However, constructing such a DSL requires human expertise or significant computational costs. Multi-modal foundation models (MMFMs) have shown remarkable in-context abilities for tasks across vision and text domains, but not graphs. We explore an unconventional solution: we render the molecule as an image, describe it using text, and cast the DSL construction into an equivalent problem of constructing a tree decomposition for the molecular graph. The MMFM performs a chain of discrete decisions to replace traditional heuristics used within the execution of the decomposition, enabling the smooth integration of its prior knowledge without overstepping the limits of the soundness of the algorithm. Furthermore, we collect MMFM’s reasoning for each decision into a design story, have non-expert agents evaluate stories for correctness and persuasiveness, and close the feedback loop to improve the DSL. Our method, Foundation Molecular Grammar (FMG), demonstrates significant advantages in synthesizability, diversity, and data-efficiency on molecule generation benchmarks. Moreover, its compelling chemical interpretability offers built-in transparency over the molecular discovery workflow, paving the way for additional feedback and oversight." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "multimodal foundation models", "molecular design", "interpretability" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/b2a84387681c5079e8e343175625f9205203237b.pdf" }, "presentation": null, "primary_area": { "value": "applications to physical sciences (physics, chemistry, biology, etc.)" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/4326ddc419b79c58b24297e0909398555dca7df8.pdf" }, "title": { "value": "Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- Some comparison against existing EV charging algorithms (there are a lot) would be useful." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The problem being considered is an interesting one. \n- The idea of controlling an distribution to look like another (more optimal) distribution is certainly useful." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper considers using optimal transport (OT) in mean field control, with constraints on the moments of the distributions. An algorithm (Sinkhorn algorithm) is proposed and an example of EV charging is considered." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "It's important to note that I'm not that familiar with the field of optimal transport. But I felt the following are the weaknesses:\n- The abstract, intro conclusion seem to promise a lot more than what the math actually delivers? The algorithm relies on Gibbs kernels, which feels pretty standard. How broadly applicable is this? \n- The EV problem presented is somewhat strange. The paper seems to say that the controllable variables are the EV arrival time and state-of-charge? But these are typically the main source of randomness in EV problems. The controllable knobs are typically the charging profiles. \n- How many EVs does there need to be for a mean field approximation to be valid? At any single charging station, there won't be that many EVs." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "Please see my comments about the weakness." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The EV Charging problem has received much attention recently. This work aims to optimize the consumption while satisfying the grid constraints." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the application of optimal transport to mean-field control. The main contribution is a representation of the mean-field control, Moment Constrained Optimal Transport for Control (MCOT-C), that aims to coordinate the agents and enforce constraints. The authors propose a variant of the Sinkhorn algorithm and apply the proposed algorithm to an EV charging application." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The theoretical part of this paper is hard for me to follow. The introduction starts with the mathematical problem settings without sufficient discussion about the background, significant challenges, and the motivation for using optimal transport in mean-field control. Besides, the theoretical problem setting, assumptions, and propositions are difficult to interpret. I suggest the authors add more discussions about the connections between the general framework and a specific example (e.g., the EV charging problem) that is easier to understand. Discussing the intuition/significance after stating each proposition or lemma is also helpful.\n\nFor the experiment part, I encourage the authors to compare the problem setting and the performance with related works on EV charging (e.g., [1]). Such comparisons can help the readers understand the advantages/limitations of the proposed approach.\n\n[1] B. Alinia, M. H. Hajiesmaili, Z. J. Lee, N. Crespi, and E. Mallada, \"Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak Constraints,\" in IEEE Transactions on Sustainable Computing, vol. 7, no. 3, pp. 537-548, 1 July-Sept. 2022." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "- What is $l$ in equation (7)?\n- Could you provide what the core observations and messages are that you want to share through the experiment sections?\n- Could the authors reorganize the contributions they want to claim in an itemized format? It would be helpful if the reorganized contributions included a clear explanation of the new approaches, the challenges faced, and the advantages compared to previous approaches. Additionally, if the authors feel there are any points that the reviewer may have overlooked, highlighting those would be welcome." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The approach of leveraging computational techniques from optimal control theory for control problems and the observations obtained from experiments applying the approach can be interesting. They provide the background theoretical derivation of such an approach. The approach focuses on a finite set of moments, so it could be more tractable in practice." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces moment constrained optimal transport for control (MCOT-C), which leverages computational techniques from optimal control theory for control problems. They provide an algorithm obtained by modifying the Sinkhorn algorithm by replacing the update on the second marginal with gradient descent on the dual. Then, they provide how their proposed approaches apply to mean field control applications, further providing an online version of MCOT-C." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Writing:**\nThe reviewer thinks the writing of this paper needs to be improved. The reviewer was confused by the abstract and couldn't understand what contributions were made in this paper at first. The authors barely use phrases like 'this paper' or 'we,' so the actions taken in the paper were not distinguished clearly. It seems this issue occurs throughout the paper as well. The reviewer feels that the authors didn't clearly articulate the prior approaches, what they did new, and what the advantages are. This makes it challenging to understand the contributions they are claiming.\n\n**Contribution:**\nTo the best of the reviewer's understanding, the contribution of this paper is that they are establishing the theoretical background to leverage computational techniques from optimal control theory to the mean field control problem, as presented in sections 2.1 and 2.2. In Proposition 2, they provide the calculation of derivatives for their problem and introduce a gradient descent-based algorithm in section 2.3. Then they directly jump to the experiments, and sections 3 and 4 consist of explanations of their experiments. However, the reviewer believes they should have provided more discussion about the method before proceeding to the experiments, for example, the motivation behind the specific design of the algorithm, or theoretical analysis, or some justifications for why they expect it would work well.\n\nAdditionally, the reviewer couldn't grasp what the authors were trying to claim with the experiments. The reviewer believes the authors should have provided guidance on interpreting their experimental results and offered clear conclusions or messages derived from the experiments. However, most of the content in the experiment section seems to focus on the details of the experiments.\n\n**Minor issues:**\n- Typo in line 072; a space between \"S.It\" is required.\n- Typo in line 077; \"litterature\" should be corrected.\n- Typo in line 419; \"(i)\" should be changed to \"(ii).\"" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "Why is the complexity in section 3.1 $N_t^3 \\times N_b$, when the state consists of only two variables with time, the arrival time and the plugging time?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The authors propose an interesting and relevant control application.\nThey bridge the fields of optimal transport and mean field control by modifying the Sinkhorn algorithm, where the update on the second marginal is replaced by gradient descent." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a solution for an application concerning the charging of a fleet of electric vehicles, where a central planner decides on the plugging time of the vehicles. Their proposed method applies a modified version of the Sinkhorn algorithm, typically used in optimal transport problems, to the considered control problem." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The main weakness of the paper is its structure.\nInstead of introducing the considered problem in the introduction, the authors start with their definitions of optimal transport and mean field control. The considered problem is formulated later. This makes it hard for the reader to follow.\nWhen presenting a paper which mainly focuses on one application, it would be better to first clearly describe the application and then introduce the math and methods needed for the solution.\n\nAnother major issue is the lack of consistency in the notation. Some examples:\nIn Eq. 7 there is the variable $l$ which has not been introduced before.\nIn Eq. 16 there is $h$ on the left hand side and $f$ on the right hand side. Are these equivalent?\nIn section 3.1 it say the gradient is calculated on $\\mathcal X \\times \\mathcal W$. Should this be $\\mathcal S \\times \\mathcal W$?\n\nAdditionally there are many typos, missing punctuation and missing comma placement, further reducing the readability of the paper.\n\nFinally the experiments do not compare to a baseline, apart from a naive decision rule, making it hard to evaluate the efficacy of the proposed method." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I am not sure I have specific questions; perhaps one is \"is the control specification in your EV charging case study truly realistic?\". My issues are not necessarily fixable by a small change in the paper." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- the paper seeks to connect two important areas of applied mathematics, and does it in an elegant fashion that is amenable to an analytical solution\n- the paper is largely well-written and I suspect that it would be considered fairly easily readable by experts in the field\n- the rigorous approach of the paper is refreshing and exposes the mathematical meat of the problem" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This is a half-theoretical, half-application paper which initially seeks to propose and solve the moment-constrained optimal transport for control problem, a variation of the constrained optimal transport problem for mean field control. After solving its first objective, the paper seeks to illustrate, and partially adapt, the developed results on an example of charging a large fleet of electric vehicles." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "In short, I like this paper a lot, but I am just not convinced that ICLR is the right venue for it. I urge the authors to consider once again whether the ICLR audience is what this paper is shooting for (without any offense at all to either the paper or the ICLR audience). For instance, the L in ICLR stands for \"learning\". Yet, any connection to learning -- if there is any -- remains unexplained. My issues largely stem from this perceived mismatch:\n\n- relegating the proofs, including the central results (Proposition 1/2), to the appendix probably does not make the paper more readable to the non-experts (who will find notation burdensome to start with), and only signals (possibly incorrectly) that the authors do not see these results as their primary contribution. In that case, I am not sure what is the primary contribution\n\n- there seems to be some dissonance between the claimed contributions (which speak about control of an ensemble of agents), the actual technical contributions (which can indeed serve this goal, but the connection is not really described in detail), and the application (which is farfetched at best: why should the power consumption exactly track a reference trajectory?)\n\n- the \"second\" technical part of the paper, coming within the application section, applies largely domain-agnostic mathematical theory to a problem that is tailored to the application (which is, as I already mentioned, farfetched). My suggestion would really be to split this paper into two: the first one dealing with the general problem of MCOT-C (and its online version in as much generality as possible), with perhaps only a small academic example if there is no room for more, and the second one applied, with a realistic case study and a detailed description of the algorithm implementation and possible minor modifications\n\n- partly because of decoupling of the proof (and a weird ordering of the proofs in the Appendix), it is not clear to me how challenging the main result actually is: the duality seems rather straightforward. If this is not the case, that should be emphasized. If it is, perhaps it would be good to try to develop online results on MCOT-C in more generality" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "Please see the \"Weaknesses\" section for my questions." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "-\tThe paper tackles the important, contemporary problem of charging EVs and uses a dataset to do so.\n-\tThe combination of mean field control and optimal transport to optimize EV charging appears to be interesting.\n-\tThe paper contains theoretical results to complement empirical findings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper combines the fields of mean field control and optimal transport to solve multi agent control problems. When doing so, the authors constrain some of the marginal distributions to specific moment classes and adopt the Sinkhorn algorithm to their setup. The theoretical and algorithmic considerations are complemented by an extensive example of EV charging in the Netherlands, based on a real dataset." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "-\tIntroduction: In my opinion, the introduction should be less technical in the sense that there shouldn’t be several extensive mathematical expressions. In this way, especially non-expert readers can get a first impression of the paper’s contributions without being confronted with mathematical details.\n-\tLine 74: EVs are mentioned for the first time here (excluding the abstract). In my opinion the authors should lead with a motivating example and then move on to the technical tools like MFC. In in its current form, the introduction reads like a random collection of mathematical concepts. The authors should put more emphasis on their goals and high-level ideas.\n-\tLine 84-90: The contributions are formulated far too vague, for example, ‘‘Coordination of an ensemble of agents to achieve a desired goal’’ basically describes any cooperative multi-agent problem. Similarly, a discussion and comparison to the existing literature is missing. What sets the contributions of this paper apart from the existing literature?\n-\tAssumptions (A1) to (A3): The assumptions are neither explained nor is there a discussion of how realistic or restrictive they are.\n-\tProposition 1, Proposition 2, Lemma 1: Like the assumptions, the theoretical results are just stated but not discussed or explained. This presentation style makes it very hard to follow the train of thought in this paper.\n-\tSection 2.2: It would be helpful for first time readers to explain why the dual problem can be useful for solving these types of problems. Just stating that it is “needed for the algorithm” (line 140) does not provide any intuitive insight.\n-\tSection 2.3: In this section it is hard for me to understand the algorithmic contributions of the paper. If the contribution, compared to the existing Sinkhorn algorithm, is just the update of $\\zeta^k$, the authors should explain when and why this update makes an important difference.\n-\tSection 3: I am not very familiar with the EV charging literature, but I wonder if there are not any existing papers that focus on similar use cases. Since there is not a single reference in Section 3, it seems like this model is completely new and has no connections to existing work. Is this really the case?\n-\tSection 4 (like the previous concern): Aren’t there any existing methods to compare against? What exactly are the advantages of the proposed approach?\n\n\nMinor Comments:\n\n-\tLine 35: Is it supposed to be “… common state space $\\mathcal{X}$ …’’? How is this state space defined? Are there any restrictions on $\\mathcal{X}$ or is it completely arbitrary? (Line 101 seems to contain the precise definition)\n-\tLine 36: the extensive mathematical definitions should appear later in the paper, but not in first paragraph of the introduction. That aside, I think that $\\mu_1$ and $\\mu_2$ are not properly defined here.\n-\tLine 58: What values can the variables $S_k$ and $W_k$ take?\n-\tLine 73: space missing at “… $S$.It“\n-\tLines 79-82: Although the sentence “Inspired by … of optimal control solutions” is somewhat vague, it is nevertheless informative about the goals of this paper. I think it needs to appear earlier in the introduction.\n-\tLine 103: Are the marginals defined correctly? Shouldn’t the two $dx$ for the first marginal just be $x$? The same question applies to the second marginal and $dy$.\n-\tLine 104: there is one word “problem” too many.\n-\tLine 104: How are the probability kernels $T^\\lambda$ in this family defined?\n-\tLine 118: I think the notation is wrong here. It should be “… \\leq 0 for all 1 \\leq m \\leq M}” instead of “… \\leq 0: 1 \\leq m \\leq M}”\n-\tLines 119-120: While I do understand that an equality can be equivalently written as two inequalities, I am unsure how this applies to the previously defined moment class. Does that mean that for equalities, we would define a moment class with more than $M$ inequalities?\n-\tLine 383: Why is a quadratic penalization chosen? If it is standard in the literature, corresponding references should be added.\n-\tLine 397: Why is the infinite norm a good candidate?\n-\tLine 419: It should be “(ii)” instead of “(i)”, right?" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We introduce a new formulation of Mean Field Control as a Moment Contrained Optimal Transport problem and illustrate it on a use case of EV charging, using real data." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024moment,\ntitle={Moment Constrained Optimal Transport for Control Applications},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2kje23LSOE},\nnote={under review}\n}" }, "abstract": { "value": "This paper concerns the application of techniques from optimal transport (OT) to mean field control, in which the probability measures of interest in OT correspond to empirical distributions associated with a large collection of controlled agents. The control objective of interest motivates a one-sided relaxation of OT, in which the first marginal is fixed and the second marginal is constrained to a “moment class”: a set of probability measures defined by generalized moment constraints. This relaxation is particularly interesting for control problems as it enables the coordination of agents without the need to know the desired distribution beforehand. The inclusion of an entropic regularizer is motivated by both computational considerations, and also to impose hard constraints on agent behavior. A computational approach inspired by the Sinkhorn algorithm is proposed to solve this problem. This new approach to distributed control is illustrated with an application of charging a fleet of electric vehicles while satisfying grid constraints. An online version is proposed and applied in a case study on the ElaadNL dataset containing 10,000 EV charging transactions in the Netherlands. This empirical validation demonstrates the effectiveness of the proposed approach to optimizing flexibility while respecting grid constraints." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Optimal Transport", "Mean Field Control", "Signal Tracking" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/74c9a4cc1dc4629d467576a91c3e7c4f04c61df6.pdf" }, "presentation": null, "primary_area": { "value": "optimization" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/6e10727c28fcf7a8f40c3efe3610ae0c7e13dfb2.zip" }, "title": { "value": "Moment Constrained Optimal Transport for Control Applications" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I think overall this paper is good. Currently I give 6 since I have not checked the proof very carefully. I am willing to raise the score to 8 if the proof is proved to be right by other reviewers." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1. I think this paper focuses on a very important problem. DARTS is a very crucial framework in NAS, but it has some well-known problems. It is very important to have some theoretical analysis on this framework. \n2. This author provides large-scale theoretical analysis, focusing on very important aspects, such as the stability of bi-level optimization, the loss trajectory, etc. I think the analysis is insightful. \n3. The proposed method can reduce the search costs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper focuses on Differentiable Architecture Search (DARTS). They conduct theoretical analysis over DARTS and propose a concept called Stable Equilibrium State. Upon it, they propose an effective framework called BOSE-NAS to identify the optimal state during the searching procedure. Experiment results show that the proposed method shows competitive results over state-of-the-art methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. I think the figures in this paper can be polished to be more clear (maybe in the camera ready version). \n2. The accuracy of the proposed method is just comparable with sota, but not superior to sota. I think it is not a serious problem, but I just list it as one weakness." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Although the problems within the bi-level optimisation process of differentiable NAS have been widely studied for years, e.g., BONAS [1], the proposed EI metric and Stable Equilibrium State still bringing some new insights to the NAS research. But differentiable NAS are often sensitive to the hyper-parameters, I wonder how sensitive is the Stable Equilibrium State identification process to the choice of hyper-parameters such as the learning rate and batch size? Can authors provide some ablation studies? It would be helpful to understand how the proposed method handles changes in the hyper-parameters, as well as its robustness.\n\n2. The proposed methods are only applied to the differentiable NAS, however, the interest of NAS research has been largely shifted to training-free NAS methods, as they are offering more flexibilities to different search algorithms and search spaces, as well as better performance and much less computational overhead compare with differentiable NAS, e.g., Zen-NAS [2] and SWAP-NAS [3]. Can author discuss the potential adaptation that extend the concept the Stable Equilibrium State and EI metric to non-differentiable NAS methods? \n\n\n\n[1] Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, and Tong Zhang. Bridging the gap between sample-based and one-shot neural architecture search with bonas. NeurIPS 2020.\n\n[2] Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin. Zen-nas: A zero-shot NAS for high-performance image recognition. ICCV 2021.\n\n[3] Yameng Peng, Andy Song, Haytham. M. Fayek, Vic Ciesielski and Xiaojun Chang . SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NAS. ICLR 2024." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The introduction of Stable Equilibrium State is somewhat novel and interesting, the theoretical analysis of architecture parameter dynamics provides a solid foundation for understanding the bi-level optimisation in differentiable NAS.\n\n2. The Equilibrium Influential (EI) metric for operation evaluation is an innovative approach and offers a more reliable measure of operation importance to the bi-level optimisation process in the differentiable NAS. \n\n2. The proposed BOSE-NAS achieves competitive performance as well as less computational overhead in benchmark datasets like CIFAR-10 and CIFAR-100, compare with other differentiable NAS methods." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors propose BOSE-NAS, a novel differentiable neural architecture search method that addresses critical challenges in existing differentiable architecture search (DARTS). The core idea of BOSE-NAS is around the the concept of a ‘Stable Equilibrium State’, which offering insights into the validation loss trajectory across architectural spaces to stabilise the supernet’s bi-level optimisation process. The proposed method introduces a novel metric called Equilibrium Influential (EI) to evaluate the importance of operations during the architecture search phase. By choosing operations based on the EI metric at the Stable Equilibrium State, BOSE-NAS uses bi-level optimisation to find the optimal architecture operations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The propose method heavily depends on the accurate identification of the Stable Equilibrium State, specifically, the EI metric evaluates each operation independently, which could overlook potential dependencies among network operations within the architecture. This could make the proposed method not always generalise well.\n\n2. The biggest concern of the proposed method, e.g., EI metric and the concept of Stable Equilibrium State, are the limited use scenario. It may not be easily applicable to non differentiable NAS methods, e.g., the evolutionary or pruning-based search algorithms." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "pls see weaknesses" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tThe paper is easy to read.\n2.\tThe problem of DAS is clear." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Differentiable Architecture Search (DAS) often faces the issue where the magnitude of architecture parameters fails to reflect the true importance of operations. This paper addresses this problem by proposing BOSE-NAS, a DAS method guided by the Stable Equilibrium of architecture parameters (i.e., the point where the rate of change of the architecture parameters is minimal). The authors provide relevant experiments to support their method. However, the experimental section has several issues, such as limited improvement in performance and a lack of ablation studies." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "This paper was submitted to NeurIPS 2024, compared with NeurIPS 2024, there are still some important issues that need to be addressed.\n1. The ablation studies are not convincing. To be specific, in Figure 3, we can clearly see that the proposed method is sensitive to hyperparameters.\n2. There still exist some typos/grammatical errors in the paper.\n3. The format of references is still wrong.\n4. Exploring the reasons behind the success of these techniques and providing intuitive explanations would contribute to the overall scientific contribution of the work.\n5. I don't understand the theoretical analysis. Why use \" Influence Function\"? What relationship between \" Influence Function\" and your method? why validate the \"reliability\" of your proposed metric? Please provide detailed motivation and clear proven process in step by step. What is the difference between stability and reliability? Please provide a step-by-step proof process for validating their metric. And, clarification on the relationship between the Influence Function and their method.\n6. In page 7, \"I(z, L)\" denotes?\n7. The main limitation of this paper is that proposed method lacks comparison with larger datasets (i.e., COCO2017, VOC), and more competitors (i.e., β-DARTS++, Λ-DARTS).\n8. Pls to prove your statement of generalizability.\n\n[1] β-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture Search\n[2] Λ-DARTS: MITIGATING PERFORMANCE COLLAPSE BY HARMONIZING OPERATION SELECTION AMONG CELLS" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See Weakness." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "+ The experimental results clearly show the effectiveness and the efficiency of the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new operation importance evaluation metric in network architecture search. The authors first introduce the concept of stable equilibrium state, which shows the stability of the bi-level optimization process in differentiable NAS. By analyzing the supernet training dynamics, the metric named equilibrium influential is proposed for fair differentiable NAS. The experimental results show that the proposed metric and search method can achieve competitive accuracy with significantly reduced search cost." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The writing can be improved. The abstract and the introduction are redundant. For the abstract, there are too many contents to introduce the background. For the introduction, many details especially the experimental results don’t have to be elaborated. I think demonstrating the main results is enough to show the effectiveness of this method.\n\n- The technical soundness can be further verified. There are some strong assumptions without verification or explanation. For example, the assumptions to transit (6) to (7) should be verified. Why they have little effect on $\\alpha$?\n\n- Some exact calculations can be put in the Appendix part.\n\n- The reason why the proposed method has less search cost should be analyzed in the result analysis, which is an important benefit from the new metric.\n\n- The performance of the proposed method underperforms the SOTA NAS methods such as IS-DARTS. More clarification is required for the performance analysis." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "This paper clarifies the ambiguities surrounding the actual role and impact of architecture parameters in DARTS and leveraging this insight proposes a more effective and robust NAS method." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024bosenas,\ntitle={{BOSE}-{NAS}: Differentiable Neural Architecture Search with Bi-Level Optimization Stable Equilibrium},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2l301qUdor},\nnote={under review}\n}" }, "abstract": { "value": "Differentiable Architecture Search (DARTS) has gained prominence in the neural architecture search community for its efficiency and simplicity, achieved through optimizing architecture parameters via gradient descent. However, the magnitude of these architecture parameters frequently fails to accurately represent the true significance of the corresponding operations, adversely affecting the performance of the resultant architectures. While numerous studies have introduced alternative metrics to evaluate operation significance, the actual role and impact of architecture parameters remain inadequately explored. This lack of understanding creates critical ambiguity in the architecture search process. Resolving these ambiguities is essential for the effective utilization of architecture parameters, thereby facilitating the development of more effective differentiable NAS methodologies. In this work, we first conduct a rigorous theoretical analysis, revealing that the change rate of architecture parameters reflects the sensitivity of the supernet’s validation loss in the architecture space. Building on this foundation, we introduce the concept of the ‘Stable Equilibrium State’, which offers essential insights into the validation loss trajectory across architectural spaces and elucidates the stability of the supernet’s bi-level optimization process. We further investigate the supernet training dynamics to assess the influence of operations on the Stable Equilibrium State, leading to the proposal of a novel metric for evaluating operation importance, termed Equilibrium Influential ($E_\\mathcal{I}$). Integrating these elements, we introduce BOSE-NAS, an effective differentiable NAS method that utilizes the Stable Equilibrium State to identify the optimal state during the search process, subsequently deriving the final architecture based on the $E_\\mathcal{I}$ metric. Extensive experiments conducted across diverse datasets and search spaces demonstrate that BOSE-NAS achieves competitive test accuracy compared to state-of-the-art methods while significantly reducing search costs." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Neural Architecture Search", "Stable Equilibrium State", "Equilibrium Influential" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/5b882efc6f53263f72ffd7a98245c0507bbad0a9.pdf" }, "presentation": null, "primary_area": { "value": "other topics in machine learning (i.e., none of the above)" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "BOSE-NAS: Differentiable Neural Architecture Search with Bi-Level Optimization Stable Equilibrium" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "- Please address the concerns in the weakness section." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "- I liked the initial idea of investigating localized unlearning based on memorization. \n- The proposed method was partially successful on some forgetting benchmarks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work attempted to tackle the problem of localized unlearning by investigating it based on the memorization assumption and proposed DEL for some parameters with resetting and fine-tuning. The proposed method showed promising results on forgetting on a couple of benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The method is based on a lot of assumptions without much justification, but with intuition. Thus, it is very hard to see if the proposed method is indeed ok in terms of unlearning (while preserving the rest!). \n- It is very hard to see the core contribution clearly due to poor writing. It was very hard to read and follow.\n- Experiments look quite limited in terms of benchmarks (datasets, compared methods). I am afraid that the localized unlearning approach may hurt the preservation of remaining parts, but it is unclear if it is true." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Q1: From the Appendix A.4's algorithmn, the localization strategy is mainly from the magnitude of each weighted gradient for each mini-batch. Is the localization mask determined by each mini-batch? Is the localization mask fixed for different networks? If the mask is not accurate, does it affecting the accuracy? How sensitive is DEL to different choices of localization strategy.\n\nQ2: Does the DEL method has any specific limitations when facing more complex or diverse data distributions?\n\nQ3: Can DEL method adapted to other network architectures? What's the differences if it adapted to a customized network structure?\n\nQ4: Does the performance different if using different hyper-parameters, such as learning rate, batch size, etc?\n\nQ5: In Table 7, the accuracy is getting better with higher percentage of parameters. Will the accuracy still getting better with 40%/50%?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This method achieves state-of-the-art unlearning performance while requiring a small modification on a subset of model parameters.\n\nThis method also minimized unnecessary parameter while preserving the model efficiency." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduced Deletion by Example Localization (DEL) method, which aimed at enhancing the machine unlearning by focusing on localized, a targeted data subset in neural networks. The traditional unlearning methods are removing the influence of certain data, making the model performance worse or requiring extensive retraining. However, DEL method used a selective approach by identifying a small subset of parameters that influenced by specific data points. This method can effectively remove the memory of specified data subset while persevering the model accuracy." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The weakness of this method is limited experiments on the public dataset, only applied on CIFAR-10 and SVHN datasets, as well as the limitation on larger models." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. I think the memorization property can vary from model scale. So, I am wondering if this memorization and proposed algorithm is available for most models since the evidence provided is empirical findings." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The localized unlearning is new and meaningful research area and the motivation to leverage the memorization is reasonable and insightful.\n\n2. The experiments and findings are validated with various metrics and existing unlearning algorithms and show consistently good results.\n\n3. The paper is well formatted and arranged so that easy to understand." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes the local unlearning algorithm Deletion by Example Localization, leveraging the memorization issue. The proposed algorithm first resets the parameters that are most critical based on the localization strategy and then finetunes them. The algorithm can be paired with various existing unlearning algorithms. The author validates experiments on different datasets with different metrics to show that the performance achieves state-of-the-art." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. There are several mathematical definitions such as the Unlearning and Label memorization. However, I did not find close connections or logical relations between them. If necessary, I expect the author to use these definitions to derive some theorems closely based on the proposed algorithm. For example, it is difficult to see theoretically or empirically if the proposed algorithm can make distribution the same as the model trained without that data.\n\n2. Following the above, I understand in this area, most justifications are more empirical. So, I think it's better to use some metrics that can support the definition (I.e., the same distribution as a retrained model)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "none" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "In Section 5.1, your paper presents several hypotheses. Could you provide a more detailed explanation of how your results support these hypotheses?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1.\tThe paper is well-written, with a clear and concise logical flow. It begins by introducing localization as a preferred approach for model unlearning and then presents a cohesive and insightful perspective—that unlearning can be viewed as an extreme form of no memorization (lines 165-169)—which lends coherence and unity to their proposed method.\n2.\tThe paper provides a comprehensive review of existing methods, thoroughly examining current approaches and establishing its own assumptions, such as the advantages of data-dependent over data-agnostic methods and the reasoning for utilizing gradient information. These insights serve as the foundation for their proposed method, DEL.\n3.\tThe proposed method is both simple and effective, achieving state-of-the-art performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the challenge of machine unlearning in a localized context by introducing a novel approach based on the concept of memorization. Following a comparison of existing methods, the authors identify data-dependent and gradient-dependent techniques as particularly effective. They refine the current criticality-based localization strategy, resulting in a new unlearning algorithm, “Deletion by Example Localization” (DEL). DEL enables localized unlearning by resetting and fine-tuning parameters identified as essential based on the calculated criticality of the parameters." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. This paper extensively discusses related work and motivations, primarily focusing on comparisons between existing methods. The proposed approach appears to be a straightforward combination of existing techniques, which may limit its novelty.\n2. The results in Section 3 do not necessarily support the hypotheses in Section 5.1, as the observed improvements could be attributed to other factors. Thus, a more thorough theoretical explanation of the proposed method is needed.\n3. This paper focuses exclusively on classification models, but I believe that “unlearning” in LLMs (i.e., model or knowledge editing) is a more pressing concern. It remains uncertain whether the conclusions drawn from vision classifiers in this paper can be directly applied to LLMs.\n4. There are a few typos, although they don’t impact comprehension. For instance, in line 159, “$f(; \\theta)$” might be intended as “$f(x; \\theta)$.”" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024improved,\ntitle={Improved Localized Machine Unlearning Through the Lens of Memorization},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2m5XI3nM46},\nnote={under review}\n}" }, "abstract": { "value": "Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is important for key applications, including making the model more accurate by removing outdated, mislabeled, or poisoned data. In this work, we study localized unlearning, where the unlearning algorithm operates on a (small) identified subset of parameters. Drawing inspiration from the memorization literature, we propose an improved localization strategy that yields strong results when paired with existing unlearning algorithms. We also propose a new unlearning algorithm, Deletion by Example Localization (DEL), that resets the parameters deemed-to-be most critical according to our localization strategy, and then finetunes them. Our extensive experiments on different datasets, forget sets and metrics reveal that DEL sets a new state-of-the-art for unlearning metrics, against both localized and full-parameter methods, while modifying a small subset of parameters, and outperforms the state-of-the-art localized unlearning in terms of test accuracy too." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Machine Unlearning", "Memorization", "Localized Unlearning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/150921e5d33db3b86c10e698cedf4711f038cb96.pdf" }, "presentation": null, "primary_area": { "value": "other topics in machine learning (i.e., none of the above)" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Improved Localized Machine Unlearning Through the Lens of Memorization" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "- How does ControlAgent handle model uncertainty? While you discuss robustness through phase margin, could you elaborate on whether the framework considers parametric uncertainties or unmodeled dynamics?\n- For higher-order systems, you mention manual design of 50 cases. Could you explain your methodology for ensuring these cases are representative and unbiased? What criteria guided your selection?\n- For the history and feedback module, how do you handle the context window limitations of LLMs? Could you provide more details about the memory management strategy?\n- Could you provide a more detailed analysis of failure cases, particularly for higher-order systems where performance was lower? Understanding these cases would help assess the framework's limitations." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The core strength of this paper lies in how it successfully addresses the fundamental performance-robustness trade-offs inherent in classical control theory. The framework intelligently uses loop-shaping and PID tuning methodologies, employing settling time and phase margin as key tuning parameters - a sophisticated approach that mirrors established control engineering practices. The iterative design process is noteworthy for its theoretical soundness. Rather than treating controller design as a single-shot optimization problem, ControlAgent mimics the systematic approach used by human experts, progressively refining controller parameters while managing the complex interplay between performance metrics. The empirical results validate this approach, showing success across various system types and complexity levels, with particularly impressive results in handling unstable and higher-order systems. The framework's ability to achieve 100% success rates for first-order and stable second-order systems, while maintaining high performance even for complex higher-order and unstable systems, demonstrates its robust theoretical foundation and practical effectiveness." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces ControlAgent, a framework that automates control system design by integrating large language model (LLM) agents with domain expertise. The framework uses multiple collaborative agents to emulate human iterative design processes, gradually tuning controller parameters to meet user-specified requirements for stability, performance, and robustness. ControlAgent consists of a central agent that analyzes tasks and distributes them to specialized agents, task-specific agents that handle detailed controller design for different system types, a Python computation agent that performs control calculations and evaluations, and a history and feedback module that enables iterative refinement of designs. The system addresses the inherent complexity of control design by breaking down the process into manageable steps and incorporating domain knowledge into the decision-making process. The authors also develop ControlEval, an evaluation benchmark comprising 500 control tasks across various system types including first-order, second-order, systems with delay, and higher-order systems, with different response modes and specific performance criteria. This benchmark serves as a standardized way to evaluate control design workflows." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The evaluation methodology raises several concerns. While ControlEval includes 500 control tasks, the paper doesn't clearly justify the distribution of these tasks or demonstrate their representativeness of real-world control problems. The generation process for higher-order systems is particularly problematic - the authors admit to manually designing these cases, which could introduce bias and may not reflect the true complexity of higher-order system control.\n- The comparison with baselines is somewhat limited. The paper primarily compares against relatively simple LLM-based approaches (zero-shot, few-shot) and a single traditional tool (PIDtune). Modern control design often employs more complex methods like robust control, model predictive control, or optimization-based approaches, which are notably absent from the comparison. The performance metrics are also relatively basic, focusing mainly on settling time and phase margin while overlooking other important characteristics like disturbance rejection and noise sensitivity.\n- The iterative design process lacks theoretical guarantees of convergence or optimality. The paper doesn't provide analysis of when or why the iteration process might fail, nor does it establish bounds on the number of iterations needed for convergence. \n- The framework's heavy reliance on proprietary LLM models raises questions about reproducibility and practical deployment. The authors don't thoroughly explore how the system's performance might vary with different base LLMs or how it might degrade with smaller, more practical models." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "As mentioned above, I suggest the authors improve their academic writing skills and design specific application scenarios, such as robotics and transportation, to verify their framework.\n\nI recommend several papers, as shown below, in which authors can learn how to improve academic writing skills and organize corresponding ideas from them.\n\n1) Yang, Q., & Parasuraman, R. Bayesian strategy networks based soft actor-critic learning. ACM Transactions on Intelligent Systems and Technology (TIST).\n\n2) H. Hamann and H. Wo ̈rn, “A framework of space–time continuous models for algorithm design in swarm robotics,” Swarm Intelligence, vol. 2, no. 2-4, pp. 209–239, 2008." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "This paper proposes a new paradigm that automates control system design via novel integration of LLM agents and control-oriented domain expertise to bridge the the complexity and specificity in control system design." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a new paradigm that automates control system design via novel integration of LLM agents and control-oriented domain expertise. However, the writing style is confusing, making it hard to follow their ideas. I suggest the authors improve their academic writing skills by making the abstract more precise and brief, adding the approach section, and reorganizing the corresponding method section. Moreover, I do not know what scenarios the authors implemented or simulated for the experiments. There is no background information or introduction. Generally, this paper needs to improve largely." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper's writing style is confusing, making it hard to follow their ideas. I suggest the authors improve their academic writing skills by making the abstract more precise and brief, adding the approach section, and reorganizing the corresponding method section. Moreover, I do not know what scenarios the authors implemented or simulated for the experiments. There is no background information or introduction. Generally, this paper needs to improve largely." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "How much sampling is done of LLM-generated designs? e.g. is the budget 10 designs?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "This paper addresses the issue of designing controllers using LLMs, in particular with specific stability, phase margin, and settling times. \n\nThe overall system runs in a loop where a the designed controller is run and the system provides feedback based on a history of designs and how well they performed." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper describes a composite LLM-based system for control tasks which attempts to design controllers, represented as Python code, for control problems with specific requirements, namely stability, phase margin, and settling time. \n\nWhile this paper is decently presented and seems to achieve decent results, I am uncertain about recommending it for ICLR. Primarily, the paper seems highly domain-specific and engineering-focused, rather than more general cutting-edge academic research. Still, it is a good engineering system. Secondly, I am uncertain about the evaluation. \n\nThe proposed method is essentially a domain-specific application of LLM-modulo, e.g. an interative prompt with a verifier and critiques [1].\n\n[1] Kambhampati, S., Valmeekam, K., Guan, L., Verma, M., Stechly, K., Bhambri, S., ... & Murthy, A. B. Position: LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks. In Forty-first International Conference on Machine Learning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "It seems guarantees would be desirable when working with control systems, and I assume the problem requirements are meant to be guarantees. However, I feel the paper would be made a lot stronger by discussing guarantees at length. \n\nThe evaluation methods seem like they could be improved, in particular I would like the authors to clarify about \"a system is considered successfully designed if at least one of the multiple independent trials results in a successful design\". It seems this would greatly skew the statistics, since failures are being filtered out. I also don't see reporting of how many samples are taken to achieve the reported success rates. \n\nGiven the unpredictable and error-prone nature of LLMs, I am skeptical that the overall system can work without a human in the loop or method for filtering correct answers. Also, it seems like intermediate mistakes in generation (e.g. a hallucinated constant) would collapse the entire system, so I would expect it to be rather fragile. To the extent that the proposed method works, I am curious what the authors attribute it to?\n\nWhile the method is interesting, it seems to be an incomplete solution to a highly domain-specific problem, so I'm unsure about the larger impact of the work, e.g. the paper doesn't give much insight into designing general LLM-based systems." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024controlagent,\ntitle={ControlAgent: Automating Control System Design via Novel Integration of {LLM} Agents and Domain Expertise},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2mGFmAQWUI},\nnote={under review}\n}" }, "abstract": { "value": "Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors, including aerospace, automotive systems, industrial processes, power grids, and robotics. Despite advances made by Large Language Models (LLMs) in various domains, their application in control system design remains limited due to the complexity and specificity of control theory. To bridge this gap, we introduce **ControlAgent**, a new paradigm that automates control system design via novel integration of LLM agents and control-oriented domain expertise. ControlAgent encodes expert control knowledge and emulates human iterative design processes by gradually tuning controller parameters to meet user-specified requirements for stability, performance (e.g. settling time), and robustness (e.g., phase margin). Specifically, ControlAgent integrates multiple collaborative LLM agents, including a central agent responsible for task distribution and task-specific agents dedicated to detailed controller design for various types of systems and requirements. In addition to LLM agents, ControlAgent employs a Python computation agent that performs complex control gain calculations and controller evaluations based on standard design information (e.g. crossover frequency, etc) provided by task-specified LLM agents. Combined with a history and feedback module, the task-specific LLM agents iteratively refine controller parameters based on real-time feedback from prior designs. Overall, ControlAgent mimics the design processes used by (human) practicing engineers, but removes all the human efforts and can be run in a fully automated way to give end-to-end solutions for control system design with user-specified requirements. To validate ControlAgent's effectiveness, we develop **ControlEval**, an evaluation dataset that comprises 500 control tasks with various specific design goals. Comparative evaluations between LLM-based and traditional human-involved toolbox-based baselines demonstrate that ControlAgent can effectively carry out control design tasks, marking a significant step towards fully automated control engineering solutions." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Automated Control System Design", "LLM Agent" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/2187e1c331af5f074bbada711de5db6d110db9b7.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "- Possibly harmful content in the part of the dataset not validated by humans \n- 3 Volunteers reviewed around 194,100 examples (30% of the total 642,700.) That's a significant time investment on the part of volunteers without compensation" }, "flag_for_ethics_review": { "value": [ "Yes, Privacy, security and safety", "Yes, Responsible research practice (e.g., human subjects, data release)" ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Q1: What was the human score distribution for the 30% of data that was validated on those 8 metrics? \n\nQ2: Could you elaborate the choice of threshold in validating the data? \n\nQ3: What percentage of those 30% data was invalidated due to significant harmful content by humans? A similar fraction of such harmful content could still be a part of the remaining 70% remaining data. \n\nQ4: Do the mentioned LLMs perform perfectly on those 647 original chinese puzzles? If not they could be used to test the generalizability of the puzzleverse framework. \n\nQ5: Is the 30% data from test split the samples that were validated by volunteers?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "S1: The authors propose an automated synthetic data generation approach for evaluating and inculcating lateral thinking in LLMs \nS2: The generated dataset is significantly larger than previous works" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper contributes a new dataset of Lateral Thinking Puzzles for training and evaluation of the lateral thinking abilities of LLMs in the Chinese language. They further introduce the Puzzleverse framework where LLMs are instruction fine-tuned and aligned with a reward model on 70% of the dataset. Training with Puzzleverse shows improved performance in the created dataset and other reasoning tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "W1: The GPT-4 model is used to create, and evaluate the quality, consistency and correctness of most of the data limiting the upper bound of the performance of any model trained on this data to the GPT-4 model. Previous work [1] shows that even the GPT-4 model performs poorly on lateral thinking limiting the potential of this dataset. \n\nW2: There is no human verification of whether the puzzles included in the dataset created using GPT-4 can actually be solved. There is no human performance on the test set reported. \n\nW4: During inference, there's a 70:30 split of the training set. Since a large amount of data is generated using an LLM there could be significant overlap between questions across the dataset. \n\nW5: In a setting like Lateral thinking, an LLM's performance might differ a lot if evaluated multiple times on the same question. There are no variance studies or standard errors across multiple trials reported. \n\nW6: Only 30% of the total data validated for correctness by humans. Within this filtered data - \"Puzzles scoring below 6 are discarded, resulting in a final average score of 6.65\" The justification for this threshold is unclear, as the questions should absolutely satisfy all those conditions for the puzzle to be lateral thinking puzzle. Furthermore the exact distribution of the scores is missing. \n\nW6: The models with and without puzzleverse are not evaluated on existing lateral thinking datasets like [1]. \n\n\n[1] LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. How to make sure the qulity of GPT-4 generated puzzles? Since these puzzles are quite challenging to GPT-4. With in-content learning, GPT-4 is able to creatively create new puzzles? Any evaluation on the qulaity?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Novel Lateral Thinking Puzzles Dataset: The paper introduces the largest lateral thinking puzzle dataset. Each puzzle includes a riddle, unconventional solutions, a sequence of yes-or-no questions, answers, and clues. The dataset is carefully constructed to capture the nuances of lateral thinking and is validated through both automated and manual review processes to ensure high quality and coherence.\n2. The PuzzleVerse framework combines supervised fine-tuning with reinforcement learning, utilizing a reward model that ranks questions based on relevance and coherence with the puzzle solution.\n3. Experiments demonstrate significant performance gains, with LLMs achieving an average improvement of 101.9% after PuzzleVerse training on lateral thinking tasks. These results are benchmarked against powerful LLMs like GPT-4, providing a robust comparison." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper explores the lateral thinking abilities of LLMs in puzzle-solving scenarios, where solutions require creative, non-linear approaches. The authors introduce the “Lateral Thinking Puzzles” dataset. It includes unconventional riddles designed to test LLMs' lateral thinking. They propose a framework, PuzzleVerse, to improve LLMs' performance on these tasks through question generation and reinforcement learning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. LLM-judge/metrics might be a good help other than only relying on human evaluation. BLEU/ROUGE is not useful here. \n2. The data creation part is not quite convincing since the challenging puzzle is not a easy task to generate. Some evaluation or human quality check might be needed.\n3. Language is Chinese only." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. The paper mentions that the dataset is designed with a focus on the Chinese language. However, the inclusion of GPT-4 in the benchmark raises a question regarding its suitability. Given that GPT-4 is known for its superior performance in English, it would be beneficial for the paper to discuss the rationale behind incorporating a model that excels in a different language context. This discussion could provide insights into how the model's strengths in English might influence the results within a Chinese-centric dataset or whether there are specific reasons for expecting GPT-4 to perform well despite the language discrepancy.\n2. When assessing the performance of various LLMs on the dataset, it is crucial to consider the impact of model size and complexity. The paper compares the performance of different LLMs but does not explicitly mention the number of parameters for each model. Model performance can be significantly influenced by the number of parameters, which affects their capacity for learning and generalization. It would greatly enhance the analysis if the paper could provide details on the parameter count for each model included in the comparison." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Lateral thinking promotes creative reasoning in LLMs, helping them move beyond straightforward logical solutions and explore unconventional answers, which could be valuable for complex problem-solving. \n2. The performance of the framework is good." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "To test and enhance lateral thinking in LLMs, the paper introduces a large dataset called Lateral Thinking Puzzles (LTP), composed of riddles with unconventional solutions. It also proposes a framework, PuzzleVerse, which guides LLMs in incrementally questioning and deducing answers through yes-or-no questions, designed to stimulate creative problem-solving strategies. In experiments, LLMs trained with PuzzleVerse demonstrate significant improvements in solving puzzles creatively, thus providing a new perspective to reasoning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper asserts that its approach significantly enhances the creativity of LLMs by extending the scope from text-based riddles to a broader category of puzzles. However, this claim might be overstated.\n2. The dataset and framework's aim is commendable in seeking to bolster LLM creativity through lateral thinking. However, the use of clues in the SFT and RL training processes seems to contradict this goal. By providing clues, there's an implicit guidance that may limit the LLMs' ability to explore solutions outside of the predefined parameters." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. For baseline evaluations the authors choose a zero-shot setting. I am curious why experiments with few-shot settings are not done? The dataset is novel as puzzles like these are not commonly seen but I would assume that these puzzles follows some intrinsic patterns as of how the solution is derived from the question. In other words in zero-shot settings the model might not grasp what kind of questions are good to ask but this problem is instantly solved in few-shot settings (similar to how human would quickly get better in \"HaiGuiTang\").\n2. (This question might be somewhat vague and I'm not being critical just curious to see what the authors think) How does the author justify the idea of language models even being ABLE to do lateral thinking? The training objective of LMs naturally leads to models selecting the most possible outcomes so I would be surprised to see LLMs thinking out of the box to such extreme extent as shown in these puzzles." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The authors nicely justify the importance of lateral thinking as a crucial ability for LLM reasoning. The paper is well-written and clarifying.\n2. The author novelly yet carefully curated a large set of lateral thinking puzzles which can effectively measure lateral thinking abilities. They also proposes a comprehensive set of creativity metrics for evaluation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors constructs the largest lateral thinking puzzle dataset by far as well as a novel set of metric to evaluate lateral thinking ability. They also proposes a PuzzleVerse framework that consists of SFT, RM, and RL. Extensive experiments are conducted to evaluate performance on the LTP dataset as well as to evaluate performance in other similar tasks like story generation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The dataset is only available in Chinese due to a loss of cultural context during translation. This limits the use case of using this dataset for more extensive comparison of LLM reasoning capability as cultural context will be crucial for solving puzzles in this dataset (for example models trained using English dataset would not understand \"square dancing\"). I would suggest the authors to develop a culture-neutral subset.\n2. The evaluation dataset chosen outside of the LTP dataset seems debatable. I would not really consider story understanding or reading comprehension task to be using lateral thinking. One immediate way to improve this is simply evaluating the framework on previous LTP dataset." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Evaluation and Enhancement of Lateral Thinking in Puzzle-Solving Games of Large Language Models." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024do,\ntitle={Do Large Language Models have Lateral Thinking in Puzzle-Solving Games?},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2mbDATzUOt},\nnote={under review}\n}" }, "abstract": { "value": "Large Language Models (LLMs) show exceptional skills in a wide range of tasks, with their ability in lateral thinking standing out as a particularly intriguing area. Lateral thinking in LLMs allows them to understand deeper or suggested meanings from the context, which is essential for making sense of complex scenarios, especially in puzzle-solving games. To delve deeper into and improve the lateral thinking capabilities of LLMs in the realm of puzzle-solving, we introduce the ``Lateral Thinking Puzzles'' and construct the accompanying dataset.\nOur novel $\\mathcal{P}$uzzle$\\mathcal{V}$erse framework aims to enhance LLMs' lateral thinking in puzzle-solving games. Complementing this, we propose a creativity metric to ensure comprehensive evaluations. \nExperiments show that the selected LLMs, after being trained with $\\mathcal{P}$uzzle$\\mathcal{V}$erse, have an average improvement of 101.9\\% compared to their performance before $\\mathcal{P}$uzzle$\\mathcal{V}$erse training among all metrics. \nWe also validate the robustness of $\\mathcal{P}$uzzle$\\mathcal{V}$erse that trained LLMs perform better in other reasoning tasks." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Large Language Models", "Lateral Thinking", "Puzzle-Solving Games" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/eafa610166559d96f4c7a221b601dfaadaf3a345.pdf" }, "presentation": null, "primary_area": { "value": "datasets and benchmarks" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Do Large Language Models have Lateral Thinking in Puzzle-Solving Games?" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "Regarding Weaknesses 1 & 4: Since GNP is a model for solving Multi-Choice QA tasks, a direct comparison with our work is not appropriate. However, we utilized GNP’s core module, the Cross-Modal Pooling Layer, to conduct an indirect performance comparison.\n\nAdditionally, there seems to be a misunderstanding regarding the term \"efficiency.\" The efficiency we described refers to representing only the information relevant to the query in a distributed manner (not concerning time or space complexity).\n\nWe address the comparison of efficiency with other baselines in terms of time complexity with the same response given to reviewer M81S's question.\n\nLet n denote the number of nodes, t the number of prompt text tokens, g the number of GNN layers, and k the number of LGPTs. The time complexity of our Graph Encoder, which utilizes 3 GNNs, is therefore O(3g(n+k)). Meanwhile, the time complexity of G-Retriever and GraphToken is O(gn). Considering that k << n, the time complexity of our method and that of other baseline graph encoders remain the same at O(gn).\n\nThe time complexity required for LLM computation is proportional to the square of the prompt length due to the self-attention mechanism. In our model, t+k tokens are passed in the prompt, while t+1 tokens are passed in GraphToken and G-Retriever. We set k=8 so that k << t, ensuring that the time complexity of LLM computation in our model is identical to that in other baseline models at O(t^2).\n\n----------\n\nRegarding Weakness 2: There are two main approaches to combining LLMs and GNNs. One approach uses LLMs for solving Graph Centric Tasks, such as Node Classification or Link Prediction, while the other uses Graph Encoders for solving general NLP tasks, such as QA (https://arxiv.org/pdf/2312.02783). Our study focuses on the latter.\n\nAs you suggested, investigating whether our methodology could work in the context of Graph Centric Tasks would be a valuable research direction and a worthwhile topic for future work. One of our key contributions is the development of a Graph Encoder model that can adapt to changing queries. Since Graph Centric Tasks typically involve less diverse queries than NLP tasks, further exploration is necessary to assess its applicability in that context.\n\n\n----------\n\nRegarding Weakness 3: We regret that we could not explore a wider range of settings. Due to constraints on computational resources and time, we prioritized verifying our core modules. As you pointed out, further experiments on various hyperparameters will be essential in future studies.\n\n\n----------\nRegarding Questions 1 & 2: We did not conduct an ablation study on the Text Encoder. Although, as you noted, experimentation with different Text Encoders could yield valuable insights, we did not pursue this as it does not critically impact the function of the core modules we propose.\n\n\n----------\nRegarding Question 3: We focus on information transmission at the embedding level. Thus, visualizing the amount of information contained in an embedding is very challenging. At our current knowledge level, we do not have a method to visualize and compare the degree of information loss. Could you suggest a visualization approach?\n\n\n----------\nRegarding Question 4: S_g is represented as a graph rather than a single matrix, so expressing it in dimensions may be challenging. We assume your question pertains to the dimensionality of the Node Embeddings in S_g. S_g consists of n nodes, each embedded as a vector of dimension d, resulting in an nxd Node Embedding matrix.\n\n\n----------\nRegarding Question 5: S_p is structured similarly to S as a graph containing Node Embeddings after Pooling. However, we only project the LGPT Tokens to the LLM, so S_p information is not utilized.\n\n\n----------\nYour insightful feedback has greatly contributed to the improvement of our research. Thank you for dedicating time to review our work. We hope that our study will be accepted, leading to further discussion. We kindly request you to reconsider your evaluation score." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "Regarding Weakness 1: GNP is a model designed for addressing Multi-Choice QA tasks. Since our task is not a Multi-Choice QA problem, direct comparison with GNP is not feasible. However, we incorporated the core module of GNP, the Cross-Modality Pooling Module, to perform comparative experiments.\n\n----------\n\nRegarding Weakness 2: Let n denote the number of nodes, t the number of prompt text tokens, g the number of GNN layers, and k the number of LGPTs. The time complexity of our Graph Encoder, which utilizes 3 GNNs, is therefore O(3g(n+k)). Meanwhile, the time complexity of G-Retriever and GraphToken is O(gn). Considering that k << n, the time complexity of our method and that of other baseline graph encoders remain the same at O(gn).\n\nThe time complexity required for LLM computation is proportional to the square of the prompt length due to the self-attention mechanism. In our model, t+k tokens are passed in the prompt, while t+1 tokens are passed in GraphToken and G-Retriever. We set k=8 so that k << t, ensuring that the time complexity of LLM computation in our model is identical to that in other baseline models at O(t^2).\n\n----------\nRegarding Weakness 3: Due to page limitations, we specified the source of our dataset (https://arxiv.org/pdf/2402.07630) as a substitute. However, this is a very relevant point, and we will address this in the Appendix.\n\n----------\n\nRegarding Weakness 4: We plan to organize the code and make it available on GitHub once the anonymous review period concludes. We apologize for the lack of a comprehensive README file due to concerns about premature exposure of our results. Thank you for your patience.\n\n----------\n\nRegarding the Question: It appears a typo occurred before submission. We apologize for any confusion this may have caused.\n\n----------\n\nYour insightful comments have significantly contributed to the improvement of our research. We appreciate the time you dedicated to reviewing our work, and we hope this paper will be accepted to enable further discussions. We kindly ask you to reconsider your evaluation score." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "Regarding Weaknesses: Our Early-Fusion approach adopts the methodology of QA-GNN (https://arxiv.org/pdf/2104.06378), as described in the main text. Although it is not a novel concept, we believe our application of this method in conjunction with LLMs represents a significant contribution as the first instance of its kind.\n\nThe concern of potential information loss when conveying information through a single parameter has been previously discussed in studies that introduce attention mechanisms to seq2seq models (https://arxiv.org/abs/1409.0473). In our experiments, we quantitatively verified this by comparing the results with LGPT configured with 1, 8, and 32 instances. However, as you pointed out, further analytical studies are required to understand precisely why this approach is effective and what specific types of information loss it mitigates.\n\n----------\n\nRegarding Question 1: We observed that performance improvements were more pronounced when applying LGPT to larger graph samples. However, since we lack a clear quantitative method to validate this, it was not included in the main text. We plan to conduct additional experiments on large-scale graph cases as soon as sufficient computational resources are available.\n\n----------\n\nRegarding Question 2: ExplaGraph showed similar trends to SceneGraph, and for WebQSP, performance was better with 32 tokens than with 8 tokens. We attribute this to the larger graph size in WebQSP compared to the other two datasets, but as this is only an assumption, we did not mention it in the main text due to the difficulty of quantitative validation.\n\n| Prompt Token | Expla Graphs | SceneGraphs | WebQSP | Average |\n|--------------|--------------|-------------|--------|---------|\n| 1 | 88.17 | 83.51 | 70.33 | 80.67 |\n| 8 | **88.62** | **85.19** | 70.70 | 81.50 |\n| 32 | 80.32 | 84.21 | **70.86** | 78.46 |\n\n\n----------\n\nYour comments and questions align closely with the insights we gained during our research process, and we plan to investigate these areas further. However, we hope that this paper, as an interim work, will stimulate further discussions and contribute to the advancement of this research topic. We kindly ask you to reconsider your evaluation of our study." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "Regarding Weakness 1: \n\nThank you for your valuable comments. As you rightly pointed out, the use of Graph Embedding as prompts for LLMs is indeed a well-established research topic. However, our study introduces the concepts of Early Query Fusion and Learnable Pooling specifically for LLM prompts, which we believe constitute key contributions of our research.\n\nAlthough each module was inspired by prior works, as you and the cited research have noted, our approach to integrating these modules in the context of combining LLMs and GNNs is novel, and we consider it a primary contribution. It seems that we did not sufficiently highlight these aspects in our writing, which we will address in the revised version.\n\n----------\nRegarding Weakness 2: \n\nTo ensure a fair comparison, we minimally modified the G-Retriever while adding our proposed model. We also preserved the hyperparameters from the official code of G-Retriever. Our results show an improvement beyond the standard deviation range of G-Retriever’s average performance (1.96–5.33 standard deviations, depending on random seeds). While our sample size limits statistical testing such as t-tests, given the improvement relative to standard deviations, we believe these findings are not the result of cherry-picking.\n\n----------\nRegarding the Question: \n\nWe are unsure if we have understood your question accurately. If our answer does not align with your intent, please feel free to ask us again.\n\nIn Figure 3, we report the performance of fine-tuning the LLM without a GNN. The addition of a GNN without fine-tuning the LLM resulted in higher performance compared to fine-tuning the LLM without a GNN. Furthermore, the combination of GNN addition and LLM fine-tuning showed superior performance over all other baselines.\n\n----------\nYour thoughtful comments have greatly contributed to enhancing our research, and we are deeply grateful for this. However, we would appreciate further feedback on the rationale behind your lower rating to better address these areas. We kindly ask you to reconsider your evaluation after reviewing our response." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. How sensitive is the model's performance to the choice of text encoder in Equation 7?\n2. Have the authors experimented with different text encoders (e.g., BERT variants, RoBERTa, T5) and observed any significant variations in performance?\n3. Regarding Equation 5, how does the choice of graph encoder architecture impact the model's performance?\n4. Can the authors provide case studies or visualization analysis demonstrating how LGPT addresses information loss compared to baseline methods?\n5. In Equation 9, please clarify the definition and dimensionality of $S_g$\n6. For Equation 10, please provide a detailed explanation of $S_p$ and its role in the architectur" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper introduces an innovative early fusion mechanism that addresses a fundamental challenge in graph-language modeling: the seamless integration of structural and textual information; The learnable pooling tokens (LGPT) provide a flexible and adaptive approach to graph representation, offering advantages over traditional static pooling methods. \n\n2.The authors conduct extensive experiments across three diverse graph QA datasets, demonstrating the robustness and generalizability of their approach. The method achieves competitive performance compared to state-of-the-art baselines, while potentially offering improved computational efficiency." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a novel approach for integrating graph representations with large language models (LLMs), addressing the critical challenge of efficient graph-text interaction. The primary contributions are twofold: (1) an early fusion mechanism that performs message passing between sub-graph node representations and query text embeddings, and (2) a learnable pooling strategy utilizing dedicated tokens (LGPT) that act as information aggregators within the graph structure.\nThe early fusion mechanism is particularly noteworthy as it enables direct interaction between textual and structural information at the embedding level, potentially capturing more nuanced relationships compared to traditional late fusion approaches. The authors implement this through message passing operations that allow bidirectional information flow between the sub-graph nodes and query text representations.\nThe learnable pooling strategy introduces fully-connected LGPT tokens that serve as dynamic information hubs within the graph. These tokens effectively aggregate information from all nodes through message passing, potentially creating a more comprehensive and adaptable graph representation. This approach appears to offer more flexibility than static pooling methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper's scalability argument lacks sufficient comparative analysis against existing methods like G-retriever and GraphToken; The authors do not provide a detailed complexity analysis or empirical benchmarks to substantiate their efficiency claims; While the authors assert improved efficiency compared to Tian et al. 2024 (Line 210), this claim requires further scrutiny since: a). The dominant computational cost typically lies in the LLM inference; b). The relative improvement in message passing efficiency may be marginal in the overall computational pipeline; c) No concrete timing or memory usage comparisons are provided. \n2. The evaluation is primarily confined to GraphQA tasks, leaving several important questions about generalization unexplored: a). The method's effectiveness on standard graph learning tasks (node classification, link prediction) remains unvalidated; b) The paper lacks a theoretical or empirical bridge between GraphQA performance and the claimed improvements in node-level and graph-level information integration. A broader evaluation across diverse graph-based tasks would strengthen the paper's contributions. \n3. The hyperparameter analysis in Section 4.4 shows significant gaps in the experimental design: The LGPT token count investigation only examines extreme values (8 and 32), omitting crucial intermediate points; The impact of other critical hyperparameters (e.g., message passing steps, fusion layer configurations) is not thoroughly explored. \n4. The paper should improve the methodological clarity from a). a more rigorous theoretical justification for the chosen LGPT architecture; b). Clear computational complexity analysis compared to baseline methods." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. What's the perfomance of LGPT in figure 1 without GNN and fine-tune lanaguage model (i.e. GraphToken with LLM fine-tuning)? It would be interesting to see whether design of graph pooling is still neccessary when LLM is tunable given that GNN introduces additional parameters." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper identifies a critical disadvantage of graph pooling method; the granularity control is either graph-level or node-level. \n2. On this pain point, the proposed multiple tunrable prompt (LGPT) effecvtively imrpove the performance on benchmarks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper leverages graph neural networks and large language models for task of knowledge graph question answering. Based on recent proposed techniques include graph soft prompt and query-aware graph prompting. The author proposed query-aware graph pooling to overcome the limitations of node-level and graph-level representations. In experiments, it shows competitive performance on recent proposed graph QA benchmarks in different domains." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The novelty of the paper is questionable. As the author mentioned, recent work such as G-Retriever;Graph Token and GNP (Graph Neural Prompting) has covered most of the techniques used in the paper except the graph prompt paramters. However, the learnable graph prompt is proposed in multiple related work including [1] and supernodes (connect every node to a virtual node for pooling) in graph pooling [2] literature.\n\n2. The proposed work re-uses most of the component of G-Retriever, which also causes my concern on cherry-picking hyperparameters given the performance improvements over G-retriever is subtle.\n\n\n\n\n[1] Universal prompt tuning for graph neural networks, Neurips 2023\n[2] Understanding Pooling in Graph Neural Networks," }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. “ However, the key difference from these methods is that, instead of pooling into a single graph embedding, our approach uses multiple learnable tokens for pooling, thereby reducing information loss” - Is there a pattern in the information loss. Is there a way to quantify this loss other than looking at the accuracy? What kind of data samples perform better when we increase the number of LGPT? \n\n2. How does the number of LGPT performance vary with the different datasets?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper is easy to read and understand. Extensive experiments and analysis have been shown to prove the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper addresses the problem of Textual-Attributed Graph QA, divided into two main steps: sub-graph retrieval and answer generation. For answer generation, their approach transforms the sub-graph into textual embeddings through a prompt, generates embeddings, and then uses a graph encoder with learnable parameters to process them. The paper highlights scalability issues in node-level prompting (where each node is treated as a separate token in the language model) and information loss in graph-level projection (where the entire graph is compressed into a single vector). To address this, the authors propose Learnable Graph Pooling Tokens (LGPT), a pooling method that introduces learnable parameters (tokens) that connect to all nodes and perform message passing. This method allows for flexible, efficient graph representation that balances fine-grained and global information, achieving improved performance on Graph QA tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The idea of “early fusion” by forming an external node and fully connecting to other nodes in the graph is not novel to the field. The LGPT idea seems intuitive that increasing the number would increase the performance but would like to see more analysis here." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- What is the meaning of Sf in the author keywords?\n- See weaknesses and make some revisions to the paper." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The combination of LLM and GNN is an important research topic.\n- The design of this paper is reasonable." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "- This paper proposes a learnable graph pooling module to enhance LLM-based GraphQA." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The novelty seems to be limited in this paper because authors only made a new incremental design in the graph encoder. The core paradigm of graph QA is preserved compared with other baselines.\n- Some important GNN+LLM baselines are missing in the experiments. For example, GNP [1].\n- The training/inference efficiency of the method should be compared with other baselines.\n- The detailed information about the graphs in each dataset is not reported.\n- The original dataset and README instructions are not provided in the code, making it difficult to reproduce the performance.\n\n\n\n\n[1] Graph neural prompting with large language models" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "This paper proposes a novel method using Learnable Graph Pooling Token (LGPT) and Early Query Fusion techniques to enable efficient graph representation in large language models, achieving a 4.13% performance improvement on the GraphQA benchmark." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024queryaware,\ntitle={Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2mg5FvBz0J},\nnote={under review}\n}" }, "abstract": { "value": "Graph-structured data plays a vital role in numerous domains, such as social networks, citation networks, commonsense reasoning graphs and knowledge graphs. While graph neural networks have been employed for graph processing, recent advancements have explored integrating large language models for graph-based tasks. In this paper, we propose a novel approach named Learnable Graph Pooling Token (LGPT), which addresses the limitations of the scalability issues in node-level projection and information loss in graph-level projection. LGPT enables flexible and efficient graph representation by introducing learnable parameters that act as tokens in large language models, balancing fine-grained and global graph information. Additionally, we investigate an Early Query Fusion technique, which fuses query context before constructing the graph representation, leading to more effective graph embeddings. Our method achieves a 4.13\\% performance improvement on the GraphQA benchmark without training the large language model, demonstrating significant gains in handling complex textual-attributed graph data." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Graph Neural Network", "Large Language Model", "Continuous Prompting", "Sf" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/bc08711e4442572f09eb4695d652f57667d5d0d3.pdf" }, "presentation": null, "primary_area": { "value": "learning on graphs and other geometries & topologies" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/1c7c3d7ac9421d0e6e88d2e60aba453c0df8bc0c.zip" }, "title": { "value": "Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See \"Weaknesses\"." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "SCALE introduces a multi-agentic approach to content analysis, enhancing scalability and efficiency by reducing the human resources required to make large-scale, high-quality analysis feasible. SCALE demonstrates high flexibility, adapting across multiple datasets without modifications. The paper might have a contribution to social sciences by enabling large-scale analysis traditionally constrained by labor-intensive methods." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose SCALE, which is a tool to perform content analysis in social science. By automating text coding and facilitating multi-agent discussion, SCALE approximates human judgment in text annotation tasks. The framework integrates AI-human collaboration, which mitigates algorithmic bias. The paper evaluates SCALE on diverse social science datasets, showing its effectiveness in improving large-scale content analysis." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "One big concern about the paper is that it does not provide prior benchmarks. The datasets used by the authors are not very commonly used in this literature. I recommend that the authors use at least one dataset with prior benchmarks on multi-label classification (e.g., COCO, Peskine et al. 2023) or apply prior methodologies of multi-label classification on your datasets. How does the plain-vanilla GPT-4o perform on your dataset without including multiple agents?\n\nIt is well-known that agentic approaches improve LLMs’ performance. However, the approaches typically require more computational resources and time. It would be helpful if the authors could include each ablation's cost and processing time. The authors acknowledge this issue in Section 6, but it will be helpful for the readers to see the informational gain of this framework along with computational requirements.\n\nIn Section 5.4.3, the authors might want to include some desired codebook structures in their prompt. They could add layer of agents that review the final product by including several instructions, e.g., examining whether there are overlapping categories by providing some theory backgrounds. They might even try fine-tuning the LLMs using some domain knowledge instead of using the plain-vanilla versions.\n\nMissing citations: Several works have already explored how the discussion among LLM agents can improve overall performance. For example, see Chan et al. (2023) and Kim et al. (2024). I’m surprised that the authors do not acknowledge any of these studies. At a high level, what this paper shows is similar to the value of a multi-agentic approach in classification problems.\n\nReferences\nPeskine, Youri, et al. \"Definitions Matter: Guiding GPT for Multi-label Classification.\" Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.\nChan, Chi-Min, et al. \"Chateval: Towards better llm-based evaluators through multi-agent debate.\" arXiv preprint arXiv:2308.07201 (2023).\nKim, Alex, Keonwoo Kim, and Sangwon Yoon. \"DEBATE: Devil's Advocate-Based Assessment and Text Evaluation.\" arXiv preprint arXiv:2405.09935 (2024).\n\nMinor Comments\n1)\tYou list five contributions but say the contributions are fourfold on page 2.\n2)\tWhy are some datasets benefiting heavily from discussions while others are not (Figure 4)? It would be helpful to include some insights on where the discussions will likely improve the model performance more and why.\n3)\tIn Table 3, it is concerning that you achieve the highest accuracy when human intervention is frequently made, and the LLM strictly follows it. Doesn’t this suggest that human interventions are required and LLMs alone cannot perform the task reliably?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "It appears that some names used as example scenarios (see row 237) actually exist and refer to real-life situations (as confirmed by a simple web search). In my opinion, these can and should be omitted (e.g., by replacing them with placeholder nicknames)." }, "flag_for_ethics_review": { "value": [ "Yes, Other reasons (please specify below)" ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- How do the authors ensure that SCALE is grounded in social science knowledge beyond simple prompting?\n- Similarly, the authors claim (row 233) that agents [...]do not rely on external knowledge or data beyond what is provided in the codebook [...]. How do they ensure that agents do not leverage their own knowledge/biases in conducting content analysis, going beyond the received guidelines?\n- Do the authors experiment with different initializations for the agents? That is, what is the effect of specifying agents' instruction, gender, and experience within prompts?\n- As hallucinations are likely to occur with LLMs, how do the authors handle them?\n- What is the default behavior when agents do not reach agreement within k iterations?\n- As the temperature is not enough to reduce randomness in LLMs, which values did the authors use for top_p and top_k?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The core idea of this manuscript to leverage LLMs for augmenting content analysis is interesting, as can lead to improvements in capabilities (as the LLMs' intrinsic word model is rather rich and varied) and scalability (e.g., by alleviating the human burden in annotating large-scale content)." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes SCALE, a multi-agent framework to simulate content analysis using LLMs. The overall idea is to incorporate different phases of content analysis, such as text coding, inter-agent discussion, and codebook updating, in a comprehensive framework carried out by LLMs as a multi-step process. Additionally, the authors allow the framework for human intervention, to enhance AI-human expert collaboration. The SCALE framework was tested on five real-world datasets, spanning seven multi-class and multi-label classification tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Despite focusing on an interesting and promising idea, this manuscript presents different criticalities, as follows:\n- There is much emphasis on the capability of SCALE to incorporate domain knowledge of social science, yet this process seems limited to one (of many possible) prompting strategies, undermining the robustness and technical depth of the proposed framework. \n- The experimental setup is not appropriate, as there is no comparison with baseline models (e.g., ML-based ones for sentiment analysis). Indeed, it is just confined to testing different prompting strategies, with two commercial models (i.e., GPT-4O and 4O-mini). Similarly, some experimental choices (e.g., the very low number of agents despite the sensitivity results) are not adequately motivated.\n- The experimental results turn out to be particularly weak for 3 out of 7 tasks, with very low coding accuracies. Also, some additional quantitative measures (e.g., inter-agent agreement) would be beneficial for a better understanding of how SCALE handles the annotation processes.\n- Despite aiming at fostering better human-AI interaction in content analysis, as well as strong capabilities, there is no human qualitative evaluation of the SCALE's capabilities. This would be needed to further validate the helpfulness of the proposed framework.\n- The entire study relies solely on the GPT family of models. Experimenting with other (e.g., open) models would be beneficial for a broader applicability and adoption of the proposed framework.\n- There are no technical details on the agents deployment and interaction. This is a key aspect for multi-agent systems, and should be stated in the manuscript to also foster reproducibility. Similar considerations hold for the human-in-the-loop setting.\n- To properly validate how SCALE complements humans, there should be some more emphasis on the patterns occurring within it, and critical analysis on how the different phases differ or resemble humans. For instance, for RQ2, certain datasets see limited to no improvement after agents' discussion, why?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- The process of content analysis is always subjective isn't it? How does the method reduce subjectivity and is that even the goal? \n- The appendix provides an overview of the different prompts associated with the different steps. How much manual effort is involved when applying the framework? Is the codebook really updated automatically or do the researchers have to manually extract codebook changes and copy them into their codebook? \n- The framework is designed to iteratively update a codebook and use it as base for the coding. There are labels for each dataset. Were these labels the starting point for the coding task? How exactly were the experiments conducted? Did you only evaluate the coding step or did the experiments include the development of a codebook for each dataset? \n- Why did you conduct the first experiments with only 2 agents? \n- Are all tasks used for the experiments multi label tasks?\n- Does the average accuracy of 0.7 refer to all models? You could also add a new column where you could plot the average.\n- How much prompt engineering was involved in the process of building the framework? How did you come up with the different prompts? Do the results depend much on the wording of the prompts?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper has a clear strucure and demonstrates strengths and limitations of the method through several experiments. The topic is very relevant for the Social Science." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a framework for automatic content analysis of unstructured data with LLMs called SCALE. The framework includes the steps of automated text coding, inter-agent discussion and dynamic codebook updates, while also allowing for human interventions. The goal is to develop a tool for social scientists that is able to support the process of content analysis at a large scale." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The central Figure (Figure 2) is not that easy to understand. It should be self-explanatory by reading the caption. It would be very helpful to know which dataset is taken as an example here. Unfortunately, the paper does not always read fluently. Sometimes articles are missing and there are some grammatical errors. Some technical details are missing, such as the implementation of the chain of thought and tree of thought baselines (or at least the references are missing - see below). Also the formula for the used accuracy measure should be written out (in my opinion). \nThe human intervention experiment is not really explained well. How much did the humans intervene? Is there a certain number of rounds? Is it the same setup as in the previous experiments?\nOverall the idea of the framework and the process of inter-agent discussion for automated content analysis is good, but some important details are missing. It is also not clear from the paper how much manual effort is required to apply the whole framework. What are the necessary steps (e.g. developing personas, a first version for a codebook..)? \nAs the authors note at the end the inter-agent discussion introduces significant computational overhead. This leaves the question how practical the framework is. \n\nMissing references:\n- Chain of thought prompting as introduced by Wei et al (2022): Wei, Jason, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 2022, 35. Jg., S. 24824-24837.\n- Tree of thougt prompting: Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., & Narasimhan, K. (2024). Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36. \n- Self Consitency: Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., Narang, S., ... & Zhou, D. Self-Consistency Improves Chain of Thought Reasoning in Language Models. In The Eleventh International Conference on Learning Representations.\n\n\nSmall errors:\n* Line 088 should probably have a period at the end of \"Human Intervention\" to be consistent with the other items.\n* line 190 is missing a period before (c)\n* line 208: it should be N personas P, which ..., are derived from.. s\n* The abbreviation NES is not introduced in the text\n* line 241: \"a K-round discussion\" instead of \"an K-round..\" \n* Line 320: It would be very nice if the Hamming loss was explicitly written out here in the formula. \n* Line 461: lLM > LLM" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "None" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "As weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The idea of using LLM agents and ai-human collaboration for content analysis is interesting.\n2. The paper is easy to follow. For example, Fig. 2 is pretty detailed to explain the overall workflow of SCALE framework.\n3. The paper could attract a large number of audience interested in using LLM agents to simulate social science research." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents SCALE, an interesting multi-agent framework designed to simulate and augment the content analysis process using LLMs and AI-human collaboration. Automating key phases of content analysis, including text coding, inter-agent discussion, and codebook evolution could reduce the time, human resources, and costs traditionally required for content analysis. It also incorporates human intervention to mitigate algorithmic bias and improve contextual sensitivity. The paper suggests that SCALE could transform social science research by providing an efficient tool for analyzing large volumes of data." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In the introduction, the authors mention that one of the drawbacks of using humans experts is the time and labor cost. The analysis of the proposed framework would benefit significantly if there is any analysis in terms of time/cost spent by humans for annotation vs LLMs.\n2. I think Section 5.3 SUPERIOR PERFORMANCE OF SCALE should emphasize the overall quality of the whole framework instead of the single coding accuracy. The classification task is relatively trivial for LLMs.\n3. Human evaluation (or detailed results) might be needed to assess the overall quality of using LLM agents to simulate content analysis beyond Codebook Update Phase." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose SCALE, a novel LLM multi-agent framework to automate content analysis, traditionally labor-intensive in social science, while integrating human oversight, enabling scalable, high-quality annotations approximating human judgment." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024scale,\ntitle={{SCALE}: Augmenting Content Analysis via {LLM} Agents and {AI}-Human Collaboration},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2miMc8FR0j},\nnote={under review}\n}" }, "abstract": { "value": "Content analysis is a fundamental social science research method that breaks down complex, unstructured texts into theory-informed numerical categories. It has been widely applied across social science disciplines such as political science, media and communication, sociology, and psychology for over a century. This process often relies on multiple rounds of manual annotation and discussion. While rigorous, content analysis is domain knowledge-dependent, labor-intensive, and time-consuming, posing challenges of subjectivity and scalability. In this paper, we introduce SCALE, a transformative multi-agent framework to $\\underline{\\textbf{S}}$imulate $\\underline{\\textbf{C}}$ontent $\\underline{\\textbf{A}}$nalysis via large language model ($\\underline{\\textbf{L}}$LM) ag$\\underline{\\textbf{E}}$nts. This framework automates key phases including text coding, inter-agent discussion, and dynamic codebook updating, capturing human researchers' reflective depth and adaptive discussions. It also incorporates human intervention, enabling different modes of AI-human expert collaboration to mitigate algorithmic bias and enhance contextual sensitivity. Extensive evaluations across real-world datasets demonstrate that SCALE exhibits versatility across diverse contexts and approximates human judgment in complex annotation tasks commonly required for content analysis. Our findings have the potential to transform social science and machine learning by demonstrating how an appropriately designed multi-agent system can automate complex, domain-expert-dependent interactions and generate large-scale, quality outputs invaluable for social scientists." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Content Analysis", "Large Language Model", "Multiagent", "Simulation", "Computational Social Science", "AI for Science" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/b1368c5045677d98b05b10cf240a54a95c3ee81c.pdf" }, "presentation": null, "primary_area": { "value": "other topics in machine learning (i.e., none of the above)" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "SCALE: Augmenting Content Analysis via LLM Agents and AI-Human Collaboration" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- How does the method perform when architectural differences between small and large models are more significant? Are there specific architectural compatibility requirements?\n- The improved prompt alignment for stylized models is intriguing. Could you provide more analysis of why this occurs and how generally applicable this finding is?\n- What are the primary failure modes of T-Stitch? Are there specific scenarios where the method consistently underperforms?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "### Novel & Foundational Insight\n - Deep understanding of diffusion models' behavior across timesteps\n - Thorough empirical validation of latent space similarity between models\n - Clear frequency analysis supporting the theoretical foundation\n - Novel perspective on leveraging model size differences temporally\n\n### Practicality\n- Training-free nature enables immediate deployment\n- Compatible with existing acceleration techniques\n- Works across various architectures and model families\n- Clear implementation guidelines and deployment considerations\n\n\n### Comprehensive Empirical Validation\n- Extensive experiments across multiple architectures\n- Thorough ablation studies covering various aspects\n- Clear demonstration of speedup-quality tradeoffs\n\n\n### Broader Impact & Applications\n- Unexpected benefits in prompt alignment for stylized models\n- Natural interpolation between style and content\n- Practical applications in Stable Diffusion ecosystem\n- Potential implications for efficient model deployment" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces T-Stitch, a training-free approach to accelerate sampling in diffusion models by strategically utilizing different-sized models across the denoising trajectory. The key insight is that small and large models trained on the same data distribution learn similar encodings, particularly in early steps where low-frequency components dominate. By leveraging this property, T-Stitch uses smaller models for early steps (global structure) and larger models for later steps (fine details), achieving significant speedup without quality degradation. The method demonstrates broad applicability across various architectures (DiT, U-Net, Stable Diffusion) and shows interesting benefits for stylized models' prompt alignment. Extensive experiments validate the effectiveness across different settings, samplers, and guidance scales." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "### Critical Absence of Limitations Analysis\n- Paper lacks a dedicated section for discussing limitations\n- No systematic analysis of failure cases\n- Insufficient discussion of edge cases and potential risks\n- Missing critical self-reflection on method boundaries\n\n### Theoretical Gaps\n- No mathematical justification for the 40% threshold\n- Lack of theoretical guarantees for quality preservation\n- Missing analysis of optimal model size ratios\n- Incomplete understanding of feature compatibility requirements\n\n### Architectural Considerations\n- Limited analysis of cross-architecture compatibility\n- No clear guidelines for multi-model (>2) scenarios\n- Insufficient investigation of feature space alignment\n- Missing discussion of architecture-specific optimization\n\n### Practical Implementation Challenges\n- Memory overhead management not thoroughly addressed\n- Pipeline complexity implications understated\n- Limited guidance for scenarios without suitable small models\n- Deployment considerations in resource-constrained environments lacking\n\n\n### +)\n- The absence of a dedicated limitations section limits the paper's completeness" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- Your idea reminds me a bit of works on early-exit diffusion models [1,2] where the depth on the denoising network is made adaptive based on the estimated difficulty of the sampling step. Could be interesting to draw further parallels between early-exit and your stitching approach\n\n\n[1] [AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation](https://arxiv.org/abs/2309.17074)\n\n[2] [DuoDiff: Accelerating Diffusion Models with a Dual-Backbone Approach](https://arxiv.org/abs/2410.09633)" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Their main idea of leveraging model of various sizes throughout the diffusion sampling process is simple, yet it is shown to be effective. The simplicity is an added benefit in my opinion, as it makes the method more reproducible and more likely to be adopted\n- I also believe their idea to be novel (though I am not fully up to date with the diffusion literature due to its high pace)\n- The experiments are very comprehensive, they try out their trajectory-stitching approach on various backbone architectures (DiT, UNet), with various samplers, for unconditional/conditional cases etc.\n- Also, I like how instead of proposing yet another new efficient diffusion model (and thus contributing to the model zoo), the authors find a smart way to combine/reuse the existing models via their trajectory-stitching approach" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a method to accelerate sampling in diffusion models by using a sequence of two denoising networks, a smaller one followed by larger one (instead of using the same network for all sampling steps as is traditionally done). In their experiments, they show their method can lead to meaningful computational savings at little to no cost to the quality of generated images." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- I think the writing can be improved. For camera-ready it would make sense to move the background/preliminaries to the main text and perhaps to move some of the experiments to the appendix. Also I find Section 3 quite chaotic (it talks about too many different things, from motivation to model design and connection to the other efficiency techniques like speculative decoding)\n- It is not clear how to select the switching point/threshold between the small and large model (r1). While I understand that by varying it you can get a Pareto frontier, however, that still requires running/evaluating a large number of candidate thresholds." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "The work primarily relies on the observation of the alignment of noise predictions between models of different scales during the early stages of the denoising process (Fig. 3). While this is an intriguing phenomenon, the paper does not provide sufficient explanation for why this occurs. Furthermore, the magnitude of the latent vectors is also important. Does the $L^2$-distance exhibit a similar pattern as shown in Fig. 3?\n\nI believe that the requirement for a shared latent space is a strict condition for this method. It is unclear whether this method is also robust for models trained with different configurations, such as varying noise schedules (like variance-preserving versus cosine) and different diffusion steps (e.g., T=100 versus T=500).\n\nIs it possible that small models trained only over larger T steps (for example, $t \\sim [70, 100]$ with a total $T=100$) yield better results?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Overall this is a well-written paper which presents a simple but effective approach for accelerating sampling speed of large diffusion models. The authors convey their ideas clearly and support their approach through extensive experiments. I guess the key significance of this stitching approach is that it is orthogonal to other techniques, like model distillation or improved ODE solvers, allowing it to be easily combined with other methods to further reduce inference time." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a new method to speed up the sampling efficiency of diffusion models. By using smaller models for the initial denoising steps and larger models in later stages, this approach greatly boosts sampling speed while keeping quality on par. Notably, this method is distinct from existing training-based or training-free techniques aimed at improving sampling efficiency. The authors demonstrate the effectiveness of their approach through extensive experiments, highlighting improved quality-efficiency trade-offs with a clear Pareto frontier." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Even if we ignore the inference time needed for running the small models, the time to generate samples of comparable quality can still be reduced by 30-40% at most. It is hard to say this method is a groundbreaking technique for improving sampling efficiency in diffusion models. While the paper presents a comparison of the Pareto frontiers between T-stitching and M-stitching, it might be more insightful to compare it with methods like progressive distillation, which can be much faster and does not need store multiple models.\n\nAdditionally, the approach uses models of different scales along the same denoising trajectory, which necessitates that both the small and large models predict score functions in the same latent space. This requirement may limit its applicability." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "I do not think I have any concerns here." }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "Please see the two points about limitations I have raised in my Weakness section" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Generally, I believe this is a good paper backed by solid experimentation. It has extensive comparative analysis involving various timesteps and samplers, and also compares itself against other methods, including those that are training-based, training-free, and search-based.\n\nThe paper is also well written and clearly motivated." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a training-free acceleration technique named T-Stitch for diffusion models. The core spirit of the approach is to employ a compact model for early timesteps and a more substantial model for later stages. The authors have provided empirical evidence that model performance remains unaffected even when the lighter model is employed for 40% of the initial steps. While the proposed method is simple and efficacious, parts of its evaluation appear to rely heavily on empirical evidence, and in my opinion, falls to a typical trap of this type of papers of not including further limit studies." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "In my view, theoretical insights and empirical studies hold equal value; the simplicity of an idea does not de-value from its merit if it is proven effective, especially if it is a topic like Efficient AI. However, my primary issue with such papers lies in my preference for a clear explanation of the method's limitations, also through sets of experiments.\n\nFirst, the authors of the T-Stitch paper state that 40% is an appropriate cutoff for switching models, a decision purely grounded in empirical evidence. This raises the question of how well-founded this choice is. If I were to apply this switching method to a different set of diffusion pairs of the models, would the 40% value still be relevant? Intuitively, the cutoff point likely hinges on the performance disparity between the more and less powerful models. From that perspective, if you put the model difference (Strong model FLOPs - Weak Model FLOPs) on x-axis, and the best cut-off point on y-axis, do you simply expect a flat line at 40% cut-off?\n\nSecond, although the authors did claim that the method can go beyond pari-wise, and have demonstrated how switching (I would maybe actually call this switching rather than stitching) can happen across 3 models, it is unclear about the limitation on this. Clearly the increased number of models would complicate the decision making on where to switch, and potentially make this method becomes a search-based one. More importantly, there must exhibits certain limitation on this switching especially when one has limited diffusion time steps. When you have N models to stitch/switch with M time steps. When N becomes larger or M becomes smaller, the return of this optimization should inevitably becomes diminishing. \n\nAlso something minor: Figure 5: the bottom of the images are cropped." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose Trajectory Stitching, a simple but effective technique that leverages small pretrained diffusion models to accelerate sampling in large pretrained diffusion models without training." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024tstitch,\ntitle={T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2mqb8bPHeb},\nnote={under review}\n}" }, "abstract": { "value": "Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality image generation and typically requires many steps with a large model. In this paper, we introduce sampling Trajectory Stitching (T-Stitch), a simple yet efficient technique to improve the sampling efficiency with little or no generation degradation. Instead of solely using a large DPM for the entire sampling trajectory, T-Stitch first leverages a smaller DPM in the initial steps as a cheap drop-in replacement of the larger DPM and switches to the larger DPM at a later stage. Our key insight is that different diffusion models learn similar encodings under the same training data distribution and smaller models are capable of generating good global structures in the early steps. Extensive experiments demonstrate that T-Stitch is training-free, generally applicable for different architectures, and complements most existing fast sampling techniques with flexible speed and quality trade-offs. On DiT-XL, for example, 40% of the early timesteps can be safely replaced with a 10x faster DiT-S without performance drop on class-conditional ImageNet generation. We further show that our method can also be used as a drop-in technique to not only accelerate the popular pretrained stable diffusion (SD) models but also improve the prompt alignment of stylized SD models from the public model zoo. Finally, the explicit model allocation strategy of T-Stitch significantly reduces the need of training or searching, delivering high deployment efficiency." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "diffusion model", "transformers", "model stitching" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/e4a67cf762625cede46883126e136009211af00b.pdf" }, "presentation": null, "primary_area": { "value": "generative models" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "**Major Questions**\n1. I am curious why text-based evaluation metrics such as Clip Score were not used. It seems like an obvious choice to do.\n2. In section 2.1, how were the mixing coefficients $wj$ actually set? Is the model capable of adjusting the weights for mixing? I am also curious about how $N$ for the individual forward process was actually set.\n3. The method overview on page 5 mentions that pre-trained diffusion models can be used, but I am curious if the only one actually used is CIFAR-10, as shown in Table 1. (The experiment by providing the models with CIFAR-10 with two sets of labels divided into five and five) I think if the authors provide the results using the output of various datasets, the paper will be stronger.\n\n**Minor Questions**\n1. I think there should be punctuation after *\"...a superposition of elementary vector fields\"* on page 3, lines 140 and 141.\n2. I think the introduction of the abstract is too long. This could be reduced since the intro occupies 1/3 of the entire amount.\n3. It would have been interesting if there was a comparison according to the distance of the disjoint set." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper is well-written and easy to understand. There are almost no grammatical errors. By developing the idea of superposition and using theoretical principles, the authors prove the idea's potential and present a reasonable result.\n2. They apply their work to two individual tasks, which could be divisive among readers, but I found it interesting.\n3. Also, it is interesting that the authors discover their model follows traditional operators such as logical OR and logical AND, making it intuitive. Similarly, the background of explaining how the superposition emerged from the diffusion models by using the vector fields and propositions is interesting.\n4. They use nine propositions, two theorems, and one lemma to support their idea, which helps readers understand why their algorithms work." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose the method to combine the multiple pre-trained diffusion models at the inference time without retraining the models. They come up with the theoretical principles using the continuity equation to show how diffusion models can be viewed in the superposition of elementary vector fields. Here, they implement two algorithms to combine pre-trained diffusion models. One is a mixture of densities (sampling from one model OR another), and the other is equal densities(samples that are likely to belong to one model AND another). They also overcome the challenges with existing diffusion models, such as (1. Differences of Marginal super-positional vector field between different models) and (2. Divergence operation’s time complexity) by introducing their density estimator. They apply their approach in various ways, such as combining models trained on disjoint datasets, concept interpolation in image generation, and improving the structure of protein design." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**(Main) Qualitative Results and the Quantitative Results with figures**\n1. Figure 1 is weak to verify the novelty of the model. I also think the generated images in the appendix, as well as the qualitative results, are mediocre.\n2. The author only uses the AND operation (sampling equal densities) for qualitative results, and OR operation for the quantitative results. I believe that including the results for the OR operation in qualitative results and the AND operation in quantitative results would strengthen the paper. This would provide a more comprehensive view of the statement on line 104 on page 2: \"improvements in designability and novelty generation\".\n3. Figure 2 does not show how the generated images are actually arranged. It is necessary to verify if the same results occur when directly arranging the generated images with the trained datasets.\n\n**Evaluation metrics and ablation study**\n1. The comparative group for the paper's qualitative results is insufficient. Comparisons with other recent models that produce dual images, such as factorization diffusion or visual anagram (Geng et al.,2024), could be added. Since it is clear that the latent diffusion result for just adding the prompt 'that looks like' would indeed be worse than the proposed method.\n2. Similarly, in the process of making baselines for concept interpolation, I wonder if the value of the ablation study would have increased if the direction of A->B and B->A was changed and the comparison group was chosen by using the better result.\n3. The execution times for the experiments were not provided. The authors claim to have solved the computational expense issue, but no results support this claim.\n\n**Clarity of the paper**\n1. Proposition 8 appears to be quite important but confusing because it was cut off the page. Listing the individual terms of $Aκ = b + o(Δt)$ on the same page would improve comprehension.\n2. The related work section comes out almost at the end of page 10, and I think this part should come out more front. It comes out so out of the blue that it somewhat interferes with understanding.\n3. The protein generation part is not clearly introduced. The authors compare Designability, Novelty, and Diversity, and there is no separate explanation of how this part is meaningful in protein generation. I didn't feel that logic was connected smoothly." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "* Why are there no quantitative results on SD, and detailed discussion of other very relevant methods as referenced earlier?\n* FID statistics on CIFAR-10 are computed on the whole dataset. Is it fair to evaluate models trained on a partial dataset using such statistics, especially when the two partitions are generated by splitting the classes?\n* What are the practical implications of the OR operator, especially in the field of image generation?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* The theoretical framework is solid.\n* The method is well-motivated and supported by the theory.\n* The method is training-free, and could be applied to diffusion models with different architectures.\n* The results of protein generation outperform other baselines." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a novel algorithm for combining multiple pre-trained diffusion models at inference time, by the principle of superposition of vector fields. The method demonstrates more diverse generation results, better prompt following on image data, and improved structure design of proteins as well." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The practical implications of AND, and OR operators are not explained clearly in both image and protein generation settings. What effect will the OR operator create on images, compared to the AND operator?\n* Lacks quantitative results on SD. Could have used metrics such as TIFA Score [1] and Image Reward [2]. I wonder if there is any reason that no such metric was used. \n* Lacks comparison against other relevant methods [3-6]. In particular, [3,4,6] are all inference-time methods that sample from some sort of mixture of scores and demonstrate multiple practical uses, such as composing objects, styles, scenes, or improving text-image alignment. Need more discussions on the capabilities of the proposed method versus others: besides the different theoretical perspectives, how SUPERDIFF performs differently, the strengths and weaknesses of SUPERDIFF than the other methods. If experiments are not possible, please include a more detailed discussion. The comparison could help readers understand the proposed method in a broader context. \n\n[1] Hu, Y., Liu, B., Kasai, J., Wang, Y., Ostendorf, M., Krishna, R., Smith, N.A.: Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering. arXiv preprint arXiv:2303.11897 (2023)\n\n[2] Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., Dong, Y.: Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems 36 (2024)\n\n[3] Du, Y., Durkan, C., Strudel, R., Tenenbaum, J.B., Dieleman, S., Fergus, R., SohlDickstein, J., Doucet, A., Grathwohl, W.S.: Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc. In: International conference on machine learning. pp. 8489–8510. PMLR (2023)\n\n[4] Golatkar, A., Achille, A., Swaminathan, A., Soatto, S.: Training data protection with compositional diffusion models. arXiv preprint arXiv:2308.01937 (2023)\n\n[5] Biggs, Benjamin, et al. \"Diffusion Soup: Model Merging for Text-to-Image Diffusion Models.\" arXiv preprint arXiv:2406.08431 (2024).\n\n[6] Liu, Nan, et al. \"Compositional visual generation with composable diffusion models.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- How do the authors explain the source of their numerical improvements using SuperDiff OR?\n- What density is being sampled from when using SuperDiff AND?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The main strength of the paper is the important observation that the probability density function of generated images can be efficiently evaluated without the need for computing the divergence of the score. It is leveraged to sample from mixtures of densities, where the weights can be defined implicitly and adaptively (in the case of the logical AND operator as defined here). The experimental results convincingly demonstrate the effectiveness of the resulting approach." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a novel, principled, and efficient way to combine diffusion models trained on different datasets (or conditioned on different prompts) to generate images from the mixture and the \"intersection\" of the corresponding distributions. It is based on a clever way to evaluate the densities $\\log p^i_t(x_t)$ of the current iterate $x_t$ under each (noisy) distribution $q^i_t$ during synthesis." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "In my opinion, the main weakness of the paper is in the clarity of the presentation of the central theoretical result (culminating in Proposition 7) and the motivation for the approach. I believe it can be significantly improved, which could enhance the impact of the paper. \n- I found section 2.1 to be unnecessary complicated and rather irrelevant for the rest of the exposition. To my understanding, the main ideas are (1) that SDEs define linear equations on the densities, so that a mixture of clean distributions $\\sum w_i p^i$ leads to a mixture of noisy distributions $\\sum w_i p_t^i$ and (2) the relationship $\\nabla \\log (\\sum w_i p^i_t) = \\sum w_i p^i_t \\nabla \\log p^i_t / \\sum w_i p^i_t$. These motivate the need for evaluating $p^i_t$ to combine scores in the correct way to sample from mixtures.\n- The equations are obscured by the use of general schedules with arbitrary $\\alpha_t$ and $\\sigma^2_t$. I encourage the authors to state the results in the main text with e.g. $\\alpha_t = 1$ and $\\sigma_2^t$ (known as the variance exploding SDE) to simplify the exposition and relegate the general case to the appendix. \n- Some results are also less intuitive (in my opinion) due to the choice to work in discrete time. For example, Proposition 6 and Theorem 1 are nothing but approximating the kernels $k_{\\Delta t}$ and $r_{\\Delta t}$ with Euler-Maruyama discretizations of the corresponding forward or backward SDEs (and analyzing the discretization error in Theorem 2). Similarly, Proposition 7 can be obtained in continuous time first (and then discretized) by applying Itô's formula to $\\log q_t(x_t)$ where $x_t$ is a solution of the backward SDE (and using the fact that $q_t$ solves a Fokker-Planck equation). As an example, in the variance-exploding case, one obtains that $\\mathrm{d} \\log q_t(x_t) = \\frac{\\mathrm{d}t}2 ||\\nabla \\log q_t(x_t)||^2 + \\langle \\mathrm{d}x_t, \\nabla \\log q_t(x_t)\\rangle$, which is the $\\Delta t \\to 0$ limit of Proposition 7 with $\\alpha_t = 1$ and $\\sigma^2_t = t$. I believe this result to be of independent interest, and would thus benefit from being highlighted and stated as simply as possible.\n\nAnother issue I have is regarding the logical OR and AND operators as defined in this paper.\n- The logical OR operator corresponds to a fixed-weight mixture of distributions, and it is thus trivial to sample from. One can simply select one diffusion model with probability corresponding to the mixture weight, and then use exclusively the score of the chosen diffusion model during generation. Using SuperDiff should be equivalent to this algorithm. So either the improved results in section 4 can also be achieved with this simple baseline, in which case the theoretical results are not needed, or the baseline underperforms, in which case the improvements come from unknown implementation choices which are completely orthogonal from the theoretical analysis. In both cases, this raises questions.\n- The real strength of the approach, I think, is when the mixture weights are adaptive (i.e., they are allowed to depend on the current iterate $x_t$). In that case, however, it is not clear what density we are ultimately sampling from. If I understand correctly, here the logical AND operator is defined implicitly, and produces samples $x$ such that $q^1(x) = q^2(x)$. A perhaps more usual definition is that one would aim to sample from the normalized product $q^1(x)q^2(x)/Z$ (or geometric mean $\\sqrt{q^1(x)q^2(x)}/Z$), but this seems difficult to achieve with the formalism of this paper. It could be beneficial to include a short discussion of this matter in the paper.\n\nFinally, I could not see where the parameters $\\omega$ and $T$ in Table 2 were explained." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "the principled way to combine the outputs of several diffusion models" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024the,\ntitle={The Superposition of Diffusion Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2o58Mbqkd2},\nnote={under review}\n}" }, "abstract": { "value": "The undeniable success of deep generative models for learning complex and high-dimensional data distributions has led to the proliferation of large-scale diffusion models across the entire machine-learning application spectrum. This Cambrian explosion of easily accessible pre-trained models, including fine-tuned open-source models on user-specific data, suggests a demand for methods that combine multiple different pre-trained models without incurring the significant computational burden of re-training a larger combined model. In this paper, we cast the problem of combining multiple pre-trained diffusion models at the generation stage under a novel proposed framework termed superposition. Theoretically, we derive superposition from rigorous first principles stemming from the celebrated continuity equation and design two novel algorithms tailor-made for combining diffusion models in SuperDiff. We demonstrate that SuperDiff is scalable to large pre-trained diffusion models as superposition is performed *solely through composition during inference*, and also enjoys painless implementation as it combines different pre-trained vector fields through an automated re-weighting scheme. Notably, we show that SuperDiffis efficient during inference time, and mimics traditional composition operators such as the logical $\\texttt{OR}$ and the logical $\\texttt{AND}$. We empirically demonstrate the utility of using SuperDiff for generating more diverse images on CIFAR-10, more faithful prompt conditioned image editing using Stable Diffusion, and improved unconditional *de novo* structure design of proteins." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "generative modelling", "protein generation", "image generation", "diffusion models" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/595e9c223fb038f65e634c9f7c0329a7b1c715a7.pdf" }, "presentation": null, "primary_area": { "value": "generative models" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "The Superposition of Diffusion Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "N/A" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- Q1 Could the authors provide some insights on the choice of clustering algorithm and patching interval? Specifically, the choice to use METIS for clustering is not directly tied to empirical or theoretical benefits specific to TGTOD’s design.\n\n- Q2 How does the partitioning of the temporal graph affect spatio-temporal correlation?\n\n- Q3 Have the authors tried directly using an efficient Trasnformer (e.g. Nodeformer) with single global-attention but not patching? \n\n- Q4 Could the authors provide a more clear comparison between TGTOD and Nodeformer, since they share the same kernelized message passing with GNN embedded? Is FiGraph that used C=1 cluster (Table 6) corresponding to this case?\n\n- Q5 How does TGTOD’s scalability compare to non-Transformer-based methods, such as GNNs?\n\nWu, Qitian, et al. \"Nodeformer: A scalable graph structure learning transformer for node classification.\" NeurIPS'22" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The hierarchical Transformer structure, combined with spatiotemporal patching is a promising approach to improving scalability.\n\n- TGTOD performs in the evaluation, validating the feasibility of using patch-based methods for financial and fraud detections in temporal graphs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies how to use Transformer for outlier detection in temporal graphs at scale. A temporal graph transformer with hierarchical architecture is proposed to handle partitioned temporal graph patches with improved scalability. The proposed TGTOD is evaluated on three datasets and outperforms standard Transformer baselines in both performance and computational efficiency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Partitioning large graphs into clusters is a well-established technique for dealing with scalability issues, e.g., ClusterGCN, GraphSAINT.\n- Current model designs (e.g., choice of clustering algorithm, patch size, and hierarchy) lack clear, evidence-based justification.\n- Results appear to be highly tailored to specific datasets for outlier detection, while the broader applicability of TGTOD to other temporal graph domains or for general purpose of spatio-temproal graph learning remains uncertain.\n\nChiang, Wei-Lin, et al. \"Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks.\" KDD'19. \nZeng, Hanqing, et al. \"Graphsaint: Graph sampling based inductive learning method.\" ICLR'20" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "**Q1**: How does the graph partitioning approach in TGTOD differ from that used in ClusterGCN? Additionally, how does TGTOD's focus on temporal graphs influence its partitioning strategy compared to the static graph approach of ClusterGCN?\n\n**Q2**: Can existing scalable node-level anomaly detection methods, such as XGBGraph [R4], be directly applied to address the challenges of temporal outlier detection? If not, what specific modifications or adaptations are necessary to ensure these methods effectively handle the dynamic nature of temporal graphs? If they can be applied directly, how does TGTOD compare with XGBGraph in terms of effectiveness and efficiency when dealing with temporal outlier detection?\n\n**Q3**: It appears that the authors may have omitted necessary parentheses in the loss function presented in Equations 2 and 3.\n\n**Q4**: To provide a comprehensive efficiency analysis of TGTOD, it would be helpful to report the results of other baseline models.\n\n---\n\n[R4] J. Tang, , F. Hua, Z. Gao, P. Zhao and J. Li. GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection. 2023. NeurIPS(36): 29628-29653." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "**S1**: The paper studies the problem of graph outlier detection by focusing on temporal graphs. This problem is important and has many practical applications in real-world scenarios.\n\n**S2**: The authors conduct extensive and thorough experiments to demonstrate the effectiveness of their proposed transformer framework across three real-world datasets." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper focuses on the challenge of graph outlier detection in temporal graphs. The authors argue that existing transformer-based models are inadequate for temporal graphs due to their quadratic computational cost and suboptimal generalization capabilities. To overcome these limitations, they propose partitioning the given graph into multiple subgraphs and applying hierarchical transformers to these subgraphs. Their method, TGTOD, integrates both graph neural networks and transformers to effectively capture structural and temporal dependencies within the temporal graph. Experimental results demonstrate the superior performance of TGTOD on three real-world temporal graphs, outperforming general graph neural networks, graph transformers, and graph outlier detectors." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**W1**: The primary concern regarding this work centers on its substantial lack of novel insights and originality in the proposed framework. The core components of the proposed framwork appear to be largely derivative of existing approaches, with minimal innovative additions. Firstly, the idea of graph partitioning as a strategy for reducing computational complexity, while effective, cannot be considered a novel contribution, as this approach has been extensively explored and implemented in existing models like ClusterGCN [R1]. Secondly, both the temporal transformer and cluster transformer essentially replicating the vanilla transformer architecture without substantial modifications or improvements tailored to graph-specific challenges. Similarly, the patch transformer component appears to be a direct adaptation of NodeFormer [R2]. Thirdly, integrating different components through weighted summation of GNN and transformer outputs has been previously introduced in SGFormer [R3].\n\n**W2**: The time complexity analysis is cursory and lacks rigor. It omits crucial considerations regarding the complexity of the METIS clustering algorithm, and the presentation lacks formal asymptotic notations. Additionally, the numerical examples provided are overly simplified, neglecting critical constant terms that could significantly impact real-world performance, such as the number of clusters, hidden dimensions, and attention head counts. A more rigorous analysis should encompass these factors and present complexity bounds with appropriate asymptotic notation. \n\n**W3**: The efficiency analysis is insufficient. The authors only compare their proposed TGTOD with DyGFormer, which does not offer a comprehensive assessment of its efficiency. It is imperative to include comparisons against a wider array of state-of-the-art methods and other baseline models for a more thorough evaluation. \n\n**W4**: The authors claim that existing transformer-based models suffer from restricted receptive fields. However, transformers are renowned for their ability to leverage a global receptive field, which is a significant advantage over traditional graph neural networks. As such, transformers can effectively address the constraints imposed by graph structures and capture long-range dependencies. This statement requires further justification and clarification to be convincing.\n\n---\n\n[R1] W. Chiang, X. Liu, S. Si, Y. Li, S. Bengio and C. Hsieh. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. 2019. SIGKDD: 257–266. \n\n[R2] Q. Wu, W. Zhao, Z. Li, D. Wipf and J. Yan. NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. 2022. NeurIPS(35): 27387-27401.\n\n[R3] Q. Wu, W. Zhao, C. Yang, H. Zhang, F. Nie, H. Jiang, Y. Bian and J. Yan. SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations. 2023. NeurIPS(36): 64753-64773." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please refer to the weakness" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "1. Outlier detection in dynamic graphs is an important problem.\n2. Given the limited number of existing models for outlier detection in dynamic graphs, this paper makes a valuable contribution by focusing on this direction and proposing a new method specifically for outlier detection in dynamic graphs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces TGTOD, a new end-to-end Temporal Graph Transformer designed for Outlier Detection in dynamic graphs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Unclear Motivation: The motivation behind this work is not well-founded. For example, the authors mention \"Limited receptive field\" as a motivation; however, neither DyGFormer nor SimpleDyG was specifically designed for outlier detection. The use of first-order neighbors is a deliberate design choice to avoid aggregating irrelevant information, which has proven effective in link prediction tasks. Thus, this choice is not inherently a limitation of receptive field. Additionally, the concept of \"task misalignment\" seems misplaced since previous models were not intended for outlier detection, making \"pretraining\" irrelevant in this context.\n\n2. Poor Organization: The paper dedicates substantial space to background knowledge and related works, yet fails to incorporate these works in the experimental comparisons. This organizational choice limits the paper’s coherence and weakens its argument for contribution.\n\n3. Limited Experiments: The experimental section is insufficient to convincingly demonstrate the model’s efficacy. Although several related works (e.g., NodeFormer, DIFFormer, SGFormer, CoBFormer) are discussed, none are included in the experimental comparisons. Furthermore, the baselines used (e.g., GCN, GraphSage) are basic, while more advanced temporal models like CAWN and TCL would be more appropriate. The limited metrics (AP and AUC) are inadequate for evaluating performance on an imbalanced dataset with a low anomaly rate; metrics such as F1-score would provide a more complete evaluation. The absence of ablation studies and hyperparameter analysis further detracts from the experimental rigor.\n\n4. Limited Novelty: The novelty of the model is minimal, as it merely combines three existing transformer architectures without any modification, contributing little innovation in terms of model design." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "+ Why is SimpleDyG mentioned in the related work but missing from the comparative analysis?\n\n+ Since the primary focus is on outlier detection, I suggest including some static outlier detection methods for comparison, instead of relying solely on common GNNs like GCN and SGC.\n\n+ The use of only three datasets is insufficient. Common benchmarks for temporal outlier detection, such as Wikipedia, Reddit, and Mooc[1], are notably missing from the experiments.\n\n+ The definitions in lines 192-193 are inaccurate. Generally, node labels are dynamically changing and are usually defined with a timestamp $t$.\n\n+ Some state-of-the-art baselines are missing, such as SAD[2] and SLADE[3].\n\n+ The claim that “existing Transformers are pretrained on link prediction…” is not entirely correct. Many temporal Transformers (e.g., TGAT, DyGFormer, SimpleDyG) are trained in an end-to-end manner for node- or link-level tasks.\n\n+ In Table 4, TGTOD shows good efficiency over DyGFormer. However, DyGFormer was not designed to be an efficient method for temporal graph learning. The authors should include more relevant baselines like SimpleDyG and TGAT for a comprehensive comparison.\n\n+ Ablation studies on varying time slots and the number of clusters are missing.\n\n+ In Table 6, the time slot is set to 1 for most datasets, which is a common setting in temporal graph learning. What is the necessity of the “patching” step in this context?\n\n\n\n[1] JODIE: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks. KDD 2019.\n\n[2] SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs. IJCAI 2023\n\n[3] SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning. KDD 2024." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "+ The proposed method is simple, effective, and scalable.\n\n+ The experimental results show overall improvement over baselines.\n\n+ Code for reproducing the experiments is provided." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors address the problem of anomaly detection over temporal graphs, a relatively less explored area compared to anomaly detection on static graphs. They highlight limitations in learning temporal signals using Transformers for this task.\n\nBased on these limitations, the authors propose an end-to-end Temporal Graph Transformer for Outlier Detection (TGTOD). TGTOD improves scalability by dividing large temporal graphs into spatiotemporal patches, followed by three Transformer networks to model both structural and temporal dependencies in temporal graphs. The experimental results demonstrate the effectiveness of TGTOD against leading baselines in outlier detection tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "+ The focus on Transformers for temporal graph learning raises concerns about novelty, as similar approaches have been extensively explored.\n\n+ The experiments are not fully convincing. Important datasets, baselines, and ablation studies are missing (see detailed comments below).\n\n+ Some claims and illustrations are vague and require more clarity (see detailed comments below)." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose TGTOD, an end-to-end temporal graph Transformer for outlier detection, conducting global spatiotemporal attention at scale." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024tgtod,\ntitle={{TGTOD}: A Global Temporal Graph Transformer for Outlier Detection at Scale},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2o7wxbKEQY},\nnote={under review}\n}" }, "abstract": { "value": "Graph outlier detection aims to identify anomalous substructures in graphs that deviate significantly from normal patterns. Traditional methods primarily focus on static graphs, overlooking the dynamic nature of real-world networks and ignoring valuable temporal signals crucial for outlier detection. While Transformers have revolutionized machine learning on time-series data, existing Transformers for temporal graphs face limitations in (1) restricted receptive fields, (2) overhead of subgraph extraction, and (3) suboptimal generalization capability beyond link prediction. In this paper, we propose TGTOD, a novel end-to-end Temporal Graph Transformer for Outlier Detection. TGTOD employs global attention to model both structural and temporal dependencies within temporal graphs. To tackle scalability, our approach divides large temporal graphs into spatiotemporal patches, which are then processed by a hierarchical Transformer architecture comprising Patch Transformer, Cluster Transformer, and Temporal Transformer. We evaluate TGTOD on three public datasets under two settings, comparing with a wide range of baselines. Our experimental results demonstrate the effectiveness of TGTOD, achieving AP improvement of 61% on Elliptic dataset. Furthermore, our efficiency evaluation shows that TGTOD reduces training time by 44×compared to existing Transformers for temporal graphs. To foster reproducibility, we make our implementation publicly available at https://anonymous.4open.science/r/tgtod." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Graph Outlier Detection", "Temporal Graph Learning", "Graph Transformers" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/4c46588159d3bffc4113cc331539fdfbedcae0ce.pdf" }, "presentation": null, "primary_area": { "value": "learning on graphs and other geometries & topologies" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Memory Mechanism Scalability:\nWhile the memory-based approach in GR-DUET performs well in your experiments, how does this method scale to larger or more complex environments? As the environment size or the number of instructions increases, the memory bank may become too large to manage efficiently. Could you provide further analysis or experiments that demonstrate how the method performs with continuous accumulation of data in larger datasets or more complex environments?\n\n2. the paper lacks a detailed discussion on how the memory is utilized, including how similar tasks are stored, how memory is retrieved and assessed for relevance and validity, and how prior knowledge is leveraged. Is the memory bank pre-set or updated dynamically? If it is updated dynamically, how is the correctness of the stored memories ensured, especially when handling diverse memories? How are the initial model parameters (L194, L198) initialized to ensure sufficient generalization? Please provide more details\n\n3. Furthermore, other memory-based VLN methods, such as SG-Nav [1], provide more detailed storage and query mechanisms based on topological graphs and memory updates. Could you compare your approach with SG-Nav in terms of performance or highlight any differences and advantages?\n\n4. Adaptation to Instruction Styles:\nYou mention using GPT-4 and a three-stage process to generate different instruction styles, but it remains unclear how the agent adapts to these varying styles over time. Could you provide more quantitative and qualitative results on how GR-DUET handles changes in style, particularly in OOD environments? A deeper analysis of how different speaking styles affect agent performance and adaptability would offer valuable insights into the robustness of your method in real-world scenarios, where user communication patterns may vary significantly.\n\n5. Unsupervised Learning and Adaptation Efficiency:\nThe paper suggests that agents in GSA-VLN can improve their performance over time using unsupervised learning techniques. Could you clarify how quickly the agents adapt in different environments? Are there any cases where adaptation is less effective or slower? Are there specific environments where the memory mechanism struggles to adapt? A more detailed breakdown of adaptation speed and efficiency across different environment types would help clarify the limitations of your approach and guide future improvements.\n\n6. Practical Deployment and Real-World Use Cases:\nThe GSA-VLN task is well-motivated by real-world scenarios, but the paper does not provide a detailed discussion on how the proposed method could be deployed in practical systems. Could you elaborate on the computational and memory overhead of your approach in real-time systems, such as those used in robotics or autonomous agents?\n\nReference:\n[1] Yin, Hang, et al. \"SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation.\" arXiv preprint arXiv:2410.08189 (2024)." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Novelty:\nThe paper introduces a new task, GSA-VLN, which focuses on the long-term adaptation of agents within specific environments, a capability with significant potential for real-world applications.\n\nDataset Contribution:\nThe authors present the GSA-R2R dataset, which extends the existing R2R dataset by using GPT-4 and a three-stage method to generate instructions in various speaking styles. The dataset is divided into residential and non-residential environments, serving as in-distribution (ID) and out-of-distribution (OOD) data, respectively.\n\nMethod Design:\nThe GR-DUET method integrates topological graphs with memory mechanisms, effectively preserving historical information and updating it continuously during navigation. This approach demonstrates notable improvements in performance, particularly in OOD (non-residential) scenarios.\n\nExperimental Results:\nThe paper compares GR-DUET with optimization-based and memory-based methods across different environment and speaking style splits. The experiments highlight the feasibility of the GSA-VLN task and the effectiveness of the GR-DUET method in various settings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes GSA-VLN (General Scene Adaptation for Vision-and-Language Navigation), a task designed to enhance the performance of navigation agents by enabling them to adapt to specific environments, particularly when exploring in the same environment over an extended period. The authors also introduce GSA-R2R, an expanded version of the HM3D and MP3D dataset, offering richer environments and more diverse instructions. Additionally, they present a novel method, GR-DUET, which improves navigation performance by utilizing memory mechanisms and updating graph structures." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Please see the Questions section for detailed improvement suggestions and questions.\nI look forward to the authors' responses to these questions, as addressing these points could significantly clarify some of the paper's contributions and limitations. I am open to adjusting my score if the authors provide further insights or resolve the concerns raised above." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please refer to the weaknesses section." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper introduces the novel General Scene Adaptation for Vision-and-Language Navigation (GSA-VLN) task, filling a critical gap in VLN research by focusing on adaptation in persistent environments. Rather than assuming agents will encounter only unseen environments, GSA-VLN models a more realistic scenario where agents learn and improve over time within a familiar setting. This shift in task formulation is both timely and innovative, especially as VLN moves toward practical applications.\n2. The paper demonstrates rigorous methodology in creating the GSA-R2R dataset, expanding on the Room-to-Room (R2R) dataset with a variety of environments, instruction styles, and out-of-distribution examples to thoroughly test agent adaptability. The proposed Graph-Retained DUET (GR-DUET) model is well-designed, combining memory-based navigation graphs with a scene-specific training strategy, and shows significant performance improvements across metrics. \n3. The paper is clearly organized and effectively conveys the importance of long-term scene adaptation in VLN." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents General Scene Adaptation for Vision-and-Language Navigation (GSA-VLN), a new VLN task where agents adapt to and improve in a specific environment over time, making it closer to real-world applications. To support this, the authors introduce GSA-R2R, a dataset that expands on Room-to-Room (R2R) by adding more diverse environments and instruction styles, including out-of-distribution examples. They also propose Graph-Retained DUET (GR-DUET), a method that uses memory-based navigation graphs and scene-specific training to help agents learn and retain scene-specific information, achieving strong results on the GSA-R2R benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The GR-DUET method involves a memory bank and a global graph that retains historical information across episodes. As the memory and graph size increase, the model’s computational requirements may grow significantly, particularly for long-term navigation in large environments. While the paper includes an environment-specific training strategy to limit graph expansion, providing an analysis of computational costs and potential trade-offs between memory retention and scalability would strengthen the model's practicality for deployment on resource-constrained systems.\n2. While the GSA-R2R dataset is a notable improvement over existing datasets for testing scene-specific adaptation, it may still fall short in representing the full diversity of real-world environments and interaction styles. The dataset includes a mix of residential and non-residential scenes, but further validation with a broader set of real-world environments could strengthen the model's applicability. Including additional scene types, such as commercial or outdoor spaces, or testing in dynamic environments where the layout changes over time, would push the dataset closer to real-world settings.\n3. Although the paper’s three-stage instruction generation pipeline enhances instruction diversity, more detailed analysis on how different instruction styles (e.g., Basic, User, Scene) impact agent performance would be valuable. For instance, specific ablation studies on each instruction type could clarify how robust the GR-DUET model is to variances in language, phrasing, and style. Additionally, investigating how the model generalizes across speakers with different dialects or levels of detail in instructions could provide actionable insights into improving instruction handling." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Line 283-286, how is the navigation model used here implemented? How can we ensure that it is a good instruction discriminator? I am concerned that if the navigation model is not trained well, it will not be enough to explain the quality of the instruction." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1、This paper is written with details and clear presentations, easy to follow.\n\n2、The author solves the VLN problem from a new perspective and divides the scenarios into Residential and Non-Residential." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a task that requires the VLN agent to execute VLN task in only one environment while storing its historical information at the same time. To make the initial parameters of the agent more general, the authors generate more environments and more instructions by using LLM. Finally, the paper also provides some experiment results according to their proposed metrics to further highlight the efficacy of the proposed methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1、\tThe novelty of this paper is limited. The GSA-VLN TASK proposed in the paper is still a standard VLN task. The so-called “standard VLN task” mentioned by the paper also includes fine-tuning based on historical information and trained models, which are claimed as the novelty of GSA-VLN in Section 3.2. \n\n2、\tFollowing the previous comment, the GSA-R2R DATASET proposed in the paper uses more environments (HM3D), and then uses tools such as LLM to refine the dataset's quality, which has been a common practice in VLN. Also, the author should not choose to ignore the existing works (e.g., HM3D-AutoVLN[1], Scale VLN[2], YouTube-VLN[3]) have also expanded and refined the VLN dataset when comparing (Table 1). I recommend the authors compare the datasets mentioned above and include them in the main manuscript (e.g. in Table I).\n\n[1] Learning from Unlabeled 3D Environments for Vision-and-Language Navigation\n\n[2] Scaling Data Generation in Vision-and-Language Navigation\n\n[3] Learning Vision-and-Language Navigation from YouTube Videos\n\n3、The comparison metrics in the experimental part are all newly proposed by the authors, which cannot correctly reflect the effectiveness of the method proposed. I suggest that the authors conduct experimental comparisons in standard VLN using common VLN metrics, and compare them on other VLN datasets besides R2R, such as REVERIR, RxR, CVDN and SOON." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "No" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "In section 3.3.4, the authors invite 15 participants to evaluate the instructions. Are the backgrounds (e.g., ages, etc.) of these 15 participants sufficiently homogeneous to demonstrate the refinement of the assessment? Also, I recommend disclosing the questionnaire they used for the test." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The proposed GSA-VLN task and GSA-R2R dataset which considers real-world robot adaptation in persistent environments, is an interesting research direction.\n2. Overall, the writing is fluent and the figures convey the character of the task well.\n3. The proposed GR-DUET method outperforms the baselines, demonstrating its effectiveness in helping agents adapt to specific environments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new task, named GSA-VLN, which require agents to execute navigation instructions within a specific scene and simultaneously adapt to it for improved performance over time. This paper also proposes a new datast, GSA-R2R, which significantly expands the diversity and quantity of environments and instructions for the Room-to-Room (R2R) dataset to evaluate agent adaptability in both ID and OOD contexts. The biggest difference between the proposed task and dataset and previous work is the diversity of instructions, i.e., different individual features and linguistic conventions are taken into account." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Some baselines are missing. I suggest to add some baseline methods based on LLM, especially the Zero-shot VLN methods. For example, InstructNav[1], NavCot[2]. The reason for this suggestion is that LLM's reasoning is now so powerful that it may be able to adapt well for different personal characteristics and language styles without the need for an additional adaption process, i.e., in zero-shot manner. Also, these different styles are essentially generated by LLM, so I'm concerned that these understanding these different styles is a very easy and undifferentiated thing for LLM to do. \n2. In this paper, the authors generated instructions for only five different character styles. However life can be much richer in terms of characters. The paper's contribution would have been greatly enhanced if the authors could propose a method for generating instructions for different character styles in a nearly infinite number of ways.\n3. The authors propose GR-DUET, but there are few details about it. For a reader who does not know DUET well, it may cause some difficulties in reading, and I suggest the authors to add some descriptions and details in the appendix.\n\n\n\n[1] InstructNav: InstructNav: Zero-shot System for Generic Instruction Navigation in Unexplored Environment.\n\n[2] NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please see above." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper is generally well motivated and the proposed tasks makes sense. For a pre-trained VLN agent, it is important to leverage the information and instructions in the new environment to further enhance it's knowledge and adapt to the new environment and uses.\n\n- A new dataset GSA-R2R based on the HM3D dataset is introduced with new instruction data collected to support the VLN task. The dataset can potentially be useful for the community.\n\n- Extensive evaluation of current VLN methods on the new dataset and different adaption methods are benchmarked. The proposed GR-DUET method demonstrates competitive performance compared to prior work." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a novel task, GSA-VLN (General Scene Adaptation for Vision-and-Language Navigation), which trains agents to follow navigation instructions within a specific scene while adapting to it for enhanced performance over time. A new dataset, derived from the HM3D dataset, is introduced to support this task. Additionally, the authors propose a new method that serves as a baseline and achieves state-of-the-art performance in this domain." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The concept of adapting a model to a test scene is not entirely new, as numerous prior methods have explored unsupervised exploration or adaptation within embodied environments.\n\n- The Related Work section could be more comprehensive. For instance, some discussions are postponed to Section 4, but it's crucial to review prior work that also employs adaptation methods in VLN, particularly those utilizing memory-based approaches, and also highlight the main differences and contributions of the proposed method.\n\n- Additionally, beyond the VLN literature, how does the proposed method relate to Lifelong Learning and Test-time Adaptation?\n\n- Table 3 presents the navigation performance of various VLN models on the new dataset. Is the performance of these methods consistent with results on other benchmark datasets?" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a new dataset, GSA-R2R, which significantly expands the diversity and quantity of environments and instructions for the Room-to-Room (R2R) dataset to evaluate agent adaptability in both ID and OOD contexts." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024general,\ntitle={General Scene Adaptation for Vision-and-Language Navigation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2oKkQTyfz7},\nnote={under review}\n}" }, "abstract": { "value": "Vision-and-Language Navigation (VLN) tasks mainly evaluate agents based on one-time execution of individual instructions across multiple environments, aiming to develop agents capable of functioning in any environment in a zero-shot manner. However, real-world navigation robots often operate in persistent environments with relatively consistent physical layouts, visual observations, and language styles from instructors. Such a gap in the task setting presents an opportunity to improve VLN agents by incorporating continuous adaptation to specific environments. To better reflect these real-world conditions, we introduce GSA-VLN (General Scene Adaptation for VLN), a novel task requiring agents to execute navigation instructions within a specific scene and simultaneously adapt to it for improved performance over time. To evaluate the proposed task, one has to address two challenges in existing VLN datasets: the lack of out-of-distribution (OOD) data, and the limited number and style diversity of instructions for each scene. Therefore, we propose a new dataset, GSA-R2R, which significantly expands the diversity and quantity of environments and instructions for the Room-to-Room (R2R) dataset to evaluate agent adaptability in both ID and OOD contexts. Furthermore, we design a three-stage instruction orchestration pipeline that leverages large language models (LLMs) to refine speaker-generated instructions and apply role-playing techniques to rephrase instructions into different speaking styles. This is motivated by the observation that each individual user often has consistent signatures or preferences in their instructions, taking the use case of home robotic assistants as an example. We conducted extensive experiments on GSA-R2R to thoroughly evaluate our dataset and benchmark various methods, revealing key factors enabling agents to adapt to specific environments. Based on our findings, we propose a novel method, Graph-Retained DUET (GR-DUET), which incorporates memory-based navigation graphs with an environment-specific training strategy, achieving state-of-the-art results on all GSA-R2R splits." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "vision-and-language navigation; scene adaptation; multi-modal learning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/e41d197ecb143849b8edd1e0630e5d8bb035b465.pdf" }, "presentation": null, "primary_area": { "value": "datasets and benchmarks" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/6da67309fa3a6bf5493cd70f73e535394be1c171.zip" }, "title": { "value": "General Scene Adaptation for Vision-and-Language Navigation" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- The soundness of the paper is high, but my primary concerns center around the novelty of the algorithm beyond tPCN itself. Simply applying a non-negative constraint and applying to a new task does not seem like a sufficiently novel contribution for ICLR. It is unclear what enhancements of the algorithm could be necessary in the context of spatial navigation." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "-\tThe general finding of the tPCN is encouraging, and the generalization to a different task than the Millidge 2024 paper is promising. \n-\tThe robustness experiments (4.4) show that the emergent grid-like activity is robust to model architectures. This is encouraging, since many experimental neuroscience manipulations show grid cells to be robust to manipulations of the environment of neural activity." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors investigate how a temporally-dependent version of predictive coding can extract compact latent spaces in the form of periodic grid activity from temporally structured place cell input. The findings are of general interest to theories of learning in biological settings, and replicate many previous results with a more biologically plausible learning mechanism." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "-\tOverall, the study seems like an incremental follow on of the tPCN paper applied to a new domain, but which does not require fundamental changes to the original algorithm. \n-\tThe path integrating tPCN assumes input in the form of place cell activity, but does not account for how place cells and grid cells form from the combination of visual and self-motion information. Combined with the lack of anatomical constraints of direction of connectivity, the study is more about the formation of compressed latent spaces than the medial temporal lobe. Several existing studies, largely cited in the paper, already investigate the formation of such successor representations by predictive coding.\n-\tThe authors dismiss previous examples of learned grid cells (Dordek, Stachenfeld, Schaffer, etc) on the basis that these are not biologically plausible learning methods, but then move to use real-valued activation functions. There is no evidence from the methods presented in this paper that a spike-based temporal predictive coding network would converge." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I have two questions:\n\n1. In both model architectures presented, grid cell activity depends on input from place cells. However, in biological systems, place cell activity varies significantly across different environments, showing a phenomenon known as global remapping, whereas grid cells maintain a stable 2D toroidal manifold across environments. How does this model account for this discrepancy? If place cell activity, the input source for grid cells, changes substantially across environments, how does the model explain the stability of grid cell activity?\n\n2. In the medial entorhinal cortex (MEC), grid cells are organized into modules with distinct spacings. In the model proposed in this paper, do the network’s grid cells display discrete spacing distributions, and are there any indications of modular independence in their connectivity?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "To the best of my knowledge, this paper is the first to suggest that a predictive coding network can serve as a biologically plausible model for learning grid cells and perform simulations to validate this hypothesis. Additionally, the paper extends the application of PCN’s locally-based learning method to approximate backpropagation (BP) in temporally processing networks, using tPCN. While not formally proven, the authors draw comparisons between tPCN and 1-step BPTT, indicating that with multi-step inferences, tPCN’s performance could approach that of BPTT." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes that the mechanism by which grid cells are learned in biological systems may involve predictive coding. To test this hypothesis, the authors trained both a predictive coding network (PCN) and a temporal predictive coding network (tPCN) on path integration and non-path integration tasks. They observed that hexagonal firing patterns, characteristic of grid cells, emerged in both paradigms. Since PCN and tPCN introduce error cells that enable learning with spatially and temporally local rules, this discovery suggests a biologically plausible mechanism for grid cell formation. The authors also analyze the learning process in tPCN, comparing it analytically with 1-step backpropagation through time (BPTT), to explain the emergence of grid cells. Finally, they assess the robustness of grid cell emergence in their model by testing various activation functions, environments, and network sizes." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The main limitation lies in novelty. First, previous studies have already shown that grid cells can be learned either through non-negative PCA or via a single-layer BP-based network from place cell activity. Likewise, RNNs trained via BPTT for path integration to predict place cell activity have also been reported (see Sorscher et al., 2022). Additionally, the ability of PCN to approximate BP using local learning rules has been demonstrated previously (see Song et al., 2020), and the t-PCN structure’s capacity to approximate BPTT is a straightforward extension of prior work (Millidge et al., 2024). The robustness analysis in this paper largely follows procedures established in earlier RNN studies and does not report new phenomena (Schaeffer et al., 2022). Other biologically plausible learning algorithms, such as those using Oja’s rule, have also achieved grid cell-like activity, suggesting that this paper’s algorithm is not unique in this regard. Overall, the contribution seems to synthesize existing ideas without introducing significant innovation." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "see weakness" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper is clearly written, and the question is well-defined." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This study demonstrates that predictive coding can effectively train neural networks to develop hexagonal grid representations from spatial inputs, providing a biologically plausible explanation for the emergence of grid cells in the medial entorhinal cortex. By analytically comparing predictive coding with existing models, we offer new insights into the learning mechanisms of grid cells and extend predictive coding theory to the hippocampal formation, suggesting a unified learning algorithm for various cortical representations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "My major concern is that the work may lack novelty.\n\n1. The use of non-negative and sparse network designs to produce grid cell-like patterns has been extensively discussed. For example, [1] reported that non-negative and sparse properties can generate grid cell -like patterns and theoretically demonstrated why non-negativity is the main driver of grid cell formation (which the author's paper does not address) instead of sparsity. Similar findings were also reported in [2]. Earlier, [3] proves that a nonnegativity constraint on firing rates induces a symmetry-breaking mechanism which favors hexagonal firing fields. [4] further explored, through extensive experiments, the conditions necessary for generating grid cells.\n\n2. Prediction tasks, including path integration, that produce grid cell-like patterns have also been widely reported, especially when the input data takes a place cell-like form. For instance, [5] also used place cell like input and path integration tasks to train networks and generate grid cells, while [6] theoretically analyzed the role of predictive learning in forming low-dimensional representations.\n\n3. In my understanding, tPCN is very similar to a one-step RNN (apart from the difference in local learning rules), so the fact that its training process resembles that of one-step tBPTT is not surprising. As previously noted, the key to forming grid cells lies in the predictive task, not the RNN network itself. Therefore, the similarity between tPCN and RNN does not offer significant insight into the generation of grid cells.\n\nFor the reasons above, I believe this paper does not offer substantial novelty or make a clear contribution to the field.\n\n\n\n[1]Whittington, James CR, et al. \"Disentanglement with biological constraints: A theory of functional cell types.\" *arXiv preprint arXiv:2210.01768* (2022).\n\n[2]Dorrell, William, et al. \"Actionable neural representations: Grid cells from minimal constraints.\" *arXiv preprint arXiv:2209.15563* (2022).\n\n[3]Sorscher, Ben, et al. \"A unified theory for the origin of grid cells through the lens of pattern formation.\" *Advances in neural information processing systems* 32 (2019).\n\n[4]Schaeffer, Rylan, Mikail Khona, and Ila Fiete. \"No free lunch from deep learning in neuroscience: A case study through models of the entorhinal-hippocampal circuit.\" *Advances in neural information processing systems* 35 (2022): 16052-16067.\n\n[5]Whittington, James CR, et al. \"The Tolman-Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation.\" *Cell* 183.5 (2020): 1249-1263.\n\n[6]Recanatesi, Stefano, et al. \"Predictive learning as a network mechanism for extracting low-dimensional latent space representations.\" *Nature communications* 12.1 (2021): 1417." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "The authors find that grid cells emerge under various configurations and constraints, even in the absence of velocity input. Could they expand on the implications of this finding for the role of predictive coding in spatial learning?\n\nYou claim that tPCN approximates tBPTT; however, the RMSE indicates that when the inference has fully converged, the tPCN outperformed tBPTT. Path integration is a Markov process, and it therefore makes sense that tBPTT should work. However, as you show, having the extra inference steps helps. Is it then tPCN that approximates tBPTT or the other way around (tBPTT approximates tPCN)\n\nMoreover, this begs the question: what is the difference between $g_{t-1}$ from RNNs and $\\hat{g}_{t-1}$ tPCNs that give this performance boost? \n\nIs there a qualitative difference in grid cells between the models, or are there other cell types that make $\\hat{g}_{t-1}$ \"better\"? One way to hint at this would be to ablate neurons in $g$ and rank them according to their effect on the loss. Are there any differences between these two populations? Another way would be to perform a detailed analysis of the predictive power of $g$ cells in the two models, for example, according to Ouchi et al.\n\nRelated works, such as the work from Giocomo in 2011, are outdated. Whether oscillatory dynamics are important for grid cells started as you point out with the work by [Burgess](https://pmc.ncbi.nlm.nih.gov/articles/PMC2678278/) and Hasselmo, but it was later included in CANNs by [Bush and Burgess](https://pubmed.ncbi.nlm.nih.gov/24695724/). The importance of oscillations in grid cells has been tested experimentally by [Lepperød et al](https://www.science.org/doi/full/10.1126/sciadv.abd5684), [Schmidt-Hieber et al.](https://www.nature.com/articles/nn.3340), [Robinson et al.](https://www.sciencedirect.com/science/article/pii/S2211124724009197)\n\n**Minor**\n\n - $\\hat{g}$ is used but not introduced as inferred before line 392; this can be nice to point out earlier.\n - Whether grid cells are learned or are there from birth is disputed; I would present this in less certain terms." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "**Originality:**\nThis paper provides a new perspective on grid cell formation by applying predictive coding. While previous work has used RNNs trained with BPTT to simulate grid cells, this study introduces predictive coding networks (PCNs) and temporal PCNs (tPCNs) as biologically plausible alternatives. While predictive coding has been addressed in hippocampal formation previously (Stachenfeld et al. ++), the proposed learning rules are novel in this context. \n\n**Quality:**\nThe authors demonstrate grid cell emergence in PCNs and perform a comparative analysis with existing RNN models. By analytically showing that tPCNs approximate truncated BPTT, they provide a theoretical solid grounding for their approach. Further, the robustness analysis—exploring different model architectures, non-linearities, and environments—addresses shortcomings proclaimed in recent work (Sorscher vs. Schaeffer). The theoretical and empirical sections are well-integrated.\n\n**Clarity:**\nThe authors use clear visual representations of presented ideas, making interpretation intuitive. The derivations are well-presented, especially in demonstrating the correspondence between tPCNs and truncated BPTT. However, some technical details on the inference dynamics of tPCNs might benefit from additional clarity or simplification, especially for readers less familiar with predictive coding. \n\n**Significance:**\nThe findings are interesting for neuroscience and machine learning. They suggest that predictive coding may underpin not only perceptual but also spatial and navigational representations. For neuroscience, predictive coding may unify perspectives across cortical functions. For machine learning, it offers an alternative to backpropagation-based learning in dynamic systems." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper investigates the emergence of grid cells, known for their hexagonal firing patterns in spatial navigation, using predictive coding—a biologically plausible learning rule. The authors propose that grid cells can be learned by neural networks through predictive coding, which aligns well with the principles of local computations and Hebbian plasticity.\n\nThe key contributions are:\n\n - Demonstrating that predictive coding networks (PCNs) can naturally develop grid cell representations with sparse, non-negative constraints, and a temporal extension (tPCN) achieves similar results in dynamic tasks like path integration.\n - Establishing that tPCNs approximate the truncated backpropagation through time (BPTT), highlighting a biologically plausible alternative to BPTT for learning grid cells.\n - Analyzing the robustness of grid cell emergence in PCNs and tPCNs across varied architectural and environmental conditions, showing grid cells can still form even without velocity input." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Although it is nice to see grid cells emerge in the proposed setup, it is not that surprising given the setup with static place cell readout. The comparison between BPTT and tPCNs is more interesting, in my opinion, than the grid cell results and can have broader implications beyond this particular setting; I would present this as the main result and, therefore, consider moving this result to an earlier stage and presenting the grid cell stuff as a test case.\n\nThe model operates under certain assumptions (e.g., reliance on sparsity, non-negative constraints, simplified path integration tasks, and place cell readout) that may not generalize well across different types of neuronal representations or tasks. However, the discussion lacks a critical assessment of these assumptions, specifically regarding where the predictive coding model might fall short compared to other frameworks for grid cells, such as the recent development of self-supervised learning for grid cells ([Schaeffer et al.](https://arxiv.org/abs/2311.02316)), conformal isometry, or distance preservation ([Xu et al.](https://arxiv.org/abs/2210.02684), [Dorell et al.](https://arxiv.org/abs/2209.15563)). For example, the choice of static read-out place cells limits studies of remapping (but can be done; see [Schøyen et al.](https://www.sciencedirect.com/science/article/pii/S258900422302179X), different geometries [Krupic et al.](https://www.nature.com/articles/nature14153) etc.\n\nThe proposed predictive coding model successfully generates grid cells, but the mechanistic explanation for how and why grid cells emerge under predictive coding is lacking. Moreover, the field suffers from challenges in comparing representations across studies, barring visual inspection. Grid scores are used to assess grid cell likeness; however, these give little insight beyond 60-degree symmetry. I suggest you use something else to assess the function of the networks, such as ablation studies and studying the full representational setting of the network. For example, do you see border cells, band cells, etc? At least provide examples, preferably representations from the full network, in the supplementary.\n\nAll in all, since the title and introduction of the paper highlight grid cells, I would expect more analysis of this finding and a broader comparison with the existing literature. However, I think the more interesting finding is the comparison between BPTT and tPCNs. Therefore, I would recommend lifting this part of the paper and proposing the grid cell story as a potential application motivating further studies on this line of work, although I do see your point on extended analysis on this being out of scope." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We show that grid cells can be learned in neural networks via predictive coding, a biologically plausible learning rule." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024learning,\ntitle={Learning grid cells by predictive coding},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2ofVtMvRil},\nnote={under review}\n}" }, "abstract": { "value": "Grid cells in the medial entorhinal cortex (MEC) of the mammalian brain exhibit a strikingly regular hexagonal firing field over space. These cells are learned after birth and are thought to support spatial navigation but also more abstract computations. Although various computational models, including those based on artificial neural networks, have been proposed to explain the formation of grid cells, the process through which the MEC circuit ${\\it learns}$ to develop grid cells remains unclear. In this study, we argue that predictive coding, a biologically plausible plasticity rule known to learn visual representations, can also train neural networks to develop hexagonal grid representations from spatial inputs. We demonstrate that grid cells emerge robustly through predictive coding in both static and dynamic environments, and we develop an understanding of this grid cell learning capability by analytically comparing predictive coding with existing models. Our work therefore offers a novel and biologically plausible perspective on the learning mechanisms underlying grid cells. Moreover, it extends the predictive coding theory to the hippocampal formation, suggesting a unified learning algorithm for diverse cortical representations." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "grid cells", "predictive coding", "computational neuroscience" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/df75e1a04211e59ed875105274fb984d58338ff0.pdf" }, "presentation": null, "primary_area": { "value": "applications to neuroscience & cognitive science" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/f95a6409e1a1770ccbd3bcf75fe0f183e1377605.zip" }, "title": { "value": "Learning grid cells by predictive coding" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- Why not evaluate on a more commonly used benchmark such as dataset B from 2008 BCI competition? https://www.doi.org/10.1109/TNSRE.2007.906956\n- Line 322: The term “fune-tuned” is confusing in this context, it suggests that the supervised methods are pre-trained. Is it the case?\n- Could you include the training times of the different methods?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "As pointed out by the authors, fine-tuning strategies are relatively underexplored with EEG foundation models. They start to fill this gap by proposing a novel fine-tuning algorithm.\nThe paper is well-structured and easy to follow, with good-quality figures. The diagrams are clear, and the use of pictograms makes their understanding intuitive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this article, the authors investigate fine-tuning techniques for pre-trained models in the context of EEG-based BCI. They present a method which combines adding adapter layers to the transformer (adapter form approach) and learning additional vectors which are concatenated to the key and value vectors in the transformer (prefix-finetuning). Both approaches are used in order to reduce the number of parameters to finetune." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- On the DREAMER and motor imagery (MI) datasets, the method proposed by the authors consistently produces relatively low results, underperforming compared to the supervised baseline. Lines 370-373, the authors suggest that this comparison is not fair and only compare the adaptive methods within themselves. However, I respectfully disagree and maintain that it is indeed appropriate and relevant. Indeed, all models had access to the same quantity of data from the target distribution. The fact that the pre-trained models perform poorly means that they are not able to correctly use the available target data. This issue is called “negative transfer” and it needs to be tackled, not ignored. \n- The models based on BIOT systematically perform around chance-level (50±2%) on the MI and DREAMER datasets. This raises questions about the statistical significance of these results. At the moment, this issue is not discussed or even mentioned by the authors. For transparency, I would suggest that the authors include the theoretical chance level in all tables and figures.\n- The MI dataset used is relatively unknown (only cited once on Google Scholar), which does not make it a good benchmark. As this is the only MI dataset used, I believe it is necessary to conduct additional experiments on another, more common, MI benchmark.\n- Line 124, the authors point out that few discussions were made on how to fine-tune models to downstream tasks in the BCI literature. While it is true that there are few, they are not nonexistent. As this is the main topic of the article, the few works that were done in that direction should at least be reported, if not compared to. The following two studies compared different downstream architectures combined with different fine-tuning regimes. In particular, they both explored additive fine-tuning algorithms, which is in contradiction with the statement line 145.\n - Kostas et al. (2021) https://doi.org/10.3389/fnhum.2021.653659\n - Guetschel et al. (2024) https://doi.org/10.3217/978-3-99161-014-4-003\n- The method proposed by the authors can only be applied to transformer-based pre-trained models and requires doing “surgical” modifications to the architecture. This is not easy to implement compared to simple finetuning.\n- The appendix is missing." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- What’s N_d in paragraph 3.2?\n- What is a “self-supervised modeling method”?\n- What is “SMM SOTA”? is it a neural network trained from scratch?\n\nTypo:\n- Eq (3): wrong matrix-vector shapes for “xW_q”" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Comprehensive related work situates TaKF+ well in EEG model adaptation literature.\n- Tackling parameter-efficient tuning for EEG is timely and could make a significant impact if successful.\n- TaKF+ is tested on 4 datasets and 2 recent pre-trained models.\n- There is no overlap between the evaluation datasets and the training datasets used in the pre-trained models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces TaKF+, a parameter-efficient tuning method for adapting EEG foundation models to a variety of downstream tasks. TaKF+ combines a Task-Adaptive Key-Feature Extractor (TaKF) with adapter modules to extract and refine task-specific features while keeping the foundation model's parameters largely unchanged, thus minimizing computational costs. Through experiments on diverse EEG tasks like motor imagery and seizure detection, TaKF+ shows superior adaptability and stability compared to existing tuning methods, particularly in data-scarce settings. The study highlights TaKF+ as a versatile and efficient tool for EEG-based applications, addressing critical challenges in EEG model adaptation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- I understand you want to show that TaKF+ is more robust than the baseline but the tables are hard to read: sometimes TaKF+ is better sometimes not. To show this, you could use normalized plots as in [1, 2].\n- Heavy use of acronyms impacts readability: SMM, FT, LP, PT.\n- An analysis of the performance with respect to the number of training samples would be interesting.\n- A comparison of computational time with other methods would also be interesting.\n\n[1] Mellot, A., Collas, A., Chevallier, S., Gramfort, A., & Engemann, D. A. (2024). Geodesic Optimization for Predictive Shift Adaptation on EEG data. arXiv preprint arXiv:2407.03878.\n\n[2] Kim, M. J., Grinsztajn, L., & Varoquaux, G. (2024). CARTE: pretraining and transfer for tabular learning. ICML 2024." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1.\tHow does TaKF+ handle cases where downstream tasks have significantly different label distributions from the pre-trained EEG foundation model?\n2.\tCould the authors clarify how TaKF+ performs on larger, more heterogeneous EEG datasets that may have different sampling rates or noise levels?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1.\tTaKF+’s integration of the Task-Adaptive Key-Feature Extractor (TaKF) and adapter modules is novel and effective in tuning EEG foundation models with minimal parameter updates.\n2.\tThe method is designed to work efficiently in low-data settings, demonstrating strong performance in few-shot learning scenarios.\n3.\tTaKF+ supports a broad range of downstream tasks, making it highly adaptable and suitable for diverse EEG-based applications." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The \"TaKF+\" paper presents a parameter-efficient tuning method aimed at enhancing EEG foundation models for diverse downstream tasks, such as seizure detection, emotion recognition, and motor imagery. The method, TaKF+, introduces a Task-Adaptive Key-Feature Extractor (TaKF) and adapter modules to adapt EEG foundation models in a task-agnostic manner, maintaining generalization and minimizing computational cost. Through evaluations on multiple datasets, the authors demonstrate TaKF+’s superior performance in few-shot scenarios and its adaptability across various EEG-based applications." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tWhile TaKF+ introduces Task-Adaptive Key-Feature Extractor and adapter modules, the motivation behind this specific design choice seems not clear. The paper lacks a more detailed comparison of how TaKF+ improves upon existing methods, both in terms of unique technical contributions and in addressing specific limitations of previous EEG foundation models\n2.\tThe novelty of TaKF+ could be strengthened by discussing how it differs fundamentally from other parameter-efficient fine-tuning approaches beyond its application to EEG. \n3.\tAlthough the empirical results are promising, the paper needs a deeper theoretical rationale supporting the choice of parameter-efficient tuning for EEG foundation models. Specifically, a clearer explanation of why the TaKF+ structure is particularly suited for EEG data, as opposed to alternative architectures, would strengthen the paper’s foundation.\n4.\tAlthough TaKF+ shows improvement over some baselines, the paper should include more comparisons with recent advancements in EEG model tuning or transfer learning.\nI will reconsider my assessment after checking authors' response" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. In Section 6.1, the paper mentions that \"Although the Adapter performed more stably than other baselines, it did not achieve the versatility of TaKF+.\" What is meant by the versatility of TaKF+ in this context, and how is it quantitatively or qualitatively better than the Adapter in terms of versatility? More clarification is needed to justify this claim." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The proposed method maintains competitive performance while reducing computational cost by tuning only a small fraction of parameters, making it resource-efficient for real-world applications in EEG-based tasks.\n2. The few-shot learning experiments demonstrate that TaKF+ approaches or even surpasses the performance of fully fine-tuned models in some datasets, which is a highly promising result." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents TaKF+, a new approach for parameter-efficient fine-tuning of EEG foundation models. The Task-Adaptive Key-Feature Extractor (TaKF) combined with adapter modules enables efficient extraction of task-relevant features with minimal computational overhead, while maintaining or exceeding the performance of fully fine-tuned models in few-shot learning scenarios." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While the paper states that 3% of parameters are tunable in additive fine-tuning methods, the exact tunable parameter ratio for TaKF+ is not provided. This lack of explicit comparison may lead to an unfair assessment of baseline methods. Clearly stating the tunable parameters for TaKF+ would provide a more transparent comparison.\n2. The core idea of TaKF+—combining the well-established Adapter technique with a Q-former-like cross-attention mechanism—might be seen as a simple extension of known methods, limiting the novelty of the contribution.\n3. The results indicate that TaKF+ does not consistently outperform all additive fine-tuning baselines across datasets. This inconsistency raises concerns about its general robustness and effectiveness.\n4. Some widely used baselines, such as LoRA, Adaptformer, and UniPELT, are absent from the experimental comparison, limiting the comprehensiveness of the evaluation. \n5. In Table 3, the performance of the proposed method's variants fluctuates significantly across different datasets, which casts doubt on the consistent effectiveness of individual components." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. How much smaller is the amount of parameters added during the fine-tuning phase compared to the original model? Is it worth the potential reduction in effectiveness?\n2. Both proposed modules increase trainable parameters to aid the fine-tuning process. Could they be demonstrated through interpretable methods, such as visualization, to substantiate the different effects described in the text?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.Innovation: Pioneering attention to the issue of high parameter counts during fine-tuning in large EEG models. \n2.Significance: Fine-tuning usually requires adjusting all parameters, which can be computationally and temporally expensive. If the hypothesis holds, the corresponding optimizations could facilitate the widespread application of large models. \n3.Clarity of writing: The descriptions of the proposed TAKF method and Adapter model are highlighted effectively. \n4.Rich experimentation: A broad range of baseline comparisons including supervised and self-supervised learning SOTA methods were selected, and different approaches to fine-tuning with additional parameters were compared. \n5.Reproducibility: The paper provides extensive code, and the reported results seem reproducible based on the documentation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper focuses on reducing computational demands during the fine-tuning phase of large EEG models by training only newly added parameters. It introduces the TAKF method to enhance the model's expressiveness by extracting task-specific features, and incorporates an Adapter module to transfer foundational knowledge of the base model to specific tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.Innovation: In terms of methodology, only the TAKF module is newly introduced, while the Adapter model merely combines existing methods. \n2.Notably unimpressive experimental results: As shown in Table 1, the performance on most datasets using LaBraM as the base model is significantly lower than LaBraM's fine-tuning results; intuitively, the lower computational cost may lead to a substantial decrease in effectiveness; on the TUEV dataset, it performs worse than the Adapter-only approach, which requires further analysis and explanation; according to Tables 1 and 2, it underperforms the MAM Adapter method in 3/8 of the metrics, showing no significant advantage. \n3.Significant errors in tables: In the Appendix, Tables 7 and 8 present the same series of methods across four different datasets, yet the data for methods from LaBraM-LP to (Ours) LaBraM-TaKF+ are identical in both tables; there are also significant errors in table titles, e.g., Table 7 includes data for LeftRight Hand, which does not belong in the emotion recognition category. The authors are advised to carefully proofread the content." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024takf,\ntitle={Ta{KF}\\${\\textasciicircum}\\{+\\}\\$: A versatile and parameter-efficient tuning for {EEG} foundation model},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2og3oWsC5n},\nnote={under review}\n}" }, "abstract": { "value": "Electroencephalogram (EEG) data, widely used in brain-computer interfaces (BCIs), pose challenges for reusing deep learning models trained on specific datasets due to variations in recording configurations and domain gaps. While foundation models pre-trained on large-scale EEG datasets have emerged as a promising solution, the challenge of effectively adapting them to downstream tasks has yet to be fully explored. To address this, we propose a novel tuning method, TaKF$^{+}$, which consists of the Task-Adaptive Key-Feature Extractor (TaKF) and adapter modules. TaKF$^{+}$ is designed to efficiently extract task-relevant features from EEG foundation models for downstream tasks while preserving the model’s parameters and significantly reducing computational overhead. We evaluate TaKF$^{+}$ across a diverse range of tasks, including motor imagery, emotion recognition, and seizure detection, and demonstrate its superior performance and adaptability compared to existing methods over publicly available datasets. Our research paves the way for more efficient and versatile applications of EEG foundation models across various domains." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "EEG", "Foundation model", "Parameter-efficient fine-tuning", "Additive fine-tuning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/ba6947c0e5f83258d252d273b17b600d99b195dc.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/1768c026fad123e2ec45d0361f6452430dc4b929.zip" }, "title": { "value": "TaKF$^{+}$: A versatile and parameter-efficient tuning for EEG foundation model" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "How does this method compare to traditional GAN ​​methods (Real-ESRGAN, BSRGAN) in terms of running costs?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The idea of using the pretrained diffusion model to train a real-sr GAN is new. The introduction of the Noise-Aware Discriminator (NAD) and the edge-aware DISTS (EA-DISTS) perceptual loss seems novel and effective. \n2. Comprehensive experimental results on three real-world datasets show the proposed DFOSD achieves competitive or superior performance in both no-reference (NR) and full-reference (FR) image quality metrics.\n3. The overall writing is good and the paper is easy to understand." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a new GAN-based real-world image super-resolution method (Real-ISR) using pretrained diffusion models. The work introducesa Noise-Aware Discriminator (NAD) and an edge-aware perceptual loss function (EA-DISTS) for the GAN training. The paper presents extensive experimental results demonstrating that the proposed method achieves superior performance in both quantitative metrics and visual quality compared to state-of-the-art diffusion-based and GAN-based methods for Real-ISR." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Although the authors claim that the proposed method, DFOSD (Distillation-Free One-Step Diffusion), is a diffusion SR model, it is essentially a GAN-based method. The model only uses the parameters trained by a diffusion model, but there is no Markov process. This term may cause some misunderstanding of the method.\n2. While the paper emphasizes the reduction in training overhead and computational complexity relative to distillation-based methods, the overall framework still relies on heavy pre-trained models (e.g., Stable Diffusion UNet). The method may not be as lightweight as simpler GAN-based approaches, which could limit its adoption in resource-constrained environments. A more explicit comparison with simpler non-diffusion-based methods in terms of memory and computational requirements would provide a clearer picture.\n3. Although the authors report visual comparisons and use several no-reference and full-reference metrics, the paper would benefit from subjective user studies to evaluate the perceived quality of the generated high-resolution images. \n4. The paper does not provide an analysis of how sensitive DFOSD is to hyperparameter choices, such as the weights of the loss function components." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please referring to the weaknesses above." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "+ The paper presents DFOSD, a novel model that significantly advances real-world image super-resolution by offering a distillation-free one-step diffusion approach, which is highly innovative in the field.\n\n+ Two standout contributions are the noise-aware discriminator (NAD) and the edge-aware DISTS (EA-DISTS) loss. The NAD leverages prior knowledge from pre-trained models to enhance realism, while EA-DISTS improves texture detail restoration.\n\n+ The writing is clear and methodical and the experimental section is robust, providing not only quantitative metrics but also qualitative assessments that demonstrate DFOSD's superior performance and efficiency in image super-resolution tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a model named DFOSD, addressing the problem of real-world image super-resolution. The authors propose a noise-aware discriminator (NAD) and an edge-aware DISTS (EA-DISTS) loss to optimize the model, resulting in superior performance on quantitative metrics and qualitative assessments. DFOSD achieves remarkable results on tasks such as image restoration, demonstrating significant improvements in realism and detail generation across various real-world datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Usually, the training cost is not very large for diffusion-based SR methods compared to text-2-image tasks, so I think the distillation-free optimization is not much necessary. Besides, we also could pre-compute the output of teacher models with multi-steps predictions before starting the complete training. Can you elaborate further on the advantages of non-distillation training?\n\n- The DFOSD proposed in this paper is just a marginal optimization based on OSEDiff[1] and other adversail training-based methods[2,3,4].\n\n- The proposal of EA-DISTS loss lacks of novelty, just an experimental trick.\n\n- Noise-aware discriminator is not new, the same ideas are shown in SD3-turbo[2] and TAD-SR[3]. Although the NAD seems simpler and effective, but is not a very innovative method.\n\n- The experimental setting is not rigorous and unfair, will you release the 200K high-quality images to public?\n\n\n[1] One-Step Effective Diffusion Network for Real-World Image Super-Resolution, 2024.\n\n[2] Adversarial Diffusion Distillation, 2023.\n\n[3] Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation, 2024.\n\n[4] One Step Diffusion-based Super-Resolution with Time-Aware Distillation, 2024." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. DFOSD uses learnable text embedding without DAPE and the text encoder, reducing inference time compared to OSEDiff. However, it's unclear if this fully accounts for the 0.24s speedup. The authors should provide a breakdown of inference times for each major component (e.g., text embedding, main network, etc.) for DFOSD and OSEDiff on the same device. This would help clarify where the speedup is coming from.\n\n2. In Table 4, DFOSD's performance with the LSDIR+10K FFHQ training dataset is worse than OSEDiff with the same training dataset in no-reference metrics (MUSIQ, ManIQA, ClipIQA). It would be useful to clarify if these improvements in no-reference metrics are primarily due to the high-quality training dataset. A more detailed analysis in Sec. 4.3 would be helpful.\nTo avoid the influence of input resolution, I suggest the authors evaluate the DFOSD's performance with different training datasets on the pre-cropped test dataset (https://huggingface.co/datasets/Iceclear/StableSR-TestSets) from StableSR [1]. \n\n[1] Wang, Jianyi, et al. \"Exploiting diffusion prior for real-world image super-resolution.\" International Journal of Computer Vision (2024): 1-21.\n\nI will consider raising my score if my primary concerns are addressed." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper proposes a noise-aware discriminator, leveraging the prior knowledge from the pre-trained SD UNet. This enhances the realism and details of the reconstructed images without much memory usage and training time.\n\n2. The proposed EA-DISTS can enhance texture detail restoration.\n\n3. The writing is well, and the idea is easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes DFOSD, a Distillation-Free One-Step Diffusion SR model that enhances image detail and visual quality. Key contributions include a Noise-Aware Discriminator (NAD), which improves realism through adversarial training, and Edge-Aware DISTS (EA-DISTS) loss, which leverages image edges to enhance the authenticity of reconstructed details." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. This paper introduces learnable text embedding to replace the text extractor, significantly reducing inference time. How it is implemented and trained and more explanation of learnable text embedding are needed for clarity. \n \n2. This paper evaluates image quality without cropping (Sec. 4.1, Lines 362-364), which is unusual for comparing SD-based SR methods, as they are sensitive to input resolution. I suggest evaluating the methods on the pre-cropped test dataset from StableSR [1] (https://huggingface.co/datasets/Iceclear/StableSR-TestSets), which has a fixed resolution with $512\\times512$ avoiding random crop and non-reproducible results. This test dataset is widely used in various SD-based SR methods, ensuring a more standardized and fair comparison while addressing the authors' concerns.\n\n[1] Wang, Jianyi, et al. \"Exploiting diffusion prior for real-world image super-resolution.\" International Journal of Computer Vision (2024): 1-21.\n\n3. The idea of NAD is similar to UFOGen [2] and LADD [3]. Relevant references and comparisons should be provided.\n\n[2] Xu, Yanwu, et al. \"Ufogen: You forward once large scale text-to-image generation via diffusion gans.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n\n[3] Sauer, Axel, et al. \"Fast high-resolution image synthesis with latent adversarial diffusion distillation.\" arXiv preprint arXiv:2403.12015 (2024)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "N/A" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. While NAD operates on noisy latents domain, an alternative approach would involve operating on decoded images. The reviewer acknowledges that the VAE decoder has a large parameter count, yet it would be insightful to see experimental results in the image domain.\n\n2. As Weakness 4, could the authors provide details about the collected dataset, specifically regarding its scale, resolution, and diversity?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Quantitative and qualitative analyses clearly demonstrate the effectiveness of the proposed method. Specifically, Figure 3 illustrates how DFOSD successfully aligns mid-level features with real image distributions. DFOSD achieves significant improvements in both distortion-based metrics (PSNR and SSIM) and perceptual metrics, which is interesting. Additionally, computational costs are significantly reduced, as shown in Table 3." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces DFOSD, a one-step diffusion model for real-world image super-resolution that bypasses multi-step diffusion processes and teacher models, reducing training and inference time. It integrates a noise-aware discriminator (NAD) within an adversarial framework to boost perceptual SR quality and employs an EA-DISTS loss to further enhance perceptual performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The relationship between the proposed NAD and EA-DISTS remains somewhat unclear. Both components aim to enhance perceptual performance, but it would be beneficial for the reviewer if their complementary relationship, if any, were explicitly clarified.\n\n2. Although Table 5 provides ablation studies on different loss functions, other perceptual losses should be included for a more comprehensive comparison. The table currently highlights the superiority of DISTS over LPIPS, but this might be due to the larger parameters used in DISTS. It would be useful to include additional perceptual losses, such as NIQE, MUSIQ, ManiQA, and ClipIQA, in both their original and EA-enhanced versions.\n\n3. What distinguishes NAD from *? What specific advantages does NAD offer over these approaches?\n\n*A. Sauer, Adversarial diffusion distillation\n*A. Sauer, Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation\n\n4. Since this paper follows the Real-ESRGAN degradation pipeline, it can use any high-quality images for training, as shown in Table 4. However, as this is not a unique contribution of the paper, it would be helpful, if any, to include detailed information on \"our dataset.\"" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024distillationfree,\ntitle={Distillation-Free One-Step Diffusion for Real-World Image Super-Resolution},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2ogxyVlHmi},\nnote={under review}\n}" }, "abstract": { "value": "Diffusion models have been achieving excellent performance for real-world image super-resolution (Real-ISR) with considerable computational costs. Current approaches are trying to derive one-step diffusion models from multi-step counterparts through knowledge distillation. However, these methods incur substantial training costs and may constrain the performance of the student model by the teacher's limitations. To tackle these issues, we propose DFOSD, a Distillation-Free One-Step Diffusion model. Specifically, we propose a noise-aware discriminator (NAD) to participate in adversarial training, further enhancing the authenticity of the generated content. Additionally, we improve the perceptual loss with edge-aware DISTS (EA-DISTS) to enhance the model's ability to generate fine details. Our experiments demonstrate that, compared with previous diffusion-based methods requiring dozens or even hundreds of steps, our DFOSD achieves comparable or even superior results in both objective metrics and subjective evaluations. Our DFOSD also abtains higher performance and efficiency compared with other one-step diffusion methods. We will release code and models." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "One-Step Diffusion", "Image Super-Resolution", "Distillation-Free", "Diffusion Models" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/c61ee9811492740d26c79cc3f8a24400782ed2cf.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/fd901f94e9844c76224f2236ec3bc5619ed5f3b3.pdf" }, "title": { "value": "Distillation-Free One-Step Diffusion for Real-World Image Super-Resolution" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "- Is convolution kernel $H$ trainable? What is the extra training cost of this design in diffusion forward process?\n- How does the convolution kernel capture spatio-temporal correlations? In my opinion, kernel $H$ seems to be able to capture only **temporal** pattern correlations within a series, but the author claims that STDM captures **spatial** correlations (for example, the contribution section). The method cannot capture spatio-temporal correlations of multivariate series anyway, I don't understand why the author named it **Spatio-Temporal** diffusion model." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Novel research perspective. As far as the reviewer is aware, this is the first paper to improve the performance on time series analysis tasks by redesigning the diffusion forward process.\n- The proposed method is flexible and extensible, and can be seamlessly integrated into the time series diffusion model to improve performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes Spatio-Temporal Diffusion Model (STDM) which redesigns the diffusion forward process for capturing correlations in time series data, and can be seamlessly integrated into current diffusion models to improve their performance in time series analysis tasks. Experiments explore the performence of STDM in time series anomaly detection and forecasting tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Motivation is not clear and does not tell a coherent story. I do not understand the motivation and significance of paying attention to the temporal patterns in the diffusion forward process of adding noise. It appears that the temporal correlations introduced by the noise in the forward process may enable the model to effectively consider and learn these correlations for denoising in the reverse process. However, the writing of the paper does not clearly explain this.\n- In Section 3, the author mentioned that \"our methodology innovatively manipulates the forward process. This adjustment facilitates faster convergence during training and enhances robustness during inference\". Nevertheless, the mechanism by which STDM accelerates training and improves inference robustness are not sufficiently explained, and both theoretical analysis and lacks empirical evidence to support this assertion.\n- The experiment results only evaluate the DiffusionAE and TimeGrad models, which are not enough to support the effectivenenss of the proposed method. And there is a notable absence of baselines for time series forecasting and anomaly detection, which limits the comprehensiveness of the evaluation.\n- The writing and charts are extremely crude and rudimentary." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "Please refer to weankess" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The motivatin of producing entirely new sample via diffussion model seem to be interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a Spatio-Temporal Diffusion Models for generating entire samples of time series. Experiments are carried on synthetic and real-world datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Figure 1 is clear to illustrate the strength of STDM in comparison with vanilla DDPM.\n2. What does ``Spatio-Temporal' mean and is related to the proposed approach?\n3. More relevant works are needed to be discussed and compared, including Csdi: Conditional score-based diffusion models for probabilistic time series imputation (NIPS 2021); Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask (KDD 2024)\n4. The contribution and novelty are unclear. What is the superiority of STDM in comparison with current time series SSL methods? \n5. Vital baselines are missed, e.g., SimMTM (NIPS 2023), TS-TCC (TPAMI 2024), TS2Vec (AAAI2022)......\n6. More datasets should be analyzed, e.g., ETTh1/h2/m1/m2 for time series forecasting, and SMD/SWAT for anomaly detection" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Motivation issue. See weaknesses. Does using convolution-based noise addition/removal versus Gaussian-based noise addition/removal have a substantial impact on sample generation? Can this be theoretically proven?\n \n2. Eq(16). If I understand correctly, $x_0$ should be $x_{k-1}$.\n \n3. Eq(14). As $k \\to \\infty$, will this distribution converge to $N(0, I)$? This is relevant because in the experiments, you directly sample $x_K \\sim N(0, I)$.\n \n4. The experiments are too simplistic. I recommend adding more baselines to compare with diffusion models that use different noise addition processes." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Overall, the writing is fluent and easy to follow. The key details are well-explained. The paper replaces the traditional linear transformation in the noise addition process of diffusion models with convolution operations, which, to my knowledge, has not been seen in other work." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work introduce a novel approach to enhance denoising diffusion models for time series tasks, addressing the challenge of conditioning for accurate reconstruction and sampling based on past time steps. Unlike existing methods, STDM guides the diffusion model's forward process by leveraging the high correlation between neighboring time steps. This is achieved through a diffusion step-dependent convolutional kernel and correlated noise to capture spatial relations and refine the degradation of inputs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The motivation of the paper is not very clear. From the perspective of guided diffusion, the conditioning approach used here doesn’t seem different from existing works. In my view, the main contribution of this paper lies in the use of convolution operations in the noise addition process, which introduces a smoothing effect on the signal distinct from traditional diffusion models. However, this smoothing approach doesn’t appear particularly meaningful, as in diffusion models we generally don’t focus much on the intermediate states in the noise/denoising process but rather only on the final generated samples. Additionally, the experiments are weak, as the paper only compares against the original DDPM and overlooks recent work from the past few years." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- Could author provide a step-by-step derivation of equation (16), highlighting how it differs from the traditional diffusion process derivation?\n- Could author provide a clear definition of $c$, regarding each of the evaluated tasks?\n- How is the proposed method applied on both autoregressive and non-autoregressive generation process? Particularly, how it works with TimeGrad?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The idea of incorporating explicit capture of temporal patterns within the time series during forward process is inspiring. The step-dependent convolutional operator for executing the forward process is novel and reasonable." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The author proposed Spatio-Temporal Diffusion Models (STDM), introducing a new forward process for time series diffusion models. The new forward process tries to use step-dependent convolutional kernels for capturing spatial relations and a combined, correlated noise to degenerate the input. The method can be integrated seamlessly into existing time series models like DiffuisonAE and TimeGrad, replacing the original forward process. Experiment results show the effectiveness of the proposed method on two tasks: time series anomaly detection and forecasting, with one baseline model examined for each task." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The eq. (16) needs to be validated, since the forward process is modified, the differences in the derivation should be noted. \n- The method section seems incomplete, for example, the definition of $c$ is not clearly stated. \n- The experiments are only on one baseline method for each task, which seems not adequate. The content of Table 2 is not as described in the caption (MG-TSD is mentioned in caption but not shown in table content).\n- In TimeGrad, the multi-variate time series are generated autoregressively, which seems to contradict with the proposed method where the $x^0$ denotes a multi-step series. It's not clear to me how the convolution kernel is applied on cross-sectional data (containing only one time step). Please correct me if I misunderstood some steps here." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "A novel approach for manipulating the forward process of time series diffusion models to benefit from temporal correlations" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024stdm,\ntitle={{STDM}: Spatio-Temporal Diffusion Models for Time Series Analysis},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2orBSi7pvi},\nnote={under review}\n}" }, "abstract": { "value": "Denoising diffusion models have emerged as a formidable method, consistently surpassing previous state-of-the-art benchmarks. However, a notable challenge in time series-related tasks like anomaly detection and forecasting is the conditioning for models to reconstruct inputs accurately or generate samples based on past time steps rather than producing entirely new samples. To address this, we introduce a novel technique that enhances the sampling capabilities of denoising diffusion models for time series analysis, namely Spatio-Temporal Diffusion Models (STDM). While recent methods fall short of mapping contextual neighborhood dependencies directly into the sampling of a noisy sample, we focus on guiding the forward process of the diffusion model. The degeneration of a sample is based on the idea that values of neighboring time steps are highly correlated. We benefit from this assumption by presenting a diffusion step-dependent convolutional kernel to capture spatial relations and a combined, correlated noise to degenerate the input. Our method can be integrated seamlessly into various existing time series diffusion models. We compare the results of anomaly detection and forecasting when using the traditional and our novel forward process. In our experiments on synthetic and real-world datasets, we show that an adaption of the forward process can be beneficial, as our approach outperforms diffusion models with the ordinary forward process in task-specific metrics, underscoring the efficacy of our strategy in enhancing time series analysis through advanced diffusion techniques." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Diffusion Models", "Time Series Analysis", "Anomaly Detection", "Forecasting" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/8cc3958ec00a76d15fad5fe9c6fc82625cb639b8.pdf" }, "presentation": null, "primary_area": { "value": "generative models" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "STDM: Spatio-Temporal Diffusion Models for Time Series Analysis" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "See weakness." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "(1) The paper is well-structured.\n\n(2) The research topic is significant." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the challenge of enhancing adversarial transferability in deep neural networks (DNNs) by proposing new strategies to avoid local optima during the generation of adversarial examples." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "(1) The novelty is limited. I believe that the essence of the proposed AGI and dual examples (DE) are equivalent to reducing the variance of attack directions, since RGI and DE accumulated/averaged multiple attack directions for each perturbation updating. While accumulating multiple attack directions for stabling each perturbation updating has been proposed in [1][2].\n\n(2) Insufficient Evaluation: The evaluations presented are not robust enough. Given the similarity of the proposed approach to methods in [1][2], it is crucial to include these as baseline comparisons. Moreover, widely recognized transferable attacks such as DIM [3] and TIM [4] should also be included as baselines \n\n(3) The attack success rates reported in Table 3 against defense methods like NRP, RS, HGD, and AT are notably low. In contrast, prior methods like DIM and TIM have achieved higher success rates against these defenses, raising concerns about the fairness and validity of the evaluation.\n\n(4) Since AGI and DE introduce additional steps in generating perturbations, it is unfair to compare the proposed methods and baselines with differing numbers of optimization steps.\n\n\n(5) typos and format errors:\n (1) In abstraction, line 4, \"samplesoften\" (2) in Section 2.2, the reference format is not correct. \n\n\n[1] Wu, Lei, Zhanxing Zhu, and Cheng Tai. \"Understanding and enhancing the transferability of adversarial examples.\" arXiv preprint arXiv:1802.09707 (2018).\n\n[2] Huang, Tianjin, et al. \"Direction-aggregated attack for transferable adversarial examples.\" ACM Journal on Emerging Technologies in Computing Systems (JETC) 18.3 (2022): 1-22.\n\n[3] Xie, Cihang, et al. \"Improving transferability of adversarial examples with input diversity.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.\n\n[4] Dong, Yinpeng, et al. \"Evading defenses to transferable adversarial examples by translation-invariant attacks.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "N/A" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "see weaknesses" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "**1.** This paper has well orgnized visualization, which effectively helps theoretical derivation. For example, Figure 2 clearly explains the different paths of the FGSM during the iterative process.\n\n**2.** The novel method achieves SOTA results in the major experiments, which aligns with those inferences." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduced new optimization strategies for adversarial attacks called Randomized Global Initialization and Dual Example. These methods trade-off computational cost for improved transferability by exploring more of the loss landscape. The authors demonstrated through extensive experiments that Randomized Global Initialization and Dual Example significantly boost the performance of gradient-based attack methods by leveraging broader optimization paths." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**1.** The authors claim that their methods enhances the adversarial transferability of attacks, but does not conduct enough evaluation under defences to prove this.\nFor example, some novel adversarial defence methods claim that they can defend the attackers by reducing adversarial transferability [1,2,3,4]. If these strong adversarial defence algorithms could be used as a benchmark and given the success rate of the attack, it would better demonstrate the validity of the advantage in adversarial transferability.\n\n**2.** The authors don't seem to mention the limitations of their paper.\n\n[1] G. Carbone et al., “Robustness and interpretability of neural networks’ predictions under adversarial attacks,” 2023.\n\n[2] Y. Ma, M. Dong, and C. Xu, “Adversarial robustness through random weight sampling,” in Advances in Neural Information Processing Systems, A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., vol. 36. Curran Associates, Inc., 2023, pp. 37 657–37 669.\n\n[3] M. Dong, X. Chen, Y. Wang, and C. Xu, “Random normalization aggregation for adversarial defense,” Advances in Neural Information Processing Systems, vol. 35, pp. 33 676–33 688, 2022.\n\n[4] B. Li, C. Chen, W. Wang, and L. Carin, “Certified adversarial robustness with additive noise,” Advances in neural information processing systems, vol. 32, 2019." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please refer to the **Weakness section**. I might raise my score if the authors address my concerns, especially regarding the computational overhead of the algorithm." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. It is necessary to dive into transferable attacks to discover hidden defects of neural networks, especially for realistic black-box environments.\n\n2. The authors conducted extensive experiments, reporting performance metrics across a diverse set of models (ResNet-18, DenseNet-121, ViT, etc.) and testing both single-model and ensemble settings. The results consistently show improvement in attack success rates, particularly for transformer-based models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces two key strategies—randomized global initialization (RGI) and dual example generation (DE)—which leverage multiple optimization trajectories to explore the loss landscape more comprehensively. This is a novel addition to adversarial attack literature, aiming to improve transferability by avoiding local optima." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Main concerns:**\n\n**1. Concerns about innovation:**\n\nThe generation method of Dual Examples is still unclear. In line 293, the author claims to use random Gaussian noise to generate N initialization samples. Are the sampling methods for random initialization of momentum and dual samples consistent? If so, there may be some overlap in the sampling methods. The random perturbation sampling in momentum initialization (such as randomly initializing multiple perturbations) is basically consistent with the generation of Dual Examples (sampling multiple trajectories from the neighborhood), both of which are sampled in the neighborhood and optimized on their own independent trajectories. This means that Dual Examples actually overlaps with the strategy of momentum initialization, does not really provide new information or optimization paths, and only increases the complexity of the calculation. Could I ask the authors to provide a detailed comparison of the sampling methods used for random initialization of momentum and dual samples?\n\nIn addition, since there are already numerous works **[1] [2] [3]** that combine the gradient information of neighborhood samples to improve transferability, could I think that the core of this paper is essentially combine neighborhood exploration (through random initialization and Dual Examples) with the pre-convergence momentum strategy of GIMI-FGSM? The pre-convergence momentum strategy has been reflected in GIMI-FGSM, and more gradient information is introduced by neighborhood increasing exploration (random initialization and Dual Examples) to calculate the average momentum, mainly by sampling multiple examples in the neighborhood. Could I ask the authors to provide a more detailed comparison of their method with existing works, particularly focusing on how their approach differs from or improves upon the combination of neighborhood exploration and pre-convergence momentum strategies?\n\n**2. Randomized Initialization Without Sufficient Parameter Analysis:**\n\nThe paper proposes randomized global initialization but does not provide a systematic study on how different levels of randomness affect convergence and transferability. Specifically, there is no ablation to explore the sensitivity of RGI to the number of random initializations or perturbation magnitude.\n\nRGI uses a predefined number of samples, yet the impact of this parameter **N** remains unclear. Testing different sample sizes or introducing an analysis of the trade-offs between computation cost and performance gain would make the method more practical and understandable.\n\n**3. Vagueness on Empirical Validation:**\n\nWhile the experimental results are promising, the paper’s reliance on empirical data without deeper technical analysis limits the work’s robustness. For instance, t-SNE visualizations show trajectories across random initializations but fail to address how these trajectories relate to transferable gradient directions in high-dimensional space. \n\nThe contribution of Figure 2 is ambiguous. In lines 214-215, the author says \"running GIMI-FGSM from different random starting points often causes adversarial examples to converge to distinct local optima\", but Figure 2 is only a visualization of adversarial sample updates and does not reflect the concept of \"local optimum\". In addition, the author claims in lines 197-198 that \"even with the same step size and number of optimization steps, each attack pushes the adversarial example to different distances from the benign sample.\" Obviously, when the input perturbations are inconsistent, the update directions of the adversarial samples generated by random initialization are different. This phenomenon does not explain the contribution of random initialization to transferability. I suggest modifying Figure 2 to more clearly reflect the motivation.\n\n**4. Computational overhead of multiple trajectories:**\n\nThe core method of this paper relies on multi-trajectory optimization of adversarial examples, including random initialization and Dual Examples, which means that each update requires separately calculating gradients on multiple trajectories. This process significantly increases the computational cost because each trajectory needs to be forward and backward propagated independently, and then the gradient information of different trajectories is integrated for update. This multiple optimization trajectories increase the demand for computing resources and memory to a certain extent. Especially on large-scale models or datasets (such as ImageNet), such consumption may not be negligible. Comparing the inference time of the proposed method with other baselines can effectively evaluate the efficiency of the algorithm.\n\n**References:**\n\n**[1]** Wang, X., & He, K. (2021). Enhancing the transferability of adversarial attacks through variance tuning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1924-1933).\n\n**[2]** Zhu, H., Ren, Y., Sui, X., Yang, L., & Jiang, W. (2023). Boosting adversarial transferability via gradient relevance attack. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4741-4750).\n\n**[3]** Wang, X., Jin, Z., Zhu, Z., Zhang, J., & Chen, H. (2024, October). Improving Adversarial Transferability via Frequency-Guided Sample Relevance Attack. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 2410-2419).\n\n\n**Minor concerns:**\n\n**1. Ambiguity of pseudocode parameters：**\n\nThe value of the **T'** parameter in line 5 of the pseudocode is not specified. Since its function is similar to that of the parameter **P** in GIMI-FGSM, can it be assumed that it is set to 5 with reference to the parameter selection of GIMI-FGSM? Could I ask the authors to clarify the value of **T'** and explain its relationship to the **P** parameter in GIMI-FGSM?\n\n**2. Possible typo errors in pseudocode:** \n\nIn line 16 of the pseudocode, should **$\\frac{1}{N} \\sum_{n=1}^{N}$** be **$\\frac{1}{K} \\sum_{k=1}^{K}$**? I'm not sure. **$g_{k,t}$** is based on the gradient of **K** Dual Examples, so **1/K** should be used instead of **1/N** when averaging **$g_{k,t}$** (here **N** is the number of samples used to calculate the randomly initialized momentum, and it has ended the loop in line 9).\n\n**3.Reproducibility Concerns:**\n\nGiven the complexity of the proposed strategies and the lack of specific initialization parameters, reproducibility may be challenging for future researchers. If possible, open-sourcing the code would help improve transparency, allowing the community to validate and build upon the results." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1.\tThe introduction of randomized global initialization and dual samples strategies increases the computational overhead. Does the paper quantitatively evaluate the time complexity and computational resource requirements of these strategies? How computationally efficient is this approach in practical applications?\n2.\tThe method is sensitive to some hyperparameters in different models; have the authors evaluated the optimal values for these parameters on different models? How are these parameters chosen in practical applications? \n3.\tThe paper primarily compares its method with gradient-based approaches but does not address non-gradient-based adversarial attack methods. What are the advantages and disadvantages of this method compared to non-gradient-based approaches?\n4.\tIs there an issue with adversarial example stability due to certain random initializations when using randomized global initialization? Has the author evaluated the variance in attack success rates across different random initializations?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "First, this paper provides a novel approach with meaningful technical contributions and well-supported experimental validations. The introduction of Randomized Global Initialization (RGI) and Dual Examples demonstrates a unique perspective in enhancing adversarial transferability, which could inspire further research in adversarial robustness. Additionally, the paper is well-organized and readable, with detailed descriptions that make complex technical methods accessible. Extensive experimental results on multiple models and datasets, including both CNNs and vision transformers, reinforce the method’s general applicability and strengths in adversarial attack scenarios. The main technical contributions of this paper include the following:\n1.\tThe RGI technique formalizes an approach to initialize adversarial examples across multiple random perturbations, capturing a more representative global momentum for better generalization and reduced local optima entrapment.\n2.\tThe Dual Example Strategy enhances the transferability of adversarial examples by generating parallel trajectories, effectively exploring a larger portion of the loss landscape. This broad approach ensures more robust adversarial optimization across different models. \n3.\tThe proposed RGI and Dual Examples are seamlessly integrated with existing gradient-based methods, highlighting the flexibility and adaptability of the proposed approach across various adversarial attack frameworks.\n4.\tExtensive experiments on the ImageNet-1K dataset demonstrate that the method outperforms other adversarial transfer techniques. The paper provides theoretical insights that underscore the importance of initialization and trajectory exploration in adversarial attacks, contributing to the broader understanding of optimization in high-dimensional spaces." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents an innovative approach to enhance adversarial transferability by introducing two primary strategies: Randomized Global Initialization (RGI) and Dual Examples. The RGI strategy leverages multiple random perturbations around an initial sample to create a more representative global momentum, thus broadening the optimization landscape and reducing the likelihood of adversarial samples being trapped in local optima. Meanwhile, the Dual Examples strategy generates parallel trajectories for adversarial optimization, effectively exploring a larger portion of the loss landscape and further enhancing transferability. Experimental results on the ImageNet-1K dataset demonstrate that this approach significantly improves attack success rates across various models, including CNNs and vision transformers, underscoring the proposed method's efficacy in increasing adversarial transferability." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe Randomized Global Initialization and Dual Example strategies introduce significant computational requirements, especially when optimizing multiple trajectories simultaneously. This could limit the method's practicality in resource-constrained environments. \n2.\tThe approach relies on several hyperparameters (e.g., number of random initializations, step size sequence), which may require fine-tuning for different models. This sensitivity could hinder straightforward application and scalability across diverse model architectures.\n3.\tThis article may lack a theoretical proof for the validity of the global initialization. In addition, there is a lack of experimental proof of the optimal settings for the number of samples to compute the global momentum and dual examples." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024enhancing,\ntitle={Enhancing Adversarial Transferability Through Exploiting Multiple Randomized Trajectories for Better Global Guidance},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2ozEpaU02q},\nnote={under review}\n}" }, "abstract": { "value": "Deep neural networks are well-known for their vulnerability to adversarial examples, particularly demonstrating poor performance in white-box attack settings. However, most white-box attack methods heavily depend on the target model and often get trapped in local optima, leading to limited adversarial transferability. Techniques such as momentum, variance reduction, and gradient penalty mitigate overfitting by combining historical information with local regions around adversarial examples, but exploration of the global loss landscape remains constrained, hindering further performance improvements.\n\nIn this work, we find that initialization influences the optimization of adversarial examples, often guiding them toward multiple local optima, providing an opportunity to explore the loss landscape more effectively. Based on this insight, we propose two strategies: randomized global initialization and dual examples. These strategies utilize multiple trajectories from benign samples to capture global optimization directions, enhancing adversarial transferability. Our approach integrates seamlessly with existing adversarial attack methods and significantly improves transferability, as demonstrated by empirical evaluations on the standard ImageNet dataset." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "adversarial transferability" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/03368974831e5a9913d88a74dc94fe2b60b5926d.pdf" }, "presentation": null, "primary_area": { "value": "alignment, fairness, safety, privacy, and societal considerations" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Enhancing Adversarial Transferability Through Exploiting Multiple Randomized Trajectories for Better Global Guidance" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "1. What does $c_i$ represent in Equations 5 and 6?\n2. For zero-shot anomaly detection, can the transformed image features still match the text features effectively?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The authors explore a valuable research topic that contributes to the current body of knowledge—how to adjust decision boundaries using language to enhance CLIP’s anomaly detection performance. \n- The proposed method stands out due to its training-free nature, which provides flexibility in application across various tasks with limited data." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a feature transformation method aimed at focusing on specific image attributes guided by language. The approach, termed Language-Assisted Feature Transformation (LAFT), leverages the shared embedding space of vision-language models (specifically CLIP) to modify image features according to user-defined concepts expressed in natural language, enabling enhanced anomaly detection capabilities without additional training." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The paper uses the vector difference between two textual descriptions to represent a single attribute and maps this attribute directly to image feature transformation. However, this simplification raises at least three issues:\n - The properties of objects cannot be adequately represented by the difference between two concepts.\n - Real-world attributes are often complex and may involve different colors or textures across various parts of an object.\n - The text embedding space and the image embedding space in CLIP are not perfectly aligned; therefore, vectors derived from the text space may not be directly applicable to the image space.\n\n- To validate the effectiveness of feature transformation, using a CLIP-based classification task would be more suitable than anomaly detection.\n\n- The paper lacks results on anomaly localization, which is crucial for industrial applications.\n\n- The language throughout the paper could be clearer. It is recommended to refer to previous works using proper method names and provide concise descriptions of these methods.\n\n- The axis labels in Figure 3 are inconsistent. How were the attributes 'Number' and 'Color' derived?\n\n- The dataset chosen for experiments, SEMANTIC ANOMALY DETECTION, focuses on distinguishing simple concepts. Why not test the method on widely recognized OOD datasets such as ImageNet-1k and OpenOOD? Industrial anomaly detection would benefit from validation on datasets like VisA and Real-IAD as well.\n\n- The comparison methods included are relatively weak. Why not compare with more recent OOD detection approaches such as NegLabel [1] and ClipN [2]?\n---\n- \\[1] X. Jiang, F. Liu, Z. Fang, H. Chen, T. Liu, F. Zheng, and B. Han, “Negative label guided OOD detection with pretrained vision-language models,” in The Twelfth International Conference on Learning Representations, 2024.\n- \\[2] Hualiang Wang, Yi Li, Huifeng Yao, and Xiaomeng Li. ClipN for zero-shot OOD detection: Teaching CLIP to say no. ICCV, 2023.\n---\nIf the author can address my concerns, I will consider increasing the score." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. In Table 8, the header refers to \"bird,\" which is inconsistent with the title of the Colored MNIST dataset mentioned (maybe a typo). Could the authors clarify this discrepancy?\n2. What are the sizes of the training sets for each dataset used in the experiments? Given that these samples serve as candidates for kNN search, how might the number of training samples affect the final performance of the model?\n3. The experimental results on the MVTec AD dataset in Table 3 suggest that InCTRL might outperform WinCLIP+LAFT when considering deviation, especially when the number of shots exceeds 2. Could the authors provide detailed experimental results for each of the five different reference sample sets?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. LAFT bridges a gap in anomaly detection by allowing users to express preferences using natural language, providing more control over what is considered \"normal.\"\n2. Unlike other feature transformation models, LAFT does not require additional training, making it efficient for settings with scarce data.\n3. The experimental results demonstrate that LAFT outperforms state-of-the-art methods, particularly in semantic anomaly detection tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces Language-Assisted Feature Transformation (LAFT), a novel framework that leverages vision-language models (like CLIP) to enhance anomaly detection. Traditional anomaly detection methods often struggle to capture user-defined nuances of normality, particularly when attributes are entangled or datasets are incomplete. LAFT tackles this by enabling feature transformations guided by natural language prompts. These prompts align visual features with user intent by projecting image features onto specific concept subspaces within a shared embedding space. The paper also proposes LAFT AD, a k-nearest-neighbor (kNN)-based method combining LAFT with anomaly detection, and extends this work into WinCLIP+LAFT, designed for industrial applications. The effectiveness of LAFT is demonstrated across datasets like Colored MNIST, Waterbirds, CelebA, and MVTec AD, showing superior performance in both semantic and industrial anomaly detection." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While LAFT demonstrates significant improvements in controlled environments, such as the Colored MNIST dataset, its performance gains appear less pronounced when applied to complex real-world datasets. This discrepancy suggests that the model may struggle to maintain robustness across multiple intricate attributes, highlighting the need for further refinement in handling multi-attribute scenarios.\n2. The experimental setup lacks comprehensive comparisons, particularly between language-assisted and vision-assisted approaches. For instance, incorporating image guidance by utilizing related reference normal images (e.g., normal digits in various colors) or color-augmentation for kNN baseline could provide valuable insights. A thorough examination of both language-based and vision-based assistance would strengthen the evaluation of LAFT's efficacy.\n3. The impact of the number of PCA components, which is the sole hyperparameter in LAFT, is not adequately investigated. Given that this parameter influences the model's performance, it is crucial to explore its effect across different datasets. Specifically, an analysis of whether a larger number of components may be beneficial for more complex datasets would provide valuable insights into optimizing the model’s performance." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please address the points raised in the Weakness section. Also:\n\n1. What is the purpose of including Aux. prompts? \n\n2. How does different CLIP architecture and also different VLMs affect performance?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The methodology is interesting and a solid contribution to this direction of research in vision-language modelling for anomaly detection.\n\nThe results appear to be promising in the experiments presented, although a wider range of experimental setups would be more convincing (see weakness) \n\nThe ablation study is comprehensive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a feature transformation methodology using concept axes, which are the principal components of the difference vectors between text embeddings of prompts specially designed to ignore nuisance attributes/highlight important attributes for anomaly detection." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Figure 1 is not particularly intuitive or clear, and it is not explained in the text. \n\n2. As the exact formulation of prompts is absolutely critical for this methodology, it should have more dedicated explanation in the main text of the paper, not relegated almost entirely to the appendix. \n\n3. There are not many baselines, and it would have been more convincing if you compare more baselines with and without LAFT transformations. \n\n4. The range of experiments presented are quite restricted. For example with Coloured MNIST, it appears that only one number-colour combination as the normal set was tried. It would be more proper to conduct multiple experiments with different combinations of attributes and show the average result. The same can be said for the other datasets." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See the weakness." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper is with clear motivation.\n2. This paper is well-organized and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "On the basis of existing anomaly detection methods based on visual language alignment, this paper proposes using task related languages for task oriented feature information screening and transformation to improve the model's anomaly detection capability. The experiment was conducted on multiple datasets and demonstrated better performance compared with existing methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The criteria for selecting text prompts are ambiguous. Some datasets utilize the category names of the samples, while others employ diverse descriptions. These approaches rest on the critical assumption that anomalies are distinctly defined, exemplified by MNIST, where anomalies arise from differences in numerals rather than variations in handwriting styles or colors. Should the actual anomalies diverge from these presuppositions, might the proposed model's performance diminish relative to methods devoid of textual guidance? In other words, could the model forfeit its capacity to detect all possible anomalies?\n\n2. In the MVTec dataset experiment, the author opted not to employ the concise anomaly descriptions provided by the dataset itself for text prompts, instead relying solely on item categories, mirroring the approach of WinCLIP. What rationale informed this decision?\n\n3. The proposed model is an extension of WinCLIP, yet it appears to forgo the anomaly segmentation functionality inherent to WinCLIP. Is this omission attributable to certain design elements that potentially diminish the model's anomaly localization capabilities?\n\n4. Experiments have been conducted on synthetic datasets like MNIST and CelebA by altering the original datasets. While I acknowledge the challenge of selecting appropriate text prompts for real-world datasets such as MVTec, the author should endeavor to incorporate more authentic datasets into their study, such as the VisA dataset utilized in WinCLIP or the medical AD benchmark employed in MVFA [a].\n\n[a] Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images. CVPR 2024." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We present Language-Assisted Feature Transformation (LAFT) to guide normality boundaries in image anomaly detection using natural language." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024languageassisted,\ntitle={Language-Assisted Feature Transformation for Anomaly Detection},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2p03KljxE9},\nnote={under review}\n}" }, "abstract": { "value": "This paper introduces LAFT, a novel feature transformation method designed to incorporate user knowledge and preferences into anomaly detection using natural language. Accurately modeling the boundary of normality is crucial for distinguishing abnormal data, but this is often challenging due to limited data or the presence of nuisance attributes. While unsupervised methods that rely solely on data without user guidance are common, they may fail to detect anomalies of specific interest. To address this limitation, we propose Language-Assisted Feature Transformation (LAFT), which leverages the shared image-text embedding space of vision-language models to transform visual features according to user-defined requirements. Combined with anomaly detection methods, LAFT effectively aligns visual features with user preferences, allowing anomalies of interest to be detected. Extensive experiments on both toy and real-world datasets validate the effectiveness of our method." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "anomaly detection", "feature transformation", "vision-language model", "language guidance" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/603120385c6a245c85cd75ff7e558343c6556c26.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/a5fff50c521dcc95360f3c415581f7517ff8c882.zip" }, "title": { "value": "Language-Assisted Feature Transformation for Anomaly Detection" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Consider a causal model with three variables $(X_1, X_2, X_3)$ with edges $X_1 \\to X_2 \\to X_3$ and $X_1 \\to X_3$. Following the definition of possible parent in Definition A.4, $X_2$, which is an actual parent of $X_3$, is not a possible parent of $X_3$. Is this correct?\n\n2. Given that the output of Algorithm 2 is a partially oriented DAG, is the definition of possible parent set the same as in Definition A.4?\n\n3. Should the faithfulness assumptions be defined on the augmented graph?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The organization of the paper is easy to follow, although some technical parts are not clear (see Weakness 3).\n2. The authors provide detailed simulation results to demonstrate the performance of the proposed algorithm. In particular, the authors present multiple variants of the proposed RCG algorithm with different graph inputs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors provide a method for identifying the root cause based on marginal invariance tests. The proposed method includes two steps: first, recovering the skeleton of the underlying causal model using normal (pre-failure) data, and second, constructing an augmented graph using invariance tests to identify the root cause by computing conditional mutual information. The authors demonstrate that, if the underlying causal model is known, the root cause can be identified using $O(\\log(n))$ marginal invariance tests, where $n$ is the number of observed variables. Additionally, given observational data, the root cause can be recovered using $O(n)$ invariance tests according to the proposed algorithm." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The main theoretical results in Sections 4 and 5 are based on the implicit assumption of atomic intervention (i.e., only one variable is affected by the failure mode). This is a very strong assumption, and existing methods such as RCD do not rely on this assumption. For example, in Figure 5, given that $F$ is not independent of $X_2$, it might be the case that both $X_2$ and $X_3$ are directly affected by $F$.\n\n2. Section 5 lacks a theoretical guarantee of the recovery output, which may make the comparison with RCD unfair. It has been shown in RCD that the true root cause can be uniquely identified given infinite data (without knowing the graph structure or the number of root causes), although it may require an exponential number of invariance tests. The authors claim that only $O(n)$ invariance tests are needed in the RCG algorithm. However, there is no guarantee of recovery accuracy; that is, it is unclear under what conditions the true root cause is the only variable adjacent to $F$, as stated in Lemma 5.3.\n\n3. Some technical details are either missing or provided in the appendix; including them in the main text would improve the presentation. For example, Lemma 5.3 relies on the possible parent set $PossPa(X)$, which is defined in Definition A.4 without explanation. Further, it appears that not all actual parents are possible parents (see Q1 below), which may lead to incorrect theoretical results." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Would you be able to try various other methods besides C-PC for the essential graph search, trying to find the best performers in the literature?\n\nWould you be able to go through the literature more thoroughly and give a more substantive literature review?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "An attempt is made here to give an algorithm for root cause analysis that considers some of the literature. Preliminary results are promising." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Using causal analysis, the authors provide and review methods to determine the root cause(s) of failure for up to 100 nodes." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "This paper needs more work. There are claims throughout that seem like they're not quite thought through. I will list a few, but generally, the paper needs to focus more on comparing the best available methods in the literature and be rewritten with this in mind. That requires a bit more work than was taken up here.\n\nFor example, the reference to Chickering 2004 as evidence that causal search algorithms are slow is a bit forced since there are implementations of Chickering 2002 that are quite fast (e.g., FGES). The Lam et al. 2022 paper referenced is pretty slow for 100 nodes, but a follow-up paper to this in Neurips 2023 is quite fast for 100 nodes and scales to 1000 nodes with near-perfect accuracy. Also, whether a causal search algorithm is slow largely depends on the model class. For the linear Gaussian or multinomial cases, algorithms can be quite fast, but general distributions can become very slow, as general tests or scores need to be employed. The speed and accuracy also depend on the density of the graph. FGES (above) is very accurate for sparse graphs (sparsity = 1 - avg degree / # nodes, so for 100 nodes, average degree 4 might be considered sparse). But for dense graphs, its accuracy falls off quickly. PC (and derivatives) tend to have decent adjacency precision, but adjacency recall can be low, and orientation accuracy can be low. The devil is in the details. So those comments were a little too hand-wavy. For the version of PC you're using, you need to cite accuracy statistics not just for adjacency but also for orientation, as you are making claims about whether ancestral paths exist. This is completely missing from the draft.\n\nAs a general strategy, one should compare one's novel methods to _the best-performing alternative methods in the literature_, not just a few chosen methods one has on hand. As for the methods compared, these don't seem like the best methods that could be devised in the literature, so more work needs to be done to find what those methods might be (or devise them) and compare them. The PC version you're using should be compared to other plausible alternatives, such as the one mentioned above, or to the R method, BIDAG, which is also quite good. Again, for timing results, just give the _actual timing results_ for the various methods and let the reader decide. If the C-PC method turns out to be the winner, this should be evident from one's figures.\n\nIn addition, there are more papers on root cause analysis than are given in the literature review; this could well be expanded.\n\nSome minor comments.\n\n1. The definition of Markov given is for DAGs in particular, not for arbitrary graphs. It doesn't even work for what you're calling \"essential graphs.\"\n\n2. There is a little confusion about soft intervention. If you do a \"soft intervention\" on a variable X, X can still have parents. The case where it cannot have parents is where you have a \"hard intervention,\" in which case you replace its distribution with a parent-free distribution of your choice. This is a terminological problem that can be fixed.\n\nThere is a little confusion between the lemmas given on p. 4 and the algorithms later in the paper. On p. 4, you claim that \"The following two lemmas use the fact that there is only one single root cause,\" leading me to think that you are only considering the case where there is a single root cause of failure in the system. It's a strong assumption, but fair enough. But later, in the algorithms, you say you list the top l root causes. I could not discern any transition between these two ideas.\n\nTypos p. 5 \"Algorithm the only\" ==> \"Algorithm that only\"; \"C avid\" ==> \"C to an avid\"\n\nYou say proofs are to be left to the avid reader, but for a paper for ICML, you should supply the proofs of your claims.\n\nIn Algorithm 2, circle endpoints suddenly appear out of nowhere. What are these? Are you dealing with PAGs here?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "N/A" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "The work certainly has some novel and great insights. However, I am concerned about the (more high-level) novelty here. The related work that identifies the node with a mechanism shift without assuming a graph naturally needs to run this on a potentially exponential number of combinations. Their main claim is also about not needing the graph structure, as this is an obvious way to reduce the required number of independence tests one needs to perform. Running causal discovery on the normal operation period of a system is a logical first step if a method requires a causal graph or, as in your case, to reduce the search space. In that sense, I am concerned about the novelty claim that one needs fewer tests if the graph is given, as this is obvious. I might be missing a crucial part here in the, admittedly, over-simplification of the idea and hope the authors can comment on this.\n\nSome further remarks:\n- The related work focuses on certain types of work in the domain of root cause analysis but lacks discussion about other types of work that utilize a causal graph directly, such as:\n\n\"Identifying patient-specific root causes with the heteroscedastic noise model\" by Strobl et al. \n\"Causal structure-based root cause analysis of outliers\" by Budhathoki et al. \n\"Counterfactual formulation of patient-specific root causes of disease\" by Strobl et al. \n\"Root Cause Analysis of Outliers with Missing Structural Knowledge\" by Okati et al. \n\n- The difference between the complexities mentioned on lines 101 and 103 is not clear, and further clarification would be helpful.\n- In Definition 2.1, the formal definition of the graph is lacking, which you then later use in Assumption 2.3. You could move this to Definition 2.1 already.\n- The notation Z(X, Y ∉ Z) in line 130 is confusing; can you clarify this?\n- As mentioned before, the need to introduce SCMs is unclear as a mechanism shift can also be purely introduced using the Bayesian network formulation.\n- A clear assumption statement that you assume a single root cause is lacking.\n- The faithfulness assumption is important for causal discovery via CIs, but that connection could be emphasized more clearly.\n- Applying causal discovery on the 'normal operation' period alone implies the assumption that the causal structure has changed in the anomalous regime. While this is a valid assumption, it is also only made implicitly. In a general setting, anomalous data can even be particularly helpful in identifying cause-effect relationships.\n- The introduction of the notation for having multiple metrics for a node over time does not seem to be used afterward. While I am not very familiar with the C-PC algorithm, it is unclear how one would perform causal discovery in such a setting with high-dimensional nodes and temporal dependencies without employing more time-based causal discovery approaches. Does the data you used in the experiments reflect such data?\n- A drawback of your approach that lacks further discussion is the requirement of a \"sufficiently\" large sample size of the anomalous population. Since you argue that the root cause needs to be identified in a timely manner, this would only work in a system that produces a lot of data. If, for example, the system only produces a metric observation every few minutes, you would not have enough samples. This aspect could be discussed further as the works mentioned in the first point would work on single observations." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "+ Insightful analysis and fair discussion of causal discovery in a large-scale setting\n+ Good introduction to the problem\n+ Extensive additional information in appendix for certain details" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes an approach for identifying the root cause in a system where we observe anomalies. For this, the authors propose to utilize data from the normal operating regime to infer a (partial) causal graph. The graphical information is then used to reduce the number of independence tests to identify the root cause node based on the assumption of a shift in the underlying data generating mechanism. The approach has been evaluated using artificial and real-world data." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- While the related work section has a fair discussion about different works, it also lacks work involving the direct use of graph structure (see Questions section for more details).\n- The proposed method certainly has its novelty, but it seems rather limited as it boils down to the idea of first reconstructing the causal graph using causal discovery and by this, naturally, reducing the search space when running independence tests. The arguments for papers that address the problem without graph knowledge explicitly avoid a causal discovery step.\n- Some definitions (like SCMs) are introduced but then not really needed. A shift in the mechanisms can be defined without this.\n- The formulation of some of the definitions could be improved (e.g., when introducing a causal graph). However, these are minor issues that could be easily fixed in a revision.\n- Some assumptions are not clearly stated and implied. For instance, the assumption that there can only be one root cause.\n\nFor more details, see the Questions section." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. The authors claim that RCD performs an exponentially large number of CI tests, but I'm not sure this is correct for Hierarchical Learning in (Ikram et al., 2022).\n\n2. Lemma 4.1 and 4.2 are both based on the fact there is only one single root cause, but it is possible that there are multiple root causes in real-world scenarios, limiting applicability of this work.\n\n3. There is too much space between references in page 14." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The motivation of this work makes sense. I totally agree that RCA is time-sensitive only after the failure occurs. We can use the time before a failure to learn the causal graph which can help us to reduce the number of tests of conditional independence in the period of RCA.\n\n2. The authors provide extensive experimental results and detailed discussion. In particular, I very appreciate Appendix I." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This research links root cause analysis in network failures to Interactive Graph Search (IGS), establishing a mathematical lower bound for the number of tests needed. With a fully known causal graph, the authors then propose an optimal algorithm that achieves this bound. With a partially known causal graph, they can identify the root-cause with linear number of invariance tests." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. This work relies heavily on previous works. First and foremost, it borrows the idea of modeling a failure as an intervention and transforming RCA into a problem of finding adjacency of the F-NODE from (Ikram et al., 2022). Also, it directly uses the theoretical results in (Shangqi et al., 2023) and C-PC in (Lee et al., 2024). More specifically, (Ikram et al., 2022) has already linked RCA to causal discovery and most causal discovery techniques used in this paper are also proposed by previous works. In my opinion, the major contribution in causal discovery of this paper lies in Lemma 4.1, 4.2, and 5.2. Considering that the authors list \"causal discovery\" as the first keyword, I think their contribution in this aspect is limited.\n\n2. The organization of this paper should be improved. The discussion on related works is spread across many sections. The authors can use a dedicated section to introduce existing techniques used in this paper and the detailed differences between this work and previous works, rather than giving too many details in Sec. 1, 4, 5, which makes it harder for readers to grasp their contributions. Besides, I strongly suggest the authors move Appendix I to the main text.\n\n3. Some minor concerns are detailed in Questions.\n\nIf the authors can address my concerns, I would like to raise my score." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We show how to use the causal graph for identifying failures in software systems." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024root,\ntitle={Root Cause Analysis of Failure with Observational Causal Discovery},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2pEqXce0um},\nnote={under review}\n}" }, "abstract": { "value": "Finding the root cause of failures is a prominent problem in many complex networks. Causal inference provides us with tools to address this problem algorithmically to automate this process and solve it efficiently. The existing methods either use a known causal structure to identify root cause via backtracking the changes, or ignore the causal structure but rely on invariance tests to identify the changing causal mechanisms after the failure. We first establish a connection between root cause analysis and the \\textit{Interactive Graph Search (IGS)} problem. This mapping highlights the importance of causal knowledge: we demonstrate that any algorithm relying solely on marginal invariance tests to identify root causes must perform at least $\\Omega(\\log_{2}(n) + d\\log_{1+d}n)$ many tests, where $n$ represents the number of components and $d$ denotes the maximum out-degree of the graph. We then present an optimal algorithm that achieves this bound by reducing the root cause identification problem as an instance of IGS. Moreover, we show that even if the causal graph is partially known in the form of a Markov equivalence class, we can identify the root-cause with linear number of invariance tests. Our experiments on a production-level application demonstrate that, even in the absence of complete causal information, our approach accurately identifies the root cause of failures." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "causal discovery", "root cause analysis" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/09edf32d7432b49ae89cc030f58002444c89aaa9.pdf" }, "presentation": null, "primary_area": { "value": "causal reasoning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Root Cause Analysis of Failure with Observational Causal Discovery" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]