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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": 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. Why can’t the augmentation be agnostic about what the patch contains, what is the theoretical bottleneck here? What impact would a uniformly random mask have? Could there be a more realistic setting where distribution-agnostic data augmentation could still achieve similar results?\n2. While the theory here follows very closely to that of AllenZhu and Li [2023], it seem to have missed some previous works exploring the effects of augmentation on feature learning process [1,2]. The authors can refer to the designs of augmentations and their corresponding analysis in these papers.\n\n[1] Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning. Zixin Wen, Yuanzhi Li [ICML 2021]\n\n[2] The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning. Zixin Wen, Yuanzhi Li [NeurIPS 2022]" }, "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 provides a new theoretical analysis of the FixMatch method, particularly on multi-view structured data distributions, demonstrating its effectiveness in learning features and its advantages over supervised learning. The characterization of FixMatch's two-stage learning process is insightful, offering a clearer understanding of how the model learns from both supervised and unsupervised data.\n\n2. The authors propose a new semantic-aware augmentation technique that aligns with their theoretical findings, which improved the performance of FixMatch." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper studies the feature learning process of neural networks trained with the FixMatch method, which is a semi-supervised learning method, demonstrating its theoretical advantages on data distributions with a “multi-view” structure. The authors characterize the FixMatch learning process as a two-stage process: initially, the model learns like supervised learning and learns most of the features, followed by a second stage where it learns the missing features through unsupervised learning from augmented data. Based on these theoretical insights, the authors introduce a semantic-aware augmentation in FixMatch to enhance its performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The assumptions regarding data augmentation appear artificial. The augmentation method knows which feature is in each patch and can distinguish between feature and noise patches. The augmentation randomly mask the noise patch and one of the feature, to enable the FixMatch to focus on the unlearned features. Even though such augmentation can be easily achieved in the theoretical setting, it is smarter than what is originally used in FixMatch.\n2. The proposed SA-FixMatch, although is interesting and shares closer connection to the theory, introduces added complexity by using Grad-CAM for augmentation, which can slow down training." }, "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": 3 }, "primary_area": null, "questions": { "value": "1. Do you feel there is more potential to be extracted from the CutOut-SA line of thinking? For example, could doing multiple cutouts on the image to enforce exactly one classifying feature being present in the strong augmentation be a future avenue of improvement? Or did you already try multiple variants of such schemes and found the one you eventually presented in the paper to be the best?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The paper is well-written and gave me the impression that I was able to follow its goal.\n2. The results from FixMatch-SA seem to confirm the pertinence of the analysis, and intuitively I found it made logical sense.\n - Some gains from CutOut-SA are truly impressive, including for recent FixMatch derivatives.\n3. I particularly liked that the paper didn't limit itself to a theoretical analysis but also provided an experimental validation on common SSL benchmarks.\n4. I find the FixMatch-SA method very elegant and effective and appears simple to implement which I consider a quality." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes two contributions:\n1. A theoretical analysis to explain why semi-supervised learning (SSL) techniques such as FixMatch generalize better than classical supervising learning (SL).\n2. A new method FixMatch-SA (semantically aware) which builds on the analysis to further enhance FixMatch.\nThe improved performance of FixMatch serves to experimentally corroborate the theoretical analysis.\n\nI understood the substantiating argument of the theoretical analysis as follows: the correct classification of sample is typically based on multiple features (at least 2). In SL, learning of all features is not necessary to minimize the loss. Meanwhile, in FixMatch, the strong augmentation drops some features and therefore requires the network to learn all the features to minimize the loss. \n\nDisclaimer: the theoretical analysis felt above my skill, mathematically speaking. I tried to follow it to the best of my ability but there could be alternate conjectures which I am not aware of to explain the observed generalization gains." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. My own lack of knowledge on the theoretical side made it hard for me to estimate the originality of the approach. It's not per-se a weakness of the paper but rather a warning that I simply don't know.\n\nTypos (obviously this didn't influence my rating, it's for authors to polish their manuscript)\n- Line 87, wrong citation \"FixMatch (Xie)\" => \"FixMatch (Sohn)\"" }, "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": 4 }, "primary_area": null, "questions": { "value": "Questions were asked in the section above." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The presentation of this work is impressive. The paper is not only easy to read, but the authors do a good job of highlighting their contributions and how it differs from previous works. The writing is clear and concise, and the figures and tables (although there are not that many) are not needlessly overcomplicated.\n- The proposed SA-FixMatch seems like a intuitive improvement to FixMatch, and does show to improve on the performance of FixMatch.\n- The theoretical justification in Section 4 seem to be sound." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "- This paper provides a theoretical analysis, aimed at answering why FixMatch-like algorithms (for Semi-Supervised Learning, or, SSL) generalizes better than supervised learning. \n- The analysis is focused on CNNs (unlike previous comparison works that provide analysis by using linear model assumptions)\n- The paper proposes a improvement to FixMatch, called Semantic-Aware FixMatch (SA-FixMatch). The SA-FixMatch essentially masks out the semantically relevant parts of a high-confidence image sample (the region that is identified by GradCAM) in a CutOut-like fashion." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- My main concern of this paper is the overall motivation. My main question for the authors is: Why do we need to have a good theoretical understanding of why FixMatch generalizes better than Supervised Learning? The following is my thought process: Let's say we have a dataset that is fully labeled. In this case, we would obviously use supervised learning (since we have all labels) to train the model. But now, let's consider the case where only 10% of the data is labeled. Obviously, given that SSL can leverage 90% of the dataset while SL can only leverage 10% (9x the size), we would apply SSL to train the model. We already know that leveraging more data will lead to better performance - so then what is the point of trying to theoretically understand why FixMatch generalizes better then SL, given that SL in this case is using a subset of the data that FixMatch is using? The worst case for SSL is that it performs equally as SL. As shown in the paper, FixMatch learns more semantic features, but that seems a bit obvious, since FixMatch is able to utilize the unlabeled samples, while SL receives no training from these unlabeled samples. Perhaps a fairer (and more interesting) setting would be to compare SSL vs Supervised learning, given the same number of total training samples (where the 'unlabeled' samples of the SSL dataset is labeled for SL). I hope I am not coming across as too offensive with this comment, but I am just trying to understand the significance of such analysis. I hope the authors can convince me otherwise. \n\n- The implications of the analysis is somewhat underwhelming. \n - The proposed SA-Cutout does not feel like a novel contribution, given that there are previous works that use guided data augmentation for other tasks (e.g., \"Crafting Better Contrastive Views for Siamese Representation Learning\" in CVPR 2022). Also, there are some gradient-based masking techniques, such as \"Adversarial Dropout for Supervised and Semi-supervised Learning\" in AAAI 2018 that have very similar motivations as SA-Cutout, and the resulting solution is quite similar as well (masking out highly semantic regions).\n - Are there any other takeaways from this analysis? For example, could this type of analysis be extended to a broader scope?" }, "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": "1. The theory relies on 3-layer ConvNets. However, the experiments obviously hold for a wider range of architectures. Is it possible to extend it to more sophisticated architectures. For example, ConvNets with residual connections, additional layers, ViTs? If so, would it change the results somehow? Can we derive conclusions that certain architectures generalize better with SL compared to other architectures? That could be really exciting!\n\n2. Can you explain in theorem 4 why the margin scales as log(k) (where k is the number of classes). How come we get better classification margin for a more complex task with more classes? \n\n3. In theorem 4 you use $T=poly(k)/\\eta$ to represent the amount of iterations until convergence. What should I expect the degree of the polynomial and its leading coefficient to be? I want to have some concept of how many iterations we need." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The theory presented is compelling. The authors provide a strong argument, without relying on overly strict assumptions, that training a realistic neural network (a 3-layer ConvNet) with FixMatch-type algorithms allows us to (1) fit the training data and (2) generalize well to unseen samples. This stands in contrast to supervised learning, where the model often fails to generalize well to certain types of samples within the distribution.\n\nAdditionally, the authors propose an improved variation of a FixMatch algorithm, demonstrating that their theory not only explains the success of this family of algorithms but also predicts new results." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores the theoretical aspects of why the SSL method FixMatch outperforms supervised learning method in generalization for deep neural networks (DNNs). Previous studies have shown that SSL methods like FixMatch achieve higher test accuracy, but the mechanisms behind this advantage are not obvious. The authors provide theoretical justification for the enhanced generalization of FixMatch for convolutional neural networks. Their analysis reveals that FixMatch captures all relevant discriminative features for each class, whereas SL approaches tend to capture only a random subset of features, an effect attributed to the lottery ticket hypothesis. This framework is shown to extend to other SSL methods similar to FixMatch, such as FlexMatch, FreeMatch, Dash, and SoftMatch. Based on these findings, the authors propose an enhanced version of FixMatch, called Semantic-Aware FixMatch (SA-FixMatch), which is validated experimentally, demonstrating improved generalization." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The main weakness of this paper lies in its technical presentation.\n\nWhile I appreciate that the theoretical framework developed here is complex, making it challenging to present in an accessible way, I believe certain aspects could have been simplified for clarity.\n\nThis could be achieved by following these guidelines:\n1. Use standard notations. For instance, the authors use symbols like $Z_l$ to denote a labeled dataset, whereas $S$ is typically used for sample sets.\n2. Avoid re-using variables. In lines 126-128, for example, the symbol $i$ is used for multiple purposes, such as indexing both patches and classes, which can be confusing.\n3. Simplify complex definitions. Concepts like Definition 1 could be broken down and explained in more detail, with examples illustrating each component. Providing an example of a distribution that meets these conditions would clarify the distinction between single- and multi-view samples and help readers appreciate the significance of the conclusions in lines 284-287.\n\nMinor comment:\nIn the theorems (e.g., Theorem 4), instead of writing \"for any \\((x,y) \\sim D\\) with probability ..., we have ...,\" I would suggest phrasing it as \"with probability ... over the selection of \\((x,y) \\sim D\\), we have ...\". It is just more mathematically accurate and is consistent with the appendix." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024towards,\ntitle={Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=25kAzqzTrz},\nnote={under review}\n}" }, "abstract": { "value": "Semi-supervised learning (SSL), exemplified by FixMatch (Sohn et al., 2020), has shown significant generalization advantages over supervised learning (SL), particularly in the context of deep neural networks (DNNs). However, it is still unclear, from a theoretical standpoint, why FixMatch-like SSL algorithms generalize better than SL on DNNs. In this work, we present the first theoretical justification for the enhanced test accuracy observed in FixMatch-like SSL applied to DNNs by taking convolutional neural networks (CNNs) on classification tasks as an example. Our theoretical analysis reveals that the semantic feature learning processes in FixMatch and SL are rather different. In particular, FixMatch learns all the discriminative features of each semantic class, while SL only randomly captures a subset of features due to the well-known lottery ticket hypothesis. Furthermore, we show that our analysis framework can be applied to other FixMatch-like SSL methods, e.g., FlexMatch, FreeMatch, Dash, and SoftMatch. Inspired by our theoretical analysis, we develop an improved variant of FixMatch, termed Semantic-Aware FixMatch (SA-FixMatch). Experimental results corroborate our theoretical findings and the enhanced generalization capability of SA-FixMatch." }, "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": [ "deep semi-supervised learning", "generalization error", "feature 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/961afa0290a0841924253ce00810f787489068b7.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/8dc5731d46ade159a79cf5558355c1da9c9fbb97.zip" }, "title": { "value": "Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning" }, "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": { "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. Why IFAdapter enables it to be seamlessly applied to various community models. For example, appearance queries are trained , how do the authors ensure that the feature has sufficient generalization capabilities.\n2. Why can appearance-related features be extracted from the bounding box through Fourier and MLP, especially when the model inference cannot obtain image input?\n3. Is the model-free method based on the same backbone as the proposed method? Are all the comparison methods that require training trained on the provided dataset? The VLM used to annotate the proposed dataset is the same as the VLM used in the evaluation metric, which may be unfair to methods that are not trained on the proposed dataset.\n4. VLMs are likely to produce hallucinations. How do the authors ensure that the annotations of the provided dataset are free of hallucinations?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "+ The authors propose an important task, namely Instance Feature Generation. In addition, the authors also provide a benchmark and a verification pipeline\n+ The proposed method seems to achieve good results. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper aims to improve the capability of Text-to-Image diffusion models in generating precise features and positioning multiple instances in images. The proposed IFAdapter enhances feature accuracy by integrating additional appearance tokens and constructing an instance semantic map, ensuring that each instance's features align accurately with its spatial location. The IFAdapter is designed as a flexible, plug-and-play module, allowing enhanced control without retraining." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "-\tCompared with instance diffusion and dense diffusion, this paper shows a small number of objects and does not show the situation where the object is denser.\n-\tThe details of the method are not explained clearly. The details of the dataset construction and the baseline setting are not presented clearly. Ablation experiments lack a basic baseline presentation.\n-\tAuthors should carefully check for errors in the text. For example, a sentence appears twice in succession in Introduction." }, "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": "Is the proposed model able to generate images with some intricate prompts. For example, a blue dog with red legs running on a colorful river, (with dog, legs, river assigned to different boxes). I want to see some limitations of the proposed method, or in other words, I want to know how IFA copes with the semantic issues which may inherit from the base model given out-of-domain prompts and instructions. I would love to revise my rating after further discussion with the authors." }, "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 well written and easy to follow. \n2. The designed approaches efficiently incorporate the external object semantics and layout information into the generation process. The proposed Appearance Tokens aggregate the textual semantic and bbox information with learnable tokens. The proposed Instance Semantic Map accurately reallocates the spatial area of different objects, and solves the semantic fusion and feature leakage problem. \n3. The illustrated visual results are impressive, which shows clear superiority against competing baselines.\n4. The proposed method is compatible with various community models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents Instance Feature Adapter (IFA) for layout-to-image generation. The key insight of IFA is to incorporate additional appearance tokens corresponded to different objects, so as to steer the T2I models to generate images that convey precise layout information and text semantics. To achieve this, IFA first leverages learnable instance tokens to aggregate the textual and bounding box information of the specific objects. To cope with the feature leakage problems, IFA further introduce Instance Semantic Map strategy to reallocate the spatial area for different semantics, so as to alleviate the feature conflicts between different objects during external feature tokens injection process. A new benchmark is proposed, the visual improvement over different baselines is significant. Further, the proposed method is a plug-and-play module, which can be adapted to various community models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The design module is supposed to interface with the text encoders, and both Appearance Tokens and Instance Semantic Map introduce the attention mechanism. Will it be computational costly during the inference process. There should be a detailed discussion.\n2. The size and shape of different objects seem unstable when applying IFA to different community models (Fig. 4), is it caused by the re-weight strategy from Instance Semantic Maps?\n3. The L2I problem is not a novel task, and the main novelty mainly lies in the implementation detail of the layout incorporation strategy, which may not bring significantly inspirations to the 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": 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": "A few suggestions to improve the paper. \n- Please remove point 3 in contributions. Comprehensive experiments are not a contribution but are rather required to support the claims made in the paper. \n- The related works section has not been used correctly in this work according to my opinion. Authors just cite all relevant work but fail to differentiate their work from the literature. Please discuss how IFG is different from prior work and how their method is different from previously proposed methods in the related works section.\n- If authors believe local CLIP score is suboptimal. I would recommend authors show (quantiatively) why VLMs are better than CLIP for this task. Please refrain from introducing a new metric and benchmark unless absolutely necessary.\n- L77-78 is a repetition. \n\nI'm willing to improve my rating if authors address the weakness section satisfactorily." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The perceiver like Resampler design to extract fixed set of appearance tokens is novel and effective. This addresses the problem of only utilizing the EoT token from the text encoders. \n- The gated semantic fusion to address multiple overlapping instances is very useful for layout to image generation methods. \n- The proposed method is simple and is architecture agnostic." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "- This work tackles instance Instance Feature Generation (IFG) task, i.e. train a model that can generate images given a global caption, spatial locations and detailed local captions as conditioning.\n- Authors introduce IFAdapter, a plug-and-play module for improving IFG.\n- The IFAdapter first extracts a fixed number of appearance tokens from a detailed instance caption and its spatial location. Next, a 2D map called Instance Semantic Map (ISM) is constructed using the bounding boxes of instances to aid the adherence of the model to spatial location conditions.\n- IFAdapter is architecture agnostic and can be incorporated into existing open-source text to image models and authors show its effectiveness on the newly introduced IFG Benchmark constructed from the COCO dataset." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Authors claim IFG as their first contribution. But this task has already been introduced in InstanceDiffusion [Wang et. al 2024c]. InstanceDiffusion uses detailed instance captions in addition to the global caption. What is the difference between their setup and IFG?\n- The experimental section needs heavy work to make this work ready for publication. With exponential progress in generative modeling, it is hard to control the experimental settings with other models, especially the training data. But that doesn't mean all other settings can be held constant to properly understand the contributions. InstanceDiffusion and GLIGEN use SD1.5 as their base generation model but authors use SDXL an already powerful base generator. This makes it hard to understand the improvements in Table 1 and 2. I recommend authors report numbers with SD1.5 as the base generator or retrain InstanceDiffusion with SDXL (since their code is available) to properly support their claims.\n- Authors introduce a new benchmark and evaluation metric for this task. Why can't they use the evaluation setup and metrics as InstanceDiffusion? If authors find flaws in InstanceDiffusion's setup, I recommend authors point it out and discuss the advantages of the IFG Benchmark (setup) and IFS Rate (metric). There is no point in creating multiple new benchmarks when existing ones are already rigorous. For IFS Rate authors use Grounding DINO whereas InstanceDiffusion uses YOLO. Please compare with InstanceDiffusion using their exact setup (COCO and LVIS val set and their metrics) to support your claims.\n- Authors claim that the a lightweight network $f$ provides an \"importance\" score for location (x,y) in the ISM construction and use it to compute $D(x,y)$. Please show qualitative or quantitative evidence that the network $f$ infact does what is claimed in the paper. While the idea sounds reasonable, I suspect how $f$ learns to predict the right \"importance\" scores without supervision." }, "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 kindly provide visual comparisons between the ground truth (GT) and the generated images." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. A plug-and-play component that can be integrated with various existing models to enhance layout control capabilities.\n2. The paper also includes a user study, offering valuable insights into the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors aim to tackle the challenge of achieving controllability in generating precise instance features. To do this, they introduce the Instance Feature Adapter (IFAdapter), designed for instance-level positioning and feature representation. Specifically, the IFAdapter employs learnable appearance queries that extract instance-specific feature information from descriptions, creating appearance tokens that complement EoT tokens. Additionally, the IFAdapter constructs a 2D semantic map to link instance features with designated spatial locations, providing enhanced spatial guidance. In areas where multiple instances overlap, a gated semantic fusion mechanism is utilized to mitigate feature confusion.\n\nTo validate their approach, the authors have created a new dataset, referred to as the COCO IFG benchmark. They leverage existing state-of-the-art Vision Language Models (VLMs) for annotation, resulting in a dataset with detailed instance-level descriptions. Experimental results indicate that the proposed plug-and-play component surpasses baseline models in both quantitative and qualitative assessments." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The quality of the proposed dataset has not been evaluated. It appears that all ground truths (GTs) are generated by existing Vision Language Models (VLMs). A human-level quality assessment would be beneficial for greater impact within the community.\n2. The assertion that the proposed component can “seamlessly empower various community models with layout control capabilities without retraining” (l.113) may be misleading. The IFAdapter is fundamentally a training-based method, and the phrase “without retraining” only holds true when applied to spaces closely aligned with COCO IFG, as the IFAdapter does not demonstrate zero-shot capabilities in this paper.\n3. The semantic-instance map does not appear to be novel. Please refer to \"BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion\" (ICCV 2023) and other zero-shot L2I methods for comparison.\n4. The appearance tokens show only minor improvements in Table 3. Additional explanations regarding this observation would be appreciated." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024ifadapter,\ntitle={{IFA}dapter: Instance feature control for grounded Text-to-Image Generation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=25l4SWH2eS},\nnote={under review}\n}" }, "abstract": { "value": "While Text-to-Image (T2I) diffusion models excel at generating visually appealing images of individual instances, they struggle to accurately position and control the features generation of multiple instances. The Layout-to-Image (L2I) task was introduced to address the positioning challenges by incorporating bounding boxes as spatial control signals, but it still falls short in generating precise instance features. In response, we propose the Instance Feature Generation (IFG) task, which aims to ensure both positional accuracy and feature fidelity in generated instances. To address the IFG task, we introduce the Instance Feature Adapter (IFAdapter). The IFAdapter enhances feature depiction by incorporating additional appearance tokens and utilizing an Instance Semantic Map to align instance-level features with spatial locations. The IFAdapter guides the diffusion process in a plug-and-play module, making it adaptable to various community models. For evaluation, we contribute an IFG benchmark and develop a verification pipeline to objectively compare models’ abilities to generate instances with accurate positioning and features. Experimental results demonstrate that IFAdapter outperforms other models in both quantitative and qualitative 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": [ "Generative diffusion models", "Layout to image 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/feb460b82087722d3ac83234da7c328bfc80b3e8.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/734519134f65a9596cab0c41d134d8e3a58e21a3.pdf" }, "title": { "value": "IFAdapter: Instance feature control for grounded Text-to-Image Generation" }, "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": "In the OOD setting, how the model’s performance varies under different guidance settings, especially for extreme or non-physical property values?" }, "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 molecule generation models, allowing multi-property control and self-assessment of generated molecules. It’s well-designed, with detailed experiments showing strong results across different datasets. The writing is clear and structured." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents STGG+, a model for molecule generation with conditional property control. The model also has the ability of self-criticism to select optimal outputs. It achieves high validity and diversity in generated molecules, efficiently handling both typical and extreme properties." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The self-criticism mechanism for filtering generated molecules based on property predictions is a key feature, but there is limited evaluation of its accuracy. Detailed analysis will be necessary.\n\nI am not an expert of this field so I will lower my confidence 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": 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. In SMILES notation, the same molecule can have multiple string representations. Based on my (limited) understanding of STGG, this ambiguity seems present as well. How are the molecules canonicalized?\n2. In Tables 3 and A.10, do all methods reach peak performance only after generating 1 million molecules? Is the search space the same across methods?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. To my knowledge, this manuscript is the first to thoroughly examine STGG for reward conditioning and/or optimization.\n2. The work reflects a substantial effort to assess STGG+'s capabilities. Overall, the approach appears methodologically sound.\n3. Molecular property optimization is an open challenge. Given its competitive performance compared to existing algorithms and its use of a (somewhat) unique molecular representation, I expect this work will attract reasonable interest." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors build upon spanning tree-based graph generation methods to produce valid molecules with desired properties. They enhance the original network architecture by adding property embeddings and incorporate a properties predictor head for joint training. Through the use of classifier-free guidance and conditioning on these properties, the authors demonstrate that STGG+ can generate molecules conditioned on specific properties or with high reward values." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The primary limitation of this paper is that generating 'valid' molecules does not guarantee synthesizability. Many molecules presented in the appendix would be very challenging, if not impossible, to synthesize. Meanwhile, some baseline methods may perform slightly worse on reward but produce molecules that are easier to synthesize, avoiding \"reward hacking.\" A fairer comparison would involve evaluating the reward optimization performance of synthesizable molecules across different algorithms.\n2. While novelty is not an ideal measure of a paper's value, this work is highly empirical, with limited theoretical insight. This puts more emphasis on competitive performance, yet the paper lacks adequate baseline comparisons for reward optimization. Previous work has shown that methods such as GraphGA, LSTM-HC, and Reinvent are effective at maximizing OOD reward, and these baselines should be included in Sections 4.3–4.5 (particularly Section 4.5, which currently lacks any baseline comparison). This is especially relevant as the random guidance approach for OOD generation resembles slightly enhanced random sampling with a reward proxy.\n3. The molecular properties selected for optimization in this study are _very_ simple. For instance, molecular weight can be adjusted by adding or removing atoms, and logP by incorporating ionic groups (which the model does). Optimizing HOMO-LUMO gaps within the QM9 dataset is not useful, as these molecules contain only 9 atoms. These problems are generally considered solved.\n4. Although the work is extensive, certain details are presented inconsistently or lack substantiation. Some claims are unsupported by data (e.g., statements like \"other [configurations] were not beneficial/did not improve performance\" lack any data references). Additional issues include appendix figure captions that are unclear and lack cross-references in the main text (e.g., molecules with QED > 1 in figures 8 and 14), and captions inappropriately implying low QED correlates with implausibility (e.g., figure 9). Many terms are undefined, including \"synthe. MAE,\" \"HOMO/LUMO,\" \"SAS,\" as well as the precise definition of diversity used here. Additionally, error bars are missing in all tables." }, "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 2, why not report the MAE at different percentiles, instead of only the MinMAE? It's possible that the model simply memorizes some extreme cases seen in training so as to achieve a good minimum MAE." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- This paper is presented with sufficiently clear descriptions.\n- The authors explored a wide range of techniques that can be applied in the under-explored context of multi-property conditional generation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed to extend Spanning Tree-based Graph Generation (STGG) to multi-property conditional generation, namely STGG+, with improvements to successfully engineer and implement the concept. STGG+ achieves SOTA on both in-distribution and OOD conditional generation, as well as reward maximization." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- It seems to me that the authors invented complicated ad-hoc designs and specifically engineered to fix any issues that may arise, for example by masking the creation of rings when reaching max number (100), or alternating the use of CFG and ranking via a property-predictor. I'm afraid this hampers the overall generality of the proposed method.\n- Ablation studies are missing. What's the effect of the improved Transformer architecture against the vanilla one? How does the auxiliary property prediction loss contribute to the results? The same applies to CFG w/ and w/o random guidance, the masking mechanism, the ring overflow treatment, the order randomization, and the automatic construction of vocabulary instead of a predefined one. Detailed ablations are needed to validate the authors' special designs, and provide more insight to the 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": 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": "My questions are closely related to the weaknesses mentioned above. The authors are encouraged to address the points raised in the weaknesses section. Some questions include but are not limited to:\n1. The STGG+ approach claims improvements over SMILES-based molecule generation methods. **What are some of the improvements that the SMILES representation fails to achieve or is difficult to achieve compared to the STGG+ representation?**\n2. The spanning-tree representation allows both explicit and implicit hydrogen atoms. Are the generated molecules restricted to explicit hydrogen atoms or can they also have implicit hydrogen atoms?\n3. How does the model/evaluation handle canonicalization of the generated sequences/molecules?\n - Does uniqueness consider canonicalization or does it only consider differences in the generated sequences?\n4. For reporting the property MAEs, was an external property predictor used for the evaluation? How is MinMAE reported?\n - If an external property predictor was used, provide the details of the external predictor for MolWt, LogP, QED, and HOMO-LUMO gap." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "### Originality\n1. This work proposed an improved method of the STGG model for any property conditional molecule generation.\n1. This work introduced classifier-free guidance and self-criticism into the transformer architecture.\n\n### Quality\n1. The proposed method is shown to improve the validity and diversity of generated molecules.\n1. The method is also shown to better generate molecules with desired/conditioned properties.\n1. The results are shown across multiple datasets and properties.\n\n### Clarity\n1. The STGG+ architecture is clearly explained.\n\n### Significance\n1. Any-property conditional generation is a challenging yet important task in technical applications. For instance, in drug discovery, it is important to generate molecules with desired curative properties and avoid molecules with toxic properties." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed the STGG+ method, an improved method of the Spanning Tree-based Graph Generation (STGG), for generating novel molecules with multi-property conditional generation. Architecture-wise, this work introduced\n\n1. an improved Transfomer with Flash-Attention. RMDProp, etc.\n1. an extended STGG model for more robust graph generation of molecules.\n\nBy randomly masking some properties at training time and using Classifier-Free Guidance (CFG), the model was shown to generate novel in- and out-of-distribution molecules with any property conditional generation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "As a computational chemist with expertise in molecules (and SMILES), I am concerned with\n1. the contribution and improvement of this work, STGG+, to the original STGG model or the original SMILES-based molecule generation methods.\n2. this work's representation of chemistry and molecules in terms of correctness and novelty.\n\n**The STGG+ representation of molecules is within the capabilities of SMILES representation.**\n1. The STGG+ improvements to STGG are not significant enough\n - the proposed improvements such as masking of invalid tokens, automatic calculation of valency, etc., seem similar to adding if-else conditions to improve the original STGG model.\n - In my opinion, these improvements should be learned by the model itself during training. The model itself should learn to avoid invalid tokens and keep track of valency. These are all fundamental grammar rules that the model should learn by itself.\n - If these constraints are manually added, it limits the efficiency of the generation process. For example, valency calculation can be intricate in the generation process when rings are involved.\n - Because of these manual implementations, I am not convinced that the STGG+ representation is significantly better than the SMILES representation in terms of ensuring valid molecules.\n2. The proposed benefits of STGG+ (spanning-tree representation) compared to SMILES representation are not entirely true. SMILES can also achieve the claimed benefits with similar modifications. For example,\n - In SMILES, rings are represented by two identical numbers at the beginning and end of the ring. For cyclohexene, its SMILES representation can be `C1CCCCC=1` (or `C1-C-C-C-C-C=1`) and its spanning-tree representation can be `[bos]C[bor]-C-C-C-C-C=[eor1][eos]`. In the spanning-tree representation, a `[bor]` token must be paired with a `[eor#]` token before `[eos]` to form a valid molecule. Whereas in SMILES, a ring-starting number must be paired with the same number before the end of the string.\n - Automatic calculation of valency can be done in SMILES as well in the same fashion as STGG+ since the spanning-tree representation and the SMILES representation are interchangeable during the generation process.\n\n**This work lacks clarity on the spanning-tree representation such as explicit/implicit hydrogen atoms and canonical representation.**\n1. In Figure 1, line 140, the spanning-tree representation used a combination of explicit and implicit hydrogen atoms. The nitrogen atom was shown with an explicit hydrogen atom, and all the carbon atoms were shown without hydrogen atoms (implicit). However, from Appendix A.4, it seems that the vocabulary is collected for spanning tree representations with explicit hydrogen atoms.\n1. The above point leads to the question of canonical representation - The same molecule can have different SMILES representations and different spanning-tree representations. In other words, different sequences of tokens can point to the same molecule. For example, `[bos]C[bor]-C-C-C-C-C=[eor1][eos]`, `[bos]C[bor]-C-CH2-C-C-C=[eor1][eos]`, and `[bos]C[bor]-CH2-C-CH2-C-C=[eor1][eos]` can all represent the same cyclohexene molecule.\n1. For the reported generative efficiency (% of valid, novel, and unique molecules), was canonicalization performed/considered? If not, the reported efficiency might be overestimated. Additionally, the authors should provide some examples of the generated sequences and their corresponding molecules to clarify the canonicalization process.\n2. **Suggestions:** In Section 3.3, the authors should discuss the issue with explicit/implicit hydrogen atoms and canonical representation. Try to clarify the following points:\n - Does STGG+ generate molecules with explicit hydrogen atoms only or can it also generate molecules with implicit hydrogen atoms? The spanning-tree representation should allow both.\n - How is the canonical representation handled in the training and generation processes?\n - **What is the definition of valid, novel, and unique molecules?** Are molecules considered unique if they have different sequences of tokens but represent the same molecule?\n\n**The reported property MAE of the conditional generation needs more explanation.**\n1. For the properties of the generated molecules, were they calculated with the property predictor of the STGG+ architecture or with an external property calculator such as RDKit? The external predictor should provide the ground truth for the property values and should thus be used for the evaluation.\n2. The `MinMAE` reported in Table 2 needs more clarification: is it the minimum absolute error across the 2K generated molecules? What does \"minimum mean\" refer to?\n - If the minimum is reported, what about the mean of the absolute errors?\n - For such a large number of generated molecules, the mean absolute error is a better metric to evaluate the performance of the conditional generation. This is related to the application of the model (line 57) - validating the properties of the generated molecules in real life can be costly. Conditional generation aims to generate a small set of potential candidates. Minimum error implies that one has to test all 2K molecules (too large) to find the best candidate, while average error better represents the overall conditional generation performance.\n - The minimum absolute error might be more convincing if reported on a small population of generated molecules such as 10x molecules with multiple batches." }, "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 authors improved the structure of the original Transformer, but the results do not seem to reflect the improvements, such as whether the generation time and the quality of the generated molecules have been improved.\n2. For the self-criticise, the authors should discuss the trade-off between performance gains and computational cost. Including a comparison of computational time for different values of k would clarify the model's efficiency.\n3. The authors should optimize the structure of the result table, as it is not clear what is being compared, e.g. modify the table head.\n4. For property-conditional generation. The authors only compare the MinMSE and should add some property distributions to demonstrate that the generated molecules approximate the given conditions." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This paper used a property-predictor-driven self-criticism mechanism that allows STGG+ to evaluate and select the best out of multiple generated molecules, improving fidelity to target properties." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes an enhanced version of Spanning Tree-based Graph Generation (STGG+), tailored for multi-property conditional molecule generation. Based on the STGG, STGG+ includes improvements in the Transformer architecture, a flexible conditioning mechanism for any subset of properties, and a self-criticism mechanism that filters generated molecules based on a property predictor." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The model’s effectiveness relies heavily on the internal property predictor, which may be less reliable for out-of-distribution samples. This dependence could reduce fidelity in less representative scenarios.\n2. Although the model improves conditioning performance, it’s unclear how it balances molecule diversity and property, diversity is also a crucial metric in molecular generation." }, "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": "Please address (to the extent possible) Q1-Q4 in the Weakness section. Specifically for Q1-Q3, please state if the alternate techniques were considered when designing the experiments/writing the paper. If so, please explain why those alternate ideas were not incorporated into the paper. If not, please discuss how these alternative techniques might be relevant to the problem considered in the paper." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* The problem considered in this paper (molecule generation) has clear practical importance and given that there exists standard 1D string representation of molecules, using generative AI to create new molecules is definitely a promising avenue worth pursuing.\n\n* The paper has a pretty comprehensive experimental results and they show the efficacy of the proposed system.\n\n* While some of the techniques used in the paper might be `standard' for say language modeling, being able to apply these techniques in a completely new application domain and show improvement is very nice.\n\n* The ability to impose certain properties on molecules being generated and having the model be able to self-criticize seems like really nice capabilities." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper considers the problem of generating (hopefully) new representations of molecules. Existing techniques either work with a 1D string representation of molecules or a 2D graph representation. This work considers the former representation and builds on a method called Spanning Tree-based Graph Generation (STGG) that was specifically created to use generative AI models. The present work differs from existing work along the following two high level aspects:\n\n1. Previous work would generate molecules without any restrictions. However, this paper considers the problem of generating molecules that has to satisfy (some) subset of a given set of properties that the generated molecules must satisfy.\n2. Unlike existing results the paper creates models that can _self-criticize_ by allowing the model to predict properties of the molecules it generates and uses that to prune out molecules that do not satisfy the required properties.\n\nThe paper lists conceptual improvements made in this work in the context of generating molecules but since I'm more familiar with Transformers and related literature, I will focus my review on those. Specific to the Transformer model used in this work (as opposed to the STGG work), the paper uses improvements made to the Transformer architecture (e.g. FlashAttention) over the last three years.\n\nThe paper presents a pretty comprehensive (at least to me) set of experiments and show that the proposed new system works better than existing systems on benchmarks that are used in this area." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Below are some questions that I was not able to answer based on what is there in the paper. (Again, as mentioned earlier, I focused in the Transformer aspects of the paper so all of the questions below are on that axis.) Also some of these questions might be asking for intuitive answers and not necessarily something that could potentially be answered with experiments-- but having these answers might be useful for the reader to understand some of the design choices made by the paper:\n\n* (Q1) The paper uses causal Transformer models-- is there any reason a non-causal Transformer model cannot be used? E.g. non-causal Transformer models have been used on applications other than language modeling (e.g. ViT in image processing)-- and pre-Transformer language models like BERT were non-causal. In theory non-causal models are more expressive than non-causal model (just because a causal model is trivially a non-causal model as well).\n\n* (Q2) The idea of having the model self-criticize the molecules it generates reminded me a lot of GANs. Have GANs been tried to generate molecules? If so, have they worked or is there some intuition for why they did not work?\n\n* (Q3) Over the last ~3 years there have been a fair amount of work on `Transformer-free' models for language generation. One such line of work is based on state space model (Mamba [see https://arxiv.org/abs/2405.21060 and https://arxiv.org/abs/2312.00752] being a model that has garnered a lot of attention in the language modeling literature). Some of these ideas have been used in genomic sequencing (e.g. Hyena DNA-- https://arxiv.org/abs/2306.15794). Where these recent models considered in this work?\n\n* (Q4) In lines 227-228, it is mentioned that the _number_ of masked properties $t$ was picked uniformly at random between $0$ and $T$. However, which of the $\\binom{T}{t}$ subset of properties were actually chosen to mask?\n\nBelow are some minor comments (that are purely related to presentation):\n\n* Lines 354-355: Instead of saying \"similar\" performance-- please quantify, i.e. within what percentage of existing work?\n\n* Table 1: Is _Distance_ in the table column name the same as FCD?" }, "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": 4 }, "primary_area": null, "questions": { "value": "1. I didn't completely follow the details of best-of-k filtering described in Section 3.6. It would be good if the authors could explain how this is exactly done. \n2. Based on observations: In Table 1, STGG+ shows an improved synth MAE, but other metrics appear comparable. In Table 2, STGG+ outperforms by design. In Table 3, the settings aren't directly comparable. Does this imply that the primary novelty lies in achieving property conditioning? If so, is the novelty somewhat limited, given that the extension feels intuitive?\n3. Considering the non-comparable nature of Table 3, could the experiments be repeated under consistent settings for a fair comparison?\n4. It’s currently unclear which modifications specifically drive the improvements in STGG+. Conducting ablation studies based on the points listed on page 2 (points 1-5) would provide more clarity.\n5. For conditional generation, online methods like GFlowNets could serve as an additional baseline. Would it be possible to include this baseline in Table 2?\n\n**Paper suggestions:**\n\na) I think, including Figure 3 (suplementary) instead of Figure 1 in the main paper would better showcase the contributions.\n\nb) For section 3.6, I found the figure a little confusing. It may help to have a better figure to explain the same." }, "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 is well written.\n2. The authors suggest modifications that make sense and feel intuitive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper tackles the issue of generating valid molecules by extending the Spanning Tree-based Graph Generation (STGG) to support multi-property conditional generation. The proposed STGG+ integrates a Transformer architecture, property masking, and an auxiliary loss for model self-evaluation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. I find the changes to be intuitive but somewhat obvious, leading me to believe that the paper lacks significant novelty.\n2. Please see questions." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Generating molecules conditional on desired properties using Spanning Trees with a model that can self criticize its own molecules" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024anypropertyconditional,\ntitle={Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=26kgSlMmhA},\nnote={under review}\n}" }, "abstract": { "value": "Generating novel molecules is challenging, with most representations of molecules leading to generative models producing many invalid molecules. Spanning Tree-based Graph Generation (STGG) is a promising approach to ensure the generation of valid molecules, outperforming state-of-the-art generative models models for unconditional generation. In the real world, we want to be able to generate molecules conditional on one or multiple desired properties rather than unconditionally. Thus, in this work, we extend STGG to multi-property conditional generation. Our approach, STGG+, incorporates a modern Transformer architecture, random masking of properties during training (enabling conditioning on any subset of properties and classifier-free guidance), an auxiliary property-prediction loss (allowing the model to self-criticize molecules and select the best ones), and other improvements. We show that STGG+ achieves state-of-the-art performance on in-distribution and out-of-distribution conditional generation, as well as reward maximization." }, "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": [ "molecules; transformers; masking; molecule generation; property conditional 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/4f2a4371eecd2e40578a79e388935d5613843a6f.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/de6aae8daabbf656de2966aa6e66e8689d782074.zip" }, "title": { "value": "Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning 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": 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": "1. \"Table 6\" seems incorrectly labeled and should be \"Table 1.\" As far as I can see, there is only one table in the entire paper. \n2. In Table. 6, the right side of the table extends beyond the text area, making the layout appear cluttered." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The abstract and introduction repeatedly emphasize that the Appearance Preservation Module (APM) ensures the natural continuity of object characteristics in generated videos. However, the paper does not provide metrics similar to CLIP-I to quantify the preservation of subject consistency. \n2. When considering long video generation, users typically seek dynamic visuals rather than frames with the same semantic content. While methods like SEINE or DynamiCrafter may appear to have lower visual quality than this work, the APM module proposed in this paper, while enhancing content continuity, also restricts the range of generated video content. In my opinion, this is a trade-off with drawbacks. The authors could consider adding experiments to demonstrate that even with CAM and APM, the model can still generate content with semantic variation. \n3. This paper employs CAM to ensure short-term consistency in the video, a method that significantly increases the parameter count. In contrast, SEINE’s method, as mentioned, only slightly increases parameters. The paper lacks a clear ablation study to compare the two methods and determine which is superior." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents StreamingT2V, a method for generating high-quality, extended videos from text prompts, specifically addressing the challenge of ensuring smooth transitions in long-form content. Existing methods often struggle with abrupt cuts in longer videos. In contrast, StreamingT2V introduces three core components: (i) the Conditional Attention Module (CAM), a short-term memory mechanism that aligns each generated segment with its predecessor for seamless transitions; (ii) the Appearance Preservation Module (APM), a long-term memory unit that retains key features from the initial frames to maintain scene consistency; and (iii) a randomized blending technique that enables a video enhancer to be applied autoregressively, ensuring coherence over extended durations. Experiments demonstrate that StreamingT2V achieves high levels of motion and continuity, outperforming other models that tend to stagnate during prolonged autoregressive use." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The abstract and introduction repeatedly emphasize that the Appearance Preservation Module (APM) ensures the natural continuity of object characteristics in generated videos. However, the paper does not provide metrics similar to CLIP-I to quantify the preservation of subject consistency. \n2. When considering long video generation, users typically seek dynamic visuals rather than frames with the same semantic content. While methods like SEINE or DynamiCrafter may appear to have lower visual quality than this work, the APM module proposed in this paper, while enhancing content continuity, also restricts the range of generated video content. In my opinion, this is a trade-off with drawbacks. The authors could consider adding experiments to demonstrate that even with CAM and APM, the model can still generate content with semantic variation. \n3. This paper employs CAM to ensure short-term consistency in the video, a method that significantly increases the parameter count. In contrast, SEINE’s method, as mentioned, only slightly increases parameters. The paper lacks a clear ablation study to compare the two methods and determine which is superior." }, "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": "About video length stress test. In auto-regressive video generation, there exists error accumulation problem, i.e. the generated frames have different distribution from the training data distribution, which makes the subsequently generated frames degrades further. How does StreamingT2V address the error accumulation problem? What is the upper-bound generation length of this 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 generated videos are sufficiently long, natural and with relatively large motion. The quantitative performance outperforms existing methods.\n2. The paper identifies a noise mismatch problem when enhancing long videos using chunk-wise SDEdit, and proposes a randomized blending method to address this problem." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a streamable text-to-video method which can generate up to 2 minutes or longer videos with seamless transitions. Three innovative methods are proposed to ensure the long video consistency and overall quality. Firstly, conditional attention module injects previous-chunk information into the pre-trained video diffusion model to ensure smooth transitions between chunks. Secondly, the CLIP feature of the first frame is injected to the video diffusion model to ensure a coherent scene and object appearance within the whole video. Thirdly, a randomized blending approach is introduced to address inconsistent transitions caused by noise mismatch within the video enhancer's denoising process. A novel motion aware warp error metric is proposed to assess both motion amount and consistency. Experiments are conducted to evaluate the proposed method qualitatively and quantitatively." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The novelty is limited. Firstly, generating subsequent frames with the condition of previous frame chunks has already been explored [1]. Secondly, the appearance preservation module (APM) in this paper is much like the anchored conditioning method in ART-V [2]. \n2. The paper states that the training data is collected from publicly available sources, but the corresponding URLs or papers are provided or mentioned. Please provide the URLs or citations for these sources.\n3. Comparisons on general video quality benchmarks are missing, such as FVD and FID on MSR-VTT or UCF datasets.\n4. The paper is not well written. The formatting issues make the paper unfrendly to read, e.g. it is better to use brackets when citing papers; Table 6 exceeds the width limit.\n\n[1] Gao, Kaifeng, et al. \"ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models.\" arXiv preprint arXiv:2406.10981 (2024).\n\n[2] Weng, Wenming, et al. \"ART-V: Auto-Regressive Text-to-Video Generation with Diffusion Models.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. Anchor frame influence: During the training and sampling stages, anchor frames are randomly sampled. How significant is the impact of choosing different anchor frames on the final video generation? Why can't all frames from the first chunk be used as anchor frames to guide generation?" }, "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 autoregressive approach effectively leverages both short-term and long-term dependencies, facilitating the seamless creation of extended video content. This method adeptly addresses the challenges associated with producing longer video sequences by ensuring smooth transitions and continuity.\n\n2.Through the integration of the Conditional Attention Module (CAM) and the Appearance Preservation Module (APM), the model ensures that generated videos exhibit natural continuity and maintain consistent scene and object characteristics across their entire length." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a novel text-to-video diffusion model aimed at generating long videos. Addressing the challenge of abrupt transitions in extended videos, the model incorporates three key mechanisms: a Conditional Attention Module (CAM) for smooth short-term transitions, an Appearance Preservation Module (APM) to maintain scene consistency, and a randomized blending technique for refining generated videos." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. CAM design : In the W.A.L.T[1] method, a very straightforward auto-regressive generation approach is provided for frame prediction tasks, where past generated frames are used as conditions to guide the generation of subsequent video content through the standard classifier-free guidance method. Can the authors explain why this approach was not adopted in the design of the CAM module, but rather a ControlNet method was used? Additionally, can the authors provide a comparison of the FVD metrics for the CAM and WALT frame prediction methods on the UCF-101 or K600 datasets? \n\n2. Training details are missing: Can the authors provide details related to the training data?\n\n3. Evaluation is a bit weak: Can the authors provide a comparison of FVD with other methods on the UCF-101 or K600 datasets?\n\n---------\n[1].Gupta, Agrim, Lijun Yu, Kihyuk Sohn, Xiuye Gu, Meera Hahn, Li Fei-Fei, Irfan Essa, Lu Jiang, and José Lezama. \"Photorealistic video generation with diffusion models.\" arXiv preprint arXiv:2312.06662 (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": 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": "Please see the weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "-An autoregressive long video generation framework is designed, which is novel, and shows stable video quality." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a long video generation framework from single text prompt. The main contribution is the proposed conditional attention module (CAM) and appearance preservation module (APM) for temporal consistent long video generation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "-I'm wondering the necessity of generating very long clip with only one short caption. In example videos provided in the supplementary material, it seems the content of video is limited to a very narrow domain with little variation, due to the design of APM. It is not suitable for very long video generation.\n\n-In line 299, \"...fuse x with output of the first temporal transformer block of CAM.\" Just curious about the fusion here, as in Figure 3, x seems to be added with the noised input after one encoding layer. Can this encoding layer described as the first temporal transformer block of CAM? As generally, the first block of CAM should have the skip connections to decoding part.\n\n-In line 417, the mean warp error W(V) is the average squared L2 pixel distance from a frame to its warped subsequent frame. So is it computed by calculating the warp error between anchor frame and all other frames? Or between two consecutive frames? What's the definition of warp error?\n\n-The quantitative comparison only includes the long video generation quality evaluation, lacking the common metric evaluation, such as FVD, LPIPS. Also lacks the evaluation on common datasets like MSRVTT and UCF101." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024streamingtv,\ntitle={StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=26oSbRRpEY},\nnote={under review}\n}" }, "abstract": { "value": "Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, simplifying the process of producing diverse and individual content.\n Current methods excel in generating short videos (up to 16s), but produce hard-cuts when naively extended to long video synthesis.\n To overcome these limitations, we present $\\textit{StreamingT2V}$, an autoregressive method that generates long videos of \\textbf{up to 2 minutes or longer} with seamless transitions.\n The key components are:\n (i) a short-term memory block called conditional attention module (CAM), which conditions the current generation on the features extracted from the preceding chunk via an attentional mechanism, leading to consistent chunk transitions, \n (ii) a long-term memory block called appearance preservation module (APM), which extracts high-level scene and object features from the first video chunk to prevent the model from forgetting the initial scene, and (iii) a randomized blending approach that allows for the autoregressive application of a video enhancer on videos of indefinite length, ensuring consistency across chunks. \n Experiments show that StreamingT2V produces high motion amount, while competing methods suffer from video stagnation when applied naively in an autoregressive fashion.\n Thus, we propose with StreamingT2V a high-quality seamless text-to-long video generator, surpassing competitors in both consistency and motion." }, "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": [ "Text-To-Video; Diffusion Models; Long Video; Autoregressive" ] }, "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/d9731227d154b2493aff20dd8a0c1cc03673e1c7.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/7442f7987b5c4890d9900a3c9b78eec7419752b6.zip" }, "title": { "value": "StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text" }, "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": "See comments in the Weaknesses part." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "+ This paper is well-written and nicely organized. \n+ The proposed framework is novel and is nicely motivated." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a novel contrastive learning approach based on neural subgraph matching, i.e., PharmacoMatch, and the authors claim that it reinterprets pharmacophore screening as an approximate subgraph matching problem and enables efficient querying of conformational databases by encoding query-target relationships in the embedding space." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- I suggested the authors consider conducting more experiments over other datasets instead of only DUD-E.\n- I wonder if the proposed contrastive learning approach can be applied to other domain datasets?\n- This paper does not provide any unique and novel insights about why the proposed architecture that works well for the current dataset." }, "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. How sensitive is the model to the choice of tolerance radius (r_T = 1.5Å)? Could you provide an analysis?\n2. How did you select the 10 DUD-E targets? Could you demonstrate the method's robustness on more targets?\n3. What is the impact of different augmentation strategies? For example, how much does performance degrade if you remove node deletion or displacement?\n4. Could you compare PharmacoMatch with recent methods like DrugClip and PharmacoNet on the same benchmarks?\n5. The speed advantage is clear, but can the author better justify the conceptual novelty? (Contrastive learning is not new, GNN for molecules is well-established and order embeddings have been used before)\n6. How much 3D geometric precision is actually lost during embedding? Could you quantify the tradeoff between speed and accuracy?\nI wonder if there are specific types of 3D arrangements where the method may fail." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The acceleration is impressive, with a thorough evaluation of embeddings and runtime analysis. The model provides a practical impact for screening billion-compound libraries. Besides, the reformulation of pharmacophore screening as neural subgraph matching is creative combining self-supervised training approach using augmentation strategies." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors introduce PharmacoMatch, a deep learning approach that reframes 3D pharmacophore screening as a neural subgraph matching problem, using a graph neural network trained through contrastive learning to encode pharmacophore structures into an embedding space. Their method achieves comparable screening performance to traditional alignment-based approaches while being approximately two orders of magnitude faster in matching speed, making it particularly valuable for screening extremely large molecular databases. The model is trained in a self-supervised manner on over 1.2 million unlabeled molecules from ChEMBL, learns to encode both structural and spatial relationships of pharmacophoric points, and demonstrates robust performance across multiple DUD-E benchmark datasets in a zero-shot setting." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Despite mentioning recent works like DrugClip and PharmacoNet in the related work section, there are no direct comparisons with these methods. It seems the paper only compares against the traditional CDPKit alignment algorithm, missing comparisons with other more current learning approaches. Besides, there is no comparison or detailed discussion with simpler baseline models (e.g., basic GNN architectures without contrastive learning)\n\nThe discussion on the model details is not sufficient. Lacks systematic ablation studies of model architecture components (e.g., the impact of different GNN layers, the importance of skip connections). Missing analysis of the impact of different augmentation strategies on model performance and investigation of how embedding dimension and model size affect performance, as well as contrastive loss function impacts." }, "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": "## **Methods:**\n**Q1. Clarification of Pharmacophore Representation:** In the section detailing pharmacophore representation, it is mentioned that $\\mathcal{L}$ comprises only pharmacophoric descriptors. However, distances are subsequently incorporated into the representation. The notation and description should be revised for consistency and clarity.\n\n**Q2. Model Input Specification:** Within the model input section, node labels are currently denoted as $V_p$, which is a set of node. It may be more appropriate to represent these as node element to enhance the clarity.\n\n**Q3. Negative Data Augmentation Strategy:**\nThe current approach limits displacement directions to a single direction to avoid cancellation effects. Allowing for all displacement directions except for the case of cancellation can highly increase the diversity of negative training data, might lead to better model performance. Are there specific reasons why the authors chose to use only a single direction for negative displacements?\n\n## **Results:**\n\n**Q4. Evaluation on Recent Datasets:** To better assess the method’s applicability to real-world scenarios, evaluation on more recent datasets like LIT-PCBA[1] is recommended. Additionally, utilizing metrics beyond AUROC, such as enrichment factors (EF) (similar to BEDROC for early recognition of hit candidates), could provide a better understanding of the model’s performance in virtual screening tasks.\n\n**Q5. Benchmarking with other methods:** Since one of the main contributions of this paper is \"*fast virtual screening in the embedding space and evaluate the performance of our method through experiments on virtual screening benchmark datasets*\", the authors should benchmark their virtual screening performance with other models. It seems that there are no significant differences in the objectives or methods of the compared models relative to the current work. Are there reasons why the authors did not benchmarked their model with the previous works such as DrugClip or PharmacoNet?\n\n**Q5-1. Ambiguous criteria for showing virtual screening performance:** The authors compared their performance with CDPKit's alignment algorithm. It only make sense that the propose method does well on virtual screening only if CDPKit alighment algorithm's performance is already good enough. Authors may provide the performance of CDPKit alignment algorithm for virtual screening explicitly in their manuscript.\n\n**Q6: Limitation in Protein-Specific Virtual Screening Capability:**\nThe methodology seems similar to ligand-based approaches, where ligand structures are pre-generated and graph matching determines activity. This may limit the model's applicability, since considering only ligand structure of protein-ligand complex cannot fully consider protein pocket. For example, if protein has large binding site that various ligands with different pharmacophoric sites can interact with, considering only a single ligand might give a bias during virtual screening on the protein target. Why did authors adopted ligand-based approach instead of protein-based one, such as using protein binding site's pharmacophore instead its binding ligand?\n\n## **References:**\n[1] Tran-Nguyen, Viet-Khoa, Célien Jacquemard, and Didier Rognan. \"LIT-PCBA: an unbiased data set for machine learning and virtual screening.\" Journal of chemical information and modeling 60.9 (2020): 4263-4273." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "**S1. Alignment of Contribution and Results:**\n\tThe study presents a coherent alignment between its contributions and the resulting outcomes. The objectives set forth by the authors are consistently addressed throughout the work.\n\n**S2. Effective Representation Learning via contrastive learning:**\n\tThe approach to representation learning through a contrastive learning with data augmentation appears to function as intended. The learned representations for pharmacophores are well-clustered in embedding space." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a contrastive learning approach that emphasizes augmentation strategies by incorporating the concept of pharmacophore in neural subgraph matching. Furthermore, they apply this concept to ligand-based virtual screening, demonstrating that the results are well-aligned with CDPKit’s alignment algorithm. Although the learned representations effectively capture the proposed pharmacophore concepts, the performance in virtual screening—a primary objective of the model—does not appear sufficiently strong. This is primarily due to the lack of benchmarking against other models and the use of additional evaluation metrics." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**W1. Limited Methodological Novelty:**\n\tThe proposed methods do not introduce significant novel approaches. As mentioned in the manuscript, the concepts of Neural Subgraph Matching or neural network architectures are already proposed by other previous works. While the formulation applied to pharmacophores—particularly the augmentation strategies for contrastive learning—is noteworthy, it may not sufficiently advance the general methodologies handled in the ICLR community. If there's any other novelty compared to previous works, the points should be clearly explained in the manuscript. Authors may propose novel input processing schemes or model architectures better suited to the pharmacophore graph, or improve neural subgraph matching techniques.\n\n**W2. Insufficient Experimental Results:**\n\n- **W2.1 Lack of Comprehensive Benchmarks:**\n\tFor a study proposing algorithms intended for virtual screening, it is essential to benchmark against established methods such as DrugClip or PharmacoNet to demonstrate comparative advantages. The absence of such comparisons, without a compelling justification, weakens the evaluation of the proposed method’s efficacy. Additionally, reliance on the outdated DUD-E benchmark and the arbitrary selection of only 10 targets out of 102 weakens the robustness of the experimental validation. (see Q4, 5)\n\t\n- **W2.2 Ambiguous goal of benchmark experiment:**\n\tThe goal of \"*to achieve comparable values between our model and the alignment algorithm*\" would be meaningful only if the alignment method inherently guarantees superior results compared to the previous methods, which is not adequately demonstrated in the current manuscript. (see Q5-1)\n\t\n- **W2.3 Suitability for Virtual Screening:**\n\tThe method appears to focus on ligand interactions with only parts of the protein pharmacophore, potentially neglecting the comprehensive information of the global protein binding site. This partial consideration might introduce bias, and it remains unclear whether the method can outperform existing techniques that utilize complete protein-ligand information. Empirical evidence showing superior performance in this regard would strengthen the study. (see Q6)" }, "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": "NA" }, "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) From Methodology point of view, is there any major novelty in the current paper?\n2) Other than the efficiency in terms of the runtime, any other clear advantage of the current model?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "It is interesting to reinterpret pharmacophore screening problem as an approximate subgraph matching problem." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors propose a contrastive learning approach for pharmacophore screening, based on subgraph matching. The key idea is to employ approximate subgraph matching for querying conformational database, a main step in pharmacophore screening. The subgraph matching is done through a contrastive learning approach by encoding query-target relationships in the embedding space. Their model has been validated based on benchmark dataset including DUD-E." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "From the methodology point of view, the paper lacks novelty. The contrastive learning framework and augmentation module are all rather standard approach in GNN models. The contribution is not significant. Further, the performance is not very impressive as shown in Table 1. Even though the authors have emphasized that \"our goal is to achieve comparable values between our model and the alignment algorithm\", the only advantage of the current model seems to be the runtime efficiency." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "PharmacoMatch, a new contrastive learning approach, accelerates pharmacophore screening by encoding query-target relationships in the embedding space." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024pharmacomatch,\ntitle={PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=27Qk18IZum},\nnote={under review}\n}" }, "abstract": { "value": "The increasing size of screening libraries poses a significant challenge for the development of virtual screening methods for drug discovery, necessitating a re-evaluation of traditional approaches in the era of big data.\nAlthough 3D pharmacophore screening remains a prevalent technique, its application to very large datasets is limited by the computational cost associated with matching query pharmacophores to database ligands.\nIn this study, we introduce PharmacoMatch, a novel contrastive learning approach based on neural subgraph matching. Our method reinterprets pharmacophore screening as an approximate subgraph matching problem and enables efficient querying of conformational databases by encoding query-target relationships in the embedding space.\nWe conduct comprehensive evaluations of the learned representations and benchmark our method on virtual screening datasets in a zero-shot setting. Our findings demonstrate significantly shorter runtimes for pharmacophore matching, offering a promising speed-up for screening very large datasets." }, "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": [ "Contrastive Representation Learning", "Neural Subgraph Matching", "Virtual Screening", "Pharmacophore Modeling" ] }, "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/38dac929af05471ea9e07a634cd86ccbe1cac832.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": "PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching" }, "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) Can you comment a bit more on Assumption 2.1 - I know that the mutally diagonalizable structure is quite restrictive, in particular, can you comment on why the tasks you chose follow these assumption(s). Which tasks can you not study, give these assumptions? \n\n2) Can you give a bit more experimental results, especially when training your networks. Maybe a hyperparameter table in the appendix is nice. Can you give more details how you derived hyperparameters when training networks, you for example mention some in Figure 2. I missed if these hps are analytically derived. \n\n3) I find the discussion wrt to compositionality and modularity, you mention \"strictly modular\" in the abstract, a bit unclear. Can you clarify, or even define in the work, what you mean with this - and then contrast this to your findings. I guess the same applies for an up-front (maybe even in the intro) definition of what you mean with this. I would find it easier to follow the paper, having these things more clearly explained." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "Disclaimer: I did not study the proofs of the paper, as I was very late reviewing this paper. I will try to find more time in the coming weeks. \n\nI find the paper clearly writtin and well presented, the authors make an effort presenting the dense results in a clear manner. The authors, to the best of my understanding, extend the theory around GDLNs and allow for a more in-depth study of the training dynamics of ReLU networks. I find the exposition of quite toyish tasks enlightning and make the paper more easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work builds provides a step towards theory of feature learning in finite ReLU neural networks by building on an equivalence between ReLU networks and Gated Deep Linear Networks (GDLNs). The authors introduce \"Rectified Linear Networks\" (ReLNs), which are GDLNs specifically designed to imitate ReLU networks, allowing them to derive exact learning dynamics.\nThe key contributions are:\n\nThe introduction of ReLNs as a theoretical framework to analyze ReLU networks, providing tractable dynamics for finite-width networks during feature learning. A demonstration that ReLU networks exhibit an implicit bias towards structured mixed selectivity - where neural units are active across multiple contexts but in structured, reusable ways. \nEvidence that this bias towards mixed selectivity and node reuse is amplified when: 1) more contexts are added to the task\nand 2) additional hidden layers are included in the network\n\nThe authors support their theoretical findings with analytical solutions and empirical demonstrations on several tasks, including an adapted XOR problem and multi-context classification tasks. They show that while ReLU networks aren't biased towards strictly modular or disentangled representations, they do learn structured features that can be somewhat reused across contexts.\nThe work takes a step towards understanding how and why structured representations emerge in ReLU networks during learning, bridging a gap in theoretical understanding between linear networks and modern deep learning architectures." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I can not judge fairly the novely of the paper, especially its relation to Saxe et a., 2022. For me, it would have been helpful to highlight the novelty a bit more." }, "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. Could the main findings (e.g., mixed selectivity) be directly observed in the ReLU network without using ReLNs?\n2. Does the method extend efficiently to larger datasets (e.g., ReLU networks trained on MNIST), and can a ReLN adequately explain such networks?\n3. Could the authors clarify their definition of \"feature learning\" and what they mean by a pathway \"doing feature learning\" (line 302)?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The introduction of ReLN as a GDLN variant to study ReLU dynamics is innovative and offers a new angle on understanding feature learning in finite ReLU networks.\n2. The paper provides theoretical insights into inductive biases in ReLU networks, particularly regarding structured mixed selectivity and node reuse." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces Rectified Linear Networks (ReLNs), a subset of Gated Deep Linear Networks (GDLNs) designed to capture the training dynamics of finite-dimensional ReLU networks. By drawing an equivalence between ReLU and ReLN, the authors aim to provide theoretical insights into feature learning and structured mixed selectivity in ReLU networks, especially in tasks involving multi-contextual inputs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. It remains unclear why ReLU’s training dynamics and mixed selectivity properties cannot be derived directly from ReLU networks, as they’re already nonlinear.\n2. The approach is demonstrated on relatively simple tasks, raising concerns about scalability. For instance, applying ReLNs to realistic datasets like MNIST remains unaddressed.\n3. Terms like \"feature learning\" and \"pathway doing feature learning\" (line 302) lack precise definitions. More clarity is needed to distinguish “feature learning” in this theoretical context." }, "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": "**Questions**\n1. [L159] Would be helpful to comments on cases for each of the mentioned assumptions in `Assumption 2.1` since these seem to be not very general assumptions and can be easily violated.\n2. [L183] What is the network architecture for the XoR dataset? Is it a 2-layer network? How would the gating structure look like? It would be helpful to explicitly write down the equation or numerical examples instead of vague description of “2 pathways” and “4 pathways”.\n3. [L257] How do these pathways get identified? Are they identified manually based on the prior knowledge about the task? Since identifying gates would be a major bottleneck for the applications of ReLN, it would be helpful to provide more information on how to identify the pathways.\n\n**Suggestions**\n1. In the Contribution section, clearly state the models and dataset in which the theoretical and empirical results are obtained (single-layer network for theoretical results, and simple synthetic dataset with hierarchy and context (maybe would be helpful to give this dataset a name for easy reference?) to provide a clear expectation for the readers.\n2. Spending more space in the main text to explain how to identify the gating structure and pathways from ReLU to ReLN." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "**Originality**\n1. The paper presents a novel framework Rectified Linear Network to analytically investigate the dynamic of finite-width single-hidden layer neural networks via an extension of Gated Deep Linear Network.\n\n**Quality**\n1. The paper provides theoretical proof for the equivalence between ReLN and ReLU for the case of finite-width single-hidden layer network and verified that the predicted loss matches with the empirical loss in a synthetic dataset.\n\n**Clarity**\n1. The paper clearly present the background relevant work, including the formulation of Gated Deep Linear Network, decomposition of eigenmodes, and derived of learning dynamic based on these eigenmodes.\n2. The theoretical proofs are presented clearly with both informal and formal versions, along with a proof sketch in the main paper, and a detailed proof in the appendix, facilitating the reader's understanding\n3. The empirical results (including the dataset and figures) are clearly presented with clear figure and caption, along with descriptions in the main text.\n\n**Significance**\n1. The paper showed that there's an equivalent ReLN for ReLU for a single-hidden layer network, and it's possible to identify the gating structure of this equivalent ReLN via a clustering algorithm.\n2. By investigating the gating structure of the equivalent ReLN network, the paper shows that there's an implicit bias for structured mixed selectivity (e.g, one gating pathway can encode multiple context)." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduced \"Rectified Linear Network\" (ReLNs) as a subset of Gated Deep Linear Networks (GDLN), and showed that for single-hidden layer network, for a ReLU network, it's possible to find a ReLN that have same loss and outputs at all time-steps (provided that the assumption 2.1 at L159 are satisfied) in a simple synthetic dataset. Using the ReLN as the equivalence of ReLU, the authors provided an analytical solution for the training dynamic for the finite single-hidden layer network with synthetic dataset, and also demonstrated that the predicted loss of ReLN matches with the empirical loss of ReLU networks. In this specific synthetic dataset (which includes hierarchical structure (animals, plants, fish, birds), and multiple contexts), the papers show that the equivalent ReLN network employs an implicit bias for structured mixed selectivity (e.g, one gating pathway can encode multiple context)." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "While the paper proposed a novel framework ReLN to study learning dynamic and feature learning in finite-width neural network, a significant topic in the machine learning community, the paper has several limitations in both theoretical and empirical results, which I would clarify below:\n\n1. Theoretical results: The main theoretical results of the paper requires very strong assumptions (L159, *Assumption 2.1*) in both (1) input dataset (*The dataset correlation matrices are mutually diagonalizable*) and (2) model training trajectory (*The neural network weights align to the singular vectors of the dataset correlation matrices.*). These are very strong assumptions, and the authors did not offer any analysis on specific scenario in which these assumption holds or not hold. As we see in section 6, assumption (2) is violated in the case of 2-layer hidden network. As the paper only uses a very simple synthetic dataset (one-hot vector input and sparse binary-vector output), it's difficult to tell whether assumption (1) can hold for realistic dataset with complicated distribution, even in the 1-layer hidden network case.\n\n2. Empirical evaluations: The empirical experiments that supports the claim and theoretical results only includes single-layer hidden network on a simple synthetic dataset (the paper did include 2-layer hidden network but this experiment did not align with the theoretical results, due to violation of the theoretical assumption). While it's reasonable to use this simple synthetic dataset as a proof of concept to verify the theoretical results and illustrate the mixed-selectivity phenomenon, it would be helpful and more convincing if the authors can demonstrate that the proposed approach work in a realistic image dataset (MNIST, CIFAR, etc.) with a more realistic and variety of model architectures (multiple layer perceptron, convolutional neural nets, etc.). The inclusion of real dataset and variety of architecture is even more important since the theoretical results require such strong assumptions.\n\n3. Identification of gating structure: The gating structure identification is a central bottleneck of this framework, since gating *g* is treated as inputs, and needed to be identified before training. Identifying a fixed gating structure, or varying gating structure through training, would be one of the major difficulty of the framework. The paper did propose a clustering algorithm to identify the gating structure of ReLN from the representation of ReLU models. However, since the paper only operates with very small synthetic dataset and models, it's unclear whether this clustering algorithms can scale with realistic dataset and larger models. Therefore, this is another reason that it is critical to evaluate this proposed framework on more realistic dataset, instead of only on the simple synthetic dataset." }, "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": "**Critical questions (order: most important to least important):**\n\n1. Your analysis looks very particular to the structure of your synthetic task. Do you think it will be feasible to find unique or representative GDLNs for more complex datasets and architectures?\n\n a. Are the assumptions more generally broken, even for 2-layer ReLU nets beyond this toy task of hierarchical structure without noise? Atanasov et al. (2021) show that non-whitened data can weaken the silent alignment effect.\n\n b. ‘Once the common pathway has finished learning there is no correlation between the context features and labels’. But in practice, likely learning is not perfectly separated. Which impact does this have on the generalizability of your results?\n\n2. You claim that the learning dynamics of the ReLN exactly describe those of the ReLU network. Why are the curves in Figure 4 then not exactly matching?\n3. How do your results depend on the number of neurons? You never explicitly mention this.\n4. Your proposed clustering algorithm in Appendix B evaluates the model at different stages of training. Is this not dangerous in cases where the learned function evolves in a more complex way than in your toy task and systematically changes? Then the algorithm tries to cluster outputs from intermediate functions that are rather unrelated to the final learned function. I would like to see whether clear gating mechanisms can be identified when applied to real data.\n\n**Questions out of curiosity:**\n\n1. In Figures 4 and 5, why is not the largest SV learned first?\n2. Where does the bias towards node reuse come from? Can you see this in the learning dynamics equations?\n3. Can your analysis be extended to SGD? Would you expect fundamentally different dynamics?\n4. Can this theory also inform how to maybe slow down learning to learn disentangled representations?\n\n**References:**\n\nAtanasov, Alexander, Blake Bordelon, and Cengiz Pehlevan. \"Neural networks as kernel learners: The silent alignment effect.\" *arXiv:2111.00034* (2021)." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The approach of identifying the linear submodules learned by ReLU networks and using this for tractability of the analysis is interesting. At least on toy datasets, natural modules to learn the structure in the data are exactly recovered by ReLU networks together with the learning speed. This provides mechanistic understanding how the bias toward learning speed explains why 2-layer ReLU networks learn common and context-specific but entangled representations." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper shows that, under strong alignment assumptions of the data and network weights, finite feature learning ReLU networks learn modules that can be represented by Gated Deep Linear Networks (GDLNs), for which the exact learning dynamics are analytically tractable. For a synthetic task with hierarchical structure, the training dynamics of 2-layer ReLU nets are shown to exactly match those of a GDLN constructed for the task. Through this equivalence, it is shown that ReLU networks learn mixed-selective representations due to a bias for optimal learning speed." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I cannot strongly recommend acceptance as some key questions remain unaddressed. While this is a nice and tractable toy model for the learning dynamics of 2-layer ReLU networks, generalizability of the findings is questionable. The results only hold for the synthetic and very specific structure considered. The relevance on real data sets or practical architectures remains elusive, as it is not evaluated. The authors show and acknowledge that the silent alignment Assumption 2 already does not hold for 2-hidden-layer ReLU nets. In Figure 6, against the authors’ claim of ‘sharing the same characteristics’, ReLU networks appear to make larger, sharper steps in the loss. What happens in more practical architectures and real datasets, and whether an appropriate and interpretable GDLN can still be identified remains completely unclear. Experiments that show that this approach works on real data would greatly alleviate these concerns. See other critical questions below.\n\n**Typos in lines:** 30, 392, many in 462-463, 473, 476, 477, 503, 527" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We establish an equivalence between finite feature learning ReLU networks and Gated Deep Linear Networks to analyse the full learning dynamics of the ReLU network. We find a bias towards structured mixed selective representations on a set of tasks." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024make,\ntitle={Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning Re{LU} Networks},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=27SSnLl85x},\nnote={under review}\n}" }, "abstract": { "value": "In spite of finite dimension ReLU neural networks being a consistent factor behind recent deep learning successes, a theory of feature learning in these models remains elusive. Currently, insightful theories still rely on assumptions including the linearity of the network computations, unstructured input data and architectural constraints such as infinite width or a single hidden layer. To begin to address this gap we establish an equivalence between ReLU networks and Gated Deep Linear Networks, and use their greater tractability to derive dynamics of learning. We then consider multiple variants of a core task reminiscent of multi-task learning or contextual control which requires both feature learning and nonlinearity. We make explicit that, for these tasks, the ReLU networks possess an inductive bias towards latent representations which are *not* strictly modular or disentangled but are still highly structured and reusable between contexts. This effect is amplified with the addition of more contexts and hidden layers. Thus, we take a step towards a theory of feature learning in finite ReLU networks and shed light on how structured mixed-selective latent representations can emerge due to a bias for node-reuse and learning speed." }, "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": [ "Gated Deep Linear Networks", "Feature Learning Dynamics", "Structured Mixed Selectivity", "ReLU Networks" ] }, "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/6e4fab4f52cb82b1055707f2fd8a68af9c44ab4f.pdf" }, "presentation": null, "primary_area": { "value": "learning theory" }, "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/a56e7a21e3e062c676bdb198aa320dbd51e45938.zip" }, "title": { "value": "Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks" }, "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": "1.\tDuring the process of selective dimension tuning, the authors select the target channels and states based on magnitude, any other metrics have been tried?\n2.\tWill SDLoRA's training speed be slower compared to vanilla LoRA? How much slower will it be?\n3.\tWhat is the accuracy of SDLoRA on a larger data set, such as ImageNet?\n4.\tSome other advanced parameter-efficient tuning method like DoRA [1] can be adapted to Mamba? or the proposed SDLoRA can be adapted to Jamba?\n\n[1] DoRA: Weight-Decomposed Low-Rank Adaptation" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tReasonable theoretical analysis and comprehensive experiments.\n2.\tThe introduced SDLoRA is novel and effective.\n3.\tThrough extensive experiments, the findings in this paper is useful and inspired." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents the first study on the performance of PEFT methods applied to SSM-based models. Specifically, prompt-based and parameter-based methods are involved. With theoretical analysis and extensive experiments, LoRA tends to achieve better performance. To further improve the performance, this paper introduces SDLoRA, which selectively updates certain channels and states on SSM modules while applying LoRA to linear projection matrices." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe speed of SDLoRA is nor reported.\n2.\tExperimental results on larger datasets are needed." }, "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 the weaknesses" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "(1) Efficiency and Scalability: By focusing on selective parameter updates, the SDLoRA method enhances computational efficiency, which is crucial for large-scale models. Experimental results show that SDLoRA consistently outperforms traditional LoRA across several benchmarks, proving its efficacy in SSM architectures.\n(2) Adaptability: The proposed SDLoRA method demonstrates adaptability across multiple tasks, including NLP tasks and vision tasks" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper explores parameter-efficient fine-tuning (PEFT) methods for deep State Space Models (SSMs), especially in the context of language modeling tasks. It investigates the effectiveness of various PEFT methods, such as prompt-based prefix-tuning and Low-Rank Adaptation (LoRA), applied to SSM architectures like Mamba. A new variant called SDLoRA (Selective Dimension LoRA) is proposed in this paper to selectively update channels and states in the SSM modules, aiming to enhance the fine-tuning performance while reducing parameters. The results indicate that SDLoRA outperforms conventional LoRA when fine-tuning SSM-based models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "(1) Limited Applicability Beyond SSMs: The focus on SSMs means SDLoRA may not generalize well to non-SSM architectures or hybrid models such as Transformer models or Transformer-SSM combinations. Its broader applicability to other architectures remains untested.\n(2) Parameter Selection: The dimension selection process in SDLoRA relies on parameter magnitudes, which may not be optimal and could benefit from a more sophisticated selection algorithm. And what if the magnitude of each channel changes during the fine-tuning stage?" }, "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": "Have you looked at all into Hybrid Architectures?" }, "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 looking at something which definitely needs to be studied, as it is not a foregone conclusion that adapters, prompt tuning, etc... will have the same benefits for SSMs as they do for transformers. A detailed study understanding the different tradeoffs brought on by the SSM architecture should be explored.\n\nThe paper has a good mix of theoretical and empirical analysis." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper aims to explore the application of LoRA to SSMs. It explores different adapter types as well as different ways of how to apply them to the SSM block. The paper additionally includes theoretical results to justify their choices." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper, to me, does not have enough substance. It needs either more detailed theoretical results, which result in some innovation or needs more empirical results which help the community understand how different adapters perform with transformers vs SSMs.\n\nHere are some specifics:\n1. Mamba2 is not included in any of the results, the paper should be updated to look at this architecture as well\n2. The theoretical analysis is largely only for the S4 block, its not clear to me the conclusions would extend to S6, and aren't convincing to me for that reason.\n3. The empirical results are lacking. The SDLoRA module is slightly different application of the standard LoRA block, essentially targeting different layers within the block instead of new a design. Also only two adapters are compared within this work. To have a true empirical study of this, many more experiments need to be conducted, even in the presence of theoretical results.\n\nI think the paper might be more interesting if it were to do something like the following:\n- Compare many adapters in a standardized for Transformers, Mamba1, and Mamba2\n- Isolate differences in which adapters perform well for each class of model\n- See if these insights give rise to a new adapter design specifically for this model class\n- Draw theoretical results to try to explain why certain adapters perform differently than in Transformers\n\nThe current structure doesn't feel as though it is contributing much" }, "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. Could the authors comment on how SDLoRA benefit to hybrid models that combine SSMs and Transformers? \n2. On Mamba-130M, the authors use GLUE and DART benchmarks, while on Mamba-1.4B, they use SAMSum and Spider benchmarks. Could the authors elaborate on the considerations behind this benchmark selection strategy?\n3. In Table 4, SDLoRA outperforms Full Fine-Tuning. Was this outcome expected, and if so, could the authors provide insights into why this might be the case? Additionally, have the authors considered conducting experiments on more challenging visual tasks, such as Imagenet-1k, to further validate the effectiveness of SDLoRA?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper systematically analyzes how existing PEFT methods perform on SSM-based models and which modules are most effective for fine-tuning, revealing that prompt-based methods like prefix-tuning are no longer effective. Additionally, it shows that applying LoRA to linear projection matrices without modifying SSM modules yields the best results. The paper further introduces SDLoRA, a novel approach to parameter-efficient fine-tuning (PEFT) for SSMs. This method's innovativeness lies in its selective dimension updating strategy within SSM modules. The document also references various datasets used for evaluating the proposed methods, and the clarity of the paper is generally good." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a systematic study on the application of parameter-efficient fine-tuning (PEFT) methods to Deep State Space Models (SSMs). The paper reveals that prompt-based methods are less effective for SSMs, while LoRA remains effective. The authors further propose SDLoRA, which selectively updates certain channels and states on SSM modules while applying LoRA to linear projection matrices, demonstrating improved performance over standard LoRA." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While the paper presents a novel approach for parameter-efficient fine-tuning SSMs, the innovation seems to be more incremental than groundbreaking. \n2. The paper lacks a detailed analysis of the computational overhead from the selective dimension tuning process. This is crucial to understanding the trade-offs between parameter reduction and computational efficiency in SDLoRA.\n3. The paper would benefit from a detailed analysis of SDLORA with hybrid models that combine SSMs and Transformers, as these models are becoming increasingly popular and have shown promise in various domains." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024parameterefficient,\ntitle={Parameter-Efficient Fine-Tuning of State Space Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=27n0kvWgqT},\nnote={under review}\n}" }, "abstract": { "value": "Deep State Space Models (SSMs), such as Mamba `(Gu \\& Dao, 2023)`, have emerged as powerful tools for language modeling, offering high performance with efficient inference and linear scaling in sequence length. However, the application of parameter-efficient fine-tuning (PEFT) methods to SSM-based models remains largely unexplored. This paper aims to systematically study two key questions: (i) How do existing PEFT methods perform on SSM-based models? (ii) Which modules are most effective for fine-tuning? We conduct an empirical benchmark of four basic PEFT methods on SSM-based models. Our findings reveal that prompt-based methods (e.g., prefix-tuning) are no longer effective, an empirical result further supported by theoretical analysis. In contrast, LoRA remains effective for SSM-based models. We further investigate the optimal application of LoRA within these models, demonstrating both theoretically and experimentally that applying LoRA to linear projection matrices without modifying SSM modules yields the best results, as LoRA is not effective at tuning SSM modules. To further improve performance, we introduce LoRA with Selective Dimension tuning (SDLoRA), which selectively updates certain channels and states on SSM modules while applying LoRA to linear projection matrices. Extensive experimental results show that this approach outperforms standard LoRA." }, "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": [ "parameter-efficient fine-tuning", "state space model", "mamba", "lora" ] }, "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/92dbb956d1528dcba9b76b94ddc2ba428238cd53.pdf" }, "presentation": null, "primary_area": { "value": "transfer learning, meta learning, and lifelong 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": "Parameter-Efficient Fine-Tuning of State Space 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": 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": { "value": "I have read and agree with the venue's withdrawal policy on behalf of myself and my co-authors." } }, { "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 169: what is the index t?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* Some of the problems identified in this work seem convincing -- the use of only two outputs, and the problem of decreasing likelihood of preferred responses. \n\n* Results show improvement compared to baselines on mt-bench and alpaca-eval\n\n* Analyasis shows reguarlization in SPO-abs in effective, indeed the prob. of the preferred response does not decrease anymore." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new offline alignment method - SPO and SPO-abs (I have seen SPO already at least twice, here is one instance https://arxiv.org/abs/2401.04056 so maybe a different acronym is needed).\n\nSPO is different from DPO is a few ways:\n1. Extension from binary classfication from multiclass classifiction by changing the sigmoid cross entropy loss in DPO to softmax cross entropy in SPO. This\n\n2. Adding softness by assuming a teacher model provides a distribution over the multiple possible responses and minimizing cross entropy with respect to that distribution (instead of assuming only a single response is labeled as gold). Notice this strongly assumes we can get a scalar reward for each response, which seems problematic unless the annotator is a machine learning model and not humans (see weaknesses below)\n\n3. in SPO-abs - adding a term that interprets rewards as logits of a sigmoid and essentially tries to maximize the log probability of samples from the base model. This is meant to combat the problem of decreasing probability in DPO and is somewhat orthogonal to the other points made.\n\nResults show that when using ultrafeedback as a reward/preference dataset and evaluating on MT-Bench and Alpaca-eval one obtains some gains compared to DPO and some natural extensions." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The authors claim that using a list of rewards is more efficient and convenient than using preferences. This runs in counter to past work even from the original summarization paper by Stiennon et al. (learing to summarize...) where the motivation to get preferences is that it is very hard to get scalar rewards from humans. And also counter to many other works (for example, self-play preference optimization, which is also coined SPO, where they show that scalar rewards are problematic because human preferences do not obey transitivity. So most empirical evidence points against using scalar rewards. The only reason to do this seems to be if you just have a model from which you distill the reward which is what happens in this work -- but the authors don't talk about this or argue for this. Is there claim that this approach is good assuming you are anyway going to use a teacher model to learn from? If the method is mostly applicable for model teachers that would be good to state precisely and as a limitation\n\n* The authors seem to not acknowledge/know relevant past work.\n(a) Using soft labels instead of hard labels - the paper \"robust preference optimization\" by fisch et al. from about 5 months ago already discusses at length use of soft labels. I think simple use of soft instead of hard lables was done even earlier in \"Human Alignment of Large Language Models through Online Preference Optimisation\" but I am not 100% sure.\n(b) There have been a few papers already addressing the problem of reducing likelihood - one is Pal et al. that the authors do cite but don't really mention the fact that they have a positive-DPO variant that adds a similar term for regularization as well as the Fisch et al. paper from above as well as Liu et al from 5 months ago (your SFT loss is implicity an adversarial regularizer)\n(c) Googling for a minute I found work on using lists of generations in alignment - LIRE -- https://arxiv.org/pdf/2405.13516 \n\n* The extension of the binary case to multiclass (when you don't consider softness) is somewhat incremental. Moreover, without softness it doesn't really exploit the full information in the list of generations - it only maximizes the probability of the single preferred response but doesn't take into account the relative preference of generations that are not the top-ranked ones. In assistant setting it is very hard to assume there is a single gold response, and thus modeling this as multiclass where there is a single correct class seems like a problematic modeling assumption.\n\n* The statement of what the authors are trying to solve is unclear - is it addressing multiple responses? is it addressing the case with scalar rewards? is it just the conjunction of both? is it the likelihood ratio decrease of DPO? It is hard to understand what the authors view as the key contribution.\n\n* Related work - the first paragraph in my humble opinion distracts from the flow - we don't need to go all they way back to BERT for this paper.\n\n* Experimentally - I did not understand the motivation for choosing the reasoning setup - is there any reason to think that SPO will be good for this setup? Is this an arbitrary choice? Also, there is a mismatch between the dataset used for training the the reward model / aligning the model and the actual benchmarks used for evalution and it is hard to reason about the results with the need to also generalize to out-of-distribution settings as part of the experiment.\n\n* minor: \"The soft label represents the important dark knowledge from the teacher LLM” I would encourage rephrasing - what does dark knowledge mean?\n\n* minor: The authors use the acronym LPO instead of SPO in the figures in the end, probably by mistake." }, "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 see weaknesses above.\n\nThere are also many small grammatical errors throughout the paper. While most of these do not significantly affect readability, they may be worth addressing, e.g.:\n* Abstract: “a adaptive loss” -> “an adaptive loss”\n* Abstract: “that inject listwise” -> “that injects listwise”\n\nOther nits:\n* Should use \\citep vs. \\citet in several places, e.g. lines 43 and 47\n* “dark knowledge“ seems like an odd way to describe the information contained in the knowledge distillation loss\n* Table 2 - the underline indicating the second highest number appears to be in the wrong place for the AlpacaEval column" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* The paper studies a generalization of the DPO objective to the multiclass setting, which could be a more efficient way to train models when there are multiple generations per prompt.\n* The paper evaluates the performance of the proposed objectives across multiple settings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Broadly, this paper studies the problem of alignment with offline algorithms such as DPO. The paper is distinguished from prior work in two ways:\n* While most prior work has focused on the setting where only pairwise preference judgements are available, this paper focused on the setting with scalar rewards assigned to a set of generations.\n* The authors propose new algorithms based on DPO, termed SPO and SPO-abs. \n\nSPO can be seen as a generalization of DPO, with two modifications:\n* The objective considers >2 generations per prompt, generalizing from the binary (i.e. K=2) case to the multiclass (K>2) case.\n* The prediction target is “soft” (i.e. based on the distribution from some teacher model) as opposed to “hard” (i.e. one-hot label from preference dataset).\n\nSPO-abs adds an additional term to the objective function that incentivizes assigning higher likelihood to preferred generations.\n\nThe authors compare SPO and SPO-abs with DPO and other baselines across several settings." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I think this paper could provide some interesting insights with some modifications. However, I think there are some serious weaknesses with the current version. To recap, the proposed algorithms vary from DPO in three ways:\n\n1. They generalize DPO from the binary to multiclass setting.\n2. They use a soft distribution from a teacher model as opposed to the “one-hot” labeled distribution.\n3. The `-abs` variant includes an additional term aimed at regularizing towards higher likelihood for preferred responses.\n\nI have concerns about each of these contributions individually.\n\n1. The generalization of DPO to the multiclass setting seems to be the most interesting contribution, as I am not aware of prior work studying this. However, it does not seem like this contribution was evaluated sufficiently on its own. Setting aside the other differences between SPO and DPO (i.e. “hard” vs. “soft” labels), when multiple generations per prompt are available, should we use the proposed multiclass objective or a binary objective over pairs of generations? What are the pros and cons? This is a very interesting question, but the DPO-1vs0 and DPO-pw baselines seem to conflate the other differences in SPO vs. DPO. It would also be good to consider the computational efficiency of such an approach. Is it more memory intensive to have an objective over K>2 generations?\n\n2. The use of a soft target distribution from a teacher vs. a hard target distribution has been proposed by prior work, which is not discussed here, e.g. the “Distilled DPO” (d-DPO) objective of Fisch et al. 2024 (https://arxiv.org/abs/2405.19316). I have not checked the math rigorously, but the proposed objectives seem to be equivalent for the K=2 case. It is still interesting to study a generalization of this objective to the K>2 case, but prior work should be discussed, and it feels misleading for the paper’s title to stress the *novelty* the proposed methods. The comparison between “hard” and “soft” labels also seems to be confounded by the fact that the “teacher model” is so much larger and more powerful than the “student” model being used as the policy. If we have the resources to train such a large “teacher” model, why not train an equally large “student”?\n\n3. For the additional objective in SPO-abs vs. SPO, this also seems to be lacking contextualization in prior work. For example, the authors say “We hypothesize the main cause of this decreasing likelihood is that SPO methods only adjust relative rewards among responses, rather than optimizing their absolute value.”, but this is more or less exactly the hypothesis proposed and studied by prior work (e.g. Pal et al. 2024 (https://arxiv.org/abs/2402.13228)). The proposed new term in the SPO-abs loss did not seem well motivated, i.e. why choose this specific formulation vs. some other? There was a mention of a connection to NCE, but this seemed underdeveloped and the connection was not clear to me. And, more importantly, it’s not clear why some approach from prior work, e.g. based on “DPO-Positive” from Pal et al. 2024 would not be sufficient? Minimally, this should be compared with empirically. Finally, some claims related to SPO-abs seemed confusing, e.g. the authors state “SPO-abs can also guarantee convergence to the optimal LM policy” but it’s not clear what guarantees are offered, or what evidence is provided to support such guarantees.\n\nTherefore, I think the paper would greatly benefit from a revision that more clearly establishes the connection to prior work and experiments that better disentangle the impact of the various aspects of the proposed methods. Proper contextualization with prior work and understanding the impact of the individual contributions is especially important given how crowded the space of proposed DPO variants has become.\n\nWhile some reviewers may take issue with the focus solely on the offline setting (and not comparing with online methods) or the limited model scales explored in the experiments, these seem like reasonable choices to me given the expense and complexity of experiments in this area." }, "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": "I find the initial related work to be too broad without sufficient coverage of recent work in offline alignment. TBH, both the first two paragraphs in related work seem too generic and unlike typical alignment papers. While this is not a problem in itself, the paper also does not sufficiently address more recent work in the direct-alignment/RLHF space and does not allude to any works in knowledge distillation. I also have concerns about claims that algorithms like KTO are only applicable to pairwise preference data since KTO clearly applies to pointwise preferences as a valid data point. I request the authors to provide more clarification in this: as this will help motivate their scalar reward learning with KD formulation and make it much clearer for the reader. \n\n\nLine 154: “Compared with constructing preference datasets, annotating each response with scalar rewards can\nbe more flexible and convenient.” —> are there any citations to back this claim? As far as preference annotations by humans of LLM responses are concerned, it is intuitively easier to get preferences/choices given as pair than to get exact scalar rewards for responses. This is because getting preferences only depends on the pair in focus while the annotator has to calibrate wrt the data distribution in assigning scalar rewards [1]. \n\n\nLine 469: The fig.1 plot seems to suggest expected rewards are plotted against steps and the numbers of the Y-axis are named as *rewards* in the legend. However, the prose in the paper claims y-axis represents likelihoods of the chosen and rejected responses. Are these rewards (as defined in the paper as log-ratios with the baseline policy) or average likelihoods of chosen and rejected responses? What is also concerning is that both fig.1 and 2 has typos in legends: SPO-abs is written as LPO> Can the authors provide some clarification on these issues and also for how many steps were these DPO and SPO-abs models trained since the x-axis in fig.1 represent percentage of total steps which does not clarify the total steps used for training.\n\n\nPresentation issues/typos;\n\n—“In order to\ndraws latent knowledge from the these powerful LLMs..”—> In order to draw..\n—Line 323: “We exam the following baseline methods:”—> We examine …\n\n—Line 467: “As shown in Figure 1. The likelihood of preferred responses” —> should be a comma with no caps on “the” likelihood…\n\n—Line 395: “Preferecne dataset” —> Preference dataset" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper clearly specifies its goal to devise an algorithm that learns from both preference labels as well as pointwise scalar rewards, that are both typically found in popular preference alignment from human feedback datasets. The benchmarks and baselines chosen are relevant and reasonable." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes two methods SPO and SPO-abs that are specifically designed to learn from both preference as well as scalar reward datasets. They conduct experiments with SPO policies learning from both reward and preference datasets and evaluate on benchmarks like MT-Bench, AlpacaEval and Ultrainteract. Their experiments suggests that their methods surpass previous preference learning baselines like DPO as well as pointwise learning methods like KTO on many of these benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper has some crucial weaknesses (unless I am missing something): their motivation for choosing a soft-label based knowledge-distillation (KD) approach is not clear and their related work does not mention any KD-based or Plackett-Luce based works that their main contribution (eq. 9) is clearly based off of. Furthermore, there are non-trivial presentation issues, many typos, mislabelling in figures and inconsistencies in prose vs figures etc in the current manuscript. Please read the questions for more 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": 5 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "Please see summary." }, "flag_for_ethics_review": { "value": [ "Yes, Research integrity issues (e.g., plagiarism, dual submission)" ] }, "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": "N/A" }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "N/A" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "**This paper is almost the same as [1].**\n\nSome Evidence:\n- The \"Preliminary\" section in this submission copies the \"Background\" in [1]\n- The \"Method\" section (section 4) is exactly the same as section 3 in [1], with the same derivation and objective.\n- Table 1 in this submission is almost the same as Table 1 in [1].\n- Baseline results in Table 2 in this submission is exactly the same number as in Table 2 in [1].\n\n**This is plagiarism and should be desk rejected.**\n\nReference:\n[1] Noise Contrastive Alignment of Language Models with Explicit Rewards. Chen et al. NeurIPS 2024. https://arxiv.org/abs/2402.05369." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "N/A" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@misc{\ndou2024a,\ntitle={A Novel Soft Alignment Approach for Language Models with Explicit Listwise Rewards},\nauthor={zhihao dou and Yi Zhao and Mige Zhu and Kaizhu Huang and Aaron Xuxiang Tian},\nyear={2024},\nurl={https://openreview.net/forum?id=28TLorTMnP}\n}" }, "abstract": { "value": "Existing alignment methods, such as Direct Preference Optimization (DPO), are mainly tailored for pairwise preference data where rewards are implicitly defined rather than explicitly given. In this paper, we introduce a general framework for large language model alignment, leveraging a novel optimization objective to bridge the gap in handling reward datasets with a list of responses explicitly annotated with scalar preferences scores.\n\nOur work comprise a novel algorithm, soft preference optimization, SPO, which enables the direct extraction of an LM policy from reward data as well as preference data. The core of SPO is a novel listwise preference optimization objective with the exponential-logarithm function form and a adaptive loss coefficient that inject listwise preference signals into the large language model. \n\nWe evaluate our methods in both reward and preference settings with Mistral models in different sizes. Experiments suggest that our method surpasses various preference baselines when reward datasets are available. We also find our method significantly outperforms DPO in complex reasoning tasks like math and coding." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": { "value": [ "~zhihao_dou2", "~Yi_Zhao18", "~Mige_Zhu1", "~Kaizhu_Huang1", "~Aaron_Xuxiang_Tian2" ] }, "authors": { "value": [ "zhihao dou", "Yi Zhao", "Mige Zhu", "Kaizhu Huang", "Aaron Xuxiang Tian" ] }, "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", "preference alignment", "listwise optimization objective" ] }, "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": { "value": "dou|a_novel_soft_alignment_approach_for_language_models_with_explicit_listwise_rewards" }, "pdf": { "value": "/pdf/c399d9168ad17867d01f90a88bdd54068cb71d19.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": "A Novel Soft Alignment Approach for Language Models with Explicit Listwise Rewards" }, "venue": { "value": "ICLR 2025 Conference Withdrawn Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Withdrawn_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. Why is Line 1035 equal to Line 1038?\n2. Why is Line 1378 equal to Line 1381?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The theoretical results are solid and novel. Specifically, it is interesting to see the transferability error can be connected with the empirical Rademacher complexity in a similar form with generalization bound, and the Hellinger distance can be used to quantify the inter-dependency across surrogate models.\n\n- The theoretical results can have a broader impact, as the analysis tools, such as those for bounding dependent random variables and the empirical Rademacher complexity for ensemble, can be applied elsewhere.\n\n- The writing is clear and easy to follow in general." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This submission theoretically studies the transferability error - the chance of being successful if the attack is generated by transferring from an ensemble of models. The core result is an upper bound of transferability error involving a vulnerability term and a diversity term, which further boils down to empirical ensemble Rademacher complexity and the Hellinger distance between joint model distributions and i.i.d. model distribution. The key insight is that the transfer attack needs to involve both more and diverse models and reduce model complexity to be powerful. Results are empirically verified on multiple datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The studied problem and practical implications may be limited. The analysis is only applicable for ensemble-based transfer attacks and it can only directly guide the design of more powerful attacks of this kind. How to leverage the analysis to better defend the model, or how to generalize the results beyond L2-bounded attacks, are worth further exploration.\n\n- Some insights from the theory may not be justified enough. For example, in Line 333-335, the paper mentioned that we need to increase the diversity of parameters in surrogate models to reduce $H_\\alpha(\\cdot)$. It seems that surrogate models need to be independently trained to achieve a minimal $H_\\alpha(\\cdot)$. However, in practice, encouraging model diversity, e.g., introducing some diversity-promoting regularizers, can sometimes further improve the attack efficiency. As a result, encouraging model diversity introduces model-level dependency and increases $H_\\alpha(\\cdot)$ but reduces transferability error. From this point of view, the theory may not reflect the full picture of adversarial transferability.\n\n- The experiment part is relatively not clear. For example, in Figure 2, good to mention that $\\lambda$ is the weight decay, explain what the $x$-axis is, and discuss detail training parameters in the main text. \n\n\nMinor:\n1. Line 106: combine -> combines\n2. Line 153: the hypothesis space maps to a discrete label space, and then the loss function $\\ell: \\mathcal{Y} \\times \\mathcal{Y} \\mapsto \\mathbb{R}_0^+$ has a discrete domain $\\\\{-1,1\\\\} \\times \\\\{-1,1\\\\}$ which is weird, may need some fix.\n3. Line 279: the redundant phrase \"provided in Appendix\"\n4. Line 1061: please define $R$ beforehand.\n5. Line 1281 - 1290: seems that there is a missing $1/N$ coefficient before all $\\sum_{i=1}^N f(\\theta_i; x)$." }, "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": "Other than the concerns pointed out in the weaknesses I have some additional questions for the authors:\n\n1. I have some confusion about the presented plots in the experiments which are not well-explained. Regarding the experiments, are you using mini-batch SGD as the optimizer? By \"# step\" on the x-axis do you mean the number of epochs? For loss value, is this the loss value of the expectation of logits on a training sample or test sample? Isn't that supposed to be decreasing as all the models are being trained?\n\n2. In figure 4, the variance of the logits from the models in the ensemble is shown to be increasing for CIFAR-10, but the number of epochs is too small and it is not clear whether the same trend continues. Could authors plot them with a higher number of epochs?\n\n3. The plots with increasing values of the variance of the logits from the models of the ensemble seem contradictory to Lemma 5 of [1]. The authors also mention for some datasets they see a decreasing trend similar to what is expected from Lemma 5 of [1]. Could the authors comment on the potential reasons for their different observations for other datasets?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper is well-written and well motivated with a good survey of related works.\n\nBy defining the transferability error, authors make a good analogy to generalization error and derive some corresponding results to provide a better understanding of model ensemble attacks.\n\nAuthors avoid the independence assumption that is used for studying generalization and derive an upper-bound is based on the divergence of the joint distribution of the parameters of the models from the case where they are independent." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a theoretical framework to explain the observations by prior empirical methods on increasing the effectiveness of model ensemble attacks. They define transferability error to measure the effectiveness of an adversarial example which is basically analogous to the generalization of the adversarial example to unseen trained models belonging to a specific function class. They also define an empirical version of Rademacher complexity as a measure of complexity for the input space for an ensemble of N classifiers and show that the transferability error is upper-bounded by a combination of this measure of input space complexity and the divergence of joint distribution of the model parameters of the ensemble from the product of their marginals which accounts for non-independence of the models of an ensemble." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) Authors connect their theoretical results with empirical observations in prior work regarding the diversity of the models; however, their definition of diversity does not match with many of these prior works. For example in [1] and [2] the diversity is defined as having gradient vectors (with respect to the inputs) with low cosine similarity. What authors consider as diversity here actually is supposed to decrease naturally according to Lemma 5 in [1]. Could authors clarify how their definition of diversity relates to these previous definitions in the literature, particularly those based on gradient similarity. \n\n[1] Yang, Z., Li, L., Xu, X., Zuo, S., Chen, Q., Zhou, P., ... & Li, B. (2021). Trs: Transferability reduced ensemble via promoting gradient diversity and model smoothness. Advances in Neural Information Processing Systems, 34, 17642-17655.\n\n[2] Kariyappa, S., & Qureshi, M. K. (2019). Improving adversarial robustness of ensembles with diversity training. arXiv preprint arXiv:1901.09981.\n\n\n2) The complexity of the models in the ensemble and the complexity of the input space seem to be used interchangeably sometimes. Equation 12 shows the complexity of input space defined by the authors, but in the follow-up discussion (line 342) it is mentioned that the model complexity has to be controlled when using stronger and more diverse ensembles.\n\n3) The interpretation of input space Rademacher complexity defined by the authors does not seem clear! The presented results suggest decreasing this complexity to achieve a tighter upper bound on the transferability error. However, decreasing this complexity means achieving a state where the sample in the input space is not able to achieve a high loss value for the models in the ensemble. This basically means that the optimization in equation 3 will achieve a lower value for equation 1 which authors are seeking to increase. This seems contradictory and it would be great if authors could clarify that. \n\n4) The experiments do not seem to be comprehensive in evaluating the presented theoretical results. For example, there is no analysis with respect to the complexity of the input space or the trade-off of diversity and complexity." }, "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": 3 }, "primary_area": null, "questions": { "value": "- Given the identified trade-off between vulnerability and diversity, could the authors suggest any criteria or metrics for balancing these components during ensemble model selection?\n\n- The experiments use standard datasets like MNIST and CIFAR-10, which may not fully represent the complexity encountered in real-world applications. Have the authors considered testing on more complex datasets (e.g. CIFAR-100, SVHN, ImageNet, etc.)\n\n- Can the author give the specific method of generating adversarial samples in the experiment and the specific meaning of \"steps\" in fig. 2, 3 and 4." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The paper demonstrates strong originality by addressing the theoretical gaps in model ensemble-based adversarial attacks, introducing the novel concepts of transferability error, vulnerability-diversity decomposition, providing well-founded upper bounds for transferability error." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper provides a theoretical foundation for model ensemble methods used to generate transferable adversarial examples. The authors introduce three new concepts: transferability error, diversity, and empirical Rademacher complexity, which together decompose transferability error into two primary components: vulnerability and diversity. Futhermore, the authors establish a bound on transferability error and propose practical guidelines to reduce it, such as increasing model diversity and managing complexity to prevent overfitting. Extensive experiments validate these findings​." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Although this paper provides a strong theoretical foundation, some limitations affect its overall impact. \n\nWhile the experiments are broad in scope, they can be enhanced by testing on a wider range of real-world scenarios or datasets outside of standard benchmarks such as MNIST and CIFAR-10 to verify applicability in more diverse contexts (e.g. CIFAR-100, SVHN, 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": "My main concerns are the implicit assumptions in Eq. (3), making the derivations much less interesting. Besides, the concluded practical guidelines are already widely applied in the literature, and there is also a lack of empirical comparisons to previous baselines for ensemble-based transfer attacks." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The Weaknesses of this paper include:\n\n- The writing is clear with intuitive explanations are in Figure 1.\n- Notations are clearly defined with neat formulations, the derivations are self-consistent.\n- The concluded practical guidelines are correct and already used in the literature." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents theoretical insights into model ensemble adversarial attacks. The authors define transferability error, which measures the error in adversarial transferability. They also discuss diversity and empirical model ensemble Rademacher complexity. The authors then decompose the transferability error to explain how it originated in the model ensemble attack. Furthermore, they derive bounds on the transferability error using complexity and generalization terms, and conclude three practical guidelines for reducing transferability error: (1) incorporating more surrogate models, (2) increasing their diversity, and (3) reducing their complexity in cases of overfitting. Some empirical evaluations are done on MNIST, CIFAR-10, and ImageNet." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The Weaknesses of this paper include:\n\n- **Implicit assumptions in Eq. (3).** The authors define the most transferable adversarial example $z^*$ in Eq. (3) as $z^*=\\textrm{argmax} L_P$, where $L_P$ in Eq. (1) is defined by taking expectation over $\\theta\\sim P_\\Theta$. This formulation has implicit assumptions that **1)** the target model share the same parameter space $\\Theta$ with the surrogate models, i.e., they have the same architectures; **2)** the target model follow the same distribution $P_\\Theta$ with the surrogate models, i.e., they apply the same (or same distribution) of training configurations. Both of these assumptions make the transfer problem overly simplistic, because in practice, the target model typically employs different model architectures and training configurations (including different datasets) than the surrogate models.\n\n- **Using Rademacher Complexity in deep cases.** First, I personally don't believe that Rademacher Complexity can convey reliable information when we are talking about deep networks. Second, Rademacher Complexity is more useful for asymptotic analysis, otherwise a lower upper bound of TE (i.e., Eq. (12)) does not indicate a lower value of TE.\n\n- **The three practical guidelines are already well-known.** While the authors demonstrated some theoretical bounds, the three guidelines they concluded—(1) incorporating more surrogate models, (2) increasing their diversity, and (3) reducing their complexity in cases of overfitting—are all well-known in literature. There is also a lack of empirical comparisons to previous baselines for ensemble-based transfer attacks." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024understanding,\ntitle={Understanding Model Ensemble in Transferable Adversarial Attack},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=28U5Olm32r},\nnote={under review}\n}" }, "abstract": { "value": "Model ensemble adversarial attack has become a powerful method for generating transferable adversarial examples that can target even unknown models, but its theoretical foundation remains underexplored. To address this gap, we provide early theoretical insights that serve as a roadmap for advancing model ensemble adversarial attack.We first define transferability error to measure the error in adversarial transferability, alongside concepts of diversity and empirical model ensemble Rademacher complexity. We then decompose the transferability error into vulnerability, diversity, and a constant, which rigidly explains the origin of transferability error in model ensemble attack: the vulnerability of an adversarial example to ensemble components, and the diversity of ensemble components.Furthermore, we apply the latest mathematical tools in information theory to bound the transferability error using complexity and generalization terms, contributing to three practical guidelines for reducing transferability error: (1) incorporating more surrogate models, (2) increasing their diversity, and (3) reducing their complexity in cases of overfitting. Finally, extensive experiments with 54 models validate our theoretical framework, representing a significant step forward in understanding transferable model ensemble adversarial attacks." }, "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 examples", "adversarial transferability", "model ensemble attack" ] }, "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/9e5f5feff8a7dd02f34ea09ba97c335a0b026dd5.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": { "value": "/attachment/88e7d3355cd7bed0e8b253e8aa4d52b09970bc12.pdf" }, "title": { "value": "Understanding Model Ensemble in Transferable Adversarial Attack" }, "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": "1. Please provide details of how synthetic data was generated. \n\n2. In Fig. 2M and O, I observed the high inconsistency in reconstruction accuracy in other models, e.g., mp_rSLDS_Gauss (per tial). This is synthetic data and, I assume, each session contains similar level of noise. It is very unclear how other baseline models nearly completely fails but performed near perfectly in some sessions. My question is, does this inconsistency supposed to support the robustness of CRIEMBO? \n3. With real data, the authors demonstrated the validity of CRIEMBO. While I agree with the author’s claim, it does not necessarily lead to the strength of CRIEMBO. Are there any interesting differences or unique patterns that can be obtained from CRIEMBO vs. other baseline models? \n4. The authors checked the robustness of CRIEMBO over different noise levels, with real data. More common practice is evaluating the robustness of the models with synthetic data, one with grountruth. If there were any specific reasons why it was done with real data, please specify it." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The major strength of this study comes from its conceptual advances embedded in the proposed CRIEMBO. While there have been efforts to model the high-dimensional brain dynamics via deep learning, many of models failed to yield interpretable features, which is the most important aspect in the neuroscience field. Thus, the neuroscience field still tends to rely on relatively simpler yet interpretable models. In this regard, I believe CRIEMBO proposed in this study can be a good solution for this gap, clearly upholding the originality of this study. The authors thoroughly examined the validity of the proposed model using simulated- and experimental data, leading to the high quality of this work. Plus, the clarity of this paper is relatively high as the model was well described in the manuscript, whereas the reviewer believes there are some rooms requiring the attention of the authors. Altogether, the scientific significance of this paper is clear and can be interesting to the electrophysiology field." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Through the study, the authors have proposed a novel analytical approach (named as “CREIMBO”) for learning dynamics of latent representations from high-dimensional electrophysiology. The major advances of CREIMBO comes from that compression of high-dimension and extraction of dynamics were conducted simultaneously in CREIMBO, while keeping the interpretability. Through experiments with synthesized- and real data, the authors have properly demonstrated the validity of CREIMBO in the study. As CREIMBO contains conceptual novelty and its validated effectiveness, I believe this model can be one of useful candidate models for studying high-dimensional, partially overlapped, data, such as intracranial EEG or multi-array spike data. Thus, CREIMBO will be useful to neuroscientists." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "While the overall strength of this study is obvious, there is a major weakness: Insufficient details in simulation study with synthetic data. It is unclear how the synthetic data was generated, e.g., what was the noise level, what kinds of parameters used for. Due to this uncertainty, it is nearly impossible to understand some of the intriguing findings in this study, especially with synthetic data. For example, there is very high inconsistency in performance across sessions in other methods, but not for CRIEMBO (Fig. 2M). While it can be interpreted as robustness of CRIEMBO, it is also possible the choice of benchmark models was not optimal for this type of synthetic data. Related to it, there was no comparison work with real data. Thus, the superiority of CRIEMBO needs further validation." }, "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": 4 }, "primary_area": null, "questions": { "value": "Can this model be applied to other organisms, like mice or rats? Extending the application to neuroscience animal models would greatly increase the impact of the presented work.\n \nHow difficult would it be to extend this model to use a Poisson observation model to better capture neural spiking activity?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The manuscript is clearly presented, referenced in the context of the relevant work and technically sound. The method was tested in simulations coming from the generative model, where the model was able to recover the parameter settings, and was able to better capture the variability compared to existing models. They also tested the model in neural recordings identifying functional connectivity between and within brain regions. Moreover, they tested the robustness to increasing levels of noise in the data." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The recent developments in neural recording technologies allow recording from large populations of neurons from multiple brain regions. Latent space models are often used to analyze these datasets, but they are generally limited to the study of single or few populations of neurons recorded simultaneously. To overcome these limitations, the presented work introduces a new algorithm that can capture variability across recording sessions and across and within brain regions. The method assumes a shared latent representation across areas and structured priors given each session. The authors validated the method on simulated data and neural data, showing that the model can capture variability across and within brain regions." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Further work in needed to fully understand the impact of this work. The authors motivate their work from their ability to better understand behavioral tasks from asynchronous recordings of spiking neural activity. However, the authors only tested the model in a single dataset where they limit the analysis to functional connectivity. It would be relevant to assess behavioral variables, such as ability to decode the present image stimuli from the learnt representations. Since the emphasis of the model is brain functional connectivity, the method should be compared to alternative recording methods such as fMRI or the LFP present in the dataset. Moreover, the comparison to alternative models in the neural data is limited to one qualitative test. A comprehensive quantitative comparison, such as decoding or reconstruction performance, is needed to understand the capabilities of the proposed method. Along the same lines, it is not surprising that the model outperforms alternative methods when the simulated data is tailored to the given model. A more relevant comparison would be simulating neural data from a neural process with temporal, task and/or behavioral variability and fit the different models there. It would also be relevant to highlight the model strengths and weaknesses in simulated data. One of the motivations for this work is its application to spiking data, but the Gaussian assumption limits its application to this kind of observation model and must be handled in preprocessing. While the authors verbally list this and other limitations in the discussion, it would be informative to show the limitations and, more importantly, capabilities of the model as 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": "- In line with my previous comment in weaknesses item 2, the authors show subject 10 in Fig. 3 which has a small number of available neurons compared to some other subjects (for Screening task). Are the example subcircuits and latent states extracted for the Screening task? Do the identified subcircuits and A matrices look denser for a higher number of neurons?\n- In the ablation study in Fig. 2 ‘All regions (sparse)’ A matrix, authors show that the CREIMBO cannot infer the true underlying connectivity matrix, which makes sense since the inductive bias on block-diagonal A matrix is removed. Do authors use the same A matrix initializations in this ablation study? If not, how would the results change if the same block-diagonal initialization is applied for ‘All regions (sparse)’ case? Can authors provide an intuition on why inferred latent factors deviate significantly from the true latents? Would the same hold in the K=1 case, in which, a similarity transform would exist between not block diagonal sparse, not block diagonal not sparse, and block-diagonal sparse A matrices? Also, are the authors showing trial and session averaged latent states in these figures? If so, how does single-session latents look like for CREIMBO?\n- The block structure imposed on A matrix seems like no regions have shared latent states and interregional interactions are captured by temporal dynamics. Did the authors try having some latent factors shared across all regions? How would it change the performance?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Authors provide a new perspective over existing models of multi-regional neural models by allowing their model to leverage multi-session recordings that can help extract global and robust interregional interactions. While doing that, they keep the interpretability in-tact unlike deep-learning approaches. \n- Authors performed exhaustive simulation experiments to show the importance of their model formulation. \n- The paper is well-written and easy to follow. The proposed model architecture and training framework are intuitive and effective." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose CREIMBO that can extract interpretable neural subcircuits underlying multiregional neural signals by utilizing multi-session recordings that can have a variable number of recording units, trials, and durations. Through simulations, they show that their model successfully uncovers the ground truth dynamics when multi-session recordings are modeled together. In real data analysis, authors show that identified subcircuits can be sparse indicating specialized functionality, and reveal across-region interactions." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Even though the paper is well written, the subfigures are very small and too crowded, which makes it hard to understand. Also, I think captions/labels for some appendix figures such as Fig. 11 should be improved. \n- It seems like the number of ensembles per region ($p_j$) is an important hyperparameter for CREIMBO. I wonder if the model would reveal some consistent subcircuits with small $p_j$, such that these subcircuits explain most of the variance and are consistent across sessions and subjects. Also, how would training times vary with large $p_j$ values such that $p \\approx N$? Overall, I think the scalability of such a model is an important aspect since modern neural recordings can include hundreds of neurons from one region, in such case, max($p_j$) = 7, can limit the interpretability of the identified subcircuits. \n- In simulations (Fig. 2F), the authors show that the single-session model underperforms CREIMBO by a large performance gap. This can be caused by a small number of trials in each simulated session and short trials, but I could not find these details in the text (if it is in Fig. 11, I think it requires explanations in the caption). If this is indeed the case, I wonder how single-session models' performance would increase with longer sessions. \n- I think the biggest contribution of CREIMBO is its multisession modeling over other approaches like mDLAG. However, the modern recording session can have hundreds of trials of data that can be sufficient to train models to understand multiregional dynamics. Therefore, I think it would be nice to see how their model compares to mDLAG even if it operates on a single-session basis. Based on Fig. 22, for the real dataset considered in this study, using multiple sessions in modeling seems important, and a comparison to mDLAG would highlight the importance of multisession modeling. Even though mDLAG does not learn dynamic matrices for temporal evolution, its learned readout matrices and lag parameters would still indicate interregional interactions, and I wonder if interregional interactions learned by mDLAG would be as poor as 'Session # (SPARSE)' in Fig. 22. Also, did authors compare their model to SLDS variants in real data as done for simulations? Overall, I think this work would benefit from more baseline comparisons to existing approaches in real data." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a framework for uncovering latent multi-regional neural sub-circuits by leveraging the richness and variability of multi-session neural data." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024creimbo,\ntitle={{CREIMBO}: Cross-Regional Ensemble Interactions in Multi-view Brain Observations},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=28abpUEICJ},\nnote={under review}\n}" }, "abstract": { "value": "Modern recordings of neural activity provide diverse observations of neurons across brain areas, behavioral conditions, and subjects; presenting an exciting opportunity to reveal the fundamentals of brain-wide dynamics. Current analysis methods, however, often fail to fully harness the richness of such data, as they provide either uninterpretable representations (e.g., via deep networks) or oversimplify models (e.g., by assuming stationary dynamics or analyzing each session independently). Here, instead of regarding asynchronous neural recordings that lack alignment in neural identity or brain areas as a limitation, we leverage these diverse views into the brain to learn a unified model of neural dynamics. Specifically, we assume that brain activity is driven by multiple hidden global sub-circuits. These sub-circuits represent global basis interactions between neural ensembles---functional groups of neurons---such that the time-varying decomposition of these sub-circuits defines how the ensembles' interactions evolve over time non-stationarily and non-linearly.\nWe discover the neural ensembles underlying non-simultaneous observations, along with their non-stationary evolving interactions, with our new model, **CREIMBO** (Cross-Regional Ensemble Interactions in Multi-view Brain Observations). CREIMBO identifies the hidden composition of per-session neural ensembles through novel graph-driven dictionary learning and models the ensemble dynamics on a low-dimensional manifold spanned by a sparse time-varying composition of the global sub-circuits. Thus, CREIMBO disentangles overlapping temporal neural processes while preserving interpretability due to the use of a shared underlying sub-circuit basis. Moreover, CREIMBO distinguishes session-specific computations from global (session-invariant) ones by identifying session covariates and variations in sub-circuit activations. We demonstrate CREIMBO's ability to recover true components in synthetic data, and uncover meaningful brain dynamics in human high-density electrode recordings---capturing cross-subject neural mechanisms as well as inter- vs. intra-region dynamical motifs." }, "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", "multi-regional brain interactions", "sparsity", "cross-session variability", "dynamical systems modeling", "neural dynamics", "non-simultaneous neural recordings" ] }, "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/223a42ca12add4f0fedc7a8476550ac941614a00.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/867767addb71089d7402d432375a4ca0b5d64609.zip" }, "title": { "value": "CREIMBO: Cross-Regional Ensemble Interactions in Multi-view Brain Observations" }, "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": "- **Q1**: Do you have any analysis of the dynamic sparsity ratio compared with PyramidKV?\n- **Q2**: Do you have any analysis of the dynamic approximated window size across different layers and heads?\n- **Q3**: Do you have results for other long-context benchmarks and longer context windows, such as RULER[1] and InfiniteBench[2]?\n- **Q4**: Typo corrections needed for quotation marks, e.g., #390, #511, #713-715. And incorrect references, e.g., in Figure 3's legend, \"Sec 4.2\" might be \"Sec 3.2\" (#294, #298)." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- This paper focuses on a practical and relevant topic.\n- The proposed matrix entropy-based method with dynamically allocated sparsity is intuitive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper focuses on the high latency and memory cost associated with long-context LLM inference by proposing UNComp, a training-free method that combines model compression and KV cache compression with a matrix entropy-based, dynamically allocated compression ratio. Specifically, the approach involves identifying similar layers and heads through offline search for compression, while using SnapKV with dynamic compression ratio of the KV cache with an approximated dynamic window size during inference. This paper test their method on the LongBench and NIAH benchmarks across four LLMs (Llama-2-7B/13B, Llama-3-8B, Mistral-7B-v0.1). Results indicate that UNComp offers slight improvements over baselines such as SnapKV, PyramidKV, and CHAI at the same compression ratio, although performance loss occurs when applying model compression." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The proposed approach is relatively straightforward and could be viewed as a combination of existing methods. For instance, the model compression component can function independently, yet no necessary baselines are provided for comparison in this area.\n2. The paper lacks sufficient ablation studies and analysis to demonstrate the contribution of each module in the proposed method. Specifically:\n - The improvement over PyramidKV appears to mainly derive from the dynamic approximated window size selection based on matrix entropy. However, there is no ablation study examining the effect of applying dynamic approximated window sizes to PyramidKV, or the performance impact of applying PyramidKV’s dynamic sparse ratio within UNComp. A comparison of dynamic sparsity ratios between this method and PyramidKV is also missing.\n - There is no analysis of the dynamic approximated window size across different layers and heads.\n3. The experiments are limited to LongBench and NIAH with a maximum context length of 32k, with no results for other state-of-the-art long-context benchmarks or longer contexts, such as RULER[1] and InfiniteBench[2].\n\n[1] RULER: What’s the Real Context Size of Your Long-Context Language Models? \n[2] InfiniteBench: Extending Long Context Evaluation Beyond 100K Tokens, ACL 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": "Please see the weaknesses 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 introduces a \u001dmatrix entropy to quantify the amount of information in each layer across the token sequence, which is then effectively applied for compression.\n- Using the metric at both the layer and head levels, the authors propose customized inter-layer and inter-head compression strategies, allowing for a more targeted approach to model compression.\n- The method undergoes extensive evaluation on diverse benchmarks, consistently delivering superior performance at comparable compression ratios." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces UNComp, an uncertainty-aware compression method designed to address memory and computational challenges associated with large language models (LLMs) during long-context inference. UNComp uses matrix entropy to estimate model uncertainty, applying selective compression across layers and attention heads based on these uncertainty levels. This approach preserves crucial information while enhancing efficiency in both memory and computational requirements." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* While Figure 3 aims to illustrate the overall workflow of the proposed method, it presents too much information at once, which makes it difficult to follow. One suggestion to improve readability is to break it down into subfigures or add step-by-step numbering to guide the reader through each part of the process. This adjustment would make the method’s workflow clearer and easier to understand.\n* An essential aspect of evaluating compression methods is understanding the trade-off between accuracy and throughput (or latency). However, this paper separates these metrics: Table 1 presents only accuracy, while Table 3 focuses solely on latency, making it challenging to assess the accuracy-throughput balance across different methods at a glance. Adding a combined table or figure that displays both accuracy and throughput would better support comparisons of this trade-off.\n* The paper primarily addresses end-to-end accuracy and latency but lacks an analysis of the compression ratio at each layer or head level within a single model (e.g., Llama3-8B-Instruct). Including this breakdown would provide greater insight into the internal dynamics and behavior of the model when applying the proposed method.\n* Although the authors claim that the proposed method achieves faster performance than CHAI despite a lower compression ratio, the reasons for this improvement are not sufficiently explained. Offering more details on which specific aspects of the method contribute to greater hardware efficiency and speed, beyond just compression ratio, would make this claim more convincing." }, "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. This method has many hyperparameters; how did you select them?\n2. If different heads retain a different number of tokens, does it affect parallel computation? If padding is used, how can true acceleration be achieved?\n3. Why does a deeper layer necessarily retain fewer tokens? From the picture, it appears that the effective rank may fluctuate." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The writing is clear and easy to follow.\n2. The source code is provided." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents UNComp, an innovative uncertainty-aware compression scheme designed to enhance the efficiency of large language models (LLMs) during long-context inference." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The number of groups seems to have little impact on performance, and sometimes fewer groups even yield better results. So why the complex design? However, if a uniform compression rate is applied, it feels like the paper doesn't contribute anything new.\n2. Different layers have varying levels of attention to tokens, so \"using the attention scores of the current layer to predict the tokens to be evicted in the next layer\" may pose significant issues.\n3. Lack of some baselines: streamingLLM[1],Quest[2],doublesparse[3] \n\n[1] Efficient Streaming Language Models with Attention Sinks https://arxiv.org/abs/2309.17453\n[2] Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference https://arxiv.org/abs/2406.10774\n[3] Post-Training Sparse Attention with Double Sparsity https://arxiv.org/abs/2408.07092" }, "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 simplication of the calculation of importance score may be the fulture major direction." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The concept of matrix entropy and effective rank is novel and useful for determining the token redundancy." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper finds that:\n1. higher-layer tokens gather more information, and a small number of tokens can represent the entire sequence\n2. For heads on the same layer, those with a higher effective rank should evict fewer tokens because this head is more informative\n3. Tokens of the same head in different layers gradually share information as the layers deepen, while tokens of different heads do not share information as the layers deepen. \n\nTherefore, based on the matrix entropy and effective rank, the KV cache and hidden states is compressed with training-free method, which achieves a compression rate of 4.74%, with a throughput increase of 6.4× and a 1.4× inference speedup in a single batch, incurring only a 1.41% performance loss." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The compression is based on the calculation of attention score and related accumulation, which introduces the onlince cost." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024uncomp,\ntitle={{UNC}omp: Uncertainty-Aware Long-context Compressor for Efficient Large Language Model Inference},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=28oMPC5bcE},\nnote={under review}\n}" }, "abstract": { "value": "Deploying large language models (LLMs) is challenging due to their high memory and computational demands, especially during long-context inference. While key-value (KV) caching accelerates inference by reusing previously computed keys and values, it also introduces significant memory overhead. Existing KV cache compression methods—such as eviction and merging—typically compress the KV cache after it is generated and overlook the eviction of hidden states, failing to improve the speed of the prefilling stage. Additionally, applying a uniform compression rate across different attention heads can harm crucial retrieval heads in needle-in-a-haystack tasks due to excessive compression. In this paper, we propose UNComp, an uncertainty-aware compression scheme that leverages matrix entropy to estimate model uncertainty across layers and heads at the token sequence level. By grouping layers and heads based on their uncertainty, UNComp adaptively compresses both the hidden states and the KV cache. Our method achieves a 1.6x speedup in the prefilling stage and reduces the KV cache to 4.74% of its original size, resulting in a 6.4x increase in throughput and a 1.4x speedup in inference with only a 1.41% performance loss. Remarkably, in needle-in-a-haystack tasks, UNComp outperforms the full-size KV cache even when compressed to 9.38% of its original size. Our approach offers an efficient, training-free Grouped-Query Attention paradigm that can be seamlessly integrated into existing KV cache schemes." }, "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": [ "KV Cache", "GQA", "Matrix entropy", "Uncertainty", "Efficient Inference" ] }, "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/22fe44bc81569da5032668e2194c25808b322186.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/958b8ed6cf78ac0777a1758b467cfad7be6a5781.zip" }, "title": { "value": "UNComp: Uncertainty-Aware Long-context Compressor for Efficient Large Language Model Inference" }, "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 do the prompting techniques generalize to other complex data structures beyond hypergraphs?\nCould the authors elaborate on the potential scalability issues of the prompting techniques with increasingly large and complex hypergraphs?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "Originality: The paper proposes a new benchmark and prompting techniques tailored for hypergraphs, addressing a gap in the assessment of LLMs' capabilities.\nQuality: The benchmark is comprehensive, covering a wide range of tasks and hypergraph types, which strengthens the validity of the findings.\nClarity: The paper is well-organized, with clear explanations of the hypergraph languages and prompting frameworks.\nSignificance: The work is significant as it pushes the boundaries of LLMs' understanding of complex data structures, which has implications for various real-world applications." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces LLM4Hypergraph, a benchmark designed to evaluate large language models' (LLMs) understanding of hypergraphs, which can capture complex, multi-way relationships beyond pairwise correlations found in traditional graphs. The benchmark includes 21,500 problems across low-order, high-order, and isomorphism tasks using both synthetic and real-world hypergraphs. The study evaluates six prominent LLMs and introduces novel prompting techniques to enhance LLMs' performance on hypergraph tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper could benefit from a deeper analysis of the limitations of the current LLMs in handling hypergraphs, beyond performance metrics.\nWhile the benchmark is comprehensive, it may lack diversity in terms of the types of real-world hypergraphs used, which could affect the generalizability of the findings." }, "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. Why is prompting a promising research direction for hypergraph understanding in light of other techniques such as function calling?\n2. What are the empirical graphs used in the experiments? What are the selection criteria?\n3. Some tasks involve computing tasks whose answers are numbers. How does the accuracy is computed for these tasks? Is it an exact match? Or allow some error under a certain threshold?\n4. Does the BAG and CoT outperform beyond statistical variations attributed to the variations of individual graph data and stochatic behaviors of LLMs?" }, "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 easy to read and the experiments are comprehensive and thorough." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper provided a new benchmark to evaluate the LLM's ability to understand hypergraphs and developed a new prompting framework to improve the hypergraph comprehension. The prompting framework demonstrated that CoT and BAG, adapted to hypergraphs, can improve the LLM's performance on hypergraph tasks, especially for high-order tasks such as Vertex Set Connection Checks and Vertex-Set--in-Hypergraph Checks using synthetic hypergraphs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Main arguments**:\n1. The paper adapts existing benchmarks and prompting techniques for hypergraphs. While the results offer some insights into the extent to which LLMs understand hypergraphs, they largely mirror findings for simple graphs---specifically, that CoT and BAG can enhance LLM performance. The only notable point is that using suitable language to describe hypergraphs can aid LLM comprehension, which is novel but trivial.\nGiven that the proposed techniques are naive adaptations of existing techniques and new insights specific to hypergraphs are not found, the contribution of the paper is incremental and not significant.\n2. The paper lays out a main motivation by the underexploration of (i) investigating the LLM's ability to understand hypergraphs and (ii) developing prompting framework for hypergraph understanding and argue that they are promising research directions. This is not a strong motivation, i.e., \"underexploration\" alone does not justify the promising research directions. More specific question is: why is prompting a promissing research direction for hypergraph understanding in light of other techniques such as function calling?\n3. Unsupported claim 1: In abstract, ``our specialized prompting framework incorporates seven hypergraph languages and introduces two novel techniques, Hyper-BAG and Hyper-COT, which enhance high-order reasoning and achieve an average 4% (up to 9%) performance improvement on structure classification tasks.'' This is not sufficiently supported by the empirical results. The performance improvement for Hyper-COT is 4\\% on average. However, for the Hyper-BAG, it is 2\\% for low-order hypergraphs and 2.8\\% for high-order hypergraphs.\n\n**Minor arguments**:\n1. Give the stochastic nature of the LLMs and the graph data, it is crucial to report the variation of the results across different runs (e.g., confidence intervals, standard deviations), given the performance gain of the proposed prompting techniques (Hyper-BAG and Hyper-COT) is slim.\n2. Unsupported claim 2: The paper claimed in the supporting information (B.4) that the benchmark represents the first instance that includes isomorphism checks. This is not precise. Isomorphism checks are a special case of Maximum Common Subgraph (MCS) problem, which is included in the existing benchmark cited in the paper (GraphArena Tang et al. (2024)). The author used \"in this domain\" to limit the scope of their claim, and it is crucial to spell out the \"domain\" (e.g., general graphs, or hypergraphs specifically) to be more precise.\n3. The paper did not provide descriptions about real-world graphs used in the experiments and their selection criteria." }, "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": "- How does the performance of LLMs depend on the hypergraph domains (e.g., emails, coauthorship)?" }, "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 the first benchmark for evaluating LLMs on hypergraphs.\n- The authors thoroughly address questions about hypergraphs.\n- The problems are well-structured and clearly categorized according to their objectives.\n- The code is released for reproducibility." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces LLM4Hypergraph, the first benchmark aimed at evaluating the ability of LLMs to understand hypergraph data. The authors design a series of tasks of varying difficulty levels and evaluate six different LLMs. Then, they identify their strengths and weaknesses. While this work represents a first step and provides a comprehensive study, there are several areas where improvement is needed." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The motivations for this research are not sufficiently discussed. Why is it important to enable LLMs to understand hypergraph structures? Are there potential practical use cases? Are there any motivations beyond the fact that similar research has been done with graphs?\n- The datasets used in the study are not comprehensive. To be specific:\n - The definition of \"hypergraph size\" is unclear. Is it referring to the number of nodes, the number of hyperedges, or the sum of hyperedge sizes?\n - The specific sizes of the hypergraphs (both real-world and synthetic) are not mentioned in the main content. How large are the synthetic hypergraphs used for evaluation?\n - According to the appendix, even the so-called \"large-scale hypergraphs\" only contain 15 to 20 vertices, which is too small to meaningfully capture higher-order structures typically expected in hypergraphs.\n - The synthetic hypergraphs are not sufficiently representative. There are other synthetic hypergraph models (e.g., configuration models) available.\n - It is unclear how the random walk approach for sampling sub-hypergraphs from real-world hypergraphs (Appendix A.2) ensures that the sampled hypergraphs \"retain the intricate and authentic correlations inherent in the original data.\"\n- The definition of task \"difficulty\" is unclear.\n- The authors may consider discussing/citing the recent work \"When LLM Meets Hypergraph: A Sociological Analysis on Personality via Online Social Networks\" (CIKM 2024) in the related work.\n\n**In summary**, this paper makes a valuable contribution to LLMs and hypergraph analysis. However, the benchmark datasets lack comprehensiveness and have room to consider additional synthetic hypergraph generators. Also, the paper lacks detailed statistics on real-world hypergraphs. Scalability is also a concern; if large-scale hypergraph handling poses challenges for LLMs, these limitations should be clearly discussed." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024beyond,\ntitle={Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=28qOQwjuma},\nnote={under review}\n}" }, "abstract": { "value": "Existing benchmarks like NLGraph and GraphQA evaluate LLMs on graphs by focusing mainly on pairwise relationships, overlooking the high-order correlations found in real-world data. Hypergraphs, which can model complex beyond-pairwise relationships, offer a more robust framework but are still underexplored in the context of LLMs. To address this gap, we introduce LLM4Hypergraph, the first comprehensive benchmark comprising 21,500 problems across eight low-order, five high-order, and two isomorphism tasks, utilizing both synthetic and real-world hypergraphs from citation networks and protein structures. We evaluate six prominent LLMs, including GPT-4o, demonstrating our benchmark’s effectiveness in identifying model strengths and weaknesses. Our specialized prompt- ing framework incorporates seven hypergraph languages and introduces two novel techniques, Hyper-BAG and Hyper-COT, which enhance high-order reasoning and achieve an average 4% (up to 9%) performance improvement on structure classification tasks. This work establishes a foundational testbed for integrating hypergraph computational capabilities into LLMs, advancing their comprehension." }, "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": [ "LLMs", "Hypergraph", "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/2a703467541d1cda061b2bd22caa36eb2a606536.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/9ade7404203f8ba5113bbb35f0beeb8efdb09d67.zip" }, "title": { "value": "Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?" }, "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": 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: Figure 2 is missing? \n\n2: What is the value for 'depth' in Figure 11? From top to the bottom, is the key information located at the beginning of the context to the tail of the context? \n\n3: Gemma series have a unique attention head dimension of 256 rather than 128. It might have interesting impact on the long context things. It would be better to have results with Gemma series as the tested models." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1: The benchmark is comprehensive. It covers most real-world long context use cases.\n\n2: The investigation of performance correlation among all task types are insightful. It provides a new perspective to understand LLMs' long context ability.\n\n3: The improvement to prompting strategy and evaluation method effectively stabilizes the evaluation results." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper constructs a comprehensive benchmark to test LLMs' long context abilities. It covers various types of tasks such as RAG, ICL, LongQA, Retrieval, Re-rank and so on. The used prompts and evaluation metrics and carefully designed to ensure both IFT models and base models can give predictions. This benchmark also evaluates most commonly recognized LLMs and accordingly provides insights about LLMs' long context performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1: The so called \"expected\" ranking of LLMs is a bit subjective. \n\n2: Lack of some deep analysis to interesting results, such as why the json-kv task has higher correlation with re-rank than RAG or LongQA\n\n3: The RoPE scaling settings are not suitable for 128k/64k testing. With ABF, usually, the scaling factor should be at least 2x the target extension ratio. With 8k context, Llama3 should use at least a scaling factor of 32 for 128k testing." }, "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 address the weaknesses in the previous section." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\t**Diverse Task Design**: HELMET includes seven categories of tasks, enhancing the representativeness of LCLMs in real applications.\n\n2.\t**Support for Ultra-Long Inputs**: This benchmark accommodates input lengths over 128k tokens, making it suitable for evaluating the long-context capabilities of frontier models.\n\n3.\t**Reliable Model-Based Evaluation**: HELMET’s evaluation metrics reflect human judgment better than traditional n-gram matching, offering more reliable model ranking.\n\n4.\t**Compatibility with Base Models**: The benchmark allows evaluations of base models that haven’t undergone instruction fine-tuning, broadening LCLM applicability." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a new benchmark called HELMET, which is designed to comprehensively evaluate the performance of long-context language models (LCLMs). Current LCLM evaluations largely rely on synthetic tasks, like Needle-in-a-Haystack (NIAH), or arbitrary subsets of some datasets. However, these methods present issues such as high noise, insufficient coverage of downstream applications, inadequate dataset lengths, and unreliable metrics. HELMET aims to address these shortcomings by expanding task diversity across seven application-centric categories (including long-document QA, citation-based generation, etc.), supporting controllable input lengths up to 128k tokens, and implementing model-based evaluations for more reliable results. Through testing 51 LCLMs, this study finds that synthetic tasks are poor predictors of downstream performance, open-source models fall behind closed-source models on complex long-context tasks, and there is low correlation among task categories, highlighting the need for multi-dimensional LCLM evaluation ." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\t**High Complexity**: With multiple tasks and model comparisons involved, HELMET’s setup and evaluation process is intricate and demands considerable effort from researchers.\n\n2.\t**Low Correlation Among Some Tasks**: The low correlation between different tasks may make it challenging to assess a model’s overall long-context handling ability if it performs exceptionally in only certain tasks.\n\n1. **High Resource Consumption**: Running the full suite of HELMET tasks is time-intensive. It would be beneficial to identify a few key subtasks that can maintain consistency with the results of full testing, allowing for time-saving evaluations." }, "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. In your analysis, you conclude that existing benchmarks like RULER and ∞BENCH are unreliable because larger models sometimes perform worse than smaller ones, which contradicts human expectations. Could you elaborate on why you attribute these unexpected results to benchmark unreliability rather than potential issues with the larger models themselves? Did you investigate alternative explanations for the performance discrepancies?\n2. Do you have any results from human evaluation that validates the model-based evaluation metrics? What were the human-model agreement rates? Were there any notable discrepancies between the human judgments and model-based evaluations?\n3. Other than RAG, which types of tasks in HELMET are compatible with the base model without instruction following capabilities?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* The paper attempts to provide a standardized, holistic benchmark for LCLMs, whose adoption can potentially improve consistency and reliability in model evaluation and comparison.\n* The evaluation is extensive -- 51 LCLMs across multiple dimensions, tasks, input lengths, and model types (open-, closed-source)\n* The paper provide some valuable findings and insights into the performance of LCLMs, e.g. the limitations of synthetic tasks as predictors of real-world performance and where the performance gaps are between open- and closed-source models. This can guide future research and model development." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents HELMET, a benchmark for evaluating long-context language models (LCLMs) that try to address limitations in existing evaluations, which often rely on synthetic tasks lacking real-world applicability. HELMET includes 7 diverse, application-centric tasks and supports input lengths up to 128k tokens. Through evaluating 51 LCLMs, the authors demonstrate that synthetic tasks are poor predictors of downstream performance, different task categories exhibit distinct trends, and open-source models significantly lag behind closed-source models on complex tasks requiring reasoning over long contexts. They advocate for holistic evaluation across diverse tasks to gain a comprehensive understanding of LCLM capabilities." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The authors observe that on existing benchmarks like RULER and ∞BENCH, smaller models (e.g., Llama-8B) sometimes outperform larger ones (e.g., Gemini Pro, Llama-70B), and they conclude that these benchmarks are unreliable because they do not reflect human expectations that larger models should perform better. This reasoning may be premature and somewhat biased. It's possible that the larger models genuinely underperform on these benchmarks due to specific issues, such as overfitting, architectural limitations, or difficulties in handling certain tasks. The benchmarks might be accurately capturing these performance discrepancies. Dismissing unexpected results as benchmark unreliability without thoroughly investigating the underlying causes undermines the validity of the authors' argument. More analysis considering both the possibility of model issues and benchmark limitations would strengthen the conclusions.\n* While the paper introduces model-based evaluation metrics using 4o to address the unreliability of traditional metrics like ROUGE, it provides limited details on how these metrics were validated against human judgments. Including more detailed results or analysis of human-model agreement would strengthen the validity of the evaluation methodology.\n* Although the paper critiques existing benchmarks, it could offer more in-depth analysis demonstrating how HELMET improves over them in practice. Figure 1 seems to be the only place where a direct comparison is shown. Conducting more direct comparisons of model rankings or performance differences on HELMET and existing benchmarks and providing concrete evidence of HELMET's advantages would strengthen the paper's arguments." }, "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": "- How well does HELMET handle variations in domain-specific tasks, such as medical or financial documents?\n- Could open-source models trained on synthetic datasets achieve comparable results with additional tuning on HELMET's diverse tasks?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- HELMET covers diverse tasks such as retrieval-augmented generation, passage re-ranking, and long-document QA, providing a comprehensive test bed for evaluating the full capabilities of long-context models.\n- By introducing controllable length settings and using model-based metrics instead of n-gram matching, HELMET offers a better reflection of human judgments and real-world performance.\n- The authors evaluate 51 models, providing valuable insights into how different architectures and model sizes handle long-context tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes HELMET, a benchmark designed to evaluate long-context language models across seven application-focused categories, addressing issues such as inadequate dataset length, noisy evaluation metrics, and inconsistencies in current benchmarks. Through empirical evaluation on 51 models, the authors argue that HELMET offers better differentiation among models compared to traditional synthetic tasks and demonstrates the inadequacy of simple benchmarks in predicting real-world performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- While HELMET’s application-oriented tasks are extensive, they may not fully capture long-context models’ capabilities in highly specific domains like legal or medical texts, limiting its applicability in niche areas.\n- The heavy reliance on closed models such as GPT-4 for comparison leaves open questions about the efficacy of HELMET in an entirely open-source setting, which may limit reproducibility for some researchers." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024helmet,\ntitle={{HELMET}: How to Evaluate Long-context Models Effectively and Thoroughly},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=293V3bJbmE},\nnote={under review}\n}" }, "abstract": { "value": "Many benchmarks exist for evaluating long-context language models (LCLMs), but developers often rely on synthetic tasks like needle-in-a-haystack (NIAH) or arbitrarily selected subsets of datasets. It remains unclear whether these evaluations translate to the diverse downstream applications of LCLMs, and the inconsistency further complicates model comparison. We investigate the underlying reasons behind current practices and find that existing benchmarks often provide noisy signals due to low coverage of long-context applications, insufficient dataset lengths, unreliable metrics, and incompatibility with base models. In this work, we present HELMET (How to Evaluate Long-context Models Effectively and Thoroughly), a comprehensive benchmark encompassing seven diverse, application-centric categories. We also address many issues in previous benchmarks by adding controllable lengths up to 128k tokens, model-based evaluation for reliable metrics, and few-shot prompting in all tasks for evaluating base models. Consequently, we demonstrate that HELMET offers more reliable and distinct rankings of frontier LCLMs. Through a comprehensive study of 51 LCLMs, we find that (1) synthetic tasks like NIAH are not good predictors of downstream performance; (2) the diverse categories in HELMET exhibit distinct trends that do not correlate well with each other; and (3) while most LCLMs achieve perfect NIAH scores, open-source models significantly lag behind closed ones when the task requires full-context reasoning or following complex instructions---the gap widens with increased lengths. Finally, we recommend using our RAG tasks for fast model developments, as they are easy to run and more predictive of downstream applications than existing synthetic tasks; but ultimately, we advocate for a holistic evaluation across diverse tasks. We hope HELMET serves as a valuable resource for future long-context model development." }, "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": [ "long-context language models", "benchmarking" ] }, "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/131ca2c1addef801223575e751b8ad41c31fb549.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": "HELMET: How to Evaluate Long-context Models Effectively and Thoroughly" }, "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": 2 }, "primary_area": null, "questions": { "value": "See above." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Proposing an exciting application for cGANs and generative models in simulating EM propagation\n- Providing a dataset of 64M simulated heatmaps with various indoor models\n- Adding physical inductive bias to the model so that the generations are physically plausible." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a generative framework for simulating Electro-Magnetic wave propagation, as a faster replacement for ray tracing approaches usually used in this application. Authors propose a method based on using cGAN and regularized by physical constrains to generate plausible propagation heatmaps given the structure of the scene. They show through experiments that although the performance is not on-par with Ray Tracing methods, this method allows for a faster simulation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Main:\n- The proposal to use random input to a GAN to avoid mode collapse is not very well-justified. The proposed set-up is very similar to the common conditional GANs which can easily have mode collapse. Further when adding regularizations, such as the physical regularizations proposed, the risk for mode collapse is increased. The authors mention building the model from simpler problem up to the aimed taks and this helps with fine-tuning and perhaps mode collapse. It would be great to have more experiments/analysis on what is the breaking point and why the model is stable in its final version.\n\nMinor:\n- What representation is used for the conditional geometry? A more thorough description of the modality in line 162 would be helpful. It is unclear how the 3D model is encoded and given to a GAN.\n- Figure 2 should be labled with yours vs baseline so its easier to read. The interpretation in the caption as what is the weakness vs strength of your method is not easily understandable from the heatmaps and would be great to highlight them visually." }, "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": "- Could you clarify how the method handles true 3D propagation versus 2D layout information? The current results only show 2D heatmaps. Could you provide vertical propagation results at different heights? How does the network architecture specifically process and maintain height information?\n- Please describe in detail how the 2K+ room models were created/sourced. What is the distribution of room types, sizes, and configurations in your dataset? How to ensure the synthetic scenes are physically realistic? How are different materials modeled and validated?\n- How to determine the weights (α, β, γ) in the physics loss function? What measures are taken to ensure training stability? How to handle varying room sizes in the network?" }, "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 an interesting application of conditional GANs to EM simulation. While both GANs and EM simulation are established fields, their combination for real-time indoor propagation simulation represents a fresh approach to an important practical problem.\n- The method achieves notable acceleration (reported 5X speedup) compared to traditional ray tracing methods. If these results can be thoroughly validated, this could be valuable for real-time applications.\n- The attempt to incorporate electromagnetic principles through specialized loss terms (direct propagation, reflection, and diffraction) shows thoughtful consideration of the physics involved, though the theoretical guarantees need more examination.\n- While the dataset generation process needs better documentation, the collection of indoor scenes and EM simulation results could be useful for future research in this direction." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents EM-GANSim, a learning-based approach for real-time electromagnetic (EM) propagation simulation in indoor environments. The core technical contribution is a modified conditional GAN architecture that incorporates both geometric information and transmitter location to predict power distribution heatmaps while adhering to electromagnetic propagation principles. The authors propose a physically-inspired learning framework that integrates direct propagation, reflection, and diffraction effects through specialized loss terms in the GAN's objective function.\n\nThe method claims to achieve comparable accuracy to traditional ray tracing-based simulators while offering significant speed improvements (reported as 5X faster). The authors evaluate their approach on 15 indoor scenes and provide ablation studies examining the impact of noise and physical constraints. They also introduce a dataset comprising over 2,000 indoor scene models with corresponding EM simulation heatmaps.\n\nWhile I am not an expert in electromagnetic propagation simulation and wireless communications, the paper appears to address an important practical challenge in real-time EM simulation. However, there is some ambiguity in how the method handles true 3D environments versus 2D representations, and the room generation and data preparation processes could benefit from clearer documentation. The paper presents an interesting application of deep learning to physics-based simulation, though both its theoretical foundations and physical accuracy need closer examination." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- A weakness is the unclear treatment of \"3D\" simulation. While the paper claims to handle \"3D indoor environments,\" the evidence presented is primarily 2D heatmaps. There's no clear explanation of how height information is processed in the network, no visualization of vertical propagation effects, and no analysis of height-dependent signal variations. Table 2 only specifies area (square meter) without height information. The paper needs to either demonstrate true 3D capability or clarify that it's a 2.5D approach.\n- Critical details about the \"2K+ models and 64M heatmaps\" are missing. The paper doesn't explain how these indoor scenes were generated, validated, or processed. Without this information, readers cannot assess data quality or reproduce the results.\n- The method description lacks important specifics. The GAN architecture details, training process, and hyperparameter selection are not fully described. The physics-based loss weights lack justification, and there's minimal discussion of training stability.\n- The experimental validation relies mainly on MSE comparisons. The performance measurements lack important context - hardware specifications, memory requirements, and preprocessing costs are not reported. The gap between training (3 dbm²) and testing (8.5 dbm²) MSE also needs explanation." }, "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": "The organization of this paper is pretty \n1. Why not show the quantative results in the manuscript?\n2. Why report the avg. mse?\n3. what is the method name for 'the traditional RT approach'?Please make this clear and cite." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The employment of cGAN itself is novel, though it seems to me not much significant change is made to the cGAN architecture and it's more like a domain adaptation. The equations in the paper are quite solid, showing the authors good understanding. The paper has strong experiments across 15 scenes with clear performance metrics showing 5X speedup. The ablation studies are well-structured and there is thorough comparison with established methods demonstrate robust methodology.\nThe proposed method achieves an impressive real-time speed while maintaining robustness, which suggests the method is of good quality. \nThe authors claim to release the code and data to benefit the community." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a novel approach to real-time electromagnetic (EM) propagation simulation in complex 3D indoor environments, utilizing a physics-inspired conditional generative adversarial network (cGAN) model. This research is positioned as the first real-time algorithm for EM simulation in these environments, and it holds potential value for applications such as 5G network planning, wireless communication system design, and dynamic indoor environments requiring rapid signal strength calculations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper lacks comparisons with state-of-the-art methods. It only presents scores in comparison to DCEM, and the detailed quantitative results are relegated to supplementary materials rather than the main manuscript. I've calculated the detailed score: GAN-based (dBm²): 8.69; DCEM (dBm²): 7.89.\nThis suggests that the GAN-based method performs slightly worse than the DCEM method introduced at VTC 2022. Given the recent methods proposed in the literature (Vaganova et al., 2023; Wang & Manocha, 2023; Haron et al., 2021; Gómez et al., 2023), incorporating more experimental results could lead to a fairer and more comprehensive evaluation.\n2. Though the proposed method is faster than traditional RT-based methods, it still takes 3-4 seconds to simulate one room. This is far from what the author frequently claimed to contribute to the 'real-time data analysis.' If the real-time is for per data point, then the traditional RT-based methods are real-time, too.\n3. The introduction could be strengthened by including a more detailed rationale for using cGAN in this context, specifically on how its features address the problem. Although section 5 provides some of this analysis, highlighting it earlier would improve the logical flow.\n4. The qualitative comparison is not sufficient, and the conclusion \"We see with GAN-based methods that the heatmaps show less MSE in general captures and exhibit more pronounced areas of both high and low signal strength, suggesting a finer granularity in the simulation of received powers.\" is ambiguous. How can the simulation judge by \"more pronounced areas of both high and low signal strength\"? Besides, the mean mse is higher!\n\nMinor:\n1. The plots and tables are not well designed, which makes them hard to understand. e.g. Figure 1 fails to demonstrate the overall method clearly. There is space to improve with regard to color design (Figure 3) and table format. In Figure 2, labels should be inserted into the plot instead of writing the first row: xxx, second row: xxx...in the caption.\n2. There are too many {enumerate} and {itemize}, which is not so common in papers. It would take a lot of space and make the paper look loose. \n3. Minor inconsistencies in grammar and terminology, such as a misplaced comma and inconsistent use of terms like \"ray tracing\" versus \"ray-tracing,\" which should be standardized.\n4. Table 3 is hard to understand. Why \"Generation time per data point (seconds)\" is a column?" }, "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": "Have authors tested the Wasserstein loss for GAN? If yes, were the results worse or better? What applications do authors supposed to test the GAN on? Is it possible to verify the trained model on tracking tasks? In the results section the different materials that objects are made of are mentioned. Did authors analyze the dependance of GAN performance on material? What kind of sensors were used for dataset collection? How do authors measure the sensors’ accuracy? What filters were used to process the raw data of sensors?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "In my opinion applying the GAN-based model to electromagnetic signal processing seems to be the strongest point of the paper: traditional approaches to this task were studied and comprehensively modified by many authors. For this reason, I find the GAN-based approach to be original and good scientific ground. Moreover, the presented method provides a robust result and achieves a 5X speed compared with other pipelines which is important in real-time applications. The Generator and Discriminator training process is described carefully.\n\nDespite the weaknesses, I recommend accepting this work because the authors used an interesting and difficult architecture in a complicated task, and the numerical results were provided. The main reason to accept this paper is strong description of the GAN training, which stresses that the authors comprehensively researched GAN opportunities in the EM propagation domain." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The new algorithm of real-time electromagnetic signal processing is presented in this paper. Authors trained a GAN-based neural network to predict electromagnetic power distributions in 3D indoor environments. The indoor electromagnetic signal processing is fundamental problem of indoor tracking, so the work is valuable and actual." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "However, several weak parts were figured out despite the strong points of the paper. The first question concerns the dataset that authors present in the paper. It seems that this dataset was used for training. This dataset is new so it is expected that there will be a pipeline of dataset generation. However, there is no description how this dataset was collected. This information allows us to judge how accurate the GT labels are. The absence of the source code and developed dataset deprives the opportunity to test the pipeline." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "A novel GAN-based approach for real-time 3D indoor electromagnetic simulation, drastically reducing computation time while maintaining accuracy." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024emgansim,\ntitle={{EM}-{GANS}im: Real-time and Accurate {EM} Simulation Using Conditional {GAN}s for 3D Indoor Scenes},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=29JDZxRgPZ},\nnote={under review}\n}" }, "abstract": { "value": "We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation that is used for wireless communication simulation in 3D indoor environments. Our approach uses a modified conditional Generative Adversarial Network (GAN) that incorporates encoded geometry and transmitter location while adhering to the electromagnetic propagation theory. The overall physically-inspired learning is able to predict the power distribution in 3D scenes, which is represented using heatmaps. Our overall accuracy is comparable to ray tracing-based EM simulation, as evidenced by lower mean squared error values. Furthermore, our GAN-based method drastically reduces the computation time, achieving a 5X speedup on complex benchmarks. In practice, it can compute the signal strength in a few milliseconds on any location in 3D indoor environments. We also present a large dataset of 3D models and EM ray tracing-simulated heatmaps. To the best of our knowledge, EM-GANSim is the first real-time algorithm for EM simulation in complex 3D indoor environments. We plan to release the code and the 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": [ "Generative Adversarial Networks (GAN)", "Electromagnetic Propagation", "Real-time Simulation", "3D Indoor Environments" ] }, "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/14594ce9613897746e50c37f5ce7df92ad457b46.pdf" }, "presentation": null, "primary_area": { "value": "learning on time series and dynamical systems" }, "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/4ba4ac8ffde4dcec176ff3b6f9fa566e06700166.zip" }, "title": { "value": "EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes" }, "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. The paper addresses injecting clean-label backdoors into LLMs under the assumption that the attacker has full control over the training process, making this setup counterintuitive and somewhat confusing. The main advantage of clean-label backdoor attacks is their stealthiness, as they can bypass human inspection. Most existing clean-label backdoor attacks operate under a data poisoning assumption, where the attacker only provides poisoned data without controlling the training process [1, 2, 3, 4]. In this scenario, model trainers may inspect the received data before using it for training. Due to the label consistency in clean-label backdoor attacks, simple human inspection cannot detect the poisoned samples, which makes them stealthy. However, in a training control setup [5, 6], the stealth advantage of a clean-label backdoor is irrelevant because the attacker will only release the poisoned model, without exposing the poisoned training data. This means there is no data inspector in such a scenario, and attackers can freely manipulate data to ensure successful backdoor injection while maintaining benign performance. Therefore, the motivation for studying clean-label backdoors in a training control setup is unclear.\n\n2. The paper claims that PEFT algorithms struggle to successfully inject backdoors into LLMs. According to Table I, even dirty-label attacks (e.g., BadNets) using PEFT only achieve a 15.51% ASR on the SST-2 dataset. This observation contradicts recent literature on LLM backdoors [7, 8, 9]. For example, [7] reports successful backdoor injection into LLMs using QLoRA, and [8] proposes a fine-tuning method similar to PEFT that achieves effective backdoor injection. Can the authors clarify the reasons behind these contradictory findings?\n\n3. One of the main advantages of W2SAttack is its ability to inject backdoors into models that cannot be trained using full-parameter fine-tuning due to computational constraints. Therefore, it would strengthen the paper if the authors included results from applying W2SAttack to larger open-source LLMs, such as Llama-2-70B or Mixtral-8x7B. This would further support the argument for the proposed attack.\n\n4. Another point of concern is that the paper focuses primarily on LLM discriminative tasks, such as sentiment classification, whereas LLMs are now predominantly used for generative tasks. Recent works have also explored backdoors in LLMs for generative tasks [10, 11]. It would be valuable if the authors extended their proposed attack to generative tasks to determine if the same observations hold in those contexts.\n\n---\n\nReference \n\n[1] Liu, Yunfei, et al. \"Reflection backdoor: A natural backdoor attack on deep neural networks.\" Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16. Springer International Publishing, 2020.\n\n[2] Barni, Mauro, Kassem Kallas, and Benedetta Tondi. \"A new backdoor attack in cnns by training set corruption without label poisoning.\" 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019.\n\n[3] Zeng, Yi, et al. \"Narcissus: A practical clean-label backdoor attack with limited information.\" Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. 2023.\n\n[4] Turner, Alexander, Dimitris Tsipras, and Aleksander Madry. \"Clean-label backdoor attacks.\" (2018).\n\n[5] Cheng, Siyuan, et al. \"Deep feature space trojan attack of neural networks by controlled detoxification.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 2. 2021.\n\n[6] Doan, Khoa, et al. \"Lira: Learnable, imperceptible and robust backdoor attacks.\" Proceedings of the IEEE/CVF international conference on computer vision. 2021.\n\n[7] Huang, Hai, et al. \"Composite backdoor attacks against large language models.\" arXiv preprint arXiv:2310.07676 (2023).\n\n[8] Li, Yanzhou, et al. \"Badedit: Backdooring large language models by model editing.\" arXiv preprint arXiv:2403.13355(2024).\n\n[9] Li, Yige, et al. \"Backdoorllm: A comprehensive benchmark for backdoor attacks on large language models.\" arXiv preprint arXiv:2408.12798 (2024).\n\n[10] Rando, Javier, and Florian Tramèr. \"Universal jailbreak backdoors from poisoned human feedback.\" arXiv preprint arXiv:2311.14455 (2023).\n\n[11] Hubinger, Evan, et al. \"Sleeper agents: Training deceptive llms that persist through safety training.\" arXiv preprint arXiv:2401.05566 (2024)." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The research topic is interesting and important to the community\n\n2. The idea is novel and intuitive.\n\n3. The paper is overall well-written and easy to follow" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces W2SAttack, a method for injecting clean-label backdoors into LLMs. The approach stems from the observation that successfully injecting clean-label backdoors during fine-tuning becomes challenging when using Parameter Efficient Fine-Tuning (PEFT) algorithms. The authors analyze the limitations of PEFT from the information theory perspective and propose Weak-to-Strong Attack (W2SAttack) to enhance attack effectiveness under PEFT. Inspired by teacher-student knowledge distillation, W2SAttack first injects backdoors into a smaller teacher model using full-parameter fine-tuning. It then transfers the backdoor knowledge to a larger student model through PEFT, incorporating feature alignment loss terms during the distillation process to support the backdoor learning. Evaluation results demonstrate that W2SAttack can effectively inject various types of backdoors into LLMs using PEFT." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The threat model combining clean-label backdoor attacks with training control is counter-intuitive and lacks practical value.\n\n2. The observation that PEFT cannot successfully inject backdoors is inconsistent with findings in recent literature.\n\n3. The paper lacks evaluation on larger LLMs to demonstrate the scalability and effectiveness of the proposed method.\n\n4. The paper lacks evaluation on generative tasks, which are a major use case for LLMs today." }, "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 definition of ASR(f(x^' )_peft) in Obj. 1 needs further clarification.\n\tWhat is the definition of Z_t? Additionally, the author needs to further explain why I(Z_t;Y) is related to backdoor features.\n\tI didn’t find the implementation details for Eq. 9 and 10, particularly for Eq. 9. Thus I have a concern about their correctness. Please provide more details.\n\tThe author said they use the clean-label backdoor attack. Why don’t use the poison-label backdoor attack? Is there any difference between those two attacks in your method? Please clarify. Besides, the author should provide the details for attack, such as target label to solve my concern.\n\tI wonder if continuously increasing the number of poisoned samples would improve the attack success rate in the PEFT setting?\n\tThe method of inserting triggers also affects the attack success rates. The author needs to further explain the implementation details of BadNet and InSent, as well as the SynAttack algorithm.\n\tThe caption for Fig. 3 should provide a detailed description of the motivation for each subfigure.\n\tWhat is the meaning of ‘Efficient-tuning’ in Tab. 5?\n\tAlso a concern about reproducibility, in Tab. 9, there is few details provided in terms of defense. For example, which trigger you used?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tThe article proposes a counter-intuitive but effective framework, that is, using small models as teachers and large models as students. This makes me think it's quite novel\n2.\tThe writing is fluent and clear, easy to understand" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed a method called W2SAttack. The author claims 1. that full-parameter fine-tune for achieving backdoor attack is not feasible due to high occupied VRAM 2. PEFT such as LoRA causes poor performance.\nTo address the posed problem, the author proposed the W2SAttack. They use the PEFT for smaller LLM first, and then set it as the teacher model to distill the larger LLM.\nThe results showed that the method can significantly reduce the computational cost." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Some weaknesses can be found in the Questions." }, "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": "More Explanation:\n\tFigure 1's y-axis should have a label; I was confused about its unit and what it represents.\n\tCA and ASR should be clearly mentioned in the caption.\n\tDuring the process of poisoning the teacher model, the authors added an additional linear layer. Is this layer necessary? Equation 4 requires further modification. What are the impacts of the teacher model on backdoor attacks?\n\tThe expression of the attacker's Objective 1 indeed requires additional explanation. The authors have noted in their third stage pilot study that deploying effective backdoor attacks using the PEFT algorithm is challenging. However, Objective 1 suggests that ASR(f(x^' )_peft)≈ASR(f(x^' )_fpft), a statement that seems to contradict the earlier assessment, which requires further explanation.\nExperimental Section:\n\tIs the caption for Figure 4 correct? The authors discuss the impact of different trigger lengths on backdoor attacks in the experimental analysis section, therefore this part needs to be revised.\n\tCompared to the BadNets backdoor attack, the backdoor attack algorithms based on InSent or SynAttack seem to achieve more desirable effects. Could the authors provide further analysis of the reasons for this, or present a more detailed analysis?\n\tAlthough the W2SAttack algorithm can guide the model to learn backdoor features, it requires the design of an additional teacher model. Existing experiments have only analyzed the effectiveness of the backdoor attack, but lack necessary analyses of communication costs, such as the training costs induced by changes in updatable parameters, which are essential for assessing the feasibility of the algorithm." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tEnhancing the effectiveness of backdoor attacks targeting the PEFT algorithm is a worthwhile research problem.\n2.\tThe authors design an effective backdoor attack algorithm that saves computational resources compared to full-parameter fine-tuning.\n3.\tOverall, the presentation is clear, and the experiments are comprehensive. The details are clear." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The preliminary experiments in this paper discovered that the PEFT, which updates only a small number of model parameters, hardly implements backdoor attacks effectively. Based on these findings, the authors proposed a weak-to-strong backdoor attack algorithm targeting PEFT, named W2SAttack. They leverage a small-scale teacher model to facilitate the student model's learning of backdoor features, thereby enhancing the effectiveness of the backdoor attack." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Some aspects are not clear, 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": 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": "1. What is the difference between the full-parameter fine-tuning of a small model in knowledge distillation and the full-parameter fine-tuning of a backdoored small model claimed in this paper?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. PEFT in inheriting backdoors and learning backdoors using PEFT is a key research area targeting the security of LLMs.\n\n2. Extensive experiments proved the feasibility of the attack." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a backdoor attack from weak to strong based on feature alignment-enhanced feature distillation. Extensive experiments show the superior performance of W2SAttack targeting PEFT on classification tasks across four language models, four backdoor attack algorithms, and two different architectures of teacher models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**1: Motivation**\n- The authors claim that LLMs cannot learn the backdoor under PEFT, but as far as I know, a lot of work reveals the vulnerability of PEFT against LLMs, e.g., references [1-2]. In addition, using LoRA (e.g., r=4) to implant a backdoor on NLU and NLG tasks, the ASR is very easy to reach 100%. \n\n- knowledge distillation to enhance backdoor learning, defend against backdoors, and transfer backdoors needs to be discussed in depth. Therefore, related work is a crucial part of the main body. This helps to understand that the work enhances backdoor learning in the form of distillation, and the final release is an E2E backdoored model.\n\n**2: Overclamming and misleading statement**\n\n- The author claims to be the first to study the effectiveness of the PEFT backdoor. In fact, there are many works in this field, referring to references [1-3].\n\n- When using Onion against W2SAttack, the results barely drop. However, Onion's effectiveness on word-level attacks can make attacks drop to at least around ASR of 50%.\n\n**3: Presentation**\n\n- In the Introduction section, the author does not assert that it is a backdoor attack based on the clean label, which may confuse the reader. \n\n- The manuscript lacks an explanation of the attacker's goals and capabilities. As I understand it, despite being a backdoor to clean labels, it requires poisoning the training set. Therefore, this assumption must be clarified in knowledge distillation or it will become impractical.\n\n- Related work and experiment details are introduced in the appendix. The main body is not self-contained. \n\n- E should be corrected to $\\mathbb{E}$ in Equation 3, 5, and 6.\n\n**Reference**\n\n[1] Unleashing Cheapfakes through Trojan Plugins of Large Language Models.\n\n[2] A Gradient Control Method for Backdoor Attacks on Parameter-Efficient Tuning\n\n[3] PPT: Backdoor Attacks on Pre-trained Models via Poisoned Prompt Tuning." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Backdoor Attacks with Knowledge Distillation" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024backdoor,\ntitle={Backdoor Attacks for {LLM}s with Weak-To-Strong Knowledge Distillation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=29LC48aY3U},\nnote={under review}\n}" }, "abstract": { "value": "Despite being widely applied due to their exceptional capabilities, Large Language Models (LLMs) have been proven to be vulnerable to backdoor attacks. These attacks introduce targeted vulnerabilities into LLMs by poisoning training samples and full-parameter fine-tuning. However, this kind of backdoor attack is limited since they require significant computational resources, especially as the size of LLMs increases. Besides, parameter-efficient fine-tuning (PEFT) offers an alternative but the restricted parameter updating may impede the alignment of triggers with target labels. In this study, we first verify that backdoor attacks with PEFT may encounter challenges in achieving feasible performance. To address these issues and improve the effectiveness of backdoor attacks with PEFT, we propose a novel backdoor attack algorithm from weak to strong based on feature alignment-enhanced knowledge distillation (W2SAttack). Specifically, we poison small-scale language models through full-parameter fine-tuning to serve as the teacher model. The teacher model then covertly transfers the backdoor to the large-scale student model through feature alignment-enhanced knowledge distillation, which employs PEFT. Theoretical analysis reveals that W2SAttack has the potential to augment the effectiveness of backdoor attacks. We demonstrate the superior performance of W2SAttack on classification tasks across four language models, four backdoor attack algorithms, and two different architectures of teacher models. Experimental results indicate success rates close to 100% for backdoor attacks targeting PEFT." }, "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": [ "Backdoor Attacks", "Large Language Models", "Knowledge Distillation" ] }, "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/caaa9bf12a1cca2444e903263c94276c67523450.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": { "value": "/attachment/fdb532cb398071926b09947a1330895733d892db.zip" }, "title": { "value": "Backdoor Attacks for LLMs with Weak-To-Strong Knowledge Distillation" }, "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": "See Weaknesses" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Effectively uses SAVi to extract object-centric frame features, enhancing computational efficiency and model accuracy.\n- Compares against two baseline models (Seer and Susie), highlighting the advantages in efficiency and success rate of the proposed approach.\n- Demonstrates generalization capabilities to unseen tasks and objects, showing robustness in diverse environments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a language-guided, object-centric world model for predictive control, which is both computationally efficient and effective in robotic and autonomous tasks. Using slot attention for object-focused representation and language guidance, it outperforms diffusion-based models in task success, speed, and generalization." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The contribution in terms of \"object-centric\" design feels limited, as it primarily substitute the encoder for SAVi without introducing distinct object-centric innovations.\n- The lack of an experiment comparing your proposed model with a VAE-based variant (ours + VAE in Tab.1) makes it difficult to conclusively justify the benefits of slot attention.\n- Comparison against video diffusion models would be more appropriate than models like instructpix2pix, as diffusion models are more aligned with the proposed model's multi-frame prediction capability.\n- The analysis suggesting that future state prediction alone suffices for action decoding is questionable; the low accuracy for \"instruction + 0 future steps\" (2.5%) compared to near-zero performance for Seer implies that baseline results may lack rigor, potentially outperforming when future states are not predicted.\n- The dataset used is overly simplistic, limiting the scope of validation for the world model. Testing across multiple, varied environments would better demonstrate the model’s general applicability and robustness." }, "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 is trained during \"Predict future dynamics\" Figure 1(b)? If nothing remome \"fire\" sign near the world model? \n\n- \"nature of slot attention not being robust to variable object number\" this could be clarified" }, "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 well-written and mostly easy to follow. \n- The authors provide a comparison with several image generation baselines adapted for the robotics domain, showing large gap from them. \n- The authors study how robust their method to some changes in the environment, such as changing the block type or changing the task to unseen one." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes to extend Slot Former to conditioned on language instruction object-centric dynamics prediction model. Such model could be used for decoding future actions for given state and instruction. Such predictions are in tern used for decoding the best action for the next time step. The paper showed that in the synthetic environment with large dataset, using such structured repression leads to better performance in comparison to using diffusion models for future state prediction. In addition, authors showed that such model is able to generalize to unseen tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Overall, the proposed method is a simple modification of the SlotFormer adding language goal conditioned predictions and trained on a large dataset of the demonstrations. On it its own it is not a big problem, if the proposed methods would be studied on diverse and challenging environments and compared with other methods that are state-of-the-art world models (e.g. [6, 7])\n\n- While the improved performance on the synthetic dataset is encouraging, it is still not clear how the method would perform in more realistic scenarios where both object-centric models and corresponding agents can struggle. As mentioned by authors, recently it was shown that object-centric methods are able to decompose much challenging images or videos (e.g. see DINOSAUR (Seitzer et al. (2023)) for images or VideoSAUR [5] /SOLV (Aydemir et al., 2024) for videos). Thus, it would be important to test how object-centric world models perform in more realistic environments with visual more complex scenarios, e.g. by training LSlotFormer on VideoSAUR or SOLV slots on environments like ManiSkill2). \n\n- It is not clear how the methods compare to the standard baselines on this task: while outperforming diffusion models for video prediction, it is not clear if usage of world model with object-centric representations are comparable or not with state-of-the-art algorithms using the same data for training. \n\n- Some experimental results would benefit from further analysis: for example, it not clear why using language conditioning for the agent itself is decreasing success rate. \n\n- Some potentially missing related work in video-based object-centric learning, control with object-centric representation and world models based on the object-centric representations: \n\n1. Focus: Object-centric world models for robotics manipulation - also proposed a world model using object-centric representations. (https://arxiv.org/abs/2307.02427)\n2. Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning, NeurIPS 2023 (https://arxiv.org/abs/2307.09205) - learns dynamics graph for more efficient policies. \n3. Self-Supervised Visual Reinforcement Learning with Object-Centric Representations, ICLR 2020 - proposed a goal-conditioned transformer based policy (or action decoder in authors notation), https://arxiv.org/abs/2011.14381\n4. Entity-Centric Reinforcement Learning for Object Manipulation from Pixels (https://arxiv.org/pdf/2404.01220)\n5. Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities (extention of SAVi to more complex real-world videos using DINOSAUR), https://arxiv.org/abs/2306.04829\n\n\n6. TD-MPC2: Scalable, Robust World Models for Continuous Control\n7. PWM: Policy Learning with Large 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": "A few questions (some overlapping with Cons. section above):\n- Is language table the de-facto setting for studying object-centric control? It seems fairly limited and biased towards object centric approaches since it is clearly possible to discard the background information quite easily. Studying it in cases of ambiguity, where sometimes the background is obvious to ignore and sometimes not would bring more of the community to investigate this topic. \n- In section 4.6, Is a world model really necessary? - Have the authors reported a pixel2action baseline, basically that does the same learning procedure, and except for learning from images directly, extract from some off the shelf network. The current results only ablate the absence of future slots, which makes sense, but that doesn't answer the question generally about needing a world model or not." }, "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 and easy to understand. \n- The problem of building a world model for predictive control is a useful and relevant one to solve. \n- The authors have ablated the components of their approach fairly well, including how to do the best action decoding, how many past steps to use in the world model etc." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "- The work proposes to inject language control in object centric world models and show its effectiveness in control. \n- It argues that object centric models, specifically based on slots as studied in the paper, are more efficient and performant than large scale video generation models based on diffusion. \n- They conduct experiments on a simulated table top manipulation benchmark to justify their method and various design choices.\n- They present an analysis on how to tune these world models in terms of action decoding, look ahead steps, access to past states to achieve good performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The authors do not have a SlotFormer baseline, which does not use any language conditioning. Given that one of the key claims of the paper is that language conditioned object centric world models help downstream tasks, checking the importance of being language centric is critical. Adding that baseline would be helpful. \n- For the evaluation of this approach, the authors have used the language table simulation environment, which involves some objects to be manipulated on a table top setting. This makes sense since there is a clear distinction between foreground (the objects) and background, which favors object centric approaches over general video generative models. However, showcasing some other scenarios or evaluation setups where maybe lets say intuitively a video generative model would have an edge, would have been interesting and more convincing to see. \n- Minor: The qualitatives in figure 3 are not the easiest to parse, if the author’s method works well, but having a video to see the predictions would make the difference much clearer, I couldn’t find anything in the suppmat." }, "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": "- It’s interesting that Seer gets basically 0% on all tasks. What are the qualitative failure cases there? \n- Since SuSiE was already evaluated on CALVIN, which is simulated, why not evaluate your approach in that setting?\n- In what qualitative settings would object-centered world models be more or less effective than image ones? Do the authors have any intuitive examples of this, and is there any experimental evidence to back that up?" }, "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 written quite clearly, I think the authors presented their ideas quite well\n- I appreciate the ablation discussions in 4.6." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose to train a language-conditioned latent dynamics model whose state representations are object-centric “slots” provided by a frozen pre-trained model. They then train an inverse dynamics model that predicts the actions corresponding to the transitions of the autoregressively-generated latent slot representations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Despite the paper being relatively clearly written, I would highly recommend using ICLR 2025’s extended page limit to increase the size and quality of your visualizations. For instance, for Figure 3, it’s very hard to see where the cube and moon are in the scene. I likewise cannot see any empirical quality differences between your approach’s generations and SuSiE’s.\n- The approach is not tested on a wide range of tasks – only the simulated LanguageTable benchmark. It’s not at all clear to me that it would generalize to the real world. Given that SuSiE was evaluated both in sim (CALVIN) and on real-world robots on Bridge-like tasks and showed good performance (compared to strong real-world baselines like RT-2), it is unclear if the present paper’s approach would similarly scale to such more complex tasks.\n- Similarly, the authors claim: “However, the major drawback of language-guided video-generation models is the requirement of large-scale labeled language-video datasets and the corresponding high computational cost. Therefore, latent predictive models, which abstract video to predict forward in compact latent state spaces, can serve as an alternative from a computational efficiency perspective.” \n - If this is true, it seems more sensible to evaluate in the real world, where data is more limited than in sim.\n - Additionally, SuSiE does show that an off-the-shelf image generation model pre-trained on general image-language data can be fine-tuned to work well with robot data just on existing relatively limited robot datasets. If that’s the case, it seems highly unclear that sample efficiency is a problem.\n- “The task is to move a block to another block based on the description of the colors or shapes … which are the red moon, blue cube, green star, and yellow pentagon.” This likewise seems very limited – I understand that there are generalization experiments, but the Bridge dataset used for SuSiE’s real-world experiments contain a much wider range of actions and objects, and thus also a much wider range of language (including many noisy labels). It has thus demonstrated to be scalable to a wider range of language and visual entities, which I think would similarly benefit this approach (as it stands, being able to generate latent state trajectories for such a limited number of objects and actions does not say much about its scalability).\n- As it stands, given that the approach was only evaluated on a single task setting and said setting is not that representative of real-world language-conditioned visuo-motor robotics tasks, I do not think that this approach has sufficiently demonstrated its general applicability. I think more experiments in a wider variety of domains would be very helpful, especially in real world experiments.\n- Finally, I think it would be important to include results that showcase in what settings visual and object-centric world models each excel or break down. I can imagine some cases wherein image or video generation is bad: for example, if I ask a robot to fetch me something from a closed cabinet, the image generator would have to effectively “imagine” what the inside of that cabinet looks like. However, I do not have corresponding intuition for object-centered world models (though it seems like their weaknesses might be quite similar). See last question for more." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a language-guided object-centric world models to predict future states and corresponding actions." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024languageguided,\ntitle={Language-Guided Object-Centric World Models for Predictive Control},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=29p13QihRM},\nnote={under review}\n}" }, "abstract": { "value": "A world model is essential for an agent to predict the future and plan in domains such as autonomous driving and robotics. To achieve this, recent advancements have focused on video generation, which has gained significant attention due to the impressive success of diffusion models. However, these models require substantial computational resources. To address these challenges, we propose a world model leveraging object-centric representation space using slot attention, guided by language instructions. Our model perceives the current state as an object-centric representation and predicts future states in this representation space conditioned on natural language instructions. This approach results in a more compact and computationally efficient model compared to diffusion-based generative alternatives. Furthermore, it flexibly predicts future states based on language instructions, and offers a significant advantage in manipulation tasks where object recognition is crucial. In this paper, we demonstrate that our latent predictive world model surpasses generative world models in visuo-linguo-motor control tasks, achieving superior sample and computation efficiency. We also investigate the generalization performance of the proposed method and explore various strategies for predicting actions using object-centric 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": [ "Object-Centric Representation", "World Model", "Predictive Control" ] }, "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/3f173253f745a65762ca596eada7280c88284504.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/b5866674110a97042a07991a5b09e30eb0f8ca2e.zip" }, "title": { "value": "Language-Guided Object-Centric World Models for Predictive Control" }, "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": "- what are the closest existing approaches to the proposed HyperAdapters, only multiple adapters?\n- what are the goals of the experiments?\n- is it established that CL-100 is a better benchmark / good enough to be able to obtain conclusive empirical evidence (wrt the main goal of the experiments)?\n- why the hyperparameters in Equations 3 and 10 are both set to 0.1\n- how good/representative the initial pre-trained model should be for the main conclusions to hold?\n- what is the expected efficiency gain compared to learning more adapters? does it depend on how similar/dissimilar different tasks are?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- An intuitive high-level connection to the Complementary Learning Systems theory.\n- A simple approach with promising performance wrt accuracy.\n- Promising approach wrt scalability - HyperAdapter allows to avoid the excessive number of adapters.\n- Overall, the paper is well-written and is comprehendable to a wide audience." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a novel rehearsal-free approach for continual learning based on hypernetworks that generate so-called adapters to adapt the pre-trained model to different tasks.\nThe idea is intuitive and at high level is brain-inspired:\n- the task dictionary is sort of an episodic memory in the hippocampus\n- the hypernetwork is sort of the neocortex, storing past knowledge. \n- task-specific embeddings are updated rapidly; and the general hypernetwork is updated slowly \nThe empirical results on the introduced by the authors CL-100 benchmark look promising." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- It is not fully clear how the technical novelty is positioned, as well as what baselines should be included for demonstrating what ideas actually work (e.g. other hypernetworks-basd CL? other rehearsal-free CL approaches?). \n- Overall, the technical novelty is rather modest: hypernetworks have been used for CL in the past. The ideas of exploring a combination of faster and slower adapting models have been explored in the past. The idea of recognizing already observed/recurrent tasks/concepts have been studies in the past too. (however, the studied combination of ideas in the context of adapting pre-trained models is novel to the best of my knowledge).\n- The code and the experimental workbench is not available yet. Hence it is not easy 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": 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 fundamental differences exist between hyperadapters and existing methods that utilize hypernetworks for continual learning [1-6], aside from variations in application scenarios? Please clarify the key innovations.\n- Is it possible to extend this approach to other continual learning settings, such as class-incremental, domain-incremental, or task-incremental learning?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The proposed HyperAdapter leverages hypernetworks to generate task-specific adapters for pre-trained models, addressing data privacy concerns and enabling effective knowledge transfer.\n- HyperAdapter requires fewer additional parameters as the number of tasks increases, making it suitable for long-sequence continual learning.\n- Experiments demonstrate that HyperAdapter consistently outperforms some methods in rehearsal-free continual learning.\n- The paper is clearly written and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper tackles the problem of catastrophic forgetting in continual learning, highlighting the limitations of traditional rehearsal buffer methods in data-sensitive contexts. The authors introduce HyperAdapter that employs hypernetworks to generate task-specific adapters for pre-trained models, thereby requiring fewer additional parameters as the number of tasks increases and promoting positive knowledge transfer across tasks. Comprehensive experiments demonstrate that HyperAdapter consistently outperforms existing methods on benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The core idea proposed in this paper, using hypernetworks to generate model parameters (whether for all parameters, some parameters, or even applied to pre-trained models) to tackle the continual learning problem, has already been extensively explored in the literature [1-6]. This paper merely applies these existing methods in the context of prompting-based continual learning with pre-trained models, which significantly limits its novelty and contribution.\n- Several of the innovative designs introduced, such as block-wise hyper-adapters, bear strong similarities in motivation and methodology to chunk embeddings and network partitioning discussed in [1]. This further constrains the novelty of the work.\n- One of the claimed main advantages, \"eliminating the necessity of knowing the task identities during inference,\" was previously addressed in [1] under the concept of unknown task identity inference. Additionally, the query-key matching mechanism commonly used in prompt-based continual learning to address this issue is a well-established practice [7-9].\n\n[1] Continual learning with hypernetworks. ArXiv:1906.00695 2019.\n\n[2] Continual learning with dependency preserving hypernetworks. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 2023.\n\n[3] Continual model-based reinforcement learning with hypernetworks. 2021 IEEE International Conference on Robotics and Automation.\n\n[4] Hypernetworks for continual semi-supervised learning. ArXiv:2110.01856 2021.\n\n[5] Partial hypernetworks for continual learning. Conference on Lifelong Learning Agents 2023.\n\n[6] Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.\n\n[7] Bridging pre-trained models to continual learning: A hypernetwork based framework with parameter-efficient fine-tuning techniques. Information Sciences, 2024.\n\n[8] Learning to Prompt for Continual Learning. CVPR 2022.\n\n[9] DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning. ECCV 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": 3 }, "primary_area": null, "questions": { "value": "- How is the task dictionary initialized? The definition of $q(x)$ in Section 4.1 requires more clarity: is [CLS] a one-hot vector, or does it represent the class token embedding of ViT (which is then multiplied by f(x))? \n\n- Does the size of the task dictionary correspond to the number of training tasks (i.e., classification tasks) or the total number of classes across these tasks?\n\n- How is Equation 10 optimized? Including the training algorithm or detailed description of the training process of different model parts would be beneficial.\n\n- What is the parameter scale unit in Figure 3? Does it measure the parameters of the hypernetwork or the generated model parameters (e.g., U,W) during inference?\n\n- How does the proposed method compare with other hypernetwork-based continual learning approaches?\ne.g. \n * Ding, F., Xu, C., Liu, H., Zhou, B., & Zhou, H. (2024). Bridging pre-trained models to continual learning: A hypernetwork based framework with parameter-efficient fine-tuning techniques. Information Sciences, 674, 120710.\n * Hemati, Hamed, Vincenzo Lomonaco, Davide Bacciu, and Damian Borth. \"Partial hypernetworks for continual learning.\" In Conference on Lifelong Learning Agents, pp. 318-336. PMLR, 2023." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Generates parameters specifically for pre-trained model adapters rather than the entire network, enhancing training efficiency.\n- Introduces a task embedding dictionary for efficient retrieval of task embeddings for incoming tasks.\n- Provides a thorough and detailed experimental analysis." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a class-incremental continual learning framework HyperAdapter, which uses hypernetworks to generate adapters to adapt a pre-trained model to different tasks. Specifically, it uses the pre-trained model's class embedding as a key to query the task embedding that generates the adapter parameters. Extensive experiments are performed on image classification benchmarks, showing improved performance over other regularization-based and prompt-based CL frameworks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The approach appears to be a direct adaptation of a hypernet-based continual learning framework to adapter-based fine-tuning. Task relationships rely solely on the pre-trained model’s class token embedding for input, making it resemble a method of training separate models for distinct classes, with conditional parameter generation handled by the hypernet. This setup may not effectively handle scenarios where new classes can not be easily matched with labels of the pre-trained model, such as domain-specific tasks. e.g. for complex facial emotion classification tasks, the pre-trained model would give similar class embeddings (e.g. human, face, eye glasses, etc) regardless of which emotion class the image belongs to. \n\n- The paper draws an analogy between the proposed method and the brain’s Complementary learning system (CLS). However, unlike the human brain, which can dynamically adjust its internal representations, such as merging or acquiring new concepts, the task dictionary here has keys fixed by the pre-trained classes, lacking the true flexibility of dictionary learning to adapt and integrate new concepts. It's suggested to consider ways to make the task dictionary more dynamic or adaptive over time." }, "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": "The values of the hyperparameters are stated on page 7. How does varying the hyperparameter values affect performance?\n\nWhat are the memory requirements, and what do they depend on?\n\nWhat are the time requirements, and what do they depend on?\n\n“Improving this selection mechanism is left as a direction for future work.” – can you suggest some possible directions for improvement?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The research will be of interest to the ICLR community.\n\nOriginality: There is currently a lot of interest in avoiding catastrophic forgetting in the continual learning setting. The authors have summarised and categorised the main approaches. \n\nExperimentation: The experimentation is carried out on the standard data sets.\n\nReproducibility: I believe that the details are sufficient for reproducibility of the experiments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper addresses the problem of catastrophic forgetting in continual learning. The authors introduce a pre-trained model-based continual learning framework, HyperAdapter, which utilizes a hypernetwork to generate adapters based on the current input, adapting the pre-trained model to the corresponding task. A key to the method is that HyperAdapter uses representative features from pre-trained models, eliminating the necessity to know the task identities during inference or the dependence on any rehearsal buffers. Experimentation shows that it outperforms other methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Clarity: The paper discusses the main approaches at a high level and fails to clearly describe the key novelty of the proposed approach. Additionally, the comparison of the proposed method with how people learn is repeated in a number of places. The points around this are well known and widely documented. Removing the replication provides space to describe the novelty in more detail and room to discuss the implications of the results.\n\nTypos: Please check your paper carefully for typos including the repeated use of “regularation” on page 3. A grammar checker will pick up some of the errors.\n\nDiscussion of results: The paper is missing a discussion of the results. Adding this will provide a deeper understanding of the advantages of the approach.\n\nDiscussion of limitations: The paper is missing a discussion of the limitations of the approach and potential ways to address them. Adding this will provide a more balanced presentation of the research.\n\nDiscussion of braider impact: What are the open problems or future directions related to your work? Adding this to the paper would improve the paper's discussion of broader impact and potential future 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": 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": "Please refer to the Weaknesses." }, "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 essentially well-organized and easy to follow.\n\n2. The proposed hypernetwork seems to be a simple but effective strategy, applicable to both adapter-based and LoRA-based parameter-efficient tuning.\n\n3. Less requirement for additional parameters is a good feature for continual learning that ensures scalability." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed pre-trained model-based continual learning method that employs a hypernetwork to generate adapters based on the current input. The proposed method features positive transfer and fewer additional parameters as the number of tasks increases. It outperforms a variety of continual learning methods in many representative benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. As acknowledged by the authors, the hypernetwork itself is a large linear layer, and the use of a separate hyper parameter for each layer results in much more parameter cost. For fairness, it is therefore desirable to compare the total parameter cost with other baselines methods.\n\n2. Does the hypernetwork need to be updated in continual learning? If so, how does it overcome catastrophic forgetting?\n\n3. The authors only considered one particular pre-trained checkpoint of supervised ImageNet-21K. Does the proposed method apply to other pre-trained checkpoints, especially for self-supervised pre-training?\n\n4. The authors compared only a representative selection of pre-trained model-based continual learning methods. It would be more informative to consider other concurrent competitors, such as SLCA (ICCV’23), LAE (ICCV’23), RanPAC (NeurIPS’23), HiDe (NeurIPS’23), etc." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a novel hypernetwork-based framework to generate task-oriented adapters for pre-trained model-based continual learning." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024hyperadapter,\ntitle={HyperAdapter: Generating Adapters for Pre-Trained Model-Based Continual Learning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=29sul3tAEa},\nnote={under review}\n}" }, "abstract": { "value": "Humans excel at leveraging past experiences to learn new skills, while artificial neural networks suffer from the phenomenon of catastrophic forgetting during sequential learning. Efforts have been made to alleviate forgetting by introducing a rehearsal buffer into the model, but this way is impractical in real-world scenarios with data privacy. Recently, pre-trained model-based continual learning methods have provided new insights into addressing this issue by effectively utilizing the powerful representational capabilities of pre-trained models to avoid catastrophic forgetting without a rehearsal buffer. In this work, we propose a novel pre-trained model-based continual learning framework, HyperAdapter, which utilizes a hypernetwork to generate adapters based on the current input, adapting the pre-trained model to the corresponding task. This paradigm requires fewer additional parameters as the number of tasks increases, which is a critical advantage for scaling to long sequences continual learning. Unlike methods that partition task-related knowledge into relatively independent subspaces, it promotes positive knowledge transfer across tasks. Comprehensive experiments across various datasets demonstrate that HyperAdapter consistently outperforms all existing methods and even exceeds the upper bounds of multi-task learning, establishing a new state-of-the-art for pre-trained model-based continual learning. Our code will be released." }, "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": [ "hypernetworks", "adapter tuning", "class-incremental 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/9e8aad06895211e44d2347bffb84b9e13c03e911.pdf" }, "presentation": null, "primary_area": { "value": "transfer learning, meta learning, and lifelong 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/92801106eefb87d7445f58f7ba94e631e0cebc27.pdf" }, "title": { "value": "HyperAdapter: Generating Adapters for Pre-Trained Model-Based Continual Learning" }, "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": "How should $m$ be selected in practice? From the experiments, this selection appears as an important choice for the quality of prediction sets, however the paper lacks discussion on this aspect." }, "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 is well-organized for the most part.\n2. The paper is technically sound in its description of problem formulation and the marginal and PAC guarantees.\n3. Construction of prediction sets in the structured prediction setting and in the context of nodes in a directed acyclic graph is an important problem." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a framework for conformal structured prediction i.e., conformal prediction in the structured prediction setting. The proposed framework outputs structured prediction sets that achieve marginal or PAC coverage guarantees while minimizing prediction set size. In the context of a set of nodes in a directed acyclic graph, the prediction set as a small subset of coarse labels corresponds to the prediction set of fine-grained descendants of the coarse labels. The paper presents empirical analysis of the approach in three domains to demonstrate the performance of their approach." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Missing discussion of important related work [1, 2]: I believe the paper misses citing and comparison with important related work on conformal risk control [1]. [1] considers hierarchical image classification in ImageNet similar to the paper and controls the graph distance between nodes. Additionally, RAPS method in [2] is a conformal prediction method that introduces regularization to encourage smaller and stable sets, and is worth comparing to given the focus of the paper on reducing average set size.\n2. The empirical evaluation can certainly benefit from more analysis. In the current form, the contribution and significance of the method are not demonstrated very clearly:\n - It is hard to understand the utility of the method without comparison with more baselines. I believe doing this is especially possible for the marginal guarantees. Qualitative comparison of the prediction sets will also help demonstrate the utility of structured prediction sets. I see the paper discusses one example in the main text, however there is certainly value in adding more examples in this case (also mentioning the error level used for standard conformal prediction and other details for fair comparison).\n - Following from above, I appreciate Table 1 in the paper as it helps understand the influence of hyperparameters better. I would suggest adding similar examples for other datasets as well.\n\n3. The motivation of the paper is not very clear in the beginning and only becomes clearer as the method and examples are discussed later in the paper. While the introduction has sufficient details about the method, I would suggest making the motivation for structured prediction sets clearer early on.\n\n\n\n\n**Minor comments:**\n1. L60: parameter $\\tau$ has not been defined and referenced early I believe without sufficient context here.\n2. Similar comment for Figure 2. The caption makes reference to $\\tau$, whereas the notation has not been introduced earlier in text or in the caption.\n3. L306: typo/incomplete -> (2)\n4. L416-417: possibly missing word after ‘values’; “ in contrast, for the PAC guarantee, coverage for all values within one standard deviation...”\n\n[1] Anastasios Nikolas Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei, and Tal Schuster. Conformal Risk Control. International Conference on Learning Representations, 2024.\n\n[2] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan, and Jitendra Malik. Uncertainty Sets for Image Classifiers using Conformal Prediction. International Conference on Learning Representations, 2021." }, "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 above)" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* It is an interesting problem, particularly how best to use the external structure of the labels to generate a better 'curve', i.e. recall at given output size.\n* The experimental setups were quite interesting, e.g. MNIST with number ranges.\n* The proposed method seems to extend well to DAG spaces (beyond trees). Though I suppose it is still restricted to DAG instead of Graphs to sum up the probs of final leaf nodes." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose conformal structured predictions which addresses the interpretability issue of conformal predictions when the output set is complex. Their algorithm also differs from existing algorithms as in conformal structured predictions the search space over $\\tau$ is not monotone anymore. This approach can be applied to tasks where the output space can be represented as a directed acyclic graph and has a hierarchical structure. The authors provide formal coverage guarantees using PAC and marginal coverage and evaluate their approach in numbers predictions with MNIST digits, ImageNet classification and temporal Q&A." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* I would love to see a baseline where we dont use the structure at all and instead rely on regular P/R curve characteristics. Does the AUC of this model behave better? It is not clear to me as such.\n\n* Even if we do use the external structure and forced to only predict internal nodes in the DAG (as opposed to arbitrary set of leaf nodes), it would still be useful to understand the P/R curve look significantly different with the proposed models. There are plenty of baselines where we can do prediction on internal nodes in addition to leaf nodes." }, "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. How does the proposed method for conformal structured prediction fundamentally differ from or improve upon prior hierarchical prediction approaches? \n\n2. The framework assumes that the label space is represented by a DAG. How does this assumption impact generalizability to label structures that are non-hierarchical, cyclical, or have overlapping dependencies?\n\n3. Integer programming (IP) can be computationally intensive, particularly for large DAGs. Have you measured or benchmarked the runtime performance and scalability of the IP formulation, especially in tasks with larger label hierarchies?\n\n4. Could you elaborate on practical scenarios or domains where marginal coverage versus PAC coverage would be preferable? How should a practitioner decide between the two guarantees in real-world settings?\n\n5. Have you considered comparing this approach to other recent extensions of conformal prediction tailored to structured outputs or complex tasks (e.g., those applied to natural language or image data)?\n\n6. How sensitive is the framework to the hyperparameter m\n(the maximum number of nodes in the prediction set)? Is there a recommended method for tuning \nm based on the domain or task?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Extension of conformal prediction to structured outputs using DAGs, combining conformal prediction with hierarchical representations.\n2. Rigorous theoretical development with both marginal and PAC coverage guarantees, validated through experiments in diverse domains.\n3. Generally well-organized with clear explanations and helpful visual aids, making complex concepts accessible.\n4. Addresses an important gap, potentially impacting applications that require structu" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper \"Conformal Structured Prediction\" introduces a novel framework to extend conformal prediction methods to complex, structured output spaces. Conformal prediction typically provides sets of labels with a high probability of containing the true label, giving a quantified uncertainty. However, for tasks involving structured or hierarchical outputs—such as text generation, image classification with hierarchical categories, or question answering with date ranges—traditional conformal methods produce prediction sets that can be large and hard to interpret.\n\nThe authors propose a method to generate interpretable structured prediction sets using a conformal predictor that works within a directed acyclic graph (DAG) representing the label hierarchy. This approach maintains coverage guarantees while reducing prediction set complexity. The paper introduces algorithms for efficiently determining the smallest structured prediction set that meets a specified confidence level, and it adapts these methods to provide both marginal and Probably Approximately Correct (PAC) guarantees.\n\nThe authors demonstrate the utility of their approach through experiments on MNIST digits, hierarchical ImageNet classification, and a question answering dataset focused on years as answers. The results show that their framework achieves desired coverage rates while keeping prediction sets concise and interpretable." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While the paper’s application of conformal prediction to structured outputs is valuable, similar approaches have been explored in hierarchical classification and structured prediction. For instance, previous works have used hierarchical structures (e.g., DAGs or trees) to improve interpretability in label prediction. The paper could benefit from a more thorough comparison to these existing methods, as well as a deeper explanation of what sets its approach apart. Highlighting any unique technical advancements in how the proposed framework constructs prediction sets in hierarchical settings (e.g., advantages of the DAG-based approach) would further clarify its contributions.\n\nSet-Valued Prediction in Hierarchical Classification with Constrained\nRepresentation Complexity. Mortier et al. (2022): This work focuses on hierarchical classification with constrained set-valued predictions, utilizing hierarchical label spaces in which classes are structured in a directed acyclic graph (DAG) or tree. It emphasizes creating interpretable predictions that adhere to hierarchical constraints, much like structured conformal prediction but without formal coverage guarantees.\n\n\n2. The experiments are limited to specific domains (MNIST digits, ImageNet, and SQuAD). Although these domains represent a variety of structured prediction tasks, they are relatively controlled environments and may not fully reflect the challenges of deploying conformal structured prediction in more complex real-world applications. For instance, the framework’s performance on prediction sets for multi-label classification or in contexts with high label ambiguity (e.g., complex multi-class categorization in medical or legal documents) remains untested.\n\nDeploying conformal structured prediction in a healthcare setting, particularly for diagnostic tasks with hierarchical or multi-label structures (e.g., identifying conditions or diseases from imaging data or lab results), would offer insights into the model's reliability and interpretability under more variable, high-stakes conditions. This field often requires nuanced coverage guarantees and interpretability to support clinical decision-making.\n\nReal-world document classification often involves hierarchical categories (e.g., legal documents, financial reports) and multi-label classifications. Testing in this setting could reveal how well the model scales with complex, unbalanced label hierarchies, providing additional insights into its generalizability to larger, noisy datasets that are typical in business and legal contexts.\n\n3. The paper assumes that the label hierarchy can be represented by a DAG, which works well for hierarchical classification but may be restrictive for tasks with overlapping or cyclical dependencies. In complex scenarios where relationships between classes are non-hierarchical or not acyclic, this assumption may not hold, potentially limiting the framework’s applicability to structured outputs with more intricate dependencies.\n\n4. The inclusion of PAC guarantees is a strong point, but the differences between the marginal and PAC guarantees could be better explored. The PAC guarantee is inherently conservative, and the experiments demonstrate that it leads to larger prediction sets in some cases. However, there is little analysis of scenarios where a PAC guarantee might be more beneficial than a marginal guarantee, or vice versa, depending on the task or risk tolerance." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We construct structured conformal prediction sets via integer programming." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024conformal,\ntitle={Conformal Structured Prediction},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2ATD8a8P3C},\nnote={under review}\n}" }, "abstract": { "value": "Conformal prediction has recently emerged as a promising strategy for quantifying the uncertainty of a predictive model; these algorithms modify the model to output sets of labels that are guaranteed to contain the true label with high probability. However, existing conformal prediction algorithms have largely targeted classification and regression settings, where the structure of the prediction set has a simple form as a level set of the scoring function. However, for complex structured outputs such as text generation, these prediction sets might include a large number of labels and therefore be hard for users to interpret. In this paper, we propose a general framework for conformal prediction in the structured prediction setting, that modifies existing conformal prediction algorithms to output structured prediction sets that implicitly represent sets of labels. In addition, we demonstrate how our approach can be applied in domains where the prediction sets can be represented as a set of nodes in a directed acyclic graph; for instance, for hierarchical labels such as image classification, a prediction set might be a small subset of coarse labels implicitly representing the prediction set of all their more fine-descendants. We demonstrate how our algorithm can be used to construct prediction sets that satisfy a desired coverage guarantee in several 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": [ "Conformal Prediction", "Structured Prediction", "Integer Programming" ] }, "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/647fd0bf0755077a9bfeb957543a959cc1412d25.pdf" }, "presentation": null, "primary_area": { "value": "probabilistic methods (Bayesian methods, variational inference, sampling, UQ, 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": "Conformal Structured Prediction" }, "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": "Can the authors add some \"cartoons\" to ensure that readers less inclined with differential geometry can still understand the main (pictorially easy to be fair) intuition behind the paper? \n\nCan the authors expand the discussion on relevant literature (maybe with some table comparison?) as per the \"Weaknesses\" section? \n\nCan you provide insights into the computational complexity of the projection operation and discuss potential scalability issues (needs not be FOCS-style theory). Include analysis on the numerical stability and error propagation introduced by the projection maybe if possible?\n\nRe the comment above \"The experiments focus on systems where the constraint manifold is relatively straightforward to compute. It would be valuable to test PNDEs on \"less trivial\" systems with high-dimensional constraints or where the constraint manifold has nontrivial topology maybe? \" can you maybe design such a larger instance and maybe harder topology problem? I would not decline the paper based on this but I do think it would massively strengthen the paper. \n\nNote the unique typos I found was that no $ sign was used in a couple of T_uM instances, just make sure you fix this. \n\nBy addressing these points, the paper would be strengthened in terms of positioning within the existing literature, theoretical rigor, empirical validation, and clarity, ultimately enhancing its significance and impact on the field and would make it a super strong paper for ICLR 2025." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper presents a substantial advancement in the field of neural differential equationss given that the authors address a, indeed, crucial limitation of standard NDEs—the inability to enforce known constraints in the learned dynamical systems—which often leads to poor generalization and numerical instability. While many solutions to this have been discussed, e.g. within Hamiltonian neural nets, the strengths of the paper are several and go beyond the literature, to the best of my knowledge. c\n\nThe introduction of PNDEs is novel contribution and the authors provide a principled method to incorporate hard constraints directly into the learning process. Their approach differs from e.g., Stabilized Neural Differential Equations by ensuring that the constraints are satisfied exactly, rather than asymptotically or approximately. Using projection operators is creative and less common within \"deep learning\" as opposed to more traditional convex optimization.\n\nThe paper demonstrates rigorous, but bried, theoretical development and provides clear mathematical derivations. The authors derive the projection operator using the Jacobian of the defining constraint functions, and in detail explain and ensure that the projected vector field remains within the tangent space of M and they further go to propose in detail that solutions to the PNDE remain on the constraint manifold is well-founded, and the proof is succinct yet thorough. Some extra discussion as well as graphical illustration would be beneficial here! The experimental section is robust, and covers a range of systems.\n\nIn terms of clarity, I am happy to see a quite clear and well-written paper which manages to convey complex ideas effectively. That said, certain sections would be harder to follow for people with more ML/DL background but I won't count this as a limitation. Note that the motivation behind enforcing hard constraints in NDEs is very clearly articulated, and the limitations of existing methods are adequately discussed. The derivation of the projection operator is presented step-by-step (again, a graphical illustration would do miracles here), making it accessible to mathematically include readers with a background in differential geometry and dynamical systems. The experimental figures and tables are informative and enhance the understanding of the results. The experimental setup is described in sufficient detail, allowing for reproducibility (although I could not locate a link with a repo).\n\n\nAs mentioned earlier, incorporating hard constraints into NDEs has significant implications for modeling realistic dynamical systems that inherently possess constraints, such as conservation laws and algebraic relationships. The ability of PNDE to enforce these constraints exactly enhances the reliability and accuracy of the models, which is crucial in safety-critical applications like power grid management. However, many practicioners, especially for this example, would claim that the lack of rigorous guarantees is a problem. Returnign to the main ideas of the paper, when suitable examples are considered, improving generalization and numerical stability, PNDEs contribute to advancing the state-of-the-art in data-driven modeling of dynamical systems. This work arguably opens up new possibilities for applying NDEs to a broader class of problems where constraints play a vital role." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper addresses the challenge of learning constrained dynamical systems in the context of neural differential equations (i.e., NDEs herafter). This term of NDEs includes the 2018 class of NODEs and generalization therefore such UDEs and bagging them together the authors are interested since indeed they allow for flexible modeling of dynamical systems by parameterizing the vector field f_\\theta with neural networks. The authors observe however, that they do not inherently enforce possible known constraints that the system may have, such as conservation laws, holonomic constraints (both applying quite well in Hamiltonian systems for example), or algebraic relationships and this can lead to learned models that to different levels of severity may violate essential properties of the system, resulting in poor generalization and numerical instability.\n\nTo overcome this limitation, the authors introduce the \"Projected Neural Differential Equations\" (PNDEs) whose key idea is to enforce constraints by projecting the neural network vector field f_\\theta onto the tangent space T_M of the constraint manifold M, which is defined by (algebraic equations) g(u) = 0. Specifically, they define the projected vector field as Proj_u (f_\\theta) \\in T_uM\nwhere \\mathrm{Proj}_u is the orthogonal projection operator of from T_uE onto T_uM. By integrating this projected vector field, the solutions remain on the manifold M, ensuring that the constraints are satisfied for all time. So in some sense, they try to \"get rid\" of the components of the vector field/neural net that would be learned but would live outside the constrained submanifold the physical system actually lives on.\n\nThe authors provide a detailed derivation of the projection operator using common decomposition techniques. They demonstrate that for an embedded submanifold M defined by smooth constraint functions g(u) the projection can be explicitly computed using the Jacobian of the latter, i.e., the constraints. This allows for efficient computation of the projected vector field during numerical integration.\n\nTo validate their approach, the authors conduct experiments on several challenging dynamical systems: the Fermi–Pasta–Ulam–Tsingou lattice system, a damped pendulum system with holonomic constraints and power grid models that incorporate power flow equations. \n\nCompared to various existing methods they cite, such as SNDEs, the experiments seem to verify claims that the proposed method offers exact constraint enforcement without introducing additional hyperparameters and/or suffering from stiffness issues in numerical integration which arguably distinguishes PNDEs from penalty-based methods or those that incorporate constraints as soft losses during training, which may not guarantee constraint satisfaction during inference.\n\nOverall, the paper presents a principled and general framework for incorporating hard constraints into neural differential equations by projecting the neural network vector field onto the tangent space of the constraint manifold. This approach enhances the modeling of constrained dynamical systems, improving accuracy, generalizability, and numerical stability." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "While the paper, as discussed, presents a novel and effective method for incorporating hard constraints into neural differential equations there are several areas where the work could be improved.\n\nOne of the (few) main weaknesses lies in the discussion of related work and positioning of the proposed method within the existing literature. The paper focuses primarily on comparing PNDEs to SNDEs. However, there is a rich body of research on incorporating physical constraints and conservation laws into neural network models of dynamical systems that is not adequately addressed.\n\nFor instance, the (indeed) cited HNNs [Greydanus et al., 2019] and Symplectic ODE-Net [Zhong et al., 2020] (since the author do mention inductive bias in the intro) are significant contributions that leverage the symplectic structure of Hamiltonian systems to enforce conservation of energy and other invariants. These methods learn the Hamiltonian function directly and ensure that the learned dynamics preserve the symplectic form, inherently satisfying certain physical constraints. Therefore, it's not clear to as wether the PNDEs would be relevant in systems where HNNs seem to perform very well. As a matter of fact, in recent work on learning generalized Hamiltonians using fully symplectic mappings [Choudhary et al. 2024], addresses the challenge of modeling non-separable Hamiltonian systems using implicit symplectic integrators within neural networks which should be a class of problems where previously I would had assumed PNDEs to be prime candidates to work on but its just not clear to me as to what the best approach would be in such situations. So, overall, I would prefer a more thoorough discussion here. Finally, I would be keen for the authors to portray further unerstanding of the literature of projections. For example, it is known that such projections, introduce certain symmetries. These symmetries ideally can be quotioned out in order to fascillitate easier training, see for example a similar construction in convex optimization and SDPs where the tangential projection symmetries need be addressed [Bellon et. al. 2210.08387].\n\n\nWhile the paper provides a clear derivation of the projection operator and proves that solutions to the PNDE remain on the constraint manifold, the theoretical analysis could be strengthened. Specifically, the paper lacks a discussion on the computational complexity and scalability of the projection operation in high-dimensional systems or with complex constraints. Maybe it's too hard? From the practicioner's point of view this is important too. Given the experiments discuss power grid (we would use BnC methods normally and not gradient based methods for a number of reasons) this is important. Also, computing the projection onto the tangent space requires solving a system involving the Jacobian of the constraints, which can be computationally intensive for large-scale systems.\n\nMoreover, the paper does not provide theoretical guarantees on the convergence or stability of the PNDEs beyond the preservation of the constraints. Are there some assumptions that can be made that would allow for an analysis of the numerical errors introduced by the projection and their impact on the overall solution accuracy? Additionally, insights into how the method performs under approximate constraints or in the presence of noise would enhance the understanding of its robustness.\n\n\nRe the experimental section, while demonstrating the effectiveness of PNDEs on several systems, could be expanded to provide a more comprehensive evaluation. The experiments focus on systems where the constraint manifold is relatively straightforward to compute. It would be valuable to test PNDEs on \"less trivial\" systems with high-dimensional constraints or where the constraint manifold has nontrivial topology maybe? \n\nFurthermore, the comparison is primarily with SNDEs and unconstrained NDEs. Including additional baseline methods, such as HNNs, Symplectic Neural Networks, or other constraint-enforcing techniques, would strengthen the empirical evaluation. This would provide a clearer picture of the advantages and limitations of PNDEs relative to existing approaches.\n\nWhile the paper is generally well-written, certain sections could be clarified for better accessibility. The derivation of the projection operator, although mathematically rigorous, might be challenging for readers not deeply familiar with differential geometry. Providing more intuitive explanations or illustrative examples could help bridge this gap.\n\nAdditionally, the notation used in some equations, such as the use of adjoints and pseudoinverses, could be explained in more detail. Ensuring that all symbols and operations are clearly defined would improve the readability of the paper.\n\n\nAnother point is that the proposed method assumes that the constraints can be expressed as explicit algebraic equations and that the Jacobian of the constraints is full rank. In practice, many systems might have constraints that are implicit, differential-algebraic, or have singular Jacobians. What happens then? Discussing how PNDEs could be extended or adapted to handle such cases would enhance the significance and applicability of the 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": 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": "My concerns regarding this paper are as explained above." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper is well-written and easy to read. Some experiments were conducted to support the effectiveness of the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, a method for learning differential equations while preserving conservation laws is proposed. Specifically, to preserve conservation laws, the authors project the learned vector field onto the tangent bundle of the manifold defined by the conserved quantities." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The proposed method is not novel because this method has already been proposed; the continuous-time model is shown in [1], and also the discrete-time model is shown in [2]. \n\n[1] Kasim, M.F. and Lim, Y.H. (2022) Constants of motion network, NeurIPS2022\n\n[2] Matsubara, T. and Yaguchi, T. (2023) FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities, ICLR2023\n\nIn [1], the learned vector field is designed to be orthogonal to the gradient vectors of the conserved quantities. Precisely, the learned vector field is projected onto the tangent space at each point of the manifold defined by the conserved quantities, which is the same as the approach proposed in this paper. In [1], the QR decomposition is used for orthogonalization and hence the method of computing the projection operator is a little different from that of this paper, which uses the pseudo inverse.\n\nIn [2], exactly the same approach as this paper is proposed; in [2], the manifold defined by conserved quantities is first introduced. Then, they consider tangent bundles of this manifold, and project the leaned vector field onto the tangent space at each point. More precisely, in [2], a continuous-time model is first considered. Equation (6) in [2], which represents the continuous-time model, is completely identical to (7) in this paper. The pseudo-inverse matrix is used for projection in [2], the pseudo-inverse matrix is specifically computed though (so the model looks a little different.) In addition, in [2] a discrete-time model is also discussed. In the discrete-time model, the discrete gradient, which is a discrete version of the gradient, is considered, and the discrete tangent space is defined using the discrete gradient. The discrete-time model is essentially the projection onto this discrete tangent space.\n\nIn addition, it seems that the conserved quantities are assumed to be given in this paper; however, the methods shown in the above papers can handle cases where these quantities are unknown. \n\nConsidering the above, the contributions of this paper is quite limited." }, "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": "Please address the concerns mentioned in the Weaknesses above." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Constrained dynamics are present in real-world problems, and existing NODEs that do not consider these constraints run the risk of failing to satisfy them. In contrast, the proposed method achieves hard constraint satisfaction through projection." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a projection-based approach to ensure hard constraint satisfaction in constrained dynamics, which is important for several real-world applications. However, several concerns outlined in the weaknesses section remain, and further investigation of these limitations is needed to improve its practical applicability." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe methodology is based on the assumption that the analytic form of the constraint function $g$ is known, which seems impractical. In real situations where the dynamics are unknown and the states $u$ are given only by the data, there are often many cases where the analytic form of $g$ is also unknown.\n2.\tWhat is the difference and advantage of the proposed method of projecting the forcing function $f$ onto the tangent space compared to projecting the predicted state $u$ onto the constraint manifold? Projecting $u$ onto the constraint manifold seems simpler than projecting $f$ onto the tangent space, while still satisfying the constraints.\n3.\tThe authors suggest restricting $f$ to the tangent space to satisfy the constraints. This leads to a question related to the one above: Is the original problem Eq. (1) with constraints on the state equivalent to the constrained problem Eq. (8) considered by the authors with the forcing term in the tangent space? Eq. (8) may limit the range of expressible dynamics.\n4.\tThere is a concern that the computation of the adjoint differential and pseudoinverse in Eq. (7) would be quite difficult for general $g$.\n5.\tInstead of enforcing hard constraints, constraints could be incorporated into the loss function by penalizing it. For instance, this could involve adding the $L^2$ norm of $g(u)$=$(g(u)-0)$ as a regularization term to the existing NODE loss. An experimental comparison with this approach seems necessary.\n6.\tWhile hard constraints ensure that the constraints are satisfied, they are not necessarily superior to soft constraints. Hard constraints can limit the representational power of the network and may negatively impact training because of their complex computational structure. It is crucial to understand and experimentally verify the trade-off between satisfying constraints and the model's capacity." }, "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": "1. Could the paper provide a detailed formula of computing the \"Relative State Error\" and \"Constraint Error\"?\n\n2. Could the paper explain in detail how the trajectory of the figure was selected?\n\n3. What does the vertical axis of the leftmost subgraph in Figure 2 mean？\n\n4. Section 4.2: Why does NDE using generalized coordinates that satisfy constraints perform poorly, and does this mean that preserving constraints cannot directly indicate better predictions? Can the results highlight the importance of constraints?\n\n5. Section 4.3: Do we know the governing function for this example, or are the dynamics learned from the given data? Additionally, the statement 'apply random perturbations to each grid (see Appendix A) and learn the dynamics from the resulting transients' feels unclear. Could the paper clarify this? Also, what are the sizes of the training and test datasets used for this example?\n\n6. For all examples, the paper only presents the state error for a single test trajectory. Could the paper provide more comprehensive quantitative evaluations? \n\n7. The assumption of known constraints may be too restrictive. It would be helpful to discuss scenarios where the constraints are known, but the governing function is unknown and data is provided." }, "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 presents an approach to integrating known constraints into neural differential equations, which is a relatively unexplored area in the field of machine learning and dynamical systems. The introduction of the projection method to enforce constraints on the vector field is innovative.\n\n2. The empirical results demonstrate that PNDEs outperform existing methods in terms of accuracy and stability. The experiments conducted on challenging examples, including chaotic systems and power grid models, further validate the robustness of the proposed method.\n\n3. The paper is well structured and clearly written, making complex concepts accessible to readers." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces Projected Neural Differential Equations (PNDEs), a novel method designed to incorporate known constraints into neural differential equations. Specifically, PNDEs project the vectors of the vector field onto the tangent spaces of the manifold defined by known constraints. This allows for the enforcement of various constraints without the need for specific coordinate systems. The paper provides empirical evidence through experiments showing that PNDEs satisfy constraints more accurately than state-of-the-art baselines." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. There is a potential ambiguity in the notation used in the paper. Specifically, the definition of $\\mathcal{E}$ is lacking; is it $\\mathbb{R}^n$? The notations $f_{\\theta}$ and $\\bar{f}_{\\theta}$ in equations 1, 3, and 8 are not entirely consistent.\n\n2. The paper primarily focuses on known constraints. In many cases—such as the first example presented—it shows that if the constraints are known, the corresponding total differential equation can be determined.\n\n3. The effect of the ODE solver is not discussed in this paper. Ideally, the constraints should be preserved exactly, as stated by Proposition 1 in the paper. However, the numerical results indicate that they are only preserved approximately, albeit with small errors. These discrepancies may arise from numerical errors introduced by the ODE solver.\n\n4. The setup of the first example differs from that in Section 2. Is it possible to explicitly write out the manifold for the example?\n\n5. The empirical results demonstrate that the proposed PNDEs outperform existing methods in terms of the consistent error. However, the improvement in terms of prediction error is less pronounced. I am not certain whether the ability to preserve the given constraints is the most critical indicator. If we aim to improve this, the simplest and most straightforward approach would be to project the predicted state onto the known manifold during the prediction phase." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024projected,\ntitle={Projected Neural Differential Equations for Learning Constrained Dynamics},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2AWZTv6kgV},\nnote={under review}\n}" }, "abstract": { "value": "Neural differential equations offer a powerful approach for learning dynamics from data.\n However, they do not impose known constraints that should be obeyed by the learned model.\n It is well-known that enforcing constraints in surrogate models can enhance their generalizability and numerical stability.\n In this paper, we introduce projected neural differential equations (PNDEs), a new method for constraining neural differential equations based on projection of the learned vector field to the tangent space of the constraint manifold.\n In tests on several challenging examples, including chaotic dynamical systems and state-of-the-art power grid models, PNDEs outperform existing methods while requiring fewer hyperparameters.\n The proposed approach demonstrates significant potential for enhancing the modeling of constrained dynamical systems, particularly in complex domains where accuracy and reliability are essential." }, "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 differential equations", "neural ordinary differential equations", "constraints", "dynamics", "scientific machine 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/6f4494dd3c83939c7d33f4ba6198bf960496e7b9.pdf" }, "presentation": null, "primary_area": { "value": "learning on time series and dynamical systems" }, "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": "Projected Neural Differential Equations for Learning Constrained 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": "I think the paper will be much more ready if the authors could address the following questions (from high to low priority):\n\n1. Could the authors provide comparisons with other layer-wise compression strategies in terms of accuracy and system performances?\n\n2. Did the authors investigate the relationship between dis-similarity ranking and the acceptance rate by the thresholding condition? It's possible that the cosine similarity check, rather than dissimilarity ranking, plays a primary role. In principle, if the \"higher dis-similarity --> inter-layer KV cache sharing gives better performance\" hypothesis holds, then a higher rank should correspond to a higher acceptance rate. Could the authors provide additional results and justification on this point?\n\n3. There is an important threshold in this work: cos-similarity (representation similarity) threshold that determines whether to accept a KV cache pair, Can the authors provide explanations on how the value is determined/searched? Moreover, the number of target shared KV cache layers is also an important hyper-parameter, and this is discussed in the paper an ablation study on in Table 1. But can the authors provide some guidance/calculation on how this number translate to memory saving and inference speedup?\n\n4. For KV cache dissimilarity distance, why did the authors choose Euclidean distance? Could the authors ablate on other distance metrics? Similarly, for cosine similarity from the final layer hidden states, what if some other metrics like angular distance is used (less important, just wondering)?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "This paper and the technique introduced have the following strengths:\n\n1. Paper writing is easy to follow with good figures and illustrations.\n2. The experiment sections demonstrate KVSharer can be used in orthogonal with other intra-layer KV compression techniques like H2O and PyramidInfer to achieve higher memory saving and more significant speedup.\n3. The paper brings up a new angle" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a new inter-layer KV cache compression technique through layer-wise KV cache dis-similarity search and sharing. The layers are ranked pairwise in accordance with their dis-similarity score. For each pair, an earlier layer's KV will be shared and reused by an later layer for efficient pre-filling and generation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I have several concerns about the paper:\n\n1. Even though layer pairs are ranked from high dis-similarity to low dis-similarity, whether to use the pair still depends on the cosine similarity between the the KV-cache compression model and the original model. There is a possibility that the cosine similarity check, rather than dis-similarity ranking, plays a major role.\n\n2. A major claim in the paper is dis-similarity metrics is better than similarity metrics when it comes to inter-layer KV cache sharing. Empirical evidences are provided in Section 5.1 and Figure 6 when changing the Euclidean-distance based ranking from descending order (dis-similarity) to ascending order (similarity). However, I didn't find any theoretical and empirical evidence that \"Euclidean distance for KV cache is a sufficient good metrics\" in comparison with the other SOTAs. More specifically, how does KVSharer compare with other layer-wise compression strategies, for example miniCache [1], LCA [2], CLLA [3] and simpleLayerKV [4]? Without the experiment results, I don't think the paper is ready at this stage for publication.\n\n[1] Liu, Akide, et al. \"MiniCache: KV Cache Compression in Depth Dimension for Large Language Models.\" arXiv preprint arXiv:2405.14366 (2024).\n\n[2] Brandon, William, et al. \"Reducing Transformer Key-Value Cache Size with Cross-Layer Attention.\" arXiv preprint arXiv:2405.12981 (2024).\n\n[3] Yang, Zhen, et al. \"Lossless KV Cache Compression to 2%.\" arXiv preprint arXiv:2410.15252 (2024).\n\n[4] Zhang, Xuan, et al. \"SimLayerKV: A Simple Framework for Layer-Level KV Cache Reduction.\" arXiv preprint arXiv:2410.13846 (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": "Not applicable." }, "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 address the corresponding concern listed in the weakness section." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- S1. This paper explores an important problem of improving the efficiency in utilizing KV cache in LLM generative inference. \n\n- S2. The related work and research context are well summarized." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper first presents a counterintuitive phenomenon when attempting to leverage the cross-layer pattern to improve the efficiency of the LLM generative inference computation, where sharing dissimilar KV caches better preserves the model performance. Based on this observation, this paper introduces a method named KVSharer, which integrates this observation to implement efficient cross-layer KV cache sharing. An empirical study has been conducted to verify the effectiveness of the proposed methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- W1. Heuristic-based on aggregated information. As enumerated in Section 3.1.2, the proposed method uses the averaged value of the KV-cache to consider the similarity between different layers -- it is a little confusing why such highly integrated information could guide the sharing policy, considering lots of recent work has been exploring the KV-cache utilization at token, layer, and head level jointly. My concern is whether such a highly aggregated metric is informative or not.\n\n- W2. My main concern is about the experimental setup. There is a significant mismatch between the motivation example in the introduction, e.g., \"During the LLM inference phase, the KV cache typically accounts for 80% of the total memory usage.\" and the benchmarked settings, where the context window is set to just a few thousand, e.g., up to 1024+4096 in Table-2. Unless batching to an extremely large value (not mentioned in the paper), there is a significant gap between the motivation and the experiments. I think it would be critical to evaluate the performance of the proposed method over long-context benchmarks (e.g., Infiniti-bench) where the model's context window should be from 32K to 128K (or even longer). Otherwise, the truly useful scenario is not evaluated." }, "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": "See Above." }, "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 addresses a good research topic: efficient LLM inference.\n \n2. The paper is well-organized.\n \n3. The proposed method is clearly presented." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces KVSharer, a plug-and-play method for compressing the key-value (KV) cache of large language models (LLMs) during inference. Unlike the intuitive approach of sharing similar KV caches, KVSharer is based on a counterintuitive observation: sharing different KV caches across layers does not significantly degrade model performance. KVSharer employs a search strategy to identify the optimal KV cache sharing policy across different layers, substantially reducing GPU memory usage while retaining most of the model’s performance. Additionally, KVSharer is compatible with existing intra-layer KV cache compression methods, offering a complementary approach to memory optimization for LLMs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **Lack of novelty and research depth:** This main technique is to share the dissimilar KV cache for efficient inference, which is quite simple. Although authors claim that this originates from a counterintuitive observation, there is no motivation provided in the methodology section. Therefore, both of the novelty and the research depth of this paper are not qualified for the top AI conference.\n \n2. **Unreasonable observation without further analysis:** The observation that the sharing the dissimilar KV cache brings in better accuracy than sharing the similar one sounds unreasonable, the dissimilar KV states output different attention scores, making the LLM attend to different part of the query token. It is more convincing that the obtained conclusion is just a coincidence and varies across the models and datasets, considering that no in-depth analysis has been provided.\n \n3. Lack Needle-in-a-Haystack experiment." }, "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": "* Why is it better to share dissimilar KV caches? Since the authors themselves describe this as counterintuitive, providing an explanation for this phenomenon would be highly valuable for the community.\n\n* What happens if KVSharer is unable to find $C$ pairs of layers to share KV caches while satisfying the threshold $T$? It would be helpful to include a guideline on setting this threshold and any evaluation showing its impact on search performance.\n\n* In Table 2, why does the memory usage reduction exceed the compression rate? Additionally, what is the source of the observed increase in generation throughput? Since KV cache sharing reduces memory usage but likely not memory bandwidth, it is unclear how this improves inference throughput." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* Does not require training\n* Provides an interesting and novel insight that sharing dissimilar KV caches yields better performance.\n* Offers diverse and insightful evaluation results." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces KVSharer, a post-training method for layerwise KV cache sharing. Based on the counterintuitive observation that sharing KV caches between layers with dissimilar, rather than similar, KV caches leads to less performance degradation, KVSharer employs a systematic search strategy for KV sharing. As a result, KVSharer reduces GPU memory consumption while maintaining model performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* Results show a noticeable performance drop even at low compression rates (e.g., 12.5%, 25%), which may limit the practicality of the method.\n* Lacks an explanation for why sharing dissimilar KV caches yields better performance, leaving an essential aspect of the method's effectiveness rather unclear." }, "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": "What is the time scalability of the proposed approach? Will the inference time remain acceptable when scaling up to models with over 400 billion parameters? It would be valuable to provide an estimation or analysis to address this concern." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This idea offers new insights into how memory size can be further reduced, potentially leading to more efficient model deployments and optimized hardware utilization." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a novel approach to sharing the key-value (KV) cache across different layers in a new dimension, which can lead to more efficient memory usage and improved performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) The paper lacks a comparison with other cache-sharing methods, which would provide a clearer understanding of its advantages.\n\n2) It should consider the scenario when the KV cache is quantized, as quantization is often used during inference to save energy.\n\n3) The paper also lacks a scalability analysis, which is crucial for evaluating how well the proposed method performs as model size and complexity increase." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024kvsharer,\ntitle={{KVS}harer: Efficient Inference via Layer-Wise Dissimilar {KV} Cache Sharing},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2Akf4BBCKo},\nnote={under review}\n}" }, "abstract": { "value": "The development of large language models (LLMs) has significantly expanded model sizes, resulting in substantial GPU memory requirements during inference. The key and value storage of the attention map in the KV (key-value) cache accounts for more than 80\\% of this memory consumption. Nowadays, most existing KV cache compression methods focus on intra-layer compression within a single Transformer layer but few works consider layer-wise compression. In this paper, we propose a plug-and-play method called \\textit{KVSharer}, which shares the KV cache between layers to achieve layer-wise compression. Rather than intuitively sharing based on higher similarity, we discover a counterintuitive phenomenon: sharing dissimilar KV caches better preserves the model performance. Experiments show that \\textit{KVSharer} can reduce KV cache computation by 30\\%, thereby lowering memory consumption without significantly impacting model performance and it can also achieve at least 1.3 times generation acceleration. Additionally, we verify that \\textit{KVSharer} is compatible with existing intra-layer KV cache compression methods, and combining both can further save memory." }, "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", "KV Cache", "KVSharer" ] }, "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/89b8a1b38e6cf82dd43280011e39cb6cefe942b9.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/f460ce31466303cf912af801d075b73567417681.zip" }, "title": { "value": "KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing" }, "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": 2 }, "primary_area": null, "questions": { "value": "see weaknesses" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The idea is novel and the theory is rigorous. The proposed algorithms lead to significant improvements in empirical evaluations on some datasets." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper explores programmatic weak supervision by treating weak labelers as constraints in a classification task.The authors propose a constrained optimization approach that integrates weak labeler error bounds directly into the learning objective. This forms a complex optimization problem and is solved with a novel alternating minimization algorithm." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper misses a conclusion and future extensions paragraph.\n2. On some datasets, the margin of the proposed methods and competing methods are small. Would it be helpful to run some statistical tests to compare their performances?" }, "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": 3 }, "primary_area": null, "questions": { "value": "1. I suggest the authors use different markers and line styles for different datasets instead of only using color to differentiate different lines.\n2. There are several ?? in the paper. For example, on line 1357, 1359, 1418: ??\n3. On line 491, the author mentioned that they implemented Algorithm 1 with an L2 regularization. I wonder what are the impacts of other regularization." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1. Novel approach: This work presents a novel constraint-based objective that specifically considers accuracy bounds.\n2. Scalable: A linear program for classifier constraints and a convex optimization problem for distribution constraints can effectively execute the paper's efficient alternating minimization approach.\n3. Thorough theoretical analysis: The paper offers a thorough theoretical examination of the suggested approach. These analyses offer assurances on the trained classifier's inaccuracy and draw attention to the denoising impacts.\n4. Excellent empirical performance: According to an experimental evaluation on a well-known weak supervision benchmark, the suggested approach outperforms current baselines, proving its efficacy and resilience." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "By using accuracy restrictions on weak labelers as learning constraints, this work introduces a novel method for programmatic weak supervision. The paper makes three primary contributions:\n\n1. create a constraint-based method for aggregating weak labelers; \n2. present a scalable optimization problem; and \n3. offer a theoretical analysis of the suggested constrained estimator.\n\nThe suggested method is technically sound and well-motivated, and the paper is well-written. The empirical evaluation shows the efficacy of the suggested approach, while the theoretical analysis sheds light on the denoising consequences of several weak labelers." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The authors admitted that in the case of learning on classifier solving the ILP can still be slow even with LP relaxation. Additionally, because the stochastic gradient descent relies on the population means of the weak labeler accuracies, the method is unable to use a small batch 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": "Please see the weaknesses above. And,\n1. I do not find the empirical results convincing. Ours(C) and Ours(V) rely on either hand tuning $\\eta_j$ or estimating from validation data. Did you estimate source quality in other WS setups using the same validation data? \n\n2. Can you provide some simulations with different $\\eta_j$ and labeling sources outputs, clearly showing how the method works in different scenarios?" }, "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 presents an interesting alternative to traditional generative models for weak supervision. By viewing weak labelers as error-bound constraints, the approach avoids common assumptions about label independence or probabilistic structure, which may not hold always. \n\n2. The authors provide an upper bound on error (in the union of covered region by all weak labelers) of any predictor satisfying all the constraints. The upper bound is summation of upper bounds on the errors in each weak labelers and the probability of region where weak labelers have a conflict. Implying better bound with more conflict.\n\n3. The method is evaluated on weak supervision benchmarks, where it demonstrates improved accuracy over other weak supervision methods." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a method for learning from programmatically generated weak labels by treating weak labelers as constraints rather than relying on traditional generative models. This approach uses side information in the form of bounds on weak labelers’ error rates, which are applied as constraints within a constrained optimization framework. The authors introduce an alternating minimization algorithm to iteratively project model predictions onto the feasible region defined by these constraints. They evaluate the method on multiple weak supervision benchmarks and demonstrate that it improves upon traditional weak supervision techniques, such as Snorkel, by incorporating this constraint-based learning approach." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The model relies on accurate estimates of weak labelers’ error bounds to define constraints. However, obtaining these estimates is challenging, and inaccurate bounds could lead to suboptimal model performance. In the experiments these are estimated using validation data, which could also be hard to obtain. In contrast several baselines in weak supervision (those based on generative modeling) estimate 'labelers quality' using only the unlabeled samples. \n\n2. Assumptions made on the weak labelers are not clear. In particular, what are the assumptions that the labelers have to satisfy to ensure the method will work as expected and the theoretical results will hold. Naively putting an upper bound of $\\eta_j$ on each labeler could lead to several scenarios e.g. all could have $\\eta_j$ error in different parts of the input space (~independence) or highly overlapping parts (~highly correlated). Could you explain how the method and results will turn out in these two extremes? \n\n3. Theoretical results do not explain how learning in the proposed setup leads to a classifier with good generalization error. It naively depends on the summation of errors of individual labelers. On the other hand several of the baselines provide results showing how the labelers cancel their noises and eventually lead to a classifier with comparable generalization error to a model trained on clean labels. They do make certain assumptions on labelers to get there. What can be said more specifically in this setup with similar assumptions? Even a naive majority vote with labelers with random noise of $\\eta_j$ could be shown to give good generalization error going down with the number of samples and the number of weak labelers." }, "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": "1. The paper moves away from traditional generative models, avoiding the often unrealistic assumption of conditional independence between weak labelers, making it more flexible for real-world applications.\n\n2. The theoretical analysis is quite thorough, especially with the introduction of projection techniques and alternating minimization, showing how to effectively build a classifier without labeled data.\n\n3. Good writting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a novel approach for learning from weak labelers by framing the problem as a constrained optimization task. Instead of relying on generative models or conditional independence assumptions, the paper proposes using known upper bounds on the error rates of weak labelers to guide the learning process. The paper develops an alternating minimization algorithm to iteratively generate soft pseudo-labels that satisfy the constraints and train the model accordingly. Theoretical analysis is provided to explain the denoising effects of the method, and experiments on benchmark datasets demonstrate its effectiveness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The problem addressed in this paper is certainly interesting, but as the authors themselves mention, it has strong connections to areas like crowdsourcing, noisy label learning, semi-supervised learning, and ensemble learning. Each of these fields already has well-established techniques that could be adapted, with only minor modifications, to solve the problem presented here. However, the paper dismisses these connections too quickly, with phrases like \"not directly applicable to our setting\" and \"relies on the implicit inductive bias.\" I find this explanation insufficient, as it limits the paper's significance and impact. A deeper exploration of these connections, along with additional comparative experiments, would have been much more convincing.\n\nThe authors propose two objectives and frame the problem as a constrained optimization task, introducing corresponding optimization methods. While the paper's main contribution is centered on optimization through projection, I have to admit that I'm not an expert in optimization, this approach feels somewhat intuitive. It doesn't strike me as a particularly novel or non-intuitive solution.\n\nAdditionally, regarding the problem setup and experiments, I would like to see more details about the coverage rate and noisy rate of each weak labeler and the collective coverage of all labelers. This seems crucial to the model’s performance and yet isn’t discussed in enough detail." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "A new method for learning from weak labelers given information about their average errors." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024learning,\ntitle={Learning from weak labelers as constraints},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2BtFKEeMGo},\nnote={under review}\n}" }, "abstract": { "value": "We study programmatic weak supervision, where in contrast to labeled data, we have access to \\emph{weak labelers}, each of which either abstains or provides noisy labels corresponding to any input. Most previous approaches typically employ latent generative models that model the joint distribution of the weak labels and the latent ``true'' label. The caveats are that this relies on assumptions that may not always hold in practice such as conditional independence assumptions over the joint distribution of the weak labelers and the latent true label, and more general implicit inductive biases in the latent generative models. In this work, we consider a more explicit form of side-information that can be leveraged to denoise the weak labeler, namely the bounds on the average error of the weak labelers. We then propose a novel but natural weak supervision objective that minimizes a regularization functional subject to satisfying these bounds. This turns out to be a difficult constrained optimization problem due to discontinuous accuracy bound constraints. We provide a continuous optimization formulation for this objective through an alternating minimization algorithm that iteratively computes soft pseudo labels on the unlabeled data satisfying the constraints while being close to the model, and then updates the model on these labels until all the constraints are satisfied. We follow this with a theoretical analysis of this approach and provide insights into its denoising effects in training discriminative models given multiple weak labelers. Finally, we demonstrate the superior performance and robustness of our method on a popular weak supervision benchmark." }, "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": [ "unsupervised learning", "weak supervision", "learning 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/c5e6d8ada8fc16373eb250584016316253b1e229.pdf" }, "presentation": null, "primary_area": { "value": "learning theory" }, "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": "Learning from weak labelers as constraints" }, "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": "**1)** Can you include more types of SR models in benchmarking, or explain the advantages of DSR-Rex over AI Feynman 2.0 in capturing equivalent expressions?\n\n**2)** In equation (4), You mentioned that $\\mathbb{I}$ \\{ $\\cdot$ } $=1$ if $\\tau$ can be converted to $\\phi$, however, according to the definition in line 85, $\\phi$ is one specific expression. How do you obtain the probability of the equivalent group? Do you mean $\\phi$ represents all equivalent expressions to $\\phi$ here? In line 181, \"all possible sequences\" refers to all the sequences in the same equivalent group, or all the expressions have been sampled?" }, "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 well written with clear notations, concrete technical details, and illustrative figures to explain the problem.\n\n**2)** The paper is well-motivated by an interesting topic of expression equivalency in the symbolic regression (SR) area, which is promising to attain better performance with existing SR models and develop new SR models.\n\n**3)** Theoretical analysis provides the performance lower-bound as DSR." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper identifies a problem of deep symbolic regression (DSR) for symbolic regression problems, that failure to capture equivalent expressions results in high variance of gradients and unstable training for the policy gradient estimator. The author proposed to address the problem by appending the symbolic reasoning module to a batch sampling of DSR to capture the equivalent expressions and adopting a new policy gradient method based on the group of equivalent expressions." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**1)** Expression equivalency problems exist in nearly all SR methods. Compared with the large landscape of SR model families, the baseline model DSR is a little bit out-of-date. For example, GPMeld, the successor of DSR in Figure 2, exhibits better performance than DSR, and a similar performance to DSR-REX. Besides, the benchmarking models adopted in the experiments only encompass Reinforcement Learning (RL) based methods and one RL and genetic programming hybrid method GPMeld. To make stronger conclusions, more types of SR models should be considered, such as AI Feynman 2.0 as cited in the paper which studies similar expression equivalency problems.\n\n**2)** Figure 3 only compares the efficiency between the steps within the DSR-REX with different architectures. The comparison of efficiency between DSR and DSR-REX would bring in more 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": 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. The theoretical guarantees assume fair sampling of all equivalent sequences for each expression, but in practice this may not hold. Consider two expressions φ₁ and φ₂, where φ₁ finds only two equivalent forms, while φ₂ finds N>>2 equivalent forms through the designed reasoning rules. This could lead to q(φ₂) > q(φ₁) simply due to having more discoverable equivalent forms (e.g., there are lots of trigonometric equivalences compared to other operations), rather than actual learning preference. How does this potential bias affect the training process?\n\n2. What is the value of max group size parameter, and how sensitive is the method to this parameter?\n\n3. Could you clarify if the results shown in Fig. 2 (right) are averaged across all benchmark datasets or specific ones?\n\n4. How are the 10 Feynman datasets selected? Why not evaluate on standard benchmarks like SRBench and compare against more recent SOTA methods?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* Interesting approach to leveraging equivalent expressions for variance reduction in symbolic regression, with supporting theoretical analysis\n* The methods to reason and find equivalent expressions are straightforward and fast, making them easy-to-use for future works." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents Deep Symbolic Regression via Reasoning Equivalent Expressions (DSR-REX), an enhancement to deep reinforcement learning-based symbolic regression (DSR). The key innovation is leveraging numerically equivalent mathematical expressions to reduce policy gradient estimate variance while maintaining unbiasedness. The method incorporates a symbolic reasoning module that generates equivalent expressions through mathematical transformations, leading to improved convergence and performance compared to baseline deep RL methods. The authors provide theoretical guarantees for their approach and demonstrate empirical improvements on several datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* Limited evaluation scope using primarily trigonometric datasets and a small subset of Feynman equations, rather than standard benchmarks like SRBench (all Feynman equation, black-box datasets)\n* Comparison against outdated baselines (DSR, neural guided GP) rather than current SOTA methods like PySR, uDSR, E2E, TPSR, and SPL\n* Insufficient analysis of how the theoretical guarantees translate to practical scenarios, particularly regarding the sampling distribution of equivalent expressions\n* Lack of ablation studies on the impact of different group sizes and reasoning rules" }, "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": 2 }, "primary_area": null, "questions": { "value": "1. Details/notations about problem setup need to be more precise. For instance, \n- for $\\tau=(\\tau_1,\\dots,\\tau_k)$, what is $k$? How is it determined? \n- What is each $\\tau_i$ -- is each of them a math operator/variable/coefficient? \n- In equations (1) and (2), the reward is defined for each sequence $\\tau$, but right after that, the notations override previous ones, where $\\{\\tau_1,\\dots,\\tau_N\\}$ represents multiple sequences, so here each $\\tau_i$ is a sequence, instead of an element in a sequence? \n\nPlease revise the notations and be rigorous about their meanings. \n\n2. I understand that numerically equivalent but symbolically different expressions exist, and it is reasonable to try to avoid them. However, for the motivation of this work, I was wondering how this might negatively affect DSR. Why does it make it less stable or less efficient, as the authors claim? \n\n3. Line 196, the sentence \"Since we cannot directly use the probability distribution qθ to sample a group of sequences with the\nsame reward. Instead,...\" seems to be grammatically incorrect. \n\n4. More details of the method for equivalent expressions are needed for clarity: It is claimed that \"In practice, equation 7 is not computed by enumerating every expression in Φ (as indicated by the inner summation).\" and the details are in Section 3.2. However, Section 3.2 seems difficult to understand. What is the generated equivalent expressions for? How are they used in equation 7? Or is there an equivalent way to compute equation 7 after generating the equivalent expressions? \n\n5. What if the equivalent expressions in Section 3.2 cannot enumerate all possible choices? What is the consequence, and how would limiting the number of them impact the results? \n\n6. Setup for Section 5.2: Is Figure 2 the result for one dataset, or aggregating results from multiple datasets? Please consider showing the results for all 10 datasets. \n\n7. How are the 10 tasks selected from the Feymann dataset? It would also be helpful to consider larger benchmarks like SRBench [1]. \n\n[1] La Cava, William, et al. \"Contemporary symbolic regression methods and their relative performance.\" Advances in neural information processing systems 2021.DB1 (2021): 1.\n\n8. The high-level idea of addressing numerically equivalent expressions seems widely applicable. Would similar ideas be useful beyond the context of DSR? It would be helpful to have some discussion on the broader scope." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Improving the performance of symbolic regression is an important problem, and this paper is likely to have impact for the important method of DSR. \n\n2. The motivating point of addressing equivalent symbolic equations is interesting and insightful. \n\n3. The paper is clearly structured." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes DSR-Rex, which adds a mathematical equivalence reasoning module to deep symbolic regression to improve the efficiency and stability in the training process (variance reduction). Based on a re-expression of the objective after grouping mathematically equivalent but symbolically different equations, the algorithm uses standard encoding/decoding modules of DSR plus a novel reasoning module that enumerates equivalent expressions of the generated equations, and then modifies the training objective of DSR. It is proved the equivalence of objective functions of DSR-Rex and DSR and reduced variance of the estimated objective using DSR. The performance of DSR-Rex is evaluated on Feymann datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The presentation clarity of the paper needs to be improved, including notations and method details. Please see my detailed questions below. \n\n2. The motivation needs to be strengthened to justify why numerically equivalent but symbolically different equations will pose challenges to DSR training and why the proposed methods. \n\n3. The experiments can be enhanced by adding more benchmark comparisons." }, "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": "##### Questions\n\n1. The third innovation point of the paper, 'Encours-ages RL exploration of different symbolic forms in the search space of all expres- sions' Is to make the probability of model sampling more random? Like adding entropy loss?\n2. Article line 151, additional sequences generated by a symbolic expression reasoning module. How does symbolic expression reasoning module generate additional sequences and what is their role?\n3. In Figure 1, the **Reasoned expressions** can improve the performance of the algorithm. Please analyze the reasons for the improvement in the performance of the algorithm more carefully in the article.\n4. Although your idea is good, I think it is inappropriate for the words \"high-level idea\" to appear in an academic paper." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "##### Strengths\n\n1. In this paper, DSR-REX has achieved good results in comparison with other baselines.\n2. Achieving variance reduction of the gradient estimator with a theoretical guarantee." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes DSR-REX, which improves the performance of the algorithm by embedding mathematical laws and equalities into deep models. Moreover, the variance of the gradient estimator is theoretically guaranteed to decrease. Finally, in various experimental tests, DSR-REX shows good performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "##### Weaknesses\n\n1. I think the chapter arrangement of this article is unreasonable. For example, the related work is actually behind the method, which makes the article very messy. I spent an hour not understanding what the author was doing. I think The **Related work** can be put behind the **Introduction**. The **Motivation ** part of **METHODOLOGY ** can be appropriately deleted and put into the **introduction ** part...\n2. Many related works are not mentioned.\n**Reinforcement Learning for Scientific Discovery.** such as TPSR(Discovering mathematical formulas from data via gpt-guided Monte Carlo tree search), SR-GPT(Discovering mathematical formulas from data via gpt-guided monte carlo tree search), RSRM(Reinforcement Symbolic Regression Machine)...\n\n**Symbolic Regression with Domain Knowledge:** NSRwH(Controllable neural symbolic\nregression), MLLM-SR(MLLM-SR: Conversational Symbolic Regression base Multi-Modal Large Language Models\n), LLM-\nSR(LLM-SR: Scientific Equation Discovery via Programming with Large Language Models)...\n3. This article only mentioned the symbolic regression method using reinforcement learning, but symbolic regression is not the only one, other methods should appear in the comparison method, e.g. SNIP,(https://doi.org/10.48550/arXiv.2310.02227) MMSR(https://doi.org/10.1016/j.inffus.2024.102681), DSO(NGGP)(https://doi.org/10.48550/arXiv.2111.00053), TPSR(Transformer-based Planning for Symbolic Regression), and so on \n4. The author should test your algorithm on the SRBench dataset." }, "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. Could you provide DSR-REX’s results on SRBench[4] or SRSD-Feynman[5] to assess the model's stability under varying noise levels and complexities with scientific implications?\n\n2. What level of improvement might your method bring if applied to other models like SPL[1], TPSR[2], and uDSR[3] for training?\n\n3. Could you share the recovery rate for each expression in Chapter 5.2, Experimental Analysis?\n\n4. Could you include an ablation study on parameters in Appendix Section D?\n\n5. Could you compare DSR-REX with models like SPL, TPSR, and uDSR [1, 2, 3] in the experiments in Chapter 5？\n\n[1]Sun F, Liu Y, Wang J X, et al. Symbolic physics learner: Discovering governing equations via monte carlo tree search[J]. arXiv preprint arXiv:2205.13134, 2022.\n\n[2]Shojaee P, Meidani K, Barati Farimani A, et al. Transformer-based planning for symbolic regression[J]. Advances in Neural Information Processing Systems, 2023, 36: 45907-45919. \n\n[3]Landajuela M, Lee C S, Yang J, et al. A unified framework for deep symbolic regression[J]. Advances in Neural Information Processing Systems, 2022, 35: 33985-33998. \n\n[4]La Cava W, Orzechowski P, Burlacu B, et al. Contemporary symbolic regression methods and their relative performance[J]. arXiv preprint arXiv:2107.14351, 2021. \n\n[5]Matsubara Y, Chiba N, Igarashi R, et al. Rethinking symbolic regression datasets and benchmarks for scientific discovery[J]. arXiv preprint arXiv:2206.10540, 2022." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The performance comparison between DSR-REX and previous models like DSR and NGGP highlights its superiority. The complexity of the case study equations effectively showcases the model’s symbolic regression capabilities. Additionally, the paper provides clear details of the algorithm and experimental processes." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The performance comparison between DSR-REX and previous models like DSR and NGGP highlights its superiority. The complexity of the case study equations effectively showcases the model’s symbolic regression capabilities. Additionally, the paper provides clear details of the algorithm and experimental processes.\n\nWeaknesses:\nQuestions:" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper lacks comparisons with other tasks beyond DSR, such as SPL[1], TPSR[2], and uDSR[3], across different benchmarks like SRbench[4]. It also does not discuss how this method could be applied to these models.\n\n[1]Sun F, Liu Y, Wang J X, et al. Symbolic physics learner: Discovering governing equations via monte carlo tree search[J]. arXiv preprint arXiv:2205.13134, 2022. \n\n[2]Shojaee P, Meidani K, Barati Farimani A, et al. Transformer-based planning for symbolic regression[J]. Advances in Neural Information Processing Systems, 2023, 36: 45907-45919. \n\n[3]Landajuela M, Lee C S, Yang J, et al. A unified framework for deep symbolic regression[J]. Advances in Neural Information Processing Systems, 2022, 35: 33985-33998. \n\n[4]La Cava W, Orzechowski P, Burlacu B, et al. Contemporary symbolic regression methods and their relative performance[J]. arXiv preprint arXiv:2107.14351, 2021." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We introduce a deep reinforcement learning approach for symbolic regression that reduces the instability of policy gradients by using numerically equivalent but symbolically distinct expressions." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024enhancing,\ntitle={Enhancing Deep Symbolic Regression via Reasoning Equivalent Expressions},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2CQa1VgO52},\nnote={under review}\n}" }, "abstract": { "value": "Symbolic regression seeks to uncover physical knowledge from experimental data. Recently a line of work on deep reinforcement learning (DRL) formulated the search for optimal expressions as a sequential decision-making problem. However, training these models is challenging due to the inherent instability of the policy gradient estimator.\nWe observe that many numerically equivalent yet symbolically distinct expressions exist, such as $\\log(x_1^2 x_2^3)$ and $2\\log(x_1) + 3\\log(x_2)$. \nBuilding on this, we propose Deep Symbolic Regression via Reasoning Equivalent eXpressions (DSR-Rex). The high-level idea is to enhance policy gradient estimation by leveraging both expressions sampled from the DRL and their numerically identical counterparts generated via an expression reasoning module. \nOur DSR-Rex (1) embeds mathematical laws and equalities into the deep model, (2) reduces gradient estimator variance with theoretical justification and (3) encourages RL exploration of different symbolic forms in the search space of all expressions.\nIn our experiments, DSR-Rex is evaluated on several challenging scientific datasets, demonstrating superior performance in discovering equations with lower Normalized MSE scores. Additionally, DSR-Rex computes gradients with smaller empirical standard deviation, compared to the previous DSR 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": [ "symbolic regression", "deep reinforcement learning", "symbolic reasoning" ] }, "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/6d7ba65b73dac30efbf1b0454b8adda02b05c37f.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": { "value": "/attachment/d21977842d820cb9bb9b97bebe4647c76ce57fa0.zip" }, "title": { "value": "Enhancing Deep Symbolic Regression via Reasoning Equivalent Expressions" }, "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": "See weaknesses." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper improves on existing methods, notably ARTree, for deep learning for phylogenetic inference.\n- The proposed central problem, that of finding a proper taxon insertion order is an important piece of any phylogenetic inference algorithm and deserves more highlights in deep learning-based approaches.\n- The use of language models to extra biological priors is quite novel and general.\n- Experiments are relatively extensive, at the base line of deep learning-based approaches." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper provides a new deep learning based method that incorporates language model to extract biological priors to find a node insertion ordering. They improve on state of the art methods using autoregressive models and provide comprehensive experiments." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The paper's main contribution is methodological but compared to the most closely related method, ARTree, there are only marginal improvements across datasets. This is also in light of the fact that the proposed methodology is extremely more computational intensive, both in terms of runtime and carbon footprint. With so much more computation, it is not too unfair to expect a more pronounced difference. Perhaps it is advisable to find conditions where dynamic node ordering strongly affects the tree reconstruction methods. If it is too hard to find such conditions, perhaps it is not as bad a problem as stated in the paper.\n- The paper's key insights (compared to literature) is a method to learn an insertion ordering of the taxa. However, it is not clear that the proposed methodology to find such an ordering is clearly advantageous compared to other different orderings. The baseline considered against use a lexicographical ordering, which is just arbitrary. What happens when a different ordering is used? \n- Related to the topic of choosing the right taxa ordering: theoretically, given just one correct planar ordering of the taxa (draw the true tree onto the plane and number leaves from left to right, there is a trivial greedy algorithm to find the correct tree structure and branch length from tree distance approximated from DNA sequences. As a result, find the correct order is one of the hardest subproblem of tree inference. \n- There are other line of work that uses Prim ordering of the distance matrix between taxa as the ordering to add taxa into the tree and implemented with maximum likelihood heuristics (Zhang, Rao, Warnow 2019 Constrained incremental tree building: new absolute fast converging phylogeny estimation methods with improved scalability and accuracy; Le et al. 2021 Using Constrained-INC for Large-Scale Gene Tree and Species Tree Estimation). These are not deep learning-based methods so it's not directly comparable, but at least a discussion on the existing orders that have been considered is warranted. It would also be interesting to see how the Prim ordering works in these experiments." }, "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": 1 }, "primary_area": null, "questions": { "value": "1.\tHow does node count measure runtime efficiency in figure 1 and why a lower node count is preferred?\n2.\tHow do we get the initial graph structure that is used as input to DON?\n3.\tIs DON a completely separate and preceding step from the dynamic AR tree construction? Or the order determination step is roll out iterative after each node insertion step?\n4.\tSee other questions in Weakness" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper studies an important problem in bioinformatics concerning phylogenetic tree inference. The main highlight is the introduction of DON which infers node orders and enables sampling multiple nodes for parallel computation, which makes use of the strong prior in pretrained GLM and might be more flexible than the fixed order AR method. \n\nThe method is novel and adds a significant improvement to AR method. Additional techniques are introduced to further improve efficiency, generation consistency, and optimization. The perspective of studying the influence of node orders on phylogenetic accuracy is also novel.\n\nThe authors conduct extensive experiments across multiple tasks and datasets related to phylogenetic tree inference and consistently outperform the baselines including ARTree which is likely the previous SOTA. They also showed strong metrics on computational efficiency, and a thorough ablation study showing the importance of DON." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a new method for phylogenetic tree inference which extends beyond autoregressive models and is claimed to improve both computational efficiency and accuracy. Specifically, it focuses on learning a better ordering strategy for adding a new node into the phylogenetic tree from GLM priors as opposed to using fixed orders (lexicographical order) in autoregressive models. The authors framed the problem as masked dynamic autoregressive tree generation and introduced a Dynamic Ordering Network (DON), which is an absorbing diffusion model for learning the node adding order from pre-trained genomic language models (GLMs) to better leverage the biological and evolutional information. They further introduced several techniques for efficiency improvement, including dynamic masking and parallel processing, dual-pass tree traversal for branch length estimation, and LAX model for variance reduction. Extensive experiments show improved accuracy and efficiency of the proposed framework." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper studies a very specific task, phylogenetic tree generation problem, within the bioinformatics domain. Although the task might be important in the domain, the introduced methodology seems to be highly specified for this problem alone, which might limit its significance in the general graph generation area. \n\nThe biggest concern lies in the writing clarity, particularly the DON description. Multiple key information is missing and several notations are inconsistent across the main text. Firstly, the DON module seems to assume some graph structure already known among the sequence (e.g., figure 2.A has a ring structure). How is this graph constructed? It cannot be the tree structure as the phylogenetic tree has not been generated yet at this stage.\n\nThe presentation of DON in 3.1 can be largely improved, with many key notations and parameters unexplained. The biggest gap is the lack of a proper definition of the forward and backward diffusion process, with clear correspondence to time t. It starts with directly “updating node features $h_t$” without defining what t means. It is also not clear what positional encoding $PE_t(g_i)$ means, it is used with subscription $t$ but isn’t the position of node i fixed? How does PE vary with time t and why is it varying with t? It is not clear whether the transition probability in (2) defines a forward corruption process from t=N to 0 or t=0 to N? How can we make sure only a single node is selected to be absorbed at each time step? The notation of $h_t$ is also confusing, is it a single node embedding or embedding matrix for all nodes? There is a mixed use of $h_t$ and $h_i$.\n\nThe node order generation process after the entire graph is absorbed is also not explained well. Equation (3) defines a conditional probability between node embeddings $q(h_t|h_0, h_{(<t))}$, how can this be used for order determination? Shouldn’t one predict the probability of unmasking a node in a diffusion setting? It seems the transition matrix Qt only allows jumping from a non-masking state to a mask. How can this be reused for computing a cumulative transition matrix in the opposite direction (i.e. from masked to unmasked)?\n\nFinally, it is not clear whether the DON is trained (e.g., with a certain score matching loss, and if so what is the training target given that optimal order is not available ahead of time?), or it is just a hand-crafted discrete forward diffusion process which is completely determined by the hyperparameters $\\beta_{t,i}$. There is no description regarding how network parameters of the relational graph convolutional network used for node feature computation are trained either. There is a large discrepancy between what is described in 3.1 and training loss (10) in 3.4, where $q_\\sigma(\\sigma_t|G_0,\\sigma_{(<t)})$ suddenly appears without definition. \n\nIn section 3.2 tree construction, a multi-head attention block with a query matrix Q is introduced MHA($Q, h_i, h_i$), what is the goal of Q here? It is initialized to an Identity matrix with size (N-3)*100, but was not mentioned later. \n \nThere are several typos and inconsistencies in naming terminology. E.g., the DON is sometimes referred as Diffusion Ordering Network and sometimes Dynamic Ordering Network." }, "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": 1 }, "primary_area": null, "questions": { "value": "- Why is G_t a graph? I would have thought it is a DAG.\n- How do you define \\tau and B_\\tau mathematically?\n- The mapping F takes a single species sequence to a tree topology, but in the text it states that F depends on G, which is not reflected in the notation. In addition, why is each species sequence sent to a separate tree topology? There is only a single evolutionary timeline we want to analyse.\n- Line 186 beta increases montonically as what value goes from 0 to 1?\n- What positional encoding function PE is being used?\n- Is the categorical distribution in Eq 3 the forward process of the diffusion process?\n- What is the MLL metric? Please either expand the acronym or give a citation.\n-Table 3: What was the hardware used?\n- I presume alpha in Equation 5 is highly sensitive to N? How come the authors choose to take the softmax of a softmax rather than directly adding the alpha term to L_i?\n- What is the importance of branch lengths?\n- You have 3 distinct components, could you clarify how the gradient flows? I presume that at the boundary between the modules the gradient is stopped due to the discrete decision boundary? If so, how does the ordering network for instance get any signal.\n- What modules are pretrained? Which are trained from scratch? How do you initialise the weights?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Several new ideas on the technical side.\n- (Marginally? hard to judge with the units and lack of error bars) better results.\n- Improvements in run-time." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a new node ordering network to be able to better utilise autoregressive models to generate phylogenetic trees." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "TLDR: I am willing to raise my score if the results are clarified and presented more convincingly (1. error bars 2. a clearer results table and figures 3. a better presentation of the baselines); the method is presented in a way that is much much clearer (I think starting from the problem then objective/loss function then the 3 main components, etc might help); and the method is better placed in the related work with a better discussion of the limitations. From my limited understanding of the paper it seems the technical contribution is there (albeit a little hard for me to judge), but the presentation is still far from the quality necessary for acceptance.\n\nIt should be said that I cannot judge whether the baselines are chosen appropriately.\n\nWeaknesses:\n- There are no statistical error margins (e.g. standard deviation) for the results. This is okay if the computational cost is huge (e.g. often the case in LLMs) if so please state this clearly as well as the running cost in GPU hours, the GPUs used, etc.\n- Table 2 confuses me. For instance for DS1: three are numbers are bold, but not even the 3 highest ones (when MLL higher is better according to the caption), e.g. VBPI-GNN also has the third highest score -7108.41. Also the difference between some of the results seem incredibly marginal. Furthermore, negative MLL might be clearer rather than having a minus sign in front of every number.\n- The method has many components, which on the hand is impressive that the authors managed to build this system and make it work, but also comes with limitations that aren't adequately discussed in my opinion. There are a great number of hyperparameters, but far too little space is dedicated to ablating them or acknowledging the difficulty of choosing them.\n- The related work is quite brief given that the model borrows many techniques related work it compares against. A clearer delineation would be helpful to the reader.\n\nClarity:\n- The way indices i and t are re-used is confusing.\n- It is imperative to give the citation of each baseline method considered. In the paper they are merely named, but not cited. This allows for confusion if two methods share the same name for instance and is generally poor practice.\n- The Table captions could be improved, what are the numbers in paranthesis in Table 2? What is the grey background mean? Why are multiple numbers in bold per dataset?\n- The y-axis range in Figure 3 makes the results really hard to discern.\n\nMinor:\n- Line 164 \"As discussed, ...\" please add a link to where this is discussed, this helps non-linear reading, which is standard for papers.\n- Equation 1 LHS says h_i, the text says h_t" }, "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": 2 }, "primary_area": null, "questions": { "value": "* Why is it better that \"closely related species should be placed earlier in the tree\" (175) versus, e.g. simply clustering together? Is this robust to all topologies? For instance, what happens if you have two very distantly related subtrees, each of which has many species who are closely related to another?\n * Similarly, I am interested in worst-case performance of the clustering algorithm. For instance, if you had a linear tree, would you still be able to parallelize your algorithm?\n* What should the reader take away from the Angiosperm tree in Figure 8? How does this compare to/improve on the trees generated by other models?\n* Bayesian phylogenetics methods will typically include a proof that their estimators are consistent and unbiased. Is it possible to do something similar in the case of this method? If not, the authors should justify why it is worth abandoning such guarantees in favor of their model\n* Will the model be made publicly available? If so, is it easy to use and install? Does it work on a variety of machines and operating systems?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* **Strong benchmark performance** is the main strength of this paper. Across almost all dataset + task benchmarks, the authors claim state-of-the-art performance. This is shown in Tables 1--4 and Figures 3--4.\n* **Extensive secondary benchmarks** characterize MDTree's runtime reduction, parsimony, topological diversity, and bipartition frequencies compared to ARTree (and sometimes other models).\n* **Biological ordering** is a desirable addition to autoregressive models, ensuring that the phylogenetic tree construction task can aggregate as much information across nodes of the tree as possible. This addresses a key limitation of previous autoregressive methods.\n* **Parallelism**, enabled by the biological ordering, is a desirable property for a computational method and appears to improve processing speeds substantially (as shown in Figure 1).\n* **Use of embeddings** eliminates the restriction that all sequences are the same length common in other models; it is also likely to unlock improvements in MDTree \"for free\" as better genomic foundation models are trained." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors present MDTree, a technique for inferring phylogenetic trees (topology + branch lengths) from a set of genomic sequences. To motivate their model, the authors reframe phylogenetic tree construction from the perspective of DART: dynamic autoregressive tree generation, which differs from its autoregressive predecessors by incorporating a node order learning step. To this end, MDTree uses a Diffusion Ordering Network (DON) using genomic language model embeddings to sort sequences. This enables better autoregressive generation and even makes it possible to add nodes in parallel. The authors benchmark MDTree on 8 classic phylogenetics datasets, comparing it to classical MCMC, structure generation, and autoregressive methods. In almost all benchmarks, they show state-of-the-art performance as well as improvements in secondary properties like computation speed, parsimony, and diversity." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* **Relationship to ARTree** is somewhat unclear, and although comparisons favor MDTree, the ARTree score is oftentimes quite close.\n * The authors should make it explicit what distinguishes MDTree from ARTree.\n * The ablations should make it clear which ablated forms of MDTree (if any) are worse than base ARTree\n * Since the benchmark scores of MDTree and ARTree are often quite close together, I am less impressed by \"state of the art\" results. If the authors could convince me why this position is mistaken, and their method is a *significant* improvement over ARTree, I would be amenable to improving my score.\n* **Lack of motivation** for specific architectural choices. Most notably, the diffusion ordering network (DON) is justified in terms of the limitations of other autoregressive methods like ARTree; however, the specific choice of architecture is presented as arbitrary/self-evident. To this end, I have several questions:\n * What other options have the authors considered/tested? Why was the DON ultimately chosen?\n * How does the DON compare to a simple baseline that produces biologically meaningful orders without relying on deep learning? The authors may have a better sense of what a good baseline might be. However, I propose the following baseline as a reasonable starting point:\n 1. Compute pairwise edit distances between genomic sequences (e.g. Levenshtein distances)\n 2. Perform agglomerative clustering on the pairwise distances to get a crude tree\n 3. Use an inorder traversal of the resulting tree to sort the leaves. This is your input order.\n * You cite \"evidence of robustness across different taxa orders\" in ARTree (line 163), but here you simply say \"the influence of node orders on phylogenetic accuracy has not been thoroughly examined.\" The ablation-based evidence presented in Table 7 suggests that node order has a weak influence on model performance, but it would be more convincing to see a non ablation-based characterization (e.g. what is the variance in MLLs for random permutations of node order?)\n* **Unintuitive choice of representations** to seed the DON. It is not apparent that genomic LM representations are the best candidates here, as the LMs are not actually trained to estimate evolutionary distances. Moreover, vector space representations of genetic sequences will always incur distortion, as the geometry of phylogenetic trees is inherently non-Euclidean as a result of the four-point condition.\n* **The DART formulation** seems unnecessary. What is the advantage of reformulating phylogenetic tree construction (which we have a perfectly good description of already: learning a topology and a set of branch lengths), besides that it attempts to justify the use of a DON? If that is all, I would argue that \"proper node orders improve phylogenetic inference\" is a sufficient claim.\n * If other problems in phylogenetics are better viewed from the DART perspective, I would be interested in such an example. This would go a long way towards changing my mind on the value of this part of the paper.\n* **Presentation** is unrefined throughout:\n * Figures are often cramped, and combined into figures with no clear logic (e.g. Figure 1 includes a cartoon and a runtime comparison)\n * Model details are crammed in pages 4 and 5. It is unclear without substantial cross-referencing and careful reading how all of the pieces fit together. While I understand the need to fit within the page limit, I would be interested in seeing the full architecture described in the Appendix.\n * \"Mask rate modulated by a cosine function\" (200) seems to be an essential detail of the autoregressive tree, but the equation is not given anywhere\n* **Related work** does not discuss Bayesian methods since VBPI (except for VaiPhy). There have been many developments in this field since then.\n* **Missing experiments**: oftentimes, certain models are missing from evaluations. For instance, MrBayes and structure representation models are missing from Table 1; many models are missing from Table 4; comparisons in terms of runtime, diversity, bipartition, etc., are only run for 2-3 models at a time. It is possible that these results are infeasible to generate, but the authors should make this explicit." }, "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": { "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. How does DON determine the node order based on genomic embeddings? How much does it impact the final inference if the species sequence order differs?\n2. How does the mask rate selection impact the parallel computation of nodes insertion and overall model running efficiency? \n3. There is no summarization of of the sequence divergence and evolutionary relationship distances about the dataset used in this study. It is necessary to evaluate the impact of sequence divergence on the model performance. The author can also consider adding simulated datasets experiments to better control the sequence divergence. \n4. What is the purpose of generating highly diverse tree topologies in biological research? What type of practical application needs such diverse tree topology instead of a highly confident and accurate phylogenetic tree? \n5. Consider adding bootstrap analysis for phylogenetic support estimation to better indicate how confident is the inferred phylogenies." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This is a quite impressive work that solved multiple pain points in phylogenetic inference. \nThe idea proposed by this paper is innovated and effective, improves the phylogenetic tree inference on both efficiency and accuracy. \nThe paper is well organized with a clear writing style. It is easy to follow the author’s idea. \nThe experiment design is comprehensive. The author considered about multiple aspects of phylogenetic inference, such as running time, tree quality, model robustness, empirical study. The results are convincing." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a new framework for phylogenetic inference MDTree. Traditional methods like MCMC and previous deep learning methods are limited by their high computational cost, low efficiency and low inference accuracy. The new framework uses multiple techniques to effectively resolve these limitations, including a diffusion ordering network (DON) to generate node orders according to evolutionary relationships, autoregressive constriction module with dynamic masking mechanism to generate tree structures in parallel. The model uses a dual-pass traversal to estimate the tree branch lengths. \nThis study includes an extensive evaluation which indicate that MDTree has a robust performance on datasets with variant taxa number and sequence length. Its computational cost and running time outperformed the state-of-the-art methods. This study also includes a comprehensive ablations study on models and hyper parameters to demonstrate the contribution and robustness of the modules." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "There are certain weakness about this study. The complex architecture and multi-layered optimization requirements may limit the practical application. It is worth to consider about pack the framework into a user friendly package or online service. This will not only help people who are interested in this study, but also increase the impact of this impressive work. \nThere are some details about the method and the evaluation metrics are ignored in the paper, such as how does DON determine the node order based on genomic embeddings? What is the impact of sequence divergence and species evolutionary relationship distance to the node order and inferred phylogenies? Why generating highly diverse tree topology is necessary, especially in biological analysis." }, "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": "My suggestion is to address the weaknesses above. \n1. describe baseline method settings\n2. provide code availability to reproduce the result\n3. compare all methods in each metric or explain why a certain method is not included\n4. review the related work and discuss existing method and gap in a more clear way\n5. proofread 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 authors proposed a novel method and conducted a comprehensive evaluation by comparing MDTree with several baseline methods across various datasets and metrics." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The manuscript introduces MDTree, a novel approach to phylogenetic tree inference. MDTree addresses the issues of model complexity and computational efficiency. By leveraging a Diffusion Ordering Network, MDTree dynamically learns the optimal node order, enabling more accurate and efficient tree construction. This approach incorporates graph neural networks (GNNs) and language modeling techniques to capture complex evolutionary relationships. Additionally, a dynamic masking mechanism allows for parallel node processing, further accelerating the inference process. The authors benchmark the performance in several aspects to show the effectiveness of MDTree." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper's experimental evaluation is hindered by several aspects. Firstly, the parameter settings for baseline methods are not well-documented, potentially impacting the strength of reported performance. Secondly, the absence of publicly available code limits reproducibility and hinders independent verification of the results. Additionally, the methods compared in each table are inconsistent, lacking clear explanations for these choices. For example, while MrBayes is included in Table 2, it is absent from Table 1, raising questions about the rationale behind these decisions.\n\nWhile the paper introduces a novel approach to phylogenetic tree inference, the literature review in the introduction appears to conflate different concepts. For instance, the discussion of Solis-Lemus & Ané, 2016 and Zhang et al., 2018, which focus on network inference from gene trees/multiple sequence alignments under the multispecies network coalescent model, seems to be mixed with the concept of gene tree inference from sequence data, the primary focus of the proposed MDTree method. A clearer distinction between these approaches would enhance the paper's clarity and contextual understanding.\n\nBesides the major concerns, below are some minor concerns.\n\nFigure 1: left and right are opposite. Run time unit is missing.\n\nThe name of the proposed method is not consistent in tables. E.g., Table 1: MDTree, Table 2: Ours." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024mdtree,\ntitle={{MDTREE}: A Masked Dynamic Autoregressive Model for Phylogenetic Inference},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2CYZkawsmz},\nnote={under review}\n}" }, "abstract": { "value": "Phylogenetic tree inference, crucial for understanding species evolution, presents challenges in jointly optimizing continuous branch lengths and discrete tree topologies. Traditional Markov Chain Monte Carlo methods, though widely adopted, suffer from slow convergence and high computational costs. Deep learning methods have introduced more scalable solutions but still face limitations. Bayesian generative models struggle with computational complexity, autoregressive models are constrained by predefined species orders, and generative flow networks still fail to fully leverage evolutionary signals from genomic sequences. In this paper, we introduce MDTree, a novel framework that redefines phylogenetic tree generation from the perspective of dynamically learning node orders based on biological priors embedded in genomic sequences. By leveraging a Diffusion Ordering Network to learn evolutionarily meaningful node orders, MDTree autoregressively positions nodes to construct biologically coherent trees. To further push its limits, we propose a dynamic masking mechanism that accelerates tree generation through parallel node processing. Extensive experiments show that MDTree outperforms existing methods on standard phylogenetic benchmarks, offering biologically interpretable and computationally efficient solutions for tree 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": [ "Phylogenetic Inference", "Genome Language Model", "Transformer", "Graph Structure Generation", "DNA", "Large Language 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/b44e9c9a6d482336ccdca08b6f4426b933cbae47.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": null, "title": { "value": "MDTREE: A Masked Dynamic Autoregressive Model for Phylogenetic Inference" }, "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 Weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- ES shows a reduction in training time without loss in performance, which is promising for computationally expensive tasks.\n- The use of loss evolution for sampling is an interesting approach that addresses the shortcomings of previous static and simple dynamic sampling methods.\n- The results on datasets with noisy labels are interesting.\n- Evaluation is sufficiently complete." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes \"Evolved Sampling\" (ES), a dynamic sampling method aimed at improving data efficiency during training. The method selects informative samples based on the loss values during training using a decoupled Exponential Moving Average (EMA) scheme. This reduces the number of samples needed for backpropagation, saving up to 40% in wall-clock time while maintaining model performance. The method was tested on a thorough evaluation across many different models (ResNet, ViT, ALBERT) and datasets (CIFAR, ImageNet, GLUE)." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Limited novelty: the paper largely builds on existing sampling concepts with incremental improvements.\n- The description of the method can be simplified considerably.\n\n- While the method helps reducing the number of backpropagation steps performed during training, it still requires feedforward running of all samples through the network, which is still computationally expensive. Indeed, while the results are positive, the measured gains are not particularly game-changing.\n\n- Minor: I am not sure \"evolved\" is the right term here; \"evolved\" and \"ES\" remind strongly of evolutionary optimization and \"Evolution Strategies\", which can introduce confusion.\n\n- It would be interesting to read more about the increased robustness to label noise; I might have expected the proposed method to perform worse, since samples with wrong labels would yield higher losses (unless/until the network memorizes the whole training 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": 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. Could the authors provide more insight into the sensitivity of the hyperparameters $(\\beta_1, \\beta_2)$ across different datasets and architectures?\n\n2. ES appears computationally feasible for single-machine training, but would its performance gains hold up in distributed training settings?\n\n3. ES with Pruning (ESWP) combines batch and set-level selection, but it is not entirely clear how this combination impacts overall performance in practice.\n\n4. How can ES be used for self-supervised training?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Novelty - The paper introduces decoupled exponential moving averages, which leverage first-order loss differences for more stable and robust sampling, effectively combining ideas from loss and gradient-based sampling with robust optimization principles.\n\n2. Quality - The paper provides theoretical proofs and experiments across models and datasets, demonstrating consistent gains in efficiency and robustness, especially under noisy labels.\n\n3. Writing - The paper is clearly structured, with well-organized sections and visual aids that clarify ES’s advantages over traditional methods, though some theoretical sections may be dense for general readers.\n\n4. Relevance - ES offers practical relevance for reducing computational costs without accuracy loss, making it impactful for both research and industry applications in large-scale ML." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a method called Evolved Sampling (ES) for efficient data selection in training machine learning models. The core contribution is a dynamic sampling framework that identifies informative data samples based on the evolution of loss values throughout the training process. By adjusting the selection of data at the batch level according to changes in loss values, ES significantly reduces the required training time while maintaining model accuracy." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Significance - Much of the computation cost of foundation models occurs during pre-training, which is mostly self-supervised (auto-regressive, contrastive learning, auto-encoders). All the experiments in the paper are for labeled datasets, which represent fine-tuning use cases where the computation cost is not a major concern. Thus, the significance of the method is not clearly demonstrated.\n\n2. Scalability - The paper claims that ES has only modest overheads, but lacks an in-depth analysis of computational and memory costs associated with the decoupled EMA calculations, especially in large-scale tasks or datasets.\n\n3. Assumptions - Some assumptions in theoretical analysis may not hold in practice, e.g., smoothness of loss functions, especially for complex architectures and non-convex losses. A discussion of how the method performs when assumptions deviate from theory, or empirical analysis on non-smooth tasks, would help clarify the applicability.\n\n4. Hyperparameter Sensitivity - Introducing 2 hyperparameters could be a major concern for the proposed method. The current analysis (Figure 5) is too limited, e.g., what's the impact of hyperparameters on efficiency? Besides, it does seem that hyperparameters introduce a large variance in performance. For fair comparisons, the cost of searching hyperparameters should also be considered in the overall task (e.g., on a smaller dataset to test hyperparameters and then apply to a large dataset.)\n\n5. Lack of Baselines for Noise - In the experiments on label noise, ES performs well, but the comparison is limited mainly to non-specialized sampling methods. \n\nnit - ES in this literature often refers to 'Evolution Strategy', so would be nice to have a different abbreviation for 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": 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 like to get a clarification regarding Eq. 3.8. We have access to the current loss of an example to decide whether or not we want to sample it for that epoch. I interpret this as doing the forward pass on an example that we later deselect to be part of the backward pass calculation. This means that we still maintain the gradient of that example until we deselect it. The main cost saved then is the amount of bwd passes. In Algorithm 1, the necessity for forward passes seems to be mitigated in Line 284 at least during the pruning by taking the historically weighed score s instead of the weight function. This seemingly implies that to select examples, only historic losses are considered. But this poses yet another question: How do we adjust an example’s loss if the example is no longer selected? Because then we yet again will need a fwd pass and we could have calculated the full weight. This seems to be what is done in 289; i.e. only the loss over the batch examples is calculated. The only thing to mitigate the issue of disregarding bad losses (almost) completely is in Remark 1 and discounting the existing values. Either way, this introduces non-trivial and dead-lock-ish dynamics I would like to see investigated." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "### Originality:\n\nThe main proposition lies in the recursive definition of an Exponentially Moving Average over the losses of individual examples to deselect them from the training process to gain speedups and improved; i.e. stable, learning dynamics. The single-level EMA itself is a well-known approach that is applied to this setting with a recursive definition. The other techniques, i.e. annealing and pruning, are mere adaptations from prior work and are only a minor contribution to the originality. The bridge between batch and set-level data selection, which their method allows them to do is a nice feature, but not the main contribution. The theoretic analysis is interesting overall. But insights like decoupled EMA is in fact a convolution over hyperparameters’ powers of historical losses – so their results are not really surprising.\n\n### Quality: \n\nQuite a few experimental issues are present, which I will detail in the weaknesses section.\n\n### Clarity:\n\nOverall the paper is clearly and concisely written. With the main exception of when exactly we are collecting the loss values of pruned examples; which might bias the calculation of their weight.\n\n### Significance: \n\nThe efficiency of modern machine learning algorithms and neural networks is a great issue, as it results in huge energy demand. Reducing the footprint is a critical point. One angle of attack pursued in this paper is being selective about the order and the subset of consumed examples. This is indeed an important and interesting avenue." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel framework called Evolved Sampling (ES) (and with Pruning ES-WP) aimed at enhancing data efficiency in machine learning. The authors propose a dynamic sampling method that selects informative data samples based on the evolution of losses during training. This approach aims to reduce backpropagation time while maintaining model performance across various architectures (ResNet, ViT, ALBERT) and datasets (CIFAR, ImageNet, GLUE). Key contributions include: (i) Dynamic Sampling: ES utilizes historical and current loss differences to inform data selection, allowing for batch-level sampling without the need for pre-trained models. (ii)Efficiency Gains: The method achieves up to 40% reduction in wall-clock time during training and shows improved accuracy (approximately 20%) in scenarios with noisy labels; and (iii) Theoretical Justifications: The authors provide theoretical insights into how their method alleviates loss oscillations and can be viewed through the lens of distributionally robust optimization." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Besides the weak overall originality, my main criticism is connected to the empirical evaluation:\n\nThe necessity for a burn-in period, where standard training must occur to initialize the loss adequately before applying the Exponential Moving Average (EMA) scheme, points to a limitation in the approach. This dependency on a specific loss initialization suggests that the method might not be entirely robust across various starting conditions. It would benefit the study to explore a more systematic ablation of this burn-in period as a hyperparameter. Additionally, understanding whether variations in the burn-in length affect performance could provide insight into the model's dependency on initialization stability and might even reveal opportunities to shorten or eliminate this requirement.\n\nAnother area where clarity is needed is the reporting of statistical measures. The number of seeds used for evaluation and averaging remains unspecified, and no standard deviations are provided. This omission raises questions about whether noise rather than true performance gains might influence observed differences in performance between the proposed method and baseline competitors. Including standard deviations would allow readers to assess the consistency of the results, providing a clearer understanding of the variability in performance.\n\nThe use of wall-clock time as a measure of speedup also presents challenges. Since wall-clock time is influenced by multiple factors, including the specific point of reference and the extent to which reference performance is met or exceeded, this metric is not straightforward. No details are provided on the variability of wall-clock measurements, which could make these results more challenging to interpret. An additional, complementary metric—such as the number of examples seen (similar to token counts in LLM training)—could yield a more direct and comparable measurement of processing efficiency, especially since the baseline approach involves higher computational requirements.\n\nRegarding robustness to label noise, Figure 3a indicates that while the method outperforms the baseline, the speedup advantage is lost under noisy conditions. This finding implies that the method may benefit from integrating the baseline up to its peak performance before switching to the proposed scheme. Such a hybrid approach could potentially leverage the best of both methods, maintaining efficiency without sacrificing performance under challenging conditions.\n\nIn Figure 3b, the gradients under comparison lack clarity. It is uncertain whether the gradients displayed encompass all examples (both corrupted and uncorrupted), necessitating additional forward passes and potentially affecting wall-clock measurements, or if the results only include corrupted examples selected by the method. The latter case would introduce a selection bias, affecting the integrity of the reported results. A more informative and balanced approach would be to calculate the proportion of non-informative examples selected per epoch, providing a relative measure of their influence on learning. This would give a clearer picture of how these less useful samples affect training efficiency and could allow for more balanced comparisons.\n\nIn Table 5, the ground-truth results are presented without a corresponding baseline for corruption-free performance. Including such a baseline would clarify the upper bound achievable in the absence of noise, providing a benchmark against which the \"superior\" performance in noisy conditions could be assessed. \n\n\nFurther minor Issues:\n\n* Ablations: \n * choices of \\beta. The presented heatmap tables are way too broad. I suggest using some Sobol or Latin Hypercube design and then reporting the heat surfaces. This way, we get a far more fine-grained perspective on the hyperparameters’ behavior.\n * Pruning is not ablated\n* The notation 0^+ and 1^- should probably be introduced or replaced by intervals (0, 1) instead of [0, 1]\n* The notation is at times slightly overburdened (e.g. the additional vector notation in 320), instead of just writing the actual values in there directly." }, "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": 4 }, "primary_area": null, "questions": { "value": "Can the key idea of the paper: optimization of the data space, be more cohesively or clearly presented? Currently, it's still difficult to understand the key idea of the paper without significant theoretical and literature knowledge." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper builds upon good theoretical foundations.\n\nThe paper well cites related work and the literature that leads to this contribution.\n\nThe paper creates an efficient heuristic based approach to solve a practical problem which rests on the previous theoretical contributions.\n\nThe paper well considers ablation studies and robustness studies.\n\nThe paper's theoretical arguments are well constructed." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper functions as a well-thought-out \"momentum optimizer\" in the data space. Instead of considering the presentation of data as fixed as in SGD, we take a more expansive view and think of the data space as another component of the model to optimize.\n\nThe work is somewhat novel in the large model training space." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "This should be better justified: This can be inefficient since different samples may have varied importance. Can you look at the influence functions or coresets literature?\n\nThis statement needs to be better motivated and explained, why is evolved sampling \"natural?\"\nIn general machine learning tasks, the typical behaviors of loss curves often appear decent trends\noverall, but can oscillate meanwhile due to certain noises. This introduces the sensitivity or instability\nissue of the sampling scheme (3.6). A natural smoothing operation is to use the exponential moving\naverage (EMA) of losses\n\nThe proof presentations are somewhat lacking. It's difficult for me to quickly match up concepts from the optimization literature to some of the theoretical arguments made, for example, the EMA to the minimax problem.\n\nIt may be worthwhile in explaining this better with regards to the control theory literature, specifically, control theory also deals with oscillations and rectifies them in similar manners:\n\nDecoupled EMA. To sufficiently leverage the loss dynamics in a more robust sense, we propose to\ncalculate the sampling probability as\npi(t) ∝ wi(t) = β1si(t − 1) + (1 − β1)ℓi(θ(t)),\nsi(t) = β2si(t − 1) + (1 − β2)ℓi(θ(t)), si(0) = 1/n (3.8)\nwith β1, β2 ∈ [0, 1] as two hyper-parameters. Here, the intermediate series {si(t)}t∈N, updated in\nthe EMA scheme, is also referred as the score (for the i-th sample). The scheme (3.8) is the so-called\ndecoupled EMA,\n2 which reduces to (3.7) when β1 = β2 = β. In Figure 1, it is shown by the red curve\nand appears an “interpolation” between the original loss and single EMA: When losses oscillate,\nthe decoupled EMA reacts moderately by not only capturing detailed dynamics of losses, but also\nremaining necessary robustness , exhibiting the flexibility to trade-off (by tuning two betas).\nIntuitively, by setting (β1, β2) → (0+, 1\n−), we are able to exploit the long-term historical information\nalong the training (via β2), while focusing on the importance of current losses (via β1) and thus can\nget the best of both world. This simple and elegant design turns out to be surprisingly beneficial in\npractice, which is further verified in numerous experiments in Section 4.\n\n\nThis should really be better explained. Again, this paper is moving into the \"total optimization landscape\" where both data and model parameters are considered components of the system to be optimized. It's not immediately clear whether this is a consequence of the problem the authors were solving, or the key insight that led to the approach.\n\n(ii) ES to solve a DRO problem. From another perspective, ES can be also reformulated as a\nsolution to the minimax problem..." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024dataefficient,\ntitle={Data-Efficient Training by Evolved Sampling},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2CflgSMLoK},\nnote={under review}\n}" }, "abstract": { "value": "Data selection is designed to accelerate learning with preserved performance. To achieve this, a fundamental thought is to identify informative data samples with significant contributions to the training. In this work, we propose **Evolved Sampling** (**ES**), a simple yet effective framework for *dynamic* sampling performed along the training process. This method conducts *batch* level data selection based on *differences* of historical and current losses, significantly reducing the back propagation time with modest additional overheads while maintaining the model performance. Due to its conciseness, ES is readily extensible to incorporate *set* level data selection for further training accelerations. As a plug-and-play framework, ES consistently achieves lossless training accelerations across various models (ResNet, ViT, ALBERT), datasets (CIFAR, ImageNet, GLUE), and optimizers (SGD, Adam), saving up to 40\\% wall-clock time. Particularly, the improvement is more significant under the *noisy supervision* setting. When there are severe corruptions in labels, ES can obtain accuracy improvements of approximately 20\\% relative to the standard batched sampling. Our results motivate further investigations on the data efficiency aspect of modern large-scale machine learning." }, "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": [ "learning efficiency", "evolved sampling", "data selection", "loss dynamics" ] }, "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/f3ea8b6b5d183aae1b23b2a8fe03c1b0c19a72c4.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": "Data-Efficient Training by Evolved Sampling" }, "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": 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": "It would be great if the total cost (rationale annotation cost vs. fine-tuning performance) breakeven could be revealed in dollar terms, and the operating guidance could be discussed for project owners with a limited annotation budget.\n\nOne way could be to provide a detailed cost-benefit analysis, including estimated costs for generating rationales (e.g., API costs if using a language model) versus the potential savings from reduced annotation needs. This would give project owners more concrete information to assess the method's practicality within their budget constraints." }, "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 is well written. The notations are clear.\n\n2. It provides up-to-date literature on RLHF techniques. It underscores the potential of rationale-based data augmentation in preference learning, paving ways for more effective language model alignment and encouraging further exploration of unpaired preference learning scenarios.\n\n3. Among many lines of work addressing the economic utility of dataset design and construction in RLHF, mechanism design has been recently explored to enhance the overall economic utility of dataset construction in RLHF.\nThe merits of introducing mechanism design are well supported by game theory studies, both theoretically and practically:\n\nZhang, G., & Duan, J. (2024). VickreyFeedback: Cost-efficient Data Construction for Reinforcement Learning from Human Feedback. \nhttps://arxiv.org/abs/2409.18417\n\nMatsushima, H., Noda, S.: Mechanism design with general ex-ante investments. Journal of Mathematical Economics 106, 102831 (2023)\n\n4. The experiments on Orca and UltraFeedback are convincing, with rational theoretical analysis using mutual information as a tool and in-depth ablation discussion in the appendix B.2." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a data-centric approach to RLHF by enriching preference datasets with machine-generated rationales. These rationales offer explanations for choices between preferred and non-preferred responses, addressing ambiguity and enhancing the effectiveness of preference learning. The proposed framework integrates rationales into the training process, can save annotation costs by 3x, and lands the fine-tuned model at better performance. Extensive experiments demonstrate that rationale-enriched learning outperforms traditional methods, with benefits across various preference optimization algorithms. \n\nThis work underscores the potential of rationale-based data augmentation in preference learning, paving ways for more effective language model alignment and encouraging further exploration of unpaired preference learning scenarios." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "This paper underlines the impact of including rationale in the RLHF fine-tuning process. In other words, the proposed method generally leverages auxiliary data to enhance the model performance. \n\nHowever, generating qualitative rationales alongside existing datasets might increase the annotation cost in dollar terms. Therefore, the breakeven analysis and the operating guidance could have been more straightforward to project owners with a limited annotation budget in dollar terms." }, "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. Instead of plotting data points w.r.t performance metrics, it will be worthwhile to plot the total number of text tokens used for training w.r.t the performance metrics. For example, if the rationale itself is quite longer than the original texts for comparison it can contain a lot more information which might explain the improvement in performance. Additionally, it is also worthwhile to report the average training time for the both procedures.\n\n2. For the vs DPO and vs SFT section, can you please provide the exact formula you used to compute the win rates? Are there any tie-breaking rules?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "This study is well motivated and adds to the literature of incorporating richer feedback (additional of rationales in this case) to do more efficient RLHF alignment." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates if incorporating rationales along with binary preference data can help improve alignment preference. To this end the authors propose rationale-DPO, an extension to the popular alignment method DPO. They compare the two algorithms with on different datasets (orca, ultrafeedback) and for different models (Mistral-7B-v0.1, Zephyr-7B-Beta etc.). The authors also propose a simplified information theoretic analysis to better understand rationale based preference modeling." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Weakness:\n1. While the problem is well motivated, the methodology to maximize the likelihood of generating the given preference and the rational is a very intuitive and simple method and is not significantly novel.\n2. Difficulty collecting data: procuring rationales can be more expensive as compared to getting just binary feedback. In addition, for certain cases like when you are comparing artwork it might not be possible to humans to explain their choice. While using LLMs to generate rationales is an efficient way of scaling the method, there is a risk of getting a misaligned response if that model is misaligned (for ex. not harmless) and it may also lead to an echo chamber as no new perspective beyond what the rationale generating LLM believes is true will be in the dataset. How do you envision addressing these challenges?\n3. In Figure 2, it seems that DPO win rate is only lagging behind RDPO by ~5-8% for the same amount of data points, however, RDPO requires a lot more text for a single data point." }, "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 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 proposes a new approach to integrate model-generated rationales in preference tuning, avoiding the need for additional human labels.\n2. Experimental results show that optimizing rationale likelihood alongside preference loss boosts model performance, reducing annotation needs and training data volume." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a new method for incorporating machine-generated rationales into preference fine-tuning, enhancing language models’ performance without extra human annotation. The authors demonstrate that maximizing rationale likelihood alongside preference loss improves model efficacy." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The proposed method can be seen as a combination of the preference loss such as DPO and the rationale log-likelihood. The paper lacks further exploration of how the two components contribute to improved performance. A few questions are:\n - a. In the ablation study on $\\gamma$, it seems the scale of gamma (from 1.0 to 10.0) does not matter at all. Did the authors try smaller $\\gamma$ or extremely large $\\gamma$?\n - b. How does tuning solely on rationale likelihood without DPO loss affect performance? Will the performance increase? \n - c. Justification is needed for a variable $\\gamma$ given the theoretical suggestion of $\\gamma=1$.\n\n2. Experimentation lacks rigor and thoroughness:\n - a. Reporting win-rate against DPO alone does not fully capture the rationale’s benefit. It is hard to evaluate the absolute improvement brought by the rationale loss. It would be better to report win-rate against a fixed opponent such as GPT-4 on AlpacaEval 2.0. This can ensure that the baseline DPO model is properly trained to a satisfactory performance.\n - b. Another related question is that there is no evidence that the DPO model in this paper is fully optimized. One may question if the dataset is weak or if the hyperparameters are adequately explored. For example, Llama3-8b-instruct + Ultrafeedback annotated by PairRM (see SimPO’s GitHub page for their dataset and model performance) can achieve a 40% LC win-rate, and the LC win-rate reported in the appendix is below 25%. I understand that SimPO did not release their training configuration, but the point here is that one cannot effectively conclude that the rationale loss significantly improves the performance.\n - c. The length bias is a key issue in preference fine-tuning. In the main text, it is reported that RDPO can produce much shorter responses and maintain a higher win-rate against DPO. This is quite surprising and deserves more analysis or explanation from the authors. On the other hand, in section B.4, the length on the AlpacaEval 2.0 dataset remains close to DPO or the original 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": 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 points above.\n\nSome additional questions I had when reading the paper, that I believe should be clarified:\n- How are draws measured in Fig 2?\n- I don’t understand this sentence: “the drawing rate for the RDPO model is stable and low across different training data sizes, which shows that RDPO winning rate is higher not due to flipping the draw points but the losing points.” Can authors clarify?\n- Fig2 caption typo: Winrare" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The area of generative reward modeling is important and gaining traction.\n- Promising experimental results across two datasets, performing comparable or better than DPO." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a new direct preference optimization method that leverages preference rationales (natural language explaining the reasons why one response is better than another). The proposed method adds a supervised loss term to the DPO objective, jointly training/anchoring the model to generate a valid preference rationale. Each preference rationales are generated with an LLM-as-Judge, augmenting a conventional binary preference dataset.\n\nThe method can be seen as a form of hybrid distillation from both preference data (DPO) and from LLM-as-Judge rationales." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Limited novelty and poor positioning with respect to the growing literature on synthetic preference generation and generative reward modeling (see missing references below, to be discussed in the paper). In addition, the authors focus entirely on direct preference optimization as an alignment method, but reward modeling + reinforcement learning remain a major paradigm for LM alignment. How does this work translate to this setting and compare to the following baselines?\n\nReferences\nRLCD: Reinforcement Learning from Contrastive Distillation for Language Model Alignment. Yang et al., 2024.\nWest-of-N: Synthetic Preference Generation for Improved Reward Modeling. Pace et al., 2024.\nJudging LLM-as-a-Judge with MT-Bench and Chatbot Arena. Zheng et al., 2024.\nSelf-Taught Evaluators. Wang et al., 2024.\n\n2. I found the theoretical analysis and motivation for the method unclear.\n - Equation 2 (L230) -> why is the joint probability decomposed in this way? Why doesn’t the preference label also depend on the rationale? Surely there isn’t a single ground-truth preference considering what is discussed in the intro (multiple valid preference labels based on different raters’ values)? In fact, does Section 5 not use the opposite formulation (“the preference inferred from the rationale”)?\n - Information-theoretic results may be interesting but are completely relegated to the appendix, so they cannot be counted as a contribution of the paper. Authors state “Our analysis demonstrates a closed-form relationship between rationale informativeness and its alignment with true preferences”, without including any explanation for this claim. What does this mean and what is the form of this relationship?\n\n3. Finally, the experimental setup is too weak to demonstrate the added value of the proposed method.\n - Is performance improvement statistically significant? Fig 2 suggests that DPO > RDPO with 1K Ultrafeedback data, but we obtain the opposite result in Fig 3. If the result is due to statistical uncertainty, this should be measured and shown on plots (RDPO outperforms DPO by a similar margin in Fig 2, which could therefore not be statistically significant).\n - Preference dataset sizes are typically >>11K (see top-perfoming RMs on RewardBench, for example). Why did the authors focus their analysis on such a small, non-representative dataset size. Also, why is there no improvement in performance with DPO beyond 1K preferences?\n - Related: L353, why pick the DPO model trained with 12K Ultrafeedback preferences as baseline, if its SFT performance is lower than that or models trained on less data?\n - Why not evaluated model performance on established benchmarks such as RewardBench/AlpacaEval?\n - How does RDPO with poor quality rationales (e.g. permuted / opposite) perform against standard DPO? I imagine much worse, since we are training on biased information. How can practitioners ensure that their rationales’ quality is sufficiently high to afford gains and not harm performance?\n - Why is RDPO performing similarly to DPO then trained on Llama-3-8B in Figure 5?" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Human Preference Optimization with Rationales" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024datacentric,\ntitle={Data-Centric Human Preference Optimization with Rationales},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2Cg4YrsCMA},\nnote={under review}\n}" }, "abstract": { "value": "Reinforcement learning from human feedback plays a crucial role in aligning\nlanguage models towards human preferences, traditionally represented through\ncomparisons between pairs or sets of responses within a given context. While\nmany studies have enhanced algorithmic techniques to optimize learning from such\ndata, this work shifts focus to improving preference learning through a data-centric\napproach. Specifically, we propose enriching existing preference datasets with\nmachine-generated rationales that explain the reasons behind choices. We develop\na simple and principled framework to augment current preference learning methods\nwith rationale information. Our comprehensive analysis highlights how rationales\nenhance learning efficiency. Extensive experiments reveal that rationale-enriched\npreference learning offers multiple advantages: it improves annotation efficiency,\naccelerates convergence to higher-performing models, and reduces verbosity bias\nand hallucination. Furthermore, this framework is versatile enough to integrate\nwith various preference optimization algorithms. Overall, our findings highlight\nthe potential of re-imagining data design for preference learning, demonstrating\nthat even freely available machine-generated rationales can significantly boost\nperformance across multiple dimensions." }, "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": [ "dpo", "preference learning", "alignment" ] }, "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/97321c40c8af9c1ef713912359bcc840e8b81d47.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": "Data-Centric Human Preference Optimization with Rationales" }, "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": "In practice, FID metric is widely-used in the evaluation of generative models. Can this paper cover this metric and why?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Overall, this paper is well-written and easy to follow.\n2. The proposed method (Mixture-UCB) is somehow novel, although it is inspired by classical UCB in the multi-armed bandit setting.\n3. Theoretical results about the regret bound are provided for the proposed Mixture-UCB-CAB. The proof seems right although I have not checked the proof line-by-line.\n4. Empirical results illustrate the effectiveness of the proposed method in finding the optimal mixture of text-based and image-based generative models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper aims to solve the online selection task over a group of well-trained generation models. It explores the selection of a mixture of multiple generative models and formulate a quadratic optimization problem to optimize the kernel-based evaluation scores including kernel inception distance (KID) and Renyi kernel entropy (RKE). Specifically, it proposes an online learning approach called Mixture Upper Confidence Bound (Mixture-UCB). Theoretically, regret analysis is provided for one method (Mixture-UCB-CAB). Experimental results illustrate the effectiveness of the proposed method for text-based and image-based generative models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. I am afraid that the online selection of well-trained generative models might have few applications because it is already costly for the (large) generative model inference, then why do we need online selection rather than batch selection? Discussions about practical applications can be added.\n2. Experimental results show that Mixture-UCB-OGD might be better than Mixture-UCB-CAB. However, theoretical guarantees about Mixture-UCB-OGD are missing. I know it might be more challenging and more detailed discussions can be added to clarify 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": 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": "Below are a few questions and/or comments.\n\n1. The problem appears novel, so I believe it makes sense to better motivative it. For example, in which context are we interested in picking a model to generate a sample at each round, why it is of interest to use \"the fewest possible sample queries\"? How the proposed method performs in an offline setting, with respect to performance and/or scalability?\n2. When summarizing the contribution of this paper, could the authors also provide (forward) pointers to the precise results? For example, \"proposing an online learning framework in Section ??\". I personally believe that this may facilitate the interested readers to quickly grasp the main contribution of the paper.\n3. Is the working assumption of linearly mixed model somewhat restrictive? Is there something else in the literature, or even such linear combination is (the first time) proposed by the authors in this paper? In fact, on the top row of Figure 3, there is a linearly mixtured \"dog\" that appears a bit bizarre: is this due to some limitation of this linear mixture? \n4. I personally find Theorem 1 a bit surprising: To me, kernel matrix \"estimation\" problem plus some online selection problem, and solving the former problem in general requires a lot of samples to have a tight spectral norm control on the estimated kernel matrix. I believe that the authors avoid this issue by assuming/focusing on the case of bounded. Could the authors comment more on this? For example, does this bounded kernel/loss function setting limit the practical interest of the proposed methods? Also, could the authors comment on the observed sample size $n_i$ for the proposed OGD method to make sense? We do not see this in Theorem 2 and this has an impact on the computational complexity I believe?\n5. a tiny side remark: Figure 3 appears in the main text but commented in the appendix." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* The paper consider online selection of generative mixture models, which, to the best of knowledge, is a novel problem of interest.\n* By making interesting connection to kernel-based scores and multi-armed bandit, the authors propose efficient methods to solve the above problem, with some theoretical guarantee. \n* Experiments on realistic data are provided, showing the practical applicability of the proposed approaches." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors focus on the online selection of generative models, and in particular, the optimal linear mixture among a set of such models.\nThe problem appears novel, and the authors make interesting connections to the maximization of some kernel-based scores and multi-armed bandit. \nBased on this, the authors propose Algorithms 1 and 2 to solve this online selection of mixture efficiently, with performance guarantee given in Theorem 1 and 2, respectively. (Although I have some concerns on the settings and theoretical results, see below).\nThese methods can be used for widely used kernel inception distance (KID) and Renyi kernel entropy (RKE), and are tested on realistic image and text data in Section 6." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* It would be great to discuss the limitation of the proposed approach, see below for my detailed comments/questions.\n* some settings and theoretical results need clarification, see below for my detailed comments/questions." }, "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": 4 }, "primary_area": null, "questions": { "value": "* Can the authors find some other works that also aim to find good mixtures of generative models, and compare their method to these works?\n\n* Can the authors provide the quality scores Density (Naeem et al., 2020). and Precision (Kynkaanniemi et al., 2019) in the experiments that they conducted?\n\n* Small question regarding Lines 257&259: is $\\hat{L}(\\mathbf{a};\\mathbf{x}^{(t)})-(\\mathbf{\\epsilon}^{(t)})^{\\rm T}\\mathbf{a})$ a lower or upper bound of $L(\\mathbf{a})$?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* This paper is well written and easy to follow.\n\n* The theoretical framework underlying the proposed algorithms is well grounded.\n\n* Extensive experiments were carried out to demonstrate the performance of the proposed algorithms." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The main goal of this work is to maximize the diversity of generated samples by selecting not a single but a mixture of generative models. Formulating first a population loss of quadratic form that can translate into evaluation scores including kernel inception distance (KID) and Renyi kernel entropy (RKE), this article proposes two online algorithms based on continuum-armed bandit and gradient descent, to find the optimal mixture through minimizing an upper confidence bound of the quadratic population loss. Experiments show that the proposed algorithms are efficient at approaching the optimal mixture." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* According to the literature review of this article, there seems to be little interest in finding a good mixture of different generative models. Indeed, if the goal is to approach the target distribution, it makes more sense to select the single best generative model than to use a mixture of different generative models, which are usually trained in an independent manner, therefore unlikely to complement each other.\n\n* It is true that when the objective is to find the single best generative model, the online approach can help prevent sampling from suboptimal models. However, as using a mixture of generative models requires sampling from all member models, the online approach seems to be less useful in 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": 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": "See above." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "It's interesting to see the authors formulated the generative model selection problem as an online selection problem. The authors also developed two algorithms for this new setting and provide theoretical guarantees for one of them. Experimental results demonstrate the efficacy of the proposed algorithms." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper study online selection for generative models, in order to generate diverse samples. The authors formulated the problem as a mixture multi armed bandit problem and developed two algorithms for that: Mixture-UCB-CAB and Mixture-UCB-OGD. The authors developed theoretical guarantees for the Mixture-UCB-CAB algorithm. The authors conduct many experiments to show the efficacy of their developed methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Since this is a new problem, can authors provide more motivations for online selection of generative models, e.g., how important is the ability to generate diverse samples? And how important is to save samples in the selection process.\n2. The authors provide a convergence guarantee for Mixture-UCB-CAB in Thm 2. For comparison, what is the rate of convergence for the offline approach that randomly generate $T$ samples and then optimize over $\\alpha$?\n3. Does Thm 1 holds for all $\\alpha$? Also, the guarantee in Thm 2 doesn't suffer the curse of dimensionality even if the algorithm is selection $\\alpha \\in R^m$; can authors explain why does that happen?\n4. Compared to standard bandit problem where one gets an intermediate regret term at each round, it seems that the studied problems gets $O(t)$ (averaged) terms (the first Eq in Section 5), and all these terms are related to the previous selections $x_1, \\cdots, x_{t-1}$. Can authors elaborate how do they deal with these terms in the analysis? What are some technical contributions?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024be,\ntitle={Be More Diverse than the Most Diverse: Online Selection of Diverse Mixtures of Generative Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2Chkk5Ye2s},\nnote={under review}\n}" }, "abstract": { "value": "The availability of multiple training algorithms and architectures for generative models requires a selection mechanism to form a single model over a group of well-trained generation models. The selection task is commonly addressed by identifying the model that maximizes an evaluation score based on the diversity and quality of the generated data. However, such a best-model identification approach overlooks the possibility that a mixture of available models can outperform each individual model. In this work, we explore the selection of a mixture of multiple generative models and formulate a quadratic optimization problem to find an optimal mixture model achieving the maximum of kernel-based evaluation scores including kernel inception distance (KID) and Renyi kernel entropy (RKE). To identify the optimal mixture of the models using the fewest possible sample queries, we propose an online learning approach called *Mixture Upper Confidence Bound (Mixture-UCB)*. Specifically, our proposed online learning method can be extended to every convex quadratic function of the mixture weights, for which we prove a concentration bound to enable the application of the UCB approach. We prove a regret bound for the proposed Mixture-UCB algorithm and perform several numerical experiments to show the success of the proposed Mixture-UCB method in finding the optimal mixture of text-based and image-based generative 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": [ "multi-armed bandits", "evaluation of generative models", "kernel-based evaluation scores" ] }, "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/2e5ac98ea7ebfabb4f0992b5c56b509717d1fe14.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/38e38b8b295e3069d30ca9af1d73c0dfad47a9d1.zip" }, "title": { "value": "Be More Diverse than the Most Diverse: Online Selection of Diverse Mixtures of Generative 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": 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. Add references and discussions of related work.\n2. It would be better to evaluate more long-video models (e.g., Qwen2VL) and different input frame rates (1, 8, 32, 128, and more).\n3. Since most question-answer pairs are generated by GPT-4o, could this lead to inflated evaluation results for GPT-4o? Analysis is needed regarding dataset quality, hallucination rates, and potential information leakage issues." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The questions are comprehensive and well-structured, covering multiple dimensions and employing diverse construction strategies for different types of questions.\n2. The evaluation methods are reasonable, adopting different assessment metrics for multiple-choice and open-ended questions." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces InfiniBench, a video understanding benchmark dataset featuring the longest video duration (average 52.59 minutes per video) and the largest number of question-answer pairs (108.2K) to evaluate 9 different video understanding tasks.\n\nThe authors conducted comprehensive evaluations of existing large multimodal models (including commercial models like GPT-4V, Gemini 1.5 Flash, and open-source models). Experiments show that even leading AI models still face challenges in long video understanding, with the best models GPT-4V and Gemini 1.5 Flash achieving average accuracy rates of only 49.16% and 42.72% respectively." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper lacks discussion of related work. For example, benchmarks proposed in Video-MME, LVBench, and Long VideoBench published in June 2024 are very similar to InfiniBench.\n\n2. Most of the question-answer pairs are generated by GPT-4o. Although multiple information sources were used as input, it's difficult to guarantee the quality of the dataset.\n\n3. Part of the data comes from IMDB content, which likely appeared multiple times in the training corpus of LLMs used by video models, potentially leading to dataset leakage issues." }, "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. How is GPT-4V's scoring aligned with human evaluation?\n2. Why weren't the latest models tested, and why wasn't there comparison and discussion of the latest benchmarks?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. InfiniBench provides a comprehensive evaluation of large multimodal models' capabilities in long video understanding through including the longest video duration and a large number of question-answer pairs, as well as designing diverse question types (multiple-choice and open-ended questions) covering nine different skills, thus thoroughly examining models' performance across multiple dimensions of long video understanding.\n\n2. By evaluating various models including both commercial and open-source models, InfiniBench reveals the challenges and limitations of existing models in long video understanding, especially in tasks requiring deep contextual understanding and critical thinking. This in-depth assessment helps identify model deficiencies and provides clear directions for future research and model improvements.\n\n3. InfiniBench's design not only tests models' technical capabilities but also drives models toward more human-like understanding and reasoning abilities. Through proposing human-centric questions, such as movie spoiler questions, it promotes model performance improvement in long video understanding tasks, which is significant for achieving more advanced AI applications and advancing the field of artificial intelligence." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces InfiniBench, an innovative and comprehensive benchmark focused on evaluating large multimodal models' performance in understanding very long videos. InfiniBench is notable for its ultra-long video duration (averaging 52.59 minutes per video) and massive question-answer pairs (108.2K), covering nine different skills including multiple-choice and open-ended questions. These questions are designed to be both diverse and human-centric, with videos primarily sourced from movies and TV shows. Experimental results show that even leading AI models like GPT-4V and Gemini 1.5 Flash face significant challenges in long video understanding, achieving average accuracies of only 49.16% and 42.72%, with mean scores of 3.22 and 2.71 (out of 5) respectively. This indicates that while these models perform relatively well on local skills, they still have limitations in skills requiring global reasoning and deep contextual understanding, such as scene transitions and movie spoiler questions. Open-source models generally perform below random chance on multiple-choice questions, highlighting long-sequence global reasoning as a major challenge for existing models. Additionally, models relying on both video and text information perform poorly without caption input, emphasizing the importance of processing both visual and textual information for long video understanding. The introduction of InfiniBench aims to fill the gap in long video understanding benchmarks, drive the development of open-source large language models, and motivate multimodal large models toward more human-like long video understanding and reasoning capabilities, despite current limitations such as video source restrictions and script dependency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The benchmark only uses movies and TV shows for testing, which is too limited. It should include more types of videos that show different parts of real life, like nature documentaries or home videos. The problem is that movies and TV shows follow certain storytelling patterns, so AI models might just learn these patterns instead of truly understanding the videos. They should add more casual videos like vlogs and livestreams to make the testing more realistic.\n\n2. The benchmark needs written scripts to create its questions and answers. This is a big problem because most real-world videos don't come with scripts. Without scripts or captions, the benchmark can't test how well AI models understand regular videos that people actually watch and share online.\n\n3. InfiniBench's testing does not cover current mainstream open-source models such as Qwen2VL, LLaVA-Onevision, and InternVL2. This makes it difficult to obtain a more comprehensive and in-depth comparison between open-source and closed-source models.\n\n4. In Table 1, the benchmark comparison is insufficient, especially regarding some recent video benchmarks such as Video-MME and LongVideoBench. Additionally, the authors' definition of \"very long\" is problematic - MLVU and MovieChat have only a 3-minute gap, yet MLVU is defined as very long. This is not reasonable." }, "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": "I have a concern about potential copyright infringement in this work. The proposed dataset is based on copyrighted content (video frames and subtitles of movies and TV shows) that authors have downloaded and used for experiments. The paper also includes figures of frames from TV shows. It is unclear whether the authors obtained permission from copyright owners for their use of the data. Authors do not mention whether they intend to release the dataset publicly, but if they do, this would raise further concerns." }, "flag_for_ethics_review": { "value": [ "Yes, Legal compliance (e.g., GDPR, copyright, terms of use)" ] }, "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": "Given the concerns listed above, I have doubts that this paper is suitable for publication at ICLR. I hope that authors can provide evidence to address my concerns as well as answers to the following questions.\n\n* Could authors provide evidence of transcript quality? How accurate and complete are they? How much focus do they have on vision? Could authors provide examples? \n* Why are multiple-choice questions evaluated by asking the model to generate an answer and then using GPT to match this answer to the options? Authors state in the appendix that the reason is that models often do not follow the prescribed answer format, but from my experience at least the larger VLMs are good at following instructions about the answer format. \n* I am worried that using GPT for option matching introduces additional bias. I believe this could be measured by evaluating GPT or Gemini again by giving it the answer options in the prompt and asking it to respond with only the answer letter. Results could then be compared against the GPT-matched results. \n * Also to the above point, did authors verify that event ordering type questions get matched correctly with GPT? These answers only differ in their ordering of options, so I am wondering whether GPT matches them correctly. \n* The benchmark was constructed using GPT, and GPT is the best performing model across all tasks. It would be interesting to quantify if there is bias towards GPT, e.g. by generating part of the data with Gemini and checking if relative model performance is consistent with the original benchmark. \n* How are copyright concerns handled? Did authors obtain permission from the copyright owners to use the video material for this purpose and to reproduce this content in a publication? If the dataset will be publicly released, how are copyright concerns handled? \n * l. 198: “To address this limitation, we transformed the TVQA dataset from a collection of short clips into a long video dataset by gathering and sequencing the clips corresponding to each episode thereby reconstructing the full episode frames.“ How was this done and what data source was used? \n* Appendix l. 12: “The remaining two skills, i.e., local visual questions and summarizing, do not need human verification, as the first one is adopted from the TVQA dataset, and the latter is scrapped from human responses on the web.” I do not fully agree with this statement since existing benchmarks and the humans writing the summaries that were pulled from the web could still contain errors. Do authors have evidence to the quality of TVQA annotations and summaries obtained from the web? \n* How does the number of video frames provided affect the model accuracy? \n* Appendix B is quite important to understand the evaluation results presented, so I think it would be better suited to be in the main text. \n* Appendix B mentions that the benchmark videos have no audio, so video and subtitles are provided to the model separately. Does this mean that alignment between frames and subtitles is missing? Did authors measure the effect of this? \n* Could authors explain how spoiler questions are generated and provide the prompt used? \n* How does the “I don’t know” option affect results? How accurately does GPT match model answers to this option? \n* Fig. 5 (left) is redundant with Tab. 3, so one of them should be removed. \n* l. 363: The explanation of local vision and text questions is not clear. It is not explained what these questions are nor how they were generated. \n* It would be good to have random accuracy in Tab. 5 for direct comparability. Then, Tab. 4 could be omitted. \n* l. 482: “As shown in the table 5, MiniGPT4-video and LLaVA-NeXT-Interleave match lower than the random performance” What random performance is being compared to here? It would help to add this to the table as suggested above. \n* l. 482, l. 505: How can a model’s performance be lower than random? \n* l. 488: “One reason may be that eliminating the noisy information and focus on only the related information helps more in answering the questions“ How does the Goldfish model eliminate noisy information? \n* For the human verification, how were human responses on open-ended questions evaluated?\n\nMinor points\n\n* Tab. 1: I would not agree with the “human” checkmark for InfiniBench since questions were generated fully automatically. \n* Tab. 2 is never referenced. \n* Appendix B: It would be helpful to express this in tabular form so readers can see at a glance how many frames and what modalities were used in each model. \n* Tab. 5.: I would suggest to organize this into one big table with one column per task type. Also would be nice to visualize as a radar chart. \n* It would be helpful to annotate question types in Sec 3.2.2 and Fig. 1 with whether they are MCQ or OE. \n* It would be helpful to see a listing of modalities (vision, summary, transcript) used to generate each question. \n* Please use \\\\citep for citations to place citations in parentheses. \n* In tables, please right-justify numerical columns and use a consistent number of digits after the decimal point. \n* Fig. 4: The font size in these charts is very small in print. I suggest increasing it. Also I would suggest to change the pie chart into a bar chart for easier readability. \n* Fig. 5: Same concern as above about the font size. \n* l. 373: Here, the reference to Fig. 4 is repeated, but Fig. 5 is wrongly referenced. Suggest correcting this sentence to refer to Fig. 3\\. \n* l. 406: Broken reference. \n* l. 413: The reference should point to Sec. B in the supplementary material." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* The presented benchmark has an impressive scale with 108.2k questions on 1,219 videos that average 52.59 minutes in length. \n* There are 9 different question types that test long video understanding models across a variety of skills. \n* The paper presents results of 8 long video models and draws interesting conclusions on their performance. \n* There is a large gap between human performance and model performance, suggesting the benchmark has ample room for improvement. \n* The paper has a good in-depth discussion of related work." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes InfiniBench, a novel benchmark for long video understanding based on movies and TV shows. The benchmark has 108.2k question-answer pairs on 1,219 videos that average 52.59 minutes in length. The benchmark tests 9 different reasoning abilities including visual, long-context and local reasoning. This makes InfiniBench the largest-scale long video understanding benchmark to date. InfiniBench was constructed by combining and augmenting from two existing video benchmarks, TVQA and MovieNet. Most question types were generated by prompting GPT-4 with the transcript of the video while a custom pipeline was used to generate questions on changes in character appearance. The paper presents benchmark results of 8 long video understanding models, including 6 open source ones and 2 commercial ones, and discusses insights into their performance across various tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The question-answer pairs in the benchmark were generated fully automatically without any human intervention. This raises questions about soundness and of the questions and potential bias. A human evaluation is performed on a subset of the data, but good human performance is no proof that questions are well-formed and free of hallucinations. \n* Most questions are generated from transcripts that authors obtained online, but it is unclear what information these transcripts contain, whether they are complete and error-free. It is also unclear how much visual information the transcripts contain and therefore it is unclear to what degree this is a multimodal benchmark. \n* The use of movies and TV shows raises questions about generalizability. Most MLLMs likely know the plots of popular movies and shows because their summaries or transcripts were part of their training data. So, they may be able to answer the questions in the dataset without any context, which is not the case for most videos from the web. The effect of this is not examined. \n* It is unclear how much the benchmark relies on multimodal reasoning. Questions about movies and TV shows could often be answerable from subtitles alone, which are provided as context in the evaluation. It would be interesting to see an ablation that uses (1) No context, only the question itself (2) Only the question and subtitles (3) the question, subtitles and video frames. \n* The copyright implications of using movies and TV shows and possibly releasing the dataset are not discussed and raise ethical concerns. \n* Since the dataset has \\~100 questions per video, it is likely that there are (near) duplicate questions. However there is no analysis of this and no mention of a filtering stage to remove duplicates. \n* There are several issues with the presentation such as redundant figures, tables that are not referenced, and wrong references. The limitations section also exceeds the 10-page limit." }, "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 weakness." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1 Important Topic. Long-form video understanding is a challenging but important problem. Hence, how to develop benchmark to evaluate this problem is critical.\n\n2 Experiments. The experimental results are sufficient to support the claim of benchmark." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this work, the authors propose an InfiniBench for very long video understanding. To contain local/global events and understand visual/contextual content, they define a long video understanding covering nine skills through four critical aspects in movies and tv shows." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1 Similar work has been proposed in the literature. For example, [MoVQA: A Benchmark of Versatile Question-Answering for Long-Form Movie Understanding, arXiv:2312.04817]. Please clarify the difference. \n \n2 The writing and paper organization is not good. Please refine it for easy reading." }, "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": 4 }, "primary_area": null, "questions": { "value": "Can the authors comment on the limited variety of TV shows? What about sports events like NBA, NFL, Tennis, etc. \n\nOn GPT-4o evaluation, only 250 frames are selected. Are the 250 frames selected uniformly? Have you tried to reduce the frame size and squeeze more frames into GPT-4o?\n\nWill all the videos be released to public? Are there any legal issues?\n\nAre there text scripts (screenplay) associated with all the videos (movies and TV shows)?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The videos are very long with an average length 52 minutes.\n\nThe number of (question, answer) pairs is large (108k)\n\nSome of the questions are unique such as spoiler questions, global appearance, and scene transitions. \n\nCompared to the existing benchmarks, this benchmark contains much longer videos and contains some new interesting types of questions. It'll be very useful to the researchers who work on long video understanding." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a benchmark, called InfiniBench, for the evaluation of long video understanding. The dataset consists of 1219 videos. The average length of the videos is 52.59 minutes. There are 108.2K (video, question) pairs. The questions are divided into 9 categories. Some categories require the ability to make associations across a longtime span. Some categories require in-depth understanding and reasoning capabilities. It is a very interesting new benchmark for long video understanding." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The variety of TV show sources is limited since there are only 6 different TV shows." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024infinibench,\ntitle={InfiniBench: A Comprehensive Benchmark for Large Multimodal Models in Very Long Video Understanding},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2D0uXQbntW},\nnote={under review}\n}" }, "abstract": { "value": "Understanding long videos, ranging from tens of minutes to several hours, presents unique challenges in video comprehension. Despite the increasing importance of long-form video content, existing benchmarks primarily focus on shorter clips. To address this gap, we introduce InfiniBench a comprehensive benchmark for very long video understanding which presents 1)The longest video duration, averaging 52.59 minutes per video; 2) The largest number of question-answer pairs, 108.2K; 3) Diversity in questions that examine nine different skills and include both multiplechoice questions and open-ended questions; 4) Human-centric, as the video sources come from movies and daily TV shows, with specific human-level question designs such as Movie Spoiler Questions that require critical thinking and comprehensive understanding. Using InfiniBench, we comprehensively evaluate existing Large Multi-Modality Models (LMMs) on each skill, including the commercial models such as GPT-4o and Gemini 1.5 Flash and the open-source models. The evaluation shows significant challenges in our benchmark. Our findings reveal that even leading AI models like GPT-4o and Gemini 1.5 Flash face challenges in achieving high performance in long video understanding, with average accuracies of just 49.16% and 42.72%, and average scores of 3.22 and 2.71 out of 5, respectively. We hope this benchmark will stimulate the LMMs community towards long video and human-level understanding. Our benchmark can be accessed at (https://infinibench.github.io/Infinibench-website/) and will be made publicly available." }, "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": [ "video understanding", "benchmark", "long video benchmark", "long video understanding" ] }, "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/d969256772e8bb967c084d933943fb219d1359f7.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/e4286990af860ccb3f16a8fce15b1da8635ef0b5.zip" }, "title": { "value": "InfiniBench: A Comprehensive Benchmark for Large Multimodal Models in Very Long Video Understanding" }, "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": 1 }, "primary_area": null, "questions": { "value": "Refer to the weaknesses." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The idea is simple and clear, the experimental setup is also quite clear." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors introduce MixAttention, an architecture that employs sliding window attention to store only recent tokens while sharing KV caches across layers. They train and evaluate four different variants and report the corresponding results." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. This paper lacks innovation; both the recent window and multi-layer attention are established techniques. The paper simply combines these two methods without any improvements.\n\n2. The experimental results are presented solely as bar charts. I believe it would be beneficial to include a table with some precise values.\n\n3. This paper resembles more of a technical report rather than an innovative and well-developed research paper, which does not meet the high standards of ICLR." }, "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 refer to the Weakness part" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The combination of sparsifying the token of sequence and sharing the KV cache across layers seems to be a promising method to reduce the inference cost. This paper conducts some interesting experiments, from pre-training to evaluation, to give us some insights regarding the impact of different choices of the setups of such combination.\n2. The experiment setup is reasonably designed." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper aims to optimize the inference efficiency of LLMs by reducing the amount of KV cache. The core intuition of this paper is to combine two existing approaches, i.e., sliding window attention and layer-wise sharing of KV cache, to further reduce the memory cost of inference. Although this kind of combination has already been proposed by some blog and papers, this paper aims to explore the effectiveness of this kind of method from an empirical perspective." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The novelty is limited in two ways. Firstly, it is a straightforward combination of two existing techniques without many adjustments. Secondly, this combination has already been explicitly described in the blog of character.ai, as cited by the authors.\n2. I can get that the value of this paper is to provide some empirical guidelines of this combination method, but still, the new information brought by this paper is also limited. For example, “…having the standard KV cache computed in the deeper layers is more important for long context abilities than the standard KV cache of the first few layers.” has been declared by some existing studies. In general, the experiment conclusions of this paper are some high-level phenomenons, instead of some practical methodology.\n3. The experiments are all based on a 5B MoE model, which makes the generalisability of the conclusions less convincing. \n4. There are quite a few new hyper-parameters getting involved, e.g., for a N-layer model, how to decide which layers are standard attention, which layers are sliding window? how many layers for a KV-sharing group? These decisions are pre-defined in this paper, but what’s really interesting is how to make these decisions wisely given a new 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": 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 would be interesting to see trends between performance and degree of cache-sharing for both standard attention and sliding window attention, as this would give us a better understanding of the rate at which the performance worsens.\n- More explanation for why certain choices were made for the experiments such as the eval benchmark of choice, selection of cache-sharing variants.\n- More discussion and analysis of the results that leads to deeper insights.\n- More discussion about the differences between this and the other cache-sharing paper [1].\n\n[1] William Brandon, Mayank Mishra, Aniruddha Nrusimha, Rameswar Panda, and Jonathan Ragan Kelly. Reducing transformer key-value cache size with cross-layer attention. arXiv preprint arXiv:2405.12981, 2024." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Cache sharing across layers has not been extensively studied and ablated over, and so this paper provides additional sample points that show the relationship between cache sharing approach and performance. \n- The authors tested their results on RULER which is a long-context benchmark and more conventional evals such as MMLU and HellaSwag through the Gauntlet evals framework which unveils differences in performance between different KV-cache sharing approaches.\n- Some of these KV-cache sharing variants perform as well as standard attention while being significantly cheaper in compute and memory." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper ablates over a particular modification to the transformer architecture where kv-caches are shared across layers and a portion of layers use sliding window attention, for the purpose of reducing compute and memory while retaining performance. \nTheir main findings show that sharing the KV-cache from the first layer, throughout the entire network hurts performance on RULER (at 32k ctx), and so the KV-cache for a non-sliding window attention layer should be computed at least once in deeper layers, while also controlling for the level of kv-cache sharing on the sliding window attention layers." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Lack of insight or discussion as to why certain cache-sharing approaches perform better or worse.\n- The paper lacks novelty, as it mostly relies on architectural configurations proposed by a blog by CharacterAI [1], and as a consequence, it lacks explanation as to why these configurations were selected in the first place.\n- In general, the main critique is that the paper presents only surface level analysis of the observations and does not contribute much to a deeper understanding of why certain cache-sharing approaches perform better than others.\n\n[1] Character.AI. Optimizing AI Inference at Character.AI — research.character.ai. https://research.character.ai/optimizing-inference/, 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": 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. There is no Pareto improvement shown. How does the proposed approach compare to a smaller standard MoE model with similar KV-cache size? It would be ideal to see a Pareto-improvement curve with KV-cache memory on the X-axis and model accuracy on Y-axis." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper is easy to follow and unlike most approaches that use custom device-level code to make inference efficient, the approach doesn't require any custom kernels. This makes the approach easier to adapt to slight changes in the model architecture or running inference on hardware from other vendors." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposed an approach called MixAttention which is interleaving standard attention with sliding window attention. Their MixAttention approach also shares KV-cache across the layers. All these optimizations lead to reduce memory usage for the model during inference without significantly deteriorating the model accuracy." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. There is no novelty in the approach. The paper just evaluates the approach proposed in the [blog](https://research.character.ai/optimizing-inference/) by character.AI with slight modifications. Also, there is nothing new written in the paper different from the blog.\n2. The authors have not put in enough effort for the paper. There is no optimization done in SGLang to optimize the inference for sliding window attention baseline.\n3. The paper is poorly written and there are some typos in the paper. For instance, line 199 uses the word 'sequence' twice in succession.\n4. The paper also says to refer to the appendix for a few experiments, however, there is no appendix in the paper.\n5. I don't believe that any amount of experiments can make the paper in an acceptable format since there is no novelty." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "KV cache size is a major factor in determining the inference throughput and memory footprint of LLMs. We show that KV cache sharing between layers and adding sliding window layers can speed up inference while maintaining model quality." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024inferencefriendly,\ntitle={Inference-Friendly Models With MixAttention},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2DD4AXOAZ8},\nnote={under review}\n}" }, "abstract": { "value": "The size of the key-value (KV) cache plays a critical role in determining both the maximum context length and the number of concurrent requests supported during inference in modern language models. The KV cache size grows proportionally with the number of attention heads and the tokens processed, leading to increased memory consumption and slower inference for long inputs. In this work, we explore the use of MixAttention, a model architecture modification closely related to a blog published by Character.AI. MixAttention combines sliding window attention, where only a small subset of recent tokens is stored in the KV cache, with KV cache sharing across layers. Our experiments demonstrate that MixAttention significantly reduces memory usage and improves inference speed without sacrificing model performance in both short and long-context tasks. We also explore various configurations of this architecture, identifying those that maintain quality across evaluation metrics while optimizing resource 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": [ "language models", "inference", "transformers", "architecture" ] }, "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/93ebbaa6ce0c07754ccdf1a5f99b08c05f486650.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/95f1e50ebff704730aa78f914e5a8653830a95db.pdf" }, "title": { "value": "Inference-Friendly Models With MixAttention" }, "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": "1. I actually don't see that the main results (like Thm 7) have the sample size as a factor in the bound? Is this correct? Shouldn't the bound improve as a factor of n?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Overall, the evaluation seems reasonable within the specific domain. The authors present 4 real-world datasets for different detection tasks.\n\n2. The analytical results seem quite strong. Specifically Thm 7 and Cor 2 could be quite useful results for analyzing fairness under Gaussian assumptions." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a theoretical framework for analyzing fairness in medical domains across diverse demographic groups. The authors present several strong analytical results, under some statistical assumptions. The authors evaluate on 4 datasets on two deep learning models with fairness over racial groups." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Overall, the evaluation in this work is quite weak. The primary result, Fig 1 is still a mystery to me. I dont have intuition for what the feature distribution 'ought' look like. So this seems the authors present mostly AUC over 4 detection tasks. \n\nUnless I missed it, this work doesn't actually present any bias mitigation strategy, except some discussion about sufficiently large sampling. \n\n2. there seems to be some assumptions of normality within this work that might not \n\n3. The overall scope of this work is somewhat limited. I didn't quite get the specifics that make these bounds hold under *medical domains* specifically (vs. domain independent). Why this is a domain paper is still a mystery to me, as none of the problem setting particularize it to medical.\n\nSmall: While the high level analytical results are fairly intuitive. I did seem to get lost in the theorem specifics. This could be an issue with me, or the notation, which I often found impenetrable without further highlighting or description in text. e.g. Thm 1, 6. Corr. 1,2. \n\nOverall, in my (unconfident) estimation, this could be a reasonable domain paper with strong analytical results. However, without a mitigation strategy, with some difficult interpreting the theorems, and without understanding the domain specificity (thus narrowing the paper), I'm not over the accept threshold on this." }, "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": "> We prove that under certain conditions, the local optima of the fairness problem can outperform those of the supervised learning problem, highlighting the importance of considering fairness criteria in model development.\n\nCan you please elaborate what this means?\n\nPlease see the weakness section above." }, "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 derives a range of theoretical results involving fairness error bounds, algorithmic complexity, generalization bounds, convergence rates, and group-specific risk bounds\n2. The paper also conducts extensive experiments on a variety of medical datasets to confirm the theoretical findings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents theoretical results regarding the fairness losses of machine learning models. These theoretical results are then validated on different medical datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Firstly, the authors frame the problem as specifically for AI-based medical diagnosis systems. This is also reflected by the mention of specifically the medical setting in both, the abstract and the introduction. However, the setting being considered is much more general, and therefore should not be framed as being specific to the medical setting.\n2. The motivation behind the paper is not very clear. The authors derive a number of theoretical results, however, these results are not well motivated. For example, it is not clear how these results can be useful in practice, or how they can help improve fair model training. \n3. There is no discussion of the implications of theorem 1. Why is it useful and what insights does it provide?\n4. The disease prevalence $r_i$ is not defined formally before assumption 1. \n5. In Theorem 2, what is the loss that the optimal function f^* minimises?\n6. I am not convinced that the result in Theorem 2 is correct. Firstly, there is no assumption on how close $\\hat{f}$ is to $f^*$. So in theory, $\\hat{f}$ could be very ‘far away’ from $f^*$ if it is not trained correctly. In this case, even as the number of data $n$ increases, the fairness errors of model $\\hat{f}$ could be very different from that of $f^*$. In specific, in the proof of this result, how do you get from line 790 to line 791 (i.e. from the second inequality to the third)?\n7. $\\epsilon$-optimality is not defined\n8. > The theorem suggests that to achieve a smaller fairness risk, one should have a larger sample size, a smaller VC dimension, and a smaller number of demographic groups (lines 263-265)\n\nThis is not necessarily true. This just means that the upper bound is small in this case, but does not necessarily mean that these parameters lead to a smaller fairness risk\n\n9. fairness risk in line 273 $R(f)$ is not defined explicitly.\n10. There is no discussion on how realistic the assumptions made are, and how robust the theoretical and empirical results are to these assumptions." }, "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 you highlight the main steps or nuances in the mathematical analysis that arises due to difference of loss in comparison to standard generalization bounds on loss functions? \n\n-- In the experiments section, are you comparing an upper bound with an upper bound?\n\n-- What would be the main takeaway of the experiments section?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "-- Interesting mathematical analysis using techniques from learning theory such as Hoeffding bound, VC dimension, and Symmetrization lemma. \n-- The paper is generally well-written and the ideas are quite nicely presented.\n-- They have also included experimental results to show how their upper bound can be computable in several scenarios. The main strength is that the theoretical bound seems to be computable from the experiments, so the research has both depth and applicability." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work presents an interesting learning-theory-inspired analysis of fair machine learning, focussing on fairness measures that aim to equalize the loss across all groups. Specifically, the fairness measure is of a similar spirit as equalized odds/equal opportunity and is defined as the differences in expected loss across various demographic groups. Next, they derive interesting complexity bounds on this loss difference using statistical learning techniques like Hoeffding's inequality, VC dimension, and the symmetrization lemma. They have also included experimental results on several datasets alongside their theoretical contributions." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "-- Though they mention AI-based medical diagnosis here and there including the abstract, I don't think the paper has anything unique to medical diagnosis here. I think the emphasis on medical diagnosis is a bit of a distraction and can be discussed only in experiments if needed. \n\n -- While the derivation of generalization bounds for a loss function in itself is not new, their main nuance (as per my understanding) lies in bounding the difference of the losses. They also make Lipschitz assumptions. I can increase my rating if the novelty of the analysis is distilled out.\n\n-- The problem statement is closer to accuracy-fairness tradeoffs. While the paper has referenced several early papers in this area of accuracy-fairness tradeoffs, a lot of other prior works in the last 2-3 years that are quite closely related to this work have not been discussed. \n[1] Menon, A. K. and Williamson, R. C. The cost of fairness in binary classification. In Proceedings of the Conference on\nFairness, Accountability and Transparency, 2018.\n[2] Zhao, H. and Gordon, G. J. Inherent tradeoffs in learning fair representation. arXiv preprint arXiv:1906.08386, 2019.\n[3] Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush Varshney. Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing. International Conference on Machine Learning 2020.\n[4] Garg, S., Kim, M. P., and Reingold, O. Tracking and improving information in the service of fairness. In Proceedings\nof the ACM Conference on Economics and Computation, pp. 809–824, 2019.\n\nFor instance, [1] also considers similar fairness metrics. [3] also looks into tradeoffs using equalized-odds-like measures and difference in errors across groups.\n\n-- I would also be curious if this type of analysis has been explored in the context of fairness in federated learning attempting to characterize the worst gap in loss across multiple clients.\n\n-- Another possible limitation: It might be difficult to extend this to demographic parity?" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "This work theoretically analyzes the impact of disease prevalence and data distribution on fairness in medical AI, deriving bounds and providing empirical validation on diverse datasets to understand and mitigate unfairness." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024impact,\ntitle={Impact of Data Distribution on Fairness Guarantees in Equitable Deep Learning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2E2q9t1MFp},\nnote={under review}\n}" }, "abstract": { "value": "Fairness in machine learning is paramount to human society because machine learning systems increasingly influence various aspects of our daily lives, particularly in consequence-critical tasks such as medical diagnosis. Deep learning models for medical diagnosis often exhibit biased performance across diverse demographic groups. Theoretical analyses to understand unfairness in AI-based medical diagnosis systems are still lacking. This work presents a comprehensive theoretical analysis of the impact of disease prevalence and data distributions on the fairness guarantees of deep learning models for medical diagnosis. We formalize the fairness problem, introduce assumptions, and derive fairness error bounds, algorithmic complexity, generalization bounds, convergence rates, and group-specific risk bounds. Our analysis reveals that fairness guarantees are significantly influenced by the differences in disease prevalence rates and data distributions across demographic groups. We prove that considering fairness criteria can lead to better performance than standard supervised learning. Empirical results on diverse datasets, including FairVision, CheXpert, HAM10000 and FairFace, corroborate our theoretical findings, demonstrating the impact of disease prevalence and feature distribution disparities on the equitable performance of deep learning models for tasks such as glaucoma, diabetic retinopathy, age-related macular degeneration, and pleural effusion detection. The code for analysis is publicly available via \\url{https://github.com/anonymous2research/fairness_guarantees}." }, "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": [ "Fairness in Machine Learning", "Equitable Deep Learning", "Fairness Error Bound" ] }, "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/389543dc17341c784fad19fea65a7ec69a394df7.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": "Impact of Data Distribution on Fairness Guarantees in Equitable Deep Learning" }, "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": "The paper mentions using \"clean user sequences\" to generate semantic embeddings, but actual user interaction data usually contains noise (such as accidental clicks). Will the interaction sequences that have not been denoised affect the quality of semantic embeddings?\n\nThe description of Semantic Fusion Layer in the method section is relatively brief. Can you give a clearer explanation?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Clear motivation: the article identifies the noise problem of the diffusion model in the recommendation task, and proposes to use semantic information as a conditional input to reduce the impact of noise. This motivation is reasonable and meets the actual needs of the recommendation system.\n\nThe experimental design is relatively sufficient: the paper conducts comprehensive comparative experiments with mainstream recommendation methods on multiple real data sets, demonstrating the advantages of SDREC in recommendation quality. The experimental design is relatively reasonable and verifies the effectiveness of the model." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a semantic-aware diffusion model SDREC for sequential recommendation tasks. SDREC enhances the model's use of item semantic information by introducing the Semantic Fusion Layer, making the recommendation generation process more accurate. This layer fuses the semantic features in the embedding table to help the model better understand the user's interest dynamics when making recommendations, thereby improving the quality of recommendations. In addition, SDREC uses a contrastive learning framework to improve the model's adaptability to different sequence patterns. Experimental results show that on multiple real datasets, SDREC outperforms a variety of existing methods in recommendation accuracy and computational efficiency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Lack of clear formulas and detailed descriptions: A key component of the article is the Semantic Fusion Layer, but the specific implementation details of this module lack clear formula support and detailed descriptions of its design points. This makes it difficult for readers to fully understand the actual role of this module in the model and its contribution to the recommendation effect.\n\nNoise in user interaction sequences: The paper mentions that \"the encoder receives clean user sequences and explicitly captures the semantic relationship between items through contrastive learning.\" I understand that the author's definition of clean here refers to the original interaction sequence (no noise is introduced). But my question is that the original user interaction sequence is often not clean and may contain noisy data such as misclicks and unexpected behaviors. Existing research points out that user behavior data usually contains noise, and unprocessed click data may cause the recommendation model to deviate from the user's true preferences [r1]. Therefore, whether user sequences that have not been processed with noise can generate high-quality semantic embeddings and whether such semantic embeddings are conducive to diffusion models require in-depth analysis.\n\n[r1] Hongyu Lu, Min Zhang, and Shaoping Ma. 2018. Between Clicks and Satisfaction: Study on Multi-Phase User Preferences and Satisfaction for Online News Reading. In Proceedings of the International SIGIR Conference on Research and Development in Information Retrieval. ACM, 435–444" }, "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": "See above." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Study the problem of unawareness of global semantics in diffusion recommenders.\n\n\n2. Design an encoder-decoder architecture to address the issue and demonstrate the performance on three datasets across multiple baselines." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper falls into the sequential recommendation, where a novel diffusion recommender that considers global awareness of item semantics is introduced. The proposed encode-decoder architecture is well-designed to learn from global semantics. However, the motivation is not reasonable, and the proposed technique is not practical in real-world scenarios." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The motivation is not reasonable. The sequential recommendation aims to predict the next item. The recommender generally outputs the probability distribution on the item set in this setting. In other words, items with high probabilities should be ranked first by design. According to the user's past behavior in Figure 2, category 6 is the user's major interest. As a result, the concentration of distribution among the top 10 predictions is reasonable.\n\n\n2. The proposed solution is not practical. A real-world recommender usually handles millions of items. The computational complexity of the proposed solution is related to the number of items, which makes it challenging to scale up and handle new items. Thus, the inference time comparison in Table 4 will have a different conclusion when using a large-scale dataset." }, "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. **Can You Clarify What \"Semantics\" Are Used and How They Are Encoded?**\n - The term \"item semantics\" is central to the model, but the specifics are not clearly defined. Could you provide examples (e.g., item attributes, categories) and explain how these semantics are encoded and integrated into the model?\n\n2. **What Is the Theoretical Justification for the Semantic Fusion Layer?**\n - The Semantic Fusion Layer is a novel component, but its role and theoretical basis in the diffusion process are not fully explained. Could the authors elaborate on why this specific mechanism enhances the recommendation performance compared to other methods?\n\n3. **Is SDREC Scalable to Large-Scale Real-World Applications?**\n - The paper shows efficiency on moderate-sized datasets, but how does SDREC scale to millions of users and items in real-world settings? Have the authors conducted any scalability tests or optimizations to demonstrate its readiness for large-scale deployment?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "#### 1. **Originality**\nThe paper presents a novel approach to sequential recommendation by introducing the **SDREC** model, which leverages a **Semantic Fusion Layer** to effectively incorporate item semantics into the diffusion process. This contribution stands out for several reasons:\n - It addresses a critical limitation in current diffusion-based recommendation methods, which often fail to utilize semantic information effectively.\n - The combination of a **contrastive learning framework** with a generative diffusion process is a creative and unique approach that differentiates this work from existing models.\n\nOverall, the originality arises from the integration of **semantic-aware mechanisms** in diffusion-based recommendation, which fills a significant gap in the current literature.\n\n#### 2. **Quality**\nThe paper demonstrates decent quality in terms of both methodological rigor and empirical validation:\n - The authors provide a **clear and detailed description** of the SDREC model, including theoretical motivations and the design choices behind its components (e.g., the Semantic Fusion Layer). \n - Extensive experiments are conducted on multiple **real-world datasets** (e.g., Amazon Beauty, Amazon Toys, and Movielens), showing consistent and significant improvements over state-of-the-art baselines.\n - The implementation details, including the training strategies and parameter settings, are well-documented, ensuring reproducibility and transparency.\n\n#### 3. **Clarity**\nThe paper is well-structured and clear in its presentation:\n - The introduction effectively outlines the problem and motivates the need for the proposed model. \n - The model design and methodology are described systematically, with helpful visual aids (e.g., figures and diagrams) that clarify complex concepts, such as the diffusion process and the role of the Semantic Fusion Layer.\n - The results and analysis are presented in an organized manner, making it easy for the reader to understand the comparative performance and the benefits of SDREC over baseline models.\n\nThe clarity of explanation, coupled with structured figures, enables readers to follow the technical details without ambiguity, contributing to the paper's accessibility.\n\n\n#### Summary of Strengths\nIn summary, the paper excels across multiple dimensions:\n - **Originality**: Innovative integration of semantics in diffusion processes.\n - **Quality**: Methodologically rigorous with comprehensive empirical validation.\n - **Clarity**: Clear presentation supported by visual aids and structured explanations.\n\nThe combination of these strengths makes this paper a valuable addition to the literature on sequential recommendation systems." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes SDREC, a semantic-aware diffusion model for sequential recommendation tasks, which aims to predict the next item a user is likely to interact with based on their historical interaction sequence. The authors highlight the limitations of existing diffusion-based recommendation models, which often fail to incorporate item semantics effectively, leading to suboptimal recommendations. To address this, SDREC introduces a Semantic Fusion Layer, an innovative component designed to enhance the diffusion process by integrating item semantic information through an attention mechanism. This approach, combined with contrastive and generative losses, ensures that item semantics are fully utilized, improving the model’s accuracy in predicting user preferences. \n\nThe experimental results show that SDREC outperforms state-of-the-art models, achieving over a 10% relative improvement in performance while maintaining computational efficiency, making it suitable for real-time applications. The paper demonstrates SDREC’s superiority through experiments on multiple datasets, underscoring the importance of integrating item semantics in diffusion-based sequential recommendation systems." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "#### 1. **Unclear Motivation and Explanation of Semantic Utilization**\nWhile the paper introduces SDREC as a model that integrates item semantics through the **Semantic Fusion Layer**, the motivation behind why and how semantics are critical in the diffusion process remains insufficiently explained. Although the authors mention that traditional models do not effectively leverage semantics, the paper does not provide a clear, detailed rationale for why this limitation specifically impairs recommendation accuracy. Additionally, the semantic information utilized (e.g., item categories, attributes) is not well-defined, leaving readers uncertain about what constitutes the \"semantics\" and how exactly it is encoded or represented.\n\n**Recommendation for Improvement**: \n - **Motivation**: The paper would benefit from a stronger motivation section that explicitly explains why semantics are crucial in sequential recommendation tasks and why their integration into the diffusion process is expected to enhance performance. The authors could provide theoretical justifications or empirical evidence showing the gap in current models and how the proposed method aims to bridge this.\n - **Clarification of Semantics**: The authors should clearly define what they mean by \"item semantics.\" Providing specific examples (e.g., movie genres, product categories, textual descriptions) and explaining how these elements are encoded and utilized within the model would make the approach more transparent. Additionally, it would be helpful to include an illustration or case study demonstrating how semantic information influences the diffusion process and leads to better recommendations.\n\nBy improving the clarity of motivation and the description of semantic use, the paper could strengthen its theoretical foundation and make its contributions more accessible and convincing to readers.\n\n#### 2. **Scalability Concerns with Large-Scale Datasets**\nAlthough SDREC demonstrates efficiency on moderate-sized datasets (e.g., Amazon and Movielens), the paper does not provide evidence of its scalability on **larger, real-time recommendation systems** that involve millions of users and items. Given that the diffusion process involves multiple steps and attention mechanisms, it is important to understand whether SDREC can scale without compromising latency and computational resources in a production environment.\n\n**Recommendation for Improvement**: Including a scalability analysis or experiments on larger datasets (e.g., a full-scale Amazon dataset or Netflix prize data) could strengthen the paper’s claims about the model’s efficiency and its readiness for real-world deployment. \n\n\n\n\n#### Summary of Weaknesses\nIn summary, while SDREC shows promise, the following areas need improvement:\n - Improving the clarity of motivation and the description of semantic use.\n - Evaluating the model's scalability with larger datasets." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We incorporate item semantics into the diffusion model through a Semantic Fusion Layer to enhance its performance for sequential recommendation." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024semanticaware,\ntitle={Semantic-Aware Diffusion Model for Sequential Recommendation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2E6OK8cSoB},\nnote={under review}\n}" }, "abstract": { "value": "Sequential recommendation aims to predict the next click for a particular user based on their historical interacted item sequences. Recently, diffusion-based methods have achieved the state-of-the-art performance in sequential recommendation. However, they fail to effectively utilize the rich semantic information embedded in items during the diffusion process to accurately guide the generation, leading to sub-optimal results. To address this limitation, we designed SDRec, a **S**emantic-aware **D**iffusion model for sequential **Rec**ommendation. Our model introduces a novel architecture, the Semantic Fusion Layer, which leverages the embedding table from the encoder to incorporate item semantics into the diffusion process through an attention mechanism. Together with the well-designed contrastive and generative losses, SDRec effectively utilizes the item semantics in diffusion model, unleashing the potential of sequential recommendation. Our experiments show that SDRec has over 10% relative gain with superior efficiency compared with existing methods." }, "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", "Sequential Recommendation" ] }, "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/588ae3b8d4859db67568719dca3160aa71c12cbd.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": "Semantic-Aware Diffusion Model for Sequential Recommendation" }, "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": "* I may have missed something, but I cannot understand exactly how the proposed difficulty metric is intended to be used?\n* The proposed difficulty metric seems expensive to compute, with pairwise similarity scores required for large subsets of the data. How does this compare to the cost of conducting a single model training run?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Tab 1 / Fig 2b shows the results when training different base architectures on different classification datasets from scratch. Interestingly, not all results are correlated with the trend on ImageNet-1K, indicating the optimal architecture choice depends on the dataset.\n- The code is open sourced and well documented. It seems that it would be simple for a researcher to reproduce the authors claim with limited effort (though I have not run the code myself).\n- It is interesting that sub-models consistently outperform full models on ImageNet. The fact that full models have seen more training datapoints in total may have compensated for fewer classes, which makes the result not totally intuitive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper tackles the problem of choosing image classifiers for tasks with only a small number of categories (\"Few-Class\"). To do so, they introduce a new benchmark, termed \"Few Class Arena\" (FCA) on which they train and evaluate a range of models on subsets of various full datasets (e.g wit so-called sub-models trained on between 2 and all 1k ImageNet categories). The FCA benchmark is open-sourced with code available on GitHub. The paper provides detailed discussion of how the open-source package can be used for model selection in the few-class setting.\n\nOverall, the authors show that models trained on specific sub-classes (sub-models) are better than models trained on the full dataset and evaluated on the same sub-classes, across model sizes. They further show that there is no single best model architecture for a given dataset, and that training models on different datasets result in different rankings of architecture. The authors also propose a \"dataset difficulty\" metric which can be computed without training a model, and correlates well with the few-class performance of a model on a dataset." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "My main issue with this paper is in overall utility. The high level goal of the paper is to provide a tool with which practitioners can select a model (dominantly through the lens of model *architecture*) for a few-class classification task. The tool basically allows authors to train a model (with most results presented from scratch) on subsets of a given dataset. However, this does not align with the practical problem to me, where practitioners might take a model pretrained on a large amount of *data* (e.g DINOv2 or CLIP) is finetuned for a given task (note that lightweight variants of these models are also open-source). \n\nGiven that this paper is predominantly an empirical examination which proposes a practical open-source library, I feel that the lack of experiments with pretrained models prevents acceptance. \n\nOther issues:\n\n- The citation format makes the main text quite difficult to parse\n- L52: Main text does not seem to describe Figure 1 accurately?\n- There is no discussion of few-shot literature, which is at least tangentially related to this problem" }, "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. For FC-Full, when N_{CL} decreases, how to make sure that model predicts only few classes? Are the logits of those few classes are selected to get the prediction and discard logits of all other classes?\n\n2. If a user has a custom dataset with few classes and want to find a model that works better on this custom dataset, it would be helpful to have an explanation on how this benchmark can assist the user in this case." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "•\tAddressed an important problem by proposing the benchmarking tool.\n\n•\tThe tool is designed to be user friendly and allows to run wide range of experiments by setting few hyper-parameters. The tool allows to benchmark on custom models and datasets.\n\n•\tProvided a behavioral understanding between models trained on large number of classes vs smaller number of classes in the few-class regime.\n\n•\tThe proposed similarity metric shown to be linearly corelated with the model performance on small number of classes. Such proxy helps to save computation cost and time of conducting various experiments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a benchmark tool called Few-Class Arena to benchmark models on different datasets with smaller number of classes (e.g. < 10) and propose a similarity metric called SimSS to measure dataset difficulty measure. They show that ResNet family models trained on full ImageNet 1K classes show reduced performance when tested only for few ImageNet classes (< 10 classes). On the other hand, the same models when trained on smaller number of ImageNet classes from scratch show higher performance on these classes when compared to models trained on all classes of ImageNet 1k. They show that the proposed SimSS metric can serve as a proxy to estimate the upper bound accuracy of model performance on few-class datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Despite focusing on an interesting problem setting, the analysis shown in the paper has limited scope. Authors shown experiments on models evaluated or trained on smaller number of classes, however there are no details discussed on how these few classes have been selected and how semantically close these few classes to each other? Would the analysis presented differ by choosing the those few classes differently?\n\nThe aspect of transfer learning has not been discussed. It is a common practice to finetune ImageNet pretrained models like ResNet50 or ViT, or recent foundation models like CLIP and DINOv2 to different downstream tasks that include adapting or finetuning them on few classes. The analysis presented in the paper is missing this exploration. Is it better to train the models from scratch on the few classes or finetuning these models work better for few classes? Does SimSS score also align on the finetuned models?\n\nTo compute SimSS, a score called Nearest Inter-Class Similarity requires a nearest class (C_hat) to the target class (C), it is not clear how this C_hat is acquired.\n\nOverall, I appreciate the motive and tool for benchmarking few-class regime, however the analysis presented in the paper is incomplete, and I suggest authors to extend their analysis." }, "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 original research do you expect will use this benchmark and what do you hope it will achieve or unlock ?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The benchmark is well executed and will be useful for “few-class” adaptation research. There are many models evaluated with many datasets and the analysis is thorough. In particular, the author study in depth the evolution of the performance of models trained on large set of classes compared to specialized models, as a function of the number of classes, and show the importance, and therefore propose a metric for evaluating this adaptation.\n\n- The similarity benchmark is a nice addition. It correlates well with the performance while being easy to evaluate and with a modest cost.\n\n- The presentation and writing are very clear. The figures are very informative." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a new benchmark for the “few-class” problem, which is a classification problem with very few classes. Most of the scientific literature focuses on datasets with many classes while practitioners often encounter the few-class scenario. The benchmark consist of several selected datasets and several settings, such as training on large set of classes and evaluating on a smaller set, and popular vision models are evaluated and compared. Finally, an analysis of what happens in few-class regimes is proposed." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The motivation behind few-class evaluations is not fully convincing. In practice, one will take a large model and fine-tune it (without the classification layer) to a target set of classes, hence obtaining a specialized model. Evaluating the capabilities of a full model on few-class is only interesting when there are too many subsets to consider ? When does that happen in practice, and could you just not use small adaptation layers on top of frozen backbone for each of the subsets ?\n\n- One thing that is missing from the paper is a recommendation for practitioners on which vision model to use for someone interested in the few-class problem. Basically discussing in more details the results from Table 1 and providing comparison between models in Section 4.2 and 4.3. One interesting question is, do models that perform really well on the many classes setup are the same that also perform well on the few-class setup ?\n\n- Some of the findings in the paper are fully expected. The fact that a model specifically trained on the target subset of classes perform better that a larger model trained on a superset is not very surprising or novel." }, "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. (Related to W2) The finding that the scaling law w.r.t. model size is violated for submodes is interesting. I would be curious if this only applies to supervised training or also to self-supervised pre-training combined with minimal fine-tuning or linear probing. Did you conduct any experiments into this direction or do you have an intuition on that?\n2. You mention that you conducted experiments on different architectures such as ResNet’s and ViT’s. However, the results are presented in an aggregated way. Did you find any significant differences between model architectures? Do the findings of Fig 1 equally apply for both architectures?\n3. I agree with your description and ad-hoc interpretation of Fig. 5. However, I am missing a discussion on why we see the low correlation for DCN-Full. Do you have any interpretation of this?\n\n\n**Minor comments:**\n\n- The correct use of author-year citations would improve readability. I.e.: Author (2024) for in-text citations and (Author, 2024) elsewise." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The necessity of a few-class benchmark is well-motivated by a strong finding that models pre-trained on many-class datasets perform worse than expected on few-class datasets which is an issue not addressed in literature thus far.\n2. The paper presents some interesting insights that contradict expected model behavior based on intuition.\n3. The authors provide a ready to use code base integrated with other frameworks and libraries ensuring usability." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a benchmark designed to evaluate and select efficient image classification models in scenarios with a limited number of classes. This setting, common in real-world applications (e.g., 2-10 classes), contrasts with widely used benchmarks like ImageNet and COCO, which involve hundreds or thousands of classes. The paper presents FCA as a tool to help researchers and practitioners efficiently select models for few-class tasks. The paper coins the term ``few-class regime'' and presents some interesting non-intuitive insights regarding the performance of models that are pre-trained with many class datasets and then applied in few-class settings.\nIn addition, they introduce a dataset difficulty metric by inverting image similarity measured via CLIP and DINOv2 features." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The scope of the study only covers image classification, while it is in principle also applicable to dense prediction tasks where object classes are present (e.g., object detection, semantic segmentation). It would be insightful if the same findings hold for these tasks. The authors could add a discussion or experiments (if available) for other vision tasks.\n2. The experiments cover classification tasks based on supervised (pre-)training. However, there is an increasing trend that classification models are fine-tuning based on self-supervised pre-trained models. This paradigm is not covered in this study, and therefore, the findings are limited to the more traditional fully supervised paradigm. The authors could discuss these aspects or add experimentation with self-supervised pre-trained models (if available)." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a Few-Class neural network benchmark for model selection with deep analyses." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024fewclass,\ntitle={Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty Measurement},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2ET561DyPe},\nnote={under review}\n}" }, "abstract": { "value": "We propose Few-Class Arena (FCA), as a unified benchmark with focus on testing efficient image classification models for few classes. A wide variety of benchmark datasets with many classes (80-1000) have been created to assist Computer Vision architectural evolution. An increasing number of vision models are evaluated with these many-class datasets. However, real-world applications often involve substantially fewer classes of interest (2-10). This gap between many and few classes makes it difficult to predict performance of the few-class applications using models trained on the available many-class datasets. To date, little has been offered to evaluate models in this Few-Class Regime. We conduct a systematic evaluation of the ResNet family trained on ImageNet subsets from 2 to 1000 classes, and test a wide spectrum of Convolutional Neural Networks and Transformer architectures over ten datasets by using our newly proposed FCA tool. Furthermore, to aid an up-front assessment of dataset difficulty and a more efficient selection of models, we incorporate a difficulty measure as a function of class similarity. FCA offers a new tool for efficient machine learning in the Few-Class Regime, with goals ranging from a new efficient class similarity proposal, to lightweight model architecture design, to a new scaling law. FCA is user-friendly and can be easily extended to new models and datasets, facilitating future research work. Our benchmark is available at https://github.com/fewclassarena/fca." }, "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": [ "Few-Class", "lightweight", "small neural network", "benchmark", "scaling law", "image similarity", "convolutional neural network", "CNN", "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/faee30214550acbd447b439fb62fed02f1d19dca.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/9799eb1de73a892cf597ec8162e99398f7c1eafd.zip" }, "title": { "value": "Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty Measurement" }, "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. Doesn't the perturbation e chosen is a one gradient step attack with simple targeted loss? \n2. Does x + \\epsilon x1, and x+ \\epsilon x2 necessarily classified differently? it seem plausible that the classification is different only for very big \\epsilon. have you tested it?\n3. What does the experiments shows?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Writing: The paper is well-written and effectively communicates its final goal from the outset. The intuitions behind the proofs are presented in a highly accessible manner.\n\nThoroughly Detailed Experiments: The experiments are described with great clarity and organization, providing all the necessary details for readers to fully understand the methodology and findings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper provides a theoretical analysis of adversarial robustness in several specific contexts. It examines the dataset and adversarial perturbations across different settings, starting with a random linear network and progressing to trained multi-layer non-linear networks and arbitrary multi-class datasets. The authors conduct experiments with 12-dimensional synthetic data and linear or two-layer networks to support their theoretical findings regarding the sizes of adversarial perturbations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Lack of Related Research: The paper overlooks existing theoretical work on random networks, such as \"Adversarial Examples in Multi-Layer Random ReLU Networks\" by Bartlett et al. \n\nConcepts: With the exception of Theorem 7, the settings discussed are largely unrelated to each other or to real-world scenarios. Theorem 7 relies heavily on linearity, (although the claim to apply on for highly non-linear networks) state only that changes in output due to input perturbations can be captured through projections on the relevant gradients.\n\nOverstating Generality: The paper makes broad claims about phenomena related to dimensionality that are primarily observed in random networks, a point that is only briefly mentioned in the introduction and not sufficiently discussed throughout the rest 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": 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": "ref weaknesses" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "I appreciate the clear presentation and detailed theoretical derivation of this manuscript." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This manuscript provides a theoretical investigation into the robustness of DNNs in classification tasks. Through rigorous matrix-theoretic analysis, they establish that the minimum adversarial perturbation—the smallest input modification required to change a network's classification decision—exhibits an intrinsic relationship with input dimensionality." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I am not an expert in this theoretical area, thus I cannot check all proof details and judge the theoretical contribution.\nFrom my perspective, the conclusion of this work---adversarial robustness can degrade as the input dimension d increases---is not rigorous. \n\n* What if the additional dimension of $\\bf x$ is correlated with other dimensions? I.e., the new dimension does not bring any new imformation, would it degrade the robustness? \n* On the other hand, if the new dimension brings new information, the new $\\bf x \\in R^{d+1}$ and the prior $\\bf x \\in R^{d}$ are drawn from different data distributions. How to compare the robustness of DNNs over different data distributions?\n* How to compare the norm for variables with different dimensions? I.e., let $\\bf \\delta_1\\in R^d$ and $\\bf \\delta_2\\in R^{d+1}$, can we directly compare $||\\delta_1||_2$ and $||\\delta_2||_2$? They are in different dimensions, for example, can we say volume > area > length?" }, "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 ethical concerns." }, "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 suggestions for improving the paper." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "A better understanding of adversarial attacks and robustness of neural networks remains an important topic." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a theoretical analysis of why neural networks classifiers are susceptible to adversarial perturbation, ie, adversarial fragility: specifically why small, targeted perturbations can dramatically change their classification outputs. The authors challenge existing theories, which attribute this fragility to factors like smoothness of decision functions or curvature of decision boundaries, arguing these approaches only partially address the problem. The authors present a matrix-theoretic analysis of this problem and explore how neural networks' robustness declines as the input dimension increases, theorizing that their adversarial robustness is inherently limited to approximately $1/\\sqrt 𝑑$ of optimal robustness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "To the best of my knowledge, the paper's conclusion that adversarial robustness can only be $1/\\sqrt d$ is already known [1, 2] and has been shown in more general settings.\n\n- The theoretical analysis is weak, as all the theorems make important and unrealistic assumptions, i.e. normal distribution of the data, constraints on the weight matrices.\n- $\\ell_2$ is the only distance considered, several other papers have proposed theoretical analysis with respect to the $\\ell_p$ norm (see [1, 2]).\n- Theorem 1 spans 2 full pages to show a probabilistic bound over a linear network with several assumptions, but it's unclear why the authors came to all this work, since the distance to the decision boundary for a linear network can be computed in closed form. \n- The paper proposes a total of 7 theorems, each of which is accompanied by a proof. \n- The paper does not propose any related work\n- The paper does not provide usable results \n- The experimental section only proposes toy experiments \n\nSuggestions for improving the paper:\n- Instead of presenting a list of theorems, the authors should motivate their analysis and explain why it's interesting. How can these results help the community? Even if the theoretical analysis has assumptions, how can it be useful for real-world applications?\n- Authors should propose a related work section and compare their analysis with other work. How is their analysis better or novel than the competing work?\n- Authors should propose real-world experiments, adversarial robustness is now a mature research topic, and large-scale (e.g. ImageNet) experiments should be performed. \n\n[1] Yang et al. Randomised smoothing of all shapes and sizes. ICML 2020 \n[2] Kumar et al. Curse of Dimensionality on Randomised Smoothing for Certifiable Robustness. ICML 2020" }, "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": "1.\tBased on the assumptions in Eq. (1) and Eq. (5), under what conditions can $w_i$ satisfy these assumptions? Does this imply that new constraints have been added to $w_i$? The authors should discuss the practical feasibility of these conditions and how they relate to real-world DNNs.\n\n2.\tIn Theorems 1 and 4, what does \"with high probability\" specifically refer to? Please provide a rigorous definition in the main text or appendix." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "$\\bullet$ Exploring the smallest magnitude of perturbations that can change model output is intriguing. The paper also provided detailed derivations and proofs.\n\n$\\bullet$ The comparison between the adversarial robustness of minimum-distance classifiers and DNNs is also noteworthy." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studied the smallest magnitude of perturbations that could alter the model output, particularly in linear cases under several assumptions. The authors demonstrated that the adversarial robustness of models degraded as the input dimension $d$ increased. Besides, they analytically showed that the adversarial robustness of linear networks could be $1/\\sqrt{d}$ of that of the minimum-distance classifier." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Several theorems were based on assumptions that were too strong, and provided little assurance that the analysis of \"adversarial robustness of neural networks\" can be generalized to any two-layer DNN, including: \n\n$\\bullet$ Theorem 1 analyzed the robustness of a two-layer linear network under the following assumptions: (a) each dimension of the training samples follows a standard Gaussian distribution, (b) the activation layer is identity matrix $I$, (c) the linear matrix $H$ is an orthogonal matrix, and (d) for the i-th sample, each i is a distinct label, with the model outputting a score of 1 for category i, and a score of 0 for all other categories, as stated in Eq (1). \n\n$\\bullet$ Theorem 4 used similar assumptions.\n\nTo improve it, the authors could include detailed discussions of how Theorems 1, 4, 6 and 7 might extend to, or provide insights into, more general neural network architectures used in practice. \n\n2.\tWriting: The authors could revise the manuscript to highlight the physical significance of each theorem, the limitations, and the insights for the DNN robustness community. For example, the authors could discuss potential implications of Theorem 7 for adversarial attacks on real-world DNNs.\n\n$\\bullet$ It is suggested to focus on the potential significance and application scenarios of each theorem and lemma, and the general ideas of the proofs in the main text, while moving the detailed proofs (e.g. Lines 151-244) to the appendix. For example, for Theorem 7, what is the actual context in which \"the classifier wrongly think the input is $x+\\epsilon x_2$ instead of $x+\\epsilon x_1$\"? \n\n$\\bullet$ Since each theorem has different assumptions, it would be beneficial to make a table that clearly lists the assumptions of each theorem, indicating which theorems represent purely ideal cases and which can be generalized to typical DNNs.\n\n3.\tThe abstract and introduction contained overclaims. Most of the theorems presented in the main text were derived under strong assumptions, and some conclusions had its restrictions, e.g., in linear networks. For example, in the abstract, the authors demonstrated \"neural network’s adversarial robustness can degrade … only $1/\\sqrt{d}$ of the best possible adversarial robustness.\" What is \"best possible adversarial robustness\"? Do these results apply to any DNN, or only to linear networks, or only to two-layer linear networks? Are there some strict assumptions implicit in this conclusion? The authors should revise the abstract and introduction to clarify the conditions under which the conclusions are applicable.\n\nBesides, the authors could conduct validation experiments on typical DNNs (e.g. validating Theorem 7 on the convolutional neural networks such as the LeNet or ResNet-18). And a thorough discussion of the limitations is recommended." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024towards,\ntitle={Towards unlocking the mystery of adversarial fragility of neural networks},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2ErS9Bkc3O},\nnote={under review}\n}" }, "abstract": { "value": "In this paper, we study the adversarial robustness of deep neural networks for classification tasks. The adversarial robustness of a classification algorithm is defined as the smallest magnitude of possible additive perturbations that can change the output of the classification algorithm. We provide a matrix-theoretic explanation of the adversarial fragility of deep neural network. In particular, our theoretical results show that neural network's adversarial robustness can degrade as the input dimension $d$ increases. Analytically we show that neural networks' adversarial robustness can be only $1/\\sqrt{d}$ of the best possible adversarial robustness. Our matrix-theoretic explanation is consistent with an earlier information-theoretic feature-compression-based explanation for the adversarial robustness of neural networks." }, "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": [ "deep learning", "adversarial attack", "adversarial robustness" ] }, "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/b461a246c523c9f6b290eb25edc8df6b822ee35f.pdf" }, "presentation": null, "primary_area": { "value": "learning theory" }, "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 unlocking the mystery of adversarial fragility of neural networks" }, "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": "1. In Figure 4, how are the lines fitted? For the right figure (open-ended questions), the gap between CoT and PathSeeker is very small. What is the standard deviation?\n\n2. In Table 2, Table 3, Table 6, and Figure 5, please add what metrics and units are used. Also add evaluation method in Experiment section." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Clear identification of research gap: I think it is an interesting question whether LLMs can reason on biological pathways, and how well they do it. The authors have identified the limitations here clearly.\n\n2. Innovative benchmark: BioMaze is a valuable contribution to the field, providing a systematic evaluation framework for assessing LLM performance across various dimensions of biological pathway reasoning." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses a gap in LLMs to reason biological pathways, especially with complex perturbations, interventions, and varying conditions. To address this gap, the authors first introduce a new benchmark, BioMaze, that contains 1.3k high-quality questions for biological pathways reasoning.\n\nNext, the paper evaluates LLMs on BioMaze with existing reasoning methods and finds that they struggle with perturbations. Then the authors propose a new reasoning approach, PathSeeker, that reasons through subgraph-based navigation within pathway graph. PathSeeker achieves better performance in biological reasoning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Data presentation is not very clear. For example, when the paper evaluates the performance of different models and reasoning methods, it simply writes \"performance\" without defining the metrics. Therefore, it is not clear whether a higher number means a better performance. In Table 2 and 3, the authors underline the lowest results, which is confusing.\n\n2. Baseline choice is not clear. The paper uses CoT as a baseline in 5.3.1 Task Analysis. I think a better baseline may be a method with pathway graph augmentation since PathSeeker also uses pathway graph augmentation.\n\n3. Analysis is not thorough enough. If the authors want to claim that PathSeeker reduces the performance gap between natural and intervened/perturbed groups, then they should provide more evidence and analysis on them." }, "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": "\"We then apply multiple data filters and validation steps to ensure the correctness, quality, and relevance\nto biological pathways. The correctness of each question is validated by checking whether LLMs\ncan answer it accurately using the original paper content, allowing us to exclude question-label pairs\nwith errors. Question quality is ensured through several filters, removing questions that are poorly\ndefined, unpredictable (e.g., asking for specific measurement values), query more than one fact, are\ntrivial with answers revealed in the question’s context, or are unrelated to biological pathways. After\nall the filters, BioMaze contains 1.3k high-quality questions for biological pathways reasoning\"\nCan you give me more confidence that these questions all have a single right answer that can be answered from the context? To what degree are the manually verified? Filters are great, but where does the buck stop? \n\nA pathway is essentially a knowledgebase. It would be good to connect this work to recent approaches that use knowledgebase graph structure in RAG, such as GraphRAG. Indeed, generally speaking, contextualization within prior work could be stronger.\n\nBiggest question: Why did you not run eval on cutting-edge larger models or larger open-source models like your LLaMa-3.1-405B, or fine-tuned SLMs. Bit sus. Willing to upgrade review if this concern is addressed." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "* The benchmark is a solid contribution. The authors did good work in breaking down the benchmark by various categories.\n* PATHSEEKER has promise, though I wish it were better motivated and contextualized within related work within systems biology as well as graph reasoning tasks with LLMs as well as graph-based RAG techniques.\n* The breakdown of failure modes for LLM reasoning over pathways, particularly in terms of causality, and showing how the graph augmentation helps is useful. Breaking down the reasons for failure with human validation is also a useful contribution and I wish I saw more of that." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a benchmark to evaluate LLMs' reasoning abilities about biological pathways including perturbed pathways. The benchmark is diverse and covers different biological domains and scenarios.\n\nThe authors' evaluations show that while LLMs understand natural mechanisms well, they struggle with intervention scenarios\n\nThe authors propose PATHSEEKER, an LLM agent that navigates pathway graphs using subgraph-based exploration. This approach improves reasoning accuracy, including accuracy for intervention scenarios.\n\nKey contributions:\n\n* BioMaze Benchmark\n* Evaluation of LLMs on benchmark\n* PATHSEEKER Agent, analysis of its performance on benchmark, its failure modes, and ablation study" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* With PATHSEEKER, I think there is a lack of motivation for explore the pathways via subgraphs other than \"Inspired by how humans browse web networks\". I don't disagree with this approach per se, but I don't think the authors motivate doing it this way as opposed to, say for example, adding including the whole graph or a big chunk of it into the prompt template. Indeed, as an experiment I pasted an XML file of a MAPK KEGG map into GPT-4's context window and it fits. And if something doesn't fit, context windows will get bigger. I think the authors should motivate the local approach, for example, by citing work that demonstrates failure modes for graph-based reasoning with LLMs, and citing work that shows how local approaches do better. \n* I find it concerning that the authors did not include results for a cutting edge model like GPT-4, Claude, PALM 2 and limited tests to GPT-3.5 and Llama-3 8b, neither of which were fined-tuned for performance in this domain. The gap between GPT-3.5 and GPT-4, as an example, on general medical QA performance is quite large. This makes me worry the benchmark might be already saturating on more advanced models. Budget could have been an issue, but could have fine-tuned GPT-3.5 (perhaps on hold-out data from their benchmark), or they could have used their instance of LLaMa-3.1-405B to answer questions as well as evaluate them. Similarly, they could have used other fine-tuned open source models to evaluate." }, "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": 4 }, "primary_area": null, "questions": { "value": "1. Maybe the author can try to answer the reason why particular types of errors occur in categorization.\n2. May using other models to do ground truth answers validation." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1. The study is very comprehensive. I like the rigorous experimental design that systematically evaluates different aspects of pathway reasoning.\n2. It contributed to the field of BIOLOGICAL PATHWAY REASONING by making benchmarks and the problem formulation combining biological pathway reasoning with LLM capabilities.\n3. I also found myself enjoy reading the paper and like the well-structured presentation progressing logically from problem motivation to solution." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces BioMaze, a large-scale benchmark for evaluating large language models' ability to reason about biological pathways. The authors also introduced PATHSEEKER, a new approach to enhance LLMs' performance on these tasks.\nThey found that while LLMs can understand basic biological mechanisms but LLMs struggle when asked to reason about perturbations or interventions in biological systems. \nThrough their experiments, they observed that LLMs perform worse on perturbed systems compared to normal conditions." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The author presents error categorization but it doesn't provide detailed analysis of when and why particular types of errors occur. If the authors can provide more analysis of the occurance, it would be nice.\n2. The validation of ground truth answers relies heavily on LLMs themselves (LLaMA 3.1-405B and GPT-4). This circular dependency could reinforce existing model biases." }, "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. Pathway graph limitations: This paper highlights that faulty reasoning persists even with pathway augmentation, especially with perturbations. Could the authors provide more insight into potential sources of error in the pathway graph data? Is it the case that some specific cases or graph structures are more challenging for the LLM to navigate, and some are easier for LLMs to handle?\n2. Handling multi-step reasoning decline: Given that CoT reasoning shows decreased accuracy with increased steps, have the authors considered alternative strategies or mechanisms, such as hierarchical reasoning, to mitigate this drop in performance, or are those questions just naturally challenging? \n3. Error analysis: The error analysis indicates that omissions remain an issue with PATHSEEKER. What approaches might the authors consider to address these issues, especially when key pathway branches are missed? Could further database expansion, enhanced subgraph search criteria, or developing a different graph search algorithm improve the performance?\n4. Using RAG: would authors consider incorporating RAG into this framework given the graph structure of biological pathways? Specifically, RAG could allow the model to retrieve specific or relevant information from related literature or pathway databases. This retrieval would provide the LLM with dynamic access to more detailed and more recent biological knowledge, instead of the graph structure constructed from a fixed database KEGG, as currently used in the paper. \n5. Evaluator setting in this paper: this paper proposes using the llama 405B model as the evaluator model for LLM's outputs, as this is costly to run multiple times, would authors consider any alternative evaluation approaches such as applying rule-based methods or using alternative LLMs to strengthen the statistical validity of the benchmarking results?f" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. BioMaze benchmark for biological pathway reasoning: The authors present BioMaze, a benchmark dataset designed to evaluate LLMs’ reasoning abilities within a biological context. BioMaze focuses on assessing how well LLMs comprehend and reason about complex biological pathway phenomena, including cause-effect relationships in natural and perturbed conditions. Curated from the literature, this dataset includes high-quality questions and answers generated with Llama 3.1405B and GPT-4. Covering multiple biology subfields, BioMaze undergoes extensive filtering and validation to ensure relevance, accuracy, and diversity of pathway scenarios.\n2. Pathway graph augmentation via PATHSEEKER agent model: Given that biological pathways are naturally structured as networks, the authors incorporate pathway graph data to improve LLM reasoning. They introduce PATHSEEKER, a novel graph-augmented agent that navigates pathway subgraphs to enrich LLM understanding and support reasoning in complex pathway contexts. This approach allows LLMs to access and utilize structural information essential for nuanced pathway reasoning, particularly in scenarios involving biological interventions.\n3. Comprehensive evaluation and analysis: The paper conducts a thorough evaluation across multiple LLM models and experimental settings, systematically analyzing LLM performance with and without pathway graph augmentation. Additionally, the ablation study of PATHSEEKER explores its effectiveness by examining API usage, step distribution, and performance impact. These analyses further strengthen the value of pathway augmentation, validating the importance of PATHSEEKER in enhancing LLMs’ reasoning capabilities in biological pathway contexts." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This study explores the under-examined ability of LLMs to reason about biological pathways, particularly focusing on how system perturbations affect downstream biological processes. The authors introduce the BioMaze dataset, a benchmark designed to assess LLMs’ reasoning on how various interventions, like mutations, infections, or treatments, impact downstream targets through complex pathway mechanisms across different biological contexts. With this dataset, the authors then test LLMs with reasoning techniques such as Chain-of-Thought (CoT) and graph-augmented methods, and they find that while LLMs can understand basic biological mechanisms, they struggle with predicting effects after perturbations. To enhance the reasoning ability of LLMs, the authors also developed PathSeeker. In this novel approach, the LLM agent navigates pathway subgraphs to improve performance in pathway reasoning, particularly in scenarios with biological perturbations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Limited evaluation method for open-ended questions: outputs from different LLMs are evaluated by another LLM, specifically using the Llama 3.1 405B model, which is considerably powerful but would be costly to replicate the results. It would be more helpful if the authors could consider some alternatives, such as using rule-based keyword-matching or for example, using ROUGE score or embedding-based summarization methods to compare how similar or dissimilar answers from LLMs are to the ground truth answers. Another alternative could be to construct different evaluation methods based on the failure modes discovered later from the error analysis study. \n2. see questions" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024benchmarking,\ntitle={Benchmarking and Enhancing Large Language Models for Biological Pathway Reasoning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2Ey2hkFicp},\nnote={under review}\n}" }, "abstract": { "value": "Large language models (LLMs) have demonstrated remarkable performance across various domains of biology, but their ability to reason about biological pathways remains underexplored. This includes reasoning about how perturbations in biological systems lead to various downstream effects through complex intermediate processes. Such reasoning is crucial for explaining and predicting biological phenomena, as well as for formulating hypotheses and designing experiments.\n\nIn this study, we investigate whether LLMs can effectively understand and reason about biological pathways by introducing BioMaze, a comprehensive benchmark focusing on reasoning about the effects and mechanisms of natural and synthetic interventions—such as mutations, infections, or treatments—on various downstream targets under different conditions through complex intermediate pathway processes. BioMaze spans multiple biological domains and is categorized along three reasoning dimensions, capturing various aspects of pathway reasoning.\n\nWe evaluate LLMs using the BioMaze benchmark with reasoning methods like Chain-of-Thought (CoT) and pathway graph-augmented approaches. Results show that while LLMs can understand mechanisms in natural organisms, they struggle with predicting phenomena after perturbations, highlighting their limitations in reasoning about biological pathways. To address these challenges, we propose PathSeeker, a novel LLM agent that interactively reasons through subgraph-based navigation within the pathway graph. This approach enhances LLMs' reasoning in biological pathways by leveraging pathway graph augmentation, particularly in cases involving perturbations, potentially bridging the gap between LLMs' current capabilities and the complexities of biological 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": [ "Large Language Model", "Reasoning", "Biology", "Biological System", "Pathway", "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/716718cdf9285f8e116742e52bfac964bc2253b7.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": null, "title": { "value": "Benchmarking and Enhancing Large Language Models for Biological Pathway Reasoning" }, "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": "- How does the dendritic attention layer differ from a current-based (CUBA) leaky integrate-and-fire (LIF) neuron model?\n- How are spikes produced between the SPLR convolution layers?\n- How are convolutions applied within the proposed model?\n- In equation (1), is the variable $u(t)$ a binary vector representing input spikes?\n- How does the inclusion of a decay matrix in the HiPPO framework enhance memory retention?\n- Could you clarify the setup for the Sequential CIFAR-10 and CIFAR-100 tasks? How are frames sequenced? Similarly, could you elaborate on the experimental setup for the other datasets?\n- For clarification, could you specify what spikes $i$ and $j$ refer to in line 187?\n- Is the manuscript proposing a new type of spiking neuron, or an entire network architecture?\n- Since the manuscript emphasizes improving SNNs' capacity to handle long-term dependencies, could you elaborate on why simple LIF models face challenges with this?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The manuscript presents an innovative approach by augmenting spiking dynamics with state-space model dynamics, potentially enabling spiking models to tackle more challenging tasks that require capturing long-range temporal dependencies. This direction could be of significant interest in broadening the applications of spiking models in complex temporal tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The manuscript introduces SPLR, a spiking neural network model designed to capture long-range temporal relationships by integrating state-space dynamics with spiking neuron models and augmenting the HiPPO framework to handle spike-driven inputs. The proposed model reportedly achieves high performance comparable to other models on event-based datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "While the proposed method is intriguing and addresses the relevant challenge of enabling spiking models to capture long-term dependencies, the manuscript has several critical weaknesses. \n\nFirstly, the presentation lacks clarity, making it difficult to fully grasp how the method works, interpret the experimental results, or potentially reproduce the findings. Essential details expected in a research paper, such as a discussion of related works, are missing (e.g., [1]). Additionally, fundamental concepts necessary to understand the work are not well-introduced; although state-space models (SSMs) have gained popularity recently, they are not widely understood in machine learning, so a brief overview would be beneficial.\n\nThe manuscript also omits essential citations, including the original HiPPO framework, which is central to this work, and does not offer a proper explanation of how it functions. The equations are unclear; while convolutions are frequently mentioned, no equations illustrate how or where convolutions are applied. Variable definitions are sometimes confusing or incomplete; for instance, on line 187, $\\Delta t$ is described as the time difference between spikes $i$ and $j$, but it is unclear what $i$ and $j$ refer to in the context of the matrix $F_{ij}$, as it operates over the hidden state rather than directly on spikes.\n\nRegarding the experiments, the manuscript lacks details about the setup, hindering the interpretability of the results. For example, it’s unclear what “Sequential CIFAR-10” entails, such as the sequence length or frame generation process. Similarly, for the DVS Gesture dataset, it's ambiguous whether the processing was done for independent events or if events were accumulated into event frames.\n\n[1] Stan, MI., Rhodes, O. Learning long sequences in spiking neural networks. Sci Rep 14, 21957 (2024). https://doi.org/10.1038/s41598-024-71678-8" }, "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": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The authors propose SPLR which effectively captures long-range temporal dependencies, addressing limitations in traditional SNNs and enhancing temporal modeling capabilities for complex event-driven tasks.\n2. The experiments show good results. SPLR achieves both computational efficiency and scalability." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work introduces SPLR (Spiking Network for Learning Long-Range Relations), designed to efficiently capture long-range temporal dependencies while maintaining the hallmark efficiency of spike-driven architectures. SPLR integrates a state-space convolutional layer and a Spike-Aware HiPPO (SA-HiPPO) layer, addressing the limitations of conventional SNNs in complex temporal modeling. The SPLR convolutional layer leverages state-space dynamics to enhance feature extraction, capturing spatial and temporal complexities in event-driven data while preserving the efficiency of sparse spike-driven processing. The SA-HiPPO layer adapts the HiPPO framework to spike-based formats, enabling efficient long-term memory retention. Through dendrite-based spatiotemporal pooling and FFT-based convolution techniques, SPLR demonstrates scalability when processing high-resolution event streams and outperforms traditional methods across various event-driven tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The primary weakness of this paper lies in its writing, which significantly hinders clarity and understanding. A reorganization is recommended to improve readability and logical flow.\n\n1. Writing and Structure. For instance, Section 2 presents the SPLR components sequentially but lacks an overview that connects each part to the overall model structure, making it difficult for readers to understand how the parts interact. Section 3 is dense with theoretical content and proofs but does not clearly convey the main ideas, making it hard to follow the section’s intended focus.\n\n2. Lack of Citations. The paper frequently omits citations in crucial areas. For example, although modifications to the HiPPO framework are proposed, no supporting references are provided. Furthermore, DH-LIF is introduced without citation, and the reference for this component is missing from the bibliography, weakening the academic rigor of the paper.\n\n3. Confusions and Errors. There are several errors and confusions throughout the paper, such as the incorrect abbreviation of the Spiking State-Space Model as SPLR in line 855. Such errors further impact the readability and precision of the 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": 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. This paper only compares FLOPs vs. accuracy between the proposed SPLR and other models. Does SPLR have an advantage over other methods in terms of inference latency?\n2. The ablation studies only examine the effects of removing the dendrite attention layer and replacing SA-HiPPO with LIF. What if we replace NPLR decomposition and FFT convolution with standard convolution?" }, "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 well-written and technically solid. Each module in SPLR is introduced in detail and highlighted in different colors. This paper presents a detailed theoretical analysis, including the long-range dependency capability and stability of SPLR.\n2. The proposed SPLR model achieves competitive accuracy with less computational overhead than other state-of-the-art models on the Celex-HAR dataset." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a Spiking Network for Learning Long-Range Relations (SPLR). The proposed SPLR model comprises the dendrite attention layer, the Spike-Aware HiPPO (SA-HiPPO) layer, and the SPLR convolution layer. These modules enhance the long-range temporal dependency learning capability of SPLR. Experimental results demonstrate that SPLR outperforms prior methods in tasks requiring both fine-grained temporal dynamics and the retention of long-range dependencies." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The proposed SPLR model incorporates several non-spike operations, including the NPLR decomposition and FFT convolution. It makes SPLR a hybrid architecture instead of a pure spiking neural network. The hybrid nature may compromise its hardware compatibility and make it difficult to deploy on neuromorphic hardware." }, "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": "Please see weaknesses." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. This work explores the combination of Spiking Neural Networks (SNN) and State Space Models (SSM), which is an interesting direction. Using state-space methods to improve SNNs' ability to model long-term dependencies holds promise.\n2. The experimental comparisons in this work are extensive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work proposes a spike SSM method named Spiking Network for Learning Long-Range Relations (SPLR). The proposed SPLR convolutional layer leverages state-space dynamics to enhance feature extraction while retaining the efficiency of sparse, event-based\nprocessing, and incorporates a Spike-Aware HiPPO (SA-HiPPO) matrix that allows SPLR to effectively maintain long-range memory by adapting the HiPPO framework for discrete, spike-driven inputs. The authors tested their method on several datasets, such as Celex HAR, DVS128 Gesture, Sequential CIFAR-10/100" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "This work requires comprehensive improvements, with the main weaknesses outlined as follows.\n\n1. Writing: The writing in this work requires careful and comprehensive improvement, covering the overall organization of the paper, paragraph structure, and numerous details that need refinement. (1) The authors placed the related work section in the supplementary materials and omitted citations to many key works, which can confuse readers. For instance, the authors did not cite relevant papers when HIPPO was first mentioned (in fact, there are almost no citations in paragraphs 3 and 4 of the introduction). (2) The methodology section is not clearly explained. What type of spiking neurons does this work use? How does SPLR integrate with spiking neurons? The authors repeat content introduced in the main text within the supplementary materials. Lines 147-150 reiterate the significance of SPLR, but readers are likely more interested in the methodological details and the rationale behind the proposed approach’s significance. Unfortunately, these critical details are missing. (3) In the theoretical discussion, the authors present several theorems but do not clarify why these are necessary. While sparse properties and FLOPS reduction are mentioned, the evaluation details are not provided. \n(4) The supplementary materials contain excessive repetition and are overly lengthy, making it difficult for readers to stay engaged. (5) Overuse of Abbreviations: The excessive use of abbreviations makes the paper difficult to follow. For instance, I cannot understand why \"Spiking Network for Learning Long-Range Relations\" is abbreviated as \"SPLR\"—what does \"P\" represent here? Is there a difference between \"Spiking Network\" and \"Spiking Neural Networks (SNNs)\"? Additionally, what does HIPPO stand for? Shouldn’t the authors explain these abbreviations first? (6) Overstatements: The paper is filled with terms like \"spike-driven,\" \"asynchronous,\" and \"real-time.\" As I understand it, \"spike-driven\" implies a purely additive network[2], yet the pink section in Figure 1 seems unable to achieve this. Regarding \"asynchronous,\" the authors’ explanation in lines 92-95 is too brief, making it difficult to discern what kind of preprocessing the network applies to the data.\n\n2. Motivation. The authors repeatedly state that the proposed SPLE can address the challenge of modeling both short- and long-term dependencies in SNNs. However, they fail to analyze why SNNs have limitations in this area and why their proposed method can solve this issue. For instance, this is mentioned in lines 58-67, 147-149, and 1846-1850 without providing the necessary analysis.\n\n3. Innovation.The originality of this work is limited. The dendrite modeling directly uses DH-LIF, and the SSM modeling is simply an SNN combined with HIPPO. I did not observe any standout contributions in this approach.\n\n4. Experiments. The datasets chosen by the authors, DVS128 Gesture and Sequential CIFAR-10/100, do not effectively test the model's ability to handle long-range dependencies. The authors could consider more challenging datasets, such as LRA. Additionally, the fact that no one in the SNN field has addressed the Celex HAR dataset does not imply that SNNs cannot handle it. The authors have not even provided a complete description of the size and scope of the Celex HAR dataset. If the authors aim to compare with other SNNs on challenging DVS datasets, they might try HAR-DVS[1]. Furthermore, the authors overlook comparisons with many recent SOTA methods on the Gesture dataset, where SNN performance has already surpassed 99.5%[2].\n\n---\n[1] Hardvs: Revisiting human activity recognition with dynamic vision sensors. In AAAI 2024.\n[2] Spike-driven transformer. In NeurIPS 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": 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 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 integration of SA-HiPPO and SPLR convolution enhances the model's ability to model long-range dependencies.\n2. The SPLR model designed in this paper introduces a dendrite-based pooling layer, which further improves the performance using the DH-LIF neuron model.\n3. The theoretical and experimental results in this paper confirm the effectiveness of the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper integrates state-space models (SSMs) and spiking neural networks (SNNs), and proposes the Spiking Network for Learning Long-Range Relations (SPLR) to enhance the ability of SNNs to capture long-range dependencies. Theoretical proofs and experimental results support the performance advantages of SPLR." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The core innovation of this paper is the SPLR convolution layer to enhance the long-range temporal modeling capability of SNNs. However, while this improves the performance of the SNN, the integration of the SSM and the SNN requires significant computational overhead, which counteracts the power consumption advantage of the SNN. In addition, as the authors mention SPLR is difficult to implement in hardware, making the significance of this work seem small.\n\n2. Can the authors clarify the key differences between the DH-LIF model in this paper and the one presented in [1]? How does the DH-LIF used in the Dendrite Attention Layer relate to attention? Is the author's claim of dendrite-based spatio-temporal pooling just spatial pooling after the output of DH-LIF? Where is there temporal pooling?\n\n3. I suggest that the authors compare their method with other optimized spiking neural networks capable of long-range temporal modeling, such as [2,3,4].\n\n\n[1] Zheng H, Zheng Z, Hu R, et al. Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics. Nature Communications, 2024.\n\n[2] Wang L, Yu Z. Autaptic synaptic circuit enhances spatio-temporal predictive learning of spiking neural networks. ICML, 2024.\n\n[3] Zhang S, Yang Q, Ma C, et al. Tc-lif: A two-compartment spiking neuron model for long-term sequential modelling. AAAI, 2024.\n\n[4] Shen S, Zhao D, Shen G, et al. TIM: An efficient temporal interaction module for spiking transformer. IJCAI, 2024." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We introduce SPLR, a novel spiking neural network architecture that effectively captures long-range temporal dependencies through Spike-Aware HiPPO and dendrite attention." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024splr,\ntitle={{SPLR}: A Spiking Neural Network for Long-Range Temporal Dependency Learning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2Ez4dhU3NG},\nnote={under review}\n}" }, "abstract": { "value": "Spiking Neural Networks (SNNs) offer an efficient framework for processing event-driven data due to their sparse, spike-based communication, making them ideal for real-time tasks. However, their inability to capture long-range dependencies limits their effectiveness in complex temporal modeling. To address this challenge, we present a Spiking Network for Learning Long-Range Relations (SPLR). SPLR address the limitations of conventional spiking network in two ways. First, we introduce SPLR convolutional layer that leverages state-space dynamics to enhance feature extraction while retaining the efficiency of sparse, event-based processing. Second, we incorporate a Spike-Aware HiPPO (SA-HiPPO) matrix that allows it to effectively maintain long-range memory by adapting the HiPPO framework for discrete, spike-driven inputs. Together, the preceding novel spike-aware state space dynamics, enhance feature extraction while retaining the efficiency of sparse, event-based processing. Experimental results across various event-based datasets demonstrate that SPLR outperforms prior methods for processing event-driven data in tasks requiring both fine-grained temporal dynamics and the retention of long-range dependencies. This unified framework advances the state of event-based learning, providing a scalable and efficient solution for real-time applications such as event-based vision and sensor fusion in neuromorphic computing." }, "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": [ "spiking neural networks", "long range dependencies", "event data modelling", "hippo matrix", "state space 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/3cf405d9d336b2650e21dba19b01ad7cf0dd35e7.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/556ef2959af4a2573b142d34dd3ac5a7f2864284.zip" }, "title": { "value": "SPLR: A Spiking Neural Network for Long-Range Temporal Dependency Learning" }, "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": "- In Section 4.2, it is mentioned that the effect of high quality demonstrations is multiplicative on ICL error; however, other terms like next-token prediction accuracy and prediction smoothness have an additive effect. Intuitively, I don't follow why this would be the case. It seems to me the first factor in equation 13 (i.e., the factor with several additive terms) is merely assessing how well the model is at pretraining and modeling the distribution at hand, and the second factor (i.e., demonstration shift) assesses how good the demonstrations are at eliciting the desired capabilities. Is this the right interpretation? If not, can you better explain why the first term has additive influence of next-token error and prediction smoothness (which I would have expected to themselves have a multiplicative effect)?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The prime contribution from a theoretical standpoint in this paper is introduction of the user intent as a first-class citizen in theory. This helps accommodate phenomenology around experiments where alteration of the context lead to the same outputs---if the user intent remains the same, the model is likely to produce the same output. That said, I have some apprehensions on relation to past work and general presentation / experimentation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The current paper attempts development of a unified theoretical model of in-context learning that can help reconcile the incoherent empirical results seen in prior literature (e.g., the effect of data to label map randomization). To achieve this, the authors explicitly model the notion of \"intention\", i.e., the task the user wants the model to perform on a given datapoint, and assess what conditions lead to the inferred task from the model matching the intended task. This leads to a three-part decomposition of ICL error: (i) error of next-token prediction (essentially the autoregressive training loss); (ii) smoothness of predictions (how drastically they change if context is altered); and (iii) \"quality\" of demonstrations. All terms have intuitively expected effects and hence reconcile past empirical results." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- **Relation to past work.** A crucial missing reference seems to be Lin and Lee (\"Dual operating model of ICL\", ICML 2024). Therein, authors define a prior over tasks the model can solve and assess the effect of context size scaling to reconcile prior empirical results. It would help if authors can delineate how their contributions differ from that of Lin and Lee.\n\n- **Experiments.** I understand the paper is theoretically driven, but there are several papers on the theory of ICL at this point and it is unclear which theoretical framework is in fact correct. I hence encourage authors to take a prediction-centric perspective: what predictive claim does your theory offer, and can you demonstrate that said claim checks out experimentally? I am happy with an entirely synthetic experiment. The currently existing experiments suggest induction heads may be the mechanism for intention inference, but that claim is mostly speculative and is not well corroborated by the current induction head knockout experiments (by knocking out induction heads, you might be removing general ICL ability, and it is unclear if where the model is failing is inference of the correct intent). \n\n- **General presentation.** I found the writing quite convoluted in several parts of the paper. For example, the introduction has several typos and grammatical errors, and at times has unnecessarily complicated phrasing (e.g., \"Numerous outstanding works have revealed the enigmatic characteristics inherent to ICL\"). The citation commands are also incorrectly used---only \\citet was used, with no \\citep usage. If citations are to go in parentheses, then \\citep should be used." }, "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": "What is “n” in Table 1?\nHow does Table 1 “show that larger LLMs can capture the intention”? Isn’t the result just scaling?\nL1182 “group them into 2 to 5 categories” which ones? Can you provide more details or samples for the dataset preparation?\nF.2 do the induction heads identified here affect intent recognition in section F.1? I.e, if you “knock out” the heads then extract features, does the intent prediction performance degrade?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This work introduces a latent variable “intent” based model for understanding ICL. The model is a reasonable plausible model for ICL in LLMs, outlining the weak assumption used in the theoretical analysis. Based on the intent model, several theoretical results are given, including conditions for when ICL can emerge in LLMs, under the intent model. The model also provides theoretical understanding to explain the phenomenon of demonstration selection for ICL, and adapting to random label changes (or other task shifts) using ICL. \n\nThe paper provides some experimental confirmation of their intent model and theoretical analysis. The experiments show the performance of LLMs under task shifts which appear to support the analysis. Moreover, experiments that use (2-layer) probes to classify intents and isolation inductions heads for intents are included, which provide some justification for their model.\n\nThe idea of changing the value of isolated induction heads for intent and observing its effect on the LLM is interesting. The results appear to confirm the importance of the identified heads." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a latent variable model to study in-context learning (ICL) of large language models (LLM). It contributes a theoretical framework based on hidden “intentions” which are sampled during LLM generation. A hidden Markov model is used to model the overall generation process of tokens from an intent. The paper proves a novel no free-lunch theorem for the intent model which describes the conditions for when ICL emerges in an LLM (small next-token prediction error and prediction noise). In addition, the paper relates the selection of demonstrations for ICL to matching the original intent, and provides theoretical insights for the process. Empirically, the paper reports experiments on the ability of LLMs to adapt to randomized labels in-context, linear probing for intents, and identifying induction heads for intent inference." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The amount of detailed mathematical analysis in Sections 3 and 4 is dense and obscures the key take away messages from the theory. For example, after detailing the many assumptions for the intent model and deriving the no-lunch theorem in 4.2, the conclusion of the theorem appears to be “LLMs with weak capability in prediction the next token and non-qualified (?) demonstrations fail to exhibit ICL capability.” This is very well-known from empirical evidence (since GPT4), so it is not very surprising that the intent model, under reasonable assumptions, arrived at this result. As a key result of this paper, its relevance to a broader ICLR audience is unclear. One suggestion to the authors is to reconsider whether to keep all of the technical details in the main paper, or describe the main takeaways and the theorem, but move the rest into the appendix.\n\nThe paper lacks direct empirical confirmation of its theoretical findings. In 5.2 it states that “it is challenging to calculate or estimate these values (values predicted as necessary for ICL in Theorem 1)” hence indirect experiments must be done. This is a significant weakness for the theory, as it essentially cannot be experimentally confirmed or falsified. Can the values be estimated in a toy setting? \n\nThe intent recognition experiment is not totally convincing. It sets up an intent prediction task and uses features extracts from different layers of LLMs’, along with a 2-layer network to predict intent. Can this task be solved without using an intent model? Please consider including a baseline that plausibly does not implement an intent model. Details of the task setup are also missing. For example, what are some of the 50 intents? Are they instructions or tasks? How are train/test splits done?\n\nA lot of the content in the appendix is highly What is “n” in Table 1?\nHow does Table 1 “show that larger LLMs can capture the intention”? Isn’t the result just scaling?\nL1182 “group them into 2 to 5 categories” which ones? Can you provide more details or samples for the dataset preparation?\nF.2 do the induction heads identified here affect intent recognition in section F.1? I.e, if you “knock out” the heads then extract features, does the intent prediction performance degrade?\n\nrelevant to the paper. For example, Appendix D which discusses the theoretical and empirical challenges. Moreover, the experiments that actually try to confirm the plausibility of the intent model within real LLMs are in Appendix F. Please discuss these experiments in the main body of the paper, state their conclusions and how they support the theory.\n\nWriting of the paper needs significant editing and proofreading. \nJust a few examples:\nL076 “Introducing an external to modify” external what?\nL225 “error of next-token predicting”\nL375 “It shows that ICL” what is “it”? \nL385 “can be wrapped in the way” what does “wrapped” mean?\nL498 “GTP-4”, “achieves exciting performance” what does “exciting” mean?\nL501 “matrixes”" }, "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": "- Why is it called the no-free lunch theorem? No one expects ICL to emerge in models with high next-token prediction errors, or models to perform well on under-specified ICL tasks.\n\n- Why do we need to have a neighborhood of intentions, which are exactly modeled; compared to Xie et al’s exact intention which may have some modeling error (as is generally the case with all ML models).\n\n- What is the difference between Assumption 3 and 5?\n\n- Section 5.2: Why is it difficult to estimate next-token error of LLMs? And the results of this section don't mean much. Everyone would expect the ICL performance to go down with a more complex task. This does not imply that the model is performing inference of a complex intention as presented by the theory. There is no \"introduction of an external matrix T”, it is all in the theory. Where is the causal link that implies that the model is figuring out this new matrix T?\n\nIn all, I find it hard to justify this paper because the theory it presents does not make any verifiable new predictions that Xie et al did not already make, and in my opinion does not explain the previously unexplained phenomena like Min et al.\n\nI will increase the score if the paper is clearly rewritten. I know that this is a long paper and hard to put concisely in 10 pages, but in this sort of work, the paper would greatly benefit if some of the underspecified theory (which makes it hard to understand) is moved completely to the appendix, and some more readable results like the experiments are moved to the front. The distinction between the results presented in this paper and Xie et al are unclear which could have been greatly improved in the introduction section. These are just some of my personal suggestions." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The theory is very similar to Xie et al's Bayesian Inference theory with some modifications like neighbourhood of an intention, etc.\n- The authors provide an interpretable way to connect LLM performance on next work prediction and the quality of demonstrations to the performance on ICL tasks (under their theory), which is nice." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "A theory about In-Context Learning similar to Xie et al's Bayesian Inference theory. Aims to explain some characteristics of ICL noted but not explained by prior works, for example perturbations in the label space. Aims to break down the error in predictions to interpretable quantities like LLMs' performance, quality of demonstrations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Next token error is conditioned on \\theta in assumption 4. Even if the LLM infers intention, that would be solely determined by o_1:i-1, say \\theta_inferred. If the LLM is well trained, we can assume that it mostly infers the right intention and hence the condition with \\delta_4,1 can be satisfied. But in cases when it fails to infer the right intention, this error may be quite large. So the assumption is strong. Moreover, there is no way to get predictions from the LLM given the same context and some different intention \\theta_different, as the intention inference (if that is what happens in LLMs) is implicit and can not be disentangled. The LLM will always infer the same distribution over intentions given the same context, so I don’t understand assumption 4. \n\n- Like Xie et al, the no free lunch theorem in this paper does not explain task learning capabilities of LLMs, on completely novel tasks (\\theta not in the intention family) unrelated to pretraining text.\n\n- The whole external mapping thing does not make much sense to me. Users do not provide an external mapping when getting the model outputs; they directly present demonstrations with this transformation. If the LLM infers this mapping, it can only be implicit. Making it a part of the original intention family. It is hard to tell if a mapping like flipped labels is present in the intention family learnt by the model during pretraining. If the mapping is randomly generated, this becomes a contradiction as it is surely not present in the pretraining corpus. The authors say that they will explore this in future work, but it is an important point that makes Xie et al’s theory and this paper’s theory inconsistent with Min et al’s results where the model is able to infer the right intention with randomly generated labels. \n\n- Experiment section is too small and severely cut (defered to the appendix). Which model? What ICL task? It is an important part of the paper and needs to be put in the main text. Also, the evidence is circumstantial. Intervening on model activations can imply so many things related to completely different theories. How can we claim that these results imply anything specifically about the intention model? This also highlights the difference between theory and practice, as the presented theory does not elicit easily verifiable causal experiments.\n\n- The paper is very hard to read and follow. Like\n - section 3.3, should define \\delta_1,1, 1,2, 4,1, etc. What do forbidden tokens mean, what are forbidden transitions?\n - citations are placed very poorly. Sometimes before the sentence, sometimes after, sometimes unrelated; without proper usage of citet/citep. \n - [nitpicky] “Advanced works”: what is advanced, and compared to what? 
“fortunately consistent”: while good to know that the authors felt relieved that the method worked, it maybe inappropriate in a technical report. Some words feel too artificially placed like “enigmatic characteristics”.\n - “These intriguing phenomena highlight the difference between traditional learning and ICL Kossen et al. (2024). These seminal explorations provide a fruitful guide for understanding and explaining ICL.” These sentences don’t flow well. which works?\n\n - “a relatively weak connection to empirical investigations, potentially due to strong assumptions” [ICML 2024 paper](https://arxiv.org/abs/2310.08540) illustrates this and may be appropriately cited.\n\n - Line 77: “Introducing an external to modify …”, external what?\n - Line 116: definition of Sn can be confusing to read.\n - o is used for both delimiter and document tokens. confusing.\n - Line 297: \\theta_g is now called inferred intention, previously it was ground truth. confusing.\n - Line 292: where does m come from? What does it mean? Unclear.\n - Table 1 is referred to as Table 2 in the text.\n - Many more ...\n\n Although I don't believe in reducing review scores for small clarity and writing issues, this paper seriously suffers from them and hampers the readers ability to understand the concepts conveyed. I would recommend a clear rewrite with more help from experienced co-authors." }, "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": "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 theoretical analysis breaks away from the typical assumptions about perfect model alignment. It feels like it’s providing a more grounded explanation, making it easier to connect theory with the real behaviors of LLMs.\n\n2. The writing is generally clear, and the mathematical notation is thoroughly defined, which makes it easier for readers to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a theoretical framework called the \"intention model\" to explain ICL behaviors. The authors present a \"no-free-lunch\" theorem for ICL, showing that its emergence depends on prediction error, prediction noise, the model's smoothness, and demonstration quality. Unlike previous approaches with strong assumptions, this work relaxes the assumptions on perfect model alignment and demonstration representation. The intention model helps bridge the gap between theoretical explanations and empirical observations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper's theoretical framework largely follows the derivation approach from Xie et al. (2022), particularly leveraging Bayesian Inference. Although it extends the original work by adding an error term between the LLM and the real distribution, this extension doesn’t feel groundbreaking. The contribution seems more like an incremental step rather than a major theoretical innovation.\n\n2. The use of the term \"No Free Lunch\" for the presented theorem seems a bit off. The typical connotation of \"No Free Lunch\" is about the impossibility of a universal solution that works optimally in all scenarios. Here, the theorem implies that LLM performance depends on factors like prediction error, prediction noise, and demonstration quality. While there is indeed an implication of trade-offs, if the theorem isn’t emphasizing a broad, universal limitation but rather a specific condition for ICL, then this choice of terminology could easily confuse readers.\n\n3. The experimental section lacks clarity on how each of the theoretical components, particularly the terms in Equation (13), manifests in practice. It’s unclear how specific terms like \"error in predicting the next token,\" \"prediction smoothness,\" and \"distribution smoothness\" are reflected in the real experimental observations. This disconnect makes it difficult for readers to see how well the theory aligns with the empirical results, and it weakens the overall support for the claims made in the theoretical part." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose the intention model, a novel theoretical explanation for ICL." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024intention,\ntitle={Intention Model: A Novel Explanation for In-context Learning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2F7MFqATdo},\nnote={under review}\n}" }, "abstract": { "value": "In-context learning (ICL) has demonstrated remarkable success in enabling large language models (LLMs) to learn to do a downstream task by simply conditioning on a few input-output demonstrations. Distinct from traditional learning paradigms, ICL does not require model updates, thus attracting significant interest in understanding the mechanisms behind LLMs’ ICL capabilities. Advanced works aim to understand ICL through an empirical viewpoint to provide the multifaceted nature of ICL, while some works aim to explain how ICL can emerge theoretically. However, the current theoretical analysis exhibits a weak connection to empirical explorations due to strong assumptions, e.g., perfect LLMs and ideal demonstrations. This work proposes an intention model, providing a novel theoretical framework for explaining ICL. With mild assumptions, we present a ``no-free-lunch'' theorem for ICL: whether ICL emerges depends on the prediction error and prediction noise, which are determined by \\emph{\\textbf{i)}} LLMs' error of next-token prediction, \\emph{\\textbf{ii)}} LLMs' prediction smoothness, and \\emph{\\textbf{iii)}} the quality of demonstrations. Moreover, our intention model provides a novel explanation for the learning behavior of ICL under various input-output relations, e.g., learning with flipped labels. This is fortunately consistent with our experimental observations." }, "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": [ "In-context learning", "Large language 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/365556ed034f94a9c3293c4d2ec3361856709588.pdf" }, "presentation": null, "primary_area": { "value": "interpretability and explainable AI" }, "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": "Intention Model: A Novel Explanation for In-context Learning" }, "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": 4 }, "primary_area": null, "questions": { "value": "Please respond to my two concerns in the weakness part." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- This paper conducts an in-depth study of existing benchmarks and reveals biases regarding tree-shaped queries and union operators in several datasets." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This manuscript presents a data-level study of knowledge graph complex query answering. The main argument is that the query-target pairs in existing datasets (q, t) can be somehow reduced to the easier ones (sub(q), t) if the required triple can be found in training KG. Therefore, the paper proposes to focus on the irreducible query answer pairs and empirically examine that the performance of all existing methods will drop significantly. The facts revealed above motivate a search approach highlighted by letting the edges in the train graph be memorized. The performance of the approach is compared against previous works on old and new benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I have two concerns about the content discussed and the angle studied in this paper.\n\nFirstly, the content seems to be very old. I am not sure whether this paper has been recycled on a sufficiently long time, so the author is not aware of the recent progress in this field. \n1. The dataset discussed only covers the query types in [1], which is outdated today. In 2024, several datasets covering far more complex queries are also proposed, including [2] for queries with logical negation, [3] for cyclic queries, and [4] for multi-answer variables. For the ``fair'' split of triples, the answer is also unaware of existing studies on temporal CQA [5].\n2. The baselines discussed are also no later than the year 2023. ULTRAQ is almost the same as the GNN-QE.\n3. Given the above ignorance, the proposed CQD-hybrid method is fundamentally identical to the QTO [6] proposed in ICML'23. Those two methods are all search-based approaches that involve memorizing the train edges, which is proposed in this paper and also reflected in Equation 4 in [6], noticing that normalizing link predictor scores into [0,0.9] will not change the order of solutions. \n\nI prefer to recognize methodological identicality as unawareness rather than plagiarism. Therefore I didn't raise an ethical review flag.\n\n\nSecondly, saying that \"the existing benchmark is problematic\" is questionable and somehow self-contradictory with this paper's philosophy of choosing outdated simple queries. \n- On the one hand, scores on the previous benchmarks [1-5] are far from saturated because the average score is still less than 50 out of 100. Optimizing empirical models on previous benchmarks will also benefit the performance of the proposed \"hard\" benchmark. Meanwhile, recognizing the importance of training edges, although motivating the CQD-hybrid in this paper, is not new to the community because it is practiced in QTO [6] and later followed by FIT [3]. It hardly says why these findings are essential.\n- On the other hand, the paper only focuses on the simpler query forms proposed in [1]. One might argue that such simple query forms cover a sufficiently large portion of real-world user cases, so the choice of such forms is reasonable. The same practical point of view can also apply to the easy-hard contrast produced by whether the reasoning triples of a query are observed or not. Although the previous benchmark consists of too many observed triples, as shown in this paper, it can also be reasonable by arguing that the train graph consists of a sufficiently large portion of knowledge that users are interested in.\n\n\n\nReferences:\n\n[1] Ren, H., Hu, W., & Leskovec, J. (2020). Query2box: Reasoning over knowledge graphs in vector space using box embeddings. arXiv preprint arXiv:2002.05969.\n\n[2] Ren, H., & Leskovec, J. (2020). Beta embeddings for multi-hop logical reasoning in knowledge graphs. Advances in Neural Information Processing Systems, 33, 19716-19726.\n\n[3] Yin, H., Wang, Z., & Song, Y. (2023). Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors. arXiv preprint arXiv:2304.07063.\n\n[4] Yin, H., Wang, Z., Fei, W., & Song, Y. (2023). ${\\rm EFO} _k $-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation.\n\n[5] Lin, X., Xu, C., Zhou, G., Luo, H., Hu, T., Su, F., ... & Sun, M. (2024). TFLEX: temporal feature-logic embedding framework for complex reasoning over temporal knowledge graph. Advances in Neural Information Processing Systems, 36.\n\n[6] Bai, Y., Lv, X., Li, J., & Hou, L. (2023, July). Answering complex logical queries on knowledge graphs via query computation tree optimization. In International Conference on Machine Learning (pp. 1472-1491). PMLR." }, "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": 3 }, "primary_area": null, "questions": { "value": "The comparison of 2u-filter is dubious. As the definition of union query just requires one link to hold in the graph, I do not see the necessity to do such filtering as Figure A.1 as it more resembles 2i query type after filtering." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The key observation of this paper is interesting. The partial-inference pair is prevailing in existing datasets and the paper shows that full-inference pair is empirically much harder than partial-inference pair and thus the reasoning ability of SOTA CQA models may be less powerful than our imagination.\n\n2. This paper's case study and deep analysis are praiseworthy. For example, the paper studies the query type with union and additionally finds that if we filter out such pairs that can be accessed by just one link, the performance of 2u will increase significantly, similar to that of the 1p query type." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the complex query answering task on knowledge graph and questions whether the data in existing dataset is unqualified. To be specific, the author proposes to term those pairs of complex query & answers which the corresponding reasoning process can leverage some parts of the knowledge in the training graph as partial inference pair and thus evaluate existing CQA models on full-inference pairs. This paper conducts extensive experiments to showcase this observation and analysis on some certain query types like 2u." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Firstly, the discussion of the query type is constrained in this paper. Most dominantly, almost all researches conducted on complex query answering in recent years included negative queries yet this paper avoids that completely. Perhaps it's a drawback of their model design originating from the initial CQD paper, or perhaps the reasoning process defined in this paper fails in a negative query. Either way, it's problematic as the scope of the query type it investigated is strictly contained.\n\n2. The claim of SOTA CQA models fail significantly on so-called full-inference pair is questionable, as it doesn't include recent models that are built by symbolic search algorithms, like QTO[1] and FIT[2], which use neural link predictors combined with searching algorithms and seems to bypass the challenges proposed by full-inference pair. As the paper itself proposes a symbolic search method, the missing baselines in other symbolic search methods is questionable." }, "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. Do you vary your argument in train queries? I am wondering the phenomenon that existed CQA models fails is caused by the train datasets have too many partial inference answers. Thus I am curious about the performance of symbolic search methods where these methods don not use queries to train." }, "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 find a interesting weakness of existing CQA dataset and propose a useful method and benchmark.\n2. This paper is well written and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The hard answers studied in complex logical queries are those that cannot be retrieved due to gaps in the knowledge graph (KG). This paper reclassifies these hard answers into two categories: full-inference and partial-inference The authors argue that partial inference can be reduced into simpler query types and partial inference occupies the majority of existing datasets, BetaE. They discover that current models perform poorly on full inference tasks and propose a new benchmark to highlight this issue. Additionally, they introduce a new model specifically designed to tackle partial inference answers by explicitly retrieving existing links from the training knowledge graph." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The baselines lack of the symbolic methods like QTO and FIT, which are the mainstream of CQA methods. The used CQD is a old symbolic method.\n2. BetaE have three KGs but only two KGs are presented in the paper.\n3. The argument of 'reduced to easier types' is weird because query types with less constraint will be easy to solved than original query types, for example the performance of 3i is good than 2i. I suggest the authors use a preciser expression.\n4. I disagree your arguments that your proposed CQD-hybrid is the first an hybrid solver. QTO and FIT use the information from observed KG and trained link predictor to construct the matrix and can use the hybrid information of train edges and pre-training embeddings.\n5. Because of Weak 4, I am curious that the performance of symbolic method QTO and FIT as they already have the hybrid information." }, "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": "* The problem of information leakage in training graphs can be solved well by the inductive setting in naive knowledge graph reasoning (one-hop reasoning) task. Actually, there have been some attempts to establish inductive settings in CQA[1][2], where there will be no information leakage because the training and test graphs are different. How do you think this paper differs from these works?\n* In my opinion, link leaks in the training graph only affect the GNN based and neural link predictor based methods, while the embedding-based methods do not take advantage of the information in the training graph (except for 1p queries). Why does this type of approach also degrade on the new benchmark?\n* As mentioned in weakness, what's the difference between CQD-Hybrid and QTO?\n\n\n[1] Inductive Logical Query Answering in Knowledge Graphs. In NeruIPS 2022.\n\n[2] Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs. In IJCAI 2022." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* Motivation of the paper is novel, and the re-examination of existing benchmarks is valuable.\n* Experiments in this paper can support the conclusion well.\n* Writing of the paper is good, the structure is clear, the layout is good, and it is easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, authors re-examine the existing problems of knowledge graph complex reasoning datasets. The authors propose that the current dataset cannot effectively measure the generalization ability of the reasoning model, that is, the complex queries in the dataset can be solved by the triples leaked in the training graph, and verifies their conjecture through extensive and sufficient experiments. Further, the authors propose a new set of benchmarks to more effectively measure the performance of complex reasoning models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* Lack of discussion of related work, if the space is limited, this part can be placed in the appendix.\n* In Section 5.1, the author proposes a CQD-Hybrid solver. Actually, the practice described in the paper is very similar to the QTO [1] and I think the difference should be cited and discussed.\n* As an effort to propose new benchmarks, the experiments for the new benchmark are a little less. More baselines, some case analysis, etc., should be added.\n* Some typos, such as line.468: 50.000\n\n[1] Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization. In ICML2023" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024is,\ntitle={Is Complex Query Answering Really Complex?},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2FMdrDp3zI},\nnote={under review}\n}" }, "abstract": { "value": "Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA are not really complex, and the way they are built distorts our perception of progress in this field. For example, we find that in these benchmarks most queries (up to 98% for some query types) can be reduced to simpler problems, e.g., link prediction, where only one link needs to be predicted. The performance of state-of-the-art CQA models drops significantly when such models are evaluated on queries that cannot be reduced to easier types. Thus, we propose a set of more challenging benchmarks, composed of queries that require models to reason over multiple hops and better reflect the construction of real-world KGs. In a systematic empirical investigation, the new benchmarks show that current methods leave much to be desired from current CQA methods." }, "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": [ "complex query answering", "knowledge graph", "multi-hop reasoning" ] }, "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/01186d51b6656e1c5197229eddc62b1e044efec3.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": "Is Complex Query Answering Really Complex?" }, "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": "W1 - W5" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "S1. Semantic file systems enhance file management by incorporating content context, enabling more intuitive and effective operations, which is an important direction.\n\nS2. LSFS simplifies interactions with file system, making file management more accessible and user-friendly.\n\nS3. Integrating LLMs in system-level tasks expands functionality, enabling intelligent, responsive, and user-focused file extraction." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces an LLM-based Semantic File System (LSFS), designed to improve file management through natural language prompts, rather than traditional command-based interactions. LSFS integrates large language models (LLMs) to facilitate semantic file operations like retrieval, summarization, and rollback. At its core, LSFS uses a vector database to create semantic indexes for files, enabling high-level file operations that consider the content and context of files. It also includes a comprehensive set of APIs that allow complex operations, such as CRUD, grouping, and semantic retrieval, to be executed through natural language prompts. Experimental results show that LSFS outperforms traditional systems in retrieval accuracy (with a 15% improvement) and speed (2.1x faster), proving especially effective for semantic file tasks that go beyond conventional keyword searches." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "W1. The motivation is not concretely convincing, especially the first challenge mentioned in Introduction.\n\nIn the Intro section, the authors mentioned that \"For instance, if two files have similar content–such as different versions of the same document–traditional file systems lack the capability to organize or retrieve these files based on their content similarity.\" Why the files should be organized by the similarity of their content? What are the benefits and what are the practical application scenarios? It would be better to at least add one short discussion or a few examples. \n\nW2. This paper does not point out what key problem they want to solve. Compared to a research paper, it seems more like a technical report.\n\nW3. The experimental setting is questionable. No baselines and introduction of datasets.\n\nW4. The experimental results need more explanation. The traditional file system needs to execute commands to extract files, which should be faster than calling one LLM, even though these LLMs are light-weight. The authors should also list the inference time of LLMs, which should be also counted as the interaction time between users and the file system. Then the authors can also list the time that users manually write commands, which should be a good point to prove the point that -- LSFS can not only speed up file retrieving accuracy and speed, but also can reduce the interaction time between users and file system.\n\nW5. Safety insurance mechanisms is pointed out as one contribution, however, there is no description of this mechanism and no experimental comparison between the performance of LSFS with and without the safety insurance mechanisms." }, "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": "NA." }, "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. Intro (line 57) \"The community still lacks a more general LSFS to serve as a common foundation that can be used by various agents on the application-level.\" Do you have some references backing up this lack? I mean, have people expressed somewhere they'd like/need a FS organized by an intelligent agent, i.e., an LLM here?\n2. Related Work (line 133) \"Besides, it integrates comprehensive semantic information across all aspects of the file system–from storage and file operations to practical applications\" This sentence is a bit vague, could you name some of the semantic information here, so to guide the reader?\n3. Could you add a positioning sentence in §2.3 to explain clearly where LSFS delineates from this research axis?\n4. In this stage many operations from traditional FS aren't there, from what I understood, this is typically the case for right modification of files or group affiliation… These would be particularly helpful so to \"propagate\" these rights to any retrievers, preventing typically to see the private/exclusive file of someone else appearing in _my_ search results, wouldn't it?\n5. In §4,2 (line 294), the authors mentioned that supervisor updates \"periodically\" what is the period between each check and therefore how expensive resource-wise is it? Did the authors check various values for this, searching for the sweet-spot between resource-consumption and freshness of the LFSF data? Also how does it scale in terms of file number and disk footprint?\n6. Overall, §4.4 seems to be more or less an NL2CSV tool, filling fields of a JSON, right? In such case, this is something that the community has been exploring a lot these past two years, so maybe adding some pointers wouldn't hurt. This goes also for the §5.1 associated with RQ1.\n7. Are authors considering releasing their test data for §5.1? Also, it would be good to have some examples in the body of the article.\n8. In §5.2 why no QWen or Gemma in the experimental run for Table 2, and no Gemma in Figure 6?\n9. In §5.2 still, what about very large number of files?\n10. Ibid., same for the number of versions?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "+ Well motivated, especially through the Introduction.\n+ Clearly written\n+ Very nice figures\n+ Hot topic nowadays, with many LLM-based applications reshaping the ways we interact with machines" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this article, the authors based their efforts on the hypothesis that Large language models (LLMs) have the potential to improve file management systems by enabling interactions through natural language rather than traditional manual commands. Following this idea, they proposed LLM-based Semantic File System (LSFS) to address some of the current File System limitations (to the users), by allowing typically semantic file management through natural language prompts. LSFS has through some APIs for semantic file operations, achieves better retrieval accuracy and speed compared to traditional systems or the use of standalone LLMs respectively. It supports complex tasks like semantic file retrieval, rollback, and sharing with high success rates." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "### General Remarks\n- Even though the Introduction is clear, I'd have liked a more concrete / detailed example, maybe having a finer-grained figure 1 would help.\n- The balance between §3 Vs. §4 is unexpected, I would have imagined a more detailed architecture section (§3), explaining the various design choices. Instead, the authors motivated their architecture. In addition to not being referenced, this motivation -to me- would have been better positioned directly in the Introduction. Similarly, the overview of the architecture and the description of Figure 2 would have benefit also the introduction of an example, especially since Fig2.a doesn't contain precise information, but rather e.g. a list of blocks entitled API.\n- Overall, for §5, it would have been very interesting and convincing to see an experiment involving users' usage and performances, using a TFS and the presented LSFS. The authors could have reviewed the success rate and the time efficiency of users in both settings together with collecting feedback from them, following the traditional user studies.\n- Very disappointed to have the future directions (as Appendix H) not included at all in the main article, but instead, pushed as optional reading material at the very very end of the .pdf…\n- Finally, the overall article, from §3 onwards especially, reads a bit like a technical report and lacks -to me- a step-back to better highlight the novelties and the perspectives while guiding the reading with more examples / intuitions directly in the text.\n\n### Minor Comments:\n- Abstract \"agent applications **TO** perform file operations\"\n- Introduction (line 95), \"a LLM-based\" should be **an**\n- Table 1 (line 235), typo on \"Hybrid retrieval\" better to put everything lower-case as the rest of the table\n- In §4.1, in Composite Syscall of LSFS, it would be better if the authors could make explicit the composition of atomic calls, i.e. for each entry, adding a generic formula (or examples) of how the composite call is practically chaining the atomic ones.\n- In Figure 4, \"Please summary all paper from AAA University about LLM\" there's a typo in the second word: **summarize**.\n- Similarly, still in Figure 4, \"Please use file A update the content of file B\" misses the word **to** before 'update'.\n- In §5.2, (line 450), typo: \"vary the the number of rollback versions\" remove one **the**.\n- In §5.3, (line 478), \"Therefore, we make two enhanced versions, named as TFS-grep and TFS-grep* to make the comparison\" I would be great to tell there differences in a line instead of relying on the Appendix, so to make the article (before page 10) self-contained." }, "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. Your systems seems to have advantages for searching and retrieving files based on keyword or semantic search. This could be implemented on top of a conventional file system, why implement a new API for that?\n2. Is the accuracy of the LSFS parser of about 90% enough for meaningful work? That means 10% incorrect results. How did you collect the NLP commands for this evaluation?\n3. How exactly did you perform the evaluation summarized in table 2?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- very interesting and original setup\n- includes interesting examples how to use the file system" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose, implement and describe an LLM-based semantic file system, where commands are replaced by prompts. They describe APIs and in several experiments compare how this filesystem is used and performs, compared to a conventional file system." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- weak evaluation, based on examples and not on more extensive use cases and human evaluation, evaluation setup not described in detail / cannot be reproduced\n- unclear for which users this could be helpful\n- unclear how robust the system is when translating NLP input into actual actions\n- unclear how the new API maps and extends conventional file handling APIs, and why setting up a new API set is superior to adding some APIs to a conventional file system" }, "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) Are there guardrails in place to restrict the LLM to not scan through personal data?\n2) How much cost are we saving with the new architecture?\n3) Are there any security concerns for using this architecture?\n4) How much of an operational overhead is this architecture based on traditional architecture ?\n5) What are the other use cases of this architecture in real life scenarios?\n6) This seems like an ongoing problem that needs to be resolved; are there any similar existing architectures? Have you looked at those papers?\n7) Is there an Andon Cord mechanism to stop the LLM to give out hallucinations and wonky results to the user?\n8) While scanning through the files, is the data saved in memory? Does the data contain PII information (ppersonal information about the user)?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper touches on an existing problem that exists in the day-to-day lives of developers and Mac OS users of remembering the file names and directory where the files are present and need modification. There is no way to solve this problem at the present. Even while using LLMs, sometimes developers have to hard code the file path for retrieval. LLM based file retrival system is new and useful for anyone who is fed up of the traditional based systems.They did pretty well work on describing the APIs to be used in the new framework and a commendable job in comparing the APIs to the traditional ones. The quality of the paper was good and the presentation with diagrams were very useful to get the context of the paper.\nThe architecture of the new framework was explained in detail and they have done a good job in explaining how each component in the architecture is integrated with LLMs. Evaluations are carried out based on success, performance, and performance on non-semantic based tasks like file sharing over sample data/files and are pretty easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper represents a problem in the current scenario of semantic file matching algorithms; currently, we use the traditional way of semantic matching algorithms based on file name, size, and timestamps. This involved remembering syntax and filenames. This fails in scenarios where two files have similar text; here its hard to distinguish files based on pure string matching. The paper introduces LLM with traditional file systems to do LLM based semantic file management.\n\nLSFS extracts semantic features from the file content and generates corresponding embedding vectors. LSFS incorporates semantic information into its file operations. In Linux, if we need to change a file, i.e., replace a file with another, we need to remember the path, but with LSFS the users don't need to remember the file name and can talk to LLM to make the changes for them. They have introduced a LLM-based Semantic File System (LSFS), an LSFS parser, and safety insurance mechanisms to the traditional file matching algorithms. The paper has done a great job at explaining the traditional way and modifications done with NLP. They have elaborately explained the API changes they have made over traditional architecture and given diagrams to explain the architecture. Also, they have demonstrated how components of LSFS interact with each other to achieve different functionalities.\n\nEvaluations are carried out based on success, performance, and performance on non-semantic based tasks like file sharing over sample data/files." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper could have used an example to walkthrough the implementation. Each component description could have been presented with design diagrams or a flowchart that is easy to understand; visual representation always helps! More evaluations to prove their architecture is better than the traditional ones based on performance, latency, operational burden, and cost. The paper didn't touch on any security concerns while using the LLMS. Are there guardrails in place to restrict the LLMs to scanning through the personal data? One more thing the paper lacked was elaborating on the use cases where this architecture can be used." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a LLM-based semantic file system" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024from,\ntitle={From Commands to Prompts: {LLM}-based Semantic File System},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2G021ZqUEZ},\nnote={under review}\n}" }, "abstract": { "value": "Large language models (LLMs) have demonstrated significant potential in the development of intelligent LLM-based agents. However, when users use these agent applications perform file operations, their interaction with the file system still remains the traditional paradigm: reliant on manual navigation through precise commands. This paradigm poses a bottleneck to the usability of these systems as users are required to navigate complex folder hierarchies and remember cryptic file names. To address this limitation, we propose an LLM-based Semantic File System ( LSFS) for prompt-driven file management. Unlike conventional approaches, LSFS incorporates LLMs to enable users or agents to interact with files through natural language prompts, facilitating semantic file management. At the macro-level, we develop a comprehensive API set to achieve semantic file management functionalities, such as semantic file retrieval, file update summarization, and semantic file rollback). At the micro-level, we store files by constructing semantic indexes for them, design and implement syscalls of different semantic operations, e.g., CRUD (create, read, update, delete), group by, join. Our experiments show that LSFS can achieve at least 15% retrieval accuracy improvement with 2.1× higher retrieval speed in the semantic file retrieval task compared with the traditional file system. In the traditional keyword-based file retrieval task (i.e., retrieving by string-matching), LSFS also performs stably well, i.e., over 89% F1-score with improved usability, especially when the keyword conditions become more complex. Additionally, LSFS supports more advanced file management operations, i.e., semantic file rollback and file sharing and achieves 100% success rates in these tasks, further suggesting the capability of LSFS. The code is available at https://anonymous.4open.science/r/LSFS-8CCF/." }, "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", "Semantic File System" ] }, "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/1dbf1e3e06b676078d47623aa8624498426b1171.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": "From Commands to Prompts: LLM-based Semantic File System" }, "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": "**(Q1) Trade-offs Analysis and Discussions:** \nThe paper's analysis of various trade-offs deserves deeper exploration. The proposed SFCNN shows great superiority in speed. However, I have noticed that some architectures like MogaNet show better parameter efficiency while at lower speeds. Thus, a more detailed investigation of parameter efficiency versus computational speed would provide valuable insights for practitioners choosing between different model configurations. Moreover, there are several points that are tightly associated with this work that deserve further exploration: First, the memory-compute trade-off analysis could be expanded to include different hardware scenarios and deployment conditions. Second, the relationship between training efficiency and inference efficiency deserves more attention, since these can often have different optimal choices. Third, the model scaling properties, particularly regarding the relationship between model depth and width at different computational budgets. \n\n**(Q2) Broader Architecture Considerations:**\nThe scope of this paper lies in ConvNets in vision tasks. However, there are more kinds of architecture emerged these years. A thorough comparison with emerging architectures like Vision Mamba and RWKV models would provide valuable context for the field's evolution. Besides, there are various efficient computation techniques proposed to boost the computational efficiency of these new architectures. The evaluation against attention-based alternatives could provide insights into the relative strengths and weaknesses of different vision backbone architectures. These expanded analyses and discussions would significantly strengthen the soundness and contribution of this paper and provide valuable guidance for future research in the community.\n\n---\n**Additional Comment:**\n\nI hope my review helps to further strengthen this paper and helps the authors, fellow reviewers, and Area Chairs understand the basis of my recommendation. I also look forward to the rebuttal feedback and further discussions, and would be glad to raise my rating if thoughtful responses and improvements are provided." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "**(S1) A critical research question with real-world significance:**\nThe paper shows great industrial relevance by addressing a critical need in real-world deployment scenarios, in which computational efficiency is crucial, particularly in edge devices and mobile applications. The proposed SFCNN is notably cost-effective, providing a more resource-efficient alternative to existing approaches while maintaining or improving performance metrics. The presented thin-and-deep architecture appears to show great scalability, demonstrating computational efficiency across different model sizes, and making it highly adaptable to various resource constraints. From a practical impact perspective, this work has the potential to significantly reduce infrastructure costs for computer vision applications at scale, making it valuable for industrial applications rather than merely pushing the limit of accuracy metrics.\n\n**(S2) Thorough experiments and validation:** \nExtensive experiments are conducted on multiple mainstream computer vision tasks, such as ImageNet-1K classification, COCO detection, and ADE20K semantic segmentation. The consistency of performance across different scales is noteworthy. Ablation studies are also conducted, providing a detailed analysis of the contribution of each component to the overall performance. More importantly, the authors present a clear demonstration of the impact of model depth vs. width, supported by the evaluation of different activation functions and receptive field analysis. Hardware performance evaluation is particularly thorough, encompassing cross-platform testing on GPU, TensorRT, and iPhone, with detailed latency and throughput measurements under various scenarios. All these experiments strongly support the paper’s claim." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper targets the computational inefficiency and hardware compatibility issues of recent ConvNets that rely on large kernels to capture long-range dependencies. The authors propose a new ConvNet architecture SFCNN for vision tasks, which has shown impressive performance through a thin-and-deep design philosophy. Concretely, it combines a dual 3×3 depth-wise convolutions branch with Global Sigmoid Linear Unit (GSiLU) activation, which captures both local and global dependencies without large kernels. The proposed SFCNN is evaluated on mainstream vision benchmarks, such as ImageNet-1K classification, COCO instance segmentation, and ADE20K semantic segmentation, demonstrating great performance while maintaining better hardware efficiency across different platforms (GPU, TensorRT, iPhone). The experiments seem to strongly support the claims about achieving better accuracy-efficiency trade-offs compared to existing ConvNets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**(W1) Technical Originality:** \nThe basic building blocks of SFCNN, including 3×3 depth-wise convolutions and point-wise convolutions, largely rely on well-established techniques without significant technical originality. The GSiLU activation also bears considerable similarity to existing approaches like CBAM and SE modules. The thin-and-deep philosophy, while effectively implemented, has been explored in previous works. The theoretical foundation could be strengthened significantly, as it currently lacks enough theoretical insights into the nature of convolution operations and their relationships with model depth. A more thorough analysis of the relationship between depth and receptive field would strengthen the paper's contributions.\n\n**(W2) Technical Soundness & Empirical Analysis:**\nWhile mobile testing is included, more empirical analysis could benefit this work and improve its technical soundness. For example, the Grad-CAM heat map visualization and training dynamics investigation would provide insightful and straightforward support for understanding the technical strengths of SFCNN. Moreover, the discussion of failure cases and limitations is inadequate, potentially leaving practitioners without clear guidance on the architecture's boundaries. The exploration of model behavior under extreme resource constraints could provide valuable insights for edge deployment scenarios. I strongly recommend that the authors carry out more empirical analyses that lead to more systematic conclusions for efficient ConvNet design. The thin-and-deep design philosophy is inspiring but not specific and systematic enough. Also, this work first tries stacking multiple depth-wise convolutions in a single block rather than just one. How it works for better representation capacity is still worth digging deep.\n\n**(W3) Presentation Clarity and Details:**\nThe writing organization exhibits several points that require further improvement. The technical content sometimes lacks coherence, with important methodological details scattered across different sections rather than presented in a unified manner. The description of the architecture could benefit from a more structured approach, particularly in explaining the interaction between different components. Several key concepts are offered within dense paragraphs, making it challenging for readers to extract crucial implementation details. In addition, the method description, while comprehensive, could be reorganized to better highlight the progressive development of ideas and design choices. Moreover, the tables of experimental results presentation would benefit from highlighting the performance advantages. The formatting consistency across tables and figures needs attention, with some inconsistencies in style and presentation detracting from the overall appearance. For example, the thickness of table lines is inconsistent. I recommend the authors to first go through the entire manuscript for a thorough refinement." }, "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 above my main concerns." }, "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 performed experiments of the proposed approach on several visual recognition tasks.\n2. The architecture presents good results in comparison with other approaches." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a CNN architecture for visual recognition. The main contribution is a small CNN architecture called Simple and Fast CNN with a core idea of stacking 3x3 convolutions to design a deep architecture. The work proposes an inverted residual bottleneck with two 3x3 depth-wise convolutions. Also, this paper proposes a Global Sigmoid Linear Unit activation function to capture global information." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The main concern is regarding the lack of novelty. The proposed contributions are already well known and explored in literature for quite a long time. For instance, using a sequence of stacked 3x3 convolutions to enlarge the receptive field is an approach deeply explored in computer vision community for many years (ResNets, VGG-Nets, MobileNets, etc). Depth-wise convolutions are also explored intensively for efficiency gains (Xception, MobileNet, etc). Including the proposed Global Sigmoid Linear Unit is just a form of the existing work (already quite old) Squeeze-and-Excitation Networks. After reading this work I could not find anything novel or some new insight that is not already known to the vision community.\n2. Besides lack of novelty, this work does not compensate with some new experimental findings or some new insights for practitioners. \n\nOverall, I find the contributions of this work to be quite limited to qualify for publishing the work at such high venue. Maybe a workshop contribution can be more appropriate." }, "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 contributions of this paper should be further explained.\n\n- More analysis on the advantages of using multiple small-kernel convolutions should be elaborated more.\n\n- The motivation of introducing global information in activation functions should be made clearer." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The motivation of this paper is clear. Using small-kernel convolutions may lead to faster inference but makes the receptive field of CNNs not large enough to capture the target objects. This paper presents to add more 3x3 convolutions to enlarge the receptive field of the proposed network.\n\n- This paper receives better trade-off between model classification performance and latency. Compared to recent CNN-based models, the proposed method has compact model architecture but better performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a new convolutional neural network architecture, called SFCNN. Unlike recent popular CNN works that mainly aim to explore how to better take advantage of large-kernel convolutions, this paper explains that using thin but deep network architecture with only 3x3 convolutions can still achieve good results. In addition, the authors also rethink of the design of the SiLU activation function and propose a new one, which involves in global information based on SiLU. Experiments show that the classification performance on ImageNet is better than most previous CNN-based models. In terms of latency, the proposed approach achieves better results as well." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The authors claim that large receptive field is important for CNNs. In Fig. 3, it is shown that the proposed approach has large effective receptive field. However, it is not as large as the one of UniRepLKNet. According to the numerical results on ImageNet, the proposed approach gets better numbers. Does this mean that large effective RF is not an important measurement for building CNNs?\n\n- The authors claim that their bottleneck block design with two 3 × 3 DWConvs is novel. However, as far as I know, adding two depthwise convolutions in a single basic block has been explored before, e.g., MobileNeXt (ECCV'20, Zhou et al.). Though the order of the layers is a bit different, the design intention is similar. So, I do not think this can be viewed as a core contribution for this paper.\n\n- From the paper, it seems that CNNs with thin but deep architecture and small kernel convolutions perform more efficient than those with large kernels. However, the macro model architecture of the proposed method is not actually the same to previous large-kernel CNNs. I think the authors should conduct more specific experiments to demonstrate this.\n\n- In Table 5, it is good to see the results on instance segmentation but the methods the authors compare with are not new already. I have no idea why the results of the recent published works on CNNs are not reported here.\n\n- It seems that the 7th table has two captions? Where is Table 8?\n\n- From the ablation results, it seems that the proposed GSiLU indeed performs better than other activation functions. However, have the authors analyzed why global information should be added into activation functions? The motivation of designing such an activation function is not clear. In addition, as GSiLU is already used, why the original SiLU is still used?" }, "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": "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": 4 }, "primary_area": null, "questions": { "value": "1. Minor performance gain vs large variance in different architecture hyperparams. This deserves a deep discussion.\n\n2. Due to the nature of carefully manually crafted CNN (which may be overfitted on IN1k), I am wondering how the architecture perform on IN22k-pretraining + IN1k-finetuning? \n\n*This is not a must-do due to the training cost. However, if this is provided, my concern on the performance perspective can be alleviated." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The paper writing is clear, including the motivation, main results summary, architecture design, main results, architecture ablations. All these necessary components are easy to find and comprehend.\n\n2. The experiments are adequate for a traditional architecture design work, including main results on 1N1k, coco and ADE20k. There are also component contribution ablations and architecture variant." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper is a very traditional architecture design paper. The major motivation of this work is: large kernel vs small kernel. This debate has been in the community since 10 AlexNet. In VGG, they change the large kernel from 7x7 to a stack of 3x3. Recent years observe a reverse trend that moving back to extremely large kernels. In this work, the authors argue again to use stack of small kernels, due to \"Nevertheless, these approaches are unfriendly to hardware, imposing a serious computation burden on training or inference\"\n\nBased on this motivation, the authors carefully craft a new architecture, SFCNN. The architecture shows minor performance gain on IN1k (\"+0.1% accuracy compared to SwiftFormer (Shaker et al., 2023) with 87% FLOPs\"), as well as downstream tasks like COCO and ADE20k." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The motivation / idea of this work is not new (from large kernels to a stack of smaller kernels.)\n\nThis idea dates back to VGG (2014). Authors can refer to sec 2.3 in the paper for more discussions. Placing this as the main motivation largely harm the overall contribution, because this makes the paper more like a revisit / conversation in the debate.\n\n2. Minor performance gain vs large variance in different architecture hyperparams.\n\nThe performance gain over SOTA models is minor, compared with performance variance in similar architectures with different hyperparams. As shown in Table9, searching a best setup for 1N1k is critical (min 81.3 vs max 82.6), while the performance gain over sota is only 0.x% level. This is also reflected in Table 8.\n\nI deeply appreciate the efforts in searching a best setup for the architecture. However, this makes the major performance contribution more in the \"searching\" part but no in the architecture itself. Currently, due to the development of NAS, such searching efforts can be largely automated.\n\n3. (Minor) Table 7 and 8 are mixed together in the manuscript. It is confused." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024simple,\ntitle={Simple and Fast {CNN} for Vision},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2GEiBzs2Do},\nnote={under review}\n}" }, "abstract": { "value": "Traditional Convolutional Neural Networks (CNNs) tend to use $3\\times 3$ small kernels, but can only capture limited neighboring spatial information. \nInspired by the success of Vision Transformers (ViTs) in capturing long-range visual dependencies, recent CNNs have reached a consensus on utilizing large kernel convolutions (e.g., astonishingly, 111 kernel). \nNevertheless, these approaches are unfriendly to hardware, imposing a serious computation burden on training or inference. \nThis paper introduces a Simple and Fast Convolutional Neural Network (SFCNN) that employs a sequence of stacked $3\\times 3$ convolutions but surpasses state-of-the-art CNNs with larger kernels. \nIn particular, we build a thin and deep model, which encourages more $3\\times 3$ convolutions to capture more spatial information under the limited computing complexity rather than opting for a heavier and shallower architecture. \nTo further enlarge the receptive field, we redesign the traditional inverted residual bottleneck with two $3\\times 3$ depthwise convolutions. \nIn addition, we propose a novel Global Sigmoid Linear Unit (GSiLU) activation function to capture global coarse-grained spatial information. \nOur SFCNN performs better than state-of-the-art CNNs and ViTs on various tasks, including ImageNet-1K image classification, COCO instance segmentation, and ADE20K semantic segmentation. \nIt also has good scalability and outperforms existing state-of-the-art lightweight models. \nAll materials containing codes and logs have been included in the supplementary materials." }, "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": [ "Convolutional Neural Network", "Vision Backbone", "Lightweight", "Fast" ] }, "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/df8b9b262f2ddcf33a04d6b357e180257be5df07.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/cb0f2474d3b5189982c0699b7466aeb248f3f290.zip" }, "title": { "value": "Simple and Fast CNN for Vision" }, "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": "1. What frequency range is the spec-magnitude attack being applied over in Figure 8a?\n1. Do you have any _formal_ explanation for why the time-domain attack is less successful than the spec-magnitude attack? To me this seems counter-intuitive because the STFT is a linear and (mostly) invertible function so, from the perspective of the optimization, it should not matter if the attack was computed in the frequency domain or the time domain. I would be very interested in seeing more explanation for why the time-domain attack is unable to reach the solution acheived by the frequency-domain attack. Please also provide the detailed settings for all the adversarial attacks (time, frequency and phase domain) used in AT, F-SAT and during evaluation.\n1.In principle, a frequency selective adversarial attack could be constructed entirely in the time domain by applying a band-pass filter to the adversarial perturbation after each optimization step (i.e. include the BP filter as part of the projection operation). This might be less computationally intensive than the proposed approach. Can you provide some discussion on why the proposed approach was favored?\n1. Why is the performance of the model trained on DeepFakeVox-HQ so low on the In-the-wild dataset (see Figure 3)" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Paper is generally well-written and easy to read but some important details are missing\n1. DeepFakeVox-HQ is a novel dataset containing data from prior datasets as well as novel deepfakes generated from SOTA speech synthesis models. I appreciate that the authors have curated a test set containing deepfake generation methods not covered in the training set _as well as deepfakes gathered from the internet_. I encourage the authors to consider uploading the dataset to a platform like Huggingface Hub.\n1. The proposed randaugment data augmentation method is effective at improving deepfake detection for RawNet3 models and is likely to be widely adopted if the source code is easy to use (I looked at the README in the attached supplemental material but did not find any instructions for the augmentation).\n1. The proposed adversarial training method improves deepfake detection accuracy on clean and adversarially perturbed recordings (though I have some reservations regarding the experimental setup)." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper attempts to improve deepfake detection by (1) proposing a large training and evaluation dataset called DeepFakeVox-HQ containing diverse synthetic and real speech recordings, (2) proposing a data augmentation method similar to randaugment for deepfake detectors and (3) proposing a frequency-selective adversarial training (F-SAT) method to make deepfake detectors more robust to adversarial attacks. \n\nDeepFakeVox-HQ is a large dataset containing real and synthetic speech from existing datasets, speech generated by SOTA speech synthesis models as well as deepfakes found in-the-wild (on social media, etc.). Results show that models trained on DeepFakeVox-HQ generally perform better on existing deepfake while the models trained on the existing datasets have weak performance on DeepFakeVox-HQ, which indicates that DeepFakeVox-HQ includes information that prior works do not provide. DeepFakeVox-HQ will likely be a useful resource in deepfake research. \n\nThe proposed RandAugment scheme for deepfake detection utilizes a large bank of audio augmentations during training and yields significant improvements in deepfake detection accuracy.\n\nThe key contribution of F-SAT is to add adversarial perturbations to only certain frequency bands, which apparently results in lesser degradation of accuracy on un-perturbed data while providing greater robustness than standard adversarial training." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Some important details about the proposed approaches are not mentioned in the paper. \n \n 1. The value of $\\epsilon$ and $p$ (or $q$) used in adversarial training methods should be mentioned in the main body of the paper. Currently, it is mentioned in the caption of a table in the appendix\n 1. The settings used for adversarial attacks during AT, F-SAT and evaluation need to be mentioned.\n 1. The parameters of the augmentations used in randaugment need to be mentioned at least in the appendix.\n 1. Detailed composition of DeepFakeVox-HQ needs to be mentioned including \n\n 1. the method used for generating deepfakes (particularly noisy deepfakes), \n 1. the number of audios from each deepfake generation system, \n 1. the demographic distribution of the real and fake speakers,\n 1. the number of utterances used from each of the datasets from prior works, \n 1. quantitative measurement of synthetic speech quality using metrics like DNSMOS, or NORESQA.\n\n1. The choice of accuracy as a metric seems to be inappropriate for a binary classification task. I would suggest using F1-score and equal error rate as the metrics. Moreover, reading tables and plots with two accuracy metrics for accuracy is a little confusing.\n1. Conducting adversarial attacks in the frequency domain and reverting to the temporal domain is not novel and has been done before [2].\n1. There is no comparison with other adversarial defenses for audio models. Many of the defenses created for speech and speaker recognition will also apply to the deepfake detection scenario. One method that is quite simple is [3]\n1. The common practice is to use signal-to-noise ratio (SNR) as the bound for adversarial attacks in the audio domain [1] instead of $\\ell_p$ bounds. I would highly recommend the authors use SNR as well. It is fairly straightforward to convert SNR to $\\ell_2$ bounds and vice-versa. The main advantage of using SNR is that one has an idea how _perceptible_ the adversarial attack is.\n\n1. Clarity issues:\n 1. The caption Figure 9 needs to state that the results are of F-SAT\n 1. Add results for 0-8K in Figure 8b \n\n\n[1] Carlini, Nicholas, and David Wagner. \"Audio adversarial examples: Targeted attacks on speech-to-text.\" 2018 IEEE security and privacy workshops (SPW). IEEE, 2018. \n\n[2] Koerich, Karl Michel, et al. \"Cross-representation transferability of adversarial attacks: From spectrograms to audio waveforms.\" 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020.\n\n[3] Olivier, Raphael, Bhiksha Raj, and Muhammad Shah. \"High-frequency adversarial defense for speech and audio.\" ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021." }, "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.Given that F-SAT focuses on high-frequency perturbations, have the authors considered whether these perturbations might be perceptible to human listeners? \n\n\n\n\n2. Were all baseline models subjected to similar adversarial training procedures as the proposed F-SAT model? Consistency in adversarial training across baseline models is essential to ensure a fair comparison of robustness improvements. If not, would the authors consider including adversarially trained baselines in future comparisons?\n\n3 . How sensitive is F-SAT to the choice of hyperparameters, particularly the frequency ranges and perturbation magnitudes used for adversarial training?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.DeepFakeVox-HQ stands out as a substantial addition to the field, with over 1.3 million samples, including 270,000 high-quality deepfake samples from 14 sources. This dataset addresses the limitations of existing datasets in diversity and scale, making it a valuable resource for benchmarking future detection models. Releasing this dataset would have a broad impact on the community.\n\n2.The F-SAT method is an important innovation, targeting high-frequency features that are critical for detection but vulnerable to adversarial manipulation. This frequency-focused adversarial training enhances model robustness without compromising accuracy on clean data, addressing a key gap in existing deepfake detection methods.\n\n3.Comprehensive Experimental Evaluation: \n The experimental design is extensive, evaluating performance across standard benchmarks (ASVspoof2019 and WaveFake) as well as the authors' own test dataset. F-SAT demonstrates clear improvements in robustness across multiple corruption and adversarial attack scenarios. The addition of an ablation study further supports the effectiveness of the proposed method.\n\n4. Extending RandAugment from image processing to audio is an inventive adaptation that helps improve model robustness on both clean and corrupted audio. This demonstrates the authors' resourcefulness in leveraging existing techniques and could be beneficial for future work in audio data augmentation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the challenge of deepfake audio detection, presenting two major contributions: (1) the creation of DeepFakeVox-HQ, the largest and most diverse public dataset for deepfake audio detection, which enables realistic testing conditions and exposes limitations in existing models, and (2) the introduction of Frequency-Selective Adversarial Training (F-SAT), a novel approach that improves detection robustness by focusing on high-frequency audio components. The work is well-written and logically structured, making complex concepts accessible, and holds significant potential for advancing the robustness and reliability of deepfake audio detection models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper does not specify whether baseline models were subjected to adversarial training. If only the F-SAT model received this enhancement, it could bias the results. Including adversarially-trained versions of baseline models using contemporary adversarial methods would provide a fairer comparison and highlight F-SAT’s unique advantages.\n\n2. While F-SAT’s focus on high-frequency components is intriguing, the rationale behind the reliance on high frequencies for detecting deepfake audio could be further elaborated. \n\n3.Adversarial training, especially in the frequency domain with iterative updates, can be computationally demanding. Assessing F-SAT's efficiency, particularly compared to baseline models, would improve the paper's practicality." }, "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": "My main concern is about the generalization and robustness issues listed above.\nI will consider changing my score based on the author's responses and other reviewers." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "Three main contributions involved in this work include (1) a carefully organized dataset, (2) a deepfake detection method, and (3) the ability against adversarial attacks (with the setting focusing on high-frequency signals).\nIn general, the contributions of this work are multi-fold." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Glad to review the paper.\nThis paper proposes a novel method, F-SAT for deepfake audio detection.\nThe topic of this work is promising, and the paper is easy to follow.\nI believe this work has reference values to domain-related researchers." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "My major concern is whether the contributions (or advantages) of this work are over-claimed.\nRegarding the dataset, although it is well organized and processed, the samples are generated using existing approaches, thus, \"the largest\" is not a significant contribution.\nRegarding generalization, as in Table 2, the significantly superior results of the proposed method are achieved on the self-organized dataset, DeepFake Vox-HQ. However, as the author introduced in Section 3, there are overlapped synthesis methods between training and testing data in this group of results (as in Figure 6). Thus the results in DeepFake Vox-HQ can not indicate out-of-distribution generalization.\nRegarding enhancing robustness, in the last paragraph of the related section, the referenced solutions were published in 2019,2018 and 2018, I am not sure whether any recent works focus on the adversarial issue, that should be discussed or compared." }, "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": "- Which model was used to create the plot on the left side in Figure 2?\n- How many hours of speech does the dataset encompass exactly? How long are the samples?\n- Are the samples aligned? Do all models synthesize speech using the same input sentences?\n- Is it possible to add a data sheet that outlines the exact sources and utterance lengths per source?\n- Are the WaveFake test samples also part of the DeefFakeVox-HQ test set?\n- WaveFake contains Japanese language JSUT samples.\n - Are these part of the dataset?\n - Should the Caption of Table 1 make this explicit? Since WaveFake is listed as \n English-language data set, I assume JSUT is not considered a part of WaveFake in this paper.\n - Do the Utterance numbers in table one exclude JSUT?\n - If yes, should this be mentioned somewhere else?\n\n- Is it possible to include leading works from the audio classification world, like the Audio Spectrogram Transformer (AST)[1], in the evaluations? Related work [2] found it to perform well on the WaveFake-dataset. It would be interesting if it also outperforms other methods on DeepFakeVox-HQ.\n\n- The Wavefake paper [3] trains with binary settings with fake audio from a single source and measures generalization. Training on which source network led to the numbers in Table 2? Are the numbers comparable to the related work?\n\n- Which software libraries have been used to implement this project?\n\n- Which hyperparameters underpin the network training?\n\nRelated work:\n[1] AST: Audio Spectrogram Transformer, https://arxiv.org/pdf/2104.01778,\n[2] Towards generalizing deep-audio fake detection networks, https://arxiv.org/pdf/2305.13033,\n[3] WaveFake: A Data Set to Facilitate Audio Deepfake Detection, https://arxiv.org/abs/2111.02813" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper tackles a significant problem.\n- The related work is well-researched and described.\n- The adversarial attack perspective is interesting.\n- Authors ensure their results are up to date, combining existing datasets with samples from commercial models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper \"I CAN HEAR YOU: SELECTIVE ROBUST TRAINING FOR DEEPFAKE AUDIO DETECTION\"\nintroduces the DeepFakeVox-HQ data set, which contains audio from 14 sources.\nIn addition to the dataset, the authors introduce Frequency-Selective Adversarial Training (F-SAT), a training method that focuses on the high-frequency part of the spectrum. In addition to FSAT, this paper evaluates robustness concerning various input perturbations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Traditional compression algorithms like MP3 remove high-frequency content; according to line 84, FSAT focuses on this part of the spectrum.\n- If I understand correctly, compression is not part of the corruption set, as shown in Figure 7. Including compression would have been important for real-world applicability.\n- Data-set details like the length in hours or training hyperparameters like the learning rate are missing." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024i,\ntitle={I Can Hear You: Selective Robust Training for Deepfake Audio Detection},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2GcR9bO620},\nnote={under review}\n}" }, "abstract": { "value": "Recent advances in AI-generated voices have intensified the challenge of detecting deepfake audio, posing risks for scams and the spread of disinformation. To tackle this issue, we establish the largest public voice dataset to date, named DeepFakeVox-HQ, comprising 1.3 million samples, including 270,000 high-quality deepfake samples from 14 diverse sources. Despite previously reported high accuracy, existing deepfake voice detectors struggle with our diversely collected dataset, and their detection success rates drop even further under realistic corruptions and adversarial attacks. We conduct a holistic investigation into factors that enhance model robustness and show that incorporating a diversified set of voice augmentations is beneficial. Moreover, we find that the best detection models often rely on high-frequency features, which are imperceptible to humans and can be easily manipulated by an attacker. To address this, we propose the F-SAT: Frequency-Selective Adversarial Training method focusing on high-frequency components. Empirical results demonstrate that using our training dataset boosts baseline model performance (without robust training) by 33%, and our robust training further improves accuracy by 7.7% on clean samples and by 29.3% on corrupted and attacked samples, over the state-of-the-art RawNet3 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": [ "Deepfake audio detection", "Audio augmentations", "Frequency-Selective Adversarial Training" ] }, "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/b98880e448b91432ac0226cc5e9573acbadb0ca9.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": { "value": "/attachment/da76cb98372407130972cee048e78bf77d22ee53.zip" }, "title": { "value": "I Can Hear You: Selective Robust Training for Deepfake Audio 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": 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": "- Some small typos: \n\t- Line 190: pernutation -> permutation\n\t- Line 267: exist -> exists\n\t- Line 483: It then curious -> It is then curious\n\n- How does the bound in [1,2] change when considering $\\text{tahn}_{\\gamma}$-PGD?\n- Have you tried larger $\\gamma$s? $\\gamma = 10^{5}$ seems to be very far from sign-PGD in Figure 2 (b). It would be interesting to see how does $\\text{tahn}_{\\gamma}$-PGD behave when it’s close to sign-PGD.\n- Can you construct an experiment where the dependence on $n$ is displayed? For example taking a smaller number of samples from the studied datasets in order to see how the generalization gap grows. A synthetic distribution could also be employed where more and more samples are drawn and the gap decreases to zero for finite $\\gamma$.\n\n**References:**\n\n[1] Wang et al., Data-dependent stability analysis of adversarial training, ArXiv 2024.\n\n[2] Xiao et al., Stability Analysis and Generalization Bounds of Adversarial Training, NeurIPS 2022" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Simple theory and easy to follow paper. I didn’t read the proofs in full detail, but the main paper is easy to follow and the analysis and experiments are reasonable.\n\n- I found the analysis of the expansiveness of the adversarial attack operator very interesting and up to my knowledge, this has not been considered before.\n\n- When considering finite $\\gamma$, authors can show that their generalization upper bound converges to zero with increasing number of training samples $n$." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Authors study the generalization of adversarial training with projected gradient descent. They provide uniform stability upper bounds of the generalization gap that consider the expansiveness of the adversarial attack operator. In the particular case of replacing the $\\text{sign}(x)$ operation in the PGD attack with $\\text{tanh}(\\gamma\\cdot x)$, they can show that their generalization upper bound decays to zero with the number of samples $n$ for a finite value of $\\gamma$. The experimental evaluation shows the tradeoff between generalization and robustness given by $\\gamma$, where smaller values of $gamma$ obtain good generalization but poor robustness and the opposite happens for larger $\\gamma$." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Authors claim that their upper bound converges to zero with increasing number of training samples $n$ and the ones of [1,2] do not. This is misleading as [1,2] do not consider the expansiveness of the attack operator and the bound provided in this work, in the same setup as [1,2] does not vanish with $n$ (see lines 369-371). \n\n- The difference with the previous bounds is not clearly covered in the paper. The proof technique and assumptions are very similar to [1,2], nevertheless the bounds in [1,2] are not presented in the work and there is no discussion about how to integrate previous bounds with the expansiveness setup introduced in this work, making it difficult to assess the contributions. It would be nice to add a discussion about which role expansiveness plays in the result of [1,2], i.e., can it result in a vanishing upper bound with $n$? It would also be good to have a table comparing the different upper bounds." }, "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": "- Which part of the analysis bypasses prior work and makes the bounds decay as $O(\\frac{1}{n})$?\n- In the experiments of Section 6, why do you change the threat model (finding different values of $\\lambda_p$)? One could imagine experiments with different steepest descent algorithms for the solution of the inner maximization problem, where the threat model does not change (i.e., projecting every time to the same $\\ell_\\infty$ balls around the original points). Of course, different steepest ascent algorithms (besides the commonly used sign gradient ascent) will perform worse in finding adversarial examples, so the number of inner iterations should be adjusted appropriately. However, I believe this could be an interesting experiment to conduct." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper studies an interesting problem: the large generalization gap of robust empirical risk minimization (adversarial training) in neural networks. This work leverages the framework of uniform stability, which has been rather unexplored in the robust learning community, and could potentially provide insights on this topic. Based on the theoretical analyses, the authors propose a sensible relaxation of the commonly used PGD attack, using the $tanh$ function instead. Finally, I agree with the authors that the optimization algorithm in the inner maximization problem has not received adequate attention in the literature, and thus, its study is welcome (despite its limitations—see below)." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies generalization bounds for robust training by leveraging the framework of uniform stability. The authors analyze $\\ell_\\infty$ perturbations and derive several upper bounds on the generalization gap of predictors. They then investigate experimentally the performance of adversarially trained models using several algorithms to solve the inner maximization problem of the robust objective." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper is unfortunately difficult to follow, making it challenging to assess its content due to presentation issues. Furthermore, the conclusions seem unoriginal to me. In particular, I identified the following weaknesses:\n\n- Poor presentation: There are many instances where the text is not polished, with numerous grammatical errors (see the non-comprehensive list at the end of the Weaknesses). Additionally, the presentation of the technical results could be substantially improved (e.g., Theorem 4.1: remind the reader of the constants $\\beta, \\Gamma_X$). Furthermore, the authors should mention in the introduction that all of their results are solely about $\\ell_\\infty$ perturbations.\n- Introduction of many ad-hoc terms to denote well-established concepts: In many places, the authors use obscure words to define concepts that are well-defined in learning theory. For instance, lines 258-259: \"mis-matched generalization gap\" — this is just the standard generalization gap of a predictor trained robustly. Several such choices make it difficult for readers to comprehend the contributions of this work. Similarly, with so-called \"RG-PGD\" and the \"expansiveness property\" (a relaxed notion of Lipschitz continuity).\n- Unclear contributions: The paper does not clearly describe how the derived bounds differ from those of Xiao et al. (2022b) and Wang et al. (2024). In particular, the bounds from these prior works are not presented, and they are solely critiqued on the basis that they do not vanish with increasing sample size. Furthermore, the criticism of the non-smoothness of the loss function adopted in prior work seems unfounded (\"The source of the non-smoothness is, however, not explained in their work\"). Even for linear models under $\\ell_\\infty$ perturbations, a cross-entropy loss is non-smooth. Hence, the property of non-smoothness is well-motivated.\n- Unclear motivation for experiments: The authors seem to identify the sign function in the solution of the inner maximization problem in the robust objective as problematic, and they suggest an alternative based on a smooth approximation. However, they do not show any benefits in terms of robustness with the new method. Furthermore, the fact that for small $\\gamma$ we do not observe overfitting and the generalization gap is small appears to be a trivial observation, as the evaluation basically approaches the standard case of no perturbations. In short, it is not a good method for finding worst-case $\\ell_\\infty$ perturbations.\n- Results of Section 6: The authors mention the connection between adversarial training and steepest descent methods, but it is clear that this has been the motivation for the iterative solution of the inner maximization problem since the introduction of adversarial training. Furthermore, the experiments fail to highlight anything new, in my understanding (basically optimising an $\\ell_\\infty$ objective yields better coverage against $\\ell_\\infty$ attacks).\n\nGrammatical errors (non comprehensive list):\n- in the abstract: \"These expansiveness parameters appear not only govern the vanishing rate of the generalization error but also govern its scaling constant.\"\n- line 190: \"perturnation\" -> perturbation\n- line 202: \"related with\" -> related to\n- line 241: \"draw\" -> draws\n- line 245, 256: \"descend\" -> descent\n- line 316: \"independent with\" -> independent of\n- lines 536-537: \"Like all up-bound based theoretical results, such an approach is adequate for understanding performance guarantees but may be inadequte to explain poor generalization.\"" }, "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 address my comments above." }, "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 clear and easy to understand.\n\n(2) This paper studies the algorithmic stability of adversarial training from an interesting angle of the PGD attack.\n\n(3) Experiments demonstrate that using tanh to replace sign function can improve the generalization performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the algorithmic stability of adversarial training with a focus on how the inaccuracy of PGD attack affects the stability. Theoretical analysis are provided to justify that the sign function in PGD updates can significantly harm the stability, leading to a worse generalization (gap)." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "(1) While the paper considers the algorithmic stability of PGD attack, a missing component is the convergence of PGD attack. Intuitively, if we always use a fixed attack direction, then the algorithmic stability is not largely affected by the attack. However, the attack is not efficient. When using PGD attack, there is supposed to have a trade-off: with more iterations, the stability gets worse, but the attack becomes stronger. If at the test stage the attacker uses a very strong attack, e.g., AA attack, then balancing the attack effectiveness and stability is essential to obtain a better robust testing performance. Could the authors elaborate more from this perspective?\n\n(2) Please highlight the technical challenges for the theoretical contributions in this paper.\n\n(3) Please consider using some SOTA methods from RobustBench, e.g., leveraging synthetic data in adv training, to conduct the experiments. While improving the sign function seems to be helpful as illustrated by this paper, there is no enough evidence to demonstrate that this is one of the key issues in adversarial training.\n\n(4) Minor: In one line of research, to save the computation budget of adversarial training, algorithms have been proposed to explore fast adversarial training: instead of calculating the attack at each iteration, they update the attack for each sample for one step at each iteration, e.g., \n\nCheng, Xiwei, Kexin Fu, and Farzan Farnia. \"Stability and Generalization in Free Adversarial Training.\" arXiv preprint arXiv:2404.08980 (2024).\n\nI'm wondering if the authors can provide some comments on algorithms of this type." }, "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": "In total, I think this is a good paper, but there are some points that can improve this paper. Please refer to the weaknesses part." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The new stability theory differs from existing ones in terms of assumptions and the form of bounds. I like the separation of adversarial training perturbation $J$ and the evaluation perturbation $\\pi$, which means that the theory in this paper is a more abstract framework and can be applied in many cases.\n- The writing is good." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper provides a new stability bound for adversarial training, where the inner maximization perturbation $J$ and the evaluation perturbation $\\pi$ can be different. The introduced term expansiveness can partly explain robust overfitting and experiments are conducted to validate the theoretical results." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- It seems that the framework in this paper can not provide a proper description for the $Q(c^*)$ term, we need to calculate it according to the concrete choice of $J, \\pi$. However, to make this framework more significant, examples of how to calculate $Q(c^*)$ and how to choose $c^*$ should be added. Note: I mean examples in practice but not examples with overly simple assumptions (such as the assumption on the second moment in lines 293-294 and the assumption in Corollary 5.2 that a term is bounded by $B$ with probability 1). Just like the VC dimension, if we can not calculate the VC dimension of some hypothesis classes, the bound with the VC dimension is meaningless.\n- Typo: in line 149, it should be \"the label space $\\mathcal{Y}$ is finite\"\n- A minor issue: many equations in this paper are numbered, in fact, the equations that are not used later need not be numbered. For example, equation (2) is not used.\n- In lines 87-88, the paper says that \"the bound convergence to a constant, this helps explain the robust overfitting phenomenon\". In fact, a lower bound of the generalization gap that converges to a constant can explain overfitting. However, an upper bound can not because your bound may not be tight enough." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024algorithmic,\ntitle={Algorithmic Stability Based Generalization Bounds for Adversarial Training},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2GwMazl9ND},\nnote={under review}\n}" }, "abstract": { "value": "In this paper, we present a novel stability analysis of adversarial training and prove generalization upper bounds in terms of an expansiveness property of adversarial perturbations used during training and used for evaluation. These expansiveness parameters appear not only govern the vanishing rate of the generalization error but also govern its scaling constant. Our proof techniques do not rely on artificial assumptions of the adversarial loss, as are typically used in previous works. Our bound attributes the robust overfitting in PGD-based adversarial training to the sign function used in the PGD attack, resulting in a bad expansiveness parameter. The peculiar choice of sign function in the PGD attack appears to impact adversarial training both in terms of (inner) optimization and in terms of generalization, as shown in this work. This aspect has been largely overlooked to date. Going beyond the sign-function based PGD attacks, we further show that poor expansiveness properties exist in a wide family of PGD-like iterative attack algorithms, which may highlight an intrinsic difficulty in adversarial training." }, "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": [ "algorithmic stability", "generalization", "adversarial training" ] }, "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/d1e69ced75caa73b96663f9202af3a2fd46e1e7f.pdf" }, "presentation": null, "primary_area": { "value": "learning theory" }, "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": "Algorithmic Stability Based Generalization Bounds for Adversarial Training" }, "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": "My main question is the use of \"slot attention\" - as far as I can understand there is actually no slot attention in this model, am I right? it seems that the corrector just uses cross-attention and not slot attention? (the difference would be the soft-max axis)." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Originality:\nThe model presented is a very mild variation on previously published works - using VQ-VAE encodings is nice (though probably requires a bit more analysis) and the general recurrent setup is appealing.\n\nQuality:\nThe proposed model variants (pre and default) are interesting and probably a good step towards analyzing the model's behaviour.\n\nClarity:\nThe paper is nicely structured and well written.\n\nSignificance:\nThe context of the work is important, but see below for criticism." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper present a slotted recurrent network model which uses transformers as the main backbone for \"world modeling\". In this context the resulting model is an \"object centric\" learning model which cross attends into VQ-VAE encoded input frame, updates the current state and then predicts the next state using a transformer. The model is trained for state prediction (with two variants, either next state prediction or current state prediction) and is demonstrated to mildly work better than a single external baseline (STEVE) and one ablation model (decoder only, where there is not explicit state representation, just prediction and decoding). The experiments are run on a physical reasoning task (PHYRE) and the output is a classification readout." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Unfortunately the paper suffers from several weaknesses.\n\nExperimental validation:\nThe method is only validated on one task and even on that task results are not very convincing. The models perform very closely to one another and the claims for efficient learning with the model are not well supported.\n\nAnalysis:\nIn general I don't mind when results of a model are not competitive with baselines or ablations as long as there is good analysis of why that is the case, and how this can improve our understanding of the model or problem. Here, however, these are absent - there's very little analysis of what the model learns, how it does that and what determines its performance.\n\nPresentation:\nThe experimental result figures are not to the level I would expect to see in an ICLR paper - raw training curves are fine if they tell a clear story. Here, however, they do not - there is very little signal there to observe. Export quality is also quite low and does not at the level I would expect.\n\nNovelty:\nWhile usually I don't think novelty is a determining factor for a paper, I feel here this is quite lacking and the proposed model is indeed quite close to existing literature (SAVI++, PARTS and more ). These are cited in the paper, so I have no complaints on that side, but given the generally weak results and analysis I think this hurts 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": 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": "* __Slot encodings.__ How and where do the authors utilize slot encodings, and what specific benefits do they offer in this context?\n\n* __Experimental design.__ Why do the authors limit their evaluation to a single dataset? Additionally, what is the rationale for selecting STEVE as the sole baseline, excluding other relevant related works?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The authors address an important problem of physical world modeling using structured latent representations." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes the Future-Predicting Transformer Triplet (FPTT), an architecture aimed at modeling of physical world dynamics from video data. It employs Transformers to learn object-centric representations, enabling the model to predict physical interactions between objects more effectively. The architecture is tested on synthetic video dataset PHYRE. The authors also perform an ablation study to understand the contribution of different components." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* __Missing slot encodings and object-centricity.__ \n\nWhile the paper includes _slot encoding_ in its title, the approach itself appears to lack this feature. As I understand it, $\\Lambda$ was intended to serve as slot encodings, but it is not even referred to as such. From the description, $\\Lambda$ seems more like a standard intermediate representation in transformer layers rather than a distinct slot encoding.\n\nIn Appendix A2, the authors reference [1] for their transformer implementation, where they also mention using four slots. However, the referenced implementation does not include a parameter for the number of slots, leaving it unclear how slot encodings are actually integrated into the proposed approach, or if they are implemented at all.\n\nFurthermore, the authors state that _\"the representation remains opaque and lacks interpretability,\"_ which raises questions about the motivation for using _slot encodings_ in the first place.\n\n\n* __Experimental methodology.__ \n\nFirstly, the authors evaluate their model on a single, very simplistic dataset, while using a relatively large number of parameters. This limited evaluation setup may not provide sufficient empirical evidence to support their claims.\n\nA more significant issue lies in their positioning among related works and choice of baselines. The authors overlook most recent related work (e.g., [2, 3, 4, 5]) and rely solely on STEVE as a baseline, aside from variations of their own approach.\n\n\n* __Presentation.__ \n\nIn addition to unclear explanations of their approach and its novelty, the authors fail to position it effectively within the existing literature, lacking a comparative analysis with prior work.\n\nAll the figures also present issues: they are unnecessarily large, some are in low resolution, and it is often unclear what the authors aim to demonstrate.\n\n\\\nReferences: \n\n\n[1]: Andrej Karpathy. nanoGPT: The simplest, fastest repository for training/finetuning mediumsized GPTs (Generative Pretrained Transformers), 2023. URL https://github.com/karpathy/nanoGPT. \\\n[2]: Nakano, A., Suzuki, M. and Matsuo, Y., 2023. Interaction-based disentanglement of entities for object-centric world models. In The Eleventh International Conference on Learning Representations.\\\n[3]: Villar-Corrales, A., Wahdan, I. and Behnke, S., 2023, October. Object-centric video prediction via decoupling of object dynamics and interactions. In 2023 IEEE International Conference on Image Processing (ICIP) (pp. 570-574). IEEE.\\\n[4]: Wu, Z., Dvornik, N., Greff, K., Kipf, T. and Garg, A., 2022. Slotformer: Unsupervised visual dynamics simulation with object-centric models. arXiv preprint arXiv:2210.05861.\\\n[5]: Daniel, T. and Tamar, A., DDLP: Unsupervised Object-centric Video Prediction with Deep Dynamic Latent Particles. Transactions on Machine Learning Research." }, "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 would the architecture compare against Dreamerv3 or Slotformer in world modeling?\n- What happens if u try to make the decoder only model more efficient by reducing the number of tokens or dimensionality of the token?\n- How would the paper compare against the baselines in benchmarks proposed in Steve or DreamerV3 or SlotFormer?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper focuses on world modeling which is an important problem.\n- The writing and presentation is clean, which makes understanding the paper easy.\n- The paper compares efficiency and accuracy, which helps understand the trade-offs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper addresses the challenge of creating sample-efficient models for physical world modeling, focusing on predicting object interactions in dynamic environments.\n\nThe authors propose an architecture that combines Transformers with slot encoding to improve sample efficiency and stability in world modeling. Unlike existing models that operate at the image level, this model incorporates object-based representations, enabling it to capture and predict interactions more accurately.\n\nTheir model, named Future-Predicting Transformer Triplet (FPTT), uses a corrector-predictor-decoder triplet of Transformers. The corrector aligns the internal state representation with the actual video evolution to prevent model drift, the predictor forecasts the next state based on the corrected representation, and the decoder converts this predicted state back into tokens for further training.\n\nExperiments using the PHYRE dataset (a benchmark for physical reasoning) show that FPTT achieves greater sample efficiency and training stability compared to baseline models like STEVE. The model’s structured approach enables it to generalize well in physical environments simulated with basic Newtonian physics.\n\n\nIn summary, the paper presents a architecture that leverages the strengths of Transformers and slot encoding for efficient and stable world modeling, demonstrating improvements in tasks requiring understanding and predicting object dynamics in a physical environment​" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The contribution is not significant having an internal representation of the previous timesteps is common in world modeling architectures, for instance: Dreamer: https://arxiv.org/pdf/2301.04104, Slotformer: https://arxiv.org/pdf/2210.05861. It's unclear to me how this work is a better architecture than Dreamer or Slotformer or other recent works.\n- The evaluations and baselines are weak, the paper only compares against STEVE. I dont think STEVE is a fair comparision as their objective was to get interpretable object representations and not necessarily the metric/benchmark paper uses for evaluation. Further the Decoder-Only model seems to perform as well as the proposed architecture on almost all tasks except efficiency.\n- Lastly the work only compares on a single benchmark, which is not being used in the baseline works such as STEVE. I think a fair thing to do would be to compare on benchmarks shown in prior baselines, so we assume they are tuned well." }, "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": "- It appears that slot attention (or inverted attention) is absent, with only cross attention being mentioned. Is this an oversight in the explanation, or is it indeed absent? If it's truly absent, how can we be confident that it captures object-level dynamic understanding?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The authors' approach of capturing slot-like internal representations from VQ tokens, instead of CNN embeddings, was intriguing and showed promising results.\n- They propose a novel evaluation protocol for testing world model architecture, utilizing the shared benchmark with different protocols." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a world-modeling architecture that captures object-level interactions in the scene, instead of the scene itself. The architecture consists of three transformer-based models: a corrector, a predictor, and a decoder, alongside a VQ-VAE tokenizer for image encoding.\n\nTo evaluate the proposed model’s performance as a world model, the authors provide a physical reasoning task using the PHYRE benchmark, demonstrating that their model outperforms STEVE, the baseline, in terms of prediction accuracy and sample efficiency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- **Lack of Novelty and Justification**: The main idea and direction of the paper have been explored in several existing works (OCVT[1], SlotFormer[2]). Although the authors are likely aware of this, they fail to convincingly demonstrate why their approach is unique and necessary for the proposed direction, compared to previous works.\n- **Architecture and Design Choices**: The proposed architecture appears to be a combination of SAVi[3] and STEVE[4] architectures, but with a predictive loss function instead of a reconstructive loss function. While this variant may be promising, the paper lacks sufficient details to justify the design choices, such as thorough ablation studies. Furthermore, it is unclear whether the proposed model can outperform existing works, as it is not comprehensively compared.\n- **Lack of Clarity in Architecture and Experiment Description**: The architecture section of the paper lacks clarity and detail, particularly in the description of the core components: corrector transformer, predictor transformer, and decoder transformer. Although the author provides a high-level overview of these architectural concepts, the explanation is insufficient given the emphasis on this part as the paper's core contribution. To thoroughly understand and investigate the proposed architecture, a detailed formulation of these components is necessary, including their implementation details and mathematical representations.\n \n Furthermore, the experiment section lacks sufficient details about the metrics used in the evaluation. To ensure transparency and reproducibility, it is essential to provide a clear explanation of each metric, including how the metric is calculated and what it represents.\n \n- **Limited Evaluation of Proposed Architecture:** The author only provides a single task to evaluate the proposed architecture, which is insufficient to demonstrate its generality and versatility. To thoroughly assess the world modeling ability of the proposed architecture, it is essential to evaluate it on a diverse range of tasks that require the model to infer and understand the relationships between objects and scenes. Additionally, to facilitate a fair comparison with existing works, the authors may consider including several generation tasks (e.g. OBJ3D[1], CLEVR[5], Physion[6]), as has been done in prior research.\n \n To further demonstrate the effectiveness of the proposed model, the author could compare it with a broader range of baselines, such as SlotFormer, OCVT, SAVi, and other relevant models mentioned in the paper. Although these models are typically used for generation tasks, their predicted representations can be evaluated using the same protocol as the proposed model. Additionally, comparing with image-based world models would illustrate the advantages of object-level world models over their image-based counterparts. This approach would provide a more comprehensive understanding of the proposed model's performance and allow for a more accurate assessment of its strengths and limitations relative to existing approaches.\n \n- **Ablation Results Raise Questions about Proposed Model:** The ablation results indicate that the ‘decoder-only’ model performs comparably to the proposed models. This suggests that VQ-tokenization and predictive loss might be sufficient to drive performance without explicitly enforcing object-level representations. This outcome seems misaligned with the paper's main theme, which emphasizes the importance of object-level representations. Consequently, this misalignment raises questions about the necessity and effectiveness of the proposed model's architecture.\n\n[1] Wu, Yi-Fu, Jaesik Yoon, and Sungjin Ahn. \"Generative video transformer: Can objects be the words?.\" International Conference on Machine Learning. PMLR, 2021.\n\n[2] Wu, Ziyi, et al. \"Slotformer: Unsupervised visual dynamics simulation with object-centric models.\" arXiv preprint arXiv:2210.05861 (2022).\n\n[3] Kipf, Thomas, et al. \"Conditional object-centric learning from video.\" arXiv preprint arXiv:2111.12594 (2021).\n\n[4] Singh, Gautam, Yi-Fu Wu, and Sungjin Ahn. \"Simple unsupervised object-centric learning for complex and naturalistic videos.\" Advances in Neural Information Processing Systems 35 (2022): 18181-18196.\n\n[5] Johnson, Justin, et al. \"Clevr: A diagnostic dataset for compositional language and elementary visual reasoning.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.\n\n[6] Bear, Daniel M., et al. \"Physion: Evaluating physical prediction from vision in humans and machines.\" arXiv preprint arXiv:2106.08261 (2021)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024transformers,\ntitle={Transformers and slot encoding for sample efficient physical world modelling},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2H6KhX1kJr},\nnote={under review}\n}" }, "abstract": { "value": "World modelling, i.e. building a representation of the rules that govern the world so as to predict its evolution, is an essential ability for any agent interacting with the physical world. Recent applications of the Transformer architecture to the problem of world modelling from video input show notable improvements in sample efficiency. However, existing approaches tend to work only at the image level thus disregarding that the environment is composed of objects interacting with each other. In this paper, we propose an architecture combining Transformers for world modelling with the slot-attention paradigm, an approach for learning representations of objects appearing in a scene. We describe the resulting neural architecture and report experimental results showing an improvement over the existing solutions in terms of sample efficiency and a reduction of the variation of the performance over the training examples." }, "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", "world modeling", "slot attention" ] }, "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/41545b0c8d7fd1d4588c8acde59513165fe992ef.pdf" }, "presentation": null, "primary_area": { "value": "learning on time series and dynamical systems" }, "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/01e4912bc09155b6ba09356814fea9905ca5c97d.zip" }, "title": { "value": "Transformers and slot encoding for sample efficient physical world modelling" }, "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": "What is the prediction error for runtime and energy consumption (also in comparison with baselines)?\n\nIt is well known that the lack of (remaining) run time of jobs is one of the main issues in achieving schedulings that are proven optimal under some aspects, i.e., minimizing waiting time. Can some variant of shortest job first (SJF) be used here?\n\nDifference between simple and greedy alsgotiehms should be better highlighted." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "+ The idea to leverage an LLM to create a powerful representation instead of hand-crafted features. This also brings a series of positive properties, as listed in the paper.\n+ Good results on the presented data center use case.\n+ Includes discussions on practical problems in applying the scheme." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes using LLMs to predict performance metrics of jobs submitted to a data center. Metrics include runtime, waiting time, and energy consumption. The main idea is to leverage the power of LLMs in creating meaningful representations of complex data, such as the source code, which can then be trained into specific predictions. Based on these predictions, the authors propose two scheduling algorithms, which they apply to a data center use case to show savings of up to approximately 30%." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Lack of details on what is considered a job and its source code. Implicitly (especially in the introduction), authors seem to assume Python scripts for machine learning, but real-world workloads might differ from this assumption. Please be more clear on any assumptions and restrictions on the jobs considered.\n- Authors estimate the model performance metrics solely from the source code. Still, the execution time (and all other performance metrics) for some models, e.g., for LLMs due to their autoregressive nature, depends heavily on the generated output based on the input/prompt and not only on the source code. (see also next point)\n- The paper lacks results on the achieved prediction accuracy of the considered metrics. Here, it would be nice to have some statistics on the achieved error between predicted and real values as well as a comparison with some of the mentioned related works for job prediction.\n- Also, an ablation study to see if the improvement stems more from the smarter scheduling algorithm or from the more precise predictions would have been nice. This would also likely allow to hint into how well the approach might generalize to other data centers that might use a different baseline scheduling." }, "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": "Thank you for submitting to ICLR. I really like the idea of using LLMs for predictive data center resource allocation. However, the paper seems incomplete and lacks a robust evaluation for the proposed method. Addressing this issue would strengthen the paper. \n\n\nQuestions: \n\nCould you provide more details about the LLM model used in the experiments, including\n- LLM model type, and how was it pre-trained or fine-tuned for this task\n- the dimension of the LLM output representations\n- how to leveage LLM to generate the output representation (eg, prompting method)\n\nWhat is the computational and cost overhead of running the LLM-based prediction framework? How does this compare to the benefits gained in terms of improved resource allocation and reduced energy consumption?\n\nCan you provide more details about the evaluation, including\n- detailed experimental setup\n- data center scale\n- the number and distribution of different task types\n- baseline scheduling algorithms to compare with\n- evaluation metrics\n- ablation study\n\nHow does the framework handle prediction errors? Is there any mechanism to adapt predictions based on actual execution results?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper designs a novel end-to-end solution using LLMs for predictive data center resource allocation. Also, their combination of LLM and probe network reduces the number of training data needed.\n\nTheir framework can generalize to diverse job task types including composite and unseen tasks, making it more flexible than traditional methods that required separate models for different task types.\n\nThe writing is easy to follow and clearly explains their model architecture." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents an LLM-powered automatic predictive scheduling system for AI workloads in data center, with the goal of optimizing both performance(job completion time) and energy consumption. The system consists of two main components: 1) An LLM-based predictive model that takes job's source code as input, and predicts its execution time and energy consumption; 2) A decision-making model that uses these predictions for deciding GPU resource allocation to each job. Through collaboration with a data center, the authors demonstrated 32% reduction in energy consumption and 30% decrease in waiting time. The key innovation is using LLMs to generate code representations that enable generalizable prediction across diverse task types." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper lacks information about which pre-trained LLM was used, details about its output representation, and how did they leverage LLM to generate the output representation (eg, prompting method).\n\nThe evaluation section seems to be incomplete. More comprehensive evaluation details are necessary to evaluate whether their proposed solution works.\n\nThe proposed method could cause potential privacy concerns when sending confidential user-submitted code to LLM for analysis. \n\nNo discussion of the computational and cost overhead of running the LLM-based prediction framework." }, "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": 3 }, "primary_area": null, "questions": { "value": "1. My first question is, what is the use-case that you are trying to solve?\n2. What are the accuracy and prediction metrics of your system?\n3. What is the scale of the datacenter you collaborate with?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Thank you for submitting your paper to ICLR. I enjoyed reading the paper as it is well written generally.\n2. The paper covers an important topic that many datacenter operators care about, how to better utilize accelerator resources.\n3. The paper uses data from a data center and I believe is the first paper to suggest a compound AI system with two LLMs to assign GPU resources." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a compound-AI based technique to predict the performance, energy consumption, and other key operational metrics for data center operations. The paper motivates the problem well, showing how a pre-trained LLM can possibly be used to predict the performance of workloads on different hardware types. This can be further used in scheduling of workloads on devices. The authors then device a scheduling optimization problem along with two algorithms to show how such a deployment can help datacenter operators. The authors run simulations based on a dataset acquired from a production system from a small datacenter over a period of about 2 months. The dataset has an aggregate task counts of less than 200 tasks. They adapt the pretrained model using 500 source codes. To label the data (and run their experiments), the authors use two GPU models A100 and A6000." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I think the paper however has several shortcomings that I will aim to detail next. The paper is neither strong on the systems side nor on the ML side, and this is the main shortcoming in my opinion. I will detail what I mean by that in the next points:\n1. To start with, I am not entirely sure if for the scale of the problem you define, the LLM is doing much. For an ICLR submission, I think It would have been better to focus more on the ML side of the problem and not the decisions making. After all, you have only provided an overview of the prediction results in the paper in Table 1. However, non of these results show tradtitional metrics that one would expect on the predictions, e.g., accuracy, recall, F1-Score, MAPE, etc. I would like to see some of these aspects for the method. \n2. There is not much novelty in the ML side of the paper, except maybe with the Align-LLM part. However, the authors treat this in passing only, with very little to no evaluations on even how this extra LLM helps. It would help the paper to do an ablation study with Align-LLM. In addition, you effectively have only two classes for your classifier, A100 and A6000. I wonder how your system would expand to a larger system with say 10s of accelerator types?\n3. From a systems perspective, I think there are way too many shortcomings. Since ICLR is not a systems conference, I will only include the following few shortcomings. First of all, you have a total of less than 200 tasks over a period of 2 month. That is about three tasks per day. Since you are running this in simulations, you can scale this up by, e.g., creating a set that resembles the original tasks you have. There are also multiple other public traces now available, e.g., from Alibaba with GPU workloads (https://github.com/alibaba/clusterdata/tree/master/cluster-trace-gpu-v2020#pai_task_table) . That being said, you do not need even GPU traces, you can easily simluate larger systems. \n\n- Second, what is your use-case? A task that runs for short periods? How would you know how long this task runs in a real datacenter unless it is a repetitive workload? Third, how would your system scale with 30+ accelerator types and 10s to 100s of tasks arriving per minute, per second, and per hour?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024llmpowered,\ntitle={{LLM}-Powered Predictive Decision-Making for Sustainable Data Center Operations},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2HN97iDvHz},\nnote={under review}\n}" }, "abstract": { "value": "The growing demand for AI-driven workloads, particularly from Large Language Models (LLMs), has raised concerns about the significant energy and resource consumption in data centers. This work introduces a novel LLM-based predictive scheduling system designed to enhance operational efficiency while reducing the environmental impact of data centers. Our system utilizes an LLM to predict key metrics such as execution time and energy consumption from source code, and it has the potential to extend to other sustainability-focused metrics like water usage for cooling and carbon emissions, provided the data center can track such data. The predictive model is followed by a real-time scheduling algorithm that allocates GPU resources, aiming to improve sustainability by optimizing both energy consumption and queuing delays. With fast inference times, the ability to generalize across diverse task types, and minimal data requirements for training, our approach offers a practical solution for data center scheduling. This framework demonstrates strong potential for advancing sustainability objectives in AI-driven infrastructure. Through our collaboration with a data center, we achieved a 32% reduction in energy consumption and a 30% decrease in waiting time." }, "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", "Generative AI", "Sustainability", "Real-time decision-making" ] }, "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/47a15a97f863147475d6efc0911d401fcbd0e830.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": { "value": "/attachment/a597ac0ec3e436ca69d8f78420a0320877ff1853.zip" }, "title": { "value": "LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations" }, "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": "Please address the points brought up in the weaknesses above." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "1. Significant qualitative results are included, including failure cases.\n2. The technical contribution appears to be novel for VOS." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors present Interpreter, a VOS method based on hierarchical slot attention that consists of separate image-level and video-level processing. To compute the image-level attention slots, Interpreter uses implicit slot attention to learn object-centric features from an image-trained backbone. Implicit slot attention is also used at the video level to learn object representations across frames, relying on the Sinkhorn divergence to learn correspondence between sets of slots across different frames. Experiments are conducted on the YTVIS-19 and MOVi-E datasets to compare Interpreter to other slot-attention-based methods. An ablation study is carried out on YTVIS-19 to determine the effect of number of slots and clustering distance threshold." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Issues with Experimental Evaluation.\n\n a. The paper claims Interpreter is a VOS method but performs its evaluation using a Video Instance Segmentation (YTVIS-19) and Video Semantic Segmentation dataset (MOVi-E). If Interpreter is a VOS method then it should be evaluated on VOS datasets such as DAVIS [1], and compared to the state of the art VOS methods, in order to assess the contribution of the work.\n\n b. The paper claims Interpreter targets long videos but the length of the videos in the datasets chosen are on the order of seconds not minutes, making it difficult to verify this claim.\n\n c. Qualitative results are included for Interpreter but not for competing methods, making it difficult to assess the performance quality of Interpreter.\n\n2. Exposition of method lacks mathematic details. In particular, Sinkhorn Divergence is never defined mathematically and the final loss function is not included. This makes it difficult to understand the method beyond a surface level.\n\n3. Related works section lacks mention of query-key-value retrieval-based methods such as STM [2] for VOS, which is a major and important direction for the task. The motivation for using slot attention based methods is not clear.\n\n4. Writing is not direct. For example, it should be explained why Interpreter performs \"unexpectedly well\" in l. 471 and what is \"surprising\" in l. 474. As another example, the phrase in l. 339 \"The last row shows a cute cat.\" seems out of context. \n\n[1] \"The 2017 DAVIS Challenge on Video Object Segmentation\". J. Pont-Tuset, F. Perazzi, S. Caelles, P. Arbeláez, A. Sorkine-Hornung, and L. Van Gool. arXiv:1704.00675, 2017.\n[2] \"Video Object Segmentation using Space-Time Memory Networks\". Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim. ICCV, 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": 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. Could the authors clarify the significant discrepancy observed between FG-ARI and mIoU performance in this model? In my view, both FG-ARI and mIoU should be high if object segmentation remains accurate over time.\n\n2. Have the authors considered using only frame-wise slot representations at the second level (where the same slot index per frame corresponds to the same object), rather than applying slot attention at the video level? What would be the implications of this approach?\n\n3. To what extent is the DINOv2 feature extractor crucial for this model? Would the method fail without it?\n\n4. Why is a different number of second-level slots used for the YTVIS-19 and MOVi-E datasets?" }, "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-written and structured, with clear explanations of the novel hierarchical slot attention mechanism and its advantages in scaling object-centric representation to longer videos.\n\n2. The approach of per-frame slot attention followed by video-level slot attention is both novel and elegant, allowing the model to handle temporal dependencies across the entire video without chunking." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a method called Interpreter, aimed at efficient, unsupervised video-level object-centric representation learning. Interpreter introduces a hierarchical slot attention architecture where image-level representations are compressed first, then video-level representations are derived from them using a relaxed optimal transport objective, Sinkhorn Divergence, for unsupervised segmentation. This approach circumvents the typical computational load associated with reconstructing frame-level feature maps, allowing Interpreter to process longer videos effectively. Experiments show that Interpreter achieves strong results on the YTVIS-19 dataset and synthetic datasets like MOVi-E." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The second-level slot number does not stay under ten (8 for YTVIS-19), which contradicts the paper’s claim of handling extensive temporal context effectively (L101).\n\n2. Results on the DAVIS-17-unsupervised dataset are absent, and performance on metrics (FG-ARI and mIOU) shows considerable variation across different benchmarks, suggesting limitations in the model’s generalizability.\n\n3. The discussion around FG-ARI and mIoU metrics lacks sufficient depth, especially in explaining the model’s inability to perform consistently across both benchmarks. It remains unclear why the method does not yield strong outputs on both metrics concurrently​." }, "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 is the Interpreter more efficient tham previous work? (e.g. STEVE, BA, VideoSAUR).\n* How does the model compare to previous work if normalised for the pre-trained architecture? E.g. BA uses ViT-s/8, while ViT-B/14 is used here.\n* Interpreter is developed with long videos in mind. What is the definition of “long” in this work and how is this reflected in the experimental setup?\n* How would approach compare to move naive objectives, e.g. matching the slots with Hungarian matching and minimising the corresponding distance (e.g. L1/L2)?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* I like the work’s technical contribution, the Sinkhorn divergence. However, I’d encourage the authors to include more details (what’s behind SH function in (2)).\n* The approach is technically sound. It makes a lot of sense to represent a video with a compact set of slot tokens and computing the segmentation through attention propagation, as described in ll. 206-215.\n* Fig. 1 provides a great overview for the approach, which helps in following the technical details. (Remark: It could’ve been more compact and use vectorised graphics). \n* I enjoyed that the text does not stop after the mixed quantiative results, but instead makes a good effort to analyse and explain them." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The work introduces an unsupervised approach to object-based segmentation of video sequences. The approach comprises two stages. The first stage follows previous work and trains an autoencoder that decomposes an input image into a set of slot tokens. In the second stage, another autoencoder learns to represent the set of slot tokens, extracted from each frame in a video, with a more compact set of video-level slot tokens. To train this autoencoder, the approach leverages Sinkhorn divergence, which establishes a (relaxed and differentiable) correspondence between the set of predicted tokens and the input set. The final output -- a temporally consistent segmentation -- is the result of attention propagation, which relies on the similarity between image-specific slots and the video-level slots. The results on YouTube-VOS and synthetic MOVi-E demonstrate impressive segmentation quality, but the quantitative results are a bit mixed." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The exposition, especially the technical part, feels way to congested. I would have preferred more technical details in Sec. 3.2 than the Figures 2-4 loading two full pages, which feel a bit like space-fillers. For example, the work does not really explain how the Sinkhorn divergence is computed in Eq. (2), nor does it really explain the architecture of the encoders/decoders in the two stages of training, etc. \n* The results are obviously mixed: On YouTube-VOS the approach discriminates between the objects well, but falls behind on foreground-background segmentation and vice-versa on MOVi-E. I like that the text discusses these weaknesses, but the analysis would have been more convincing with more informative qualitative examples (including the ground truth and the output from previous work).\n* The title falls short on the promise of efficiency. Perhaps the method is efficient, but I did not find convincing arguments or corresponding experiments to support this point.\n* The experiments are bit too brief. I would be curious to see the approach with another pre-trained backbone and dataset (e.g. DAVIS)." }, "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": "**Q1. What are the main architectural differences between Interpreter and VITA?**\n\nIn terms of architectural design, what distinguishes Interpreter from VITA? Could you specify the unique aspects of Interpreter’s approach, especially in how it addresses hierarchical design and temporal context aggregation?\n\n**Q2. Is there any statistical basis for the analysis beyond observations on a few samples?**\n\nBeyond observations from a limited set of examples, does the paper offer any statistical foundation for its analysis? For instance, is there evidence that specific factors like object movement or motion changes hinder the performance of slot-based approaches? A more comprehensive investigation into such cases could help substantiate the findings.\n\n**Q3. Are additional ablation studies provided to validate the proposed method’s effectiveness?**\n\nApart from the current experiments, are there further ablation studies examining key factors, such as the influence of varying the number of slots (K) or the impact of end-to-end fine-tuning in the second stage?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper introduces a straightforward design for unsupervised video object segmentation using slot attention. This architecture demonstrates remarkable performance on the real-world dataset YTVIS-19." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a hierarchical slot attention approach for handling temporal context in video segmentation. To achieve this, it incorporates a video-level slot that aggregates temporal information across all frame-level slots. Additionally, to smoothly apply the video-level slot for video representation prediction, the paper proposes an attention map propagation technique. For loss calculation, Sinkhorn Divergence is utilized. With these components, the proposed model, Interpreter, achieves state-of-the-art performance on the YTVIS-19 dataset in terms of mIoU and on the MOVi-E dataset in terms of FG-ARI." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**1. Limited Architectural Contribution**\n\nThe primary contribution of Interpreter lies in its hierarchical architecture, a concept previously introduced in the video instance segmentation method, VITA [1]. Similar to VITA, Interpreter employs a hierarchical design where video-level queries aggregate temporal context from frame-level queries. Apart from differences in target tasks and objective functions, the overall architectural design remains largely similar to that of VITA.\n\n**2. Insufficient Experimental Support**\n\nAdditional experiments are necessary to validate the proposed methods. In Tables 1 and 2, as noted by the authors, results on the MOVi-E dataset show a trend that significantly deviates from results on YTVIS-19. Section 4.3 discusses specific cases with limited examples to interpret these discrepancies. However, since these two sets of results exhibit opposite trends, it remains challenging to conclude that the proposed method is generally applicable. To address these contradictions, further analysis—such as statistical investigation—would be beneficial. Additionally, only two ablation studies are presented, even for critical hyperparameters, and key factors like the effect of varying the number K are not explored.\n\n**3. Limited Readability**\n\nThe overall structure of the paper hinders readability and comprehension. In particular, the experimental section is challenging to follow, as it combines main experimental results, qualitative findings, and ablation studies within the same section, making it difficult to discern the purpose and implications of each individual experiment. Furthermore, Figures 3 and 4 display segmentation results without the original samples, which complicates the reader’s ability to fully interpret the analysis.\n\n[1] Heo, Miran, et al. \"Vita: Video instance segmentation via object token association.\" Advances in Neural Information Processing Systems 35 (2022): 23109-23120." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We introduce a novel method for efficiently learning object-centric representations over videos and achieve state-of-the-art video object segmentation performance on YTVIS-19." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024efficient,\ntitle={Efficient Object-Centric Learning for Videos},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2HdZPEQUig},\nnote={under review}\n}" }, "abstract": { "value": "This paper introduces a method for efficiently learning video-level object-centric representations by bootstrapping off a pre-trained image backbone, which we term Interpreter. It presents a novel hierarchical slot attention architecture with local learning and an optimal transport objective that yields fully unsupervised video segmentation. We first learn to compress images into image-level object-centric representations. Interpreter then learns to compress and reconstruct the object-centric representations for each frame across a video, allowing us to circumvent the costly process of reconstructing full frame feature maps. Unlike prior work, this allows us to scale to significantly longer videos without resorting to chunking videos into segments and matching between them. To deal with the unordered nature of object-centric representations, we employ Sinkhorn divergence, a relaxed optimal transport objective, to compute the distance between unordered sets of representations. We evaluate the resulting segmentation maps on video instance segmentation in both realistic and synthetic settings, using YTVIS-19 and MOVi-E, respectively. Interpreter achieves state-of-the-art results on the realistic YTVIS-19 dataset and presents a promising approach of scaling object-centric representation learning to longer videos." }, "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": [ "Object-Centric Learning", "Representation Learning", "Video", "Segmentation", "Video Object Segmentation" ] }, "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/a0ca16772d5fe0b949efa9455797c694464d3f4f.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": "Efficient Object-Centric Learning for Videos" }, "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": "1. What are some common failure cases for the editing, especially if the text and local edits conflict. \n2. How are the number of iterations for denoising fixed for drag editing and how do the impact change with fewer to larger iterations. \n3. One of example is shown to incorporate masks for editing, can it be explained how masks are incorporated in this framework ?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Novel Approach to combine local and global gradient: Building on text inversion methods to combine text and drag signals, CLIPDrag enables pixel-level control, offering both specific and contextually aware edits.\n2. Efficient Convergence: The fast point-tracking method improves the editing process by guiding handle points toward their target positions faster.\n3. Extensive Ablations: The paper has ablations for all different components such as point tracking, GLMS and controls with edit and text showing clear performance gain. \n4. Qualitative Results: The papers presents representative set of results allowing easy intuition and help with clarity of the paper." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces CLIPDrag, a novel image editing approach that integrates both text-based and drag-based controls to achieve more precise and flexible edits. Traditional text-based editing provides general guidance but often lacks specificity, while drag-based editing offers local control but can be ambiguous without context. CLIPDrag addresses these issues by using text as global guidance for overall image context and drag points for fine-grained, localized control. The model leverages a global-local motion supervision (GLMS) system and a fast point-tracking (FPT) method to streamline and accelerate the editing process. The paper is well written and easy to understand, the paper has comprehensive experimental results which show CLIPDrag outperforms both traditional drag- and text-based methods in accuracy and image fidelity. The detailed ablations make the hypothesis clear. The paper presents an interesting path for image editing and is theoretically grounded which should be shared within the community" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The need for identity preservation beyond citing DragDiffusion is not shared, given the improvement of base models, the intuition behind it is lacking. \n2. Gradient accumulation is discussed assuming the latent code is continuous, formulating why the gradient manipulation will still lead to plausible images is unclear. \n3. Assumption around using nearest neighbors in FPT moving monotonically towards target is not explained, given the optimization is highly non linear." }, "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": "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": 2 }, "primary_area": null, "questions": { "value": "Please provide examples with and without drag edit the following examples. \"The sculpture is smiling and not showing his teeth.\" and \"The sculpture is smiling and not raising his head\"." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "For the first time, the paper provides an algorithm that integrates text-guided editing with drag-guided editing. The proposed editing algorithm attempts to provide more precise global editing and reduce ambiguity in local editing. The independent guidance or supervision of text and drag is combined interestingly by disentangling the global gradient that is perpendicular and parallel to the local gradient." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a Text-Drag Editing framework to address text-based and drag-based editing limitations. To achieve this, the authors introduce global-local motion supervision that integrates the semantic aspects of text with drag guidance. They utilize a novel approach of gradient fusion, combining gradients from text and drag conditioning based on their directional alignment to provide a unified gradient." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Lack of Comprehensive Review of Diffusion-Based Image Editing Literature:\nThe paper does not provide an adequate overview of diffusion-based image editing methods. A more thorough review of recent approaches in diffusion-based image editing is necessary to strengthen its background and situate the proposed method within the broader field. Specifically, the authors should consider discussing recent methods, such as \nSINE: SINgle Image Editing With Text-to-Image Diffusion Models (Zhang et al., CVPR 2023), \nPaint by Example: Exemplar-based Image Editing with Diffusion Models (Yang et al., CVPR 2023), \nFlexiEdit: Frequency-Aware Latent Refinement for Enhanced Non-Rigid Editing (Koo et al., ECCV 2024), \nand RegionDrag: Fast Region-Based Image Editing with Diffusion Models (Lu et al., ECCV 2024). \nIncorporating these examples will provide a more robust foundation and context for the reader, enabling a clearer understanding of how the current approach builds upon or diverges from existing work.\n\n2. Unconvincing Example in Figure 1:\nThe example provided in Figure 1 does not convincingly illustrate the motivation of the study. The intention seems to highlight the limitations of drag-based and text-based editing approaches, yet the figure only demonstrates an instance where drag-based editing is ineffective. A more persuasive example might involve a scenario where drag-based editing produces a subtle change—such as adjusting a subject's smile—which could then be further refined by the proposed text-drag editing method to achieve a more detailed, natural effect. This change would clarify the benefits of text-drag editing over existing methods.\n\nAdditionally, the similarity between the proposed method's results and traditional drag-based editing in Figure 1 and the statute example raises questions about the added benefit of the proposed approach. If these similarities are not intentional, a different example or refinement of the illustrations might better demonstrate the unique advantages of the proposed method.\n\n3. Handling of Distinct Effect Regions in Text-Based and Drag-Based Editing\nThe paper does not adequately explain how it manages distinct effect regions associated with text-based and drag-based editing despite these methods likely targeting different areas in an image. Clarifying how these regions are defined, integrated, or adjusted during editing would provide more specificity and improve understanding of the algorithm's functionality. This discussion is crucial to distinguish the contribution of the combined editing approach.\n\n4. Suggested Comparative Experiments for Method Validation\nComparative experiments should include scenarios where text-based editing is applied after drag-based editing and vice versa to illustrate the proposed method's effectiveness better. This comparison would help demonstrate the practical advantage of combining both methods in the proposed approach and establish whether there are meaningful improvements when they are applied sequentially.\n\n5. Limited Novelty in Gradient Combination Approach\nThe novelty presented in Equation (6), which combines the two editing approaches by decomposing one gradient into the other perpendicular component and then summing them, seems linear, and it is conceivable that a non-linear combination may provide a more effective result. Including alternative approaches as comparative experiments would strengthen the paper's case for its approach or help contextualize its performance relative to existing methods.\n\nThe paper introduces a combined text-drag editing approach but lacks a comprehensive literature review, convincing examples, clarity regarding region specificity, and evidence of sufficient novelty. Addressing these areas would help elevate the study’s contributions and clarify its position within diffusion-based image editing." }, "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.", "Yes, Discrimination / bias / fairness concerns", "Yes, Privacy, security and safety", "Yes, Legal compliance (e.g., GDPR, copyright, terms of use)", "Yes, Potentially harmful insights, methodologies and applications", "Yes, Responsible research practice (e.g., human subjects, data release)", "Yes, Research integrity issues (e.g., plagiarism, dual submission)", "Yes, Unprofessional behaviors (e.g., unprofessional exchange between authors and reviewers)", "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": "The comparisons in this paper in insufficient, why only DragDiff is compared to in this paper? More comparisons should be added." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tThe motivation is clear and effective, combining text and drag editing to leverage the strengths of both approaches, achieving more precise edits.\n2.\tThe Global-Local Gradient Fusion method is innovative, merging global text and local drag gradients to enhance editing quality, with experiments showing notable improvements in performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "CLIPDrag combines text and drag-based controls to improve image editing, using text for broad guidance and drag points for precise adjustments. The author introduces Global-Local Motion Supervision, which combines gradients from both text and drag inputs, and Fast Point Tracking to speed up convergence. This method eliminates common issues like vagueness in text-only edits and ambiguity in drag-only edits." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe illustration in Figure 2 is unclear in terms of workflow. If CLIP guidance is applied, the latent space should ideally be converted to the pixel domain to align with CLIP’s processing. However, the diagram uses SD rather than a VAE.\n2.\tCLIPDrag lacks comprehensive quantitative comparisons with other methods in image editing. The current evaluation only includes DragDiff in Figure 6, which is insufficient.\n3.\tThe ablation study also lacks more detailed quantitative comparisons. In Figure 8, the visual differences between (b) and (c) are subtle, making it hard to discern the impact of changes." }, "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 the context of drag-based editing where maintaining the original identity of objects while manipulating specific features is a major challenge, your manuscript suggests the integration of text-based inputs to guide the editing process. Could you elaborate on how the addition of text signals specifically contributes to preserving the object's original identity during the edit? Additionally, are there specific conditions or types of text prompts that particularly enhance this preservation aspect within the CLIPDrag framework?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. **Innovative Integration**: The paper presents a compelling approach by combining text and drag inputs to guide image editing. This dual-input strategy addresses the limitations of each method when used independently, potentially offering more controlled and precise edits.\n2. **Technical Depth**: The introduction of GLMS shows a deep understanding of the challenges in image editing, particularly in handling the complexities associated with combining different types of editing signals.\n3. **Experimental Validation**: Extensive experiments, including ablation studies, demonstrate the effectiveness of CLIPDrag against state-of-the-art methods. The results are well-presented and support the claims of improved performance in terms of both precision and ambiguity resolution." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This manuscript introduces CLIPDrag, a novel method that integrates text-based and drag-based signals for image editing, leveraging both for precise control and reduced ambiguity. The method utilizes Global-Local Motion Supervision (GLMS) and Fast Point Tracking (FPT) to enhance the image editing process, aiming to outperform existing methods by combining the strengths of both editing approaches." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **Novelty of FPT**: The paper should acknowledge that searching for handle points along the path from handle points to targets has been previously explored in methods like DragGAN and DragDiffusion. To clarify the unique contributions of FPT, the authors should provide side-by-side comparisons of point searching strategies, highlighting any improvements or distinctions in their approach.\n\n2. **Comprehensive Comparisons**: While the paper compares CLIPDrag with some existing methods, it would benefit from more extensive comparisons or discussions with recent techniques such as InstantDrag, LightningDrag, StableDrag, and RegionDrag. Although these methods may use different training approaches or inputs, incorporating their text-supervision signals could demonstrate CLIPDrag's ability to address ambiguities present in these methods, showcasing its generalizability. Additionally, these methods should be thoroughly discussed in the related work section to provide a more complete context.\n\n3. **Performance Metrics**: The paper should include a discussion or report on inference time comparisons. This information is crucial for understanding the practical applicability of CLIPDrag in real-world scenarios and how it compares to other methods in terms of computational efficiency.\n\n4. **User Input Optimization**: While the text prompt is provided in DragBench, it's worth noting that the original DragGAN paper did not require text input. The additional text prompt in CLIPDrag may increase user effort. To address this, the authors could explore incorporating vision-language models like GPT-4V to automatically interpret the input image (as shown in the first column of Figure 4). This approach could significantly reduce user burden while maintaining the benefits of text-guided editing." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024combining,\ntitle={Combining Text-based and Drag-based Editing for Precise and Flexible Image Editing},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2HjRezQ1nj},\nnote={under review}\n}" }, "abstract": { "value": "Precise and flexible image editing remains a fundamental challenge in computer vision. Based on the modified areas, most editing methods can be divided into two main types: global editing and local editing. In this paper, we choose the two most common editing approaches (\\ie text-based editing and drag-based editing) and analyze their drawbacks. Specifically, text-based methods often fail to describe the desired modifications precisely, while drag-based methods suffer from ambiguity. To address these issues, we proposed \\textbf{CLIPDrag}, a novel image editing method that is the first to combine text and drag signals for precise and ambiguity-free manipulations on diffusion models. To fully leverage these two signals, we treat text signals as global guidance and drag points as local information. Then we introduce a novel global-local motion supervision method to integrate text signals into existing drag-based methods by adapting a pre-trained language-vision model like CLIP. Furthermore, we also address the problem of slow convergence in CLIPDrag by presenting a fast point-tracking method that enforces drag points moving toward correct directions. Extensive experiments demonstrate that CLIPDrag outperforms existing single drag-based methods or text-based methods." }, "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": [ "Computer Vision", "Generative Model", "Diffusion Model", "Image Editing." ] }, "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/fcbad8308b7043bc870c5f6be924bfdfd97d3999.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": "Combining Text-based and Drag-based Editing for Precise and Flexible Image Editing" }, "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": "It would be very important if you can justify those points in the weakness part." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The authors tackle the problem of hierarchical robotic scene understanding, which is an interesting and important topic\n2. The proposed LOP is bio-inspired, to me this concept seems interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces Topo-Field, a framework designed to enhance mobile robot navigation by integrating detailed semantic information about layouts, objects, and their positions (LOP) into a neural field representation. Interestly, such structure is inspired by the role of postrhinal cortex neurons on spatial layout. By querying a learned NeRF, Topo-Field constructs a semantically rich yet computationally efficient topometric map for hierarchical robotic scene understanding. Experimental results demonstrate its effectiveness in tasks like position inference, localization, and planning, bridging the gap between detailed scene understanding and efficient robotic navigation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Unclear descriptions of target feature processing in Sec 4.1**\n1. How do you know if a 3D point belongs to the object, or the background? Do you use the GT annotations from the dataset? (the Matterport3D you show has that information I believe?)\n2. For the background features, you will get only a single feature for each image. How do you fuse those features from different views? \n3. Also, isn’t it making more sense to take per-pixel CLIP features from models like LSeg/OpenSeg, and fuse that information?\n\n**Unclear descriptions of neural scene encoding in Sec 4.2**\n1. Related to the questions above. In this section you mention that there are object-level local features and layout-level region features and MHE seems to be a good representation for learning such hierarchy. However, how exactly do you learn these two set of features respectively under MHE? No details there\n2. To learn MHE or NeRF in general, you need to actually shoot a ray for each pixel and sample along the ray. The final features are the weighted sum of all values along the ray, with volume rendering. How do you make sure your features on the 3D surface point are exactly the feature you render? \n\n**Unclear Topometric Mapping in Sec 4.3**\n1. Line 309, what is ${C_t, S_t}$? What are the differences to ${C_R, S_R}$ (I know this is the embeddings for region) in Line 304, and ${C_I, S_I}$ in Line 314? You did not specify them before. It is confusing and making it hard to understand\n2. Figure 3 (b) does not really match with what you write in “localization with text/image query” between Line 306-318. In the figure, all you get are the per-point features, and try to match with query features, omitting many important details in your description. \n3. “Matching” in Figure 3 is never really discussed. What kind of matching? Do you mean calculating the cosine similarity among the features, and take the one with the highest score?\n\n**Text query localization in experiments**\n1. How do you decide the similarity threshold for the bounding box? Do you need to choose a different threshold for each text query? My own experience is that, it is not really possible to get a single threshold for every query.\n2. One more thing: once you have the right threshold, how exactly do you get the bounding boxes out from thresholding? \n3. What are the “samples” in Table 1?\n4. How many queries are you considering for each scene, and how do you obtain the GT? Same question applies to Table 3 as well.\n\n**Image query localization in experiments** \nif I understand correctly, you show the heatmap of the query. You claim that “Topo-Field constrains the localization results to a smaller range in the exact region”. However, that is not really true to me. If you look at the washbasin in the bathroom, you also have many points highlighted in other regions, like kitchen, and even some points in the bedroom. In such a case, how can you get such good numbers in Table 3? \n\n**Ablation study**\nHow come your ablation in Table 4 is only evaluating the region prediction accuracy, which does not even require most parts of your methods (objects, the graph you build, etc). Why not evaluate on other things as well? And even that, your default strategy seems not outperform much over any of baselines, even the very simple baseline 1 in some scenes. \n\n**Writing** \n- Overall I think the writing is not good since many things are not justified well. \n- There are so many cases when the author writs (), a space is not added before, e.g. L214 …mapping(SLAM), L259 Multi-layer Perceptron(MLP), 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": 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": 2 }, "primary_area": null, "questions": { "value": "- In line 235: what is C and S? I assume it is the output of CLIP and Sentence-BERT? How are the regions r_p defined? \n- In line 239: what is m in this equation? \n- In page 5, line 241: It would seem that the partitioning of the space requires human labeling? If that is the case, it is a significant limitation of the approach. \n- In line 242: Could you clarify the sentence \"the predicted implicit representation outputs are targeted to match the features from the pre-trained models separately\", what it means in practice or how this is achieved. I assume this is what is described in 4.2, but it would be good to make it unambiguous if that is the case, as F is not referenced in that section. \n- Could you make the description in Section 4.2 more specific and formal? The only description of inputs/outputs and process we are provided is via the diagram in figure 1, it would be good to have a proper formal description of the process, description of the architecture, and format/dimensionality of inputs and outputs for each component, as well as a formal algorithm\n- In line 254: Could you provide a more in-depth argument for using MHE? The computational cost of standard NeRFs is well known, but is MHE the only possible solution? How does it compare with other fast approaches discussed in the literature, like, for example, Gaussian Splatting? \n- In line 258: Could you describe the mapping in more formal terms? Fig. 2 only provides a schematic description of the process. \n- In line 268: How is the similarity between E_pi and {C_R, S_R} calculated? It would be good to have a formal equation for this operation." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The combination of structural and semantic information in a way that is efficient for robotic system to query and plan on is a critical problem for robotics. \n- The approach seems to perform very well on the evaluation, clearly outperforming the presented baselines on those tasks. \n- The proposed approach is also reasonable in computational terms, as all experiments were performed on a single GPU (no information is given on training time though)." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This article proposes a novel approach for encoding scene information into a topometric map, for improving localisation and planning. The proposed approach is based on a Layout-Object-Position (LOP) approach. Layout information from knowledge of the environment's rooms. Object information from semantic segmentation (Detic) and a joint encoding of the segmented object patch using clip and of the object-region labels using Sentence-BERT. Finally, position information is produced by a 3D reconstruction of the scene using Multi-scale Hashing Encoding (MHE). This information is combined into a single Topometric map coined Topo-Field. \nThe proposed method is evaluated for the inference of position attributes and localisation and appears to clearly outperform the presented baselines." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The description of the approach is lacking specifics, and the reader has to infer the architecture and information flow from the provided diagrams rather than formal description in mathematical and algorithmic terms. \n- It is not fully clear how the partitioning of the environment (location) into rooms is performed and how well it would generalise to new environments. \n- The motivation of the work from neuroscience is interesting, but remains very vague. Little discussion is provided on how well the proposed approach may model the neural structures it claims to be inspired by. \n- The performance is very good compared to the discussed baselines, but it would seem that the proposed appraoch also benefits from significantly more task-specific information for those tasks (ie, the room information is provided directly). This is not a critical issue in my view, but it would be good to discuss the limitations of the presented baselines and issue of fairness of comparison some more. \n- I note that the reference for Reimers & Gurewych should probably cite the published version of the article rather than the pre-print." }, "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 experimental results are impressive when compared to baseline performances; however, it is unclear whether the benchmarks used are new proposed by the authors or following existing ones, which raises concerns about the fairness of the evaluation. What are the primary factors driving the significant improvements?\n\nComputation: the paper mentions a large batch size of 12,544. It would be helpful to clarify what specific data is contained within this batch size." }, "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 compelling problem in semantic mapping and its applications for enabling robots to navigate real-world environments. The experimental results demonstrate impressive improvements in performance. Additionally, the supplementary materials, such as the code snippets and prompts, enhance the understanding of the proposed method's details and implementation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a method for training a neural implicit field that utilizes supervisory signals from pre-trained foundation models to capture semantic features. The proposed model is applicable to several critical downstream tasks in robotics, including text/image query localization, semantic navigation, and path planning. Experimental results demonstrate significant improvements in performance metrics, supported by qualitative evidence." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Despite its potential, the system heavily relies on various input types, such as annotated room maps and camera poses, as well as off-the-shelf object detection methods for generating bounding boxes and masks. This dependence poses challenges in real-world applications, where inaccuracies in these inputs can lead to errors. Additionally, the system's reliance on ChatGPT complicates debugging and explanation when errors occur in complex real-world environments.\n\nEncoding semantic information and supervising it with pre-trained features alleviates some annotation burdens; however, this approach is already a common practice in the field of implicit representation for semantic mapping [1][2]. The overall system resembles a large engineering project, making it challenging to distill its theoretical contributions.\n\n[1] V. Tschernezki, I. Laina, D. Larlus and A. Vedaldi, \"Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations,\" 2022 International Conference on 3D Vision (3DV), Prague, Czech Republic, 2022, pp. 443-453, doi: 10.1109/3DV57658.2022.00056.\n[2] Zhu, Siting, et al. \"Sni-slam: Semantic neural implicit slam.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n\nTable 4 could benefit from clearer labeling, where Baselines 1-4 are not explicitly defined. A reference to Figure 7 could help.\n\nThe authors frequently reference the postrhinal cortex from the biological literature, but the connection to the proposed method is not clearly articulated. Topological mapping is indeed a common computer vision task relevant to navigation." }, "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": "With the issues addressed above, the authors should revise the paper accordingly." }, "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 constructing a topometric map using the implicit neural field is interesting" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper targets an interesting problem of topometric mapping but is not ready for publishing. The quality is poor regarding writing, organization, annotations, and experimental setups." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The writing is far from satisfactory. The corresponding authors should revise the manuscripts besides the abstract and introduction.\n+ Though the paper proposes a Topo-field to integrate layout-object-position, this representation is not clearly presented in Sec. 3. The definition of the topometric map (or the graph structure in Eq. 3) is vague and hard to follow, and the generation of the graph from dense field F (L199) is unclear. Note that the implicit neural field F is similar to previous methods with a distilled feature field, the novelty and the contribution of the proposed method are unclear.\n+ The hierarchical structure of point-object-room is common in scene graph generation. However, no relevant work (e.g., CLIO, HOV-SG) is referred to in the related work section or the experiments section.\n+ Multiple annotations are not formally defined in the paper (e.g., the functions $C_t, S_t$). The training stage in Sec. 4.4 should be carefully revised to make it clear.\n+ The experimental setups lack clear demonstration, and comparisons against recent methods are missing." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024topofield,\ntitle={Topo-Field: Topometric mapping with Brain-inspired Hierarchical Layout-Object-Position Fields},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2IBdk8cUdC},\nnote={under review}\n}" }, "abstract": { "value": "Mobile robots require comprehensive scene understanding to operate effectively in diverse environments, enriched with contextual information such as layouts, objects, and their relationships. While advancements like Neural Radiance Fields (NeRF) offer high-fidelity 3D reconstructions, they are computationally intensive and often lack efficient representations of traversable spaces essential for planning and navigation. In contrast, topological maps generated by LiDAR or visual SLAM methods are computationally efficient but lack the semantic richness necessary for a more complete understanding of the environment.\nInspired by neuroscientific studies on spatial cognition, particularly the role of postrhinal cortex (POR) neurons that are strongly tuned to spatial layouts over scene content, this work introduces Topo-Field, a framework that integrates Layout-Object-Position (LOP) associations into a neural field and constructs a topometric map from this learned representation. LOP associations are modeled by explicitly encoding object and layout information, while a Large Foundation Model (LFM) technique allows for efficient training without extensive annotations. The topometric map is then constructed by querying the learned NeRF, offering both semantic richness and computational efficiency.\nEmpirical evaluations in multi-room apartment environments demonstrate the effectiveness of Topo-Field in tasks such as position attribute inference, query localization, and topometric planning, successfully bridging the gap between high-fidelity scene understanding and efficient robotic navigation." }, "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": [ "Robotic scene understanding", "Neural scene representation", "Hierarchical representation", "Topometric map" ] }, "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/f20a3f3e9466efaa3a6b6cd80e4329fd31b9307d.pdf" }, "presentation": null, "primary_area": { "value": "applications to robotics, autonomy, planning" }, "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/328f1c520b9baeb43a71cf066b7043781fbdc581.zip" }, "title": { "value": "Topo-Field: Topometric mapping with Brain-inspired Hierarchical Layout-Object-Position Fields" }, "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": "See weakness." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "S1. This paper is easy to follow, and the idea is straightforward.\n\nS2. The introduction about the framework is clear, and the equations are well presented." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors propose a Fast Tensor-Based Multi-View Clustering with Anchor Probability Transition Matrix ((FTMVC-APTM) method. Within this model, to reduce the computational complexity, the relationships between data points and the selected anchors in different views are captured, and recorded by the bipartite similarity graphs. Based on these probability graphs, the cluster labels from anchors to samples are transferred, and the membership matrices can be obtained without the need for post-processing. To further exploit complementary information across views, the membership matrices are stacked into a tensor and contrained by a Schatten p-norm." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "W1. Overall, the idea of this paper is straightforward and clear. However, the novelty of FTMVC-APTM is limited, and the presented motivations/problems have been referred and solved.\n\nW2. The used datasets are too small, and the experiments provided in this paper are not convincing to show the superiority of the proposed method.\n\nW3. The running time comparison experiment is missing.\n\nW4. More recent fast multi-view clustering methods should be introduced and compared in the experiments." }, "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.\tThe sample size of the existing dataset is small and the authors should increase the experiments on large-scale datasets.\n2.\tThe available experimental results are not sufficient to support the authors' opinion. I suggest the authors to add some visualization or other experiments.\n3.\tCompared to existing methods, the authors' innovation is unclear. I suggest that the authors should carefully consider the motivation and contributions." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tThis paper is well organized.\n2.\tThis paper implements an exploration of fast tensor clustering.\n3.\tThe proposed methodology is somewhat enlightening." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, a tensor-based method is proposed to solve the MVC problem. The authors propose a simple and efficient method and verify the rationality and superiority of the method through experimental results." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I have some concerns about the paper, as follows:\n\n1. This paper is also limited by its innovative. Affiliation matrix is not a new method, it has been widely used[1,2]. Tensor Schatten-p norm[3,4] are also a common way to deal with low rank. So, the innovation made by the authors is more in the sense of incremental.\n\n[1] Zhao, J. B., & Lu, G. F. (2022). Clean and robust affinity matrix learning for multi-view clustering. Applied Intelligence, 52(14), 15899-15915.\n\n[2] Li, X., Zhang, H., Wang, R., & Nie, F. (2020). Multiview clustering: A scalable and parameter-free bipartite graph fusion method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1), 330-344.\n\n[3] Xie, Y., Gu, S., Liu, Y., Zuo, W., Zhang, W., & Zhang, L. (2016). Weighted Schatten \n-norm minimization for image denoising and background subtraction. IEEE transactions on image processing, 25(10), 4842-4857.\n\n[4] Li, X., Ren, Z., Sun, Q., & Xu, Z. (2023). Auto-weighted tensor schatten p-norm for robust multi-view graph clustering. Pattern Recognition, 134, 109083.\n\n2. The experimental results in this paper are inadequate. For example, the authors emphasize that their method enhances the interpretability of clustering. However, this needs to be verified experimentally. The superior performance of clustering alone may not provide effective support.\n\n3. In addition, the authors emphasize that their method requires only linear complexity and has a fast computational speed. However, the sample size of the dataset used is small, and I suggest the authors to increase their experiments on large-scale datasets such as AwA[5] or Youtube[6].\n\n[5] https://cvml.ista.ac.at/AwA/\n\n[6] https://www.cs.tau.ac.il/~wolf/ytfaces/" }, "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. This paper claims that this method is more interpretable than other complex multi-view clustering methods. Specifically, how does the membership matrix generated by the probability matrix help explain the final clustering structure?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The proposed method is clearly explained and easy to understand.\n\n2. This paper carefully analyzes the computational complexity of the method and illustrates the potential advantages of FTMVC-APTM in data scale expansion.\n\n3. Experimental results on eight multi-view datasets demonstrate its effectiveness." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new multi-view clustering method called Fast Tensor Multi-view Clustering Based on Anchor Probability Transformation Matrix (FTMVC-APTM). The method of directly calculating the membership matrix using the probability matrix avoids complex post-processing and enhances clustering interpretability. The nuclear norm and Schatten p-norm regularization are introduced to ensure the balance and robustness of the clustering results." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The main contributions of this paper is to combine the anchor probability transformation matrix and the Schatten p-norm regularization of the multi-view tensor structure. However, these ideas are not new in the field of multi-view clustering, and the combination of anchor selection, tensor structure and probability matrix has been applied in some methods[1][2].\n[1] Nie, Feiping, et al. \"Fast clustering with anchor guidance.\" IEEE Transactions on Pattern Analysis and Machine Intelligence (2023).\n[2] Yu, Weizhong, et al. \"Multi-View Fuzzy Clustering Based on Anchor Graph.\" IEEE Transactions on Fuzzy Systems (2023).\n\n2. This paper lacks ablation experiments on the key design of using probability matrix to calculate membership matrix. Given that this method is a core contribution of FTMVC-APTM, conducting relevant ablation experiments will help evaluate the actual impact of this strategy on the model performance.\n\n3. Although this paper demonstrates the superior performance of FTMVC-APTM on multi-view datasets, the scale of these datasets is relatively limited (the number of samples ranges from a few hundred to a few thousand), which fails to fully verify the performance of the method on large-scale data. It is recommended to supplement the experiments on larger datasets, such as the YTF dataset and the Caltech dataset.\n\n4. It is recommended that the authors appropriately increase the visualization results of clustering to help readers more intuitively understand the performance and clustering structure of the proposed FTMVC-APTM 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": 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": "A good framework for this paper." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed a Fast Tensor-Based Multi-View Clustering with Anchor Probability Transition Matrix ((FTMVC-APTM) to address some key challenges in multi-view clustering like lacking interpretability, and high computational complexity from large-scale data. Extensive experiments on various datasets are conducted to demonstrate the effectiveness and efficiency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tInnovative may not be enough for such a conference, this work simply combines a lot of work, for example, nuclear norm and Schatten p-norm regularization are both very common regular terms, and the authors don't discuss in depth why they use these two items， for example why Schatten p-norm regularization, there are many new low-rank tensor norm [1][2].\n2.\tThe article is poorly expressed, for example, whether the author employs Schatten p-norm or weighted tensor Schatten p-norm. the introduction states the Schatten p-norm, but Eq.6 uses the weighted tensor Schatten p-norm in [3]. These are two completely different concepts. If you use the weighted tensor Schatten p-norm, how did you determine the weight values for the different views?\n3.\tThis work states “fast tensor-based multi-view clustering”, but the dataset is only 4k in size and there is no runtime comparison, which is hard to believe!\n4.\tThe author states “Each experiment was replicated 5 times”, so why do the results in Table 3 not include variance?\n5.\tIn Figure 2, the performance always reaches best when anchor rate=1, which means the anchor is useless, and the complexity is also O(n^2logn), This result proves that the work proposed by the authors is not valid, At least it contradicts the author's “fast” statement.\n\n\n[1] Guo J, Sun Y, Gao J, et al. Logarithmic Schatten-$ p $ p Norm Minimization for Tensorial Multi-View Subspace Clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(3): 3396-3410.\n\n[2] Ji, Jintian, and Songhe Feng. \"Anchor structure regularization induced multi-view subspace clustering via enhanced tensor rank minimization.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n\n[3] Gao, Quanxue, et al. \"Enhanced tensor RPCA and its application.\" IEEE transactions on pattern analysis and machine intelligence 43.6 (2020): 2133-2140." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024fast,\ntitle={Fast Tensor-Based Multi-View Clustering with Anchor Probability Transition Matrix},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=2IUO0Iq5Bq},\nnote={under review}\n}" }, "abstract": { "value": "Multi-view clustering effectively integrates information from multiple data representations, yet current methods face key challenges. They often lack interpretability, obscuring how clusters are formed, and fail to fully leverage the complementary information across views, limiting clustering quality. Additionally, large-scale data introduces high computational demands, with traditional methods requiring extensive post-processing and manual tuning.To address these issues, we propose a novel multi-view clustering approach based on probability transition matrices. By selecting anchor points and constructing bipartite similarity graphs, we can capture the relationships between data points and anchors in different views and reduce computational complexity. Through probability matrices, we efficiently transfer cluster labels from anchors to samples, generating membership matrices without the need for post-processing. We further assemble these membership matrices into a tensor and apply a Schatten \\(p\\)-norm constraint to exploit complementary information across views, ensuring consistency and robustness. To prevent trivial solutions and ensure well-defined clusters, we incorporate nuclear norm-based regularization. Extensive experiments on various datasets confirm the effectiveness and efficiency 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": [ "Multi-view clustering", "Fast clustering" ] }, "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/4f6cce2849435384f8c6e704c1009c8da59dd5d0.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/9ea226b38a34a2dcae33c178dcf139521d8e9c68.zip" }, "title": { "value": "Fast Tensor-Based Multi-View Clustering with Anchor Probability Transition Matrix" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]