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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": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Q1. Do you have any idea, if the huge improvement of the performance of DeBerta after distillation is related to improving of the model's question answering ability, or just due to learning of Multiple Choice QA format?" }, "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 clearly written, and presents a practical method of LLM distillation to a smaller encoder-only model\n- A nice ablation study is provided. There are several interesting observations, e.g. increasing the performance with the distill loss + temperature adjustment, or the usage of the format correctness as an implicit sign of the model's confidence." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes the method of distillation of the large language model to the smaller one for efficient solving of Multiple Choice Question Answering task, via data generation and distillation loss. Two methods of data generation are considered: generate the whole question-answer structure with answer options in json format (via 5-shot prompting); or generate question-answer pairs obtaining each option separately. Then, the smaller model is trained on the generated data by distillation loss, learning to predict the larger model's probability of the generated options. The evaluation is done on MMLU benchmark. For the ablation study, ARC dataset is used" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The method is rather straightforward and does not contain a significant novelty, although the presented analysis is good\n- The practical usefullness of the considered task is not so clear. Indeed, Multiple Choice Question Answering is the specific QA format convenient for LLM's evaluation, but the MCQA results are not necessarily directly connected to the general QA abilities of the model. For encoder-only LLMs, classification-based approach looks more appropriate (i.e. scoring the correctness of the QA pair)\n- The model does not outperform Tasksource model which is obtained by the multi-task training of the same backbone: the improvement on MMLU is marginal (+0.5), and on ARC data the proposed approach works significantly worse." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 experiments lack a more detailed analysis of the two data generation methods (e.g., example-based analysis): Why do the two methods (JSON and Decompose) lead to different outcomes in performance? Why does JSON outperform Decompose?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The method is relatively simple and clearly explained.\n2. The paper explores the effectiveness of using LLMs to construct data for MCQA tasks, and the proposed distillation loss training method shows notable performance improvements.\n3. The paper conducted a relatively comprehensive ablation experiment." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper aims to enhance the performance of low-computation-cost, only-encode models for few-shot multiple-choice question answering (MCQA) tasks. It leverages large language models (LLMs) to generate a high-quality, task-specific MCQA dataset for training and introduces a training approach that applies distillation loss based on LLM-assigned scores. Experimental results demonstrate the effectiveness of the proposed method: LLM-driven data generation and knowledge distillation for few-shot MCQA." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The method's performance improvement is limited and depends on the strength of the base model. While the gains are more pronounced with the weaker DeBERTa-base model, they are minimal with the stronger Tasksource model, and even slightly decreases in the case of Decompose. \n2. Additionally, when using DeBERTa-base, the best performance (JSON distill) achieved by using only the constructed dataset does not surpass that of a multi-task fine-tuned model (Tasksource)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "* Has there been any comparison with recently released lightweight models, such as those with a 1B parameter size?\n* Is there a specific reason why only the DeBERTa encoder model was tested?\n* Was there a particular reason for employing few-shot learning in an encoder model instead of using a masked language model (MLM)?\n* Does this paper really aligns to the ICLR conference is questionable. Any other natural language processing conference seems more suitable." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* This paper sheds light again on the encoder-only model, which had been receiving less attention recently.\n* The methodology's adaptability to existing domain-specific benchmarks suggests its potential for broad application across diverse fields." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the possibility of encoder model with LLM-generated dataset and knowledge distillation. To address current effortful MCQA benchmark making, this paper utilizes LLM’s ability in few shot prompting with two formatting strategies. Then, by distilling the loss of bigger LLM into small, encoder-only model, the paper shows the efficient way to achieve performance nearing that of bigger LLM." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**[Novelty is limited]**\n\nThe paper’s novelty appears limited, as it does not introduce a new dataset and relies primarily on formatting prompts either in full JSON format or as segmented parts, raising questions on whether these methods constitute a genuinely novel approach. Furthermore, the distillation technique applied here does not seem particularly innovative, as it essentially reduces to a form of fine-tuning.\n\n**[Using Encoder-only Models - limited Experimental setups]**\n\nAdditionally, while the paper suggests the encoder-only model’s powerful capabilities, this claim is primarily based on improvements from distillation and model size reduction. These factors alone may not suffice to substantiate the model’s claimed \"power\" without more substantial baseline comparisons, particularly in tasks beyond fine-tuning.\n\n**[Inadequate analysis of suggested method]**\n\nThere is inadequate validation of the quality of the LLM-generated dataset, which raises further concerns about the reliability and applicability 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": 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 weakness" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The approach addresses a relevant problem in natural language processing, providing a practical solution for scenarios where computational resources are limited.\n2. The framework is straightforward, and the two methods of data generation (JSON and decomposed) are described in detail, with thoughtful consideration of their benefits and limitations.\n3. The paper presents extensive experiments, including performance comparisons, ablation studies, and evaluations on the MMLU benchmark." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a novel approach to address few-shot multiple choice question answering (MCQA) by leveraging large language models (LLMs) for data generation and knowledge distillation into a smaller, efficient encoder-only model, DeBERTa-v3-base. The study addresses the computational challenges associated with using LLMs directly in real-world applications and provides a three-step framework involving synthetic data generation, LLM-based scoring, and distillation training. Experimental results demonstrate significant improvements in accuracy over baseline models on the Massive Multitask Language Understanding (MMLU) benchmark, as well as competitive performance compared to larger models like LLaMA-7B and Flan-T5-250M. The paper also includes ablation studies on various generation methods, scoring techniques, and hyperparameters." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The method relies heavily on the availability of robust LLMs, which may not be readily accessible in languages other than English or for certain domain-specific tasks.\n2. The decomposed generation method, while reducing parsing errors, often results in noisy data due to longer and less structured answers." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "* On line 245, why is it “approximately 4000 MCQA examples”? Shouldn’t this be exact?\n* Why was number of negative examples set to 5 when MMLU and ARC only have 3?\n* What temperature was used for the MMLU experiments?\n* In Section 3.2, how is a sequence is being transformed into a scalar — [CLS] token? Pooling?\n* From Section 3.2 it is my understanding that handling a MCQ with n options requires n forward passes. Is that correct?\n* How was inference done for the baseline models?\n* When JSON samples are not properly formatted, are they resampled, or are less than 1024 samples used?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "(Originality) While synthetic data generation with LLMs and knowledge distillation into transformer based models are both widely used and studied, the authors consider the specific setting of MCQA and distilling a decoder-only model into an encoder-only model, which is a new setting.\n\n(Quality) The authors report results across several random seeds. They also do some nice ablation studies. The limitations section was also of high quality.\n\n(Clarity) The paper was generally clear and easy to follow.\n\n(Significance) As mentioned in originality, this paper explores a setting that is slightly different from past work." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors use a few task-specific multiple choice questions as seed examples to get a LLM to generate task-specific, synthetic multiple choice data. They explore two ways of prompting the LLM to generate this data. They train a small, encoder-only model via knowledge distillation using soft labels assigned by the LLM. They show that training on synthetic data via distillation is better than just training on a few non-synthetic task-specific data points directly, and also compare to some other models. The authors also conduct ablation studies regarding the amount of synthetic data, synthetic data generation temperature, and choice of LLM." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* A couple typos (these didn’t affect my review at all, but just mentioning them)\n * Line 69/70 missing a space\n * Line 179 “a scalar values” -> “scalar values”\n* When generating synthetic data, how can you be sure you’re not generating questions that are in the MMLU/ARC test sets (or that are quite close?). It would be nice to see something like nearest neighbors of generated questions, or something like overlap of answer options with answer option sets from the test sets.\n* A note on the tasksource+decompose/JSON is I don’t think it can necessarily be concluded that tasksource+JSON is better than tasksource as 0.5 is quite a narrow margin.\n* In my mind the main weakness of this paper would be lack of significance.\n * In practice, in the resource constrained setting there are already compelling alternatives to the approach described in this paper. For example, tasksource has the same amount of parameters, comparable performance, and faster inference as it only needs one forward pass. It also doesn't require synthetic data generation for each task. Furthermore, performance is less good than that of e.g., Gemma-2-2b-it and similar models which can be run quite cheaply on even a laptop (especially after quantization). I don’t see when “distillation into DeBERTa” would be used in practice because there are already very compelling alternatives. I'd be happy to hear the authors' take on this, though.\n * A paper definitely doesn't need to be the best \"in practice\" option to be useful, as it might provide surprising/intuitive insights compared to previous work. However, I don’t find the results of this paper particularly surprising in light of past work. Knowledge distillation is already widely used with LMs, and distillation from larger LLMs to smaller LLMs is done all the time with good results. Synthetic data generation with LLMs is also frequently done, and has been shown to work well. That more synthetic data works better is as expected.\n\n*Strengths & Weaknesses tl;dr*: I think the authors’ study is well thought out and put together, and mostly easy to follow. However, I don’t think it provides substantial insight/methods beyond what already seems to be common knowledge in the research community. I’ve assigned a rating of 3, but I’d choose 4 if it were an option because I think the paper is overall well made but just doesn’t have the level of impact I typically associate with ICLR papers." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "uses a large language model for few-shot multiple-choice question answering by generating synthetic training data and distilling knowledge into a smaller model, significantly boosting its performance." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024llm,\ntitle={{LLM} Distillation for Efficient Few-Shot Multiple Choice Question Answering},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1TJSnL3ywS},\nnote={under review}\n}" }, "abstract": { "value": "Multiple Choice Question Answering (MCQA) is an important problem with numerous real-world applications, such as medicine, law, and education. The high cost of building MCQA datasets makes few-shot learning pivotal in this domain. While Large Language Models (LLMs) can enable few-shot learning, their direct application in real-world scenarios is often hindered by their high computational cost. To address this challenge, we propose a simple yet effective approach that uses LLMs for data generation and scoring. Our approach utilizes LLMs to create MCQA data which contains questions and choices, and to assign probability scores to the generated choices. We then use the generated data and LLM-assigned scores to finetune a smaller and more efficient encoder-only model, DeBERTa-v3-base by leveraging distillation loss. Extensive experiments on the Massive Multitask Language Understanding (MMLU) benchmark demonstrate that our method improves accuracy from 28.9\\% to 39.3\\%, representing a gain of over 10\\% compared to a baseline finetuned directly on 5-shot examples. This shows the effectiveness of LLM-driven data generation and knowledge distillation for few-shot MCQA." }, "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-shot learning", "Multiple Choice Question Answering (MCQA)", "Data generation", "Knowledge distillation", "Multiple Choice Question Answering (MCQA)" ] }, "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/1806f3accd9e08892dfab43cf7fe9dc18ecc46fa.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": "LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering" }, "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": { "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": "I am open to discussion and willing to reconsider my score if my major concerns can be adequately addressed.\n\n\n**Claims on the Effectiveness of the Proposed Regularization Technique**\n\n- For example, lines 326-329 state:\n > We apply this regularization to MambaCL and other sequence prediction models (weighted by a scalar λ) together with the MCL objective in Eq. (7), which improves the meta-training stability and convergence for all models.\n\n- The authors do not fully support their claims about \"improving the meta-training stability and convergence for all models.\" Specifically, there are no experiments showing learning curves (or similar alternatives) for all models during meta-training to compare results with and without this technique. \n\n- A seemingly related empirical evidence is presented in Figure 4. However, the results appear to pertain to a *single* model, and it is unclear, based on the figure caption and the text in lines 481-485, which specific model (i.e., Mamba, transformers) was used in this ablation study. Although the experiment demonstrates the sensitivity of meta-testing performance to the regularization strength, it lacks comprehensive evidence across multiple models to support the authors claim.\n\n\n**Experiment Implementation Details**\n\n- In the paper, it is mentioned: \n > Following Lee et al., 2024, we set the initial learning rate to 1 × 10⁻⁴...\n\n- Cloud the authors please provide some motivations for using the same hyperparameters as in Lee et al., 2024, given that the meta-training setups differ? Specifically, the authors used a pre-trained CLIP backbone as a visual encoder and included the proposed regularization loss across all models. \n\n- Moreover, were these hyperparameters adjusted for different model architectures based on some meta-validation sets, e.g., for linear transformers and Mamba? If not, wouldn't using fixed hyperparameters for all experiments and models potentially lead to sub-optimal results? If these hyperparameters are not optimal for every models, this could produce misleading results and potentially invalidate the observations.\n\n**Meta-Overfitting in Figures 3a and 3b**\n\n- The authors observed that transformers and their variants seem to suffer from severe meta-overfitting based on the results in Figures 3a and 3b. However, the potential underlying causes for this overfitting are quite unclear. Specifically:\n\n - As previously mentioned, based on the current description of the implementation details, it's unclear whether this overfitting is due to the use of improper hyperparameters, such as learning rates.\n\n - Additionally, it is undetermined whether this overfitting is influenced by the use of regularization terms for all models during meta-training. Would removing this regularization loss for transformers significantly reduce meta-overfitting?\n\n- Could the authors please provide some insights into why Mamba did not suffer from the same degree of overfitting?\n\n- While the occurrence of meta-overfitting is expected, the degree of overfitting—particularly in relation to the number of training tasks and training shots used in meta-training—exhibited by transformers and their variants in Figures 3a and 3b is somewhat surprising. Specifically, in Figure 3b, adding more training shots per class even, and almost monotonically, decreased the classification accuracy on the queries.\n\n\n**Robustness in Figure 3c**\n\n- It is somewhat unclear how the authors performed the input noise perturbation. Specifically, what does $ x_i$ in line 473 refer to? Is it the original input image to the CLIP encoder, or the extracted image embeddings that serve as inputs to the sequential learning models?\n\n- I find it very interesting that Mamba exhibits excellent robustness to input noise, even with a standard deviation as large as 10. Could the authors potentially discuss some potential reasons behind Mamba's extreme robustness to large input noise?\n\n**General Comments on MCL**\n\n- Some important challenges in the MCL setup for continual learning include: 1) its application to long continual learning sequences, 2) the requirement for offline training datasets (meta-training), and 3) generalization to unseen long OOD meta-testing tasks. These challenges cannot be resolved simply by switching from transformers or their variants to Mamba.\n\n- Are there any differences on the problem formulation and the meta-training setups between the ones in the paper and the one in MetaICL: Learning to Learn In Context, Min et al., NAACL 2022?" }, "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 well-structured and easy to follow.\n- The authors clearly explained the issue of increased compute complexity with using transformers for MCL." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper follows the meta continual learning (MCL) framework as outlined by Lee et al., 2024. The authors meta-train sequential models on offline meta-training sequences to enhance their sequence modelling capability. The authors propose using Mamba as the sequential model instead of transformers or attention-free variants to alleviate high computational costs while still achieving satisfactory performance. Additionally, the authors introduce a selective regularization technique for meta-training, which enhances the association between query tokens and previously correlated input tokens. Experimental results demonstrate that Mamba achieves improved generalization and robustness compared to transformer variants in MCL, while using less memory for inference." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "In general:\n\n- The paper shows limited novelty. The problem formulation, specifically the recasting of the continual learning problem as a sequential modelling problem in recurrent models, mirrors the previous work by Lee et al., 2024. From the technical side, the authors propose a new selective regularization technique for meta-training and claim it improves training stability and convergence. While the technique itself is novel, there are several questionable aspects regarding this technique and the authors' claims. I cannot fully credit the novelty of this technique until these issues are addressed.\n\n- Although the authors claim better generalization and robustness when using Mamba instead of transformers based on empirical results, these results appear somewhat questionable. Furthermore, there is a lack of new insights and detailed analysis; for instance, the authors did not delve deeper into the underlying mechanisms that led to these results. This deeper analysis is crucial, especially if the primary motivation of the paper is to use Mamba (or any different model architecture) instead of transformers for the same problem settings.\n\nPlease kindly refer to the questions for more details." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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 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. It proposes MambaCL as a strong sequential approach to meta-continual learning. \n\n2. It performs thorough experiments and discover multiple interesting observations.\n- The use of Mamba may be more helpful for generalization over Transformers as discussed in Fig.3.\n- MambaCL is particularly effective in on fine-grained recognition tasks as shown in Table 3.\n- Integration of Mamba with MoE improves the MCL performance as reported in Table 6." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work addresses meta-continual learning using a state space model Mamba. It performs comprehensive experiments across various CL benchmarks and reports several interesting results, including comparison with Transformers and extension to Mamba mixture-of-experts." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The technical novelty is limited.\n- This work is largely based on the work of (Lee et al., 2024), which first formulates the MCL problem as a sequent modeling.\n- This work simply replaces Transformers of (Lee et al., 2024) with a state space model Mamba. \n- Except this replacement, there is little novelty as its application is rather straightforward, following (Lee et al., 2024). \n\n2. The use of Mamba instead of Transformers leads to little performance improvement as reported in Table 1-5. \n- The main benefit of Mamba over Transformer lies in fewer parameters and increased processing speed as shown in Table 7.\n\n3. Implementation details are missing. \n- Appendix is too sketchy to fully understand how the MambaCL is implemented. \n- The code is not provided." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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": "N/A" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* It is interesting to explore how Mamba performs in a meta-continual learning setting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors explore a key research question: Can the attention-free Mamba model effectively handle meta-continual learning (MCL) tasks? They reframe State Space Models (SSM) and Mamba as sequence-prediction-based continual learners, training them via meta-learning across continual learning episodes. To enhance this training, they introduce a selectivity regularization technique. Extensive experiments reveal that Mamba consistently performs well in various MCL settings, significantly surpassing other attention-free approaches and often equaling or surpassing Transformer models in performance—all while using fewer parameters and computational resources. Notably, Mamba demonstrates strong reliability, generalization, and robustness in complex scenarios." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The conclusion of this paper is unsurprising, as Mamba's MCL performance aligns closely with its results on standard benchmarks.\n\n* There is insufficient analysis explaining how and why Mamba outperforms other attention-free architectures and achieves comparable results to Transformer-based models." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024learning,\ntitle={Learning Mamba as a Continual Learner},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1TXDtnDIsV},\nnote={under review}\n}" }, "abstract": { "value": "Continual learning (CL) aims to efficiently learn and accumulate knowledge from a data stream with different distributions. By formulating CL as a sequence prediction task, meta-continual learning (MCL) enables to meta-learn an efficient continual learner based on the recent advanced sequence models, e.g., Transformers. Although attention-free models (e.g., Linear Transformers) can ideally match CL's essential objective and efficiency requirements, they usually perform not well in MCL. Considering that the attention-free Mamba achieves excellent performances matching Transformers' on general sequence modeling tasks, in this paper, we aim to answer a question -- Can attention-free Mamba perform well on MCL? By formulating Mamba with a selective state space model (SSM) for MCL tasks, we propose to meta-learn Mamba as a continual learner, referred to as MambaCL. By incorporating a selectivity regularization, we can effectively train MambaCL. Through comprehensive experiments across various CL tasks, we also explore how Mamba and other models perform in different MCL scenarios. Our experiments and analyses highlight the promising performance and generalization capabilities of Mamba in MCL." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Continual Learning", "Sequence Modelling" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/f426ca68936779a2ca68468d9dc7f4ec832bf0fa.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": "Learning Mamba as a Continual Learner" }, "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: Following W1, what are the assumption and failure cases of the proposed camera estimation?\n\nQ2: Following W3, please describe how the metric is calculated in detail for fair comparison against prior methods." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "S1: Sensible model design\nAlthough the masked latent conditioning is not new, the architectural modification upon SVD is sensible and allows joint training on 3D and 4D data. \n\nS2: General model for 3D and 4D generation\nThe proposed model is capable of 3D and 4D generation of both object-centric and scene-level videos, which is more general than most prior methods. On a side note, the authors should also include MotionCtrl in Table 1.\n\nS3: Good writing\nThe paper is well-written and easy to follow overall." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes GENXD, a latent diffusion model for 3D/4D generation of objects or scenes. Specifically, it adopts masked latent conditions to support various number of input views, and the alpha-fusing mechanism allows joint training on 3D and 4D data. Considering the lack of 4D scene dataset, the authors further curated a new dataset, CAMVID-30K, by estimating camera with a SfM-based method and filtering out videos without object motion. Qualitative and quantitative results show that the proposed method generates comparable or slightly more satisfactory outputs than corresponding prior arts." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "W1: Limitation of camera pose estimation\nThe proposed camera pose estimation relies on segmentation of all moving pixels. However, in scenarios where camera moves independently of object motion, especially when camera motion is large or objects take up a large portion of the scene, it would be challenging to estimate accurate camera pose. Does the method assume that these cases do not exist in the dataset?\n\nW2: Quality of 3D object generation\nThe results of 3D object generation seem to be of comparable or worse quality than the prior state-of-the-arts both qualitatively (Figure 10) and quantitatively (Table 6). Moreover, the quantitative evaluation is incomplete since some more recent methods (Zero123XL, Magic123, SV3D, EscherNet, etc) are missing and the metric is limited to CLIP-I only, while prior works usually report metrics like LPIPS, SSIM, Chamfer Distance, 3D IoU (on 3D object datasets like Google Scanned Objects).\n\nW3: Evaluation of 4D object generation\nAgain, the quantitative evaluation for 4D object generation is limited to the CLIP-I metric and more recent methods like STAG4D and DreamGaussian4D are missing. Also, it is unclear if the metrics in Table 3 are calculated on the training (synthesized) video frames only or on densely sampled views and timestamps. Since the proposed method optimizes 4D-GS only on one camera orbit without SDS loss, I suspect that the outputs look good on these frames/views but worse than other methods in novel views.\n\nW4: Small camera motion in 4D scene generation\nAll the presented results on 4D scene generation seem to have smaller camera motion compared to results shown in prior work like MotionCtrl. Although the results in Figure 5 and supplemental video show decent temporal consistency and motion, I’m wondering if it is limited to camera trajectories without much deviation from the input view.\n\nW5: Lack of results on motion strength control\nWhile the paper emphasizes the contribution of motion strength control, there is only one example of a simple driving scene. It would be more insightful to show more diverse motion cases to understand the effeteness and limitations of it." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "The author should provide rigorous analysis of the accuracy of the camera-controll capability, and how changing alpha values affects the generated motions." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The paper shares technical details on how to annotate magnitude motion and camera poses from in the wild videos. The alpha-fusion layers for motion disentangle seems an interesting design." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper trained a video generation model that can control camera trajectory and magnitude of motion and supports multiple frame conditioning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "First, I feel the claim of being able to perform 4D generation is an over-claim to me. 4D generation requires the capability of either directly generating 4D representations such as dynamic 3D GS, or at least generating synchronized multi-view videos like in SV4D. Neither of these capabilities were presented in the main paper. In table 1, the capability of generating synchronized videos were not discussed, and to me, this is a severe misrepresentation. It would be more appropriate for the author to rebrand their method as a motion-controllable and 3D-aware video model. \n\n2nd, although the idea of using alpha-fusion seems interesting, it is currently not properly evaluated. It did not show how changing alpha values affects the magnitude of generated motions, and it did not evaluate the camera control accuracy as other related papers did. Reporting CLIP-score and FID is not enough to reflect the accuracy of the proposed capability of the method.\n\n3rd, a minor point, I am not sure promoting the capability of taking multiple image input can be regarded as a major technical contribution, given it is already supported in prior works including CAT3D, and it is conceptually trivial to be implemented in most video generation 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": 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 the top case of Figure 10, the results from the proposed method appear off-center, possibly due to an inappropriate object-to-image occupancy ratio in the input images. Adjusting this ratio might improve the alignment of the results.\n2. If the learnable fusion weight, alpha, is set to 1, would it enable video generation based on the first frame? With alpha at 1, only the outputs from the temporal modules would contribute." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The proposed method is the first to generate any 3D and 4D scenes with camera control and an arbitrary number of condition frames.\n2. The proposed multiview-temporal modules with alpha-fusing enable separate multi-view and temporal information and effectively conduct both 3D and 4D generation.\n3. The paper constructs a new dataset for 4D scene generation. The dataset and the data curation pipeline potentially benefit the following video generation with camera control and 4D generation.\n4. The paper is well-written and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "1. This paper aims to jointly generate 3D and 4D objects and scenes with camera control. \n2. This paper proposed multiview-temporal modules that disentangle camera and object movements and thus can learn from both 3D and 4D data. The proposed approach employs masked latent conditions to support a variety of conditioning views.\n3. They construct a dataset CamVid-30K that consists of high-quality 4D data with camera poses for model training\n4. Extensive experiments show that the proposed method can achieve comparable or better results than baselines in 3D/4D object/scene generation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Experiments**:\n\n1. In the experiment of 4D object generation, some relevant references and comparisons are missing, such as Consistent4D [1] and STAG4D [2]. Since these works are open-source, it would strengthen the paper to include these baselines or clarify why they are not suitable for comparison. They take single-view video as input, which should be applicable for this work.\n2. In Table 3, it would also be beneficial to report temporal consistency metrics (e.g., FVD), as temporal consistency is critical for 4D object generation.\n\n**Minor Points:**\n\n1. Clarifying the selection process for the 44K dynamic data in Objaverse-XL would be helpful. According to Diffusion4D [Liang et al. (2024)], ~323K dynamic objects were collected. For instance, what filters were applied in this work? Will the selected dynamic objects be publicly available? Adding these details in the Appendix would enhance transparency.\n2. Some technical details are missing: What is the maximum number of frames the model supports? Additionally, in Table 3, Zero-1-to-3 and RealFusion were originally designed for 3D reconstruction—how were they adapted for 4D generation in this work?\n\n[1] Jiang, Yanqin, et al. \"Consistent4d: Consistent 360 {\\deg} dynamic object generation from monocular video.\" ICLR 2024.\n\n[2] Zeng, Yifei, et al. \"Stag4d: Spatial-temporal anchored generative 4d gaussians.\" ECCV 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": 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 statement in section 3 that \"when the camera is moving while the object remains static, the motion strength is significantly smaller compared to videos with object motion\" seems not so easy to understand. I assume the authors mean that this is the common case for natural captured video where cameras are still or moving in a slow motion?\n\n2. Does the $\\alpha$ needs to be explicitly set during training / inference? For example let network itself output the weight when dealing with 4D content and explicitly set it as 0 when dealing with 3D content. If so then it would be interesting to see given same conditions (more complicated than Figure 7) what would model outputs for different $\\alpha$. Like given multi-frames of a static scene but telling model do 4D generation and given multi-step frames with dynamic objects and force model to do static 3D generation.\n\n3. It's kind of confusing that the 5.4 ablation study is for model training or just inference after training? If it's after training, than the results in table 5 is somehow not so useful as it's trained with $\\alpha$ but in inference time not allowed to use it, which would certainly lead to performance drop." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The data curation pipeline for transforming existing videos into trainable 4D dataset is quite useful, and the proposed curation pipeline and the CamVid-30K should be beneficial to the field.\n\n2. Combining all source and sub-tasks' data (object/scene, 3D/4D) is fundamentally useful and a model trained on mixture of data should have better generalization ability. The proposed $\\alpha$ parameter seems can be understood as an explicit control to switch between 3D and 4D generation given same conditions.\n\n3. The results are promising and generally good. And extensive evaluations on multiple benchmarks show the effectiveness of the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In the paper the authors propose two main contributions: a curated dataset for 4D generation model learning, named CamVid-30K; and a model trained to generate in 3D or 4D given arbitrary condition images, named GenXD.\n\nAuthors proposed a detailed pipeline on how to combine existing techniques to curate a 4D scene datasets for model training. Including instance segmentation modules for static / dynamic decomposition, static structure-from-motion to recover camera parameters and sparse depth map, relative depth align with sparse depth map for spotting the dynamic object and introducing a motion strength factor as additional condition.\n\nAuthors proposed a new model GenXD to train on this dataset combining with other object-centric 3D/4D datasets and 3D scene datasets. They further design a $\\alpha$-fusing strategy to better disentangle the spatial and temporal information in the data source. Experiments across various benchmark show impressive performance of the proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "There are some minor errors or confusing points in the paper. I'll list some here and some in the following questions section.\n\n1. In L253, \"The keypoint $(u_i, v_i)^T$ in the $i$-th frame is first back-projected into world space to obtain the 3D keypoint $kp_i$\". I agree here the $kp_i$ should be in world space, but according to Eq.(3) seems it's in the camera space? From my perspective the Eq.(3) is transforming image-space coordinates to camera-space coordinates, missing the step of transforming to world coordinates.\n\n2. In all the figures with camera trajectory visualization, the legends and axis notations are very small and impossible to tell the actual information, also the trajectory only lies in a small region in the plot. I suggest authors remove the axis notations if they are too small, and zoom in to show the trajectory in a more detailed way.\n\n3. In section 5.2 4D object Generation, it seems unfair to say \"results in our method being $100\\times$ faster\", as the efficiency comes from using a different underlying 3D representation comparing to other methods, which are orthogonal to the proposed method. I think here using CLIP similarities for comparison is reasonable. Showing speed is fine but shouldn't be used as comparison.\n\n4. I think in general the paper is with good results. But according to the task of the proposed method, I expect to see more scene level 3D or 4D generation results, including larger camera trajectories and failure examples." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024genxd,\ntitle={Gen{XD}: Generating Any 3D and 4D Scenes},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1ThYY28HXg},\nnote={under review}\n}" }, "abstract": { "value": "Recent developments in 2D visual generation have been remarkably successful. However, 3D and 4D generation remain challenging in real-world applications due to the lack of large-scale 4D data and effective model design. In this paper, we propose to jointly investigate general 3D and 4D generation by leveraging camera and object movements commonly observed in daily life. Due to the lack of real-world 4D data in the community, we first propose a data curation pipeline to obtain camera poses and object motion strength from videos. Based on this pipeline, we introduce a large-scale real-world 4D scene dataset: CamVid-30K. By leveraging all the 3D and 4D data, we develop our framework, GenXD, which allows us to produce any 3D or 4D scene. We propose multiview-temporal modules, which disentangle camera and object movements, to seamlessly learn from both 3D and 4D data. Additionally, GenXD employs masked latent conditions to support a variety of conditioning views. GenXD can generate videos that follow the camera trajectory as well as consistent 3D views that can be lifted into 3D representations. We perform extensive evaluations across various real-world and synthetic datasets, demonstrating GenXD's effectiveness and versatility compared to previous methods in 3D and 4D generation. The dataset and code 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": [ "3D Generation; 4D Generation; Diffusion Models" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/bdce10e24a52c41ddaf103f9f899cbad21b41f20.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/2931e9d0738280f9869f3cc20f8888959a6e6be0.zip" }, "title": { "value": "GenXD: Generating Any 3D and 4D 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": 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": "- can authors elaborate on why the maximum likelihood is needed despite already enforcing low reconstruction error ?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- presentation: the paper is polished, clear, and well-written\n- relevance of the topics: learning models that disentangle sources of information whether attributes or objects without any prior knowledge about the type of sources but rather that rely on general prior information about the data structure like compositionality to enforce disentanglement is of great nterest to the community." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates the learning of disentangled representations in particular the adaptation of existing frameworks to the learning of representations that can disentangle both attributes (e.g., color, texture, ...) and objects in a scene which authors claim prior work only tacked one of the other. The authors propose to leverage compositionality to learn disentangled representations. The setup includes pre-trained VAEs which provide representations that are then combined. The new representations serve as input to a diffusion-based decoder which is trained to reconstruct the composition of the original images. A pre-trained diffusion model is also used to enforce consistency between the input composite representations and the representation of the generated image. The method is tested for feature and object disentanglement on multiple synthetic datasets where is shows either superior or comparable performance to attribute or object disentanglement methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- complexity of the proposed approach leads to limited applicability and impact: the proposed approach requires the use of pretrained diffusion models to operate (i.e., to maximize the likelihood of composite images) and requires access to composite images to train the model. \n- limited performance increase: while results show more consistent improvements for the **multi-seed** attribute disentanglement experiments, the gains are less consistent across metrics for the **single-seed** object disentanglement experiment.\n\n\nMinor:\n- theta should be a subscript in line 187\n- typo line 212, 281, 310\n- error in figure 1: z3 should be blue instead of orange\n- line 227: figure 1 above\n\n- Not sure I am getting lines 210-213" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "Are there any further insights on the failure cases? Is it harder to compose attributes or objects?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1. Addresses both attribute and object disentanglement by developing appropriate mixing strategy for latents. This is helpful to steer the field towards disentangling different types of factors of variation - eg properties of object and object themselves.\n2. The paper gives an in depth analysis of the intricacies involved in optimizing for compositionality.\n3. The paper is well written for the most part. There are appropriate visualizations in method and experiments that complement the text." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper attempts to tackle attribute and object disentanglement through the same mechanism as opposed to separate treatment by prior methods. Building on diffusion based decoding approaches that maximize compositionality, this paper lays emphasis on composing/mixing strategy of latents for object/attributes." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The impact of paper can be more by showing results on real world data" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "How would this generalize to more complex datasets where the exact factors of disentanglement might not be known. Does this scale to lots of disentangled factors (dozens or hundreds) or would that make the mixing strategies too complicated?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper addresses the learning of disentangled representations for both objects and attributes and makes use of a standard generative model for learning them. By introducing specific mixing strategies to combine latent representations of different images under given constraints the model is able to learn disentangled representations under a fairly simple framework.\n\nThe evaluation shows that the model learns better disentangled representations than the given baselines." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Note: I am not an expert on disentangled representation learning and know little/none of the related work.\n\nThe paper proposes an approach to learn a generative model for learning disentangled representations by maximizing the compositionality of representations. By mixing the representations of two images (given some constraints to make sure the results latent representations are valid) and maximizing the likelihood of the resulting composite images the model learns representations that can be disentangled on the object and attribute level. Experiments on synthetic datasets show that the model performs well in disentangling factors across several datasets both on the object and attribute level." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "It seems like the approach is only useable if the practitioner already knows the underlying factors they want to disentangle, as the latent mixing strategies take this knowledge under account. \nIt's also not clear to me if this would translate to real-world datasets with more complicated distributions.\nThe experiments show results for either object disentanglement or attribute disentanglement but no experiments for joint object and attribute disentanglement.\nAll experiments are done on rather simple synthetic datasets." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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 challenges do the authors anticipate in applying this model to real-world, complex datasets, and how might they address these?\n- Could dynamic/learned mixing strategies replace fixed ones to improve flexibility in complex scenes? \n- Have the authors thought about under which conditions their method can provide identifiability guarantees?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "**Strengths:**\n\n- The paper is relatively clear and easy to understand;\n\n- The general idea of enforcing compositional consistency across mixed latent representations is fairly neat, and could possibly be extended to more challenging scenarios;\n\n- The results seem to match or exceed some of the previous works on disentanglement benchmarks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a framework for disentangled representation learning that targets both attribute—and object-based disentanglement within a single model. The authors formulate disentangled representation learning as maximizing the compositionality of randomly mixed latent representations of distinct images. The method uses a pre-trained diffusion model as an image generator and introduces an additional compositional consistency loss to encourage the composite images to remain faithful to the composite latent. The authors claim that their method can obtain superior performance in standard disentanglement benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Weaknesses:**\n\n- The approach relies on a pre-trained diffusion model to ensure composite image realism, but this doesn’t guarantee alignment with the intended attribute or object combinations. As such, it is my understanding that this can compromise the interpretability and control of compositions in the general case, especially in more complex scenarios with subtle and/or hierarchical attribute/object relationships. \n- There are no guarantees that the latent representations are identifiable under the current model, and by implication, neither are the compositions;\n- The fixed mixing strategies, although appropriate for the simple cases studied, are quite rigid and likely would not adapt well to more complex scenarios in real data;\n- The scope of the evaluation is limited to toy settings which is somewhat outdated given the recent progress in generative modelling.\n- The writing is a little careless at times, there are numerous typos and/or grammatical issues some of which are mentioned below.\n\nIn my opinion, in its current state, this work largely sidesteps the key challenges in the area today, particularly the theoretical analysis of identifiability for latent representations and the development of scalable techniques that allow object-centric methods to be applied effectively in real-world settings. Therefore, I would encourage the authors to bolster their current contribution by tackling one of the two aforementioned challenges in the future.\n\n**Typo corrections:**\n\nline 34 \"theoretically prove\" \\\nline 46 \"a unique object\" \\\nline 70 \"and verify\" \\\nsection 2 heading change to \"Background\" \\\nline 77 \"incompatible with\" \\\nline 97 \"that render\" \\\nline 107 \"tailored specifically\" \\\nline 122 \"maximizing the likelihood\" \\\nline 122 \"disentangle attributes and objects\" \\\nline 147 \"to the type of\" \\\nline 163 \"While (Jung et al., 2024) rely\" \\\nline 165 sentence needs rewriting for clarity \\\nline 167 \"derive a specific\" \\\nline 177 \"of each factor\" \\\nline 177 \"derive a corresponding\" \\\nline 188 \"independent sampling of\" \\\nline 190 \"is equivalent\" \\\nline 197 \"always contains\" \\\nparagraph starting at line 206 could do with rewriting for clarity \\\nline 216 \"belong to the same\" \\\nline 259 \"While Jung et al. (2024) also maximize...\" \\\nline 295 \"to each factor of\" \\\nline 307 \"ensure reliable image generation\" \\\nline 310 \"from scratch\" \\\npage 6 footnote \"significantly\" \\\n\netc" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 address the main weaknesses listed above. These are the most critical ones, I find the paper interesting but these weaknesses do need to be tackled, specifically:\nA. Could you explain or correct the mismatch between your results and those previously reported?\nB. Could you provide results on unsupervised segmentation tasks using the three typical metrics: Adjusted rand index for foreground objects (FG-ARI), mean intersection over union (mIoU), and mean best overlap (mBO) (see Jung et al 2024 as an example).\nC. Could you provide results on at least a couple of the more complex datasets listed above (and for the tasks used in the state of art work mentioned).\n\nAdditionally these are more questions that are interesting to discuss.\n\nD. The authors state at various points in the manuscript that previous methods use inductive biases specific to either attributes or objects, making them unsuitable for both simultaneously. For instance, in the statements, “Existing disentangled representation learning methods rely on inductive biases tailored for specific factors of variation (e.g., attributes or objects). However, these biases are incompatible with other classes of factors” and “Unlike previous methods, which introduce inductive biases tailored specifically to either attribute or object.”\nHowever, the proposed method also requires a choice of mixing strategy tailored to either attributes or objects, which seems like an inductive bias itself, specific to one type of disentanglement. Could this advance choice also be considered a form of inductive bias that is specific to objects or attributes? Likewise, could state-of-the-art methods (e.g., Jung et al., 2024) also be modified to handle both attributes and objects? It’s unclear to me to what extent prior methods are fundamentally \"unable\" to address both types of disentanglement, as opposed their experiments being focused on of the the two tasks but potentially adaptable to the other in a way similar to how this proposed method can be adapted via choosing an appropriate mixing strategy.\n\nE. In Section 2 the authors make the following comment “in object-centric scenes, the same objects can appear in different spatial locations, complicating the definition of independence metrics for object representations”. It would be great to show qualitatively in examples like Figure 2 what happens when the image contains 2 identical objects and one of them is added or removed from the image. Would the proposed framework work or would there be a confusion among those object. I say this in part out of curiosity and in part because in Figure 3 (right 3rd column for inserting) it seems the model is confusing two similar objects and is adding the one in the back rather then one in the front. Could you provide those qualitative examples (if not possible in the rebuttal then in a potential future version of the paper).\n\nF. I could not find any detail (even in the appendix) about w(t). Could you please provide details about this function for both attribute and object tasks.\n\nG. The authors mention that Jung et al. use a similar prior term but since they use the same diffusion model (as opposed to a pre-trained and frozen one) they are measuring $p(x^c|z^c)$ rather than $p(x^c)$. I have two comments and questions about this:\n1. Even when using a frozen diffusion model, wouldn’t the final decoded image be conditioned on $z^c$? \n2. Regardless, I think this would be a good choice to compare. How does the current framework compare quantitatively to a similar framework that uses the term from Jung et al? Using Jung et al. solution would simplify the framework and reduce the need for training an extra model. Could you provide a comparison between these two options?\n\nH. For the DCI metric the authors say “we perform PCA as post-processing on the representation before evaluation, following (Du et al., 2021; Yang et al., 2023)”. While I appreciate that this has been done before I wonder if it is a fair evaluation of disentanglement when it is applied only to some methods. Shouldn’t each vector $z_i$ be considered one of the “dimensions”. With PCA one is not measuring the disentanglement of each dimension but rather the disentanglement of a rotated version of the linear combination of the dimensions. This does not seem the same. Please help me understand why this makes sense and it is a fair evaluation, or if you agree with me that this is not a fair evaluation please compute and report the DCI score without PCA." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The paper is easy to read. The proposed framework leverages and combines many techniques (such as diffusion models, SSL, optimal transport) in interesting way. The final framework is simple and from the reported results effective." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work proposes a framework to learn disentangled representations of either attributes (e.g., an object's color or orientation) or distinct objects within a scene. The frameworks begins by encoding a pair of images using a VAE encoder. The embeddings generated are $k$ vectors that eventually will be the disentangled representations. At this stage a mixer samples some vectors from image 1 and some vectors from image 2 generating the representation of a ``new’’ composed image. These new representation are then noised and denoised thanks to a diffusion model before going through the decoding stage of the VAE. The mixing component can be adjusted according to the desired inductive bias. For attribute disentanglement, the model enforces mutual exclusivity by ensuring each latent vector is sampled from only one of the two images. In contrast, for object disentanglement, this exclusivity constraint is removed, allowing, for instance, the first latent vector to be sampled from both images.\n\nThe objective function is composed of three terms: (1) a latent denoising objective using a diffusion decoder (as in Jung et al., 2024); (2) a term to maximize the likelihood of the composed image, implemented as a diffusion loss, where the diffusion model is pre-trained for each task and then frozen; and (3) a consistency objective, which ensures that the latent representation $z$ of a given image and the latent representation re-encoded after decoding the reconstructed image from $z$ remain close. For this last term, the authors found that using an NCE-like objective, where each representation should be close to its counterpart and distant from other batch representations, outperformed simply minimizing cosine similarity.\n\nThe proposed method is evaluated against various baselines, datasets, and metrics for both attribute and object disentanglement, showing improved performance across the board." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The main weaknesses of this paper are in the empirical evaluations. Specifically, some of the results reported do not match those previously published, a very common task used to assess object disentanglement (unsupervised segmentation) is missing, none of the experiments are done on realistic or complex datasets (although recent state-of-art works do employ those kind of datasets). These are the main points to be discussed during the rebuttal. Fixing these could increase the soundness and the contribution scores, hence, also the final recommendation score. See below for more details on all these weaknesses.\n- Results reported in this work about other baselines do not seem to match the original results reported by the respective original papers on the same tasks and datasets. For example LDS property prediction in the original paper shows much better accuracy (80.23% on Shape, compared to the one reported in this work for LSD which is only 68.25%, for comparison the proposed method accuracy is 70.90%). For the properties “material” and “shape” the differences is even higher. \n- State of art works on object disentanglement consistently use unsupervised segmentation to assess the usefulness of the generated representations, however, these tests are missing from the current work. This is an important task because it shows a concrete application of these type of representations (and for ease of comparison given that all recent works use both unsupervised segmentation as well as property prediction).\n- Both set of experiments (attributes and objects) lack of realistic or more complex datasets which state-of-the art have been using (in addition to some of the datasets used in this work). While it is not needed to have results on all of the following datasets, showing that the proposed method scales to the complexity of some of those datasets comparably to the state of art would make the contribution stronger. For example:\n - For the attribute disentanglement FactorVAE uses CelebA.\n - For Object centric Jung et al. 2024 use Super-CLEVR (multi-colored parts and textures), and MultiShapeNet (for realistic images), while other work such as Object Centric Slot Diffusion use the MOVi-C dataset (which contains complex objects and natural background), MOVi-E datasets (which contains up to 23 objects per scene), FFHQ (high quality image of faces).\n\nOther minor evaluations weaknesses:\n- Attribute disentanglement results are reported with standard deviation (great!) but it is unclear on how many runs. Results for object disentanglement are provided without any standard deviation (but they should). \n\nMinor Writing Comments. This writing suggestions are not critical but they would improve clarity and readability of the paper. No need to discuss them in rebuttal but they do need to be fixed and could increase the presentation score.\n- I find the first part of the paper (until section 3) lacking important details that could easily be provided. For example:\n - The abstract is very dry, there is no mention of which are the “strong baselines”, nor which tasks this work was tested on, nor quantitative evaluation to show that the propose method “matches or exceeds” baselines. Consider adding more information.\n - From the abstract (and even the introduction and the beginning of section 3.1) it is not clear what “mix”, “compose”, “composition operator” mean. It could be concatenation, averaging, summing… it will only become clear much later but It would be great to provide more details if not in the abstract (ideal) at least in the introduction.\n - Still by the end of Section 2 there is no formal definition of “attribute” and “object”. The first example of attributes is at page 4. Having these definitions would help the reader understanding the work much better since the beginning of the paper. From the examples at page 4 it seems that nose is an attribute and face an object but it could easily be argued that actually nose is an object in itself, or that face is an attribute of a bigger objet (human body). Again this highlight the need for a formal definition of attributes and objects.\n- In Figure1 there is a concrete image example but it is not clear if it belongs to Attribute mixing or Object mixing. The “thing” being mixed is a cylinder and a ball so why is it linked both to attributes and objects? It would be clearer to provide an example for both. Note that everything becomes clearer once the whole paper has been read but the first time the reader reaches Figure 1 this could be a source of confusion.\n- At page 6 the authors say “This occurs because the encoder can collapse the posterior pθ(z|x) into a single mode“. I know if this is an issue with posterior collapse. If the encoder collapses the posterior, then the first loss ($L_{diff}$) should become high hence preventing the collapse. The problem seems to be related to the fact that the learnt encoding is sufficiently different (hence not collapsed) to keep $L_{diff}$ while what the authors want is not just $\\hat{z} = z$ but also as different as possible with respect to other $z$s.\n- Typo (?): “we can without modifying the objective function, which will be introduced in next paragraph.” It is not clear what is that “we can”.\n- Typo: line 241 “an noised”.\n- The following sentence is incomplete: “we adjust our image encoder to take VAE features as input”. Please clarify which kind of adjustments?\n- “When back-propagate the gradient through xc, we truncate the gradient at the last iteration of decoding”. Why, it would be great to explain and motivate this choice.\n- Typo in Line 310: “model on each training dataset from the scratch”. Should be “from scratch”.\n- It would be great to explain how you understand which latent controls which factor. I believe there is a brief explanation in the appendix but it would be great if it could be explained in the main paper.\n- In table 3 and some part of the appendix the loss term $L_con$ is called $L_cycle$. Please update it so that it is consistent throughout the paper." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024unifying,\ntitle={Unifying Disentangled Representation Learning with Compositional Bias},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1UMxtR9Eb9},\nnote={under review}\n}" }, "abstract": { "value": "Existing disentangled representation learning methods rely on inductive biases tailored for the specific factors of variation (e.g., attributes or objects).\nHowever, these biases are incompatible with other classes of factors, limiting their applicability for disentangling general factors of variation.\nIn this paper, we propose a unified framework for disentangled representation learning, accommodating both attribute and object disentanglement.\nTo this end, we reformulate disentangled representation learning as maximizing the compositionality of the latents.\nSpecifically, we randomly \\textit{mix} two latent representations from distinct images and maximize the likelihood of the resulting composite image.\nUnder this general framework, we demonstrate that adjusting the strategy for mixing between two latent representations allows us to capture either attributes or objects within a single framework.\nTo derive appropriate mixing strategies, we analyze the compositional structures of both attributes and objects, then incorporate these structures into their respective mixing strategies.\nOur evaluations show that our method achieves performance that matches or exceeds strong baselines in both attribute and object disentanglement." }, "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 Representation Learning", "Disentangled Representation Learning", "Compositionality" ] }, "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/0e2b98f438c7e0cc2b5de2ea9804f3edbd62ef36.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/e60a0665754a17055fb44994d1a73776711a582d.pdf" }, "title": { "value": "Unifying Disentangled Representation Learning with Compositional Bias" }, "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": "See above" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. A novel inference-time model editing technique using Alignment Vectors that allows dynamic adjustment of LLM outputs along preference dimensions without retraining or complex prompt engineering\n2. A substantial synthetic dataset (38k examples) spanning three domains and three proficiency levels, with human-evaluated quality checks showing strong inter-annotator agreement\n3. Demonstration that AVs can be effectively transferred across different fine-tuning stages of the same model while maintaining performance\n4. A resource-efficient approach to achieving multidomain diverse behaviors that is 12x faster than traditional retraining methods" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a novel approach for adjusting Large Language Model (LLM) behaviors during inference time using Alignment Vectors (AV). The key innovation is treating alignment as a model editing problem where preference dimensions are encoded as vectors that can be dynamically combined with the base model through simple linear operations. The authors focus on three proficiency levels (expert, generic, and avoidance) across three specialized domains (medical, legal, and financial), demonstrating how their method enables flexible control over model outputs without requiring retraining. The work includes creation of a synthetic dataset with 38k query-response pairs and shows that their approach reduces resource usage by 12x compared to traditional retraining methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The evaluation based on GPT-4 judged metrics might need further validation with human study.\n2. Validation is limited to only one model (Mistral-7b) - broader testing across different open-source LLMs would strengthen the findings.\n3. Besides prompting, any test-time adaptation methods should be compare in the main experiments?\n4. Any further illustrations on \"over-generalization effect\"?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See weaknesses" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper presents a simple and effective idea to align the preferences of LLMs in inference time. The transferability of this approach across different domains is good.\n\n2. The authors have also built a large dataset that contains responses in avoidance, generic responses, and expert opinions.\n\n3. The AVs offer flexibility to adjust the level of LLMs in generation by adjusting their weights." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a preference alignment approach that only aligns during inference, using encoded representations called Alignment Vectors (AVs). The AVs are learned and tuned for the same model in different tuning stages, which shows good transferability across different domains. The authors also build a diverse domain-specific dataset with responses categorized into three levels. Extensive experiments demonstrate that AVs can help LLMs align to different domains and show promising performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The work aims to align LLMs during inference, and I agree that \"it requires full re-training when a change is needed.\" However, AVs are the subtraction of an aligned model and an unaligned model. Alignment during inference is to the unaligned one, making it return to the aligned model. If I understand correctly, this process still requires training and not fully inference-time alignment.\n\n2. Although this inference-time alignment method reduces the training cost, it requires two times inference, i.e., unaligned models and AVs.\n\n3. The dataset is built upon prompting Claude to generate different responses at different levels. Although the languages are appropriate to these levels (e.g., experts) and express relevant concepts, such as medical terms, are their content appropriate as well? For example, is a medical case resolved by LLMs, or do these LLMs only create or even hallucinate something to meet the prompts' requirements? The practicality of this alignment method is still awaiting to examine in this regard." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "* Can you please review the concerns regarding novelty and clarify the contribution of the work in that context? \n* As a suggestion, the paper structure could be improved for readability. I would recommend moving the “Methodology” section to be before the “Synthesizing Specialized Preference Data”. The “Methodology” section is the core contribution and it makes sense to center it. The “Synthesizing” section could also be combined more directly with the Experiments section, so that all relevant details concerning the experiments are presented together.\n* As a suggestion, I think it would be better to not refer to the “preference accuracy” and “GPT-4 judged generation accuracy” as accuracy metrics. This is because there is no comparison to a ground truth and thus it is not accurate to refer to these metrics as accuracy metrics. “Likelihood preference rate” and “GPT-4 judged rate” may be more appropriate names. In my opinion, calling the rates that are reported “accuracy” also lends itself to misleading claims regarding the performance of the approach (e.g., reading the reported 100% accuracy numbers as perfect performance, when it is more appropriate to think of them at the rate that a particular class of text was preferred)." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* The simplicity of the approach is a major strength, in that inference-time alignment significantly reduces computational costs in cases where it is of interest to align to many potential reward mixtures over single or multiple preference dimensions.\n* The work also includes a dataset contribution of the generated personas, which has potential for reuse in future work." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work proposes an approach to inference-time control over the alignment of a large language model to multiple, potentially competing preference dimensions. The approach defines an “alignment vector” which is the difference between the weights of a model aligned to a particular dimension (e.g., using DPO or RLHF). The approach allows for smooth interpolation between the base model and the aligned model, on for any given dimension, as well as for choosing an operating point in a trade-off space between multiple dimensions. In this work, they investigate dimensions along the axes of specialized domains (Medical, Financial, and Legal) and subject matter proficiency. This is implemented by constructing 12,000-13,000 personas related to each of the specialized domains, generating LLM outputs with a prompt that emphasizes each proficiency level (avoidance, generic response, and expert response). They observe that the likelihood of the expert responses tend to increase as the mixture weights are tuned away from the base model towards that of the aligned model." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* Unfortunately, this work may not be sufficiently novel nor sufficiently well-grounded in the related literature. I believe that the approach proposed in the present work is essentially a special case of the “Rewarded Soups” and “Personalized Soups” approaches proposed by Rame et al [1] and Jang et al [2]. In those prior works, they similarly propose inference-time weighted mixtures over models aligned to different reward functions. They also conduct much more extensive experiments and provide more rigorous theoretical motivation for the approach. \n* The theoretical motivation is relatively superficial compared to related prior work (i.e., works that connect weight interpolation to linear mode connectivity).\n* Few details are provided regarding the methodology for creating the persona dataset. For example, no details are provided about the “thorough clean-up, involving truncation, and reformatting” (Line 159).\n\n\n1. Rame, Alexandre, et al. \"Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards.\" Advances in Neural Information Processing Systems 36 (2023).\n2. Jang, Joel, et al. \"Personalized soups: Personalized large language model alignment via post-hoc parameter merging.\" arXiv preprint arXiv:2310.11564 (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": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "It appears this research has some level of human involvement participating in annotating data." }, "flag_for_ethics_review": { "value": [ "Yes, Responsible research practice (e.g., human subjects, data release)" ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- How do you check what a valid persona-query pair is? How were 13k, 12.3k, 12.8k selected? Is it based on non-repetitive persona-query samples alone or was there for Quality control involved? (section 3.1)\n- Were the annotators human or was it machine annotations? (section 3.3)\n- How can you be certain the LLM generation can serve as a ground truth? \n- Is it better to have an LLM that is aligned to one domain instead of all three domains (equation 3)? I imagine an expert in the field would feel indifferent if the specialized LLM for healthcare was also aligned with law, etc.?\n- Are there other metrics to measure outside of preference accuracy? I think the benchmark otherwise is not robust enough given preference accuracy is a hand crafted metric from the authors.\n- How are metrics like safety and helpfulness quanitfied. It was not written clearly?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper is well motivated. It is true that there has been limited study on aligning LLMs at inference time.\n- The paper presents two clear research questions that they will address.\n- results show nearly maximal performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes an LLM alignment method at inference time which has not been well studied. On top of preference tuning at inference time, they propose a model editing technique called alignment vector arithmetic (subtracting base model with aligned model at inference time) strengthening the methods sections of this paper. It appears there method on inference time alignment performs quite strongly in the three domains under three different instruction types (avoid, generic, and expert). From these three expert instruction type appears to do the best overall. Performance metrics were measured but were observed with some level of hesitancy and there were not many inference time alignment approaches making it difficult to assess. Authors can potentially show the benefits of inference time alignment versus that during training to further motivate the problem." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The selection of LLM is not well motivated? Why did you use Claude-3-Sonnet over GPT4 or even open source models like Llama-2/3?\n- minor attention to detail but keep writing conistent. I see instances were \\citep was used and where \\cite was used.\n- Not sure I gree with the multidomain preference approach. Seems that instead of building a generalist AI, experts in the field would prefer a specialized version of the LLM. However I will listen to the authors justification in the rebuttal period.\n- please formalize a mathematical definition of the preference accuracy.\n- the task is not super clear. Figure 2 looks amazing but I'm not sure what was done to achieve this.\n- Writing clarity can be improved. They talk about using Claude then in the section 5.3 they say they use mistral 7b. LLM selection is also not properly motivated.\n- Paper can motivate the need for inference time alignment over conventional approaches." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "This paper presents Alignment Vectors (AV), a new method for dynamically aligning LLMs during inference, enabling customizable outputs while reducing cost." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024inference,\ntitle={Inference time {LLM} alignment in single and multidomain preference spectrum},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1Uem0nAWK0},\nnote={under review}\n}" }, "abstract": { "value": "Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure. Existing training-time alignment methods require full re-training when a change is needed and inference-time ones typically require access to the reward model at each inference step. To address these limitations, we introduce an inference-time model alignment method that learns encoded representations of preference dimensions, called Alignment Vectors (AV). These representations are computed by subtracting the base model from the aligned model as in model editing enabling dynamically adjusting the model behavior during inference through simple linear operations. Even though the preference dimensions can span various granularity levels, here we focus on three gradual response levels across three specialized domains: medical, legal, and financial, exemplifying its practical potential. This new alignment paradigm introduces adjustable preference knobs during inference, allowing users to tailor their LLM outputs while reducing the inference cost by half compared to the prompt engineering approach. Additionally, we find that AVs are transferable across different fine-tuning stages of the same model, demonstrating their flexibility. AVs also facilitate multidomain, diverse preference alignment, making the process 12x faster than the retraining approach." }, "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": [ "LLM", "Alignment", "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/945b44aadde6e88b4cafbcf18b6c6730072dc554.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": "Inference time LLM alignment in single and multidomain preference spectrum" }, "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. Since the datasets have annotations like a matched moment and text, how to evaluate the model's ability to learn uncertainty when processing an unreasonable text query? Like the example in Figure 1\n2. In Geom-regularization, how to define accurate predictions? how to define less accurate predictions?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. It introduces the first extension of Deep Evidential Regression (DER) to Video Temporal Grounding (VTG) tasks, aiming to address uncertainties in open-world scenarios. \n2. It proposes a Debiased DER Model (DDM-VTG) that tackles modality imbalance and counterintuitive uncertainty through a Reflective Flipped Fusion block and a Geom-regularizer, enhancing the model's sensitivity to text queries and calibrating uncertainty estimation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "It presents DDM-VTG, a new model that integrates Deep Evidential Regression into Video Temporal Grounding to handle uncertainties in open-world scenarios. It addresses modality imbalance and counterintuitive uncertainty with a Reflective Flipped Fusion block and a Geom-regularizer, enhancing model robustness and effectiveness across benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The datasets used are not open-world.\n2. The performance on the video summarization task is not advantageous enough.\n3. Figure 2 shows 4 cases of the uncertainty. It is not clear how the method addresses (a)(b)(d) and how to evaluate if the methods can handle these scenarios." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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 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.\tThe proposed baseline model is innovative for its integration of Deep Evidential Regression (DER) with VTG tasks to address both aleatoric and epistemic uncertainties. \n2.\tThe paper not only identifies the existence of modal imbalance and structural flaws in regularization within the baseline model but also offers solutions to these issues.\n3.\tThe authors have conducted extensive experiments across various benchmarks, which effectively demonstrate the efficacy of their approach." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a novel approach to Video Temporal Grounding (VTG) by integrating Deep Evidential Regression (DER) to address uncertainties in open-world scenarios, such as out-of-distribution (OOD) data and open-vocabulary queries. The authors propose a Debiased DER Model for Video Temporal Grounding (DDM-VTG) that tackles modality imbalance and counterintuitive uncertainty through a Reflective Flipped Fusion (RFF) block, a query reconstruction task, and a Geom-regularizer. The model demonstrates effectiveness and robustness across multiple benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tWhile the paper presents a novel approach to addressing uncertainties in VTG, it could benefit from a deeper analysis of the limitations of the proposed model, especially in handling highly ambiguous queries or extremely OOD data.\n2.\tThe paper could provide more insights into how the DDM-VTG model generalizes to other video-related tasks beyond the tested benchmarks.\n3.\tWhen designing the baseline, whether DER provides positive assistance for the correct prediction of the model, the author needs to provide corresponding proof experiments.\n4.\tWhen introducing the baseline, the author believes that it has a modal imbalance problem, and DDM-VTG effectively alleviates this imbalance, which requires corresponding experimental evidence.\n5.\tThe method proposed by the author showed out of distribution predictions on the qv height dataset, which to some extent indicates the generalization of DDM-VTG, but it is not clear and specific enough. The author needs to provide results on charades-CD." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 Figure 2, several challenges within VTG tasks are highlighted, but it appears that targeted comparative experiments were not conducted in the study. When compared with other works, can DDM-VTG perform better in addressing these challenges? Some discussions are expected.\n- In the Query Reconstruction task, how can DDM-VTG ensure that the tokens predicted by the QR head are accurate when dealing with complex videos? What happens if the predictions are incorrect? Does it affect the accuracy of temporal localization of the whole video?\n- In the case study, the average length of the videos is 150 seconds. How would the model perform with longer videos, and would the cost increase significantly?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The basic idea is easy to follow and the main motivation is clear.\n- The innovative integration of DER into VTG tasks is a novel approach that effectively addresses key issues like OOD videos.\n- The proposed method achieves strong experiment results, both compared to its baseline and other SOTA methods." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes Debiased DER Model for VTG, tackling open-vocabulary queries and out-of-distribution videos in video temporal grounding tasks. It extends the vanilla DER to VTG and establishes a baseline. To address two critical biases in the baseline—modality imbalance and counterintuitive uncertainty—the method incorporates a RFF block for progressively enhancing modal alignment, a query reconstruction task to ensure robust cross-modal alignment capabilities and a Geom-regularizer to calibrate uncertainty estimation. The proposed method has been evaluated on 4 datasets, demonstrating its effectiveness in Moment Retrieval, Highlight Detection and Video Summarization. The ablation studies also support the analysis." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- In figure3, I can’t see the difference between the two distributions except for the color, which might be confusing as to why one is unreliable and the other is trustworthy.\n- About the presentation. In 4.3, there is a significant disparity in the level of detail explained for different modules, perhaps the arrangement of content in the main text and appendix could be adjusted to make it clearer for readers.\n- The experimental section only shows the comparison with SOTA methods on various metrics. In the appendix, only some cases of the QVHighlights dataset are shown, without visual results for the other datasets mentioned in the paper, and it also lacks displays of comparative results for the three sub-tasks. \n- It would be more complete to have a discussion of this increased cost if there are any, as well as techniques used to overcome it.\n- (Minor) Minor typos/grammatical mistakes (e.g. 4.2 “VALLINA”)" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. Why does the model only mask and reconstruct one noun? Would masking more words help enhance text sensitivity?\n2. In the conclusion, the authors claim that the model’s capabilities are limited by data quality and scale. ActivityNet-Captions and Ego4D-NLQ are large-scale datasets. Would the model perform well on these two datasets?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. This paper extends the deep evidential regression to video temporal grounding for uncertainty estimation.\n2. The authors propose a Geom-regularizer to solve the counterintuitive uncertainty and calibrate the estimation of uncertainty. \n3. The proposed method achieves comparable performance in the majority of benchmarks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the issue of open-world challenges caused by open-vocabulary queries and out-of-distribution videos in video temporal grounding. The authors adopt the Deep Evidential Regression as baseline, and propose a Reflective Flipped Fusion block to realize modality alignment and query reconstruction. Meanwhile, a Geom-regularizer is proposed to debias and calibrate uncertainty estimation. Extensive experiments are conducted on the public dataset to validate the proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The evaluation of location bias is insufficient. There are no transfer experiments on the Charades-CD and ActivityNet-CD datasets to validate the model in OOD scenarios, as done by MomentDETR and MomentDiff. \n2. The study of query reconstruction (QR) is not thorough. The authors only present performance across different QR epochs and learning rates.\n3. Insufficient performance evaluation. Ego4D-NLQ is widely used in previous works, yet this study does not report results on this dataset. Additionally, the paper fails to compare with recent works, such as \"R2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding\" from ECCV 2024." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "This paper presents SRAM, a novel approach that leverages Evidential Deep Learning to enhance model's robustness and interpretability in Video Temporal Grounding tasks." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024debiased,\ntitle={Debiased Deep Evidential Regression for Video Temporal Grounding},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1V28zvLJMg},\nnote={under review}\n}" }, "abstract": { "value": "Existing Video Temporal Grounding (VTG) models perform well in accuracy but often fail to address open-world challenges posed by open-vocabulary queries and out-of-distribution (OOD) videos, which can lead to unreliable predictions. To address uncertainty, particularly with OOD data, we build a VTG baseline using Deep Evidential Regression (DER), which excels in capturing both aleatoric and epistemic uncertainty. Despite promising results, our baseline faces two key biases in multimodal tasks: (1) Modality imbalance, where uncertainty estimation is more sensitive to the visual modality than the text modality; (2) Counterintuitive uncertainty, resulting from excessive evidence suppression in regularization and uneven sample error distribution in conventional DER. To address these, we propose an RFF block for progressive modality alignment and a query reconstruction task to enhance sensitivity to text queries. Additionally, we introduce a Geom-regularizer to debias and calibrate uncertainty estimation. This marks the first extension of DER in VTG tasks. Extensive experiments demonstrate the effectiveness and robustness of our approach. Our code will be released soon." }, "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 Temporal Grounding", "Uncertainty Quantification", "Multi-Modal Fusion", "Deep evidential regression", "Evidential deep 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/d8c1ec2d0da362eb2f91b0eea496e4d2063cb0c6.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Debiased Deep Evidential Regression for Video Temporal Grounding" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "* I find the notion of \"One-shot\" and \"fine-tuned\" experimental setting could be improved; First because the notion of fine-tuning can become confusing between the coefficients $\\alpha$ vs the model parameters $\\theta$ fine-tuning. Second, because it is not clear if it is referring to a specific method/objective for fine-tuning the task coefficients (e.g. AdaMerging or others) or simply hyperparameter search." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* The paper is well motivated and grounded in previous related work\n* The proposed method is simple and could adapt to different task arithmetic variants\n* Interesting insights on the link between the proposed regularisation and weight disentanglement\n* A more efficient implementation of the method is proposed for handling larger number of tasks (Equation 11)" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper tackles the task of task arithmetics, i.e. how to combine *task vectors/parameters* to form a multi-task model. A key issue is to determine the best combination weights as to minimise interference between tasks, and maximise sharing of information / positive transfer.\nMore specifically, the authors make use of two previously introduced notions: **(i)** the notion of **weight disentanglement** which was proposed as a measure of task interference in task arithmetic. And **(ii)** the Neural Tangent Kernel (NTK) which designates a training regime where parameter updates can be expressed with a linearised approximation.\nPrevious works have suggested that performing task arithmetics under the NTK regime can lead to better MTL performance. the authors investigate this behaviour in more depth. Based on this analysis, they also propose a regularisation technique to further reduce task interference when performing task arithmetic, which involves slightly fine-tuning the task vectors themselves." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* Missing baselines on more recent task arithmetic work: The main tables should include some recent task arithmetic results (e.g. TIES-Merging and AdaMerging) as well as standard single task and MTL baselines (although it is in the appendix), if only to better understand the existing gap in performance.\n* Missing discussion about the extra cost: The paper briefly mentions efficiency of the method (e.g. equation 11 or line 364), however I think this could be discussed in more depth: On the one hand, the proposed method seems more robust to task coefficients $\\alpha$, which could save on hyper parameter tuning; On the other hand, it involves a fine-tuning procedure which requires knowledge/access to all tasks simultaneously (Equation 10) as opposed to directly combining task vectors obtained independently from one another." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 does the computational cost of the proposed method compare to existing approaches?\n- Can the method be adapted to work with limited or no access to data from other tasks?\n- How well does the approach generalize to other domains beyond image classification?" }, "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 provides a comprehensive theoretical and empirical study of the relationship between the proposed $\\tau$Jp metric and task interference in neural networks.\n\n2. The introduction of $\\tau$Jp as a new metric for weight disentanglement is novel and well-motivated.\n\n3. The proposed regularization method eliminates the need for tuning inference-time hyperparameters ($\\alpha$), which is a practical advantage." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new metric called $\\tau$Jp ($\\tau$-Jacobian product) for measuring weight disentanglement in task arithmetic operations on neural networks. The authors theoretically analyze the relationship between $\\tau$Jp and interference between tasks, and introduce a regularization method based on minimizing $\\tau$Jp during fine-tuning. Experiments on image classification tasks demonstrate improved performance and reduced need for hyperparameter tuning compared to existing methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The method requires access to data from all other tasks during training, which is often unavailable in realistic task arithmetic scenarios. This limits the practical applicability of the approach.\n\n2. The computational cost of calculating τJp is likely very high, as it involves multiple Jacobian-vector products. The paper does not report runtime or resource requirements, making it difficult to assess scalability.\n\n3. Experiments are limited to image classification tasks. Evaluation on other domains like language tasks would strengthen the claims of generality.\n\n4. The derivation of Equation 7 from the weight disentanglement definition is non-trivial and should be explained more clearly." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": { "value": "No" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Suggestions：\n\n1.The related work section could be improved by explicitly connecting prior studies to this paper's contributions, emphasizing how the proposed method addresses existing limitations. \n2.Consider moving the related work section after the methods section, especially since the current structure delays the introduction of the proposed method until page 5. This change would allow readers to quickly understand the proposed approach before diving into comparisons, enhancing readability and engagement." }, "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 addresses an important and timely topic: in an era where foundation models are prevalent, better understanding weight disentanglement is particularly valuable for enhancing the practical applicability of these models.\n\n2.The proposed metric offers a deeper understanding of weight disentanglement, and the regularization method effectively reduces task interference, minimizing the need for coefficient adjustments.\n\n3.The success of the proposed method in incremental learning scenarios aligns well with real-world applications, demonstrating its scalability and practical relevance when future tasks are unknown." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel metric, $\\tau \\text{Jp}$ ($\\tau$-Jacobian product), to improve understanding of weight disentanglement in task arithmetic. It demonstrates that τJp inversely correlates with normalized accuracy, suggesting it as an indicator for weight disentanglement. A regularization technique is proposed to minimize τJp during fine-tuning, effectively reducing the need for coefficient adjustments in task addition and negation. It also proves valuable in incremental learning scenarios where future tasks are unknown." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.While the paper introduces the $\\tau \\text{Jp}$ metric and explains its relationship with weight disentanglement, the theoretical justification for why $\\tau \\text{Jp}$ regularization effectively reduces task interference could be further elaborated.\n\n2.The proposed regularization method lacks a comparison with other existing regularization techniques, which makes it difficult to fully assess its relative strengths and weaknesses. \n\n3.The paper mentions task addition, task negation, and task analogies in the introduction and background sections as key operations in task arithmetic, but there are no experiments evaluating task analogies. This inconsistency weakens the completeness of the experimental 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": 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": "Could the proposed regularization affect the model's plasticity? Specifically, how might the addition of this regularization impact the fine-tuning performance, potentially influenced by the strength of the regularization?" }, "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 well-written and easy to follow.\n+ The experiments are extensive, and the results sound good.\n+ The design of metric τJp is reasonable and interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a novel approach to task arithmetic in neural networks, which leverages a novel metric that quantifies the relationship between task vectors and the Jacobian of pre-trained models. The authors claim that by minimizing this metric through regularization, they can significantly reduce interference between task predictions and enhance the accuracy of task arithmetic operations. The experimental results demonstrate substantial improvements in performance for both task addition and task negation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The novelty of this paper may be limited. My consideration is that this paper seems to fundamentally align with the approach proposed by Ortiz-Jimenez et al. (2023) [1], which also emphasizes fine-tuning models in the tangent space. Although using the specific regularization term, this paper does not sufficiently differentiate itself from this existing work.\n- While the empirical results are compelling, the paper lacks a thorough theoretical explanation for why the proposed regularization leads to better performance compared to other methods, such as those discussed in Ortiz-Jimenez et al. (2023). I am confused about why a simple and soft regularization results in such improvement compared to [1]. A deeper theoretical analysis could strengthen the paper's contributions.\n- The authors briefly mention tuning the regularization strength but do not provide sufficient details on how this hyperparameter was selected. The sensitive analysis of this hyperparameter is also necessary for the paper.\n\n[1] Guillermo Ortiz-Jimenez, Alessandro Favero, and Pascal Frossard. Task arithmetic in the tangent space: Improved editing of pre-trained models. Advances in Neural Information Processing Systems, 2023." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Our proposed $\\tau$Jp regularizer improve the performance of task arithmetic and lead to its practical applications." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024mastering,\ntitle={Mastering Task Arithmetic: \\${\\textbackslash}tau\\$Jp as a Key Indicator for Weight Disentanglement},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1VwWi6zbxs},\nnote={under review}\n}" }, "abstract": { "value": "Model-editing techniques using task arithmetic have rapidly gained attention. Through task arithmetic, simply through arithmetic operations on the weights of pre-trained and fine-tuned models create desired models, such as multi-task models, models with specific tasks unsolvable, or domain-transferred models. However, task arithmetic faces challenges, such as low reproducibility and the high cost associated with adjusting coefficients in the arithmetic operations on model parameters, which have limited its practical success. In this paper, we present three key contributions in the context of task addition and task negation within task arithmetic. First, we propose a new metric called $\\tau$Jp which is based on the product of the task vector ($\\tau$) and the Jacobian of the pre-trained model with respect to its weights. We show that $\\tau$Jp has a causal relationship with the interference that occurs from arithmetic operations. Second, we show that introducing regularization to minimize $\\tau$Jp significantly mitigates interference between task inferences, which leads to eliminating coefficient tuning and better accuracy on each task. Third, in the context of incremental learning, we confirmed that our $\\tau$Jp regularization demonstrates more robust performance in environments where future tasks to be learned are not accessible, validating the scalability of the approach. Finally, we demonstrate that the $\\tau$Jp regularizer further reinforces the performance of task arithmetic by leveraging publicly available fine-tuned models, offering practical benefits for real-world applications." }, "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": [ "task arithmetic", "model editing", "task vector" ] }, "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/22d9dea5dae592047e0035c4c20f89b0f99b9681.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": "Mastering Task Arithmetic: $\\tau$Jp as a Key Indicator for Weight Disentanglement" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "Human faces appear on the road. They are not removed and blurred." }, "flag_for_ethics_review": { "value": [ "Yes, Privacy, security and safety" ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "- The authors need to consider including more baselines for evaluation." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "- The authors have compiled a comprehensive dataset with over 10,000 high-resolution images and detailed instance segmentation annotations, covering a diverse range of geographic regions within Bangladesh.\n\n- A rigorous two-stage validation process for annotations ensures high-quality data, which is essential for developing robust and accurate computer vision models.\n\n- Comparative evaluation with multiple state-of-the-art models (e.g., YOLOv5, YOLOv8, YOLOv9) showcases the benchmark's effectiveness and sets a baseline for future research on BRSSD10k.\n\n- The inclusion of region-specific object classes (e.g., rickshaws, CNGs, informal stalls) provides a unique contribution, enabling autonomous systems to better understand and navigate environments outside of structured Western road layouts." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces BRSSD10k, a segmentation dataset specifically tailored to the unique and diverse road scenarios in Bangladesh. This dataset consists of 10,082 high-resolution images from nine cities across the country, with detailed annotations covering 34 classes that reflect the region's distinct transportation environment. Classes include locally prevalent elements such as rickshaws, CNGs (auto-rickshaws), and informal roadside stalls, which are critical for developing robust autonomous driving systems for Bangladeshi roads." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The dataset only covers limited regions in one country, which is not enough to evaluate the generalization ability of segmentation.\n\n- The quality of the segmentation masks is not satisfactory.\n\n- Certain critical classes, such as traffic lights, construction vehicles, and road blockers, are underrepresented in the dataset.\n\n- The dataset currently lacks nighttime and adverse weather imagery (e.g., rain or fog), which are essential for real-world segmentation.\n\n- The paper only evaluates three versions of the YOLO model, which may limit insights into how BRSSD10k performs across different model architectures. \n\n- There is no analysis on how models trained on BRSSD10k generalize to other datasets or vice versa." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "It would make sense to extend the dataset with full panoptic labels.\n\nIt would make sense to cite and discuss related road driving datasets: ACDC, WildDash, FishyScapes, SegmentMeIfYouCan." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The dataset will likely prove as a valuable contribution to the field.\n\n- Many stuff classes are annotated (sky, road, wall, fence).\n\n- Little effort is required to extend the dataset for panoptic segmentation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The manuscripts presents a novel road-driving dataset for instance segmentation. The dataset includes more than 10000 high resolution images acquired along 9 cities in Bangladesh. The dataset taxonomy includes 34 classes that reflect typical needs of autonomous driving and regional characteristics. The taxonomy is mostly well-balanced (Figure 2). There are around 6000 training, 2000 validation and 2000 test images. The presented experiments involve object detection with stock models and report mAP50 performance on validation and test datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- it is hard to recommend n+1-th road-driving dataset for publication at a major conference\n\n- dataset focuses on typical images, for which our models are known to work well \n\n- the baseline models address only object detection (some universal segmentation model such as MaskFormer would be a better choice)" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- [Q1] How is the dataset split into train/val/test? Do you perform geographic\n splitting, or is the splitting purely at random?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- [S1] Diverse object classes from multiple cities in Bangladesh, reflecting a\n unique label distribution that is materially different from other established\n datasets such as Waymo Open and nuScenes.\n- [S2] The authors also present the results of a few detection baselines based\n on YOLO, trained and evaluated on this dataset's corresponding splits." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a new dataset focused on object detection and segmentation,\ntailored to the specific driving conditions in Bangladesh in terms of its\nappearance and taxonomy.\n\nThe dataset encompasses ~10k camera images collected in Bangladesh using a cell\nphone. The images are sourced from video chunks originating from diverse\nregions, and contiguous footage is sampled to 1 Hz. The frames are annotated\nwith object bounding boxes and segmentation masks.\n\nThe paper motivates the dataset as helpful in developing computer vision\nalgorithms specific to Bangladeshi driving scenes and performs a brief\ncomparative analysis of different YOLO-based models trained on this dataset. The\npaper helpfuly provides metadata like class and geographic distribution\nhistograms as well as many qualitative examples in order to help the reader get\na sense of the dataset.\n\nWhile it is definitely important to promote datasets which cover a diverse range\nof environments, I think the quantitative argument made in this paper to\nmotivate the dataset could be strengthened. For example, the argument could be\nimproved by showing experimental results which demonstrate the limitations of\nother dataset on data collected in Bangladesh." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- [W1] The related section could be made a bit more comprehensive. For example,\n it would be interesting to also discuss other datasets focusing on non-Western\n streets, such as the dataset introduced in [@traphic]. Even though it's\n mentioned later in the paper, the BadODD dataset should also be covered in the\n related work section and in the relevant tables.\n- [W2] While it is helpful to benchmark a few existing models on the proposed\n dataset, it would be beneficial to also compare these numbers with those from\n models trained on a mainstream dataset such as CityScapes or Mapillary Vistas.\n If models trained on a dataset like Cityscapes or Mapillary Vistas fail to\n perform well on this dataset, that would make for a good quantitative argument\n for why this dataset will help the community.\n - As a side-note, even if the taxonomy another dataset won't match the one in\n BRSSD10k perfectly, this gap could be alleviated by the use of an\n off-the-shelf VLM, which have been shown to be very good at tasks like open\n set object detection---see, for example, Grounding DINO [@liu2024grounding].\n- Minor Suggestions\n - Sections 7.3, 7.4, and 7.5 can be shortened and replaced with more\n comparisons, or additional details about the dataset or its software\n development kit. Readers can refer to the corresponding references if they\n are curious about the specific loss functions used to train these models.\n - The citation markers seem to be missing parentheses around them. For\n example, a sentence like \"... complex environments He et al. (2017)\" should\n be formatted like \"... complex environments (He et al., 2017).\"\n- References:\n - [@traphic]: Chandra, Rohan, et al. \"Traphic: Trajectory prediction in dense\n and heterogeneous traffic using weighted interactions.\" CVPR. 2019.\n - [@liu2024grounding]: Liu, Shilong, et al. \"Grounding dino: Marrying dino\n with grounded pre-training for open-set object detection.\" arXiv preprint\n arXiv:2303.05499 (2023)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "The authors strongly emphasize that the main motivation of this work is that there lack segmentation datasets in Bangladeshi. It should be clarified that the contribution of a dataset does not lay in its location, but the data quality, diversity, and scale." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "It clearly analyzes the practical scenario characteristics in Bangladeshi. The class definition and labeling process fully fits the scenarios. This dataset acts as a valuable resource for developing autonomous driving models in this country. It may also contribute to general vision perception tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a road segmentation dataset for autonomous driving purpose. It focuses on the scenarios in Bangladeshi and make specific adaptions in class definition and labeling. Validation experiments are conducted." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.The authors just use one paragraph to summarize the related datasets without any detailed comparison. I do not think the authors really understand the development of this field as there are only eight references. \n2. The scenarios in the dataset are more likely to be corner cases comparing with the mainstream segmentation datasets. Its universality cannot be verified.\n3.The structure of the manuscript is poorly organized. The logic between sections 3-6 are chaotic.\n4.It is really confusing that the authors validate the segmentation dataset with YOLO.\n5. It is really funny that the GT maps in Figure 3 are wrong." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "BRSSD10k is Bangladesh's first instance segmentation dataset for autonomous driving, with 10,082 high-res images from 9 cities. It offers detailed road element annotations, providing a key benchmark for AI models in diverse South Asian road scenarios" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024brssdk,\ntitle={{BRSSD}10k : A {SEGMENTATION} {DATASET} {\\textbackslash}{\\textbackslash}{OF} {BANGLADESHI} {ROAD} {SCENARIO}},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1W6oINj8ne},\nnote={under review}\n}" }, "abstract": { "value": "In this paper, we present a novel Bangladeshi Road Scenario Segmentation Dataset designed to advance autonomous driving technologies under the challenging and diverse road conditions of Bangladesh. This comprehensive instance segmentation dataset comprised 10,082 high-resolution images captured across nine major cities, including Dhaka, Sylhet, Chittagong, and Rajshahi, addressing the critical need for region-specific computer vision data in developing countries. Unlike existing autonomous driving datasets that primarily focus on western road conditions, BRSSD10k encompasses a wide range of environments unique to Bangladesh, including unstructured urban areas, hilly terrains, village roads, and densely populated city centers. The dataset features instance segmentation annotations with classes specifically tailored to reflect the distinctive elements of Bangladeshi roads, such as rickshaws, CNGs (auto-rickshaws), informal roadside stalls, and various nonstandard vehicles. To demonstrate its utility as a benchmarking tool for autonomous driving systems, we present comparative results from several state-of-the-art instance segmentation models tested on this dataset, achieving an mAP of 0.441. This evaluation not only showcases the dataset's effectiveness in assessing model performance but also underscores the need for adaptive algorithms capable of handling diverse and unpredictable urban environments in the context of autonomous 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": [ "Instance Segmentation", "Computer Vision", "Dataset", "Autonomous Driving", "Bangadeshi Road" ] }, "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/579a826d28a09538bc6be840565dc960b193450e.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/988c17d58f6bc59372415bbca67af3f085fe26cd.zip" }, "title": { "value": "BRSSD10k : A SEGMENTATION DATASET \\\\OF BANGLADESHI ROAD SCENARIO" }, "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. Why is the dependency of the algorithm on a nominal policy a bad idea in the given settings? Since it appears easy enough to construct one (say a PID controller like in [1]) for the environments given, is this the right direction?\n2. What is the difference between the training and inference situations in terms of the number of agents? Does the algorithm need to be retrained for every new number of agents unlike in [1] where the algorithm was trained on 8 agents and deployed on up to 1024 agents (albeit being purely concerned with single goal reaching while avoiding collisions)?\n3. With regards to the sample efficiency and computation requirements, how is DGPPO w.r.t. the baselines (I noticed the training time was listed as 12 hours on the reference specifications)? On a related note, how is the benefit of a constant set of hyperparameters demonstrated? Can we confidently say the hyperparameter search for the baselines takes significantly longer (in wall clock time on a comparable machine)?\n4.What are the restrictions on the definition of the avoid set $\\mathcal{A}_i$ and the assumptions on the function $h_i^{(m)}$? Do the avoid sets primarily represent distance to $y^k$ greater than some safe radius?\n5. The LiDAR part of the observation appears less clear. From the appendix (Sec B.2.1) is it right to say that only the LiDAR environments use the 32 equally spaced ray capturing relative positions? How are the obstacles in the VMAS environments represented to the agent?\n6. The experiments with scalability to multiple agents (Fig. 5) appear quite close to the baselines. Is there a better comparison available?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The proposed method (DGPPO) is an elegant way to solve the discrete-time distributed MASOCP (multi-agent safe optimal control problem) with unknown environment dynamics. This assumption was not present in previous work which had to differentiate through the given transition functions.\n- The theorems introduced provide a solid foundation for the applicability of DGPPO in the discrete-time setting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The proposed DGPPO framework addresses challenges in multi-agent systems (MAS) by learning both a discrete graph control barrier function (DGCBF) and a high-performance safe policy under unknown discrete-time dynamics, changing neighborhoods, and input constraints. DGPPO combines reinforcement learning and DGCBF, achieving high task performance and safety across varied environments without needing a pre-existing nominal policy or multiple hyperparameter sets, consistently outperforming other methods in both metrics of safety rate vs cost for various simulations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Scalability appears limited (only up to 7 agents) compared to the continuous time setting of GCBF+ [1] (most likely due to the present of unknown environment dynamics and the noise introduced through the sample score function gradient).\n- The stability of DGPPO compared to the baselines does not seem appropriately explained. Is it a purely empirical observation or is there some theoretical justification available?\n- In the given setting, why is the assumption of unknown dynamics interesting? To me, the environments considered are purely the settings of [1] without using the environment dynamics directly (even though they are available). Would it not be a better idea to consider an environment where the dynamics are not as simple as the ones in [1] or some complex unknown function (for e.g., common Mujoco robots)?\n\nReferences:\n\n[1] GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control, Zhang et al, T-RO, 2024" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Q1: How does DGPPO ensure that agents achieve the global objective, rather than just meeting safety constraints? \n\nQ2: Could DGPPO’s rollout requirements be reduced to improve sample efficiency without compromising safety? \n\nQ3: What are the practical scalability limits of DGPPO when applied to larger MAS, particularly with the use of GNNs?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The authors present an innovative combination of reinforcement learning and discrete-time CBFs to address the challenges of safety and task performance in MAS with unknown dynamics. The extension to the DCBF framework in discrete-time and the introduction of DGCBFs allow for neighborhood adaptability, overcoming limitations associated with continuous-time CBFs. The approach is well-motivated, tackling safety in unknown environments without requiring a predefined nominal policy—a substantial improvement for multi-agent reinforcement learning (MARL). I particularly appreciate the rigorous theoretical presentation to support the proposed approach." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel framework, Discrete Graph Control Barrier Functions Proximal Policy Optimization (DGPPO), for ensuring safe control in multi-agent systems (MAS) operating under unknown discrete-time dynamics and input constraints. Unlike prior approaches that rely on continuous-time models and known nominal policies, DGPPO incorporates discrete CBFs (DCBFs) and reinforcement learning to dynamically learn both a high-performance safe policy and a discrete graph CBF (DGCBF). Through extensive empirical validation across various simulated MAS environments, DGPPO claims to achieve both high task performance and safety without the need for hyperparameter adjustments specific to each environment." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "While DGPPO introduces a novel safety mechanism for MAS, nonetheless I believe there are few critical concerns that could limit the effectiveness and general applicability of the approach.\n\n**Lack of clear performance metrics**: \n\nwhile DGPPO is shown to achieve high safety, the empirical results focus primarily on safety metrics and the minimization of constraints. It remains unclear if the agents are successfully accomplishing global objectives. Without metrics like mean global success rate or reward, it is difficult to assess if the agents are merely achieving safety (e.g., by staying stationary) rather than making meaningful progress toward the task goals while satisfying the safety constraints. This is especially relevant as the DGPPO framework does not incorporate a nominal policy, meaning that without these metrics, the experiments risks overlooking cases where the agents avoid unsafe states at the expense of task completion. Does the proposed framework present mechanisms to ensure that safety constraints do not excessively dominate the objective?\n\n**Limited scalability experiments**: \n\nthe authors state that DGPPO scale well w.r.t to the baseline approach tested, however testing 5 agents instead of 3 as in the original experiment, I believe it is too limited to claim scalability of the proposed approach. Crucially, as stated from the authors themselves the proposed approach requires both stochastic and deterministic rollouts to enforce DGCBFs. While this approach ensures safety in discrete-time settings, it also introduces significant sample inefficiency, which may limit the framework’s scalability to larger or more complex MAS. Hence, an extensive test with for instance 10 or 15 agents would strength the results of the paper.\nMoreover while the authors employ GNNs with attention mechanisms to handle changing agent neighborhoods, the computational complexity of GNNs in larger MAS could become important. In high-density environments with frequent neighborhood changes, maintaining an updated and accurate DGCBF through GNNs could pose significant computational challenges, possibly impacting real-time applicability. A detailed discussion on the scalability of GNN-based policies for larger agent systems would add valuable context to the method’s limitations.\n\n**Dependence on hyperparameter $\\nu$ for constraint enforcement**: \n\nthe authors claim on the fact that DGPPO is less sensitive to hyperparameters does not seem to be properly backed up. From the plot in Fig. 6b the value of $\\nu$—responsible for controlling the weight on constraint minimization steps—significantly impacts performance. Misalignment in $\\nu$ could lead to either overly conservative or unsafe policies, showing that DGPPO still requires careful tuning, contrary to its stated hyperparameter 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": 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) The policy in this work only takes local observations as input, such that it is decentralized. Why do you refer to it as a distributed policy? \n\n(2) The modified constraint (11b) is too strict which cannot be satisfied by a lot of policy classes, such as the Gaussian policy.\n\n(3) In (12), please provide the explicit gradient formula when the safety condition is violated. Note that the authors provide a gradient in (41). Nevertheless, this gradient is not associated to the policy loss function (under safety violation) in (12).\n\n(4) Theorem 3 is very difficult to understand. The orthogonality assumption is impractical. The reviewers also find that the authors try to replace the stationary state distribution (which is a function associated to the policy parameter $\\theta$) with a constant state distribution in this theorem to obtain their gradient $g$ in (13). What is the purpose of giving Theorem 3? What is the relationship between (13) and (14)? \n\n(5) The reason of using discrete graph CBFs should be explained clearly. Note that we can regard the multi-agent system as a large but single agent. Then, you can directly use the discrete CBF given in Theorem 2 to learn safe policies. In this case, the distributed control nature can still be preserved as the learned observation-based policy is end-to-end.\n\n(6) Theorem 4 is hard to understand. What is the relationship between the discrete graph CBF and the discrete CBF? Similar to Theorem 1, it is important for the authors to show that the safe set is forward invariant based on the discrete graph CBF.\n\n(7) In (11b), the safety constraint is calculated using a stochastic policy $\\pi$. However, in Fig. 1, deterministic policies are used for estimating the discrete graph CBF.\n\n(8) Why do the agents have different GAEs in your algorithm? Are you suggesting that the agents are heterogeneous and that their local policies differ?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "(1) The multi-agent safe optimal control problem considered in this paper is both general and challenging, as neither the system model nor a nominal policy is available in advance.\n\n(2) The learned policy is safe, which does not require additional safety filters in implementation.\n\n(3) Extensive simulations are conducted, and state-of-the-art baselines are compared." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a safe multi-agent reinforcement learning method based on distributed control barrier functions (CBFs) for multi-agent systems with limited perception capabilities. Simulation results on several multi-agent safe coordination tasks demonstrate the effectiveness of the proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "There exist several theoretical issues in the paper. The motivation of employing the discrete graph CBF is unclear. Some implementation details should be incorporated. See Questions part for more details." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024discrete,\ntitle={Discrete {GCBF} Proximal Policy Optimization for Multi-agent Safe Optimal Control},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1X1R7P6yzt},\nnote={under review}\n}" }, "abstract": { "value": "Control policies that can achieve high task performance and satisfy safety constraints are desirable for any system, including multi-agent systems (MAS). One promising technique for ensuring the safety of MAS is distributed control barrier functions (CBF). However, it is difficult to design distributed CBF-based policies for MAS that can tackle unknown discrete-time dynamics, partial observability, changing neighborhoods, and input constraints, especially when a distributed high-performance nominal policy that can achieve the task is unavailable. To tackle these challenges, we propose **DGPPO**, a new framework that *simultaneously* learns both a *discrete* graph CBF which handles neighborhood changes and input constraints, and a distributed high-performance safe policy for MAS with unknown discrete-time dynamics.\nWe empirically validate our claims on a suite of multi-agent tasks spanning three different simulation engines. The results suggest that, compared with existing methods, our DGPPO framework obtains policies that achieve high task performance (matching baselines that ignore the safety constraints), and high safety rates (matching the most conservative baselines), with a *constant* set of hyperparameters across all environments." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "control barrier functions", "multi-agent systems", "black-box systems", "partial observability", "reinforcement 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/d19a658d0ce2d53bb4213598817fd2d41831ee94.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/a419ca7c7867d4551af7467c34c3bf123dd67fd0.zip" }, "title": { "value": "Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal 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": "See above." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The pipeline can be thought of as a way to distill knowledge from the language models and stable diffusion models to augment the dataset of CLIP. This is an interesting way to inject new information in synthetic data. \n\n- The results are good, demonstrating improvements over CLIP while maintaining the amount of data it sees since the fix the number of iterations and just change the proportions of real vs their synthetic data." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a controllable image-text generation pipeline that can augment data to improve CLIPs image retrieval, classification, and compositional performance. Specifically, they leverage strong vision models to tag images with objects and attributes, use the knowledge in language models to create new variations of the captions, and use diffusion models to generate images based on the new captions as prompts." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- They say that the language model takes an instruction on how to generate a caption given the visual tags. They show some examples in Appendix A1. The instructions don't mention any editing, it mostly just says to describe the image better. In that case, do the gains come from some hallucination in the LLM caption that makes varied images?\n\n- Have the authors tried any other variation of editing instructions? Is there any analysis on the kinds of image editing prompted by the text that improve performance more? Are there specific prompts that serve as better negatives when tuning the CLIP contrastive loss?\n\n- There are other works that edit images based on text instructions like instruct pic to pic, magic brush etc. It might have been nice to see to see if editing certain things in images based on the LLM prompts is better than just using SD to generate since SD can often lack accuracy in generating the correct attribute object relation compositions. \n\n- Nit: There are several works that either generate synthetic images based on the dataset they want to target (https://arxiv.org/pdf/2406.05184), or for cross domain retrieval (https://arxiv.org/pdf/2401.00420). A discussion for comparison could be nice." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "Refer to the Weakness." }, "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 noise and misalignment in the large-scale image-text datasets, which is a critical challenge in multimodal learning.\n- The paper introduces an innovative approach that emphasizes fine-grained control, utilizing generative models to decompose and refine images and texts at a detailed level. Notably, it is training-free and suited for integration with different pre-trained generative models.\n- The experiments presented in the paper show that the proposed method improves downstream performances of multimodal models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes CtrlSynth, a controllable image-text synthesis pipeline for data-efficient multimodal training. Addressing limitations in existing large-scale datasets that are often noisy and misaligned, CtrlSynth enables fine-grained control by decomposing images into basic elements and applying user-specified modifications to synthesize new data. This training-free and flexible pipeline can work with different models and supports closed-loop synthesis (image to text and vice versa). The proposed method also boosts the performance of multimodal model training." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper, while contributing valuable ideas, has several notable weaknesses that are significant and need to be addressed. \n\n### Methodological Weaknesses\n- The proposed pipeline shares significant similarities with GenArtist[1] on image editing. The paper does not clearly demonstrate the differences between this work and GenArtist. It is important for the authors to specify these distinctions and highlight the novelty of their approach. Additionally, a thorough comparison should be incorporated into the experimental section to strengthen the evaluation.\n- While fine-grained control is presented as the main contribution, represented by the text and image controllers in the pipeline, the design is inadequate and lacks clarity. The design of the pipeline does not effectively demonstrate how the editing condition is provided to the generative model in a fine-grained manner. The text controller relies solely on prompt concatenation, making the mapping between visual tags and policies unclear and limiting precise control. Additionally, the paper does not address how to maintain image consistency after editing, which is essential for practical use. These shortcomings contribute to potential inconsistencies and an insufficient explanation of how fine-grained control is maintained. The image controller exists the same problem.\n\n### Experimental Limitations\n- The datasets used (CC3M and CC12M) are relatively small, with no experiments conducted on larger datasets such as LAION-400M or LAION-5B.\n- The paper only tests a limited range of multimodal model structures, lacking experiments on models like BLIP and CLIP of different ViT models.\n- The study does not address data-efficiency validation. Existing data-efficiency-focused works, such as SemDeDup[2], Filter-&-Align[3], and Sieve[4], refine or filter datasets for better performance. The paper should include comparisons with these approaches in terms of model performance and the amount of training data.\n\n---\nReference\n\n[1] Zhenyu Wang, Aoxue Li, Zhenguo Li, and Xihui Liu. GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing. arXiv.2407.05600.\n\n[2] Amro Abbas, Kushal Tirumala, Daniel Simig, Surya Ganguli and Ari S. Morcos. SemDeDup: Data-efficient learning at web-scale through semantic deduplication. arXiv.2303.09540.\n\n[3] Lei Zhang, Fangxun Shu, Tianyang Liu, Sucheng Ren, Hao Jiang, and Cihang Xie. Filter & Align: Leveraging Human Knowledge to Curate Image-Text Data. arXiv.2312.06726.\n\n[4] Anas Mahmoud, Mostafa Elhoushi, Amro Abbas, Yu Yang, Newsha Ardalani, Hugh Leather, and Ari Morcos. Sieve: Multimodal Dataset Pruning Using Image Captioning Models. arXiv.2310.02110." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "Can the authors provide details on the overall efficiency of the proposed pipeline? For example, how long does it take to generate 1 million images along with their captions? It would also be good to know the time cost at each component, e.g. vision tagging, caption generation, image generation. A more complete picture of the efficiency in the pipeline would better help to assess the value of this work." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The idea is clear and effective: by combining multiple expert models, we can obtain fine-grained image tags, captions, and synthetic images, which together help to create a high-quality synthetic dataset.\n\n2. The modularized pipeline is flexible, as each model can be replaced without affecting the performance of the other components.\n\n3. Experiments are comprehensive. Compared to the baseline CLIP, the improvements from CtrlSynth are evident." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a multimodal data synthesis pipeline called CtrlSynth. Specifically, CtrlSynth includes a vision tagging model to extract key objects, attributes, and relations from an image, which can then optionally be combined with the original text for a language model to generate new image descriptions. Finally, the newly generated image caption is input into a text-to-image model to generate an image. The authors have demonstrated the effectiveness of their pipeline by comparing it with CLIP pretraining data. Overall, the enhanced dataset appears to be superior to the original one." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Practical concerns: By using several models, such as the vision tagging model, LLM, and diffusion model, the proposed method might not be efficient for scaling up to larger datasets, particularly considering the time cost associated with image synthesis.\n\n2. The assumption behind CtrlSynth is based on a fixed number of data samples, where the method helps a model achieve better performance than training on the original dataset. However, given the recent trends in LLM and multimodal LLM research, where pretraining data continues to scale up, the proposed method may not be scalable for very large datasets. While this is a challenge, under the current setting in the paper, CtrlSynth is indeed effective." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Introduces a new controllable synthesis pipeline (CtrlSynth) that allows fine-grained data manipulation, enabling user-defined policies for image-text synthesis.\n- Achieving significant performance improvements across diverse tasks such as zero-shot classification, retrieval, and long-tail recognition is inspiring.\n- Clearly explains the methodology with diagrams and examples, easy to understand the synthesis process and its components." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces CtrlSynth, a controllable pipeline for generating synthetic image-text data to improve multimodal models. By allowing fine-grained control over data synthesis, CtrlSynth decomposes and recomposes visual semantics using pretrained models, enhancing diversity and alignment of generated samples." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In the ablation experiments, it was observed that the performance improvement brought by CtrlSynth-img alone is minimal. Would it be possible to completely remove the generation of synthetic images and focus resources on improving the diversity and quality of synthetic text? Would this lead to consistent performance improvements across all tasks?\n\n2. The paper mentions that CtrlSynth uses a self-filtering mechanism to improve the quality of synthetic data, but it lacks detailed explanations about the implementation, such as how the alignment threshold for visual tags is selected.\n\n3. The paper does not explain in depth how CtrlSynth fundamentally differs from other caption augmentation methods like VeCLIP and LaCLIP. It is necessary to provide a clearer comparison, clarifying whether the increased diversity brought by the decomposition of visual tags and user control strategies is more important, or whether it is the generation of more fine-grained semantic captions that matters.\n\n4. The experiments may be limited to a few selected models (e.g., Mistral-Nemo and Qwen2-7B). Would using larger LLMs lead to better results? \n\n5. A drawback of this method is that the data generation pipeline involves multiple different models and is not end-to-end in training, requiring substantial resources and time for building the synthetic data in the early stages." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024ctrlsynth,\ntitle={CtrlSynth: Controllable Image Text Synthesis for Data-Efficient Multimodal Learning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1X85iw7tqY},\nnote={under review}\n}" }, "abstract": { "value": "Pretraining strong vision or multimodal foundation models like CLIP relies on large-scale datasets (e.g., image-text pairs) that may be noisy, potentially misaligned, and have long-tail distributions. Previous work has shown promising results in augmenting datasets by generating synthetic samples. However, they only support domain-specific ad hoc use cases (like for image or text alone) and are limited in data diversity due to a lack of fine-grained control over the synthesis process. \nWe design a controllable image-text synthesis pipeline called CtrlSynth to enable data-efficient multimodal learning and improve vision and multimodal models in various use cases. The key idea is to decompose the visual semantics of an image into basic elements, apply user-specified control policies (e.g. remove, add, replace operations), and recompose them to synthesize images or texts. The decompose and recompose feature in CtrlSynth allows users to control data synthesis in a fine-grained manner by defining customized control policies to manipulate the basic elements. CtrlSynth leverages the capabilities of pretrained foundation models such as large language models (LLMs) or diffusion models (DMs) to reason and recompose basic elements such that synthetic samples are natural and composed in diverse ways. CtrlSynth pipeline is training-free and has a modular design, making it easy to support different pretrained models. \nCtrlSynth pipeline is also closed-loop, meaning it can synthesize text data based on the image or vice versa. Our evaluation shows that CtrlSynth samples substantially improve zero-shot classification, image-text retrieval, and compositional reasoning performance of CLIP models. We will publicly release the code and pipeline for future research." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "clip", "synthetic data", "multimodal learning", "longtail" ] }, "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/4a9c22db8db43cdc1ea19455bc0fad09ada14da8.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "CtrlSynth: Controllable Image Text Synthesis for Data-Efficient Multimodal 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": 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 the FTCT learning task (e.g., Figure 1), why in the $D_{train}$, we need to add noisy tokens in the token sequence? Why in the $D_{test}$ we do not add noisy tokens in the prompt?" }, "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 investigates whether transformers are capable of generalizing to longer reasoning chains through connecting shorter ones seen in the training stage, which is an interesting and important research question.\n2. The paper is technically sound: the trained transformers behave compositionally (with few-shot chain-of-thought prompting) and the authors provide insights on its internal workings: induction head and attention assignment, demonstrating that the transformer learn a generalizable program in its internal computing.\n3. Authors also theoretically prove that Transformers have the expressivity to simulate the generalizble underlying program." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The work sets out to investigate whether transformers are capable of generalizing to longer reasoning chains through connecting shorter ones seen in the training stage. The authors introduce \"Fragemented at Training, Chained at Testing\" learning task to train a randomly initialized 3-layer 3-head GPT2-like transformer. They find that with few-shot chain-of-thought prompting, transformers can perform good compositional reasoning skills by combineing fragments together. The authors further show that the generalization performance highly correlates to model complexity (require multiple-layer attention structure) and high relative knowledge ratio of training data. The paper also discusses the internal working of the model (learn an underlying generalizable program) to interpret the transformer's generalization behaviors and provide theoretical insights on transformer's expressivity on learning a such program." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Since the experiment setting is a randomly initialized transformer trained on synthetic data, to what extent the paper's conclusion can be extended to real pre-trained language models is questionable.\n2. the notations used in the paper are quite complicated, making the paper a little bit difficult for readers to follow." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "My main concerns have already been expressed in the \"weakness\" section." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The design of the FTCT task is well-conceived, as it effectively mimics real-world scenarios where knowledge is often fragmented and must be integrated to draw comprehensive conclusions. This setup provides a meaningful and practical benchmark to evaluate the compositional reasoning abilities of Transformers, making the study relevant and valuable for advancing our understanding of machine learning models' capabilities.\n- Chapter 5, \"transformer does compositional reasoning via the underlying program\", is very interesting as it explores the possible underlying mechanisms and principles that allow Transformers to perform compositional generalization. This chapter goes beyond just presenting empirical results by looking into how these models might internally handle and integrate fragmented knowledge. This deeper investigation adds value by giving us a better understanding of how Transformers achieve complex reasoning tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces the \"FTCT\" (Fragmented at Training, Chained at Testing) task to evaluate if Transformers can perform compositional reasoning similar to humans. The task involves training models on separate knowledge fragments and testing them on integrating these fragments to form complete causal graph traces. The study finds that few-shot Chain-of-Thought prompting helps Transformers combine these fragments correctly, even without seeing such combinations during training. The results indicate that model complexity and the data's knowledge ratio play a role in enabling this skill. The authors provide theoretical and empirical evidence for their claims, showing that Transformers can learn a generalizable program to aid in compositional reasoning. The findings are interesting and suggest potential areas for further exploration." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- While the task studied in this paper requires strong compositional generalization abilities, it is simple and singular in its form. Generally, using a simple and singular synthetic dataset is suitable for highlighting the shortcomings of the Transformer architecture. However, since the paper concludes that Transformers possess this capability, the experiments on this task alone are not sufficient to support such a conclusion. I believe that more diverse and comprehensive tasks are needed, and ideally, this capability should also be validated on complex real-world tasks.\n- In the related work section, the paper discusses various tasks used to probe compositional generalization abilities. The authors mention that these existing tasks have not been studied from the perspectives of few-shot prompting and chain-of-thought reasoning. However, this distinction alone is insufficient; if the difference is merely in this aspect, it would be possible to modify existing datasets instead of creating a new one. The novelty of this newly created task is not demonstrated well. Therefore, the authors need to provide more explanation regarding the motivation and innovation behind the proposed task.\n- The experiments in this paper use Transformers with relatively small parameter sizes. It is unclear whether the conclusions drawn from these experiments would hold true for larger Transformer models. This limitation raises questions about the generalizability of the findings to more complex and sizable architectures." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "- Although it is clear that the model is learning some ability to connect reasoning chains together (e.g. during training the model might just as AB and BC and correctly chain ABC), will the model be able to correctly chain together the values of AC? This could make for an interesting experiment where we could have some skip links in the test data and check for values accuracy\n- Checking my understanding, is there a typo in Figure 1, where B=106 and B=103, should it be C=108 and C=105, respectively?\n- Are there more than one set of the causal chains? The set equation in line 155 seems to suggest there is only one sequence of length n.\n- Why are the noise vertices inserted in a predictable manner?\n- I am curious about this 0.3 relative knowledge ratio threshold where it is reported that compositional reasoning emerges. Could it be that 0.3 is when the probability that there is at-least one occurrence for every (v_i, v_{i+1}) in the train set reaches close to 1? \n- Why is there a drop in performance in Figure 2 (right) and relative knowledge of 0.25?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The paper presented a very intriguing and creative approach to testing the ability for models to learn compositional reasoning ability\n- There are some really interesting results, specifically the exact complexity (and the increased expressability) needed for the transformer architecture to optimally solve the FTCT task\n- The insights regarding the few shot CoT results are of significance and spark further research in this area\n- The empirical findings of how the transformers performs this task is enlightening and should spark some interest for further research in this area" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper investigates how well transformers are able to perform compositional reasoning tasks. To that end, the paper introduces a new dataset methodology, namely the Fragmented Training, Chained at Testing (FTCT) that simulates how models would be presented with training data in practice (with incomplete fragments of reasoning paths with noise + context) and how well the model is able to piece together the full reasoning chain in test-time. Using this methodology, the paper runs insightful experiments that ablate different lengths of partial reasoning chains during training, different transformers and neural architectures, and number of few shot CoT examples. Through these experiments, the authors find that few shot CoT plays an important role for compositional reasoning, the impact of increasing relative knowledge ratio, and the increasing expressibility of adding layers and heads in the transformers architecture. Lastly, the paper presented some empirical evidence that you need a certain complexity of the transformers architecture to simulate the optimal program for the FTCT task in training and testing." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The clarity of this paper is lacking, especially in the notation and writing. For instance, in Figure 1, there is a seeming typo in some of the values that contradicts the setup of the dataset. Separately, some concrete examples of the data (including noise + context tokens) of the FTCT dataset would really improve the readers understanding (it took me multiple re-read to get the gist of the methodology)\n- The paper's definition of compositional reasoning should be explicitly written out in the paper. The only real definition of this is in the abstract where it is stated that \"For example, if someone learns ( B = f(A) ) from one source and ( C = g(B) ) from another, they can deduce ( C = g(f(A)) ) effortlessly, even without encountering ( AC ) or ( ABC ) together, showcasing their compositional generalization ability.\"\n- With this FTCT methodology, it seems clear that the model is learning some ability to connect sequential reasoning chains together (e.g. during training the model might just as AB and BC and correctly chain ABC), but the approach does not test if the model can correctly reason about AC in test-time, which is an aspect of compositional reasoning (as mentioned in the abstract)" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. Table 1 shows that all Transformer models attain 1.0 Values accuracy, even for small models that get very low Vertices accuracy. Can you account for this discrepancy?\n2. An unintuitive pattern in the results (e.g., Fig. 2 and Table 3) is that accuracy *decreases* with the number of few-shot prompts $>1$. This results stands in contrast to a standard ICL setting, where inclusion of more examples typically improves performance. It is stated that this is “possibly due to increased dissimilarity between training and testing data with more CoT examples” (Line 277-278). Why does including more CoT examples causes the test data to be OOD? If this is the case, this seems like an important confound affecting this experiment setup that may not be adequately accounted for.\n3. It is interesting to contrast the results from Sec. 5-6 with Zhang et al., 2022 (“On the Paradox of Learning to Reason from Data”), who apply a similar methodology but find that gradient descent fails to discover the correct $\\theta^*$ for a logical induction task with very similar structure. Is there a reason why here the training succeeds at uncovering the underlying program, whereas in previous work it does not? More generally, it would be nice to see reference to this paper in the discussion." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "**Motivation:** The key research questions of the paper are clearly delineated in the introduction:\n\n1) When are Transformers able to perform compositional reasoning by connecting fragmented knowledge in training data?\n2) How do different training factors impact the emergence of this ability?\n3) What internal mechanisms enable Transformers to develop this ability?\n\nThese questions are broadly relevant to current research and the paper is structured in a way that consistently centers these 3 questions throughout.\n\n**Mechanistic interpretability analysis:** I especially enjoyed the approach to question (3). Broadly speaking, the authors approach this question by first demonstrating that there exists a program that solves the task (Sec. 5.1) and that this program can be approximated by a 2-layer Transformer (Sec. 5.2). Then, through linear probing experiments (Sec. 6), they give an empirical argument that the Transformers trained on FTCT have learn to implement this program. I am not an expert on probing so I can’t speak to the soundness of the methods, but I found the combination of Sec. 5-6 to be an elegant argument from a mechanistic interpretability standpoint." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "**Aims:** This paper seeks to understand at a mechanistic level how Transformers are able to perform compositional reasoning in a few-shot chain-of-thought setting.\n\n**Methods:** A synthetic dataset is generated consisting of chains of nodes and edges derived from causal graphs. At training time, spurious nodes are inserted randomly into the chains; at testing time, few-shot prompts consisting of intact chains (no spurious nodes) are provided to the model. Models are tested on their ability to reconstruct full causal chains from fragmented chains learned in training, with evaluation based on accuracy in predicting both the correct vertex order and values in the chain.\n\n**Results:**\n\n- Zero-shot versus few-shot prompting is compared, with findings showing that few-shot CoT prompting significantly enhances performance in compositional reasoning tasks, particularly in forming the correct vertex order.\n- A space of small, GPT-2-style models ranging from 42M-54M parameters are trained on the FTCT dataset. Results show that multi-layer, multi-head Transformers (minimum 2 layers and 2 heads) perform notably better, while single-layer/single-head models and MLPs perform poorly.\n- The impact of training data’s relative knowledge ratio (ratio of child chain length to complete chain length) is studied, with a critical threshold (ratio ≥ 0.3) identified where compositional reasoning reliably emerges.\n- Mechanisms underlying the model's success, such as induction heads for in-context learning and attention patterns facilitating parent-child relationships, are analyzed through linear probing, revealing specific mechanisms by which the model achieves compositional reasoning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Novelty:** The paper claims to “introduce a learning task” (FTCT) based on causal graphs, and yet the design of this task is nearly identical to the setup in Prystawski et al. (2024). Given that the main distinction between FTCT and the prior work is the introduction of spurious nodes (line 105-106), I would expect to see this prior work—which actually *did* introduce a novel learning task—given more prominent attribution. \n\n- (Currently this work is referenced in Line 103—”The empirical findings of our work align with the observation in (Prystawski et al., 2024)…” The wording of this reference obfuscates the underlying causal structure that this prior work likely played in informing the current paper.)\n\n**Generality:** The key findings of this paper are framed in very broad terms: \n\n> “The emergence of compositional reasoning is highly influenced by the data’s relative knowledge ratio and model complexity. Specifically, a relative knowledge ratio of at least 0.3 and a Transformer architecture with at least two layers and two heads are critical for achieving this ability.” (Lines 520-523)\n> \n\nHowever, these conclusions are all drawn relative to one synthetic dataset with a highly-specific structure; it is unclear to what extent the empirical conclusions (e.g., compositional reasoning in transformers requires a relative knowledge ratio ≥ 0.3) generalize beyond the FTCT task. To make a convincing argument that these results have meaning beyond this one benchmark, this analysis ought to be replicated on more naturalistic reasoning benchmarks where few-shot CoT prompting is commonly used.\n\n**Clarity:** The description of the FTCT dataset/task design (Sec. 3) fails to convey a clear description of the experiment setup and requires too much work of the reader. All aspects of prompt construction are described in excruciating formal detail, making it hard to separate design choices that are key to the experiment from implementation details. Overall, the formalism in this section is a barrier to understanding what’s going on at a more basic level.\n\n- Fig. 1 lacks basic signposting needed to convey what is going on.\n - First off, there is no caption. This is a major omission as the figure is definitely not self-explanatory.\n - The blue highlights draw the reader’s attention to spurious features of the task (noise nodes) instead of the actual purpose of the task (predicting values of causal nodes).\n- Other comprehension/clarity issues in Sec. 3:\n - “We assume op(e) represents operations like (+a) or (−b)” Does this mean addition/subtraction are the *only* possible operations?\n - I don’t understand how the merge operation works from the description.\n - Some unconventional choices of notation, such as using $f$ as an index over few-shot examples.\n - What is “downside processing” - do you mean “downstream”?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024are,\ntitle={Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1Xg4JPPxJ0},\nnote={under review}\n}" }, "abstract": { "value": "Humans exhibit remarkable compositional reasoning by integrating knowledge from various sources. For example, if someone learns ( B = f(A) ) from one source and ( C = g(B) ) from another, they can deduce ( C = g(f(A)) ) effortlessly, even without encountering ( AC ) or ( ABC ) together, showcasing their compositional generalization ability. In this paper, we introduce a learning task, \"FTCT\" (Fragmented at Training, Chained at Testing), to assess if Transformers can replicate this skill. In the training phase, data consist of separated knowledge fragments from an overall causal graph. During testing, Transformers must infer complete causal graph traces by integrating these fragments. Our findings demonstrate that few-shot Chain-of-Thought prompting enables Transformers to perform compositional reasoning by revealing correct combinations of fragments, even if such combinations were absent in the training data. Furthermore, the emergence of compositional reasoning ability is strongly correlated with the model complexity and data's relative knowledge ratio. We propose, both theoretically and empirically, that Transformers learn an underlying generalizable program from training, enabling effective compositional reasoning during testing." }, "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": [ "Transformer; Chain-of-Thought; In-Context-Learning; Compositional Generalization" ] }, "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/cb4822c7c17fddb5cca6f4a1f589c8e11ec6b09a.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/b8c4e05383b92ddd3e548442492bdbb451408314.zip" }, "title": { "value": "Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See weakness" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Figures clearly illustrate the point of the paper. \n2. The writing is easy to follow\n3. Articulate the attack model and assumptions\n4. Run transferability test" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduced an attack that enables adversaries to add stealthy “meta-instructions” to images that influence how visual language models respond to queries about these images" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. L33, \" but injection attacks in non-text modalities are a new, yet-to-be-explored area of LLM safety research\". This type of attack has been widely explore in [1] and [2]\n2. L81, \"users are victims of adversarial third-party content that they ask the model to process\". I'm curious whether the images are generated by the users or not. If the user create the line chart shown in Fig. 1 from their local devices, does it mean the attack studied in the paper doesn't exist anymore?\n3. Table 4, why is the transfer rate of llava on negative as low as 0.1?\n4. I'm curious what will happen if the system prompt of the VLM contradicts with the meta-instruction in the image?\n5. Overall, I think the paper is in a good quality. The major downside is the novelty, as we already know from previous work that optimizing the input image towards a certain attack target is feasible for VLM. Thus, it's not a new vulnerability in VLM. Though the author attempts to differentiate their attack setting from previous jailbreaking and soft prompt attacks, the overall attack surfaces and methods remain largely the same. I would like to the see more insights coming from the paper. \n\n\n[1] Are aligned neural networks adversarially aligned?\n[2] A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 summarize some of my comments on weaknesses of the paper into questions below:\n\n1) Do the authors agree with my comments about their portrayal of previous works, and if so what steps are the authors taking to address this? Concretely, what sections of the paper have been rewritten.\n2) Have the authors been able to run the suggested experiments I have mentioned above, and if so what did they find?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "## Originality\n\nThe question of prompt injection vulnerabilities to large language models is of significant importance. The authors demonstrate that models are vulnerable to similar attacks of this nature through their vision input as are possible through their text input. What's more, they show the vulnerability is in some cases worse through the image input.\n\nWhilst the idea of providing meta-instructions through image inputs its not entirely novel (see weaknesses section), this paper is the most thorough treatment of the subject that I am aware of, and brings to light new and concerning ways that a model's output can be tampered with using images.\n\n## Quality and clarity\n\nThe paper is well written the method is conveyed clearly. The results section contains a good depth of experiments, most importantly covering a number of popular open-source VLMs and target meta-instructions.\n\n## Significance\n\nAs VLMs are used more frequently for agentic tasks that will expose them to untrusted data from the internet, prompt injection / meta-instruction attacks will become more and more concerning. Thus the paper concerns a timely and interesting threat model that the adversarial attack community should be exploring in more detail." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a method to create image inputs to Vision Language Models (VLMs) that lead said model to respond to any user query appended to the image with a certain \"spin\", e.g. responding with a certain sentiment, or in a certain language. The authors refer to this as embedding a \"meta-instruction\" in an image. \n\nCritically, a meta-instruction attack is only successful if the models response to the users query (and the attacked image) responds to the query whilst following the meta-instruction (e.g., if the meta-instruction was \"talk in French\" and the model responded in French but did not answer the users query, then this would not be a successful attack).\n\nTo train these meta-instruction attacks, the authors perform projected gradient descent on an image to minimize the language modeling loss of the VLM inputted with this image over a dataset of synthetic question answer pairs with the answers following some target natural language meta-instruction.\n\nThe results of the paper demonstrate that this method can be used to learn adversarial images for various different types of meta-instructions. The authors also demonstrate a non-trivial transfer of meta-instruction images between models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "While the critique is long, this is only because I believe the paper has interesting results that could be improved.\n\n## Presentation of previous work\n\nThe authors make a number of claims about prior work that I believe are not completely accurate. Editing the language around these claims would help to improve the paper. Here are some examples that I believe need to be addressed:\n\n- Line 32 - \"But injection attacks in non-text modalities are a new, yet-to-be-explored area of LLM safety research.\" I do not think this is entirely true. For example, Bailey et al. [1] explore how train an image to convey a certain text prompt, which they demonstrate can be a prompt injection attack. \n- Line 83 - \"By design, jailbreaking and adversarial examples produce contextually incoherent outputs that do not actually answer users’ questions about images.\" I think this depends on how you define an image jailbreak. For example, Dong et al. [2] produce adversarially perturbations to harmful images that lead to a model answering coherently about said image --- in particular the model is able to correctly identify what is in the image. While the authors claim here is correct for other image jailbreaking work, such as Qi et al. [3] who learn images unrelated to the harmful request they are trying to elicit a response about from the model, it is not universally correct. For this reason the claim should be softened.\n- Line 84 - \"They [jailbreaking and image adv attacks] are not stealthy and cannot be used for indirect attacks because users would notice that the VLM’s outputs are wrong given the conversation context and inputs.\" Bailey et al. [1] and Qi et al. [3] both demonstrate methods to create jailbreaking images under epsilon ball constraints, which is the definition of stealthiness the authors use on line 290. \n\n## Novelty / originality\n\nFollowing on from some of the comments above, I believe there is a question of novelty / originality of this work. \n\nIn particular, the general algorithm presented to produce meta-instruction attacks essentially involves creating a dataset of input output pairs, and training an image by PGD to maximize the likelihood over this dataset. This method appears to fit into the \"Behavior Matching\" algorithm from Bailey et al. [1] \n\nDespite this, I believe the work does contain novel and important contributions. In particular: \n1. The study of the changes in semantic meaning present in images from various different attacks, with meta-instruction attacks preserving meaning.\n2. The transfer experiments in Table 4 are very interesting.\n3. This is the most thorough treatment of prompt injection image attacks I have seen.\n\n## Summary\n\nCombining the above two points, I believe the paper needs to be\nrewritten to more clearly lay out the novelty of the paper \nand more accurately represent the papers contribution. \nMy high level suggestions would be:\n1. Make it clear that prior works have examined prompt injecting image attacks, however yours is a more complete treatment of the topic.\n2. Make it clear that your method to create such attacks is a special instance of what prior works have introduced. \n3. From this, your novelty comes not from the method but rather the results. E.g. line 88 that reads \"We design, implement, and evaluate a method for creating a new type of image perturbations that act as cross-modal soft prompts for a language model while preserving the visual semantics of the image.\" needs to be adjusted.\n4. Given that I do not think the method is novel, I would suggest running the following additional experiments:\n\t1. In Table 4, add transfer results to Claude and GPT-4o. These results should feature in the transferability experiment.\n\t2. More detailed defense experiments. Appendix C shows fairly simple defenses can work to avoid meta-instruction attacks. [1] finds that training perturbations under different constraints (e.g. a moving patch) ends up being more robust to simple defenses. It would be interesting to see if this result is reproducible in your setting.\n\nTo reiterate, I think studying prompt-injection images to models is important, and the authors present valuable results. I thank the authors for their hard work! \n\n\n[1] - Bailey, Luke, et al. \"Image hijacks: Adversarial images can control generative models at runtime.\" arXiv preprint arXiv:2309.00236 (2023).\n\n[2] - Dong, Yinpeng, et al. \"How Robust is Google's Bard to Adversarial Image Attacks?.\" arXiv preprint arXiv:2309.11751 (2023).\n\n[3] - Qi, Xiangyu, et al. \"Visual adversarial examples jailbreak aligned large language models.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 19. 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": 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 weaknesses." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The concept of embedding hidden meta-instructions within images offers a new approach to prompt injection for multi-modal models, highlighting a potential vulnerability not extensively covered in existing literature.\n\n2. It is interesting to see how the method reveals hidden capabilities of instruction-tuned models. In some cases, the meta-instructions successfully steer the model's outputs in ways that explicit instructions fail to achieve.\n\n3. The study provides an empirical evaluation on a range of meta-objectives (e.g., sentiment, language, and political bias), demonstrating the effectiveness of the attack method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a new type of attack on visual language models. These attacks, termed meta-instruction attacks, involve subtle image perturbations that act as soft prompts to influence how a model interprets images and responds to queries. The idea is to steer the model’s outputs to satisfy adversary-chosen objectives, such as a specific sentiment, style, or political bias, without the user being aware of the manipulation. The authors demonstrate the effectiveness of this approach across various visual language models, showing that these perturbations often outperform explicit instructions and are transferable across models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper's reliance on just five images from a single dataset, ImageNet, limits the robustness and generalizability of its evaluation. ImageNet, which is primarily focused on object recognition, may not adequately represent the diversity and complexity of images encountered in real-world scenarios. Incorporating evaluations on datasets with more varied and complex scenes, such as MSCOCO, would provide a more comprehensive assessment of performance.\n\n2. The paper simulates user interaction by generating questions to test meta-instructions, but it provides limited clarity on whether these questions adequately cover a broad range of natural user queries. Limited prompt diversity may affect the robustness of the attack if VLMs encounter different prompts in real-world scenarios.\n\n3. Since the meta-instruction is added as noise to the image, the paper does not demonstrate the effectiveness of meta-instructions against recent inference-time defense methods like DISCO[1], DiffPure[2], and IRAD[3]. This could be valuable for understanding its performance in the context of contemporary robustness strategies.\n\n[1] DISCO: Adversarial Defense with Local Implicit Functions.\n[2] Diffusion models for adversarial purification.\n[3] IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 also provide an evaluation when random resizing or cropping is applied? Since this paper addresses practical concerns, it would be valuable to test your method under common “defenses” encountered in everyday scenarios.\n\n2. Are there any failure cases? For example, are there meta-instructions that are particularly difficult to achieve?\n\n3. Why is it necessary to evaluate cosine similarity as done in Section 5.3? Could you clarify the relevance of this metric?\n\n4. Is there an evaluation that checks whether the generated textual outputs remain consistent with the input images?\n\nOverall, I appreciate the practical focus of this paper. I would be happy to raise my evaluation if these concerns are addressed." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The focus on the dissemination of seemingly coherent misinformation is highly practical and addresses a significant real-world concern.\n\n2. The evaluation is thorough, including robustness testing against JPEG compression as a defense (which I suggest moving to the main text, given its practicality in everyday use) and examining the transferability of the attack across different vision-language models (VLMs)." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new attack objective in which the output text remains consistent with the input images but adopts an adversary-chosen style, sentiment, or point of view. The adversarial optimization is applied to the input image, ensuring that the modifications are imperceptible to humans. Experiments demonstrate that images containing hidden meta-instructions achieve significantly higher success rates compared to those with explicit instructions. This attack highlights a practical risk, as it enables the dissemination of seemingly coherent but misleading information." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. A NeurIPS 2024 paper [1] also explores the dissemination of seemingly coherent misinformation in visual language models, but through the lens of data poisoning. While this paper focuses on test-time adversarial attacks, it would be beneficial to discuss the key differences between test-time attacks and training-time poisoning, and in what scenarios each is more practical, given the similarity in objectives between the two papers.\n\n2. The evaluation of image semantics preservation seems suboptimal. In Section 5.3, semantics are defined using cosine similarity between images, but it is unclear why this metric is particularly relevant. A more meaningful evaluation would assess how well the actual text output of the visual language model aligns with the input images, which is the core focus of this paper—consistent outputs with images but in adversary-chosen styles, sentiments, or viewpoints.\n\n\nReference:\n[1] Xu, Yuancheng, et al. \"Shadowcast: Stealthy data poisoning attacks against vision-language models.\", The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We introduce a new type of indirect, cross-modal injection attacks against VLMs to influence how the model interprets the image and steer its outputs to express an adversary-chosen style, sentiment, or point of view." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024soft,\ntitle={Soft Prompts Go Hard: Steering Visual Language Models with Hidden Meta-Instructions},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1XxNbecjXe},\nnote={under review}\n}" }, "abstract": { "value": "We introduce a new type of indirect, cross-modal injection attacks against language models that operate on images: hidden \"meta-instructions\" that influence how the model interprets the image and steer its outputs to express an adversary-chosen style, sentiment, or point of view. We create meta-instructions by generating images that act as soft prompts. In contrast to jailbreaking attacks and adversarial examples, outputs produced in response to these images are plausible and based on the visual content of the image, yet also satisfy the adversary's (meta-)objective. We evaluate the efficacy of meta-instructions for multiple models and adversarial meta-objectives, and demonstrate how they \"unlock\" capabilities of the underlying language models that are unavailable via explicit text instructions. We describe how meta-instruction attacks could cause harm by enabling creation of self-interpreting content that carries spam, misinformation, and spin." }, "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": [ "security", "machine learning", "adversarial perturbations", "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/fb4adaa1ef6d33d20980028e22c625f0e811905a.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": "Soft Prompts Go Hard: Steering Visual Language Models with Hidden Meta-Instructions" }, "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": "Please address 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": "- The paper proposes a novel notion to measure causal fairness. This notion makes intuitive sense and seems easy to implement. \n- The paper proposes a new algorithm to train a model, where the causal fairness notion is cast as a regularization term.\n- On several empirical datasets, the proposed algorithm seems to perform best in terms of causal fairness, as compared to several benchmarks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a novel causality-based fairness notion called post-intervention Cumulative Ratio Disparity (ICRD) to assess the causal fairness of the decision models, and then presents a causal framework based on ICRD. The theoretical and empirical results show that ICRD can assess causal fairness and the causal framework can better balance accuracy and fairness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- It seems that the proposed algorithm is not very competitive as compared to benchmarks if one primarily cares about conventional metrics e.g., K-fair and accuracy. \n- The theoretical results are quite intuitive, and the proof is straightforward. It would be helpful to the contributions of the paper, and why the contributions are nontrivial to obtain. \n- The references of this paper do not contain a single ICLR paper. It would be helpful to better demonstrate the fit of this paper to 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": 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": "Since the main contribution of this paper is to build on the K-fair definition, what could you do specifically to include a more comprehensive and clear explanation of K-fair? How is the set of contexts C chosen, and which contexts were used in the experiments?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The overall setting is well-chosen and the contribution appears to be solid." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper adds to the causal fairness literature by proposing a new metric to measure unfairness and a strategy for training fair models. It follows previous work by Salimi et al (2019) and Ling et al (2023) on interventional fairness or K-fairness. An algorithm is K-fair if interventions on the sensitive attribute do not change the predictions, while also causally conditioning on a given context K. The current paper extends this definition by applying a 1-Wasserstein distance to the difference between the interventional distributions, with interventions on the sensitive attribute. The proposed training strategy is empirical risk minimization with a penalty term added using the aforementioned 1-W. distance. The paper includes a few basic theoretical results and experiments comparing the method with several alternate methods on several datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Compared to the existing work I believe this paper is somewhat incremental. The novelty is not high. The experiments are OK. The presentation and explanations of both current work and its context in related literature are not very clear." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "Please refer to the questions in Weaknesses part." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The authors clearly illustrated the limitations in existing interventional fairness metrics, and the related works section is comprehensive and easy to follow.\n\n2. The proposed formulation of ICRD is sound and the authors provide the theoretical analysis on how ICRD addresses the limitations of existing causal fairness metrics.\n\n3. The authors proposed a fairness framework, ICCFL, which incorporates a differentiable approximation of the ICRD metric to enable efficient training." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a new fairness metric called Intervention-based Cumulative Ratio Disparity (ICRD), which aims to address limitations in existing causal fairness metrics (K-Fair) by measuring cumulative causal effects along prediction probabilities by intervening on sensitive attributes. Additionally, the authors propose a fairness framework, ICCFL, which incorporates the ICRD metric to train fairer models. Through theoretical and empirical analyses, the paper demonstrates that ICCFL better balances fairness and accuracy than existing fairness-aware algorithms across multiple datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The proposed method assumes the causal model is known, which may be a strict assumption. It would be great for the authors to discuss the sensitivity of the proposed metric and framework to potential causal graph misspecification.\n\n2. This paper assumes the sensitive attribute is binary. Could the proposed metric be extended to handle multiple sensitive attributes?\n\n3. The method leverages causal generative models to infer the distribution of exogenous variables. It would be useful to explore the robustness of the approach when estimating interventional distributions with different causal generative models." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. How does the ICRD notion address limitation 1?\n\n2. Why does interventional fairness have fewer identifiability challenges compared to the counterfactual fairness and path-specific fairness, as mentioned on line 228, page 5? \n\n3. Can the ICCFL method be compared with any benchmark methods using other causal fairness notions, such as, path-specific fairness? This might reveal interesting observations on the comparison between ICRD and other non-intervention based causal fairness measures." }, "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 improved the existing interventional fairness notion, K-Fairness, in a comprehensive way that both develops a new fairness notion and proposes an algorithm for applying the new fairness notion. The authors also provided relevant theoretical support for the validity of both ICRD and ICCFL, which add to the technical soundness of the paper. \n\n2. The paper provided useful details in the experiment evaluation of the ICCFL method: Section 5.3 offered empirical evidence for the benefit of ICRD, and Section 5.4 discussed observations related to hyperparameter choice in ICCFL." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Motivated by shortcomings of existing interventional fairness notions, this paper proposed a new causality-based fairness notion called post-Intervention Cumulative Ratio Disparity (ICRD). ICRD measures the cumulative causal effects along prediction probabilities by intervening on the sensitive attribute. The authors explained ICRD’s superior properties over existing intervention causal fairness notions. Additionally, they developed a new fairness framework based on ICRD: Intervention-based Cumulative Causality Fairness Learning approach (ICCFL) formulates a constrained optimization problem where the ICRD metric is included in the prediction loss of the model. Empirical evidence from comparing ICCFL with several benchmark methods demonstrated that ICCFL could attain better causal fairness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Given that the differences between the K-Fair notion and the new ICRD notion are somewhat subtle, the paper could benefit from clearer explanations. For example, Example 1 used to discuss limitation 1 might be applied again after introducing ICRD to illustrate how ICRD applies here, such as, what are the possible contexts C in this example. On a related note, although the ICRD notion has clear advantages over the K-Fair notion, it is unclear whether these advantages alone justify adding ICRD to the already large number of causal fairness definitions. It would be helpful to discuss the benefits of ICRD as a causal fairness definition in general. \n\n2. The ICRD notion centers on disparity in the cumulative causal effects. This is not necessarily desirable for understanding discrimination, as we may be more interested in dissecting the causal effects associated with specific scenarios. It would be helpful to discuss potential insufficiencies of the ICRD notion, for example, when ICRD may not be identifiable, when enforcing ICRD to be 0 may be too restrictive for fairness." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 refer to the above weaknesses." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- It is reasonable and meaningful to uncover the limitations of the existing fairness notions and propose a new one.\n- The experimental results show the effectiveness of the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper demonstrates the limitations of the existing interventional fairness and then proposes a new causal fairness metric called Intervention-based Cumulative Rate Disparity (ICRD). ICRD aims to measure the post-intervention cumulative causal effects along the prediction probabilities for any intervention on the context. In addition to defining this metric, the authors propose an algorithm designed to achieve ICRD." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The motivation behind ICRD is somewhat ambiguous. Specifically, regarding sufficiency, how is a condition defined as ‘sufficient’ for evaluating causal fairness? It seems that the sufficiency aspect depends significantly on the particular causal fairness definition in use, and the current explanation feels unclear on this point. The insufficiency aspect could benefit from greater elaboration.\n- Lines 313–314 state that “ICRD encompasses K-Fair and represents the cumulative causal effect of K-Fair across all decision thresholds,” but this claim is difficult to interpret without additional clarification. Similarly, it is unclear how Table 1 was generated or how the decision threshold impacts outcomes. Could the authors further clarify these aspects?\n- Finally, I am unconvinced that the decision threshold’s impact constitutes a limitation of K-Fair. K-Fair requires two distributions to be equivalent; hence, it is unclear how the decision threshold would influence this requirement. More discussion on this would be valuable to fully understand the claimed limitation." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024interventionbased,\ntitle={Intervention-based Causal Discrimination Discovery and Removal},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1XzTxtezgj},\nnote={under review}\n}" }, "abstract": { "value": "Causal inference is a recent and widely adopted paradigm to deal with algorithmic discrimination. Building on Pearl's structure causal model, several causality-based fairness notions have been developed, which estimates the unfair causal effects from the sensitive attribute to the outcomes by incorporating the intervention or counterfactual operators. Among them, interventional fairness (i.e., $K$-Fair) stands out as the most fundamental and broadly applicable concept that is computable from observantional data. However, existing interventional fairness notions fail to accurately evaluate causal fairness, due to their following inherent limitations: (i) the causal effects evaluated by interventional fairness cannot be uniquely computed; (ii) the violation of interventional fairness being zero is not a sufficient condition for a causally fair model. To address these issues, we firstly propose a novel causality-based fairness notion called post-Intervention Cumulative Ratio Disparity (ICRD) to assess causal fairness of the decision models. Subsequently, we present a fairness framework (ICCFL) based on the proposed ICRD metric. ICCFL firstly generates interventional samples, and then computes the differentiable approximation of the ICRD to train a causally fair model. Both theoretical and empirical results demonstrate that the proposed ICRD effectively assesses causal fairness, and ICCFL can better balance accuracy and fairness." }, "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", "Causal inference", "Intervention-based metric" ] }, "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/87b17721caab96660a85c1b975f662864c76561f.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": "Intervention-based Causal Discrimination Discovery and Removal" }, "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": "- The authors should compare different base models in Figure 5 and Table 2.\n\n- The experimental setup in the experimental module should be clearly presented; for instance, in Table 2, did the responses corresponding to questions from other datasets involve generating five responses and filtering down to one based on the reward model, or was only one response generated?\n\n- The authors might discuss the effects of optimizing different question data volumes during QPO. Additionally, since the authors note that optimizing for both solvability and difficulty simultaneously in QPO is challenging, are there corresponding experimental results to support this?\n\n- The author should probably compare the generated questions with the questions in the test set (n-grams or other methods) to prevent potential data leakage." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- This article focuses on synthesizing mathematical problems using open-source large language models, which is an important topic. The fine-tuning and filtering techniques proposed by the authors demonstrate some effectiveness.\n- The article presents a thorough and detailed set of experiments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel framework for generating high-quality reasoning datasets using smaller open-source models. The primary focus is on addressing the challenges of synthesizing high-quality data at scale with affordable costs. \n\nKey contributions of the paper include:\n\n- The authors present a scalable data synthesis method that enables the generation of 1 million question-answer pairs without relying on extensive seed data or complex augmentation techniques.\n\n- The framework incorporates a two-stage process consisting of Question Fine-Tuning (QFT) and Question Preference Optimization (QPO), which enhances the question generation capabilities of the models.\n\n- The paper demonstrates that models fine-tuned with the ScaleQuest dataset achieve significant performance gains compared to baseline models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The proposed Question Preference Optimization (QPO) appears to be less effective; as shown in Figure 5, the difference between QPO and QFT is minimal, raising questions about the validity of QPO.\n\n- This paper attempts to extract training data from models, similar to the approach of MAGPIE. Therefore, the authors should conduct a more fair and detailed comparison between Question Fine-Tuning (QFT) and direct prompting methods. In Figure 5, the authors generated 1 million question-response pairs using MAGPIE with Qwen2-Math-7B-Instruct as the \"raw data\" setting. However, the other settings filtered 2 million( from DeepSeekMath-QGen and Qwen2-Math-QGen) questions down to 1 million and applied a reward model to filter the responses. Consequently, it is difficult to determine whether QFT is more effective than the MAGPIE method or if the filtration of questions and responses is more effective.\n\n- The ablation experiments are insufficient. The authors conducted experiments only on Llama3-8B, rather than comparing all four base models as presented in the main table. \n\n- The authors suggest that the data generation method proposed in this paper can produce diverse and high-quality questions at a lower cost. However, with advancements in open-source models, previous sample-driven and knowledge-driven question synthesis models can also be replaced with open-source models. Moreover, Qwen2-Math, as a response synthesizer, demonstrates superior mathematical capabilities compared to earlier versions of GPT-4. Therefore, it is difficult to assert that the data synthesis approach presented in this paper is superior to other methods in cost." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "There are plenty of LLMs used in the data synthesis pipeline: DeepSeekMath- 7B-RL , Qwen2-Math-7B-Instruct, GPT-4o-mini, GPT-4o, DeepseekMath-7B-Base, InternLM2-7B-Reward. Can you provide a Table for all the settings? Is there any specific reason to select different LLMs for different stages?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper provides a cost-effective data synthesis method for math reasoning problems. \n2. The synthetic dataset can boost the performance of multiple open-source models in both in-domain and out-of-domain evaluation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a scalable data synthesis method, ScaleQuest, for math reasoning. The augmented math datasets can enhance the model performance of mainstream open-source models such as Mistral, Llama3, DeepSeekMath, and Qwen2-Math. After finetuning the proposed dataset, the small open-source models can even outperform closed-source models such as GPT-4-Turno and Claude-3.5 Sonnet" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The main weakness of this paper is, that the proposed data synthesis pipeline is too complex and may be domain-specific. It includes the training in question fine-tuning, question preference optimization, the inference for solvability and difficulty check, reward scoring, etc. Although the API and training cost is not as expensive as GPT-4, this method is more time-consuming and requires extra effort to adapt to other domains. \n2. The proposed data synthesis method is only evaluated in the math domain. It is unsure whether this method can be easily adapted to other domains such as code or logical reasoning. Specifically, can the question finetuning and question preference optimization trained on the math domain be directly used for other domains, or the extra finetuning for each domain and each stage is needed? \n3. The experimental results are not easy to interpret: \n(i) For the baselines with different synthetic datasets, are they finetuned on the same scale of training examples? \n(ii) What does the Percentage and Accuracy in Figure 5 mean? Where is the legend of the left plot of Figure 5? \n(iii) What does the question quality in Table 2 refer to? \n4. There are many components in the data synthesis pipeline, but the impact of each component is not clear. For example, what if removing the question preference optimization and directly using the solvability filtering and difficulty sampling? This is different from the ablation study, which compares the performance w/ and w/o reward filtering while keeping all other components the same." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 did the authors select the base difficulty filtering model for fine-tuning (Lines 222-239) and the reward filtering model (Lines 251-252)? Considering that filtering significantly impacts final data quality (Figure 5), further discussion of criteria for model selection, along with any experimental comparisons, would enhance clarity on whether these models represent optimal choices.\n\n2. In Table 1, the term “Synthesis Model” in the header needs clarification. Does it refer to the model used for both question and response generation, or only response generation? This ambiguity is notable, especially as fine-tuned models such as Deepseek-QGen and Qwen2-Math-QGen are absent from the table. \n\n3. The left bar chart in Figure 5 has a confusing y-axis. Does the percentage indicate solvable/non-solvable or easy/difficult ratios? If it reflects these ratios, how does this relate to the five difficulty levels introduced in Lines 377-406? Detailing this connection would make the difficulty and solvability metrics clearer.\n\n4. Lastly, while evaluating synthesized data via difficulty distribution and solvability is helpful, a rigorous human evaluation on a random subset would better demonstrate ScaleQuest’s quality. Including human assessments of clarity, coherence, and real-world relevance could provide a nuanced verification of the synthesized data's effectiveness." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. ScaleQuest targets data synthesis for instruction tuning, focusing on affordable low-cost methods. This approach demonstrates significant cost savings (Section 3.4), making large-scale data creation more accessible for open-source communities.\n\n2. The study includes thorough experimentation with multiple baselines, assessing both question and response quality across a total of four mathematical problem-solving benchmarks, thereby increasing the credibility of ScaleQuest.\n\n3. The paper is well-structured and quite easy to follow, with sufficient implementation details to enhance reproducibility." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents ScaleQuest, a scalable and cost-effective data synthesis framework designed to enhance the mathematical problem-solving capabilities of large language models (LLMs). Motivated by the need for high-quality, large-scale data, the authors propose a two-stage synthesis process. Specifically, ScaleQuest employs Question Fine-Tuning (QFT) to activate question-generation (QG) capabilities in small base models and Question Preference Optimization (QPO) to improve question solvability and difficulty. This is followed by filtering for language clarity, difficulty, and solvability, as well as reward-based response selection to ensure high-quality outputs. Experiments demonstrate that models fine-tuned with the ScaleQuest dataset outperform several baselines on benchmarks, achieving substantial improvements in accuracy across in-domain and out-of-domain mathematical reasoning tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. As claimed by the authors in Lines 17-20 and 76-80, the main contribution of the paper is the scalable synthesis method, ScaleQuest. However, the method heavily depends on domain-specific fine-tuning and specialized models, which raises questions about its generalizability and applicability to domains beyond mathematical reasoning. For instance, the authors use Question Fine-Tuning (QFT) and Question Preference Optimization (QPO) to optimize the question generation process within the target domain of mathematical reasoning. Furthermore, the method involves components like solvability filtering, difficulty sampling, and reward filtering, each relying on different models and a specialized fine-tuned difficulty scorer, which appear tailored to mathematical data construction. This reliance on fine-tuned, domain-specific models, while effective in the tested domain, makes it challenging to adapt ScaleQuest to broader applications, potentially limiting its utility as a general-purpose data synthesis method.\n\n2. Additionally, the paper appears to make some overclaims regarding its scope and efficiency. While the title suggests an enhancement of \"reasoning capability,\" the paper narrowly addresses mathematical reasoning tasks, with little consideration given to other reasoning types, such as causal, commonsense, or logical reasoning. The claim of using “small-size” models (Lines 18-19) is also somewhat misleading. Specifically, the QPO stage (Lines 199-202) requires a larger model, GPT-4o-mini, to achieve better preference-based filtering, suggesting that smaller models alone may not fully support the quality goals of ScaleQuest. The ablation results (Figure 5) further highlight the critical role of QPO, reinforcing the notion that the trade-off between model size and final data quality is not fully acknowledged, which impacts the efficiency claims of the method.\n\n3. Lastly, despite the authors’ assertions that ScaleQuest-generated data significantly enhances performance across various benchmarks, the observed improvements are marginal. For instance, Table 1 shows only a slight average increase from 62.7 to 62.9 when comparing Qwen2-Math-7B-ScaleQuest to its baseline Qwen2-Math-7B-Instruct, even with a decrease in performance on the CollegeMath benchmark. These limited gains suggest that the effectiveness of ScaleQuest’s synthesized data may not justify its complexity. Consequently, these modest gains raise concerns about the practical value and impact of the ScaleQuest approach." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "Typo: Filering -> Filtering in line 215\n\nIn Figure 5, it seems that QPO is less effective. Does the author try the combination of QFT and reward filtering only?\n\nI am curious about the effectiveness of Solvability Filtering and Difficulty sampling. For Solvability Filtering, it seems that the final dataset still does not have perfect quality but produces a good performance. So I am curious about the influence of the quality. For difficulty sampling, I am not sure why we need to fit certain difficult distributions." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "As for the method, ScaleQuest generates questions independently from scratch, removing dependency on existing question datasets, which enhances question diversity and supports scalability. Also, the paper integrates comprehensive filtering techniques, including language, solvability, and difficulty sampling, which could be a good reference for future efforts in data filtering.\n\nThe presentation is very clear, the workflow of the method is easy to follow. All the details such as prompts are all clearly given. The authors said they will release the data and code, which will be a useful resource to the community." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a synthetic training data generation method for mathematical LLMs. Based on two small models at a 7B scale, the authors achieve state-of-the-art performance than other models trained with the data from larger LMs. The proposed method including question supervised fine-tuning, question preference tuning and reward-score-based selection." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The main experiments in Table 1 are somehow not very fair. Some of the baseline methods contain less data than the used dataset in the paper. \n\nIn Table 1, it seems that Qwen2-Math-7B-ScaleQuest achieves similar performance with Qwen2-Math-7B-Instruct, I am wondering if their performance is similar on OOD test sets like GSM-hard (https://huggingface.co/datasets/reasoning-machines/gsm-hard) and MathChat (https://github.com/Zhenwen-NLP/MathChat). I would like to see if Qwen2-Math-7B-ScaleQuest is over-fitting on GSM and MATH style questions.\n\nFor the efficiency result, it seems that the cost is similar to (even slightly higher) GPT-4o mini if we put that in the table. I am wondering why the authors choose models like Qwen2-Math-7B instead of GPT-4o mini for solvability & difficulty check, etc." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We introduce ScaleQuest, a scalable, novel and cost-effective data synthesis method to unleash the reasoning capability of LLMs." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024unleashing,\ntitle={Unleashing Reasoning Capability of {LLM}s via Scalable Question Synthesis from Scratch},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1Y5hMMuCFU},\nnote={under review}\n}" }, "abstract": { "value": "The availability of high-quality data is one of the most important factors in improving the reasoning capability of LLMs. \nExisting works have demonstrated the effectiveness of creating more instruction data from seed questions or knowledge bases.\nRecent research indicates that continually scaling up data synthesis from strong models (e.g., GPT-4) can further elicit reasoning performance.\nThough promising, the open-sourced community still lacks high-quality data at scale and scalable data synthesis methods with affordable costs.\nTo address this, we introduce ScaleQuest, a scalable and novel data synthesis method that utilizes ``small-size'' (e.g., 7B) open-source models to generate questions from scratch without the need for seed data with complex augmentation constraints.\nWith the efficient ScaleQuest, we automatically constructed a mathematical reasoning dataset consisting of 1 million problem-solution pairs, which are more effective than existing open-sourced datasets.\nIt can universally increase the performance of mainstream open-source models (i.e., Mistral, Llama3, DeepSeekMath, and Qwen2-Math) by achieving 29.2\\% to 46.4\\% gains on MATH.\nNotably, simply fine-tuning the Qwen2-Math-7B-Base model with our dataset can even surpass Qwen2-Math-7B-Instruct, a strong and well-aligned model on closed-source data, and proprietary models such as GPT-4-Turbo and Claude-3.5 Sonnet." }, "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", "mathematical reasoning", "data synthesis" ] }, "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/b1675f039fadbf1f1605ba8da3f57c72a9e783e6.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/b0686ba48b361d2ab050bccbd8998f70b8f1a693.zip" }, "title": { "value": "Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch" }, "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": 1 }, "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": "as above" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Pro:\n\n- The sampling speed in image-to-image translation is a critical problem in this area.\n- The paper combines various techniques, including DDIB and consistency models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Diffusion models are widely used for image translation. This paper identifies limitations in existing approaches: slow inference, need for paired data, and one-way translation constraints. It introduces Implicit Bridge Consistency Distillation (IBCD) to address these issues. IBCD extends consistency distillation with a diffusion implicit bridge model. The paper proposes two improvements: Distribution Matching for Consistency Distillation (DMCD) and a distillation-difficulty adaptive weighting method. Experimental results show IBCD achieves state-of-the-art performance in one-step generation for bidirectional translation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Con:\n\n- The main concern is that the method seems too incremental, appearing to be merely a combination of DDIB and consistency models.\n- In Table 3, the FID improvement from adding Cycle and DMCD is marginal. Is the author aware of what a 0.1 FID change means? If you repeat the experiment twice, the variance might be even larger. This becomes a significant issue when the FID is so high. Also, most baselines in Table 2 show the variance of FID, while the author didn't. As you can see, the variance of other methods is quite large, further undermining the ablation study in Table 3.\n- With only a single step, the stochasticity is significantly reduced. The authors should include several other related metrics that highlight diversity, such as the Inception Score. Additionally, more failure cases should be provided for better understanding of the method's limitations." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "see weakness" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1) The paper is well-written and it clearly explains the proposed method.\n2) The visualizations of component’s cumulative contributions on the toy dataset in Fig. (3) help appreciate the role of each part.\n3) Experiments on both toy and highdimensional datasets demonstrate the effectiveness of IBCD." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a framework called Implicit Bridge Consistency Distillation (IBCD) for unpaired image to image translation. IBCD connects PF-ODE trajectories from any distribution to another one by extending consistency distillation with a diffusion implicit bridge model. It introduces Distribution Matching for Consistency Distillation (DMCD) and distillation-difficulty adaptive weighting method to deal with the distillation errors and mean prediction problems from the consistency distillation. Experiments on translation benchmarks demonstrate the effectiveness of the proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) Missing comparison of the results for bidirectional translation.\n2) Missing comparison of computation cost with the existing methods to show the efficiency of the proposed method.\n3) The results in Tab. 3 show the model which added DMCD loss, cycle loss and adaptive DMCD degrades the performance in terms of PSNR and SSIM compared to the method using IBCD only.\n4) The zero in the first row of Eq. (6) might be \\chi_A\\cap\\chi_B." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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": "- The authors propose to use one generator for two domains, which may be unreasonable or hard to achieve in practice. I think the whole pipeline is compatible with two independent pre-trained DMs, i.e., one ADM on LSUN Cat and one ADM on ImageNet with some specific class." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "- The paper provides a versatile pipeline for unpaired image-to-image translation within only one step, which outperforms most previous methods even with large NFEs.\n\n- The theory part is clear and intuitive, and the toy data showcases the instability of vanilla IBCD clearly.\n\n- The experimental results are convincing and impressive, demonstrating directly the outperformance.\n\n- The novel adaptive weighting is interesting and effective, encouraging further study in diffusion model distillation with insight." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes to apply consistency distillation (CD) on previous DDIB, achieving a one-step generative model for unpaired image-to-image translation. The authors manage to extend the CD theory, which is applicable to two arbitrary distributions. The novel distribution matching and adaptive weighting techniques further stabilize and facilitate the training process. Both qualitative and quantitative experiments confirm the efficacy of the pipeline and the outperformance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- CD highly bases on PF-ODE, i.e., it needs to follow the score function trajectory. In Eq. (6), two PF-ODEs starting from different domains are connected together at $\\sigma_{max}$, how to guarantee the smoothness of score function (i.e., gradient) at this point (since one directly uses noisy $x_a$ to solver attached to domain B)? If not smooth, how will the error be like? The authors may provide analysis here similar to original CD paper.\n\n- In L261, the authors claim one of the challenge is to employ only local consistency. However, CTM [1] refers to local consistency as the case when applying PF-ODE solver with extremely smaller step. On the contrary, when using two adjacent timesteps, CTM names it global consistency, similar to original CD. So in the paper, this should also be called a global consistency. I can hardly understand why such strategy is a challenge, given that most distillation works use such a loss.\n\n[1] Learning Probability Flow ODE Trajectory of Diffusion. Kim et al., ICLR 2024.\n\n- The authors state that vanilla IBCD faces mean prediction phenomenon, but provides no convincing analysis on it. Original CD seems not to face such challenge. Does it come from the mismatch of two PF-ODEs? The visualization in Fig. 3(a) fails to convince me. The synthesized samples are not at the mean of domain B. Besides, I cannot see the efficacy of DMCD and cycle loss.\n\n- The ablation study is somewhat confusing. Why vanilla IBCD is only a point rather than a broken line like the others? From Tab. 3 and Fig. 6, it seems that adaptive weighting may harm the performance, which is not consistent with conclusion in toy data. Conversely, DMCD is helpful in real data but fails in toy data. The authors may need further clarification." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024implicit,\ntitle={Implicit Bridge Consistency Distillation for One-Step Unpaired Image Translation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1YTF7Try7H},\nnote={under review}\n}" }, "abstract": { "value": "Recently, diffusion models have been extensively studied as powerful generative tools for image translation. However, the existing diffusion model-based image translation approaches often suffer from several limitations: 1) slow inference due to iterative denoising, 2) the necessity for paired training data, or 3) constraints from learning only one-way translation paths. To mitigate these limitations, here we introduce a novel framework, called Implicit Bridge Consistency Distillation (IBCD), that extends consistency distillation with a diffusion implicit bridge model that connects PF-ODE trajectories from any distribution to another one. Moreover, to address the challenges associated with distillation errors and mean prediction problems from the consistency distillation, we introduce two unique improvements: Distribution Matching for Consistency Distillation (DMCD) and distillation-difficulty adaptive weighting method. Experimental results confirm that IBCD for bidirectional translation can achieve state-of-the-art performance on benchmark datasets in just one step 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": [ "image translation", "consistency distillation", "unpaired", "one-step", "diffusion models" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/96ceb39175e320725b7cc3c4439a14a64fa873bb.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": "Implicit Bridge Consistency Distillation for One-Step Unpaired Image Translation" }, "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": "- When conditioning the optimal path with a partial exploration path, is it equivalent to a self-reflection process (and the the self-reflection succeeds with one reflection trial)? If so, what is the novelty of GSoS over a RL reflection-tuning method, or RL finetuning with Chain-of-Hindsight [1]?\n - Furthermore, have the authors tried to compile the trajectories by exploring more than one non-subgoal node in advance of the subgoal node, and ablate the effect with those containing only one non-subgoal node ahead of each corresponding subgoal node?\n\n [1] Liu et al., Chain of Hindsight Aligns Language Models with Feedback. ICLR 2024.\n\n- The effectiveness of GSoS is only demonstrated on one benchmark. The proposed method should be benchmarked on more scenarios to demonstrate its superiority.\n\n- In Lines 192-194, it is claimed that \"Fine-tuning on these trajectories may lead to significant changes in the model’s weights, potentially degrading its search and planning abilities. Therefore, it is crucial to explore methods for effectively integrating optimal solutions to produce trajectories that maintain both high likelihood and quality.\" It would be beneficial if the authors provide more experimental supports for why direct finetuning leads to the degradation of the search and planning abilities. Specifically, if it is supported by the main experiments where GSoS outperforms SoS, additional qualitative analysis and case studies are needed for the direct comparison between GSoS and SoS, and it would be helpful to provide cases where GSoS+finetuning succeeds while SoS+finetuning fails.\n\n- In Lines 306-307, it is demonstrated that \"even when multi-step returns with GAE are used for training the value function.\" It would be beneficial if the authors could show the experiments that verify this claim.\n\n- In Line 5 of Algorithm 2: what is M(y|x)?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- This paper studies complex reasoning and planning of LLMs, which is an important topic in LLM research.\n- The idea of integrating more exploratory trajectory segments into the context of the optimal subgoal makes sense, as it steers LLMs to learn to pivot to the optimal path.\n- Setting up the RL training on the operation level effectively accelerates the learning process, which is supported by comparison experiments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes Guided Stream of Search (GSoS), a novel method that combining the optimal path as well as the search trajectories of a searching scenario into a sequence, which is used as the training data instance for LLMs to acquire better planning and search performances. The authors have conducted experiments on Countdown, a mathematical reasoning benchmark with branching factor in square complexity of the inputs at each searching step. The experimental results demonstrated the effectiveness of GSoS, especially with RL that functions on the operation level." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Please refer to the Questions listed below." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 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 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 paper presents a simple and intuitive approach for improving planning tasks in LLMs by incorporating optimal solutions into trajectory generation process, which enhances the quality of generated trajectories and overall training outcomes.\n2. The paper is clearly written, making the proposed method and experiment findings accessible." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors introduce GSoS, a method to improve the planning and reasoning capabilities of language models by integrating optimal solutions within search processes. Unlike prior approaches that rely solely on self-generated, often suboptimal search trajectories, GSoS incorporates optimal solutions progressively, guiding the model toward more structured search trajectories. These trajectories are distilled through SFT, which, combined with subsequent RL training, enhances performance on the planning task Countdown." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. A key baseline—using SFT with the optimal solutions (BC)—is missing. While the authors discuss BC's limitations on unseen tasks, including it in the evaluation would provide a more comprehensive comparison, especially since the main contribution of this approach is incorporating optimal solutions into the data construction process. \n2. The proposed approach is only validated on a single test bed, Countdown, which may leave readers questioning its generalizability to other planning tasks. Including an additional test bed, such as those from Beyond a* [1], would strengthen the paper’s claims, particularly as this work builds on and seeks to improve upon SoS (Gandhi et al., 2024).\n\n**Minor Issue:**\n- Line 111: The purpose of transforming $x$ through a series of operations to obtain $\\\\hat{y}$ is unclear, as $x$ already contains both input and output states? \n\n\n[1] Beyond a*: Better planning with transformers via search dynamics bootstrapping." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See 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 is well-written and easy to follow\n\n2. The proposed method is simple and intuitive\n\n3. Good experimental results and detailed analysis on Countdown benchmark" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work explores how to leverage optimal solutions to enhance the search and planning abilities of language models. The authors propose guided stream of search (GSoS), which seamlessly incorporates optimal solutions into the self-generation process in a progressive manner, producing high-quality search trajectories for training. GSoS can significantly enhance the search and planning abilities of language models on Countdown, a simple yet challenging mathematical reasoning task." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The experiments are only conducted on one single benchmark. There are many other datasets requiring complex reasoning. At least one of them, such as LogiQA2, should be investigated.\n\n2. The authors use a 250M model for experiments which is quite small. For complex planning and reasoning, larger language models should be considered.\n\n3. How about the comparison to this simple baseline? For the given query, we sample plenty of trajectories from the model and construct a DAG using the sampled trajectories. Then we can sample different types of search paths from the DAG for 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": 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 performance of GSoS compare with other state-of-the-art algorithms in terms of search and planning capabilities? Can leading search and planning algorithms be transferred to this benchmark and be evaluated?\n\n2. What's the reason behind choosing GPT-2 as the backbone model? Is it possible to replicate the experiments with more advanced open-sourced models?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "[+] The overall presentation and structure are well-organized. The introduction, preliminary, and method sections are well-written. The threads are easy to follow.\n\n[+] The results and analysis of the experiments are detailed and comprehensive. The authors provide extensive experiment results and analyze them in detail. In my opinion, this paper is fine with its empirical results and analysis. \n\n[+] All the codes and hyperparameters are open-sourced for reproducibility.\n\n[+] I believe this method has potential applications for larger problems and more advanced models. Augmenting search and planning trajectories could be a crucial step in training models like o1." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduced the Guided Streat of Search, which integrates optimal solutions into the self-generation process of LLMs to improve their search and planning capabilities. The main contribution is extending the existing Stream of Search (SoS) approach to Guided Stream of Search (GSoS), which incorporates optimal solutions into the self-generation process in a progressive manner. GSoS uses unsuccessful search trajectories as contexts to integrate each intermediate action from the optimal solution, producing high-quality search trajectories that are then used for SFT. GSoS is evaluated on a search benchmark and demonstrates outperformance in comparison to both SFT and RLHF baselines, regarding both seen targets and unseen targets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "[-] The evaluation benchmark is not convincing to me. It appears that this benchmark can easily be formulated as a real search problem, making the use of an LLM unnecessary. I think the authors should consider testing their framework on a more complex benchmark.\n\n[-] It's doubtful that the unseen targets in Countdown can be considered a valid evaluation of generalization, given the high similarity between the supposedly different tasks in the dataset.\n\n[-] The backbone model, GPT-2, is somewhat outdated. Additionally, I could not find an explanation provided for choosing GPT-2 over other models.\n\nOn a minor note, I do not observe any planning capability (i.e., the ability to plan ahead of actions) from this method or within the benchmark, despite its repeated emphasis in the paper." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024guided,\ntitle={Guided Stream of Search: Learning to Better Search with Language Models via Optimal Path Guidance},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1YXkDXIqVw},\nnote={under review}\n}" }, "abstract": { "value": "While language models have demonstrated impressive capabilities across a range of tasks, they still struggle with tasks that require complex planning and reasoning. Recent studies have proposed training language models on search processes rather than optimal solutions, resulting in better generalization performance even though search processes are noisy and even suboptimal. However, these studies overlook the value of optimal solutions, which can serve as step-by-step landmarks to guide more effective search. In this work, we explore how to leverage optimal solutions to enhance the search and planning abilities of language models. To this end, we propose guided stream of search (GSoS), which seamlessly incorporates optimal solutions into the self-generation process in a progressive manner, producing high-quality search trajectories. These trajectories are then distilled into the pre-trained model via supervised fine-tuning. Our approach significantly enhances the search and planning abilities of language models on Countdown, a simple yet challenging mathematical reasoning task. Notably, combining our method with RL fine-tuning yields further improvements, whereas previous supervised fine-tuning methods do not benefit from RL. Furthermore, our approach exhibits greater effectiveness than leveraging optimal solutions in the form of subgoal rewards." }, "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": [ "planning with language models", "supervised fine-tuning with self-generated data", "reinforcement learning fine-tuning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/934f37ce5f9ae39ea9004df38d8864814f28d600.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/20bb2dd6a18f3303097fd8557b8f74aad3882a57.zip" }, "title": { "value": "Guided Stream of Search: Learning to Better Search with Language Models via Optimal Path Guidance" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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": "N/A" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "As a summary, this work investigates a natural setting and models the problem in a reasonable way. The work guarantees DP through model learning and post processing, which is intuitive. The performance of the algorithm seems decent." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work extends model-based offline reinforcement learning, namely MORL, to its differentially private variant, namely PriMORL. The work intends to guarantee trajectory level DP, which means it treats two datasets that differ in at most one (entire) trajectory as neighboring datasets and asks the algorithm to output indistinguishably between them.\n\nThe differential privacy is achieved by model privacy, i.e. if the model is learned in a DP way, then by post processing the algorithm is also DP. This though comes with the limitation that the learning algorithm can no longer access the dataset once the model is obtained from the previous phase. To achieve DP model learning, it randomly draws a subset of trajectories and uses this batch of data to estimate a gradient. A clip is then applied to the gradient which bounds the sensitivity of the gradient. The work discusses different clipping techniques which fine tune the clipping threshold. The DP guarantee is then proved by moment accountant.\n\nOnce a model is obtained the policy is trained through pessimistic private MDP, which is by the intuition that being aware of the model uncertainty requires pessimisty. This intuition inspires the authors to run soft actor-critic on the pessimistic variant of MDP, where the reward is reduced by the uncertainty level. This to some extent mitigates the cost of not being able to access the data once the model training is complete.\n\nMany experiments are provided on tasks like pendulum, cartpole, and halfcheetah. The authors didn't seem to compare their algorithms with baseline methods, while there seems to be many. Though, the performance of the proposed algorithm is reasonably good on its own." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Model/Techniques are not exciting.\n2. No baseline comparison.\n3. Limited testbeds" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": { "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": "It seems that the number of ensembles $N$ plays a vital role in the results. This work has already reduce the dependency to $\\sqrt{N}$ in the sensitivity. I am just curious is it possible to further reduce it in training ensemble models." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The proposed method that provides privacy guarantees for training ensemble models is novel.\n2. The investigation of the problem is thorough." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studied differentially private model-based offline reinforcement learning (RL). The paper proposed a new algorithm that provide differential privacy for training ensemble models. Besides theoretical guarantees, the experiments also demonstrate the effectiveness of the proposed algorithm." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. There exist several typos. For example, in line 274, 'it does entirely remove...' I assume it should be 'it does not entirely remove...' because increasing $N$ will degrade the model performance.\n2. The major weakness is that there is a lack of a more explicit discussion on each term in the theoretical results, such as $\\epsilon_p$ and $\\epsilon_p^{DP}$. I would be curious about if these terms depend on privacy parameter or $N$, if so, what should be the approximate dependency." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1.In Theorem 4.2, a general formula for \\epsilon^{MA}(z, q, T, \\delta) is presented, but neither the main text nor the appendix provides a complete, detailed expression of this formula. Could you include a full derivation of this formula so we can clearly understand how \\epsilon^{MA} is calculated based on inputs like z, q, T, and \\delta?\n2.In Section 4.3.2, the paper discusses handling model uncertainty under a private setting but appears to apply existing uncertainty-handling techniques from non-private settings directly to the private setting. Could you clarify any special considerations or unique aspects of handling uncertainty in the private setting? Specifically, how might model error and uncertainty differ under a private setting, given its unique constraints? We would appreciate any further insights on this point." }, "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 clearly organized. The authors first introduce the trajectory-level differential privacy framework within the offline RL setting, then explain the method for training private models using a model ensemble approach, covering both implementation details and theoretical guarantees. Finally, they describe how policy optimization is achieved by incorporating uncertainty techniques, with theoretical support provided as well." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces PRIMORL, a differentially private (DP) model-based reinforcement learning algorithm for offline settings. PRIMORL trains policies for continuous control tasks while ensuring trajectory-level privacy by learning an ensemble of DP models from offline data. This approach protects against privacy leaks in RL, especially critical in applications where individual trajectories may contain sensitive information. PRIMORL operates in an offline, infinite-horizon setting, leveraging private models to optimize policies without further interaction with the environment. Empirically, PRIMORL demonstrates competitive performance on deep RL tasks, advancing private RL beyond simpler tabular and linear MDPs and addressing practical privacy-performance trade-offs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tIt is unclear how the private-variant experiments control for \\epsilon. In the theoretical guarantees section, \\epsilon is presented as a theoretical bound, yet here it seems to be treated as a tunable hyperparameter, with little explanation connecting these two perspectives. \n2.\tWhile the motivation for the work is compelling, the experimental design is relatively simple and basic. I would have liked to see experiments that address the unique challenges of applying DP frameworks within the RL domain, yet this paper lacks a broader experimental analysis to underscore the real-world relevance of introducing DP into RL." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please refer to the weaknesses above." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The problem of offline RL with DP is important and well-motivated.\n2. This paper proposes a practical solution to the problem.\n3. The authors do experiments to support their algorithm.\n4. The paper is well-written in general." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors consider private deep offline reinforcement learning (RL), where the goal is to train a policy on standard control tasks that is differentially private (DP) with respect to individual trajectories in the dataset. To achieve this, they introduce PriMORL, a model-based RL algorithm with formal differential privacy guarantees. PriMORL first learns an ensemble of trajectory-level DP models of the environment from offline data. It then optimizes a policy on the penalized private model, without any further interaction with the system or access to the dataset. In addition to theoretical guarantees, they empirically demonstrate that PriMORL enables the training of private RL agents on offline continuous control tasks with deep function approximations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The main concern is about technical novelty. The definition of Trajectory-level DP is directly adapted from [1]. The first part directly applies DP-FEDAVG, while the second part is about learning from the private model with pessimism. To the best of my knowledge, [1] is based on the same idea of private model + pessimism. The DP guarantee for the private model is from previous results, and the DP guarantee for learning the policy is from standard post-processing. I do not see any technical challenge in the process. It will be better if the authors could discuss about the challenges.\n\n[1] Dan Qiao and Yu-Xiang Wang. Offline reinforcement learning with differential privacy.\n\n2. Proposition 4.4 only provides an error bound for estimating the value function of $\\hat{\\pi}$, which is not standard. Is it possible to derive any results about the sub-optimality gap $V^\\star-V^{\\hat{\\pi}}$?\n\n3. In the experiments, the privacy protection is very weak (some $\\epsilon$ being close to 100). What will happen for more practical choices of $\\epsilon$? E.g. $\\epsilon \\approx 1$." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We address deep offline reinforcement learning with differential privacy guarantees, using a model-based approach." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024differentially,\ntitle={Differentially Private Deep Model-Based Reinforcement Learning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1YYp1rPRlm},\nnote={under review}\n}" }, "abstract": { "value": "We address private deep offline reinforcement learning (RL), where the goal is to train a policy on standard control tasks that is differentially private (DP) with respect to individual trajectories in the dataset. To achieve this, we introduce PriMORL, a model-based RL algorithm with formal differential privacy guarantees.\nPriMORL first learns an ensemble of trajectory-level DP models of the environment from offline data.\nIt then optimizes a policy on the penalized private model, without any further interaction with the system or access to the dataset. \nIn addition to offering strong theoretical guarantees, we empirically demonstrate that PriMORL enables the training of private RL agents on offline continuous control tasks with deep function approximations, whereas current methods are limited to simpler tabular and linear Markov Decision Processes (MDPs). We furthermore outline the trade-offs involved in achieving privacy in this setting." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "machine learning", "reinforcement learning", "privacy", "differential privacy", "deep learning", "model-based", "offline" ] }, "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/8c63b4c6f87fc38a78d80c9ce8dd01a3cc22bee5.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/715fdf070b28062bb6578bcddf11ff4dace339e9.zip" }, "title": { "value": "Differentially Private Deep Model-Based Reinforcement 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": 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": 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": 4 }, "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. This paper is well written and organized.\n\n2. The combination of Mamba and Transformer is promising for improving the performance of image super-resolution tasks.\n\n3. The ablation and main experiments are extensive and comprehensive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a hybrid network based on Mamba and Transformer for image super-resolution. Register tokens and SE-Scaling attention mechanisms are introduced to improve performance and reduce computation. The experimental results demonstrated the effectiveness of the method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The novelty of this paper is limited, and the main contribution seems to be just combining Mamba and Transformer. SE-Scaling did not show significant improvement over previous work.\n\n2. Since Mamba models perform poorly in capturing local information, why not integrate Mamba and CNNs which are good at local modeling. \n\n3. In addition to the parameters and FLOPs, it is necessary to compare the inference latency of the different methods on the device.\n\n4. From Figure 10, there is no significant difference between the proposed SST and MambaIR on the LAM attribution map. Does this indicate that the Transformer provides limited benefit?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "Why do the authors not compare their method with HAT and discuss the advantages and disadvantages between them?" }, "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 effectively integrates Mamba and Transformer, and the visualization results show that the hybrid structure can activate a wider range of pixels.\n2. This work made some improvements to Mamba to alleviate the feature artifact problem." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work introduces SST, a new model that integrates Mamba and Transformer to extract global and local information, respectively. This work is well-written, and the method is clear and easy to understand." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The authors claim the proposed SE-Scaling can significantly reduce the computational cost, but in Table 3, the Macs of using SE-Scaling are higher than Channel-Attention.\n2. Although this work integrates Mamba and Transformer, the proposed network SST simply uses Mamba and Transformer alternately and lacks deeper exploration.\n3. The performance of SST shows only a slight improvement compared to existing state-of-the-art models, such as SRFormer. The author should add comparisons with HAT, OmniSR, 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": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. The impact of different proportions of VSSM and MSA on model performance was only verified on Urban100 and Manga109. It remains uncertain whether similar results would be observed on the other three datasets.\n2. Similar to the previous question, it is uncertain whether the number of registration tokens also produces similar results on the other three datasets.\n3. In paper [1], it is noted that Vision Transformer has artifact issues. This raises the question of whether the window attention in this paper exhibits similar phenomena and whether it also utilizes registration tokens.\n\n[1] Vision Transformers Need Registers, ICLR2024" }, "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 very easy to follow. \n2. Extensive experiments were conducted to explore combinations of Mamba and Window Attention. \n3. Experiments were also conducted to investigate the impact of the number of registration tokens on model performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a single-image super-resolution network called SST, which combines Mamba and Window Attention mechanisms. The authors observed that some existing Mamba models do not effectively capture local dependencies in 2D images. Therefore, they leverage window attention to address these limitations. Additionally, the authors also observed that Mamba models tend to produce artifacts. To mitigate this issue, they introduced registration tokens before the SSM scan in SST. The authors conducted extensive experiments to explore different combinations of window attention and Mamba, and compared their method with current mainstream super-resolution networks to validate its effectiveness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Lack of comparison with some closely related approaches. With a larger number of parameters, SST-light shows lower performance metrics on almost all benchmarks compared to ATD-light[1].\n2. Super resolution is a very local computation, (at the range of a pixel). It is not demonstrated what is the advantage of exploring global interaction for such a problem.\n\n[1] Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary, CVPR2024" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 the weakness." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tBy introducing the SE-Scaling mechanism, the model reduces the computational burden typically associated with channel attention mechanisms, making it suitable for lightweight applications. \n2.\tThe paper reports that SST achieves state-of-the-art results on both classical and lightweight super-resolution tasks, demonstrating its effectiveness through extensive experiments on various benchmark datasets." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a super-resolution network called SST (State Space Transformer), which integrates Mamba State Space Models (SSM) with Transformer self-attention layers. The authors aim to leverage the strengths of both architectures to enhance single image super-resolution (SISR) tasks. The Mamba model is noted for its ability to process global information efficiently due to its linear complexity, while Transformer models, particularly the Swin Transformer, excel in local region representation but suffer from quadratic complexity, limiting their receptive fields. The proposed SST model addresses the shortcomings of both approaches by combining their advantages. The authors introduce an updateable register to mitigate feature map artifacts commonly found in Mamba models and propose a new attention mechanism called SE-Scaling to reduce computational costs while improving performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe combination of Mamba SSM and Transformer architectures allows the model to capture both global and local contextual information effectively, however, they are both from current knowledge. Besides, the idea of combining Mamba and Transformer has been already proposed.\n2.\tCompared to SOTA methods, the improvement is not significant, such as ERF in figure 2 against MambaIR and quantitative results against DAT.\n3.\tMissing in-depth motivation. This article seems to be just a simple attempt at an IR mission by combining Mamba and current Transformer structures.\n4.\tThe model complexity is still large, what is the merit by using Mamba?\n5.\tMore recent mamba-based image SR works should be referenced and compared." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@misc{\nsun2024integrating,\ntitle={Integrating State Space Model and Transformer for Global-Local Processing in Super-Resolution Networks},\nauthor={Yukai Sun and Zheng Chen and Yulun Zhang and Jinjin Gu},\nyear={2024},\nurl={https://openreview.net/forum?id=1YZw3RK2kg}\n}" }, "abstract": { "value": "Single image super-resolution aims to recover high-quality images from low-resolution inputs and is a key topic in computer vision. While Convolutional Neural Networks (CNNs) and Transformer models have shown great success in SISR, they have notable limitations: CNNs struggle with non-local information, and Transformers face quadratic complexity in global attention. To address these issues, Mamba models introduce a State Space Model (SSM) with linear complexity. However, recent research shows that Mamba models underperform in capturing local dependencies in 2D images. In this paper, we propose a novel approach that integrates Mamba SSM blocks with Transformer self-attention layers, combining their strengths. We also introduce register tokens and a new SE-Scaling attention mechanism to improve performance while reducing computational costs. The resulting super-resolution network, SST (State Space Transformer), achieves state-of-the-art results on both classical and lightweight tasks." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": { "value": [ "~Yukai_Sun2", "~Zheng_Chen11", "~Yulun_Zhang1", "~Jinjin_Gu1" ] }, "authors": { "value": [ "Yukai Sun", "Zheng Chen", "Yulun Zhang", "Jinjin Gu" ] }, "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 and Pattern Recognition", "image super-resolution" ] }, "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": "sun|integrating_state_space_model_and_transformer_for_globallocal_processing_in_superresolution_networks" }, "pdf": { "value": "/pdf/a8ef1e869f8f4c5b61dcea3af0e3e3948a9aeb16.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Integrating State Space Model and Transformer for Global-Local Processing in Super-Resolution Networks" }, "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": null, "code_of_ethics": null, "comment": { "value": "Thank you for your questions. Let me clarify.\n\nBy “orthogonal,” I mean uncorrelated—like vector orthogonality. My concern is that the performance gains you’re seeing might be due to decorrelating representations of different classes at each layer and not due to decorrelating them with the error signal.\n\nThis came up because I had trouble following the logic of applying the reverse of the theorem about error signal orthogonality.\n\nTo check this, you might compare your method to a baseline that decorrelates class representations at each layer using only the class labels without involving the error signal. I don’t have a specific algorithm in mind, but this could help show whether the key factor is the decorrelation with the error signal or just between classes.\n\nI hope this helps." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "Dear Reviewer,\nWe would like to thank you for the comprehensive review. Before drafting our response, we want to ensure we fully understand the reviewer’s points. Therefore, we kindly request clarification on the following:\n\nIn Weakness Point 2 and Question 2, the reviewer suggests that the performance gains reported in our article may result from 'orthogonalization of layer representations' and/or 'orthogonal representations of different classes.' We would be grateful if the reviewer could clarify precisely what is meant by these terms and indicate any mechanisms within our method that might be contributing to this effect. We note that in our framework, 'orthogonality' refers to being uncorrelated in a statistical sense. However, we believe the reviewer’s use of orthogonality pertains to vector orthogonality with respect to the Euclidean inner product.\n\nAdditionally, could the reviewer provide more details about the exact implementation of the “comparative baseline” that we are asked to compare against: for example, what is meant by and how is local class orthogonalization implemented in each layer? Is there an available article/codebase that the reviewer can point us to for this comparison baseline." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": { "value": "A kind request for clarification" }, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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 the above weaknesses. Moreover:\n\n1.\tWhy does BP in Table 2 show such a low performance? Did the authors try to use a different CNN architecture to get a better performance? \n2.\tThe MMSE estimator-based derivation looks great, but in terms of network training, is optimal MMSE estimator the best objective of an arbitrary defined network? (since different tasks might have different loss functions)\n\nRef:\n\n[1] Athanasios Papoulis and S Unnikrishna Pillai. Probability, Random Variables, and Stochastic Processes. 2002\n\n[2] Journe et.al., Hebbian deep learning without feedback, ICLR 2023" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tThe paper proposes a novel idea that uses the orthogonality property of the optimal MMSE, which avoids weight symmetry problem in conventional backpropagation.\n2.\tCompared with direct feedback alignment (DFA) method, the proposed EBD method provides better theoretical illustration and the results in MNIST and CIFAR-10 tasks are better." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new learning framework for neural networks that directly broadcasts output error to individual layers. The main idea is to minimize the correlation between the layer activations and output errors, which is based on the orthogonality property of minimum mean square error estimators developed by Papoulis&Pillai 2002 [1]. The framework is implemented on MNIST and CIFAR10 benchmark tasks for MLP and CNN." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe method still faces the critical issue of scaling up, as most of non-BP learning frameworks exist.\n2.\tThe experiments show EBD is only slightly better than DFA, while it is not comparable with other SOTA biologically plausible methods (e.g. Hebbian base method [2])" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 premise of this work hinges on the orthogonality of the error of the optimal estimator to the neural representations. However, the reverse is not addressed: a vector that is orthogonal to a set of functions of the input is not necessarily indicative of an optimal estimator, and functions orthogonal to the estimator may not be meaningful. Could you clarify the theoretical foundation of your algorithm and its precise connection to the theorem you reference?\n\n2. To strengthen your claims, it would be helpful to demonstrate that the performance improvements are not solely due to orthogonalizing representations in each layer. Your experimental results primarily focus on training fully connected networks on MNIST and CIFAR-10, raising the possibility that similar gains could be achieved through layer-wise orthogonal class representations with SGD applied only at the output. Can you comment on this possibility or provide additional evidence?\n\n3. Is there a potential for an online version of your algorithm that eliminates the need for batch learning? If so, how would this be implemented?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. **Compelling Biological Motivation**: Exploring error broadcasting and feedback alignment as methods for implementing gradient descent in neural networks holds significant promise for biological plausibility. These approaches circumvent the weight transport problem by directly transmitting error signals to deeper layers. This work introduces an innovative algorithm within this framework, which exhibits improved performance compared to previous error broadcasting methods.\n\n2. **Normative Approach and Theoretical Foundation**: The algorithm’s development, rooted in the theoretical orthogonality property of an optimal MMSE estimator, is intriguing. Framing this method as a normative approach that leverages optimal predictor properties is commendable. However, as noted below, a potential misuse of this theorem raises concerns.\n\n3. **Practical Demonstration with Numerical Results**: The empirical findings showcase the proposed algorithm's practicality, albeit with limitations. Its reported performance on benchmark datasets suggests that it is competitive with state-of-the-art alternatives under certain conditions." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors introduce the Error Broadcast and Decorrelation (EBD) algorithm as a novel method for implementing gradient descent in deep neural networks. This approach addresses the limitations of traditional backpropagation (BP), which requires biologically unrealistic components such as weight transport and symmetric feedback pathways. The EBD algorithm builds on a key theorem from minimum mean square error (MMSE) estimation, which states that the output error of an optimal estimator is orthogonal to any function of the input. Leveraging this property, the authors propose that the activations in each layer, as functions of the input, be orthogonal to the output error. This orthogonality condition forms the basis for their weight update rule, which aims to decorrelate activations and output errors at each layer. The proposed EBD framework demonstrates competitive performance with BP on benchmark datasets like MNIST and CIFAR-10, particularly in fully connected and simpler architectures. The authors also explore potential extensions to the algorithm, including regularization techniques and forward projection of activations, to enhance stability and performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **Theoretical Assumptions and Interpretation**: The reliance on the theorem regarding the orthogonality of an optimal estimator’s error to any function of its input, while foundational, is problematic when extended in reverse. The paper does not adequately explain the consequences of requiring orthogonality between output error and hidden layer activations. Furthermore, in most applications and architecture, the dimensionality of the hidden layers is very large. Constraining a solution to be orthogonal to a single direction is weak, and its benefits are poorly defined. This gap leaves uncertainty regarding how orthogonality aids learning or inference. Thus, the theoretical basis appears tenuous and potentially misapplied.\n\n3. **Ambiguity in Performance Implications**: Although the algorithm performs well on real datasets, this success might stem from something other than the stated theoretical premise. The observed gains could be attributed to a different mechanism, such as orthogonal representations of different classes, rather than the error signal’s orthogonality. It would be valuable for the authors to test whether the performance improvement is due to orthogonalized class representations or if it is indeed a result of their premise. A comparative baseline using local class orthogonalization with an SGD-trained readout on the penultimate layer would provide insights into the true contribution of the proposed mechanism.\n\n4. **Biological Plausibility of Batch Learning**: The paper’s claim of biological relevance is weakened by the batch learning requirement, which necessitates retaining and normalizing the entire batch at each layer. While replacing weight transport and feedback pathways with error broadcast is a step toward biological realism, the reliance on batch-based updates undercuts this claim. The authors should consider the feasibility of online, more biologically plausible approaches and address whether the proposed method truly enhances biological plausibility. Alternatively, the paper can focus on the mathematical foundation of error broadcasting and not on biological realistic implementations of gradient descent.\n\n5. **Lack of Clarity in Possible Extensions**: In Section 4, the authors introduce several extensions to the EBD algorithm. The first involves regularization techniques aimed at preventing layer collapse. While preventing collapse is essential for maintaining active and diverse representations, the specific normalization methods proposed are neither novel nor particularly informative. Their inclusion does not substantially enhance the originality of the work.\n The second extension discussed is the forward projection of neural activations onto the output or penultimate layer, followed by an orthogonalization process at that stage. The rationale behind this step remains unclear. The manuscript provides no compelling biological basis for this projection mechanism, suggesting that its primary motivation is performance optimization rather than biological plausibility. Notably, the statement in line 448—“This projection facilitates the optimization of the decorrelation loss by adjusting the parameters of the final layer”—is ambiguous. It lacks a clear, rigorous mathematical explanation that would elucidate how this projection supports the training process. A more detailed formulation or analysis is necessary to justify the inclusion and clarify the impact of this component on the algorithm’s overall functionality.\n\n6. **Performance Analysis on Complex Architectures**: The results presented in Section 5 show that the algorithm's performance is almost on par with backpropagation. However, demonstrating performance close to BP on simpler datasets, such as MNIST or fully connected networks trained on CIFAR-10, is not sufficiently informative. While useful as initial proof-of-concept validations, these comparisons do not substantiate the broader claims of the algorithm’s novelty or practical utility. Combined with the previously mentioned theoretical limitations, the results fall short of convincingly demonstrating the value and distinct advantages of the proposed EBD approach." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "- in line 156 is N_k the size of layer k? this should be specified\n- that g can be any nonlinear function seems a powerful result. How much did you explore its possibilities? It seems a big hyperparameter to choose but I didn't get a feel for what it should be\n- the error epsilon is a vector so why is it not in bold? (it currently appears as a scalar)\n- for non-linear networks the error landscape is typically non-convex and has many local optima which are found during learning instead of one global optimum. How does the main theoretical results (lemmas A.1/A.2 tie in with this?\n- for the equation in line 199 R is defined recursively, but what is R[0]?\n- does the forgetting factor lambda lie in [0,1]?\n- In section 2.3 what do W_1, W_2 mean? Do they directly related to W (e.g. the first/second column). I presume not given equations 6/7 but if they don't they should be called something else\n- to what extent does EBD depend on batch size? It seems like it would require large batches to get a good correlation estimate, but this doesn't seem to fit in with the biological plausibility of the algorithm?\n- Why is EBD a 3-factor learning rule but not backprop? is it not possible to consider the post-synaptic/modulatory signal as the error gradient with respect to the pre-synaptic neuron?\n- in 3.2 why are the corinfomax equations which involve determinants etc biologically plausible? It's not clear to the non-expert reader. Given there are lateral connections, are we also dealing with RNNs instead of feedforward nets now?\n- in algorithm 1 why are activations H and errors E and bias B now in caps? Also the link to the corinfomax equations above is not clear to me at all\n- In section 4 line 393 it's written that these extensions are 'building on the CorInfoMax-EBD algorithm', but I don't understand why they can't also be applied to standard MLP?\n- Could the power normalization equation in 4.1.1 not be written as a norm over the batch. I personally find the notation with [n] confusing\n- out of interest is 4.1.2 itself bio-plausible?\n- typos: line 398: period after stability; line 709: linear -> non-linear." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- the text is generally well written\n- the theoretical building block in which this is built - that optimal nonlinear MMSE estimators hvae error orthognoal to functions of input - is interesting and in my view certainly deserves the attention given by the authors. Its implementation - and therefore this paper - should be of value to both ML and neuroscience researchers.\n- it is clear the authors have a good grasp of the theory and technical details of the models considered, and the approach in general seems well thought out\n- relevant literature appears to be duly cited and compared, though I am a non-expert in this field\n- the numerical results presented in section 5 appear impressive, though I would prefer they were elaborated upon a bit more." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a method for training neural networks based on decorrelating layer activities with the output error. This method avoids the need for backpropagation and is a potential solution to the weight transport problem." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The paper is rather dense, and I worry that it harms its accessibility. It seems to me some of the technical details/results can be sacrificed to the appendix in place of more motivation/clarification. For example, section 3.2, and the relationship to corinfomax in general, is very difficult to grasp. The motivation seems to be that corinfomax is a biologically plausible model, but I don't understand why corinfomax-EBD is more biologically plausible than the implementation in 2.3. What was the original implementation lacking that corinfomax-EBD addresses?\n- As per above, I would appreciate any more insight into the results and comparison vs other models. E.g. do you have any intuition as to why EBD outperforms NN-GEVB and MS-GEVB? For the corinfomax models it seems that the benefit of EBD is that it avoids the two-phases (?), but is there a reason it makes significantly improvements on the CIFAR-10 dataset?\n- the notation can sometimes be sloppy (see below)" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 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": "The weaknesses section above contains most of my concerns which should be addressed for an increased score. Here a few additional questions are posed.\n- How might the mechanisms for power normalization and layer entropy be a plausible addition to biological neural networks?\n- Can this framework be extended beyond the MSE loss case? In practice, loss functions in the deep neural network literature are often very different than an MSE loss. In Appendix E.2, correlation curves are shown for the Categorical Cross Entropy loss, however it is unclear if this was used in practice to train networks. Clarity would be appreciated.\n- The computational complexity of the method and the proposed additional learning of correlation structures is not much discussed. How much might such a method cost in this regard?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "- This work contributes a new perspective for measurement of a final optimum of network training based upon the MMSE estimator in a principled manner based upon the orthogonality of error and layer-activations.\n- The paper describes this method and its drawbacks within the methods section at length and covers a set of failure modes and extensions.\n- Detailed descriptions of the mathematical steps and hyperparameters of models tested are given." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work proposes a novel optimization method called the “Error Broadcast and Decorrelation” (EBD) algorithm. This algorithm attempts to obtain the optimal minimum mean square error (MMSE) estimator by meeting its core criteria: that at optimum, there is zero correlation between an error signal and non-linearly transformed encodings of data. This enables a new perspective on error broadcasting which attempts to capture correlations between errors and neural network layer activations and uses this correlation (covariance) structure to propagate and thereafter minimise correlation. This effectively results in a decorrelation between error and layer-wise activations once converged. This method is combined with a number of additions to stabilize network activity norms and encourage activation entropy within network layers, as well as being integrated into the CorInfoMax framework. This method is finally tested by training of multilayer perceptrons and convolutional networks with the MNIST and CIFAR10 tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Of major and significant concern are the claims of this paper in comparison to existing work by Clark et al. 2021. Specifically, the results of this paper are presented in direct comparison to trained models in earlier work (Clark et al.) while claiming outperformance. However, this paper integrates a number of elements to the training scheme that are not present in the original comparison work. For example, the code and tables of this paper suggest that among other additions this work makes use of a learning rate scheduler, as well as potentially using many more epochs of training (unclear). In comparison, the original work by Clark et al. has no learning rate scheduler and far fewer training hyperparameters in general. This suggests that the comparison is entirely inappropriate. To provide a genuine comparison, I would encourage the authors to carry out a reimplementation and rigorous test against existing methods (at least against BP) in which the same degree of parameter searching/sweeping is carried out for all methods compared. Otherwise comparison is uninformative at best, and misleading at worst. For these reasons, the results in Tables 1 and 2 cannot in their current form be trusted to provide understanding of the relative efficacy of training methods.\n- This paper claims to provide a novel biologically plausible learning mechanism (even in the title), however to make this method work, a power normalizing and entropy encouraging mechanism is added to network dynamics. It is not discussed whether these are reasonable mechanisms within a biologically plausible context.\n- The current set of simulations results are sufficiently limited that it is not clear whether this method would scale. In particular, biologically-plausible rules can succeed at tasks such as MNIST or CIFAR-10 level but fail completely at large scale (see Bartunov et al. 2018, Neurips). Currently, there are no explanations of how well this method might do when scaled to harder datasets, or even how well it scales when network width or depth is modified. Without measures of performance across network scale and task complexity, it is not possible to know whether this method’s performance is robust to depth/task-complexity.\n- The description of this work’s method is comprehensive but requires a reader to go back and forth to understand it well. For example, the added extensions to EBD, which are used during training, are described with some distance after the main method (in Section 4) making it difficult to understand all moving parts of the simulation in a single read. Furthermore, the paper in general is too heavy on the methods aspects leaving zero room for interpretation of results and discussion. A refactoring of the paper in these respects would greatly help its readability and contribution as well as enabling a more complete discussion on the implications of the work." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a principled error broadcasting framework to serve as a more biologically realistic and flexible alternative to the backpropagation algorithm, based on the orthogonality property of nonlinear MMSE estimators." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024error,\ntitle={Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1YlfHUVq7q},\nnote={under review}\n}" }, "abstract": { "value": "We introduce the Error Broadcast and Decorrelation (EBD) algorithm, a novel learning framework that addresses the credit assignment problem in neural networks by directly broadcasting output error to individual layers. The EBD algorithm leverages the orthogonality property of the optimal minimum mean square error (MMSE) estimator, which states that estimation errors are orthogonal to any nonlinear function of the input, specifically the activations of each layer. By defining layerwise loss functions that penalize correlations between these activations and output errors, the EBD method offers a principled and efficient approach to error broadcasting. This direct error transmission eliminates the need for weight transport inherent in backpropagation. Additionally, the optimization framework of the EBD algorithm naturally leads to the emergence of the experimentally observed three-factor learning rule. We further demonstrate how EBD can be integrated with other biologically plausible learning frameworks, transforming time-contrastive approaches into single-phase, non-contrastive forms, thereby enhancing biological plausibility and performance. Numerical experiments demonstrate that EBD achieves performance comparable to or better than state-of-the-art methods on benchmark datasets. Our findings suggest that EBD offers a promising, principled direction for both artificial and natural learning paradigms, providing a biologically plausible and flexible alternative for neural network training with inherent simplicity and adaptability that could benefit future developments in neural network technologies." }, "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": [ "Error Broadcasting", "Biologically Plausible Neural Networks", "Backpropagation Alternative", "Direct Feedback 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/246039d9d8ca7789d7b3c2f33914c31bc0ffdeb3.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/237402f4a565b806ab9c040fc7013ee2ca9b8466.zip" }, "title": { "value": "Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism" }, "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 authors should clarify the novelty, choice of limited datasets, the use of older defense strategies, and the dependency on pretrained models." }, "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 improves traditional clean-label backdoor attacks by proposing a threat model that is more applicable in real-world scenarios.\n\n2. The method is claimed to achieve higher attack success rates with a lower poisoning rate, showcasing efficient use of resources." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a method for enhancing clean-label backdoor attacks on deep neural networks. Unlike traditional clean-label attacks that apply triggers randomly, this approach selectively poisons challenging samples within the target class, boosting attack success rates with fewer poisoned samples. The authors introduce two strategies: using pretrained models to identify \"hard\" samples and leveraging out-of-distribution data for sample selection. Tested on CIFAR-10 and GTSRB datasets, this method outperforms random poisoning and is resilient against popular defenses like STRIP and Neural Cleanse, highlighting a need for stronger countermeasures against selective clean-label attacks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In my opinion, the method primarily introduces a data selection strategy, which lacks sufficient novelty.\n\n2. The evaluation is conducted only on CIFAR-10 and GTSRB datasets, limiting insight into the method's performance across other dataset types and application domains.\n\n3. The paper primarily tests against older defense strategies. Implementing more recent and sophisticated defenses, including adaptive methods like sample-specific anomaly detection, would strengthen the evaluation.\n\n4. The pretrained model strategy relies on the availability of pretrained models in similar domains, which may not always be accessible in real-world applications." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 see the questions in weaknesses." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The main strengths of the paper lie in its studied threat model, proposed sampling strategies and experimental evaluation.\n\n- I think that the proposed threat model is important as it exposes yet another backdoor threat where an attack only needs access to the data of the target class. The demonstrations that existing backdoor attacks under this threat model are not satisfactorily effective are an important contribution of the paper.\n- The proposed sampling strategies are novel, especially when they could be used with existing backdoor attacks, such as BadNets, SIG, Narcissus, etc…) to boost their backdoor performances under the studied threat model. It’s also interesting finding where the effectiveness of the proposed strategies even when there is less and less assumptions on the pretrained models or the OOD datasets. \n- The paper includes thorough analysis of the proposed strategies, demonstrating their effectiveness when they’re used with several existing backdoor attacks across different datasets. The evaluation also shows the effectiveness against several defenses." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper studies clean-label backdoor attacks in a very constrained setting, where the attacker only needs access to the training data from the target class and has no prior knowledge of the victim model, training process, and the other classes, and focuses on data-selection strategies to boost the performance of existing clean-label attacks in this constrained setting. The proposed data selection strategies include (1) the use of a pretrained model (when such exists) or (2) the use of an OOD dataset (when the pretrained model is not available) to train a surrogate model. The experimental results demonstrate the proposed strategies significantly enhance the ASR and several existing clean-label backdoor attacks, compared to random selection strategies. In addition, the paper demonstrates that the proposed strategies are resilient against several existing defenses." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I find that the paper has the following concerns:\n* The Narcissus results, while interesting, are different from what reported in their original paper. Can the authors explain why there are such differences? \n* The OOD approach rely on out-of-distribution data but it’s not clear how this dataset could be obtained, or whether there are any specific requirements of the datasets to maintain the effectiveness of the attacks?\n* Assuming that the victim could distribute the target class data collection to multiple sources, how does the proposed attacks perform in this case? \n* Do the authors have any suggestions about potential mitigation approaches against the proposed attacks in the studied threat 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": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- Why would an attacker use the OOD strategy proposed in section 4.4, as it requires training a surrogate model and appears to work worse than using a pretrained model?\n- Why use a latent space clustering approach instead of using the loss from a pretrained zero-shot image classifier like CLIP?\n- Why use VICReg instead of a more general feature extractor like CLIP?\n- Where are the training settings used in experiments adapted from?\n\nReferences\n\n[1] Alexander Turner, Dimitris Tsipras and Aleksander Madry. \"Label-Consistent Backdoor Attacks.\", 2019. \n\n[2] He, Kaiming, et al. \"Deep Residual Learning for Image Recognition,\" 2016. \n\n[3] Gao, Yinghua, et al. \"Not All Samples Are Born Equal: Towards Effective Clean-Label Backdoor Attacks.\", 2023." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The proposed method is a widely applicable technique to enhance to clean label attacks.\n- The experiments do a good job differentiating the surrogate model from victim model and therefore the attack shows convincing transferability." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a method for improving the effectiveness of clean-label attacks. It introduces a threat model where the attacker only has access to data belonging to a specific class and has no knowledge about other classes in the dataset. The paper proposes a method for using samples with hard to learn features to create poison-efficient clean label attacks. The proposed method finds these samples by clustering the latent features of a surrogate model. The paper explores using a pretrained model and a model trained on OOD data as the surrogate model. The paper evaluates the clean-label attack against backdoor defenses and data cleaning methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The paper trains for 300 epochs which is significantly longer than it should take to train the model on CIFAR-10/GSTRB [1,2] and makes attack success due to over-fitting very likely. Around 100 epochs seems to be more standard. Ideally, to simulate a competent defender early stopping should be employed. I.e. stopping the run when validation loss plateaus.\n\n- The experiments use very weak baselines. The paper only evaluates how the method performs compared to random sampling. At minimum the paper should compare against [3]. Especially because [3] could easily be adapted to adhere to this paper's threat model by using a pretrained model. Therefore, the experiments are not sufficent to jusifty that the proposed method is stronger than a slightly adapted version of [3].\n\n- The paper claims that it's threat model represents *\"the **most** constrained data-poisoning threat.\"* However, there are other perfectly reasonable threat models that would make this attack unrealistic. For example, an opportunistic attacker that doesn't get to choose the subset of samples in the dataset they are able to manipulate.\n\n- When evaluating the attack against defenses the paper does not describe the hyperparameter settings used by each defense nor how those settings were derived.\n\nMinor:\n- Bolding of best methods or aggregation would make Tables 2 and 3 more interpretable.\n- There are many typos in the manuscript." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 authors should include more related works and advanced baselines in their paper.\n2. The authors should better clarify their main contributions than those introduced in existing works.\n3. The authors should avoid overclaims.\n4. The authors should conduct more comprehensive experiemnts. \n\nMore details are 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": "1. The paper is easy to follow to a large extent.\n2. The motivation is clear and with empirical support.\n3. The paper introduces a clean-label backdoor attack that works effectively in a constrained scenario where the attacker has limited data access (only one target class). This approach is realistic for scenarios with privacy or geographical constraints, enhancing the practical relevance of the attack model." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores a practical scenario for clean-label backdoor attacks, where an attacker’s access is limited to a single class of data within a decentralized training setup. This constrained threat model reflects real-world data collection challenges, such as privacy restrictions and geographical limitations. To enhance poisoning efficiency under these conditions, the paper introduces two sample selection methods specifically designed for this limited-access scenario." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Numerous studies [1,2,3,4,5,6,7,8] have addressed sample selection in backdoor attacks, several of which [6,8] specifically focus on sample selection for clean-label backdoor attacks. Omitting these key relevant works is a significant oversight and should be addressed to ensure a comprehensive discussion of the literature.\n2. The novelty of this paper is limited, as it leverages a pre-trained model to identify \"hard samples\" for poisoning—a concept already explored in several studies [6,7,9]. However, the distinctions between this approach and prior work are not clearly articulated.\n3. The first contribution claimed by this paper is the introduction of a new backdoor threat model, where an attacker, acting as a data supplier, has access only to the target class data yet can still execute effective clean-label backdoor attacks. However, previous studies [10,11] have already examined this threat model in depth, providing detailed discussions on \"Why are dirty-label attacks more effective than clean-label attacks?\" Consequently, the originality and contribution of this paper raise some concerns.\n4. The discussion of backdoor attacks and defenses in the related work sections of this paper is outdated. \n5. There are some potential over-claims. For example, Line 156-159: Accessing only samples from a single non-target class is more difficult setting than yours.\n6. Missing some important experiments.\n- Main Experiments\n - The authors should also include the results of methods using all training samples for references, although you have a different setting.\n - It would be better to include the results of Narcissus here instead of in the appendix.\n - I would like to see whether the proposed method is also effective for untargeted clean-label backdoor attacks (e.g., UBW-C in [12])\n- The Resistance to Defenses: The authors should evaluate their methods on more advanced backdoor defenses (such as [13, 14] and their baselines). \n\n\n\n\n**References**\n1. Computation and data efficient backdoor attacks\n2. Explore the effect of data selection on poison efficiency in backdoor attacks\n3. Boosting backdoor attack with a learnable poisoning sample selection strategy\n4. A proxy-free strategy for practically improving the poisoning efficiency in backdoor attacks\n5. Minimalism is King! High-Frequency Energy-based Screening for Data-Efficient Backdoor Attacks\n6. Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks\n7. Confidence-driven Sampling for Backdoor Attacks\n8. Clean-label Backdoor Attacks by Selectively Poisoning with Limited Information from Target Class\n9. Not all samples are born equal: Towards effective clean-label backdoor attacks\n10. Efficient backdoor attacks for deep neural networks in real-world scenarios\n11. Narcissus: A practical clean-label backdoor attack with limited information\n12. Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection\n13. Towards Reliable and Efficient Backdoor Trigger Inversion via Decoupling Benign Features\n14. IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a strategy that select data to poison to improve the success rate of clean label backdoor attacks." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024wicked,\ntitle={Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1Z3C49JQVf},\nnote={under review}\n}" }, "abstract": { "value": "Deep neural networks are vulnerable to backdoor attacks, a type of adversarial attack that poisons the training data to manipulate the behavior of models trained on such data. \nClean-label attacks are a more stealthy form of backdoor attacks that can perform the attack without changing the labels of poisoned data.\nEarly works on clean-label attacks added triggers to a random subset of the training set, ignoring the fact that samples contribute unequally to the attack's success. This results in high poisoning rates and low attack success rates.\nTo alleviate the problem, several supervised learning-based sample selection strategies have been proposed.\nHowever, these methods assume access to the entire labeled training set and require training, which is expensive and may not always be practical.\nThis work studies a new and more practical (but also more challenging) threat model where the attacker only provides data for the target class (e.g., in face recognition systems) and has no knowledge of the victim model or any other classes in the training set.\nWe study different strategies for selectively poisoning a small set of training samples in the target class to boost the attack success rate in this setting. \nOur threat model poses a serious threat in training machine learning models with third-party datasets, since the attack can be performed effectively with limited information. Experiments on benchmark datasets illustrate the effectiveness of our strategies in improving clean-label backdoor 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": [ "backdoor attack", "data selection" ] }, "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/3f2db51fbde69adef73921cfda0af2f4b493125e.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/e3fe55fbf7cd909ff7cee301c0bffd58e8b34195.zip" }, "title": { "value": "Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks" }, "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.", "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 SiT architecture reports improved results [1]; could authors clarify more about the SotA claim? \n\n- Could the authors please fix the citation format at L196 by using \\citet{}?\n\n[1] Ma et al. ECCV 2024, SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Paper is clear and well-written.\n2. The binary latent idea is new regarding Image Generation through LLMs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "BiGR is a novel conditional image generation model that uses compact binary latent codes to enhance both generative and representation capabilities.​ It unifies generative and discriminative tasks within the same framework, featuring a binary tokenizer, a masked modeling mechanism, and a binary transcoder for binary code prediction. BiGR introduces an entropy-ordered sampling method for efficient image generation and demonstrates superior performance in generation quality and representation capabilities." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- While the idea introduced is novel, it is hard for me to reason why the design choices used in the paper are leading to improving representation capabilities. It would be great if authors shed light on this much more.\n\n- The idea of the diffusion process in Binary seems interesting, however the motivation of why it should improve the overall results could be clearer.\n\n- The authors claim that they have replaced causal attention with bi-directional attention. I need help understanding how this can be done at the inference stage and what fine-tuning was done to make it work.\n\n- The LlamaGen paper reports better results for the ImageNet (256x256). So could the authors please clarify the discrepancy in the results reported?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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.\tHow does BiGR handle scenarios where binary latent codes introduce quantization artifacts? \n2.\tDoes entropy order sampling prioritize representative features (attribute) of an object or class? Is there any relation in order of sampling and semantic characteristics?\n3.\tIt would be insightful to include examples of failure cases." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "- Unified Framework for Generation and Representation. The proposed method effectively combines generative and discriminative capabilities within a single model, demonstrating strong performance in both areas.\n- Strong Experimental Validation. The model's performance is validated through extensive experiments, showing improvements over previous methods in generation quality and discriminative accuracy.\n- Fast Inference Speed. The model’s entropy-ordered sampling strategy accelerates the generation process by iteratively unmasking tokens in a confidence-guided manner. This is significantly faster compared to autoregressive models.\n- Various Applications. BiGR's ability to perform tasks such as inpainting, outpainting, and image enrichment in a zero-shot setting validates its flexibility and generalization capabilities.\n- Extensive Ablations. The paper provides thorough ablation studies that detail the impact of various components and settings on the model's performance.\n- Well written. The paper's motivation is clear and well-connected to the approach. Although some technical parts can be improved with more detail, the paper is well-written overall.\n- The limitations are discussed in the paper." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces BiGR, a conditional image generation model that leverages compact binary latent codes to achieve both high-quality image generation and strong visual representation capabilities. BiGR integrates a binary tokenizer, a masked modeling mechanism, and a binary transcoder to generate binary codes, to achieve efficient generation through an entropy-ordered sampling strategy. The model's design allows it to perform favorably in both generative and discriminative tasks. BiGR demonstrates strong performance on generation metrics, e.g., FID-50k and representation tasks evaluated via linear-probe accuracy. Additionally, the proposed method demonstrates its versatility in applications including image editing and zero-shot generalization on several tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Hyperparameter Complexity. The proposed method relies on several hyperparameters for both training and inference, such as the CFG scale, Gumbel temperature, number of sampling iterations, and number of diffusion steps. This complexity increases the time and resources required for tuning. This is discussed in the limitation section of the paper.\n\n- Fixed Sequence Length: The model’s architecture enforces a fixed sequence length during training, which restricts its flexibility to handle inputs of varying sizes. Generating images at different resolutions requires retraining the model with the new sequence length configuration. This is also discussed in the limitation section of the paper.\n\n- The diffusion and denoising process is a bit confusing. It took me a while to figure out where the noise and denoising process is applied. Clarifying that the binary transcoder is the component responsible for denoising the noise introduced in the \"Bernoulli diffusion\" section would make the flow more understandable and easier to follow." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 refer to the weakness." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Please refer to the summary." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces BiGR, a novel conditional image generation model that unifies generative and discriminative tasks within a single framework. This model is notable for several key advantages:\n\nUniformity: BiGR is the first model to integrate both generative and discriminative tasks, leveraging compact binary latent codes to achieve strong performance in both areas. This unification allows BiGR to handle tasks that typically require separate models.\n\nEfficiency: The model is designed to generate images quickly, making it more efficient than existing models. This efficiency is achieved without compromising the quality of the generated images.\n\nFlexibility and Scalability: BiGR is adaptable to various tasks, including zero-shot generalized tasks, showcasing its potential for a wide range of applications. The model's scalability is demonstrated through its performance across different model sizes and configurations.\n\nPerformance: Extensive experiments show that BiGR delivers decent performance in terms of generation quality and linear separability. The model's performance is evaluated using metrics like FID (Fréchet Inception Distance), and it is shown to perform well compared to other models like LlamaGen.\n\nInference Hyperparameters: The paper discusses the impact of hyperparameters such as the number of sampling iterations and diffusion timesteps on the model's performance. It is noted that larger models tend to achieve lower FID values, but with increased sample time, and that optimal performance varies with model size.\n\nComparison with Other Models: BiGR is compared against other models, including LlamaGen, across different settings involving tokenizers, training objectives, and modeling types. The paper highlights that while the unconditional version of the model shows better representation capabilities, the conditional version excels in generative tasks.\n\nOverall, BiGR represents a significant advancement in the field of image generation by combining generative and discriminative capabilities in a single, efficient model, with promising applications for future research and development." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Lack of Comprehensive Benchmarking: While the paper compares its model against LlamaGen and a few other settings, the scope of comparison is limited. The paper could benefit from a more extensive benchmarking against a wider range of state-of-the-art models to better establish its relative performance.\n\nSampling Strategy Issues: The paper mentions a \"nan\" issue in the sampling strategy due to logarithmic operations. Although a workaround is provided, this indicates potential instability in the model's implementation. A more robust solution to this problem would enhance the reliability of the model.\n\nLimited Exploration of Model Configurations: The paper primarily focuses on a few configurations (S0, S1, S2, S3) and does not explore a broader range of hyperparameters or architectural variations. This limits the understanding of the model's capabilities and its adaptability to different tasks or datasets.\n\nEvaluation Metrics: The paper emphasizes generative performance but does not provide a detailed analysis of other important aspects such as scalability, robustness, or efficiency. Including these metrics would provide a more holistic view of the model's strengths and weaknesses.\n\nAssumptions and Limitations: The paper acknowledges that surpassing state-of-the-art models across all metrics is not the goal, but it does not clearly outline the specific scenarios or applications where the proposed model excels. A clearer articulation of the model's intended use cases and limitations would help in understanding its practical applicability.\n\nTheoretical Justification: While empirical results are presented, the paper could strengthen its theoretical foundation by providing more in-depth explanations or proofs of why certain design choices, such as the non-deterministic binary transcoder, lead to better performance." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- Comments in Weaknesses should be resolved." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The method is easy to follow and the paper is easy to read\n- The architecture of this model seems to work very well on specific tasks such as inpainting, outpainting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a language model based image generation/discrimination model. Using binary latent code autoencoder, the model can learn binary codes from the image representation. Llama is originally a decoder-only model but this method use it as encoder-only model. The generation of an image is conducted by sampling from the Bernoulli distribution of outputs of the model." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The biggest problem of this approach is that it is unable to conduct text-to-image generation. Early stage of diffusion models were constrained to class-conditional generation but now it is hard to find models that is unable to do t2i generation. Even LlamaGen can receive various types of condition (especially text condition) since it is a decoder-only model.\n- I think that's why binary latent code has been enough to encode image representation. Even VQ-VAE inevitably suffers from loss of information because the latent variable is not continuous. But as the problem setting of this paper is limited to class conditional image generation, the amount of information is not large enough to see the malfunction of the binary code.\n- Also, I think it is not fair to directly compare with LlamaGen since it is designed to handle multiple modalities, not focusing on image generation. And also with an appropriate prompt, LlamaGen is able to conduct image discrimination task as well.\n- In conclusion, limiting the scope of the problem enabled the binary code to work well." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We present a novel conditional image generation model that unifies generative and discriminative tasks effectively." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024bigr,\ntitle={Bi{GR}: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1Z6PSw7OL8},\nnote={under review}\n}" }, "abstract": { "value": "We introduce BiGR, a novel conditional image generation model using compact binary latent codes for generative training, focusing on enhancing both generative and representation capabilities. \nBiGR is the first conditional generative model that unifies generation and discrimination within the same framework. \nBiGR features a binary tokenizer, a masking modeling mechanism, and a binary transcoder for binary code prediction. \nAdditionally, we introduce a novel entropy-ordered sampling method to enable efficient image generation. \nExtensive experiments validate BiGR's superior performance in generation quality, as measured by FID-50k, and representation capabilities, as evidenced by linear-probe accuracy. \nMoreover, BiGR showcases zero-shot generalization across various vision tasks, enabling applications such as image inpainting, outpainting, editing, interpolation, and enrichment, without the need for structural modifications. Our findings suggest that BiGR unifies generative and discriminative tasks effectively, paving the way for further advancements in the field." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Image generation", "Generative model", "Representation 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/d2b985d3f51fb18462eac1aea6d72e32bf5a1534.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": "BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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": "What will the performance if using Standard Bayesian optimization rather than IORBO proposed by this paper?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The experiments are comprehensive. Hyperparameters are chosen reasonably." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces two regularization terms for improving the performance of the tabular generative model. The authors further propose to use ranking-based Bayesian Optimization to choose the hyperparameter. They finally evaluate the proposed method in Twenty tabular datasets on 10 base generative models by using TSTR, augmentation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The proposed method is heuristic. The paper does not provide an optimality or convergence guarantee of the proposed loss. These two proposed losses are reasonable for tabular data but not general enough for other types of data. The hyperparameters are chosen by the new proposed Bayesian Optimization without theoretical guarantees." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "The individual contributions of the paper are good. However, my main concern is the overall theme of the paper. I am unable to determine the overall research question the paper is trying to address. 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": "**Originality and Quality**\n\nThe correlation- and distribution-aware loss function is new and interesting to me. I have not encountered works that display the effectiveness of enforcing correlation and high-order moments in the loss function to improve generative models. It is nice to see an improvement in existing hyperparameter tuning algorithms such as Standard Bayesian Optimization by adding an iterative refinement process.\n\n**Clarity**\n\nIndividual sections of the paper are well written.\n\n**Significance**\n\nTabular data generation is gaining traction in real-world applications such as electronic health records. This work helps bring progress to tabular data generation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "- Introduced a correlation- and distribution-aware loss function designed as a regularizer for DGMs in tabular data synthesis that displays promising results\n- Introduced a hyperparameter tuning approach, IORBO, that leverages rank-based aggregation. (concerns of units\n- They introduce a benchmarking system evaluating statistical similarity, ML TSTR performance, and ML augmentation performance, with robust statistical tests." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- I am struggling to find a central theme/research question the paper is trying to answer. It provides solutions from three different perspectives: 1) Loss Function Regularization: Improving generative model outputs by enforcing statistical properties (e.g., correlation, distribution); 2) Hyperparameter Tuning: Using methods like IORBO for iterative optimization; 3) Statistical Tests: Providing a framework for assessing model performance across metrics. I am unable to determine a flow to link the three ideas together/how one idea enforces the other.\n- L486: How does IORBA perform against other hyperparameter tuning methods such as [Randomised Optimization, GridSearch etc.](https://scikit-learn.org/1.5/modules/grid_search.html#tuning-the-hyper-parameters-of-an-estimator) in terms of performance? What about the computational cost for IORBA vs. SBO and other mentioned baselines, what is this tradeoff? Additionally, what are the optimized hyperparameters that you obtain from your method? Ablation studies of the aforementioned would make your case stronger.\n- In [TabSyn](https://arxiv.org/abs/2310.09656), the authors provided a comprehensive evaluation of synthetic tabular data using over five distinct evaluation metrics. Their metrics are straightforward and easy to comprehend. It will be nice to compare and justify why your metrics are more convincing and better than their proposed benchmark so that users should use your metrics instead of/in addition to TabSyn’s.\n- Privacy is also crucial in synthetic tabular generation. How does your proposed loss function affect privacy-preserving metrics such as DCR and C2ST?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. **Significance Levels and Decision-Making:**\n In Table 1, the column for significance levels presents $p$-value ranges. A more detailed description of the decision based on the test statistic (or $p$-value obtained) may be helpful in understanding the experiment since a two-sided test is concerned.\n\n2. **Distribution matching loss:**\nIt is possible for non-converging distributions to have similar moments, especially in lower orders. And, moment estimators of higher order moments introduce instability in the finite sample sense, and this instability goes up when the moment order goes up. It would be helpful if the author could justify using moments for distribution rather than the usual distance/score-based metrics for distribution similarity.\n\n\n\n**Some Suggestions:**\n ***Reordering Loss Components:***\n For clearer presentation, consider swapping the order of the two proposed loss components to explain what $\\mu$ and $\\sigma$ are before presenting them in Eq.2." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper presents an approach to enhancing Deep Generative Models (DGMs) for synthetic data generation on tabular data. Introduction of a correlation- and distribution-aware loss function, iterative objective refinement Bayesian optimization, and a detailed benchmarking framework are presented." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "**Review of \"Improving Tabular Generative Models: Loss Functions, Benchmarks, and Iterative Objective Bayesian Approaches\"**\n\nThis paper proposes several methods to enhance deep generative models (DGMs) for synthetic data generation with a particular focus on tabular data. While the work presents promising results in experiment, certain aspects need further clarification and further improvement." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **\"Moment Generating Function (MGF)\":**\nThe term \"Moment Generating Function (MGF)\" appears to be misused. The paper discusses empirical moments themselves rather than the empirical MGF $\\hat{M_X}(t)$ from which the $n$-th moments can be obtained by taking $n$-th derivatives wrt $t$ at $t=0$. [See *Casella, Statistical Inference, 1990* (pp61)]\n\n2. **Biased Estimator in Synthetic Data:**\n A biased estimator is used to calculate the standard deviation. This includes the estimator on synthetic data sampled at size $B$, which is not enough for the biased estimator to converge to the unbiased one. It would be beneficial for the paper to address or justify this choice. \n\n3. **Hyperparameter $\\lambda$:**\nHyperparameter $\\lambda$ in Eq.6 scales the $L_{\\text{distribution}}$ in a manner the same as $\\beta$ in custom losses, since $\\lambda$ is \n proportional to $L_{\\text{distribution}}$ in Eq.6. Simultaneous inclusion of $\\lambda$ and $\\beta$ in the hyperparameter search may lead to issues such as multi-collinearity for Bayesian optimization." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": 4 }, "strengths": { "value": "The added regularizers are described clearly and intuitively, with a well-defined methodology and comprehensive benchmark design. This approach encompasses various generative models and employs Bayesian optimization to identify optimal hyperparameter configurations. Consistent improvements over baseline models are demonstrated." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work introduces correlation and moment-matching loss functions to regularize the loss function of different deep generative models for tabular data. Its results show that with proper selection of hyperparameters, its approach consistently improves the baselines. A Bayesian optimization procedure is introduced for hyperparameter tuning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "What I miss from the paper is a discussion on how to tune the method in the case of data heterogeneity and its performance and robustness in missing data scenarios. How do the regularizers formulate in the case of counting distributions (e.g., Poisson likelihood) or ordinal variables? Do they consistently improve the results in the case of large fractions of missing entries in the database? I set my score to 6 since I feel that without a proper discussion on these aspects, the impact of the paper is limited." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a novel loss function and optimization method that significantly improve the ability of deep generative models to create high-quality synthetic tabular data for better machine learning performance." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024improving,\ntitle={Improving Tabular Generative Models: Loss Functions, Benchmarks, and Iterative Objective Bayesian Approaches},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1ZAqAmK6BM},\nnote={under review}\n}" }, "abstract": { "value": "In many applications of deep learning (DL), more data is essential to enhance model performance and generalization. A promising avenue to increase data availability is to use deep generative models (DGMs) to create synthetic data. However, existing DGMs struggle to capture the complexities of real-world tabular data, which often contain diverse variable types with potential imbalances and dependencies. To address these challenges, we propose a novel correlation- and distribution-aware loss function that works as a regularizer for DGMs. Additionally, to address the limitations of standard Bayesian optimization (SBO), which struggles to aggregate multiple metrics with different units--resulting in unreliable direct averaging and sub-optimal decisions--we introduce iterative objective refinement Bayesian optimization (IORBO) to rank metrics to enable more meaningful comparisons across diverse objectives. To ensure a rigorous evaluation, we establish a comprehensive benchmarking framework using twenty real-world datasets along with ten established tabular DGM baselines. The proposed loss function demonstrates statistically significant improvements over existing methods in capturing the true data distribution, significantly enhancing the quality of synthetic data generated with DGMs. The benchmarking framework shows that the enhanced synthetic data quality leads to improved performance in downstream DGMs tasks. Further, the proposed IORBO outperformed the SBO with mean aggregation in terms of win rate and outperformed the SBO with median aggregation overall." }, "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 network", "synthetic data", "correlation- and distribution-aware loss function", "iterative objective refinement Bayesian optimization", "benchmarking framework" ] }, "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/9cd1a0cd15dd9853b20aebf9d9629538320babca.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": "Improving Tabular Generative Models: Loss Functions, Benchmarks, and Iterative Objective Bayesian Approaches" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Could the authors elaborate on potential methods to mitigate noise introduced by style dictionary diversification, especially in fine-grained tasks?\n2. Are there specific aspects of CLIP’s architecture that are essential to this approach, or could it be adapted to other VLM architectures?\n3. In Figure 6, the style differences are not very apparent—could the authors clarify how style diversification manifests visually?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper provides a meaningful contribution to open-vocabulary customization for VLMs, especially under data-free constraints. It addresses practical issues in adapting CLIP without original data, proposing a unique approach to handle limitations posed by BatchNorm layers. Techniques like style dictionary diversification and meta knowledge distillation are well-conceived, though the performance improvements are modest. While the theoretical analysis is detailed, the practical gains might benefit from further validation. Overall, the paper offers useful insights but may require more refinement and broader evaluation to strengthen its impact." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a novel approach for open-vocabulary customization in vision-language models like CLIP, utilizing Data-Free Knowledge Distillation. The authors address limitations of existing DFKD methods, which depend heavily on BatchNorm layers incompatible with CLIP. Their method incorporates image-text matching to invert a surrogate dataset, enabling text- and image-based customization. Key innovations include style dictionary diversification, class consistency maintaining, and meta knowledge distillation to enhance the generalizability of a student model." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper's writing could be improved for clarity, as the relevance of BatchNorm (BN) statistics to the later-introduced contrastive learning method is somewhat confusing. The presentation would benefit from clearer contextualization and integration with recent advancements in VLM customization to help situate the contributions more effectively. While the proposed techniques are valuable, additional clarity around specific limitations—such as the potential for style dictionary diversification to introduce noise—could strengthen the paper. Additionally, the reliance on the CLIP model may limit generalizability across other VLM architectures. Expanding future work to include broader applications of the method across diverse vision-language architectures would help validate its adaptability." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. Why did you use VQGAN? Will the generated data have enough diversity? What other architectures did you consider?" }, "rating": { "value": 10 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "This paper presents a novel approach to open-vocabulary customization of Vision-Language Models (VLMs) like CLIP. The authors identify the limitations of existing Data-Free Knowledge Distillation (DFKD) methods and propose a novel solution to address these limitations.\n\nThe paper is well-written and easy to follow. The authors provide a clear motivation for their work and a concise overview of their proposed technique. The introduction effectively sets the stage for the paper, with a clear articulation of the research gap and the proposed solution.\n\nThe authors provide a comprehensive analysis of related work, demonstrating the novelty of their approach. The experimental findings are compelling, particularly the observation that CLIP's BN layers tend to favor faces, highlighting their unsuitability for DFKD.\n\nThe proposed framework's ability to handle both text-based and image-based customization enhances its applicability and significance. The use of instance-level contrastive loss for increased diversity is well-justified, both in practice and through theoretical analysis (Theorem 4.1).\n\nThe experimental setup and training details are described thoroughly, which is commendable. The choice of the ImageNet dataset is appropriate, given its scale and diversity. The result analysis is comprehensive and insightful, with the authors exploring various aspects of their approach, including the unique \"warm-up\" strategy." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper shows that the existing works that use DFKD methods using CLIP do not perform well. This is blamed on their use of BatchNorm that biases towards faces. The paper introduces an alternate technique for performing DFKD well using CLIP - a text-image matching technique." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While Figure 1 provides a good overview of the framework, consider replacing the \"frozen\" and \"not frozen\" symbols with more intuitive icons, such as a lock and an unlocked lock. Additionally, ensure the frozen symbol is clearly visible in the blue boxes, perhaps by changing its color.\n2. Tables 2, 4, and 5 don’t have any units for the numbers or any text mentioning the metric used for those results. Please consider adding metrics and units." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "My concern mainly lies in technique novelty. Can you summarize your contribution again based on my concern?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "-\tThis paper is well written.\n-\tThis paper is well motivated to study DFKD for vision-language foundation models.\n-\tExperiments show the effectiveness of the proposed framework." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper delves into Data-Free Knowledge Distillation for CLIP so as to distill a compact student model with customized zero-shot or few-shot image classification capacity. Specifically, the proposed framework is composed of surrogate dataset generation and knowledge distillation. For the former component, this paper uses model inversion and style dictionary diversification based on the framework of VQGAN-CLIP. For the latter component, this paper designs a meta method for knowledge distillation. Experiments validate the effectiveness of the proposed framework." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- My concern mainly lies in technique novelty. In Fig.1, the proposed framework is composed of dataset inversion process and knowledge distillation process. However, in dataset inversion process, the proposed method is mainly similar to [1] and [2], especially [2], which is also a related work to study DFKD in CLIP. In knowledge distillation, the proposed method is mainly similar to [3], which uses a MAML-like meta learning to enhance cross-domain generalization capacity. \n\n[1] VQGAN-CLIP: Open domain image generation and editing with natural language guidance. ECCV 2022.\n\n[2] Distilling vision-language foundation models: A data-free approach via prompt diversification. ACMMM 2023.\n\n[3] Learning to Generalize: Meta-Learning for Domain Generalization. AAAI 2018." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 the weakness section. I would like to increase my rating, if the proper justification of my questions will be given." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The method eables model customization without accessing the original data, preserving privacy of the users.\n2. The proposed approach captures invariant representations through style diversification and meta knowledge distillation, which is interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the customization of CLIP for specific user-defined tasks without using original data. The proposed approach involves generation of synthetic images using VQGAN in different styles to increase diversity, while following a data-free meta learning based knowledge distillation technique to adapt a lioghtweight student encoder from teacher CLIP. It aims to overcome the reliance on BatchNorm layers, which hinder customization for ViT variants of CLIP model. The authors have shown extensive experiments with significant improvement of performance of the proposed method compared to CLIP." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. As CLIP is already a very good domain-aware model, what is the motivation behind generating style tranferred images? The diversification could be better and challenging with generation of very fine-grained realistic images.\n\n2. Can pretrained diffusion models be used instead of VQGAN, as it can generate more diverse datasets very easily? What are the pros and cons of using a diffusion model?\n\n3. Why meta learning based knowledge distillation over traditional supervised learning? Any theorectical reason?\n\n experiments of distillation techniques like TinyCLIP [1], CLIP-KD [2], LP-CLIP [3] are likely to be preferable.\n\n\n [1] TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance, ICCV 2023.\n\n [2] CLIP-KD: An Empirical Study of CLIP Model Distillation, CVPR 2024.\n\n [3] Improving CLIP Robustness with Knowledge Distillation and Self-Training" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Could we distill models from CLIP without data to meet customized tasks?" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024openvocabulary,\ntitle={Open-Vocabulary Customization from {CLIP} via Data-Free Knowledge Distillation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1aF2D2CPHi},\nnote={under review}\n}" }, "abstract": { "value": "Vision-language models such as CLIP have demonstrated strong zero-shot performance, but their considerable size and inefficient inference limit customizable deployment for users. While knowledge distillation is a solution, it still requires the original data, which is not always available due to copyrights and privacy concerns. For many users seeking open-vocabulary customization, Data-Free Knowledge Distillation (DFKD) emerges as a promising direction. Upon rethinking DFKD, we find that existing methods fail on CLIP due to their heavy reliance on BatchNorm layers, which are unexpectedly unusable in CLIP. Based on our findings, we adopt image-text matching to achieve DFKD for CLIP, enabling customization based on arbitrary class texts. This involves (i) inversing a surrogate dataset from CLIP based on text prompts; and (ii) distilling a student model from CLIP using the surrogate dataset. Specifically, we introduce style dictionary diversification to enhance the diversity of synthetic images. To prevent uncontrollable semantics introduced by diversification, we propose a class consistency maintaining strategy to ensure the consistency of synthetic images. Based on synthetic images with various styles, we further propose meta knowledge distillation to train the student model with good generalization ability. Moreover, we introduce a simple yet effective method to enable customization based on few example images. Comprehensive experiments showcase the superiority of our approach across twelve customized tasks, achieving a 9.33\\% improvement compared to existing DFKD 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": [ "Data-Free Learning", "CLIP Model", "Customization" ] }, "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/5344d1ee8334909cd8ec9a48790f795aecc1f4f4.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Open-Vocabulary Customization from CLIP via Data-Free 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": 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 the Weaknesses above." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The work provides theoretical insights into neural network learning mechanisms for group operations. The discovery of algebraic structures (semi-ring) in the weight space and monomial potentials in the loss function offers a fresh perspective on how networks learn structured tasks. \n- There's strong empirical validation of the theoretical results. As shown in Table 2, around 95% of gradient descent solutions exactly match their theoretical constructions, with very small factorization errors. This provides concrete evidence that the theoretical framework accurately captures the learning behavior.\n- The analysis of training dynamics (Theorem 5 and 6) provides insights into why networks prefer low-order Fourier solutions over perfect memorization. The paper shows that gradient descent with weight decay naturally favors simpler solutions due to topological connectivity between different-order solutions, which is an interesting finding." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces CoGO (Composing Global Optimizers), a theoretical framework for analyzing how 2-layer neural networks learn group operations with quadratic activation and L2 loss. The key insight is discovering a semi-ring algebraic structure in the solution space that allows the construction of global optimizers by composing partial solutions. The authors prove that the weight space has a semi-ring structure and that the loss function consists of monomial potentials with ring homomorphism properties. They also analyze training dynamics to explain why networks prefer simpler Fourier-based solutions over perfect memorization. The theoretical predictions align well with empirical results, showing that about 95% of gradient descent solutions match their constructed solutions." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- My major concern is that the loss decomposition approach (Theorem 1) seems limited to scenarios where we already understand the underlying group structure of the data. The paper doesn't address how this framework might generalize to real-world scenarios where the data's algebraic structure is unknown or unclear. This limits the practical applicability of the theoretical insights, e.g., can we decompose the next token prediction loss easily?\n- While the training dynamics analysis (particularly around Fourier feature learning and Theorem 5) is interesting, [1] also introduced that the NN prefers to learn Fourier features by gradient descent. Can the author give a more detailed comparison of connections and differences to [1]? The paper could better contextualize its findings with existing work by providing a more detailed comparison of the mechanisms and insights, which would strengthen the paper's contribution. \n- The paper mentions connections to grokking in the Conclusion but doesn't fully explore this direction. It would be good to discuss more, e.g., why there is a gap between train loss and test loss in the beginning under the paper’s analysis framework. Given that grokking is a significant phenomenon in neural network learning, especially for arithmetic tasks, a more detailed discussion of how CoGO might explain or relate to grokking would enhance the paper's impact.\n\n[1] Depen Morwani, Benjamin L Edelman, Costin-Andrei Oncescu, Rosie Zhao, and Sham Kakade. Feature emergence via margin maximization: case studies in algebraic tasks. ICLR 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": 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": "- Line 140: Should mention l[i] is the embedding of the true label for the i-th data point.\n- Line 145: I guess l[i] should be the in d-dimension, ie, the embedding of the element g_1[i] g_2[i], rather than the element itself. \n- Line 145: How is g_1[i] g_2[i] embedded into l[i]? g_1[i] is using U_{G_1} and g_2[i] is using U_{G_2}, while it's unclear how l[i] is obtained.\n- Experiment: how to generate the training data (ie how g_1[i] and g_2[i] are sampled)? The data distribution can significantly impact the solution reached by training, so it needs to be specified for interpreting the empirical result that most solutions reached in experiments match the theoretical construction." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The work provided a new angle on analyzing the global optimizers for the considered algebraic problem. It analyzed algebraic properties of the weight space and the loss, and then gave sufficient conditions for the global optimizers. \n- The study is quite solid and thorough. It provided detailed characterization of the sufficient condition, and also gave a systematic approach to construct global optimizers." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work considered 2-layer neural networks with quadratic activation and L2 loss on learning group multiplication (an extension of modular addition). It showed that global optimizers can be constructed algebraically from small partial solutions that are optimal only for parts of the loss, due to (1) a semi-ring structure over the weights space and (2) L2 loss being a function of monomial potentials allowing composition of partial solutions into global ones. (2) is shown by representing the network weights and then the loss function using Fourier bases. \n\nIt then proposed a systematic approach using the above algebraic structure to construct global optimizers. It used this theoretical framework named CoGO to construct two distinct types of Fourier-based global optimizers of per-frequency order 4 and 6, and a global optimizer of order that correspond to perfect memorization. It empirically showed that most solutions via gradient descent match such constructions. It also analyzed the gradient dynamics, showing that it favors simpler solutions under weight decay, and that overparameterization asymptotically decouples the dynamics." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The theoretical setup is quite specific: quadratic activation and learning group multiplication. While the analysis is interesting, it is unclear if the results can provide insights into more general settings, in particular those more related to practical scenarios. The work can be strengthened if it can provide some empirical study on more realistic datasets verifying the insights (ie composition structure of the solutions), or provide generalization to more general settings (at least discussion about potential generalization and why). \n- The global optimizers constructed by CoGO is only a subset of all possible global optimizers, so the approach only partially characterizes the problem solutions. This weakens the contribution a bit, though the work does provide empirical evidence that most practically obtained solutions are in their construction. \n- The presentation can be improved. See several comments below." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "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": 1 }, "primary_area": null, "questions": { "value": "What is l[i] in (1)?\nIs it important in Section 4.1 that you are looking at solutions in a weight space, or can they just be any fixing of parameters?\nDoesn't the loss function itself change when you change the shapes of the parameters?\n\nClarify the relationship between Input and Output paragraph with what follows. \nBe consistent with subscripts with commas or multindices. I'm confused now if they have different meanings. \nClarify the construction alluded to at the beginning of 5.1. \nThe relationship between weights, w, z, and r should be better clarified. This seems to me like a lot of notation and I don't have the intuition to understand the claims. \nPlease also explain the essence of the constructions of solutions in 5.2. What is really \"going on\"?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Studies a simple and interesting class of neural networks. \nProves many nice properties of a new mathematical space.\nThere is probably a nice interpretation of the construction of the solutions in Section 5.2 (but a weakness is that I don't see this expressed in a simple way). Interesting results about behavior of gradient descent in Section 6." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The work analyzes the 2-layer network training dynamics when learning Abelian group multiplication. Gradient descent matches an analytical solution for optimality." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Numerous grammatical errors (\"which are ring homomorphism\", \"goes to infinite\", \"is called semi-ring\"...)\nOn the whole, the presentation of technical results is not clear enough to get a good picture of what is happening mathematically." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Semi-ring structure exists in 2-layer neural nets for reasoning tasks on Abelian group (e.g., modular addition), trained with L2 loss, which enables constructing global solutions analytically from non-optimal ones instead of gradient descent." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024composing,\ntitle={Composing Global Optimizers to Reasoning Tasks via Algebraic Objects in Neural Nets},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1auB9yeB9a},\nnote={under review}\n}" }, "abstract": { "value": "We prove rich algebraic structures of the solution space for 2-layer neural networks with quadratic activation and $L_2$ loss, trained on reasoning tasks in Abelian group (e.g., modular addition). Such a rich structure enables \\emph{analytical} construction of global optimal solutions from partial solutions that only satisfy part of the loss, despite its high nonlinearity. We coin the framework as \\ours{} (\\emph{\\underline{Co}mposing \\underline{G}lobal \\underline{O}ptimizers}). Specifically, we show that the weight space over different numbers of hidden nodes of the 2-layer network is equipped with a semi-ring algebraic structure, and the loss function to be optimized consists of \\emph{monomial potentials}, which are ring homomorphism, allowing partial solutions to be composed into global ones by ring addition and multiplication. Our experiments show that around $95\\%$ of the solutions obtained by gradient descent match exactly our theoretical constructions. Although the global optimizers constructed only required a small number of hidden nodes, our analysis on gradient dynamics shows that overparameterization asymptotically decouples training dynamics and is beneficial. We further show that training dynamics favors simpler solutions under weight decay, and thus high-order global optimizers such as perfect memorization are unfavorable." }, "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": [ "landscape analysis", "modular addition; gradient dynamics; reasoning; symmetry; representation 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/ac5155b9225cdb2886a75e3edb2fa0d802114b64.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": "Composing Global Optimizers to Reasoning Tasks via Algebraic Objects in Neural Nets" }, "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": "- in Section 5, what is the precise format of the explanation the authors intend to provide? Is the optimal training task sequence itself considered the explanation, as suggested in Section 5.2 (lines 429-471)?\n\n- what is the objective of the controller, and what purpose does the control policy serve? This remains unexplained.\n\n- in lines 100-102, does \"updating the policy\" equate to \"updating the agent’s learning process\"? Could the authors clarify this distinction?\n\n- could the authors elaborate on the terms “nodes linked to significant updates” and “activated nodes during the test” in Section 4.2, specifically how their correlation is analyzed?\n\n- where is Figure 3 referenced in the main text?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "the framework provides a structured approach for interpreting the learning progress of agents in long-horizon tasks using a GNN-based model." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents an interpretability framework to understand an agent’s learning process in complex tasks (e.g., ALFWorld) through a GNN-based explainer. This method examines policy updates across predefined subtasks and highlights critical sections of the policy network." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- the paper's clarity could be improved. Certain terms are referenced repeatedly early in the text (e.g., introduction) but are defined too late or not at all—examples include \"node-link diagram,\" \"policy structure,\" \"structure training tasks,\" and \"problem\" (line 91).\n- the three claimed benefits of \"understanding the agent’s performance post-training from the learning process of the policy and the sequences of training tasks\" are difficult to grasp. Benefit 1 is too abstract, and lacks contextual detail. In contrast, Benefit 3 includes broad statements unsupported by references (e.g., \"which can not deal with the big and complex problems that can not be seen in the real world\").\n- several key concepts lack references, including SCM and counterfactual methods (lines 95-96), MDP (line 167), and the node-link diagram representation (line 162).\n- the paper motivates the question \"why an agent can succeed or fail in a task\" but lacks examples or case studies that would provide a unique takeaway on RL agents' interpretability.\n- section 3’s \"Structural visualization of the policy\" is hard to understand. Goals are listed, but it is unclear how they are grounded or justified. For instance, it is mentioned that the policy visualization should use a node-link diagram to depict network architecture, but the rationale behind this choice is not explained. Additionally, it is unclear how this visualization allows users to judge the network’s robustness to translational and rotational variances or ambiguous inputs. The \"gap\" between visualization requirements and actual results remains unaddressed.\n- in Figure 1, the authors introduce the GNN-explainer as part of the proposed framework, but Section 4.2 later introduces a GNN-predictor (also in Algorithm 1) without clarifying where it fits within Figure 1, creating confusion.\n\n- the related work in explainable reinforcement learning (XRL) is not up-to-date, lacking recent advances in XRL.\n- given that this work offers neuron-level visualization, it would benefit from referencing related literature in mechanistic interpretability (which is for understanding the inner workings of neural networks).\n- the claim that prior explanation algorithms cannot model complex behaviours (lines 44-47) lacks evidence. Although (Puiutta & Veith, 2020) is cited to support this claim, it is a survey paper, which weakens the argument.\n\n- how do the authors ensure there is any semantical interpretation w.r.t. part of the policy weights (so that humans can understand) when using GNN-explainer to visualise the policy (section 4.2)? in other words, how could users understand the visualised section of the policy? how could users link the \"part of the edges (updated weights)\" to the success of the RL agent?\n\n- the GNN-based explainer is suggested to provide an understanding of each subtask’s value in training, yet this explanation seems limited to high-level progress indicators rather than deep rationales behind actions. This contradicts some of the authors’ statements like \"a proficient explanation enhances understanding of the agent’s actions and helps improve performance\" (lines 60-62). Moreover, the reliance on predefined subtasks limits the framework's applicability in real-world scenarios.\n\n- step 1 in Section 4.2 is difficult to follow, particularly the authors' claim that variability does not affect GNN training. Additionally, the connection between \"nodes linked to significant updates\" and \"activated nodes during the test\" remains unclear. The assertion that \"REVEAL-IT is distinguished from other explainable RL methods by its independence from external data and structure\" is also debatable, as saliency maps do not impose environment or algorithm-specific constraints either.\n\n- in Algorithm 1, it is unclear how the GNN optimizes the training task sequence; the sequence sampling appears to be based only on $P$ (see line 7 in Algorithm 1).\n\n- a brief comparison of REVEAL-IT with baselines is missing, which is important for understanding the reasons behind its performance advantages—whether due to improved planning steps or better-learned low-level behaviours.\n\n- figure 4, relevant to the discussion in Section 5.2, is placed in the appendix. Moving it (or parts of it) to the main text would improve readability and flow.\n\n- the first question in Section 5 (\"learning process of an RL agent\") does not appear to be fully answered. It’s unclear where this process is visualized—Figure 2 or Figure 3. How could the nodes in Figure 2 be interpretable for users, what are the verbs in Figure 3 (are they subtasks?) and which final task is Figure 3 about?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "What is the GNN Explainer’s training objective? If it is only trained to preserve the predictor’s accuracy, then it could just output the full graph. \n\nWhat is the definition of “active nodes” in Step 1 of section 4.2? \n\nHow exactly does the GNN explainer choose the distribution of subtasks to train on? That does not seem to be a direct byproduct of classifying the most critical weight updates. And how does it help on the OpenAI gym environments which do not involve any subtasks? \n\nThe conclusion states that REVEAL-IT can’t adapt to multi-modal challenges. Why would it not be able to handle non-visual modalities? It seems like it can be applied wherever a neural network policy network is used, which does not seem to be constrained to image inputs. \n\n\nIn Figure 2, what do the gray shaded regions correspond to? “Thicker connections indicate larger updates in weight amplitude (selected by GNN explainer)” - does this mean that thicker connections indicate weights that were both selected by GNN explainer, and had large updates in amplitude? How were the portions of the policy that are common to several sub-tasks identified? “as the training progresses toward the latter stage, there is a greater overlap between the region with a larger update amplitude and the region triggered by evaluation.” - what does “the region triggered by evaluation” refer to?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "REVEAL-IT addresses an important challenge in deep RL. \n\nThe method is broadly applicable, as it is agnostic to the environment or (online) RL algorithm.\n\nThe performance appears to be quite impressive for Alfworld." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a framework for interpreting the training process of RL algorithms. Policy updates are visualized with node-link graphs, where the nodes are the neurons in the policy network and the edges are the weights that were updated. A GNN predictor is then trained to predict the RL algorithm’s learning progress, defined as the increase in return on a task after one policy update. A GNN explainer is trained to find which updated weights are most critical for the success of the RL agent, by finding the subset of weights that preserves the GNN predictor’s output given only that subset. The authors demonstrate that REVEAL-IT's explanations can be used to improve training efficiency and performance of various RL algorithms in ALFWorld and OpenAI gym environments." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The writing is very difficult to follow due to excessive verbosity, vague language and grammar issues e.g. “you can record and correspond to the changes in the value of a specific part of the weights” (line 186) or “the understanding the critical nodes in the RL agent’s evaluation is a crucial pre-requisite for determining the significance of weights updating.” (line 220) The authors should review the paper for conciseness and grammatical accuracy\n\nThe GNN Explainer does not seem to provide much human-interpretability. Figure 2: “we will observe that the sections with more significant policy updates will undergo modifications” seems to be a trivial observation rather than something illuminating. It is not uncommon for deep RL models to have millions of weights, so the ability to highlight a subgraph of most important weight updates would still leave the user with far too many to interpret. Could the authors provide concrete examples of helpful insights gained from the GNN Explainer? \n\nResults tables are missing standard deviations; particularly for Table 2 it is unclear whether the improvements are significant. \n\nThe link to view the project on line 311 is broken. \n\nSome figure references are broken, e.g. the distribution of training subtasks is figure 3 but referenced as figure 4 on line 430" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "I listed the questions and suggestions together with weaknesses in the above section." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- **[Motivation]**: The motivation is generally sound; learning from policy behavior appears to be a promising approach for developing interpretable and generalizable policies in complex environments.\n\n- **[Empirical Evaluation]**: The empirical evaluation is relatively comprehensive, and the reported results show the potential of this approach." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a GNN-based explainer to identify critical nodes or components for reinforcement learning (RL) tasks, with the goal of improving interpretability and enhancing the learning efficiency of RL agents. The approach involves visualizing a node graph that represents the RL training process for sub-tasks, and training the GNN-based explainer to approximate the true learning process in order to identify important components (key weights or edges) for the tasks. The GNN explainer is then used to guide policy learning. Results show improvements over standard RL models and language-based approaches (tested on ALFworld and other RL benchmarks).\n\nOverall, the paper presents an interesting direction by learning critical components across multiple RL tasks through policy network responses. However, some technical aspects of the method are unclear and could benefit from further justification and improvement. I have outlined specific questions and concerns below and will give a borderline reject in this initial review." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**[About Problem Definition, Methodology, and Experiments:]**\n- 1. The authors assert that variability in learning across models will not pose an issue since the learned explainer is model- or task-specific. However, for applications in multi-task learning, pre-training, or other forms of generalization, this variability is crucial and challenging. For instance, training the same network multiple times may yield high variance in the training process data due to permutation invariance. Empirical evaluation or theoretical justification for this would be useful.\n\n- 2. To ensure the framework is generalizable and learns universal, principled representations, it would be beneficial to further explore the alignment between the learned structural information and the actual policies or concepts, either empirically or theoretically. Approaches could include using sparse autoencoders (potentially with larger models) [1] or examining the alignment between individual components and their corresponding concepts, modularities, interactions, and causal relations [2-5].\n\n- 3. Building on point 2, utilizing these representations could facilitate compositional and hierarchical structures in policy adaptation and generalization. Including evaluations that focus on different levels of generalization would be uesful.\n\n- 4. The impact of network size on the results should be investigated through ablation studies. If the network size is small, do the same phenomena observed in Figure 2 still occur?\n\n- 5. What are the primary benefits of using GNNs for contribution analysis of each node or weight? Why not directly use magnitude, partial derivatives, or conditional mutual information to assess the importance of each weight?\n\n**[About Clarity]**\n\n- 1. It would be helpful to list all objective functions in a separate subsection, particularly the objectives for the GNN predictor and explainers, along with an explanation of how guidance information is provided for policy updates.\n\n- 2. In line 115, the process is mentioned as being similar to a POMDP; please formulate this for clarity.\n\n- 3. There are some typos to address, such as in line 11 of Algorithm 1—should $\\pi_0$ be $\\pi_t$? Also, in line 432, figure 4 should likely be referenced as figure 3 instead.\n\n**Others**: As a side note, many causal RL works focus on learning world models, akin to a subgroup of model-based RL with interventions, rather than behaviors or policies/reward structures, which differ from the goals of this paper. The authors mention their inability to handle complicated tasks, but a more justified statement regarding this limitation should be provided.\n\n\n\n\n[1] Gao, Leo, et al. \"Scaling and evaluating sparse autoencoders.\" arXiv preprint arXiv:2406.04093 (2024).\n\n[2] Marks, Samuel, et al. \"Sparse feature circuits: Discovering and editing interpretable causal graphs in language models.\" arXiv preprint arXiv:2403.19647 (2024).\n\n[3] Gandikota, Rohit, et al. \"Erasing Conceptual Knowledge from Language Models.\" arXiv preprint arXiv:2410.02760 (2024).\n\n[4] Geiger, Atticus, et al. \"Causal abstraction: A theoretical foundation for mechanistic interpretability.\" Preprint (2024).\n\n[5] Lippe, Phillip, et al. \"Biscuit: Causal representation learning from binary interactions.\" Uncertainty in Artificial Intelligence. PMLR, 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": 2 }, "primary_area": null, "questions": { "value": "In general the authors need to be clearer about the system they have developed, and if curriculum learning is indeed part of the system, it should be discussed more thoroughly and introduced early on.\n\nAlso, very little is said about the Gym tasks, so it is difficult to understand what went on in those experiments, particularly as the standard environments do not have subtasks." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The authors introduce a new framework for RL interpretability which can provide better insights than other methods. The method also includes a curriculum learning component, which produces very strong results on the ALFWorld environment. The authors do also spend some time analysing the results from their interpretability framework, in order to showcase its capabilities." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors introduce an interpretable RL agent, in the context of environments with sub-tasks. The algorithm uses a GNN-based approach to visualise the updates to the policy, and also uses another GNN to implement curriculum learning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The authors provide a valid criticism of more generic interpretability methods e.g. post-hoc methods like saliency maps. However, this work assumes (unless I am wrong - see below) the environment provides a set of subtasks that can be trained on, which is a large assumption. Therefore the generality of this method is somewhat limited. To what extent do subtasks need to be provided/can be inferred?\n\nThe methodology is a bit unclear, as the focus appears to be on interpretability, and then halfway through the paper the authors introduce a GNN predictor and curriculum learning. Curriculum learning is not discussed at all in the Related Works section. And yet the authors show that their method significantly outperforms other methods in ALFWorld (Table 1), so it is clear that this is not just about interpretability. If this is the case, then the authors should be mentioning this in the abstract and from the introduction.\n\nI am also under the impression that environment subtasks are needed for REVEAL-IT, but the authors perform experiments on OpenAI Gym MuJoCo environments, which don't have them? Table 2 indicates that adding REVEAL-IT to existing methods improves their performance, but in Table 1 the authors present REVEAL-IT as its own algorithm, so once again it is unclear what is going on here. Is REVEAL-IT standalone or an addition to existing algorithms?\n\nThe paper should be checked for spelling mistakes, e.g., \"Strucutral\" on page 4." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024revealit,\ntitle={{REVEAL}-{IT}: {RE}inforcement learning with Visibility of Evolving Agent poLicy for InTerpretability},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1c73HCZpbo},\nnote={under review}\n}" }, "abstract": { "value": "Understanding the agent's learning process, particularly the factors that contribute to its success or failure post-training, is crucial for comprehending the rationale behind the agent's decision-making process. Prior methods clarify the learning process by creating a structural causal model (SCM) or visually representing the distribution of value functions. Nevertheless, these approaches have constraints as they exclusively function in 2D-environments or with uncomplicated transition dynamics. Understanding the agent's learning process in complicated environments or tasks is more challenging. In this paper, we propose REVEAL-IT, a novel framework for explaining the learning process of an agent in complex environments. Initially, we visualize the policy structure and the agent's learning process for various training tasks. By visualizing these findings, we can understand how much a particular training task or stage affects the agent's performance in the test. Then, a GNN-based explainer learns to highlight the most important section of the policy, providing a more clear and robust explanation of the agent's learning process. The experiments demonstrate that explanations derived from this framework can effectively help optimize the training tasks, resulting in improved learning efficiency and final performance." }, "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": [ "Reinforcement Learning", "Interpretability" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/75eeb1a99f6500eb89ef27865f2bd896a4c35956.pdf" }, "presentation": null, "primary_area": { "value": "reinforcement learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "REVEAL-IT: REinforcement learning with Visibility of Evolving Agent poLicy for InTerpretability" }, "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": "- I would like the authors to respond to the points I raised as concerns regarding the above weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The paper is well-structured and easy to read.\n- The proposed method integrates VAE and flow matching in a straightforward manner, offering novelty in its ability to learn vector fields with uncertainty. Furthermore, the high performance on MNIST and CIFAR-10 datasets suggests that the hypotheses and approaches of this study are reasonably valid." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a novel method using variational inference to address the ambiguity of velocity vector fields that classic rectified flow matching fails to capture. By introducing latent variables and modeling ambiguity through a mixture model of velocity vector fields, the method enables more accurate data distribution capture and efficient integration. Experimental results demonstrate that the proposed approach achieves comparable performance to existing methods with fewer steps on synthetic data and image datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The proposed method in this paper introduces latent variables and their inference model, enabling the capture of overlapping vector fields when they occur. However, it is necessary to clarify the setting that assumes such overlapping vector fields. The overlap and ambiguity in question here initially seemed to imply that while the flow from a specific $x_0$​ to $x_1$​ is uniquely determined, there exist different vector fields at a particular time and spatial location that may intersect. However, during inference with the proposed method, a data point $x_0$​ is sampled from the source distribution, followed by the sampling of a latent variable, which is then used as the initial value to integrate an ODE based on a vector field determined by it. This implies that there is uncertainty in the direction from the initial value $x_0$​, meaning there is an assumption that it could lead to a different $x_1$​. Is this setting reasonable? The uncertainty involving deterministic flows crossing and the possibility of tracing different flows from the initial value (i.e., the $x_0$ and $x_1$​ pairings are not unique) appear to be mixed. The authors should clearly distinguish between these and clarify which aspect they are aiming to address.\n- Considering the above points, while the proposed method may indeed enable faster transitions to the target due to the learned flow being linear even when vector fields overlap, during inference, $z$ is sampled from the prior and thus is not determined solely by $x_0$. As a result, the model could reach a different $x_1$​, which may not be desirable from the perspective of two-sided conditioning flow matching. \n- The proposed method requires an inference model and employs separate encoders for each of $x_0​,x_1$​, and $x_t$​ with the same structure as the encoder in $v_\\theta​$. This implies a significantly larger number of learnable parameters compared to existing models, and although the encoders are not used during inference, it is not entirely fair in terms of parameter count relative to previous research (even if the speed remains similar). Therefore, it would be necessary to evaluate the impact of the size of the inference model’s encoders by modifying their size and investigating how it affects performance." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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": "Refer to the Weaknesses section for my concerns and questions." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The strengths of the paper are as follows:\n\n- Clear, easy-to-follow presentation with strong empirical performance compared to baseline methods." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel framework, Variational Rectified Flow Matching, which addresses the limitations of conventional Rectified Flow (RF) methods in capturing ambiguity in velocity distributions. By incorporating an additional latent variable z drawn from a Gaussian prior, the framework models multiple modes of ambiguity. An encoder is used to derive the posterior distribution p(v∣xt,t,z) at a specific sample xt and time t. This approach is claimed to better capture ambiguity and improve the empirical performance of diffusion model generation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The weaknesses of the paper are listed below:\n- The motivation of the paper is unclear, particularly in the Introduction. The statement at line 73, “Importantly, variational rectified flow matching differs in that it enables modeling ambiguity in the data-space-time-space domain, i.e., the goal is a model where flow trajectories can intersect,” does not clarify how allowing flow trajectories to intersect resolves the ambiguity problem.\n- As I understand, using an additional latent variable z when modeling the velocity at (xt,t) can partially address the ambiguity problem, as different values of z capture different modes or sources of variation in the velocity distribution. However, VAEs are known to sometimes experience mode collapse, where the same high-density velocities may be generated from multiple modes of z. How does the proposed method handle this issue? To further address this concern, I suggest including a “diversity metric” in the experimental protocol to measure the variety of generated samples. \n- Furthermore, I believe RF can address the ambiguity problem by performing multiple rectifications. When training stabilizes, the optimal velocity at each sample xt at time t becomes unique, eliminating ambiguity. Even without rectification, existing methods such as OT-FM can mitigate ambiguity by improving the coupling between x0 and x1, resulting in less ambiguous directions at (xt, t). Are there any theoretical or methodological benefits of the proposed approach compared to these methods? Without such justification, it’s difficult to attribute the improved performance to the additional variable z.\n- Can different values of z affect the visual quality of the generated samples? If x0 is kept constant, does varying z introduce significant variance in the generated samples? Or is there a specific value of z that results in low-quality samples? \n- There is no theoretical guarantee that the proposed approach will achieve a better straight flow when addressing the ambiguity 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": 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": "I am curious whether Variational Rectified Flow Matching can enhance performance through \"reflow\", similar to classic rectified flow matching as discussed in [1].\n\n\n[1] Liu, Xingchao, and Chengyue Gong. \"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.\" The Eleventh International Conference on Learning Representations." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The paper presents a valuable observation: because the vector field is parameterized via a Gaussian at each data-domain-time-domain location, ambiguity cannot be captured.\n\n2. The analytical experiments with visualizations are well-executed and contribute significantly to validating the theoretical analysis." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces Variational Rectified Flow Matching as a method to model multi-modal velocity and ambiguity in the data-space-time-space domain. The properties of Variational Rectified Flow Matching are studied and validated through experiments with visualizations on low-dimensional synthetic data. Compelling results are demonstrated on the synthetic data, MNIST, and CIFAR-10 datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Current evaluations are too weak and this paper lacks enough experiments on more real-world and complex datasets, such as the AFHQ, CelebA and ImageNet datasets. The evaluations on these benchmarks are necessary for demonstrating the effectiveness of the proposed method.\n\n2. The authors should sufficiently discuss these missing related works [1][2][3][4][5], and compare with them in the experiments.\n\n[1] Nguyen B, Nguyen B, Nguyen V A. Bellman Optimal Stepsize Straightening of Flow-Matching Models[C]//The Twelfth International Conference on Learning Representations. 2024.\n\n[2] Song, Yang, et al. Consistency models. arXiv preprint arXiv:2303.01469 (2023).\n\n[3] Yang, Ling, et al. Consistency flow matching: Defining straight flows with velocity consistency. arXiv preprint arXiv:2407.02398 (2024).\n\n[4] Yan, Hanshu, et al. Perflow: Piecewise rectified flow as universal plug-and-play accelerator. arXiv preprint arXiv:2405.07510 (2024).\n\n[5] Kim, Dongjun, et al. Consistency trajectory models: Learning probability flow ode trajectory of diffusion. arXiv preprint arXiv:2310.02279 (2023)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 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": "- Is there any reason for designing the input of the posterior encoder as [x0, x1, xt, t]?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Significance: This paper addresses a notable limitation in flow matching models, specifically the ambiguity at path intersections that results in curved sampling trajectories. By tackling this ambiguity, the proposed approach demonstrates clear improvements over existing rectified flow models, particularly in low NFE settings.\n\n- Originality and clarity: The paper is well-written and easy to follow, clearly presenting concepts. Interpreting the flow-matching objective through variational inference to reduce directional ambiguity is conceptually sound, adding a meaningful perspective to the flow-matching framework." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents Variational Rectified Flow Matching (VRFM), a framework that improves classic rectified flow matching by incorporating multi-modal velocity vector fields based on a variational perspective. Previous flow matching approaches average out directions, leading to curved integration paths and hindering accurately fitting the target distribution. VRFM, by contrast, captures the multi-modality of flow directions, thus preserving directional diversity. Experimental results on synthetic data, MNIST, and CIFAR-10 demonstrate that VRFM achieves promising results with fewer integration steps." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- In line 53, the authors claim, “This results in trajectories that are more straight”. It is unclear why reducing ambiguity would inherently lead to straighter flows. Including a theoretical proof or a detailed explanation to clarify this result would strengthen the argument.\n\n- For completeness, the paper should include proofs demonstrating that the learned distribution from VRFM preserves the marginal data distribution, as established in Theorem 3.3 in [1].\n\n- The most concerning part of this paper is limited evaluation and performance compared to the recent papers. The empirical evaluation is restricted to MNIST and CIFAR-10, which limits the generalizability of the findings. Extending the evaluation to additional datasets, such as ImageNet 64x64, would improve the generalizability of the findings. Furthermore, the reported results of VRFM in low NFE regimes (e.g., 104 FID for 2 NFE on CIFAR-10) are less compelling, given the recent advances [1,2,3] in reducing sampling costs in diffusion (or rectified flow) models. For instance, reflow on rectified flow (e.g., 2-rectified flow) achieves a 4.85 FID with a single step [1]. Results showing VRFM’s performance with the reflow technique would provide a more competitive comparison. \n\n- It would be valuable if the authors could provide results on conditional generation setting." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024variational,\ntitle={Variational Rectified Flow Matching},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1cM0yQe3pO},\nnote={under review}\n}" }, "abstract": { "value": "We study Variational Rectified Flow Matching, a framework that enhances classic rectified flow matching by modeling multi-modal velocity vector-fields. At inference time, classic rectified flow matching 'moves' samples from a source distribution to the target distribution by solving an ordinary differential equation via integration along a velocity vector-field. At training time, the velocity vector-field is learnt by linearly interpolating between coupled samples one drawn from the source and one drawn from the target distribution randomly. This leads to ''ground-truth'' velocity vector-fields that point in different directions at the same location, i.e., the velocity vector-fields are multi-modal/ambiguous. However, since training uses a standard mean-squared-error loss, the learnt velocity vector-field averages ''ground-truth'' directions and isn't multi-modal. Further, averaging leads to integration paths that are more curved while making it harder to fit the target distribution. In contrast, the studied variational rectified flow matching is able to capture the ambiguity in flow directions. We show on synthetic data, MNIST, and CIFAR-10 that the proposed variational rectified flow matching leads to compelling results with fewer integration steps." }, "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": [ "Flow Matching", "Diffusion Model", "Generative Model" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/0060edc66ce8cc07ae9fbfae4bb8ea83f08cf582.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": "Variational Rectified Flow 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": 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": "can the authors comment on how does the method compare to other recent works?\n\ncan the authors clarify the discrepancy between figure 14 and the perfect accuracy claims made in the main text\n\n_our method reaches the solution in very few iterations, as seen in Figure 15. This contrasts with the competing methods, which often rely on loops that repeat for many more iterations to arrive to a solution_ - I could not understand the significance of this claim. Can the authors provide additional insight?" }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "- The authors propose a novel image-based pipeline for the OARSMT problem\n- The synthetic dataset generation is interesting\n- Superior runtimes are reported on a variety of synthetic benchmarks compared to classic methods" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a neural network-based framework named Mazenet for the Obstacle Avoiding Rectilinear Steiner Minimum Tree problem, an important combinatorial problem associated with circuit routing. \n\nMazenet is derived from an image classification perspective. The algorithm involves mapping an input graph and set of terminals to an image. An recurrent convolutional network is then trained on synthetic data to sequentially predict elements of the steiner tree. A termination condition module is trained to detect once a candidate path is detected. \n\nThe authors demonstrate that Mazenet recovers the OARSMT faster than classical exact algorithms and highlight its ability to generalize to problem settings beyond its training set. Some ablation experiments detailing Mazenet’s test accuracy and training time are provided. Superior runtimes are reported and perfect test accuracy." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- weak experimental results. The authors evaluate their method on synthetic benchmarks and compare to old methods.\n- some confusing results. figure 14 does not imply perfect test accuracy despite the claims made in the paper.\n- the authors may consider a more rigorous evaluation with the current state of the art, FLUTE or any number of other recent methods, e.g. Chen et al., A Reinforcement Learning Agent for Obstacle-Avoiding Rectilinear Steiner Tree Construction, 2022, Kahng et al., NN-Steiner: A Mixed Neural-algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem, 2023, etc.\n- evaluation on real datasets is critical to understand the performance benefit of the proposed method." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 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 is the threshold 0.65 decided as the TC threshold? Is there ablation study to find the optimal value?\n2. What is the step size of the solver, i.e., how many cells are the trees extended in each iteration? How many one entries are contained in the predicted binary matrix?\n3. Curious what is the performance of MazeNet on large mazes, e.g., 256 x 256?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. This paper formulates the OARSMT into a binary image prediction problem, which is easy to understand and reasonable.\n\n2. The experimental results show that MazeNet is able to achieve an impressive 100% test accuracy.\n\n3. The experimental results show that MazeNet scales well with an increasing number of terminals." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes MazeNet, a learning-based algorithm that leverages a recurrent convolutional neural network to predict a single-channel binary matrix iteratively, thereby solving the Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem. The algorithm is evaluated on different mazes with 2-8 terminals, showing 100% test accuracy and competitive planning speed." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The mazes that MazeNet is evaluated on are too small, of only 11 x 11 kernels. There is not strong evidence that MazeNet can perform well on larger mazes. \n\n2. This work only compares MazeNet with classical solvers like Dijkstra, Mehlhorn and Kou, etc. However, there are some more recent algorithms that are either learning-based or CPU-based, e.g., [1], [2]. [3]. Comparison with more and stronger baselines is needed to consolidate the conclusion.\n\n3. It is not new to learn to predict the future images, e.g., [4] also formulated the grid-like motion planning problem into a video prediction problem. From this paper, I can not see how the specific domain knowledge from OARSMT is incorporated into the network design.\n\n\n[1] Lin, Zhenkun, et al. \"Obstacle-Avoiding Rectilinear Steiner Minimal Tree Algorithm Based on Deep Reinforcement Learning.\" 2023 International Conference on Artificial Intelligence of Things and Systems (AIoTSys). IEEE, 2023.\n\n[2] Chen, Po-Yan, et al. \"A reinforcement learning agent for obstacle-avoiding rectilinear steiner tree construction.\" Proceedings of the 2022 international symposium on physical design. 2022.\n\n[3] Huang, Tao, and Evangeline FY Young. \"An exact algorithm for the construction of rectilinear Steiner minimum trees among complex obstacles.\" Proceedings of the 48th Design Automation Conference. 2011.\n\n[4] Zang, Xiao, et al. \"Robot motion planning as video prediction: A spatio-temporal neural network-based motion planner.\" 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 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": 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 what ways does the proposed method differ from prior work that applies Recurrent Convolutional Neural Networks (RCNNs) to solve maze-related problems?\n\n2. Does MazeNet require separate training for different grid and terminal configurations, such as an 11×11 versus a 9×9 node grid, or can a single model handle multiple setups?\n\n3. What strategies can be employed to reduce the time and computational complexity involved in generating training data?\n\n4. Training MazeNet reportedly took around 48.12 hours across four GPUs, which is considerable. How does training time scale with increased problem complexity and size, and what optimizations could help reduce this duration?\n\n5. In Figure 8, is the runtime of MazeNet measured with parallelization applied?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. MazeNet is designed for scalability and adaptability, making it effective for solving mazes of varying sizes and numbers of terminals that need connection.\n\n2. While RCNNs alone may struggle to identify and verify a correct solution to terminate the process, MazeNet addresses this by incorporating a search-based algorithm that reliably detects a correct solution. This approach combines the speed of graph-based approximate algorithms with the precision of exhaustive graph-based methods.\n\n3. RCNNs provide step-by-step interpretability of the method’s operations, as the head module can be applied at any iteration, allowing for observation of intermediate solution stages. These stages can be visualized as image outputs, providing insight into the solution process at each step." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "MazeNet, a recurrent convolutional neural network (RCNN) for the Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem, shows promise with 100% accuracy in initial tests but requires further validation on larger grids and more terminals to confirm scalability. Questions remain on its novelty, given similar RCNN applications in maze-solving, and on its high training time (48.12 hours on four GPUs), along with the need to reduce training data complexity and evaluate the TC module's computational overhead. Additional context through a more detailed literature review would also strengthen the work." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The proposed approach of using a recurrent convolutional neural network (RCNN) to solve the Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem may lack novelty, as RCNNs have previously been applied to similar maze-solving problems.\n\n2. Although MazeNet demonstrated 100% accuracy in the reported experiments, additional proof is needed to confirm it can consistently achieve this level of accuracy across all problem instances.\n\n3. The experimental setup appears limited; testing just on a grid of 11 × 11 nodes with up to 8 terminals may not be sufficient to thoroughly assess MazeNet’s performance, particularly regarding its scalability.\n\n4. While the TC module improves MazeNet's accuracy, it introduces significant computational overhead, which has not yet been systematically evaluated.\n\n5. The paper lacks a dedicated related work section, and a more comprehensive discussion of relevant literature would strengthen the context for this 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": 2 }, "primary_area": null, "questions": { "value": "1. The article only mentions the number of samples in the test set. What is the number of samples in the training set?\n2. In terms of problem scale, for instance in the field of chip design where there are tens of thousands of nodes with connections that must adhere to certain constraints, can this algorithm achieve good results in larger-scale tasks?\n3. The testing accuracy can reach 100%, could this be a result of overfitting?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The application is interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The article establishes a MazeNet model to solve the OARSMT problem. Specifically, it first converts the graph representation of the maze into image representation, then processes the image data using the RCNN model, and finally reduces the model's running time through a termination condition." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe motivation is not clear, as the article does not explicitly outline the problems with previous solutions to the OARSMT problem, nor does it explain how this article addresses these issues.\n2.\tThe experimental evaluation metric design is unreasonable. The OARSMT problem is an NP-hard problem. However, the evaluation metric used in this article's experimental section is accuracy. While for small-scale problems, the shortest path can be obtained using Dijkstra's algorithm for comparison to calculate precision, for large-scale problems, it is challenging to solve using Dijkstra's algorithm. \nFurthermore, the second part of the article clearly states that the optimization goal is to minimize path length. However, the evaluation metric in the experimental section does not use path length as a measure, which is confusing.\n3.\tIn line 164 of the text, it is stated that \"However, these problems were in domains where traditional methods are both fast and accurate, leaving open the question of whether RCNNs can provide similar advantages for more complex graph-based problems.\" Given that traditional algorithms can achieve good results, what is the significance of this research? Moreover, the question of whether RCNNs can provide similar advantages for more complex graph-based problems remains unresolved. How does this study address or prove this issue?\n4.\tThe resolution of figures 2b and 2c is too low. Although the generated data size is 48x48, clear images should still be placed in the article.\n5.\tThe author's proficiency in English is lacking, and the translation traces are too obvious.\nThe innovation in this article is weak. Regardless of whether it is RCNN or the conversion of graph representation to image representation, the innovation is very limited. From both a writing and experimental perspective, it resembles more of an experimental report and is not suitable for publication as a research 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": 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": "## Questions\n\nHow would researchers replicate your work?\n\nL111 How is O(T!) permutations determined for exhaustive methods?\n\nWhat is the purpose of the paragraph at L174-182? Is the progressive training algorithm of Bansal et al. used in this work? If so, be explicit and state that.\n\nAt L224, \"...position, indicating a cycle, it is terminated to prevent redundant processing.\" After finding a cycle and terminating, which single path is chosen?\n\nAlgorithm 1 L245-250 is a bit difficult to follow. \"junction found\" can only be understood by referencing back to the text. Also, what if the \"Move to the direction with highest 'whiteness'\" is in the backwards direction?\n\nL269 Why are mazes of 2, 3, or 4 terminals chosen for training? (e.g., as opposed to 5, 6, 7)\n\nL293-295 reference random variables n,k. What distributions are these sampled from?\n\nParallelization for Scalability Section 3.4 is missing specific details.\nHow many sections are images divided into? (L320)\nHow many pixels are \"sufficient\" overlap? (L322)\nFor a section with two or more terminals, what is the incentive to find additional paths to other unknown sections?\nWhat is the goal of a section with only one terminal?\nHow does parallelization work for sections without terminals?\n\nL378 What does \"20 MazeNet iterations\" refer to? Earlier sections indicated that 30 module iterations are used before checking terminal conditions (L261) and 16 training epochs are used (L310). There is no explanation in the text or table.\n\n## Feedback\n\nL55 describes a 11x11 maze, but the paper does not clarify what \"11\" refers to until L125 in Section 2.1. Explain what 11x11 means at L55 (e.g., \"11x11 node graph\").\n\nFigure 5 is first referenced at L266 but provides almost no detail or context for what the \"Projection,\" \"Batch,\" and \"Head\" blocks are. Projection was referenced once at L176 when discussing another paper's work. Multiple configurations of the batch and head modules are referenced earlier, but all blocks are uniformly labeled without any specification of the differences between them. For example, the first \"Batch\" represents 30 RB iterations and subsequent \"Batch\" represents 10 iteration (L261) but these are labeled as the exact same module in Figure 5. As another example, L177-180 reference a \"Head\" module that produces the output and a \"final head module\" that transforms the network's output to single-channel prediction. Why not add these details to Figure 5 to be more informative and accurate?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Approach for converting Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem to image-based Recurrent Convolutional Neural Network (RCNN) with extensible training images and more than 2 terminals.\n\n100% empirical accuracy on test cases (40,000 total mazes for 2-5 terminals and 3,000 mazes for 6-8 terminals). Alternatively, graph-based approximation methods of Kou et al. 1981 and Mehlhorn 1988 have errors with 3 or more terminals.\n\nMazeNet is computationally faster than Dijkstra's algorithm when 5 or more terminals are used. \n\nMaze figures are straightforward and informative (e.g., Figure 4)." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT), which seeks to find a set of horizontal and vertical connections between a set of points while avoiding obstacles using the minimum overall connection length. The paper's technical approach is to convert OARSMT graphs to images then use a Recurrent Convolutional Neural Network (RCNN) to iteratively highlight the solution. RCNN-based solutions to OARSMT were introduced in previous work, but this paper uniquely extends RCNN-based maze solving to larger maze domains with more terminals where traditional methods are computationally inefficient. In addition, this paper develops a termination condition to avoid both premature termination and excessive runtimes. Finally, this paper includes experimental results with 2-7 terminals in 11x11 mazes with 100% accuracy." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Several details technical details are unclear (see specific feedback below).\n\nDoes not provide any limitations or failure cases. For example, what happens if >> 8 terminals are used? This is only discussed as future work. Does algorithm run indefinately for unreachable terminals?\n\nA lot of overlap with Schwarzschild et. al. 2021, but with additional terminals and the terminal condition module.\n\nThe paper emphasizes that their approach is parallelizable (L23, L155, L315) but does not provide key details on how this approach works or report accuracy of experimental results on larger mazes to verify it's utility. Instead, the paper provides a vague description of the parallelization process (Section 3.4, L315) and reports only on runtime performance from parallelization on larger mazes (Figure 9, L466)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024mazenet,\ntitle={MazeNet: An Accurate, Fast, \\& Scalable Deep Learning Solution for Steiner Minimum Trees},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1ctV3yry3B},\nnote={under review}\n}" }, "abstract": { "value": "The Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem, which seeks the shortest interconnection of a given number of terminals in a rectilinear plane while avoiding obstacles, is a critical task in integrated circuit design, network optimization, and robot path planning. Since OARSMT is NP-hard, exact algorithms scale poorly with the number of terminals, leading practical solvers to sacrifice accuracy for large problems. We propose and study MazeNet, a deep learning-based method that learns to solve the OARSMT from data. MazeNet reframes OARSMT as a maze-solving task that can be addressed with a recurrent convolutional neural network (RCNN). A key hallmark of MazeNet is its scalability: we only need to train the RCNN blocks on mazes with a small number of terminals; mazes with a larger number of terminals can be solved simply by replicating the same pre-trained blocks to create a larger network. Across a wide range of experiments, MazeNet achieves perfect OARSMT-solving accuracy, with significantly reduced runtime compared to classical exact algorithms, and with the ability to handle larger numbers of terminals than state-of-the-art approximate algorithms." }, "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": [ "Recurrent Convolutional Neural Networks (RCNNs)", "Obstacle-Avoiding Rectilinear Steiner Minimum Tree (OARSMT)", "Deep learning for maze-solving", "Search algorithm for termination condition", "Graph-to-image transformation" ] }, "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/b3fdee7635f9ff4e53a1172af594ba890923c409.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": "MazeNet: An Accurate, Fast, & Scalable Deep Learning Solution for Steiner Minimum 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": 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": "Major: \n\nSee above (\"Weaknesses\").\n\nMinor:\n\n1- Given the reliance on task labels to optimize segmentation, how does the model perform on tasks with sparse or noisy labels? Does this affect interpretability? It would be interesting if authors could've addressed that and potentially compare it with the SOTA method." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1- The multi-view segmentation approach is an innovative contribution, adding potential value to explainability in machine learning for healthcare, where interpretability is crucial.\n\n2- The paper proposes a unified architecture applicable to both classification and regression tasks, which shows adaptability to a variety of physiological signal processing tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a multi-view deep learning model aimed at self-explaining predictions for various physiological signal-based tasks, such as obstructive sleep apnea (OSA) and atrial fibrillation (AF) detection. The proposed model generates “semantic views” by using mask networks to isolate task-relevant regions of the input signals. These views are used to enhance interpretability and yield clinically relevant insights." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1- The paper introduces multiple semantic views (2, 3, or 4 views) but does not explain why these specific numbers of views are optimal across tasks. This arbitrary choice may limit the interpretability and generalizability of the approach. Further discussion or empirical testing regarding the impact of varying the number of views on interpretability and performance would strengthen the approach.\n\n2- The experimental setup could have greatly benefited from ablation studies that justify the architectural decisions, such as the number of mask networks or the use of shared embedding networks. These studies would help clarify the impact of each component on the model’s performance and interpretability, providing a stronger empirical basis for the architectural choices.\n\n3- The authors claim alignment between the generated views and clinical knowledge, yet this is primarily presented through visual inspection. Providing more robust, quantitative evaluations of interpretability, ideally verified with domain experts, would lend credibility to these claims.\n\n4- The results are not entirely convincing, as the proposed model fails to outperform current state-of-the-art implementations on 3 out of 4 datasets. Additionally, the ablation study in Table 1 indicates that the multi-view architecture offers only a marginal performance improvement. \n\n5- There is a lack of comparison with established explainability approaches like SHAP or LIME. Although these methods may not offer the same level of task-specific interpretability, a comparison would clarify the relative benefits of the proposed model. \n\nMinor: \n\n1- The naming conventions in Table 1 could be clearer. Terms like “SOTA” and “ablation” could be replaced with more descriptive labels that specify the method or configuration used, making it easier for readers to understand the comparison." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "Abstract\n\nMethods in the Abstract do not need to be so extensive e.g., The section: “Specifically, the proposed network… to the task labels” could be omitted.\n\nMethods\n\nLine 181. “… each sample x in the time series S to be attributed to one of the N semantic states”.\nWhy should one sample be attributed to only one semantic state? Intuitively, it seems that specific parts of a physiological signal could reflect several latent semantic states.\n\nEquation (4). By applying softmax activation at each time sample, we allow the information to leak into all masks with varying amplitudes, which deviates from the original strict binary definition. In this case, how do we know whether this information is amplified or suppressed by the subsequent embedding networks – and hence attribute explainability to the high-amplitude parts?\n\n3.2.2. Considering that the semantic segmentation masks provide interpretability, why do we need weight sharing in the embedding network features?\n\n3.2.2. What’s the role of the differential embedding vector? Is there any empirical evidence that the decision network can’t exploit such relationships?\n\nResults\n\nTable 1. It would be preferable to report AUC metrics instead of accuracy, considering that accuracy will be sensitive to each model and binary threshold (was there any threshold tuning?) \n\nLine 337. “which suggests the effectiveness… from full-time series interval”. The comparison here may not be fair, considering that removing the mask network significantly reduces the number of parameters in the model, which will solely rely on one embedding network to receive input from the original signal.\n\nLine 370. “From Figure 3… clearly capture such information”. The heart rate variation is not very prominent in the figures. Maybe you could show smaller windows or wider X-axes?\n\nAppendix\n\nLine 926. “we enforce a minimum duration L”. How did you select L for each task? I don’t think these numbers are mentioned in the paper.\n\nGeneral\n\nPlease generate in-text citations with brackets.\n\nFrom a physiological signal interpretation perspective, how do these semantic views compare to existing post-hoc explainability methods? E.g., clustering techniques at the sample level [1].\n\nPotential Work\n\nThe assumptions behind the semantic masks (semantic states attributed to specific time samples) and the need for prior selection of the number of semantic states N may introduce limitations as a general-purpose explainability mechanism. Could the network somehow discover the optimal number of semantic states? (instead of predefining N).\n\nReferences:\n\n[1] Boubekki, A., Fadel, S.G., & Mair, S. (2024). Leveraging Activations for Superpixel Explanations. ArXiv, abs/2406.04933." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "This work presents an original idea for a generalized explainable deep learning architecture that can potentially have significant implications for AI-based medicine and XAI. Specifically, it incorporates model and sample level interpretability, by introducing a prior constraint on the number of semantic views which are trainable (model level explainability), based on which each sample produces a unique segmentation mask (sample level explainability). This is contrary to post hoc explainability techniques, where the sample-level explanation can be independent, ambiguous (model approximations), and inconsistent across techniques." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The work involves an inherently explainable AI approach for clinically relevant supervised tasks based on electrocardiogram (ECG) or photoplethysmogram (PPG) inputs. The explainability is achieved by exploiting trainable masks to identify regions of ECG/PPG contributing to clinically significant information." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The proposed deep learning architecture can be relevant for signal segmentation tasks but the level of explainability is quite coarse. The method involves qualitative (visual) exploration of the learned masks that may be quite difficult for slightly more complex tasks. This is also evident from the fact that performance is only optimum for 2 or 3 masks, with the design preventing the integration of multi-dimensional concepts. Moreover, the learned views do not necessarily seem to provide unique insights into model explainability, without knowing which semantic states, and which part of the signals in each mask, contribute to the networks’ decisions (e.g., in the example of AF, the presence or absence of P waves may be more indicative for the detection of the disease, than actual QRS peaks). The selection of tasks is also quite limited in showing interpretability properties, considering that all tasks in the paper are defined as conditions related to peak-to-peak interval variability." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": 2 }, "strengths": { "value": "- The research topic of this paper focuses on the interpretability of medical artificial intelligence, which is a field of great concern and has significant practical importance.\n\n- The method proposed in this paper maps different parts of the input signal to different semantic state spaces, revealing hidden patterns in the input signal that are related to model decisions, thereby enhancing the model's interpretability.\n\n- This paper has been validated on multiple datasets, and the experimental results show that the model's decision focus aligns with domain knowledge, verifying the effectiveness of the method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a self-explaining deep learning model architecture designed to enhance interpretability in the analysis of physiological signals, an issue often overlooked in existing deep models. The architecture employs a multi-semantic view approach, which generates multiple mask-modulated signal versions through a mask network. This process attributes model inputs to distinct semantic states, uncovering hidden patterns within the input data. The paper tests this architecture on four clinically relevant tasks involving ECG or PPG signals for classification and regression. Experimental results indicate that the multi-view approach demonstrates improved model interpretability, providing clearer insights into the model's decision-making process." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Data diversity: The dataset used in this article is limited to a single type of physiological signal, utilizing either ECG or PPG signals exclusively (although differentiated PPG signals were employed). This limitation raises questions about the effectiveness of the proposed method when applied to mixed types of physiological signal inputs, which warrants further validation.\n- Semantic state complexity: The number of semantic states in the paper is relatively small (2, 3, or 4). For complex inputs or tasks, a limited number of semantic states may not adequately reflect the model's decision-making process. The performance of the model with a higher number of semantic states requires further investigation.\n- Visualization challenge: The visualization results are discernible when the number of semantic states is small (e.g., 2). However, as the number of semantic states increases, these visualizations become difficult to recognize effectively. This can lead to a decreased understanding of the model's decision focus, thereby reducing the model's interpretability.\n- Evaluation metrics: There are concerns with the evaluation metrics used in the dataset. In classification problems, accuracy is employed as the evaluation metric, but this metric is susceptible to the impact of class imbalance. More robust metrics, such as AUC or F-score, should be considered for a more reliable assessment.\n- Temporal data representation: The method proposed in the paper classifies semantic states for individual sample time points. However, data from a single time point may not capture sufficient semantic information, especially when the signal sampling frequency is high, which could limit the representation of meaningful physiological 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": 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. \\[Line 16\\] Can you explain how xAI “ensures” reliability without casual inference study? \n2. \\[Line 31\\] Why do you believe validating on only 4 tasks with only 2 waveforms “displays universal usability”? \n3. \\[Line 37\\] Is highlighting relevant regions sufficient for transparency? How does it relate to clinical decision making? Is there any assumption to be made here for how clinicians interact with the model you developed? \n4. \\[Line 328\\] It seems 4-view is worse than 3-view. Is there any explanation?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The paper is generally easy to read, but its clarity can be enhanced by better diagrams." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents an architecture for processing medical waveforms with enhanced explainability. The author claims the learned representations are task-relevant and human-interpretable." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In general, there is no significant technical innovation specially designed for clinical applications or medical waveforms. The author should explain how the proposed method differs from previous general xAI methods and compare the performances. \n2. The author claims the method generates human-interpretable features, but the embedding and decision networks are not easily interpreted (limiting the transparency significantly). \n3. There seems to be no user study with clinicians on the relevance of extracted features. \n4. Figure 2 is too brief, consider adding some sub-figures to illustrate the ideas. It’s only about half the page width now. \n5. Some equations on Page 5 seem un-necessary, and the notation can be simplified. \n6. The tasks selected are not representative in general, and the SOTA methods cited are old in general. \n7. The ablation is only limited to the number of views. \n8. The results reported in Section 5.2 have strong selection bias (correctly-classified ones are shown)." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a generalized self-explaining multi-view deep learning architecture, that generates task-relevant human-interpretable representations during model inference, for stratifying health information from physiological signals." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024learning,\ntitle={Learning Multiple Semantic Views For Self-explaining Physiological Signal Stratification},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1d8Egv45of},\nnote={under review}\n}" }, "abstract": { "value": "Explainable artificial intelligence (XAI) offers enhanced transparency by revealing key features, relationships, and patterns within the input data that drive model decisions. In healthcare and clinical applications, where physiological signals serve as inputs to the models for decision making, such transparency is critical for ensuring reliability, identifying biases, and uncovering new insights. However, despite the potential to reveal clinically-relevant information used for inference, generalized solutions for explainability have remained limited in this domain. In this work, we propose a generalized self-explaining multi-view deep learning architecture, that generates task-relevant human-interpretable representations during model inference, for stratifying health information from physiological signals. Specifically, the proposed network architecture employs a mask network to produce multiple mask-modulated versions of the signal, referred to as “semantic views”, highlighting distinct regions of the signal that may be relevant to clinically significant information. These views offer complementary perspectives to enhance interpretability and feature extraction. A shared embedding network is used to extract task-related features from each semantic view, which are used to produce the model's output. Through supervised training with labels, each semantic view is updated based on the saliency information between the semantic view and the model's output, toward fitting the model's output to the task labels. Validated on 4 different clinically-relevant classification and regression tasks taking electrocardiogram (ECG) or photoplethysmogram (PPG) as input, the proposed multi-view architecture displays universal usability, achieving comparable or superior performance across all tasks, when compared to state-of-the-art methods designed for each task. Unlike current state-of-the-art models, which lack task-agnostic human interpretability, our model uniquely provides interpretable outputs. As it is shown, the semantic views generated by the proposed model highlight task-specific characteristic regions in the input signal, aligning closely with the domain knowledge of human experts for each task. Overall, the proposed method offers new directions for interpretable machine learning and data-driven analysis of physiological signals, envisioning self-explaining models for clinical applications." }, "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": [ "Explainable artificial intelligence (XAI)", "Interpretable machine learning", "Interpretability", "Deep learning", "Time Series Analysis", "Segmentation", "End-to-end", "Self-explaining models", "Physiological signals", "Photoplethysmogram (PPG)", "Electrocardiogram (ECG)", "Obstructive sleep apnea (OSA)", "Atrial fibrillation (AF)", "Heart rate variability (HRV)", "Blood pressure (BP)" ] }, "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/6b15d200913305505c5065677bb6970308669a7f.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": "Learning Multiple Semantic Views For Self-explaining Physiological Signal Stratification" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "(1) Could you elaborate on the purpose of this paper? For instance, why is DAG learning needed, and how does it differ from causal discovery?\n\n(2) Could you provide more evidence of the significance of your work beyond its ability to incorporate expert knowledge and quantify edge strengths?\n\n(3) Could you explain why you are confident in the accuracy of the learned edge strengths? What makes them reliable, and how would you convince others to use them in downstream tasks?\n\n(4) Could you clarify why classic causal discovery algorithms are not mentioned or compared in your paper? Additionally, why is continuous learning preferable to traditional score-based, combinatorial structure learning methods? I am not fully convinced by your statement that “In combinatorial search, local decisions about adding, removing, or reversing edges are made without clear visibility into their global impact, only revealed once the global objective is minimized,” as this issue is specifically addressed by GES." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The author provides a highly intuitive introduction to the background and existing challenges in the field, making it accessible even for readers less familiar with the topic. The writing style is clear and straightforward, which enhances comprehension. The explanations are both precise and easy to follow, contributing to a well-structured presentation of ideas. The paper presents both qualitative and quantitative experimental results that are insightful and visually intuitive, aiding in understanding the effectiveness of the proposed method. Additionally, the inclusion of the Sachs dataset as a real-world example is particularly informative, demonstrating the practical applicability of the method and adding significant value to the study." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a nonparametric method for quantifying edge strength and incorporating domain knowledge into modeling. It builds upon the well-known NOTEARS causal discovery method, which transforms the combinatorial search process into a continuous optimization problem. By leveraging nonparametric techniques such as Gaussian Processes, the NEDAG-GP method offers interpretable weights within a nonparametric modeling framework." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "(1) While the paper aims to address DAG learning for modeling causal structures and complex dependencies, the purpose could be clarified. It seems that causal structures inherently involve complex dependencies, so it would help to clarify how these terms are being distinguished in the context of this work. If the intent is to use a DAG for causal reasoning, some discussion on the identifiability of the learned DAG would strengthen the contribution. Specifically, it would be helpful to know if the learned DAG represents a unique solution given the data or if it belongs to an equivalence class that includes the ground truth. Reviewing classic works on causal discovery algorithms, such as PC, GES, or PNL, could help refine the objectives and theoretical foundation of the approach.\n\n(2) The paper introduces the idea of incorporating Gaussian Processes into DAG learning, leveraging their nonparametric properties. While this is an interesting direction, the novelty may be somewhat limited, as Gaussian Processes are a known approach for handling nonparametric modeling. Given an adjacency matrix with binary indicators, there are many established methods for estimating associated parameters, so it would be valuable to see a discussion on how this approach contributes uniquely to the field.\n\n(3) Some aspects of the writing could be more clear. For instance, in the introduction, two bolded statements emphasize the importance of incorporating expert knowledge while minimizing reliance on expert-specified parameters and distributional assumptions. Since expert knowledge can encompass information on edges, parameters, and distributions, it would help to clarify the intended balance between these elements. Addressing this and similar points throughout the paper would enhance readability and help readers better understand the author’s perspective and familiarity with the field." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "- it has been known GP and NNs share at least similarities, for example \"DEEP NEURAL NETWORKS AS GAUSSIAN PROCESSES\" ICLR 2018. However, the proposed approach did not fully explore and differentiate the use of GP from NNs, besides just a nonparametric approach in name. It would be good that authors can show, in some theoretical statement, where GP based dag learning can be superior. \n- Some related works on causal graphs and gaussian process are not discussed and compared. e.g., \nAglietti et al, \"Multi-task Causal Learning with Gaussian Processes\".\nWilson et al, \"Gaussian Process Regression Networks\". \n- typical distribution assumptions are needed to guarantee identifiablity. What can be guaraneted, in term of the identifiability or consistency, for the proposed method?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "the proposed approach to learn graph using GP partial derivative is new\n\nimproved performance over compared methods" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "the paper proposes a GP process based continuous DAG learning framework. The approach is based on nonlinear DAG constraint from NOTEARS-MLP , utilizing the partial derivaties. Authors show prior knowledge can be incorporated into this framework. Empirical evaluation shows the proposed approach is better than NOTEARS-MLP." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Unfortunately, the paper contain many imprecise statements (see below). \n\n- only one method is compared, also ignoring many literatures on GP based causal models.\n\n- motivation on using GP is not fully justified. \n\nOther comments:\n- \" local decisions about adding, removing,or reversing edges are made without clear visibility into their global impact\": this is not true, global consistency (and in some extend local consistency) properties of scores have been proven to show the optimality in these operations\n- L65: is it true that a single number that can reveal the full caual relationships, esp they often come with specific distribution assumptions? In addition, score-based approach produce specific distribution scores, constraint-based approaches offer test stats, which all represent edge weights.\n- L144: The knowledge on edge weights can be easily be via regularization, such as the L1 sparsity coefficient to achieve confidence in forbidden edges. The objective itself is data fitting + prior as regularizations. Topological order itself can be expressed by a set of forbidden edges. \n- Section 4.2: I don't see how these W constraints can not be expressed by existing continuos learning approaches. In addition, exppressing prior knowledge as an exact numerical value seems harder" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. A recent paper [1] also discusses incorporating prior knowledge in continuous structure learning framework. Can the authors comment on the connections with paper [1]?\n\n\n[1] Wang, Z,. Gao, X,. Liu, X,. Ru, X,. Zhang, Q,.(2024). Incorporating structural constraints into continuous optimization for causal discovery. Neurocomputing, Vol.595." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This work is well-situated in the literature and fills the gap of utilizing non-parametric methods and incorporating expert knowledge in continuous structure learning framework. The advantages of the proposed method are supported by both synthetic experiment and real-world experiment." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a novel method for learning DAG structure based on continuous structure learning framework. Equipped with additive Gaussian Process with RBF kernel, this method provides non-parametric estimation of edge strengths and improving the interpretability of the structure learning process. The method also incorporates several types of expert knowledge, effectively enhances its performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Although I selected “good” for presentation, it would be better if the authors could include a pseudocode of their algorithm for a clearer presentation.\n\n2. How to select the parameters of the Gaussian Processes? In supplementary B.1, the authors described the objective function, and it seems that the notation $\\theta$ is unexplained. Does $\\theta$ refer to the parameters of the Gaussian Processes? Also, It is still unclear to me how the expert knowledge is incorporated. Is it formulated as constraints of the optimization problem?\n\n3. It seems that using non-parametric estimation method and incorporating expert knowledge make NEDAG-GP outperform NOTEARS-MLP. What if we compare NEDAG-GP with NOTEARS that is augmented with non-parametric estimation methods or expert knowledge incorporated? i.e. an ablation study." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. I found it strange that none of the in-text or reference list citations included years.\n2. Equation 4: What is $H^1$? I could not see this set introduced anywhere.\n3. Equation 5: Why is $x_k$ bold here? Other references to $x_k$ are not bold.\n4. Section 4.1: It would help if $\\sigma$ and $\\ell$ are indexed by $j$ and $k$.\n5. Section 5: This section is not substantive enough to constitute a single section. I suggest merging Section 5 with Section 6.\n6. Table 2: How many replications are the results measured over?\n7. Figure 2: There is no explicit reference to this figure anywhere in the text.\n8. Appendix B.5: It would be helpful to provide the mathematical definitions of these metrics (or references to such). In particular, I am unfamiliar with the Balancing Scoring Function.\n9. Figure 3: Each method is evaluated on a coarse grid of three points across the $x$-axis. It would be better to use a finer grid." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "DAG learning is undoubtedly an important problem for areas such as causal inference. The nonparametric nature of NEDAG-GP makes it appealing for complex nonlinear data, which is pervasive nowadays. Moreover, the capacity to incorporate expert knowledge is attractive. I also found the discussion around different characterizations of edge strength insightful." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper studies directed acyclic graph (DAG) structure learning on observational data. It proposes NEDAG-GP, a new method that learns a nonparametric DAG as a Gaussian process (GP). NEDAG-GP also accommodates expert prior knowledge in the learned DAG." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The methodological innovation behind NEDAG-GP is limited. Specifically, the literature review indicates that GP-based DAG methods are already available, though NEDAG-GP sets up the weighted adjacency matrix differently. Moreover, incorporating expert knowledge is seemingly straightforward, though Section 4.2 does not actually explain how the knowledge-based constraints are enforced.\n2. The paper's primary focus is unclear as it attempts to address two distinct problems simultaneously: nonparametric DAG learning and expert knowledge incorporation. Is there any reason expert knowledge cannot be included in linear DAGs, MLP-based DAGs, or spline-based DAGs? Or is there something particular about GP-based DAGs that makes them more amenable to integrating expert knowledge?\n3. The experimental evidence in favor of NEDAG-GP (without expert knowledge) is limited. Figure 3 suggests that its good performance depends on whether the ground truth is a GP, so evaluations on a wider range of functions would be helpful. Also, DAGMA should be included as a baseline since it has superseded NOTEARS as the de facto DAG learning method in this area.\n4. The paper does not provide a discussion or results about NEDAG-GP's uncertainty quantification performance, which is odd since it uses GPs." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024nonparametric,\ntitle={Nonparametric Expert {DAG} Learning with Accurate Edge Strengths and Realistic Knowledge Incorporation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1dDxMPJy4i},\nnote={under review}\n}" }, "abstract": { "value": "Directed Acyclic Graphs (DAGs) are crucial for modeling causal structures and complex dependencies in domains such as biology, healthcare, and finance. Effective structure learning must not only align with domain expert knowledge but also produce interpretable model decisions. Though continuous structure learning methods like NOTEARS are gaining popularity, an underexplored feature is their ability to open up the black box of decisions made by traditional combinatorial search by quantifying edge strengths in weighted adjacency matrices. Yet challenges persist in systematically integrating expert knowledge and ensuring learned weights accurately reflect true edge relationships. We present Non-parametric Expert DAG (NEDAG), a novel method that formulates accurate weight matrices using Gaussian Processes (GPs) and incorporates realistic domain knowledge into the continuous structure learning framework. Experiments on both synthetic and real-world datasets demonstrate that NEDAG not only surpasses existing methods in structure accuracy but also produces more accurate edge strengths. NEDAG thus provides a robust and interpretable solution for structure discovery in real-world applications." }, "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": [ "probabilistic inference", "nonparametric method", "knowledge representation" ] }, "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/9f14a2251e051f15e8e2a0e77d391388062d79d1.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": "Nonparametric Expert DAG Learning with Accurate Edge Strengths and Realistic Knowledge Incorporation" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "We would like to thank all reviewers for their insights on the paper and suggestions for improvement. We have taken on all actionable suggestions and updated the manuscript accordingly. We have also submitted individual comments to each reviewer with further details relevant to their specific reviews.\n\nWe thank each reviewer in advance for taking the time to read our comments engage in further discussion." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": { "value": "Updates to the manuscript and individual comments" }, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "We would like to thank the reviewer for their precise and informative review. We are glad that the reviewer sees our paper as “significantly improving the scalability of automated instruction-following benchmarks” and finds it “interesting that the checklist can help LLMs refine their initial responses”.\n\nWe directly address the weaknesses raised by the reviewer in the updated manuscript, as described below.\n\n> Lexical-matching metrics should be replaced with more semantic ones [such as] BERTScore.\n\nWe thank the reviewer for suggesting this and have included BERTScore as a column in Table 2 of the updated manuscript. These results further demonstrate the similarity between LLM-generated and gold standard human-written checklists, thus strengthening this part of the paper. We acknowledge that reporting the percentage of recalled gold standard checklist items would be another informative metric, however doing so would require further costly human annotations, as judging whether items from two different checklists are precisely the same is an ambiguous task. However, we hope that the addition of BERTScore in combination with reporting the count MAE provides sufficient evidence that the checklists are similar in terms of count and content. Finally, we would also like to point the reviewer to Table 3 (a), which shows that the downstream evaluation scores from using gold standard or LLM-generated checklists are highly correlated, demonstrating that the checklists also lead to similar evaluations on aggregate.\n\n> The paper fails to discourse the details of human study.\n\nWe would again like to thank the reviewer for identifying this shortcoming. We have updated the manuscript to include further details on the training of annotators and report the inter-annotator agreement. This information is now at the top of Appendix H.\n\nWe are confident that these changes strengthen our paper and hope that the reviewer finds that their suggestions have been directly addressed." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": { "value": "Taking on suggestions" }, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "We are pleased that the reviewer finds that the paper “conducts extensive automated and manual consistency experiments to quantify and demonstrate the advantages of the TICK evaluation method”. However, we find it a shame that the reviewer has not acknowledged Section 4 of the paper, beyond claiming that using checklists for refinement is not new, despite the fact that no prior work on using checklists for in-context self-improvement exists to the best of our knowledge. Our paper demonstrates that Self-TICK (STICK) significantly improves in-context self-refinement, an increasingly common practice for improving response quality by expending more compute at inference time [1]. Table 1 demonstrates that end-to-end checklist self-evaluations enable purely in-context self-correction in challenging code, reasoning and mathematics problems, despite the reviewer’s claims that we do not consider these task types. We are therefore led to believe that the reviewer has not considered these results in their assessment and would like to highlight their significance, especially in light of several recent works suggesting that purely in-context self-correction is yet to be demonstrated in these settings [2, 3].\n\n> Employing decomposed checklists for instruction evaluation, validation and refinement is not new, as seen in work like FollowBench, InFoBench, and Self-Contrast.\n\nFollowBench and InFoBench use expensive-to-gather, human-written checklists, which limits the use of checklist-based evaluations to their predefined prompt datasets, whereas TICK is substantially cheaper and generally applicable (as acknowledged by Reviewer sSrg), which is what enables Section 4 of the paper, where an LLM can perform checklist-based self-evaluations on-the-fly. Self-Contrast is not closely related to our paper, being a similarity-based method for alignment involving two fine-tuning phases. To the best of our knowledge, no prior work uses decomposed evaluation structure to enable improved iterative self-refinement and even self-correction, which recent work has suggested requires RL fine-tuning to achieve [2].\n\n> There is a lack of in-depth discussion regarding the efficiency of the proposed method.\n\nWe thank the reviewer for identifying this. Scaling inference compute by sampling more or longer generations is an increasingly common practice for improving LLM capabilities on problems that are otherwise challenging [1, 4, 5]. We therefore see the improvements of TICK and STICK as being due to an effective way of improving evaluation and refinement quality in exchange for additional inference costs. To further address this concern, we additionally compare to the most common approach to inference scaling of majority vote among K generations sampled in parallel (i.e., Maj@K) [5] in Table 4 of the updated manuscript. We do this for preference-based LLM-as-judge evaluation and direct scoring with K=32 and still using Chain-of-Thought for the evaluator in each case. The results demonstrate that this improves both LLM-as-judge preferences and direct scores, but that both still perform worse than TICK, highlighting that TICK makes more efficient use of additional tokens than majority vote.\n\n## Answering questions\n\n1. We acknowledge that checklists do not capture sequential dependencies by default and see the automatic construction of evaluation rubrics for agentic tasks as an exciting direction for future work. Whilst ComplexBench explicitly constructs a dataset of instructions with constraint dependencies and has human annotators write checklists that reflect this, simply prompting the LLM to “opt for ’NO’ if the generated text provides no information that could be utilised to answer the question” implicitly captures the fact that a checklist question should be answered ‘NO’ if a question higher up a dependency chain is answered ‘NO’, as is done in this work and InFoBench.\n\n2. We explicitly prompt the LLM to include “implicit criteria that are generally important for an instruction’s problem domain” in the checklist (line 174 in the manuscript), the positive effect of which can be observed in the examples of generated checklists in the appendix and in the positive STICK results on precisely the fields mentioned by the reviewer (Table 1 of the manuscript).\n\n3. We have included results for Llama-3.1-8B-Instruct in Table 2 and Table 3 (b) to address this. We see that it performs only marginally worse than larger models at both generating and answering checklist questions.\n\n[1] Snell et al, Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters, 2024\n\n[2] Kumar et al, Training Language Models to Self-Correct via Reinforcement Learning, 2024\n\n[3] J. Huang et al, Large Language Models Cannot Self-Correct Reasoning Yet, 2023\n\n[4] Madaan et al, Self-Refine: Iterative Refinement with Self-Feedback, 2023\n\n[5] X. Wang et al, Self-Consistency Improves Chain of Thought Reasoning in Language Models, 2022" }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": { "value": "Addressing concerns" }, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "We thank the reviewer for their detailed review. We are pleased that the reviewer sees our paper as “offering valuable insights” and that the “overall experimental analysis is thorough”. We strongly believe that the weaknesses raised by the reviewer are addressed by the following clarifications.\n\n> [The approach] requires the LLM to first follow a complex set of instructions to generate the checklist.\n\nOur results demonstrate that current LLMs are already capable of generating checklists that are similar to gold standard human-written checklists (Table 2 and Table 3 (a)), including smaller, open-source models, such as Llama-3.1-8B for which results have been added in the updated manuscript. Additionally, checklist self-feedback (i.e., STICK) proves effective at enabling self-correction where unstructured self-feedback fails (Table 1), demonstrating that checklist generation and answering in fact *eases* the problem of answering the original instruction and thus cannot be more difficult than answering the original instruction.\n\n> It only examines three benchmarks: Internal, InFoBench, and WildBench.\n\nAs shown in Table 1, we also evaluate on LiveBench, which spans a range of task categories covering reasoning, mathematics, code, language and more. Additionally, both Internal and WildBench cover a very broad spectrum of instructions, with WildBench instructions being taken from a wide range of real-world interactions with chatbots. We believe that the four benchmarks considered cumulatively provide strong evidence for the benefits of using automatically generated checklists to structure automatic evaluation.\n\n> The existing design is computationally expensive during inference time.\n\nScaling inference compute as an alternative to scaling training compute has emerged as an exciting paradigm for further improving LLM capabilities [1, 2], with self-refinement [3] and self-correction [4, 5] becoming popular research directions. We convincingly demonstrate that checklist-based self-evaluations are an effective way obtaining greater benefits from increased inference compute, whether by iterative refinement (Table 1 & Figure 3), or Best-of-N selection (Table 5). As a further investigation of how TICK compares to alternative approaches to assigning more inference compute to the task of evaluation, we have added a comparison to majority vote among 32 parallel sampled evaluations (i.e., Maj@32) for preference and direct scoring in Table 4 of the updated manuscript. We see that doing so improves agreement between the subsequent preferences or scores and human evaluations, but that they remain worse than TICK.\n\n## Answering questions\n\n1. We thank the reviewer for this suggestion. We have included results using the semantic similarity metric BERTScore in Table 2 of the updated manuscript.\n\n[1] Snell et al, Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters, 2024\n\n[2] OpenAI, o1, 2024\n\n[3] Madaan et al, Self-Refine: Iterative Refinement with Self-Feedback, 2023\n\n[4] Kumar et al, Training Language Models to Self-Correct via Reinforcement Learning, 2024\n\n[5] Gou et al, CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, 2024" }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": { "value": "Taking on comments and providing clarifications" }, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "We would like to thank the reviewer for their clear and focused review. We are glad that the reviewer sees the presented method as “novel and significant”, including “comprehensive experiments”. In light of this, we are surprised that the reviewer’s concerns should warrant the current score and thoroughly address each one below.\n\n> Previous work has also used decomposition techniques...\n\nWhilst it is true that prior work decomposes the evaluation task, our work is the first to take an approach to decomposition that has proven powerful in fixed datasets and fully automate the decomposition and evaluation itself using an LLM. Our work is also the first to show that such a decomposition technique enables in-context self-improvement/ self-correction in settings where unstructured self-critiques fail.\n\n> The construction details and statistics of the Internal dataset are not sufficiently explained.\n\nWe thank the reviewer for drawing attention to this and have included further details on both the annotator pool and Internal dataset construction in Appendix H of the updated manuscript. The Internal dataset and its full construction details are scheduled for public release within the next month (footnote 1 of the manuscript). \n\n> The authors use metrics like ROUGE and BLEU.\n\nDue to the expense of human annotation, we were unable to additionally acquire human annotation results for this comparison. As suggested by reviewers KpU7 and sSrg, we have included semantic similarity (BERTScore) between checklists in Table 2 of the updated manuscript, where we see that LLM-generated checklists maintain strong similarity to gold standard human-written checklists.\n\n> The preference labelling approach of annotators does not fully align with the checklist-based method.\n\nWe would like to raise two key points that we are confident address this claim. Firstly, as can be seen in the prompt for checklist generation and as is stated in line 174 of the manuscript, the LLM is prompted to include “implicit criteria that are generally important for an instruction’s problem domain” in the checklist. Secondly, the superior agreement with gathered human preferences achieved by TICK relative to asking an LLM-as-judge for a preference or score empirically demonstrates that it is better aligned with the preference labelling approach of annotators.\n\n> The low inter-annotator agreement for direct scoring raises concerns.\n\nTICK’s effectiveness was demonstrated by comparing to preference annotations on Internal, for which we provide the inter-annotator agreement in Appendix H of the updated manuscript (0.684 Krippendorff’s alpha). This difference reflects the fact that WildBench involves particularly long and sometimes low quality instructions, direct scoring yields lower agreement than preference labelling, and annotators are familiar with the Internal instruction set. \n\n> Evaluations with fine-tuned models or well-established frameworks could provide a fairer assessment.\n\nGiven that TICK requires no additional data or fine-tuning, we firmly disagree that comparing to fine-tuned evaluator models would be a fairer assessment. As an alternative inference scaled baseline, we have additionally provided results for a majority vote (Maj@K) [1] version of preference evaluation and direct scoring among K=32 parallel samples in Table 4 of the updated manuscript. Notably, both remain inferior to TICK. \n\n> The baseline comparison is limited to vanilla self-refinement, which is insufficient.\n\nSelf-refine [2] is itself a relatively new method, with no well-established, fine-tuning free alternatives. There are numerous papers indicating that purely in-context self-refinement in fact generally fails, with a prominent recent paper [3] claiming that RL fine-tuning is absolutely necessary to achieve this behaviour in self-correction settings. Yet, in Table 1 we show that STICK is able to reliably self-correct across almost all task categories in the challenging benchmark LiveBench. We believe that this is a very significant result. \n\n## Answering questions\n\n1. We thank the reviewer for identifying a potentially out-of-sequence figure caption, but are unable to identify which they mean. Could the reviewer please clarify whether they mean Table 3 (a) or Figure 3 (which has no subfigure labelled (a))?\n\n2. As shown in [3, 4] and in Table 1, in-context self-refinement is typically prone to response quality degradation, as the LLM can misidentify issues with its own response. The small performance dip in the fourth iteration on WildBench simply shows that the number of iterations STICK can sustain improvements is still limited.\n\n[1] Wang et al, Self-Consistency Improves Chain of Thought Reasoning in Language Models, 2022\n\n[2] Madaan et al, Self-Refine: Iterative Refinement with Self-Feedback, 2023\n\n[3] Kumar et al, Training Language Models to Self-Correct via Reinforcement Learning, 2024\n\n[4] Huang et al, Large Language Models Cannot Self-Correct Reasoning Yet, 2023" }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": { "value": "Addressing concerns" }, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "There is a potential risk that using STICK for harmful instructions (e.g., those involving discrimination or violence) may increase the harmfulness of LLM responses. Ethical safeguards should be considered to mitigate such issues." }, "flag_for_ethics_review": { "value": [ "Yes, Discrimination / bias / fairness concerns" ] }, "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 caption for Figure 3(a) appears to be out of sequence or unclear. Could the authors clarify or reorder the content for better coherence?\n2. The self-refinement process using STICK results in a minor decline in the last iteration, could the authors make a further explanation?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The automatic evaluation method using LLMs as judges is novel and significant. The authors present an effective and interpretable protocol for evaluating and refining generated text.\n- Comprehensive experiments and detailed analyses are provided to support the effectiveness of the proposed methods.\n- The paper is well-written and easy to follow, making it accessible to a broad audience." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose TICK, a method that uses LLMs to decompose instructions into checklists composed of several YES/NO choices to address limitations in standard evaluation metrics like Elo rating and direct scoring. This approach provides a more interpretable evaluation by breaking down instructions into specific criteria. They further introduce STICK, which refines LLM responses using self-assessment based on these checklists, achieving substantial improvements compared to traditional refinement methods. Experiments demonstrate that using LLMs for checklist generation is feasible and reliable. Also, using checklists for evaluation aligns with human annotations. Based on TICK, STICK enhances the quality of LLM outputs beyond vanilla-refinement approaches. Additionally, the authors find that using checklists in human annotation significantly increases inter-annotator agreement, making the evaluation process more consistent and reliable." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Leveraging LLMs with simple prompts to generate checklists is a straightforward approach. Previous work has also used decomposition techniques to evaluate responses across multiple dimensions, similar to step-by-step verification of LLMs' instruction-following abilities. While this method has been applied to various evaluation metrics, to my knowledge, this is the first time it has been specifically focused on instruction-following.\n2. The construction details and statistics of the Internal dataset are not sufficiently explained, which reduces confidence in the reliability of the results when using LLMs for checklist generation.\n3. When evaluating the generated checklists against gold labels, the authors use metrics like ROUGE and BLEU. However, these metrics are less effective in knowledge-intensive contexts, suggesting a need for additional manual annotation or alternative metrics. However, the human annotation results are missed.\n4. The preference labeling approach of annotators does not fully align with the checklist-based method for evaluating instruction-following capabilities. Human annotation will consider the quality of the response while TICK only considers instruction-following ability.\n5. The low inter-annotator agreement for direct scoring raises concerns, as the authors only demonstrate TICK's effectiveness through pairwise correlation with human annotations. If the inter-annotator agreement for pairwise scoring is similarly low, it might undermine the validity of this correlation.\n6. The comparison of TICK to other evaluation methods is limited to direct scoring and an ablated version (Check-then-Score). This restricts the scope of the comparison. Evaluations with fine-tuned models or well-established frameworks could provide a fairer assessment.\n7. In self-refinement experiments, the baseline comparison is limited to vanilla self-refinement, which is insufficient. Incorporating additional strong baselines would provide a more comprehensive understanding of STICK's effectiveness.\n\nReference:\n\nKalpesh Krishna, Aurko Roy, and Mohit Iyyer. Hurdles to progress in long-form question answering, 2021. URL https://arxiv.org/abs/2103.06332.\n\nShashank Sonkar and Kangqi Ni and Lesa Tran Lu and Kristi Kincaid and John S. Hutchinson and Richard G. Baraniuk. Automated Long Answer Grading with RiceChem Dataset, 2024. URL https://arxiv.org/abs/2404.14316" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. For table 2, why don't you consider the semantic similarity metrics such as scores generated by natural language inference models? BLEU and Rouge style metrics sometimes can be unreliable." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Originality: This paper analyzes the quality of checklists generated by advanced LLMs and how they can be used to improve LLM-as-judge and high-quality instruction selection. It can provide experiment results for practitioners who want to use these checklists to enhance the performance of LLMs as judges, offering valuable insights.\n\nQuality: The overall experimental analysis is thorough, including validation of LLM-generated checklists to human-generated checklists. It also features corresponding analyses on the use of checklists for self-refinement and their application as the reference for human annotators.\n\nClarity: The paper is written clearly, making it easy to follow and understand.\n\nSignificance: The topic of LLMs as judges is highly relevant, and the findings of this study may offer significant insights for the industry." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper aims to measure and enhance LLM performance in instruction-following tasks by leveraging a powerful model to generate checklists based on the given instructions. \nThe key contributions include: \n1. Proposing a prompt to generate checklists for each instruction. \n2. Validating the high similarity between checklists generated by advanced LLMs and those created by humans across several benchmarks. \n3. Showing that the judge score derived from aggregating checklists yields a pass ratio that closely aligns with human scores, highlighting the potential of using the checklist to improve the performance of LLM-as-judge. \n4. Showcasing that self-refinement guided by the generated checklists leads to higher performance improvements compared to unstructured feedback. \n5. Allowing human annotators to reference the model-generated checklists results in enhanced inter-annotator agreement." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Novelty: Given multiple works on using checklists to enhance the performance of LLMs as judges, this paper’s contribution lies in enabling LLMs to generate their own checklists and validating their feasibility. The approach involves introducing a specific prompt to elicit the checklist from the LLM. However, this requires the LLM to first follow a complex set of instructions to generate the checklist, which places even higher demands on the model’s capabilities than the instruction-following task itself.\n\nExperimental Limitations: From an experimental perspective, the study could benefit from considering a wider range and a larger scale dataset. Currently, it only examines three benchmarks: Internal, InfoBench, and WildBench. \n\nExpense: The existing design is computationally expense during inference time since it requires a large number of tokens and multiple generations during the self-refinement stages. How to distill this ability or reduce this expense can be a good direction." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. Although checklists introduce a certain level of structure, they typically only express parallel relationships. When the content to be verified involves more complex logical relationships, such as selective, chain relationships, or their combinations (for example, tasks in ComplexBench), how can the effectiveness of checklists be ensured?\n\n2. A notable feature of instruction-following tasks is that verification points are directly reflected in the instructions (such as text style, word count limits, etc.), making it relatively easy to break down the task into different verification points and generate checklists. However, for a wider range of task types, especially in fields involving symbolic reasoning like mathematics and programming, how can the application methods and advantages of checklists be demonstrated?\n\n3. For models with different capability levels, particularly some weaker or smaller-scale language models (LLMs), how do they perform in terms of decomposing checklists and accurately scoring?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. TICK enhances the transparency and interpretability of the evaluation process by breaking down the assessment task into a series of specific YES/NO questions. This fine-grained evaluation approach helps to more accurately identify the strengths and weaknesses in the model's output.\n\n2. This paper conducts extensive automated and manual consistency experiments to quantify and demonstrate the advantages of the TICK evaluation method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "To evaluate the instruction-following capabilities of large language models (LLMs), this paper introduces a method called TICK (Targeted Instruct-evaluation with ChecKlists). TICK leverages the in-context learning abilities of LLM to break down complex instructions into a series of yes/no questions, forming a checklist. The LLM is then used to score this checklist. Initially, the paper demonstrates the advantages of the TICK assessment method through extensive human consistency experiments. Subsequently, the effectiveness of the TICK method is validated through experiments involving self-refinement, Best-of-N selection, and assistance with human annotations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The core of the proposed method in this paper lies in using in-context learning to break down instructions into a checklist for self-validation and refinement, as well as for best-of-N selection. However, employing decomposed checklists for instruction evaluation ,validation and refinement is not new, as seen in work like FollowBench, InfoBench, and Self-Contrast. The fundamental differences and substantive contributions of this work compared to existing approaches, particularly in terms of evaluation methods and self-improvement strategies, need to be more clearly defined.\n\n2. There is a lack of in-depth discussion regarding the efficiency of the proposed evaluation method." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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 see the suggestions in Weaknesses." }, "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 removes the major constraint of manually constructing checklists of prior works, significantly improving the scalability of automated instruction-following benchmarks.\n2. It is interesting that the checklist can help LLMs refine their initial responses.\n3. The paper is well-written and well-organized." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores developing an automated evaluation benchmark to assess the instruction-following ability of large language models. Their study is based on the idea that asking LLMs to evaluate response qualities with a set of detailed requirements provides more reliable assessments than asking LLMs to provide a holistic evaluation directly, as proposed by InfoBench. The major finding of this paper is that LLMs can also prepare the decomposed questions (i.e., the checklist) for arbitrary user prompts, scaling up this framework to the next level of automation. Also, they find that the LLM-generated checklist could further help LLMs to provide self-refined responses." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The metrics to evaluate the similarities between the human-crafted and LLM-generated checklists can be improved. In particular, those lexical-matching metrics (i.e., BLEU and ROUGE) should be replaced with more semantic ones. For example, [1] evaluates the quality of LLM-generated rubrics versus to human-crafted ones with BERTScore. Further reporting the percentage of recalled human-crafted check items and the percentage of precise LLM-generated check items will be better. \n\n2. This paper fails to discourse the details of human study. In this paper, many experiments are conducted with human annotators. The authors should discuss some basic information about the annotations, such as the statistics of their demographic information, the training procedures for the annotators, and the internal agreement among the annotators.\n\n[1] Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We show the advantages of using generated checklists to structure evaluation, including for response self-improvement." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024ticking,\ntitle={{TICK}ing All the Boxes: Generated Checklists Improve {LLM} Evaluation and Generation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1dUdNzLJRF},\nnote={under review}\n}" }, "abstract": { "value": "Given the widespread adoption and usage of Large Language Models (LLMs), it is crucial to have flexible and interpretable evaluations of their instruction-following ability. Furthermore, as human annotation is slow and costly, LLMs are increasingly used to make these judgments, at the expense of reliability and interpretability. In this work, we propose TICK (Targeted Instruct-evaluation with ChecKlists), a fully automated, interpretable evaluation protocol that structures evaluations with LLM-generated, instruction-specific checklists. We first show that, given an instruction, LLMs can reliably produce high-quality, tailored evaluation checklists that decompose the instruction into a series of YES/NO questions. Each question asks whether a candidate response meets a specific requirement of the instruction. We demonstrate that using TICK leads to a significant increase (46.4% $\\to$ 52.2%) in the frequency of exact agreements between LLM judgements and human preferences, as compared to having an LLM directly score an output. We then show that \\textbf{STICK} (Self-TICK) can be used to improve generation quality across multiple benchmarks via self-refinement and best-of-N selection. STICK self-refinement on LiveBench reasoning tasks leads to an absolute gain of $+$7.8%, whilst best-of-N selection with STICK attains $+$6.3% absolute improvement on the real-world instruction dataset, WildBench. In light of this, structured, multi-faceted self-improvement is shown to be a promising way to further advance LLM capabilities. Finally, by providing LLM-generated checklists to human evaluators tasked with directly scoring LLM responses to WildBench instructions, we notably increase inter-annotator agreement (0.194 $\\to$ 0.256)." }, "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", "evaluation", "instruction following", "self-critique" ] }, "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/7788fffa186c55a3464ac319117ec8c89a8b819e.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": "TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and 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": 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": 4 }, "primary_area": null, "questions": { "value": "1. What are the possible research avenues to mitigate some of the limitations highlighted above?\n2. How practical is your method for highly complex networks with very intricate computational graphs?\n3. How severe are the poisonous attacks that are considered? Are there newer and more severe attacks that could evade your method?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper is quite well written. The problem that is addressed is clearly defined and seems quite relevant for this venue. The proposed method has shown compelling results against all the baselines. Overall, this is an enjoyable paper to read." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Neural network models often underperform when faced with data shifts. Due to their opaque nature, addressing this issue typically involves extensive data cleaning and retraining, resulting in significant computational and manual demands. This drives the need for more efficient model correction methods. This paper introduces a rank-one model editing approach to correct unreliable model behavior on corrupted inputs, aligning it with performance on clean data. The proposed method uses an attribution-based technique to identify the primary layer contributing to the model's misbehavior, incorporating this layer localization into a dynamic model editing process. This enables adaptive adjustments during editing. The authors performed extensive experiments which show that that their method effectively corrects issues related to neural Trojans and spurious correlations with as little as a single cleansed sample." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The key limitations of this method include its reliance on identifying unreliable behaviors and the requirement that both corrupted and cleansed samples are available for effective correction. While the method has shown compelling results on almost all the benchmarks, this heavy reliance on the identification of unreliabilities makes the method less practical. Unfortunately, the authors didn't provide any clues or research directions for how to mitigate this issue. Also, it seems to me that this method is mostly applicable to models with simpler computational graphs. For instance, models that involve lots of skip connections, group norm, layer norm, etc. might be quite difficult to correct. It is also not clear to me how effective the proposed method is when dealing with stronger more \"aggressive\" poisonous attacks. Mentioning how severe the attacks that are considered are and how they compare with other types of attacks might convince the reader more about the efficacy of the 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": "Question: The model editing process, as illustrated in Figure 3, involves clean samples and their corresponding corrupted samples. How would one edit a model trained on a dataset containing poison and Trojan horses when the original training dataset or clean samples are unavailable?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Strengths:\n1. The method's approach of retaining original samples alongside corresponding corrupted samples effectively addresses issues related to performance degradation and data volume constraints.\n2. Algorithm 1 employs a clever strategy for dynamically editing the model using attribution methods by appropriately setting thresholds for $\\delta$ and $\\epsilon$.\n3. The experimental section considers critical issues such as backdoor attacks and spurious correlations, providing an analysis of the method on the real-world ISIC dataset, which showcases the method's extensive effectiveness." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Summary: This paper proposes a novel method for editing unreliable models, drawing inspiration from rank-one editing techniques. The authors demonstrate the effectiveness of their approach through experiments focused on backdoor attacks and spurious correlations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Weaknesses:\n1. The proportion of the backdoor subset within the training set is not clearly specified. To better evaluate the robustness of the proposed method, I recommend varying the proportion of backdoor data and comparing results across different configurations.\n2. While the experiments demonstrate strong performance on image data, additional experiments in other domains, such as sequence recognition, would help establish the method's scalability and versatility, highlighting its broader applicability.\n3. The paper lacks a comparative analysis of the time complexity of the proposed method relative to existing techniques. Including this analysis would offer valuable insights into the method's efficiency and practical feasibility." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "n/a" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "What is the difference between your method and ROME, aside from (i) applying it to image classifiers (ii) editing iteratively (or 'dynamically'), and (iii) the inclusion of corrupted samples in training (pls correct me if I am interpreting this wrong -- see next question)?\n\nDoes this mean you train on the corrupted samples? What if your model has already been trained? Also, don't the corrupted samples need to be included in training to begin with, so that the attack is successful? This part is unclear to me. \n> L207: \"Our proposed process of model editing to correct unreliable behaviors involves integrating both original samples x and their corrupted counterpart x˜ into the training procedure\"\n\nMore minor:\nDon't these sentences contradict each other? Maybe you need a name for your method to distinguish it from the standard ROME (which you say doesn't work out of the box).\n> L43: \"we formally pinpoint two key challenges when applying rank-one editing to domain adaptation, which inevitably lead to diminished model performance and necessitate labor-intensive data preparation (details in § 4.1). Next, we establish that rank-one model editing is\nwell-suited for correcting model’s unreliable behavior as it intrinsically sidesteps these challenges\" \n\nSome questions for figure 3:\n- Should it be key k*? Instead of key v*? (step 3 panel)\n- Yellow arrow is pointing wrong way and should be under 'attribution flow'?\n\nWhat patterns? I think this is important -- it is a lot easier to edit out a reliance on a spurious pattern that does not vary much in its appearance (like in the Trojan case) than it is to edit real spurious features (e.g. to backgrounds). \nUpdate: I see in the appendix the patterns added are the same as the Trojans -- the only difference in the settings are that the added patterns flip the label in the Trojan case, while they do not in the spurious case. I think this makes your spurious correlations setting unrealistic. \n> L410: \"we pollute a proportion of samples of class y by attaching patterns to create spurious samples\"\n\nSuggestion for table 4: highlight the accuracy on the samples without the spurious correlation (is this what you mean by clean?) or performance drop for these samples instead of showing the performance for samples with the correlation. It reads a little cleaner to see your method improves accuracy, and better highlights the cost of relying on spurious features (i.e. performance is worse when the feature is absent + correlation is broken)." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The method seems to work well! ASR is dropped to nearly zero without compromising accuracy, and bias toward spurious features are removed without affecting accuracy. \n\nThe result could be an important result for the mechanistic interpretability community (which is currently garnering lots of attention) as it shows editing techniques can be applied to a second modality and to alleviate existing concerns.\n\nI really liked that a realistic setting was also considered, and that the method seemed to work well in this case too." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors apply model editing (very similar to ROME) for the task of removing neural trojans and mitigating reliance on spurious correlations (which in the paper, closely resemble neural trojans) for image classifiers. About half the paper is dedicated to i. explaining ROME, ii. describing some challenges in applying editing to the desired settings, and iii. detailing their modified approach. Their method consists of identifying the best layer to edit by comparing attributions (i.e. via Integrated-Gradients) for clean and poisoned samples, and then \"dynamically\" applying model editing, which I believe (but am unsure) means making repeated edits until the edited model's overall performance does not deviate much from the original model's overall performance. \n\nExperiments are conducted using a simple neural trojan and spurious feature for CIFAR10 and ImageNet classification. The proposed method successfully reduces to the attack success rate of the neural trojan to nearly zero without compromising overall accuracy. It also removes any bias to the spurious feature (measured as the increase in performance when the spurious feature is present). Baselines include fine-tuning and methods called P(or A)-ClArC, and are surpassed. Similar results are attained on a more realistic setting of skin lesion classification." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Clarity**: I personally found this paper hard to read. I did not find Sec 4 to be well integrated to the paper. Paragraph 207-215 feels like it should be very important, but it was not clear to me how your method changed to sidestep concerns and if there was empirical evidence showing that this methodological change was truly responsible for improved performance. The paper would have benefitted more from taking more time to clearly explain the experiments, imo. \n\n**Novelty over ROME**: Perhaps this relates to the above point, but it is unclear to me exactly how this differs from ROME, which as I understand it, also involves localizing and editing. The iterative nature (which authors term 'dynamic') is perhaps new, but it seems to only marginally improve performance over static editing. Similarly, the comparison to Santurkar's editing work was a bit lackluster (is the main difference that they choose the penultimate layer by default?)\n\nI am **unsure if this method would work for more realistic spurious correlations**, which would not have a single fixed appearance, as is the case for the spurious features studied (even for the skin lesions, the spurious patches are quite consistent in their appearance). Even a simple benchmark like Waterbirds is not studied (I personally think even Waterbirds is too simple, but it is very established and having a result on it would greatly improve the paper's claim about spurious features). \n\nSummary: I am borderline on this paper. The clarity issues are somewhat significant for me, but the experimental results are strong and the impact of showing editing is effective for vision models would be impactful. I am curious if other reviewers also had difficulty reading the paper -- if it is just me, I'd raise my score. Novelty issues are not as big for me, but I think the paper would be strengthened if the exact differences between this and the most similar related methods are clearly and concisely articulated." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "- Equation (1): What is $f(k^*;W)?\n- Lemma 2: What does $x^* \\rightarrow k^*$ mean? What is $\\mathcal{X}$?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "**Promising research direction:** Domain adaptation is a serious issue in the application of machine learning to many domains. While the use of rank-1 editing has been proposed before in this setting, the reviewer believes that the suite of tools that editing offers have still not been fully exploited.\n\n**Methodical experiments and results are strong:** Overall, the experimental section is well-written. While the reviewer has some issues with the overall scope of the experiments (see Weaknesses), those that were run seem to be fairly comprehensive and the results are well-described. In the settings where the method is deployed, it performs well relative to the other baselines that are explored." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work considers the problem of spurious correlations learned by a model during training. It proposes the use of rank-1 editing as an approach to correct such model errors while preserving the model’s overall performance. This method is motivated by two challenges in the use of rank-1 editing. One is technical and involves finding rank-1 updates that do not interfere with other facets of model performance. The second is more specific to domain adaptation and involves the need for sufficient quantities of labeled data. The method first utilizes a feature attribution-based approach to locate the layer of the model where editing will yield the biggest improvement. Then it applies rank-1 editing to this layer to correct the spurious correlation in the model. The paper evaluates the approach on models to which a trojan has been injected and models that have learned spurious features related to patches (for both toy and real datasets). Experiments suggest that the approach strikes a good balance between correcting model behavior in specific instances without degrading overall accuracy too much." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**The problem that is addressed is narrow:** This work claims to explore domain adaptation, but it only looks at mitigating the presence of very obvious spurious correlations in the data (e.g., trigger patches). The challenges associated with real-world domain adaptation are far more subtle than this. It would have made the work stronger if it had investigated instances of domain adaptation where the differences between domains were more subtle. If the proposed method worked in such situations, it would be very notable. Alternatively, the paper could shift its language to focus more exclusively on spurious correlations.\n\n**The challenges that motivate the method are not very clearly described and are never shown empirically to be issues:** The work describes two challenges that are meant to motivate the proposed approach. Overall, these are not very clearly described. Indeed, Challenge 2, which involves lack of data in domain adaptation settings, could be easily described outside of the mathematical formalism, but this is not done. Challenge 1 relates to the specific approach to rank-1 editing, specifically the failure of $k^*$ to be included in the statistics matrix $C$. This is stated as one of the fundamental challenges of using rank-1 editing for domain adaptation, but it is never explained why this is specifically a problem for domain adaptation and not a general issue. Finally, it would help make these challenges more meaningful if some empirical evidence was given to support their centrality.\n\n**Repeated editing has been explored in the past:** To this reviewer’s understanding, the main contribution of the work is the use of feature attribution to locate a layer to edit, the modification of the existing rank-1 editing technique to mitigate an issue with the statistical matrix $C$, and the introduction of dynamic editing. The first and second are new to this reviewer’s knowledge (though the reviewer is not an expert in the breadth of what has been done in the editing space). The impact of repeated editing has been explored in detail in past works (e.g., [1]). It would be good to consider how the present paper fits into such studies.\n\n### Nitpicks:\n- Line 077: “Experimental evaluations highlight our method’s remarkable performance…” It is this reviewer’s opinion that the word ‘remarkable’ should be removed and that the paper should let the results speak for themselves.\n- Line 043: The first sentence says that there are significant challenges to using rank-1 editing for domain adaptation. The second sentence says that actually rank-1 editing is well-suited to domain adaptation. What changed?\n\n[1] Gupta, Akshat, Anurag Rao, and Gopala Anumanchipalli. \"Model editing at scale leads to gradual and catastrophic forgetting.\" arXiv preprint arXiv:2401.07453 (2024)." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "A dynamic model editing technique is proposed for correcting the model's misbehavior." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024dynamic,\ntitle={Dynamic Model Editing to Rectify Unreliable Behavior in Neural Networks},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1dkL3MVBfV},\nnote={under review}\n}" }, "abstract": { "value": "The performance of neural network models degrades with data shifts. Owing to their opaque nature, rectifying models to address this problem often necessitates arduous data cleaning and model retraining, resulting in huge computational and manual overhead. This motivates the development of efficient methods for rectifying models. In this work, we propose leveraging rank-one model editing to correct model's unreliable behavior on corrupted input samples and align it with that on cleansed samples. We introduce an attribution-based method for locating the primary layer responsible for the model's misbehavior and integrate this layer localization technique into a dynamic model editing approach, enabling dynamic adjustment of the model behavior during the editing process. Through extensive experiments, the proposed method is demonstrated to be effective in correcting model's misbehavior observed for neural Trojans and spurious correlations. Our approach demonstrates remarkable performance by achieving its editing objective with as few as a single cleansed sample, which makes it appealing for practice." }, "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": [ "model vulnerability", "model editing", "feature attribution" ] }, "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/b442ecfccdc948aa8c0777024bbf7342f63ebd38.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": { "value": "/attachment/fe309804fd027ef6f09fd7568f9d077d0e8b685d.zip" }, "title": { "value": "Dynamic Model Editing to Rectify Unreliable Behavior in 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": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Hallucination Analysis: How does the model ensure that synthetic ECG signals do not contain unrealistic artifacts?\n\nBaseline Comparisons: The comparison is limited to CardioGAN and RDDM. Have you considered including additional baseline models from the literature (e.g., ArXiv:2309.15375, 2012.04949, 2204.11795, 2101.02362) to provide a more comprehensive performance evaluation?\n\nReal vs. Synthetic Data Performance: How do the model's performance metrics change when trained on real versus synthetic PPG-ECG pairs? Can you quantify the impact of synthetic data on classification accuracy?\n\nGeneralizability Across Datasets: Have you considered applying this approach to unpaired PPG-ECG datasets or datasets from different demographic groups? Would such testing be feasible within your current framework? Have you employed techniques such as cross-validation or tested on external datasets to ensure that your model generalizes well beyond the training data?\n\nRisk of Overfitting: Given the complexity of diffusion models and the size of the datasets used, what measures have you taken to prevent overfitting?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "Originality and Significance: This paper offers a novel approach to the challenging PPG-to-ECG conversion by applying a diffusion-based, uncertainty-aware model. This method effectively addresses the inherently ill-posed nature of the task, capturing the distribution of possible ECG outputs rather than a single solution. By doing so, it meets a main need in cardiovascular diagnostics, especially where paired data is limited.\n\nMethodological Rigor: The paper demonstrates strong methodological rigor with a solid theoretical foundation, including proofs of the Expected Score Classifier's (ESC) optimality. This rigorous analysis, supported by detailed equations (e.g., Theorem 3.1), supports the model’s reliability, ensuring both empirical soundness and theoretical robustness.\n\nComprehensive Evaluation: A thorough evaluation across 11 cardiovascular conditions highlights the model's generalizability and robustness. Compared to baseline models, it shows superior performance in signal reconstruction and classification, with added metrics for uncertainty quantification. This detailed analysis strengthens the case for clinical applicability.\n\nClarity and Presentation: The paper is well-organized, balancing technical details with intuitive explanations, such as figures illustrating model performance and ECG visualization strategies, enhancing clarity. The emphasis on interpretability supports practical use in clinical settings.\n\nSignificance for the Field: The integration of uncertainty-aware diffusion models for physiological signal conversion represents a meaningful enhancement in machine learning for healthcare. The interdisciplinary approach bridges machine learning and biomedical engineering with the potential to drive future innovations in cardiovascular diagnostics and medical device development." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents \"Uncertainty-Aware PPG-2-ECG (UA-P2E),\" a novel framework that employs diffusion models to convert photoplethysmography (PPG) signals into electrocardiography (ECG) signals for improved cardiovascular disease classification. Recognizing the ill-posed and inherently ambiguous nature of the PPG-to-ECG conversion—stemming from the loss of certain physiological information in PPG measurements—the authors propose a multi-solution approach to address this challenge. By leveraging diffusion models, UA-P2E captures the full distribution of possible ECG signals corresponding to a given PPG input, effectively modeling the uncertainty inherent in this inverse problem. This allows the framework to generate robust ECG signals that account for the variability and ambiguity of the conversion process. The authors validate their approach through experiments across multiple cardiovascular conditions, demonstrating state-of-the-art classification performance. They provide empirical evaluations, including comparisons with two baseline models, to substantiate the effectiveness of UA-P2E in both signal reconstruction and cardiovascular classification tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Dependence on Synthetic Data: The evaluation heavily relies on synthetic PPG data, especially for augmenting the CinC dataset (Section 5.2​). This dependence raises concerns about potential biases, as synthetic data may not fully capture the variability and complexities of real-world PPG signals. Consequently, the model's generalizability to real clinical settings might be limited, potentially affecting its practical applicability.\n\nLimited Baseline Comparisons: The paper compares UA-P2E primarily with two baseline models: CardioGAN and RDDM (Table 1). While these comparisons provide some insight, the limited scope restricts a comprehensive understanding of the model's performance relative to the broader range of existing methodologies. Including additional, especially more recent baseline models, such as those in the referenced ArXiv papers, would strengthen the evaluation and better position UA-P2E within the current state-of-the-art.\n\nPotential for Synthetic Artifacts: The possibility of generating hallucinations or artifacts in the synthetic ECG signals produced by the diffusion models is not thoroughly examined. Since diffusion-based models can introduce unrealistic features, a lack of analysis on this front may raise concerns about the reliability and clinical validity of the generated signals. Addressing this risk through quantitative assessments would enhance the credibility of the proposed approach.\n\nLimited Dataset Diversity: The study focuses on paired PPG-ECG datasets from CinC and MIMIC-III. This narrow dataset selection may not adequately demonstrate the model's flexibility or adaptability to other data sources. Expanding the evaluation to include larger or unpaired datasets would provide a more robust validation of the model's generalizability and its potential utility across diverse cardiovascular data." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. Given the stochastic nature of the diffusion model, are 3 seeds enough to capture the full range of ECG variability, or to provide significant statistical measures in your work?\n2. Although the paper reports the classification of the mean and random ECG diffusion-based solutions, you do not mention the performance of CardioGAN and RDDM directly, which were initially used to benchmark the ECG generation performance. If the end goal is to improve classification with ECG-generated signals, don't you think that it would be appropriate to use the exact implementations of CardioGAN and RDDM to compare the performance?\n3. Did you ensure that there are no segments from the same recording in both the training and test datasets? I believe this is not mentioned. If the same recording appears in both datasets, the generalization ability of the approach might be not properly evaluated.\n\nAlso some missing information:\n- The exact database(s) of CinC that was(were) used, as there are many databases from this source available on PhysioNet\n- A data summary with the number of samples used to train and test the PPG conversion and classification models (and the corresponding class distributions)\n- Only the AURC is used to assess the classification performance, but since the pathological labels often suffer from class imbalance, more insightful metrics such as sensitivity, specificity and F1-Score could be reported to improve the clinical relevance of the approach and facilitate comparison with other works." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This work uses a diffusion model to propose multiple generated ECG waves for a single PPG signal, instead of a single solution as in previous works.\n2. The paper provides the mathematical proof of the optimality of the proposed classifier, which reassures the credibility of the approach. Also, multiple performance metrics are used to evaluate the model.\n3. In general the paper is well structured, with relevant tables and figures for comparison, and explanations are thorough.\n4. The use of ECG-generated waves from PPG and its improved classification accuracy could be extended to the current PPG-based widespread solutions for cardiovascular monitoring." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a methodology to convert PPG signals into ECG ones by accounting for the uncertainty of the conversion process using a diffusion-based model. The methodology is novel arguing that by using a multi-solution approach - where multiple ECG signals can generate the same PPG wave - the combined classification of the generated ECGs is more accurate in comparison to using only the PPG or using a single generated ECG from state-of-the-art methods. The authors use two datasets, one with pairs of unlabeled ECG and PPG signals and another with only labeled ECG signals. A reverse ECG-2-PPG model is used to generate synthetic PPG for classification." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Despite the comprehensive methodology and appendix, some points require some clarification:\n1. The authors use 3 random seeds to generate the ECG signals from the PPG ones and report the mean and standard deviation for the chosen metrics. Given the stochastic nature of the diffusion model, are 3 seeds enough to capture the full range of ECG variability, or to provide significant statistical measures?\n2. Although the motivation for the use of PPG-2-ECG relies on the widespread of wearable devices, no database with signals acquired in wearable settings was used. For the diffusion model, the MIMIC-III data comes from hospital facilities (to my knowledge), and for classification, this is unclear (unless the CinC2017 database for AFib was used, for example). This should be stated as a limitation of the work, or as a pointer for 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": "1. As Figure 3 shows, the performance of the PPG-derived ECG significantly trails that of the original ECG. How do you explain this discrepancy? Is it possible that the required information for detecting certain arrhythmias is inherently absent in the PPG signals?\n2. Appendix G focuses on Atrial Fibrillation (AF), which is relatively easier to detect. It would be beneficial to include results for other types of diseases to provide a broader evaluation.\n3. How does pacing rhythm manifest in PPG signals? Is it feasible to accurately generate ECG signals with pacing rhythm characteristics from PPG?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The theoretical foundation validating the transition from PPG to ECG and then to classification is robust. The paper demonstrates that generating multiple candidate ECG sample sets can mitigate the uncertainty of ECG conversion and eliminate errors due to mismatch.\n2. Quantification of uncertainties during the conversion process enhances the interpretability of the results.\n3. The methodological design is well-justified through practical experiments showing the reliability of PPG-derived ECGs, surpassing state-of-the-art methods. Ablation studies further substantiate the validity of the proposed approach.\n4. The paper is well-written with a clear structure, making it easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel methodology for synthesizing Electrocardiogram (ECG) signals from Photoplethysmogram (PPG) signals, aimed at enhancing the reliability of cardiovascular disease diagnostics while avoiding the difficulties associated with ECG acquisition. By measuring the uncertainties involved in generating the ECG from PPG signals and in classifying from the generated signals, the authors convincingly demonstrate the feasibility and superiority of their proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper should specify which particular database the CinC dataset is derived from. The rationale behind choosing these 11 types of anomalies should be clarified.\n2. Although the authors provide a thorough theoretical foundation and extensive analysis, the task of generating ECG from PPG lacks inherent rationale. It is unclear whether the generated ECG can reliably reflect arrhythmias, making this approach seem like an uncertain application of analytical techniques with limited practical value.\n3. The authors should further compare their classification results with state-of-the-art models, as the current performance appears suboptimal for ECG classification tasks." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We developed a SOTA PPG-to-ECG conversion model and an optimal classification approach that accounts for conversion uncertainty, supported by rigorous mathematical justification." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024uncertaintyaware,\ntitle={Uncertainty-Aware {PPG}-2-{ECG} for Enhanced Cardiovascular Diagnosis using Diffusion Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1dkVCX4jlH},\nnote={under review}\n}" }, "abstract": { "value": "Analyzing the cardiovascular system condition via Electrocardiography (ECG) is a common and highly effective approach, and it has been practiced and perfected over many decades. ECG sensing is non-invasive and relatively easy to acquire, and yet it is still cumbersome for holter monitoring tests that may span over hours and even days. A possible alternative in this context is Photoplethysmography (PPG): An optically-based signal that measures blood volume fluctuations, as typically sensed by conventional ``wearable devices''. While PPG presents clear advantages in acquisition, convenience, and cost-effectiveness, ECG provides more comprehensive information, allowing for a more precise detection of heart conditions. This implies that a conversion from PPG to ECG, as recently discussed in the literature, inherently involves an unavoidable level of uncertainty. In this paper we introduce a novel methodology for addressing the PPG-2-ECG conversion, and offer an enhanced classification of cardiovascular conditions using the given PPG, all while taking into account the uncertainties arising from the conversion process. We provide a mathematical justification for our proposed computational approach, and present empirical studies demonstrating its superior performance compared to state-of-the-art baseline 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": [ "Inverse Problems" ] }, "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/03df1ff3ccf1c090d8fbb7d9df71ce8a3316de2c.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": "Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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": "● Some models use conservative forces and some don't. Do you have a sense of how much that impacts when you distill Hessians from a non-conservative model instead or if the student model is conservative? Or do you expect that not to impact performance as it seems like hessian quality doesn't?\n\n● Why are the rows sampled so different for GemNet across training set and the same for PaiNN? What would be the general suggestion to set this hyperparameter? Or should everyone be iterating on this for every dataset, model architecture, etc?\n\n● Why don't you compare the results with Kelvinius et al. 2023 on OC20-2M and COLL on which the original work was performed?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "● The distilled MLFFs are up to 20x faster than their foundation model counterparts, enabling more efficient simulations.\n\n● The distilled MLFFs achieve comparable or even better force prediction accuracy than the original FMs and demonstrate improved MD stability results.\n\n●The Hessian distillation method is model architecture agnostic.\n\n● Subsampling hessian rows significantly reduces computational costs without sacrificing performance. Its also interesting that subsampling quality doesn't impact the performance much." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work proposes a new method for improving the efficiency of Machine Learning Force Fields (MLFFs) by utilizing a knowledge distillation technique. The method distills knowledge from large, general-purpose foundation MLFF models (FMs) into smaller, faster MLFFs specialized for specific regions of chemical space. This is accomplished by aligning the energy Hessians, which are the second derivatives of the energy with respect to atomic positions, between the teacher FM and the student MLFF. By strategically subsampling rows of the Hessian, the authors significantly reduce the computational cost of the distillation process. The authors demonstrate that their approach can achieve speedups of up to 20 times compared to the original FMs while retaining, and in some cases exceeding, the accuracy of the foundation models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "● Training with hessian distillation increases the computational cost compared to undistilled training.\n \n● An anonymous link to the code is not available." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 ethics 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": 3 }, "primary_area": null, "questions": { "value": "See “Weaknesses” for detailed questions and suggestions.\n\nPotential typos:\n* Line 420: “disilling” should be “distilling.”" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* The approach is well-motivated, leveraging knowledge from large MLFF foundation models and adapting it to specific chemical space regions using knowledge distillation. The method also achieves promising results across organic and material molecular systems.\n* The paper is well-organized and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a method for transferring general-purpose representations from large ML force field (MLFF) foundation models to smaller, faster MLFFs specialized for specific regions of chemical space, with the aim of improving inference speed. The approach is formulated as a knowledge distillation (KD) process, where the smaller “student” MLFF learns to match the Hessians of energy predictions made by the “teacher” foundation model. By selectively subsampling rows of the Hessian corresponding to individual atomic coordinates, the “student” MLFF achieves a training process that is computationally efficient relative to foundation models and demonstrates improved force prediction on downstream datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* Although the method shows promising results, the rationale for using Hessian information as a distillation signal is unclear, which may impact the perceived technical contribution. Additional theoretical or intuitive insights on this choice would clarify the method’s grounding.\n* Foundation models are often trained with energy and force supervision, possibly derived from various electronic structure methods, making the physical reliability of Hessians from pre-trained foundation models questionable.\n\t* Notably, the authors mention that some student models outperform foundation models in specialized chemical spaces even before distillation, suggesting that foundation models may not fully converge in certain cases. This raises questions about the significance and reliability of using Hessians from foundation models as distillation targets.\n\t* Accurate Hessians are crucial for tasks like geometry optimization (as referenced in Figure 1). It remains unclear how potentially inaccurate Hessians from foundation models could affect the student model's performance in such applications.\n* It is uncertain whether the proposed method can be extended to MLFF architectures designed with energy-conserving forces or high-order equivariance, which are often crucial factors for stable and transferable ML force fields. Discussing the impact of these inductive biases on the Hessian-driven KD approach would strengthen 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": 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 tables in the paper show only force results, without energy, so I'm curious about the energy results after distillation.\n* A major concern is that the primary use of the distilled MLFF model is for molecular dynamics simulations, where conservation properties are crucial for scientists in physics, chemistry, biology, and materials science. I understand the authors avoided second-order derivatives to calculate the Hessian by directly predicting forces, using JVP calculations. However, a pretrained model might predict forces directly to save computation due to its large size, but the student model should compute forces using autograd, similar in the SFT in JMP[1], which makes more sense. Although Fig. 2 shows stable molecular dynamics in the NVT ensemble, following [2], energy will not be conserved in the NVE ensemble.\n* The paper claims that the student model outperforms the teacher model, which is confusing. I suspect this is because the energy and force labels used in training come from the dataset itself. While the inclusion of Hessian loss is shown to be better than using only energy and force loss, this highlights the importance of derivatives. Since the Hessian matrix introduces force derivatives, could training a traditional MLFF from scratch, with forces computed via autograd, achieve similar or better results? Additionally, the statement in Fig. 3b about \"speculating that s may play a similar role as the batch size\" is akin to conclusions from traditional MLFF training, suggesting that direct autograd training without Hessian distillation might yield similar outcomes. The authors could compare such models to illustrate the Hessian's impact.\n* Regarding the appendix experiment using force for distillation, what is the specific loss function? If I'm correct in understanding that the Hessian term in Eq. 3 is replaced by the force term, could the poor results from force distillation be due to the inherent force label loss in the data, where the teacher model's force predictions contradict the data labels? This implies that the force distillation setup might be flawed.\n\n[1] Shoghi N, Kolluru A, Kitchin J R, et al. From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction[C]//The Twelfth International Conference on Learning Representations.\n\n[2] Fu X, Wu Z, Wang W, et al. Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations[J]. Transactions on Machine Learning Research." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* This paper proposes a new method to distill MLFF foundation models into smaller, faster MLFFs with Hessians, which is highly beneficial for simulating realistic systems.\n* The paper is written in a clear and concise manner, facilitating effortless understanding." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a method to distillate MLFF foundation models to smaller, faster MLFFs with energy Hessians, achieving significant speed improvements while maintaining performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Related concerns are discussed in the questions section." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "* Why is there a tank in Figure 1?\n* While the Hessian has a nice physical interpretation, as the authors point out, do higher derivates improve transfer further? One way to improve runtime in such cases would be to use Forward on Backward differentiation." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Clarity and Readability: The paper is very well-written and accessible, making the proposed method easy to follow.\n2. Practicality: The approach is straightforward to implement and is cost-effective relative to similar knowledge distillation methods.\n3. Experimental Validation: MD simulations performed validate the method, underscoring the practical benefits of the proposed technique.\n4. Implementation Insights: The paper also offers practical implementation guidance for practitioners, which is particularly useful for real-world applications." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a novel technique for knowledge distillation from foundation model force fields to smaller, faster, and more specialized force fields. The core idea is to align the Hessians of the energies with respect to atomic positions between the teacher (foundation model) and student models, facilitating efficient knowledge transfer. Experiments demonstrate that this approach improves stability and accuracy in the derived force fields compared to simpler distillation methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The baseline methods used for comparison seem to perform notably poorly, raising questions about fairness. This might be genuine, but the absence of hyperparameter tuning for the baselines undermines this. The authors specifically tuned their method by adjusting the Hessian distillation loss term, \"we reduce the weight of the Hessian distillation loss term, λKD, by a factor of 4 during training once the student’s validation loss on the original objective, LEF (φ), becomes lower than that of the frozen FM, LEF (ψ)\" (l.207-209). Further clarification on the role of this schedule, as well as the sensitivity to λKD, would be helpful. Would similar tuning or scheduling benefit the alternative approaches as well?" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We distill large machine learning force field foundation models into small, specialized models using knowledge distillation from energy Hessians." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024towards,\ntitle={Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1durmugh3I},\nnote={under review}\n}" }, "abstract": { "value": "Foundation models trained on large datasets have transformed machine learning by leveraging general-purpose representations to solve many downstream tasks. A similar paradigm is arising in Machine Learning Force Fields (MLFFs), a powerful tool in computational chemistry for a variety of atomistic modeling tasks. Recent MLFF foundation models are enabled by a combination of increasing ab-initio data availability and larger model sizes. Although MLFF foundation models have begun to close the accuracy gap relative to first-principles methods, there is still a strong need for faster inference speed. Additionally, while model development is increasingly focused on general-purpose models which transfer across chemical space, practitioners typically only study a small subset of systems at a given time. This underscores the need for fast, specialized MLFFs relevant to specific downstream applications. In this work, we introduce a method to transfer general-purpose representations from MLFF foundation models to smaller, faster MLFFs specialized to specific regions of chemical space. We formulate our approach as a knowledge distillation procedure, where the smaller \"student\" MLFF is trained to match the Hessians of the energy predictions of the \"teacher\" foundation model. By selectively subsampling rows of the Hessian corresponding to individual atomic coordinates, we significantly reduce the number of required backward passes. This ensures that distillation incurs a small computational cost relative to training the original foundation model. We demonstrate our approach across multiple recent foundation models, large-scale datasets, and chemical subsets. Our results demonstrate that our specialized MLFFs can be up to 20 $\\times$ faster than the original foundation model, while retaining, and in some cases exceeding, its performance. More broadly, our work suggests a new paradigm for MLFF development, in which foundation models are released along with smaller, specialized simulation \"engines\" for common chemical subsets." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "machine learning force fields", "graph neural networks", "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/5ffd1a42a7d6f083746aa49c305ec342845a3076.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": "Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians" }, "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": "- What are some limitations of the method?\n- How does the runtime compare to other baseline models that you compare to?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The proposed model seems theoretically well-motivated.\n- The empirical evaluation shows a superior performance on different benchmarks compared to existing models.\n- The paper describes two ablation studies that analyze the model's robustness to noise where it performs superior to the Mamba baseline and that analyze performance as the number of base predictor models increases, which leads to improved performance as predicted by the presented theory." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a new model for predicting continuous sequences and discusses its theoretical underpinnings as well as its empirical evaluation on different benchmark datasets and in comparison to a range of baseline models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- This paper is very technical and builds on many rather sophisticated mathematical concepts that I would assume many readers not to be familiar with (and this in itself is of course not a weekness). I believe the presentation of the content could be improved so that the paper and the main concepts become more understandable, e.g. I believe a more high level introduction to the modelor some of the key concepts (e.g. graphons) would make it easier to follow the paper. Secondly, I sometimes was wondering about the notation of particular equations which were only clarified much later in the text. Examples for this are: $\\mathcal{\\nu}$, $\\mathbb{W}$ or <> in definition 2.1, or the (subscript) E in $\\mathbb{E} $$[||\\mathbb{E}X_{u_\\infty}^{α^*} (t) − y||^2_E]$ in the main text. \n\n- I appreciate the overview figure 1 and can see that a lot of work went into that. However, I think the caption could be improved, e.g. there are three subfigures but the caption only mentions \"left\" and \"right\". Also what is the difference between\"real observations\" and the (observed?) values of u? And what is y in the legend?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "- I am not very familiar with the mean field game literature, could the authors point to some references, and include earlier on in the paper, (before starting their own problem formulation)\n- Could authors explain/illustrate how long it takes for their method to converge/train as compared to baselines." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Seems to be the first paper to propose such a method to learn time series by casting as mean field game, which has been applied successfully in other areas of control and generative modeling/prediction.\n- Paper relatively well organized (see weakness)\n- Author provide numerical illustration on three datasets" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Authors cast the time-series prediction problem into a mean-field game, where they treat the time-series as arising from a controlled stochastic differential equation (mean-field graphon dynamic), which is given in terms of a continuum of mean-field predictors. Authors cast the problem thus to find an optimal control policy to the dynamic by solving the associated Bellman equation. The authors discuss how find such a policy by gradient descent in their mean-field game setting. Authors illustrate their method a number of real world data sets and perform two ablation studies." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The way things are introduced is a bit confusing in Section 2. There is little motivation to the definition 2.1, which has limited reference to related literature. Many terms are not defined/explained until much later, e.g. the function $b$ is never explicitly defined or explained anywhere in the manuscript, and there are multiple overlapping uses of the variable $W$ with different meanings.\n- Authors have specified little related literature to their work, e.g., connection of this work and (Liu et al. 2022) was not entirely clear to me.\n- Empirical evaluation doesn’t include runtime results, e.g., the convergence rate of the solution to scheme presented in 3.2 (w.r.t. the training of other competing models is not specified)\n\nErrata:\nLine 92: Why is initial condition $y_u \\sim p(u,y)$ measure dependent on $y$?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. In definition 2.1, define $\\psi$, such that in general the reader can know what it is, or maybe move definition 2.2? I was confused about \\psi (although I can guess what it is), it will be easier for other readers to understand the equation if two definitions are written together. \n2. Since the graphon is modeled by a neural network, is the linear assumption, where the mean-field drift and the Ito drift are linearly added, still necessary in Eq (1)? If not, is it possible to extend this framework to Mckean-Vlasov SDE to be more general than just mean field SDE ? (see[1], [2])\n3. Notation discrepancy in Eq (3)? Eigen function was defined as $\\phi_l$ but written as $\\varphi_l$\n4. Why do the authors assume the form of the graphon when $W_{\\alpha} (u,v)$ are already modeled as neural networks? What is the implication of completely assuming the graphon to be a neural network without assuming its form? Can one still recover the temporal decay and cyclic properties when no such form is assumed?\n5. Related to the above comment, [1] introduced implicit measure architecture that learns the mean field through a change of measure from the space of neural network weight to the observation space. Could this be implemented under this system without explicit assumption on the form of Graphon? \n6. Is there a way to combine the temporal decay and the cyclic properties into one form of graphon?\n7. Can the number of samples be incorporated in the cost function such that the most cost-effective number of samples can be obtained?\n\n[1]: Yang, Haoming, et al. \"Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes.\" International Conference on Artificial Intelligence and Statistics. PMLR, 2024. \n[2]: Sharrock, Louis, et al. \"Online parameter estimation for the McKean–Vlasov stochastic differential equation.\" Stochastic Processes and their Applications 162 (2023): 481-546." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Generally the idea is novel and nicely motivated. \n2. Although it may seem incremental to add just the mean-field SDE based on graphon, the theoretical analysis and the related algorithms are non-trivial. \n3. Demonstrated strong empirical improvement." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors described a new method of modeling continuous time series through mean-field SDE where the mean-field interaction is computed over a graphon. It can be essentially be seen as an ensemble method (over a graphon) to model timeseries, where the graphon is semi-parametric with predetermined (temporal decay and cyclic) form. A stochastic controller is then applied to control the values of the parameters of the graphon. The authors also developed a gradient-based optimization algorithm to optimize the neural network used for stochastic control. Overall the paper has sound theoretical motivation and good empirical results." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Confusion in notations. Throughout reading the manuscript, I would constantly be lost in new notations. Please make sure the notations are consistent. \n2. Lack of Limitation as there is no discussion of the limitations of the proposed methods. \n3. Lack of details in experiments and implementation." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024meanfield,\ntitle={Mean-field Continuous Sequence Predictors},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1e5fX6X44w},\nnote={under review}\n}" }, "abstract": { "value": "We propose a novel class of neural differential equation models called mean-field continuous sequence predictors (MFPs) for efficiently generating continuous sequences with potentially infinite-order complexity. To address complex inductive biases in time-series data, we employ mean-field dynamics structured through carefully designed graphons. By reframing time-series prediction as mean-field games, we utilize a fictitious play strategy integrated with gradient-descent techniques. This approach exploits the stochastic maximum principle to determine the Nash equilibrium of the system. Both empirical evidence and theoretical analysis underscore the unique advantages of our MFPs, where a collective of continuous predictors achieves highly accurate predictions and consistently outperforms benchmark prior works." }, "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": [ "Mean-field graphon games", "Mean-field games as continuous sequence prediction", "Mean-field Neural SDEs" ] }, "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/e9c4b2df8528d3d798413c65666e6cfe7aae07e7.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": "Mean-field Continuous Sequence Predictors" }, "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": { "value": "N.A." }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "How to address the issue of adding restrictions on features that significantly reduce the authenticity of generated results?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The ideas are clearly presented." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a modified LORA-based multiple-concept generation model. By introducing three loss functions, the phenomenon of concept vanishing and confusion are somewhat suppressed." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper was prepared carelessly. First, the paper exceeds the length limit. Second, the authors claim that the proposed module is training-free. However, the main part is three loss functions. Third, the visualization results are poor. For example in Figure 5, the persons are pasted to the background, and the results look unreal. I think the results of mix-of-show method are far better." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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. What would be the performance of LoRA-Composer when applied to datasets that exhibit more complex interactions among subjects?\n2. Would the fine-tuning of layers beyond the U-Net architecture lead to further enhancements in the preservation of concepts?\n3. If the background inherently includes elements of the foreground, would this affect the effectiveness?\n4. Would the presence of overlapping layout boxes influence the outcome?\n5. Are there any errors in Fig.3a and Fig.3b? It seems that m1-v1 and m2-v2 do not match.\n6. For two similar foreground concepts, such as people who look very similar, is there a possibility of concept confusion?" }, "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's technical approach appears well-founded. The concept isolation and injection constraints effectively reduce concept vanishing and confusion, supporting the paper's claims of improved performance in multi-concept generation. The latent re-initialization technique also adds rigor, ensuring spatially accurate representation of concepts.\n2. The paper is clear and logically structured, guiding readers through the model's design, methodology, and experimental evaluation. Visual examples illustrate improvements over other models.\n3. The proposed LoRA Composer is an innovative solution, and the selected baseline should be the latest. In comparison, the model performance of this paper is outstanding, and there are abundant comparative and ablation experiments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents LoRA-Composer, a training-free framework designed to manage multi-concept image customization using Low-Rank Adaptations (LoRAs) with layout and textual prompts. LoRA-Composer addresses two key challenges in multi-concept customization: concept vanishing (loss of intended concepts) and concept confusion (misattribution of characteristics between subjects). Key features include concept injection constraints, concept isolation constraints, and latent re-initialization for spatial focus. Experimental results show LoRA-Composer outperforms existing methods in qualitative and quantitative metrics." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Despite being training-free, the model’s architecture (especially concept isolation and injection constraints) is relatively complex and might limit ease of implementation.\n2. Evaluation Scope: The method is tested on select datasets, including COCO, FFHQ, and CelebA-HQ， featuring anime and realistic styles. Testing on broader datasets could enhance its robustness claims.\n3. A discussion should be added on whether this method is easy to extend, whether it is applicable to various variants of stable diffusion, and it is not yet clear which version of Stable Diffusion is used in this paper.\n4. There seem to be some defects in the figure drawing in the article, such as the arrow pointing to the text encoder in Fig. 2, and there is also a lack of explanation for the data flow related to Fig. 2.\n\nAlthough the paper has some shortcomings, its overall innovation and the integrity of the experiments are good." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 authors claim that concept injection constraints effectively avoid concept missing (at line 264), but Figure 6(d) still has that issue. So, does the concept isolation constraints (CI) also contribute to the mitigation of concept missing?\n\n- How does the value of k in topk(.) reduce function in the loss components (equation (3) and (7)) affect the results? For example, using larger k might lead to larger generated concepts?\n\n- In scenarios with overlapping box layouts, such as “A [v1] person hugs a [v2] dog,” how effectively does LoRA-Composer perform? It appears that the calculations in these situations may result in many artifacts in the outputs.\n\n- There's a minor analysis point that I think should be clarified. In my view, the Gradient Fusion optimization combined with ED-LoRA introduced in Mix-of-Show [1] is not the primary factor reducing concept identities when generating multi-concept images (e.g., prompts containing multiple concept tokens like “A [v1] man and [v2] woman”). Rather, it's more closely tied to the \"incorrect behavior\" in the cross-attention and self-attention modules that you are aiming to address. This suggests that the LoRA-Composer method could also be applied to Mix-of-Show or other methods using Gradient Fusion.\n\n[1] Gu, Yuchao, et al. \"Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models.\" Advances in Neural Information Processing Systems 36 (2024)." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper identifies and tackles significant challenges in the multi-concept customization task, which are concept vanishing and concept confusion, by examining the cross-attention and self-attention layers within the U-Net of Stable Diffusion.\n- The motivations for the contributions are explained well with informative figures. \n- Extensive experiments and ablation studies are conducted to showcase the capability of the proposed method.\n- LoRA-Composer can produce visual stunning multi-concept outputs in a training-free manner and does not require the image-based conditions like canny edge or pose estimations. It could potentially have wide applicability across several applications." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a training-free model for integrating multiple LoRAs called LoRA-Composer. From given box layouts, a global prompt and local prompts, the proposed method addresses the concept vanishing and confusion issues in multi-concept customization by proposing Concept Injection Constraints and Concept Isolation Constraints, respectively. Concept Injection Constraints modify the cross-attention layers in the U-Net to perform Region-Aware LoRA Injection and Concept Enhancement Constraint, which refine cross-attention maps using Gaussian weighting and adopt a strategy to obtain box-spread attention values. Meanwhile, Concept Isolation Constraints focus on self-attention layers to limit the interaction between queries within a specific concept region and those in other concept regions.\nThe authors also propose latent re-initialization to obtain better prior latent values for the generation process. LoRA-Composer achieves a notable enhancement compared to standard baselines." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The novelty of the proposed method is not enough for ICLR:\n + Some contributions should be clarified as either \"inspired by existing work to develop\" or simply \"adopted,\" in order to emphasize the novelty of the paper:\n . Region-Aware LoRA Injection: Similar to Regionally Controllable Sampling in Mix-of-Show [1]\n . Gaussian weighting in Concept Enhancement Constraints: Similar to the method proposed in BoxDiff [2], with Gaussian weighting from Attend-and-Excite [3].\n + For Region Perceptual Restriction, the idea of minimizing interaction between queries of the foreground and background areas in self-attention is quite popular in existing work related to attention manipulation, such as Attention Refocusing [4].\n- The writing in some parts is quite ambiguous:\n + Region-Aware LoRA Injection at line 200: After obtaining h_i in equation (2) at line 215, what do we do next?\n + L_c loss in equation (3) at line 240: What is it? It suddenly appears there without any explanation.\n + Concept Region Mask in Line 270: What do we use it for?\n- The prompts used for qualitative evaluation should be mentioned (Figure 5, Figure 6)\n\n[1] Gu, Yuchao, et al. \"Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models.\" NIPS 2024\n[2] Xie, Jinheng, et al. \"Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion.\" ICCV 2023\n[3] Chefer, Hila, et al. \"Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models.\" ACM Transactions on Graphics (TOG) 2023\n[4] Phung, Quynh, Songwei Ge, and Jia-Bin Huang. \"Grounded text-to-image synthesis with attention refocusing.\" CVPR 2024" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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. More explanation of $\\mathcal{L}_c$ in Eq. 3 is needed. What is the meaning of creating $\\mathcal{L}_c$? Its form requires the weight within the concept mask to become larger, but why increasing the weight can restrain the activation for the edge region is not clear. Additionally, why can Gaussian weight restrain the activation in the edge region? Can performing a low-pass filter such as blurring get the same results?\n2. Sec. 3.4 for latent re-initialization is hard to follow. What is replacing the layout area $z_t[M_i]$ with the latent area? What is the latent area, and how can we obtain it? Missing the latent area can make the paragraph hard to follow.\n3. It is observed that the layout for each concept discussed in the paper does not overlap. Is this necessary for the approach to work? What would the outcomes be if some of the boxes overlapped?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The authors propose useful constraints, including concept enhancement and concept isolation, which is an interesting design for the community and can be seen as the plug-and-play objective for future applications.\n2. User study is conducted to bridge the gap between human preference and machine metrics. Their results have provided a huge gap under conditions without further image conditions.\n3. The model and approach design illustrations are clear and easy to follow. Also, the visualization for different approaches and designs are well-structured." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a new LoRA-based approach for customizable generation for diffusion. Their main contributions include proposing a training-free approach and designing new strategies to inject and isolate the concept to ensure the targeted objects are generated without interference. Their approach has been shown to surpass existing SOTA (e.g., Mix-to-Show, Paint-by-Example) with a higher CLIP score on image preservation and text alignment, as well as the mIoU score with the layout." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The authors state that their approach can deal with the concept vanishing issue in Sec. 3.1 but no quantitative comparison to support this statement. For instance, the author can provide a metric that counts how many predicted boxes are obtained with GroundingDINO and compare it with the GT layout. Otherwise, only visualization cannot provide any useful information on how powerfully the proposed approach can deal with the vanishing issue.\n2. The authors propose a new dataset but do not provide the results for the existing one proposed in Mix-of-Show. Further discussion or explanation is needed." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024loracomposer,\ntitle={Lo{RA}-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1eI236MqEA},\nnote={under review}\n}" }, "abstract": { "value": "Customization generation techniques have significantly advanced the synthesis of specific concepts across varied contexts. Multi-concept customization emerges as the challenging task within this domain. Existing approaches often rely on training a fusion matrix of multiple Low-Rank Adaptations (LoRAs) to merge various concepts into a single image. However, we identify this straightforward method faces two major challenges: 1) concept confusion, where the model struggles to preserve distinct individual characteristics, and 2) concept vanishing, where the model fails to generate the intended subjects. To address these issues, we introduce LoRA-Composer, a training-free framework designed for seamlessly integrating multiple LoRAs, thereby enhancing the harmony among different concepts within generated images.\nLoRA-Composer addresses concept vanishing through concept injection constraints, enhancing concept visibility via an expanded cross-attention mechanism. To combat concept confusion, concept isolation constraints are introduced, refining the self-attention computation. Furthermore, latent re-initialization is proposed to effectively stimulate concept-specific latent within designated regions. Our extensive testing showcases a notable enhancement in LoRA-Composer's performance compared to standard baselines, especially when eliminating the image-based conditions like canny edge or pose estimations." }, "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-Concept Customization", "LoRA Integration", "Training-Free" ] }, "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/96fd9d298f7bbb76cd9721ed8caa1b242f469601.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": "LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "N/A" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- This paper can be followed easily and most heuristics are intuitive. The computation of the step size is cheap.\n\n- The experiments on the 2-dimensional example are interesting and show under certain settings (e.g., rotation) the proposed method can significantly outperform other adaptive step sizes such as Adam and RMSprop." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper derives a new step size schedule based on a quadratic model. The key observation is that \nthe optimal step size happens when current and previous gradients are orthogonal. Thus, their inner products play an important role in controlling the magnitude of the step size. Besides this, it introduces several heuristics to stabilize the training and improve the overall performance. Noticeably, it considers damping the learning rate increase when the function value rises (when compared against previous iterations); and gradually increasing the batch size when some criteria based on function values are met (to reduce the random noise when a local minimum is approached). It demonstrates the effectiveness of the proposed method mostly using vision experiments." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The proposed step size lacks theoretical guarantees even in the convex settings (or even convex quadratics?). I am not sure where the technical challenges are.\n\n- I don’t think the current method is compared fairly with other methods given all the heuristics added on top of the learning rate. It is difficult to tell where the gain (if there is any) comes from? Is it because of the step size schedule, or batch size increase, or iterates averaging (named as mean value boosting in the paper)? If iterates averaging were applied to other baselines, would the results change?\n\n- Another major concern is that there are many hyperparameters associated with the proposed method, which raises questions regarding its practical usability. How expensive are the tunings of these hyperparameters?\n\n- The experiments on language modelling are inconclusive." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "no further questions, see comments above" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The proposed algorithm introduces not much extra computation cost while tuning the learning rate, as the tuning mechanism only depends on the norms of, and inner products between, past two gradient vectors.\n\nThere is less need in tuning the initial learning rate, compared with pure SGD. The algorithm is invariant under coordinate rotation, which could be an advantage over Adam and other Ada-family algorithms." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes an optimization algorithm that dynamically tunes the learning rate and dynamics, based on its last two gradients. The algorithm is evaluated on neural networks with several standard benchmark datasets, and compared with SGD and Adam." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1: The design of the proposed algorithm is based on strong assumptions/conjectures which may not be true in practice. Specifically, it assumes that the loss function is a parabola in the line that goes through any two consecutive iterates. Moreover, it requires that the minimizer of the parabola happens at $x=0$. These conditions usually do not hold in modern deep learning where loss function is considered as highly non-convex, and is far from quadratic.\n\nIn addition, the paper did not verify these assumptions/conjectures in experiments.\n\n2: The proposed algorithm does not seem to improve empirical performance (according to the experiments of the paper). Without those additional techniques (FT, WD), ELRA actually performs quite worse than SGD. The successful run includes too many other techniques (boosting, FT, weight decay, gradient decay etc), it is not clear whether it is ELRA or those accessories that lead to a relatively good performance.\n\n3: [about the empirical term $\\kappa$]. The algorithm introduced the empirical term $\\kappa$. \nThere is no theoretical justification of introducing it\nThere is no explanation of the formula of $\\kappa$ (Line 151). Is it an empirical choice?\nThere is no (theoretical) justification of the claim “($\\kappa$) neutralizes random noise effects in neural networks”. It is hard to believe a single scalar can neutralize random noise. There must be some theory to support the claim. \n\n4: In line 123, the paper says “we expect the optimal $\\alpha_t$ for $x_t$ does not vary too much from the optimal $\\alpha_{t-1}$ for $x_{t-1}$. I could not see why this should be true. The algorithm makes discrete steps (some steps may be quite big), hence $x_t$ is not necessarily close to $x_{t-1}$. At least, the paper needs to experimentally verify the relation between $\\alpha_t$ and $\\alpha_{t-1}$.\n\n5: As for saddle points, the paper only looked at a special type $f(x)=x_1^2-x_2^2$. However, geometry near saddle points can be much more complicated than this special case, and the analysis of the special case may not generalize." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 answer the issues and questions in the Weakness and point out my potential misunderstandings. I am happy to discuss and enhance my rate." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "* The authors have fused several accelerating tricks in the field of optimization into their optimizer, it seems to surprisingly work well.\n* The authors provide interesting derivations for their design of the optimizer" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work presents a new fast gradient based momentum-free optimizer algorithm with dynamic learning rate and dynamic batch size. They evaluated their algorithm on several benchmarks and made some basic empirical achievement." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* It needs to clarify that, since this optimizer introduces adaptive learning rate for each parameter, then momentum-free shouldn’t be an advantage of this work at efficiency, the cache of learning rates is equivalent to cache momentum practically.\n* In Section 2, the definition of the local minimum of $\\alpha$ is unclear, as the authors assume that $f$ is convex, whereas it does not straightly lead that $f(x_t)$ is convex for $\\alpha$, I am not sure about the effectiveness of devoting “local minimum” for faster optimization theoretically.\n* The key derivation in Section 2 first appeared in the delta-bar-delta method in [1], in which it starts a series of following works, please at least review these works in the paper, and compare how the key idea of ELRA demonstrates advancement. In my opinion, the reasons why the old trick doesn’t last long in the application of optimization may be various. However, this work lacks a considerable review to the prior works.\n* The efficiency claim in line 257 to line 258 sounds unprofessional: This gives ELRA a roughly 10% computation advantage over Adam and Lion, when used with the same batch size. The authors should at least provide some experimental analysis to prove it.\n* It’s not clear what problem or challenges the study mainly aims to address, I checked 7 subsections in Section 3, and found no necessary or professional reason for introducing such or that trick in this optimizer. It seems like an addition of several existing works, but with poor writing.\n* Showcasing “fast” needs comprehensive experiments, evaluations on some toy datasets seem far away from the word “enough”. Could the authors add some speed analysis on those real-world benchmarks compared with baseline optimizers like Adam, Lion, SGDm?\n* Typo: line 479: Our experiments suggest that ELRA shares this behaviour with SDG.\n* I didn’t find out the definition of “ELRA+FT”, please define it somewhere conspicuous, since it looks like this line performs best in the results but I could not find what it is.\n* In conclusion, this paper has a limitation in its presentation, some words seem unprofessional. I suggest the authors to re-organize the whole writing to tell a better story and show a better result.\n\nReferences:\n[1]: Bernard Widrow, Marcian E Hoff, et al. Adaptive switching circuits. In IRE WESCON convention record, volume 4, pages 96–104. New York, 1960." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We present a novel fast optimizer with self-adjusted learning rates and batch sizes, without momentum." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024a,\ntitle={A Gradient Descent Optimizer with auto-controlled large Learning Rates, dynamic Batch Sizes and without Momentum},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1eMbYu0841},\nnote={under review}\n}" }, "abstract": { "value": "We present a novel, fast gradient based momentum-free optimizer algorithm with dynamic learning rate and dynamic batch size. The main ideas are to exponentially adapt the learning rate $ \\alpha $ by situational awareness, mainly striving for orthogonal neighboring gradients, and to increase the batch size when the gradients become too noisy, leading to random walks rather than gradient descent. The method has a high success and fast convergence rate and relies only on few hyper-parameters, providing greater universality. It scales only linearly (of order $O(n)$) with dimension and is rotation invariant, thereby overcoming known limitations. The optimization method is termed ELRA (Exponential Learning Rate Adaption). The impressive performance of ELRA is demonstrated by experiments on several benchmark data-sets (ranging from MNIST to ImageNet) against common optimizers such as Adam, Lion and SGD." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Machine Learning", "ICRL", "Optimization" ] }, "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/b5beef05b3fe5db4d444ecf7e2021014a410953c.pdf" }, "presentation": null, "primary_area": { "value": "optimization" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "A Gradient Descent Optimizer with auto-controlled large Learning Rates, dynamic Batch Sizes and without Momentum" }, "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": "- How are rotary embeddings managed for the beacon tokens? Although the LLM processes a fixed chunk at a time, the relative positions of the beacon tokens vary across chunks. How are positional embeddings applied in these cases?\n- Additional parameters are added and fine-tuned for self-attention projections specific to the beacon tokens. What is the impact of these added parameters on VRAM usage and latency? If the cost is significant, could LoRA fine-tuning be effective for the proposed activation beacons approach?\n- What portion of time is allocated to prefilling and decoding? While the proposed method reduces some recomputation, it may require customized attention masks or iterative context processing, which could lack efficient kernel implementation or result in extra kernel calls. Please provide a latency breakdown of prefilling and decoding for specific workloads (e.g., 32/128k context, 128 decoded tokens) and compare it with the flash attention full-context baseline.\n- How does the proposed approach affect fine-tuning throughput? Please compare its performance with Full-FT.\n\nI am open to adjusting my ratings if all concerns and questions are adequately addressed." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Compressing by chunks at each layer avoids the need for recomputation and addresses gradient back-propagation challenges present in some prior baselines that rely on recursive dependencies from final-layer outputs. This design enhances both training and inference efficiency.\n- The chunking approach and the interleaved insertion of beacon tokens are straightforward and intuitive.\n- Evaluations on various benchmarks indicate that the proposed approach generally outperforms the KV cache compression and “soft-prompt” compression baselines, achieving notable reductions in both inference time and memory usage.\n- Training with randomly sampled compression ratios enables flexible compression ratios during testing." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces “Activation Beacon,” a plug-in module to conduct long-context compression for LLMs. The proposed approach progressively compresses the activations at each layer and can be trained in the conventional auto-regressive way of language modeling. The authors demonstrate the benefits of this approach through evaluations on various long-context tasks for compression quality and inference efficiency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- In addition to LongBench and NIAH, it is essential to evaluate the proposed approach on newer, more challenging benchmarks, such as RULER [1].\n- Some recent context compression baselines, including CEPE [2] and LLoCO [3], are not discussed in the paper and should be included for a more comprehensive discussion or comparison.\n\n[1] Hsieh et al. RULER: What's the Real Context Size of Your Long-Context Language Models? COLM 2024. \n[2] Yen et al. Long-Context Language Modeling with Parallel Context Encoding. ACL 2024. \n[3] Tan et al. LLoCO: Learning Long Contexts Offline. EMNLP 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": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See weaknesses" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Activation Beacon reduces inference time by 2x and KV cache memory costs by 8x compared to the uncompressed baseline.\n\n2. The method supports adaptive compression ratios, allowing flexibility for different tasks and contexts.\n\n3. The proposed model maintains short-context capabilities, preserving the performance of the original LLM." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces \"Activation Beacon\", a compression method designed to enhance long-context processing efficiency in LLMs. The approach compresses the activations of keys and values in transformer layers, avoiding bottlenecks associated with traditional soft prompt methods. Additionally, a progressive compression workflow compresses each context unit in chunks, allowing the model to handle longer contexts than the original LLM's window. Experimental results show Activation Beacon achieves significant memory and computation savings, with minimal loss in performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The performance of this method may vary with model size. Current evaluations focus on medium-sized models, lacking validation on larger-scale models, leaving its effectiveness and applicability in very large models underexplored.\n\n2. The added complexity of managing beacon tokens and compression ratios increases implementation overhead for end-users, particularly when adapting to different tasks. In addition to actual inference latency, specific memory usage data across implementations would help clarify practical resource requirements." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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": "overall, this paper is novel and idea is well presented. please add more techniques for comparison so that users can choose different method." }, "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 efficient method to compress long contexts, reducing memory usage by up to 8x and speeding up inference by 2x.\n- Its progressive, fine-grained compression approach maintains high compression quality, allowing the model to handle longer inputs than its built-in context window.\n-It supports flexible compression ratios, preserving model performance across various long-context tasks without degrading short-context capabilities." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper compresses activations (keys and values) rather than using soft prompts, facilitating a progressive, fine-grained compression process. Specifically, it first partition input into small chunks, interleaving special beacon tokens that accumulate contextual activations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Lack of Comparison with KIVI: The paper does not provide a direct comparison with KIVI, a relevant compression method that could offer insights into the performance trade-offs.\n- GPU Time Omission: The paper does not report GPU training or inference time, leaving uncertainty around the practical computational cost and efficiency of the proposed method.\n- Scalability Concerns: The method requires 8 A800 GPUs to train a 7B parameter model, raising concerns about its scalability to larger models like 70B, where computational demands could become prohibitive.\n- Limited Comparative Analysis: The paper would benefit from including more baseline methods, particularly compression-based approaches like KIVI, KV-layer shared compression methods such as CacheGen, and relative-position encoding strategies like LM-Infinite.\nAdditional References Needed: Incorporating comparisons with relevant works, such as LM-Infinite [1] for dynamic context management, CacheGen [2] for efficient context loading, and KIVI [3] for asymmetric quantization of KV caches, would strengthen the evaluation and highlight the advantages and limitations of the proposed approach.\n\n[1] LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models\n[2] CacheGen: Fast Context Loading for Language Model Applications\n[3] KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "My main question is in the \"regarding differences with previous works\" above. I want to understand if the results are improved mainly from decreasing chunk size, or if there's another difference between soft tokens and beacon tokens that explains the difference.\n\nAlso, what window size do you use? From Table 1, your model has a context length of 32k. I'm guessing you use this window size, but I don't see it explicitly stated, and line 184 suggests that 1024 would be a common window size, so I'm not sure. Since LongBench has only a few examples above 32k, I'm guessing the window logic isn't really used much (unlike for Needle In a Haystack)" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper focuses on an impactful area (long-context efficiency for LLMs).\n- The paper provides a relatively simple idea that is well-explained. I view simplicity as a plus - if a simple idea can give strong accuracy improvements, it's far better than an unnecessarily complicated idea.\n- The paper demonstrates strong results. Table 2 demonstrates strong accuracy at good latency on standard benchmarks for long context. Their method is competitive with full fine-tuning and better than baselines. Table 1 provides strong accuracy as well (though latency is missing).\n- The figures do a good job of explaining what's going on. Figure 1 and Figure 2 give nice overviews of the method.\n- The method is computationally efficient compared to fine-tuning. Their \"pretraining\" (starting from an already-pretrained model) only requires 1B tokens which is very few.\n- The paper ablates design choices (Table 4).\n- The paper is generally well written." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a method called Activation Beacon for efficient long-context processing. The method adds learned \"beacon\" tokens at regular intervals in the input query. These tokens are expected to learn \"summaries\" of the text. At inference time, when processing long contexts, the beacon tokens are retained and the other context tokens are discarded. Thus, the beacon tokens essentially provide a summary of the context. The authors evaluate their method in comparison with a few other recent methods for efficient long context processing. Their method significantly improves results on LongBench and Multi-Needle-in-a-Haystack. The authors also provide ablations for various design choices." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- In Table 1, it's not obvious whether the latencies are comparable. The compression ratio isn't mentioned.\n- line 368: why do you use adaptive compression for llama-2 and uniform compression for qwen?\n\nMy main perceived weaknesses are regarding differences with previous works, and understanding why this method is performing so well:\n- line 135: \"ICAE and AutoCompressor... segment the long context into chunks and compress each chunk. However, both of them compress the context into soft tokens\" <- how are these soft tokens different than beacon tokens? (similarly, on line 373-374, you mention soft tokens being a drawback)\n- line 137: \"Their compression workflow also lacks fine-grained handling of the chunked inputs, resulting in inferior compression quality\" <- it seems like all they would need to do to allow \"fine-grained handling of the chunked inputs\" is just choose a smaller chunk size, so that the soft tokens appear more frequently. Is that right?\n- - If this is true, it seems like your main contribution is the insight that soft tokens should be distributed evenly through the context. Would doing this massively improve the accuracy of ICAE and AutoCompressor? It seems like this is the main discovery, but I'm left wondering if I'm missing some more fundamental difference.\n\n[Minor]:\nline 47: \"it it\" -> \"it\"\nline 53: \"alternamtive\" -> alternative\nline 371: \"highligh\" -> \"highlight\"\nline 483: \"scope\" -> score\nTable 2: give units of \"latency\"" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024long,\ntitle={Long Context Compression with Activation Beacon},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1eQT9OzfNQ},\nnote={under review}\n}" }, "abstract": { "value": "Long context compression is a critical research problem due to its significance in reducing the high computational and memory costs associated with LLMs. In this paper, we propose Activation Beacon, a plug-in module for transformer-based LLMs that targets effective, efficient, and flexible compression of long contexts. To achieve this, our method introduces the following technical designs. \n1) We directly compress the activations (i.e. keys and values at every layer), rather than leveraging soft prompts to relay information (which constitute a major bottleneck to encapsulate the complex information within long contexts).\n2) We tailor the compression workflow, where each fine-grained input unit is progressively compressed, enabling high-quality compression and efficient computation during both training and inference. \n3) We train the model through compression-based auto-regression, making full use of plain texts and instructional data to optimize the model's compression performance.\n4) During training, we randomly sample a compression ratio at each step, teaching the model to support a wide range of compression configurations. \n\nExtensive evaluations are conducted on various long-context tasks whose lengths (e.g., 128K) may far exceed the maximum training length (20K), such as document understanding, few-shot learning, and Needle-in-a-Haystack. Whilst existing methods struggle to handle these challenging tasks, Activation Beacon maintains a comparable performance to the uncompressed baseline across various scenarios, \nachieving a 2x acceleration in inference time and an 8x reduction of memory costs for KV cache." }, "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": [ "Context Compression", "Long Context LLMs", "LLM Memory" ] }, "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/5b3521f89b796b894da27de96764376fddf1fc25.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": "Long Context Compression with Activation Beacon" }, "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": 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": "- Could the authors clarify the computational cost of AugRevise and RegMixup?\n- The v2 ResNet50 yields an ImageNet-1k accuracy of 80.92%. The accuracies in Table 4&5 for AugRevise are significantly lower and v2 models are omitted from the Table. Are there more setups where training with AugRevise leads to a significant drop in accuracy compared to the v2 models?\n\n\nRegarding clarity:\n- Section 5.2, especially lines 360-377 introduces the central part of AugRevise, but it is very short, which is in contrast to the previous Sections where the effects of Mixup/LS were explained thoroughly with several ablations. Section 5.2, in particular the introduction of the loss function requires, more explanation and justification\n- Figures 1 and 2 are hard to read on paper. Larger markers and more distinguishable colours would help. It is not always clear which text belongs to which dot.\n- Line 475: Should it be ImageNet-1k instead of ImageNet200?\n- Throughout the paper (e.g. Figure 3 and 4, but also in most other places): Specifying for each Table and Figure which dataset (ID and OOD), which score and which model is reported would make the Tables and Figures more self-contained\n- Regarding OpenOODv1.5 reporting: Which numbers are usually reported? In OpenOOD there is commonly a split between near and far OOD (as reported in the Appendix), but in the main paper, there is only one number. Is it the average?\n- Throughout the paper: The larger Tables are hard to digest, as there are many numbers but little structure. I suggest making the best methods per model bold and grouping the same models, perhaps also adding row colours.\n- In some Tables some methods are missing that appear in others, e.g. MDS in Table 19, ASH in Table 5 for AugRevise" }, "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 straightforward: the authors identify a problem (reduced OOD detection performance models from a certain training script), provide an explanation (mixup and label smoothing) and a fix for it\n- The experiments identifying label smoothing and mixup as the problems are convincing and thorough\n- Especially AugDelete, while being a simple method, shows consistent and believable improvements\n- Investigations on the effects of training on OOD detection are often overlooked and a relevant subject to study" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors observe a drop in OOD detection performance of torchvision-v2 models compared to their v1 counterparts despite a gain in ID classification accuracy. They identify mixup and label smoothing as the root cause for the decrease in OOD detection performance, especially on logit-based detection methods via theoretical and experimental analysis. They devise two strategies to mitigate the problem: AugDelete finetunes the linear layer of pretrained models without the problematic augmentation strategies, and AugRevise adds a loss term regularizing the effect of the max-logit of samples with and without mixup for training from scratch. Experiments on the OpenOOD1.5 benchmark are provided." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- While the paper tells a consistent and mostly believable story, its scope is somewhat limited. In particular, it focuses on models from the torchvision v2 hub that were trained from scratch on the respective datasets. How the findings translate to models from other codebases (e.g. timm) with more diverse pretraining settings (e.g. ImageNet21k, Laion, CLIP, distillation, …,) or zero-shot models is unclear. As recent studies [1,2] have shown, SOTA results are often achieved for bigger models with large pretraining schemes especially with feature-based methods, so those setups would be interesting to look at. \n- It is unclear if there is additional computational cost associated with AugRevise and RegMixup. Since for those methods both the mixed and ‘clean’ sample are propagated through the network, this in principle doubles the batchsize and the computational cost (if the batch-size is fixed w.r.t. ‘Clean’ data, which is not explained in the paper). This would give AugRevise and RegMixup an unfair advantage over other baseline methods that only forward the mixed or only the clean samples. \n- The claim that “feature-based methods are likely similarly compromised” is not backed by the provided experiments. For instance, the auroc differences in Table 7/8/9 between v1 and v2 for KNN are marginal, for CIFAR10 even the best-performing model is a v2 model with KNN. For AugRevise, additional feature-based methods like Mahalanobis distance, relative Mahalanobis Distance, Vim, … are omitted in the experiments\n- AugRevise changes the from-scratch training compared to the torchvison v2 training script, but eventually still applies AugDelete, which sometimes leads to significantly lower ID accuracy (e.g. RN50 on IN-1k). It is unclear if other training methods, e.g. autoaugment or 3-Augment[4] or RSB [5] or others would not achieve similar results (potentially when combined with AugDelete). Also, to my understanding, only one model per dataset is investigated with AugRevise (ResNet-18 and ResNet-50).\n- The authors claim that their “empirical results challenge the conventional understanding of ID and OOD performance correlation”, but similar observations have already been made in previous work, e.g. in [3]\n- Proposition 4.2 relies on the assumption that the cosine similarity between ID samples is smaller than between ID and OOD samples. This is a somewhat strong assumption: If this were satisfied for most samples, it would allow to design of a good OOD detector based on cosine similarity. I would appreciate a discussion on the limitations of this assumption and how well it is justified.\n- There are several issues regarding the presentation and the clarity of the paper (details below in Questions)\n\n[1] Julian Bitterwolf, Maximilian Müller, and Matthias Hein. In or out? Fixing ImageNet out-of-\ndistribution detection evaluation. In ICML, 2023.\n\n[2] Galil, I., Dabbah, M., and El-Yaniv, R. A framework for benchmarking class-out-of-distribution detection and its application to imagenet. In The Eleventh International Conference on Learning Representations, 2023\n\n[3] Maximilian Müller, Matthias Hein. How to train your ViT for OOD detection, ICLR 2024 R2FM workshop\n\n[4] Touvron, H., Cord, M., and Jegou, H. Deit iii: Revenge of the vit. ECCV, 2022.\n\n[5] R. Wightman, H. Touvron, and H. Jégou, “ResNet strikes ack: An improved training procedure in timm,” arXiv preprint arXiv: 2110.00476, 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": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. In Figure 2, it seems that \"all augs (v2)\" does not only reduce the OOD detection performance, but also reduce the ID accuracy. Please explain this apparent reduction in both OOD detection performance and ID accuracy. \nIn addition, could the authors consider grouping RE and TA together, and mixup and LS together, then adding these two new data points to Figure 2? This might provide additional insights into the combined effects of these augmentation strategies on both OOD detection and ID accuracy.\n\n2. In Equations (3) and (7), the standard cross-entropy (CE) loss function is missing the \"negative\" sign. While optimization can still proceed with a positive formulation, it’s important to clarify this deviation from the standard notation to avoid confusion." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The motivation is clear and strong. The observation that using data-based data augmentation degrades the OOD detection of the model is new. The paper is to show its solutions.\n\n2. The authors conduct extensive experiments across multiple architectures and benchmark datasets to support their claims.\n\n3. This paper also provides theoretical analysis for the proposed approach." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the impact of certain data augmentations on out-of-distribution (OOD) detection performance. The authors observe that two popular data augmentation techniques—label smoothing and mixup—although effective at improving in-distribution (ID) accuracy, degrade OOD detection performance. They provide empirical evidence and theoretical insights to explain this issue, highlighting that these techniques reduce the separability between ID and OOD samples in logit space, which is critical for effective OOD detection." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper provides valuable insights into the effects of label-based augmentations (label smoothing and Mixup) on OOD detection. However, it would benefit from a broader exploration of other popular augmentation strategies, such as CutMix, to examine if these alternatives yield similar or contrasting impacts on OOD performance. Could you clarify the rationale behind selecting these specific four augmentation methods? It would be helpful to explain whether and how these choices align with the evolution of torchvision (from v1 to v2) and whether the findings could generalize to other augmentations." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "see weakness" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The authors observe an interesting phenomenon: the torchvision v2 models perform poorly in OOD detection compared to the torchvision v1 models. They find that this is due to the improved training techniques used in the v2 models, such as Label Smoothing and Mixup, which reduces the the maximal logits and then reduces the OOD detection performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates the impact of data augmentation techniques on OOD detection, focusing primarily on Label Smoothing and Mixup. The authors find that while these methods improve in-distribution accuracy, they lead to a decline in OOD detection performance. The authors attribute this phenomenon to the fact that both Label Smoothing and Mixup decrease the maximal logits, with this reduction being more pronounced in ID data. To address this issue, the authors propose two methods to mitigate the performance degradation caused by Label Smoothing and Mixup." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The using Label Smoothing and Mixup reduces the maximal logit is obvious, I am more concerned with the authors' statement that “this reduction is more pronounced for in-distribution (ID) samples than for out-of-distribution (OOD) samples”. The authors try to prove this in Proposition 4.2. However, the authors make so many strong assumptions without stating why these assumptions hold, so that the logic of the proof is like \"assume that A is correct, therefore A is correct\" (lines 816 to 822). It would be beneficial if the authors could provide a clearer proof.\n\n* From Table1 I observe that compared to v1 (trained with vanilla cross-entropy loss), the proposed v1+mixup-AugRevise and v1+LS-AugRevise only improve by 0.72 and 0.17, respectively, which is not exciting considering the additional computational cost and hyperparameters\n\n* The proposed fixing method requires retraining, making the method less favorable. I think the contribution of this paper could be greatly enhanced if a post-hoc method could be used for fixing.\n\nTypo: \"Proposition 4.1\" in line 255-256 should be \"Proposition 4.2\"" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "(Copied from Weaknesses) \n1. There are many studies showing that models trained with self-supervised learning are effective for OOD detection. Why must the classifier perform OOD detection simultaneously, rather than using experts for each task of classification and OOD detection?\n2. Can this method be applied to models trained with self-supervised learning?\n3. In the training recipe for achieving the best-performing classifier, are label smoothing or mixup essential components?\n4. Does this research have significance from a transfer learning perspective?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The proposed method is lightweight and easy to implement.\n2. The proposed method outperforms baselines in the tested settings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper examines why mixup and label smoothing can enhance the performance of image classifiers but, unlike RandAugment, Style Augment, and AugMix, simultaneously lead to lower out-of-distribution (OOD) detection performance. It also proposes AugDelete and AugRevise—methods that maintain the classification performance of classifiers trained with label smoothing or mixup while improving their OOD detection performance. AugDelete is lightweight, as it fine-tunes only the penultimate layer of pre-trained classifiers, whereas AugRevise achieves even better performance than AugDelete. The authors validate their claims using ResNets as well as the CIFAR and ImageNet datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. There are many studies showing that models trained with self-supervised learning are effective for OOD detection [1,2,3,4]. Why must the classifier perform OOD detection simultaneously, rather than using experts for each task of classification and OOD detection?\n2. Can this method be applied to models trained with self-supervised learning?\n3. In the training recipe for achieving the best-performing classifier, are label smoothing or mixup essential components?\n4. Does this research have significance from a transfer learning perspective?\n\n[1] Tack et al., \"CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances\" NeurIPS 2020 \n[2] Ming et al., \"Delving into out-of-distribution detection with vision-language representations\" NeurIPS 2022 \n[3] Jiang et al., \"Negative Label Guided OOD Detection with Pretrained Vision-Language Models\" ICLR 2024 \n[4] Lee et al., \"Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection\" NeurIPS 2024" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@misc{\nxiong2024fixing,\ntitle={Fixing Data Augmentations for Out-of-distribution Detection},\nauthor={Haipeng Xiong and Kai Xu and Angela Yao},\nyear={2024},\nurl={https://openreview.net/forum?id=1ebgtm7P10}\n}" }, "abstract": { "value": "Out-of-distribution (OOD) detection methods, especially post-hoc methods, rely on off-the-shelf pre-trained models. Existing literature shows how OOD and ID performance are correlated, i.e. stronger models with better ID performance tend to perform better in OOD detection. However, significant performance discrepancies exist between model versions, sometimes exceeding the impact of the OOD detection methods themselves. In this study, we systematically investigated this issue and identified two main factors—label smoothing and mixup—that, while improving in-distribution accuracy, lead to a decline in OOD detection performance. We provide empirical and theoretical explanations for this phenomenon and propose a solution that enhances OOD Detection while maintaining strong in-distribution performance. Code will be released upon acceptance." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": { "value": [ "~Haipeng_Xiong1", "~Kai_Xu7", "~Angela_Yao1" ] }, "authors": { "value": [ "Haipeng Xiong", "Kai Xu", "Angela Yao" ] }, "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": [ "OOD Detection; Data Augmentation" ] }, "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": "xiong|fixing_data_augmentations_for_outofdistribution_detection" }, "pdf": { "value": "/pdf/f0cdb36e453047e49d40ccb5a58606d49aac7173.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": "Fixing Data Augmentations for Out-of-distribution Detection" }, "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": 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. Context in Weaknesses Point 1: Can the framework solve absurd queries of type \"Rope $X$ is $1$mm, Y is $1.61$Meters how long does rope $Z$ has to be to be 10^3 time longer than squared average of $X$ and $Y$\"? \n2. Context in Weaknesses Point 2: Does the method suffer from calibration issues (ranges of probabilities are not homogenous, do not interact) within separate intermediate answers similar to CQD, meaning that each intermediate top-k answer can fall within varying ranges of probability and be omitted/filtered out during product (the t-norm chosen) aggregation?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The research includes many merits, from the novel approach to tackle numeric reasoning within the KGs that is able to achieve ~40% increase compared to prior benchmarks, to the introduction of more robust testing/evaluation query types (2b, 3b ... etc). The study is supported by straightforward benchmarks and comprehensive evaluations, showing that the method is particularly well suited for numeric reasoning and is computationally advantageous as it does not require explicit training on complex queries (trained only on atomic queries), yet generalises well to complex reasoning structures. The use of Multi-ComplEx is shown to be essential for embedding the numeric information within the KG, while the use of fuzzy sets allows robust reasoning when dealing with direct numeric operations, comparisons and assessments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors introduce a novel method for numerical reasoning over numeric knowledge graphs containing both real-valued continuous attributes and entities and relations. This is important as it allows for more robust reasoning over and modelling of real-world natural and common queries within KGs. One novelty of the method lies in the capability to encode the numeric and entity attributes separately, allowing for the use of binary operators to obtain numeric values outside of the designated knowledge graph. A more comprehensive evaluation suite is proposed for this type of OOD (meaning numbers/ents not in the graph) setup. The method allows the joint encoding (separately within a joint training process) of entity-numeric relationships that capture the semantics and abstractions of the relation. This is achieved using Multi-ComplEx, an extension of the link prediction method ComplEx (a strong method) by combining a separate set of numeric-value and numeric-entity encodings." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While the method works well with a margin of error and allows the obtaining numeric values outside of the KG, it is still limited in terms of the numeric continuous values that it predicts and the precision of such numbers through the limited amount of used binary operators and initially present Numeric values. Can the framework solve absurd queries of type \"Rope $X$ is $1$mm, Y is $1.61$Meters how long does rope $Z$ has to be to be 10^3 time longer than squared average of $X$ and $Y$\"? \n\n2. As the framework shares many similarities with CQD, a natural question arises if the intermediate answers obtained during query answering are calibrated to interact with each other (intermediate probability ranges are similar), which was a problem in CQD outlined in CQD-A. This is particularly important as the fuzzy aggregation method (the T-norm) that was chosen is the product norm, which suffers from this discrepancy." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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) Why LitCQD is mentioned but not compared? \n2) Why equation 9 is used, does it satisfy commutative, associative and distributive laws, and many others?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1) The first work considering complex queries involving binary numeric operations.\n2) Experimental improvements seem significant." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a CQA method on KGs with numeric values and binary operations. This approach can effectively handles more than 100 types of complex numerical reasoning queries. On three public datasets, the proposed method CNR-NST demonstrates SOTA performance in complex numerical queries, achieving an average improvement of over 40% compared to existing methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) A comparision with some naive baselines could significantly improve the perception of the experimental results. Especially for the query types with binary operations. The MRR numbers are very small and there is no baseline, and hence it is very hard to judge whether the results are good or not. For example, one could use some simple numeric rules mined from the training graph to derive answers. \n2) It seems that the techniqical contributions are two-fold: 1) Multi-ComplEx, which is a direct extension of ComplEx used in CQD to deal with numerical information; 2) The numerical computation framework. However, it is unclear whether the numerical computation is a reasonable or not. Does it satisfy some laws like commutative, associative and distributive Laws? I see no discussion about this but I think this is the key which influences the generalization capability of the reasoning. \n3) The test queries are generated as \"hard queries\" in the sense that as least one missing link is in the test graph. However, it is unclear for a multi-hop query, how much percent of the links are seen in the training graph. Note that this is important, as if most of the links in a multi-hop queries are seen. Then the problem can be reduced to a link prediction 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": 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": "- How does the proposed approach compare to LitCQD?\n- Section 3.1: It seems the loss function in Equation (4) can be negative (the second part). How would one train with a negative loss? Are there any settings that prevent the loss from becoming negative?\n- Lines 464-465: \"Instead of ranking based on the exact match of numerical nodes, we compute the RANK using the probability ranking of numerical nodes whose relative error compared to the correct answer is below a specified threshold (typically set at 0.001)\"\nThis evaluation metric discards some numerical nodes, which might not reveal the actual performance of a model. As an example, assume that $n$ nodes were ranked higher than the target node in the original metric (MRR). Also assume that these $n$ nodes are now removed in your new metric because they do not fulfil the 0.001 criterion. Then, the actual target node will now be ranked 1st, which does not really reveal the performance of your model. Any ideas on how to improve this metric? Why don't you use Mean Absolute Error (MAE) or Mean Squared Error (MSE) as LitCQD does?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "- The paper tackles a highly relevant problem. Numeric data is wide-spread in real-world knowledge graphs.\n- The experimental results show that the new approach outperforms the NRN baseline in most cases." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes an approach (CNR-NST) for complex query answering on incomplete knowledge graphs. In contrast to many previous works (e.g., CQD, GQE, ...), the paper supports knowledge graphs with numeric attributes. The proposed approach is compared to one of the existing works addressing the same problem (NRN)." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Lack of novelty: The previous approach LitCQD is mostly ignored leading to wrong claims about the contribution (see details)\n- Insufficient experiments: LitCQD is missing as a baseline\n- Poor technical and presentation quality (see details below and questions)\n\n### Details \n\n- Figure 1: Q3: \"What is the total population of Schleswig-Holstein and Dakar?\" The paper claims that previous approaches could not answer this query (\"cannot compute or infer new numerical answers from multiple values (like Q3).\"). LitCQD supports to answer such queries (see Equation 13 in the LitCQD paper).\n\n- \"Numerical Binary Operation Operator.\" This operator can handle \"queries that involve numerical answers\" and this operator is presented as a novel contribution. However, the Section 4.2 \"Multihop Queries with Literals and Literal Answers\" in the LitCQD paper deals with exactly this issue.\n\n- Section 4.3 : \"For the first time, we extend numerical reasoning in knowledge graphs to the real number domain, whereas previous methods were confined to the discrete numerical domain within the KG.\" This sentence is wrong (c.f. LitCQD)\n\n- Section 4.4: \"Previous approaches used the same evaluation metrics for these queries as for entities, but this method has limitations.\" This sentence is wrong. LitCQD used Mean Absolute Error (MAE) and Mean Squared Error (MSE) instead of Mean Reciprocal Rank (MRR).\n\n- Abstract: A sentence seems to be repeated in the abstract: \"The proposed frame-work is the first to enable binary…\" and \"The CNR-NST framework can perform binary…\". Apart from the fact that this sentence is wrong (see LitCQD), the two sentences should be merged.\n\n- Preliminaries: After the period, there should always be a space. For example: \"relations R.Each triplet\" --> \"relations R. Each triplet\" (lines 150-151, to show only a few. The problem occurs more often throughout the paper.)\n\n- Preliminaries \"Knowledge Graph $G = (V, R, \\epsilon)$ contains the set of all entities $\\epsilon$ and the set of all relations $R$.\" What is $V$ in this definition? How is it different from $\\epsilon$? This definition seems wrong: a knowledge graph is not only defined by its set of entities and relations. Triples define a knowledge graph, too.\n\n- Preliminaries: Lines 173-174: \"In the above equation, the variable $E$ represents a subset of entity $\\epsilon$...\" How is a variable a subset? It might be better to talk about variable bindings.\n\n- Preliminaries: The functions $r_i$ and $a_j$ are mentioned in the paragraph on lines 173-178 but never defined\n\n- Section 3.1: Confusing notations are used, e.g., it is not clear whether $\\mathbb{R}$ is the usual real numbers, see Equation (4). Also see line 309 where $\\mathcal{R}$ (instead of $\\mathbb{R}$) is defined as the real number domain. Moreover, in line 152 $\\mathcal{R}$ is defined as set of relations.\n\n- Section 3.1: There seem to be many inconsistencies in the Methodology section. First it is written that $f(h,t,r) \\in \\mathbb{R}$ then $(h,t,r) \\in \\mathbb{R}\\cup \\mathbb{A} \\cup \\mathbb{F}$ (Equation 4). Both f(h, t, r) and (h, r, t) are in $\\mathbb{R}$ (with and without the $f$)? Moreover, the notation $(h,r,t)$ is not consistent across the paper. Sometimes a triple is denoted as $(h,t,r)$. \n\n- Section 3.2: Confusing terms are used on page 6: \"relation edge\" and \"entity node\". I believe one should either use \"relation\" or \"edge\" or \"entity\" or \"node\" but not two of the words.\n\n- Section 3.2: $V^*(X = x)$ is defined as a truth value (see line 273). The transposition operation is also applied to it afterwards, see Equation (9). This makes the equation look incorrect. Important details on how fuzzy numbers and fuzzy sets are used in the proposed approach seem to be missing.\n\n- Section 3.2: Lines 270-271: It is written that $\\mathcal{U}(Q)$ denotes a probability, but it is actually defined as a set in Equation (8)\n\n- Section 3.2: Lines 274-275: If $x$ is an entity or numerical value, what is $|x|$?\n\n- Equation 10: It should be $\\phi (V_{i1}, V_{i2}, \\ldots, V_{im})$, you used $V_{i1}$ twice in the enumeration." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": 1 }, "primary_area": null, "questions": { "value": "1. L30 in what sense is the new MRR metric validated by your results?\n2. L105 what are the inherent fuzzy relationships within numerical data?\n3. L214 why is this referred to pre-training? Is it not just \"training\"?\n4. L236 what does it mean that \"X represents various relationships\"?\n5. L247 what does \"original distribution\" refer to?\n6. L264 what is an \"anchored\" entity?\n7. L267 is N here referring to the set of all possible numbers in the system? This will then be a huge vector\n8. L308 what are these \"membership functions\"?\n9. L329 what do Avg_All and the other columns denote?\n10. L408 are these values only eliminated post-training?\n11. L465 if you’re using a continuous range, is this not the only way this can be done?\n12. L524 how does your work differ from that of LitCQD?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The improvements on the baseline are very significant, with strong results reported across 3 datasets and a large variety of query types.\n\n2. The ablation study demonstrates well how different parts of their proposed architecture contribute towards its success.\n\n3. The related work is thorough." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper aims to solve the task of multi-hop queries over knowledge graphs containing both entities and numerical values. They adapted ComplEx to devise a series of encoders which are trained and then brought together to form a system that, using fuzzy logic, can answer queries on such KGs (including queries that have answers from the real numbers). They evaluate the model across 3 benchmarks datasets against an existing method and show a significant improvement on previous results." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Major:\n1. The soundness of the mathematical presentation is very poor: this is my most significant issue with the paper. Many terms and symbols are used without being defined, notation switches arbitrarily, some sections do not make logical sense, and symbols do not match up with standard mathematical practice. As such, it was impossible for me to ascertain precisely how the proposed model operates. More specific details on this can be found below.\n\n2. The paper claims to be evaluating against 3 different numerical reasoning models, but they all come from the same paper and are variants of one another. As a result, I find the evaluation to be lacking, and suggest that the authors evaluate against some other baselines, such as the ones mentioned in L508 - L515, and L524 - L527. Furthermore, the comparison on L494 between training and testing times is not relevant, since evaluation speed is most pertinent when a model is being applied.\n\n3. Sections of the paper are full of clear typos, making it difficult to read. Some of the formatting and placement of various bits of information could also use some restructuring. More details in the minor comments.\n\nSpecific concerns with the mathematics:\n1. L150 If epsilon is entities, then what is V? And if R is all relations, where are the facts specified in the KG?\n2. L154 notation for N mismatches with the one used earlier, has not been properly defined, and usually picks out the natural numbers\n3. L172 - L178 \"bf\" is bad notation, since two variables are being used for one concept.\n4. L177 the definition of bf does not parse. Furthermore, it was already defined on L172, so has been defined in two different ways\n5. L172 N is not defined\n6. L220 - L221 what do all of these arguments refer to?\n7. L226 beta is not defind\n8. L232 R', A', F' are not defined\n9. L238 which normalisation function?\n10. L251 (())\n11. L249 - L255 I don't see how this defines the above matrices\n12. L259 which matrix M?\n13. L272 - L287 this section is very confusing, and needs more clarity as to what it is actually describing\n14. L295 what is |x| here?\n15. L302 what are Vim, u_m and n_m?\n16. L309 - L310 wrong notation for cross product and real numbers. And what is F?\n\nAlso, what is the signature of the KG?\n\nOther minor concerns:\n1. L38 could use a citation\n2. L41 \"query\"\n3. L58 \"are\"\n4. L61 \"attribute\"\n5. L108 would be nice to try cut some content to bring this line onto the previous page\n6. L140 - L145 is not a contribution, bur rather a result, and does not belong in this section\n7. L150, L151, L157, L159, L166, L238, L259, L278 - no space after full stop or comma\n8. L153 extract bracket\n9. L156 - L160 list not formatted properly, and can be defined more concisely\n10. L186 \"scored\"\n11. L188 \"t- connorm\"\n12. L173 \"entity epsilon\"\n13. L239 - 241 this paragraph does not contribute to this section\n14. L256 errant full stop\n15. L326 \"method\" instead of AVG_T would convey this better\n16. L404 Hits@K is never used in the paper\n17. L420 reference the ablation study here and show it it supports your argument\n18. L450 an example of one of the queries in the main text would be nice\n19. L535 just one metric is defined, not multiple" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose a Complex Numerical Reasoning with Numerical Semantic Pre-Training Framework, which can perform binary operations on numerical attributes within numerical knowledge graphs and supports complex numerical reasoning tasks." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024complex,\ntitle={Complex Numerical Computation with Numerical Semantic Pre-training Framework},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1epaSm9QRs},\nnote={under review}\n}" }, "abstract": { "value": "Multi-hop complex reasoning over incomplete knowledge graphs has been extensively studied, but research on numerical knowledge graphs remains relatively limited. Recent approaches focus on separately encoding entities and numerical values, using neural networks to process query encodings for reasoning. However, in complex multi-hop reasoning tasks, numerical values are not merely symbols; they carry specific semantics and logical relationships that must be accurately represented. Directly encoding numerical values often leads to the loss of such semantic information. In this work, we propose a Complex Numerical Reasoning with Numerical Semantic Pre-Training Framework (CNR-NST). We designed a joint link predictor that incorporates the relationships between numerical values and entities into the learning process of numerical semantics. The proposed framework is the first to enable binary operations on numerical attributes in numerical knowledge graphs, allowing new numerical attributes to be inferred from existing knowledge. The CNR-NST framework can perform binary operations on numerical attributes in numerical knowledge graphs, enabling it to infer new numerical attributes from existing knowledge. Our approach effectively handles up to 102 types of complex numerical reasoning queries. On three public datasets, CNR-NST demonstrates state-of-the-art performance in complex numerical queries, achieving an average improvement of over 40% compared to existing methods. Notably, this work expands the range of query types for complex multi-hop numerical reasoning and introduces a new evaluation metric for numerical answers, which has been validated through comprehensive experiments." }, "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": [ "Numerical Reasoning", "Complex Query Answering", "Knowledge Graph" ] }, "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/bdafdaf627703f3572fd4e686641c2715c0b07b3.pdf" }, "presentation": null, "primary_area": { "value": "learning on graphs and other geometries & topologies" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/6f562a3227dc9ec2774d579b998e954af480f6ea.zip" }, "title": { "value": "Complex Numerical Computation with Numerical Semantic Pre-training Framework" }, "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": "Please refer to the Weakness." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. This paper leverages the outpainting ability of the foundation model to complete the generation under the spatial condition of referencing human images through the inpainting, with a novel perspective.\n2. The spatial conditions are applied in an inpainting manner, which makes sense." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a self-conditioned diffusion (SCD) model for consistent human-centric image and video synthesis, focusing on maintaining consistency with the reference subject while generating new contents like poses and garments. SCD frames the task as a spatially conditioned inpainting problem, where the reference image as a spatial condition guiding the generation. Besides, the authors introduce a causal feature interaction mechanism to enhances the flexibility and effectiveness. Experimentally, SCD outperforms existing methods in both image and video quality metrics on 10 TikTok-style videos." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While the insight of inpainting manner is reasonable, it heavily relies on the capabilities of the foundation model. Since this work takes SD1.5 as the base, which isn’t fully perfect for generating humans, there doesn’t appear to be a mechanism to address the situation when the base model lacks such an ability. This raises reasonable doubt that the effectiveness of the results is largely due to fine-tuning the base model with the dataset rather than overcoming inherent issues. Additionally, base models indeed perform outpainting, but this does not mean their results are consist, there is still a gap.\n2. The method primarily focuses on the spatial aspect, with no special treatment on the temporal dimension for video generation—just following the AnimateDiff. So, how to improve the consistency in temporal?\n3. The observed phenomenon (line247-267) , whether it is too model-specific (SD) or architecture-specific (UNet-base), this phenomenon may not be universally present. If so, please provide more observation results of models and architectures (DiT), like SD3.5.\n4. Dedicating too much of the introduction to detailing previous methods, making it difficult to quickly grasp the main contributions of this paper. It is recommended that the authors reorganize this section, using concise language to summarize the primary limitations of prior work and clearly present contributions of this work." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I have some questions that need further clarification:\n\n1. I noticed that the reported scores in Table 2, such as PSNR, are not consistent with those reported in other works like Champ and MagicAnyone. Could you please clarify this?\n\n2. I am also interested in the experimental settings for using SMPL information as controllable signals within the proposed framework." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "$\\textbf{Originality, Significance}$\n\n1. It is commendable to study controllable human animation generation using a single, unified SD network. This approach may streamline the training process in several areas, such as optimizing GPU resource usage and tuning hyperparameters.\n\n2. The causal feature interaction strategy represents a novel contribution to the single-network paradigm for this task.\n\n3. The method achieves higher scores on the TikTok and UBCFashion datasets compared to previous works.\n\n$\\textbf{Clarity}$\n\n1. The paper is easy to follow, and the ideas are well presented.\n\n2. The spatial conditioning and causal feature interaction strategies are validated and discussed in the ablation study section, which is commendable." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores controllable human animation generation. Unlike common frameworks that use separate networks for extracting reference appearance features and generating target visuals, this study approaches the task as an inpainting problem. In this framework, the target human image is inpainted based on the spatially conditioned reference image, allowing for the use of a single, unified SD network. Additionally, the paper introduces a causal feature interaction strategy, wherein reference features can only query information from themselves, while target features can access appearance data from both the reference and target images." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "My main concern is that the technical contributions of this paper appear to be incremental.\n\nIn the context of controllable human animation generation, I am only knowledgeable about several widely studied works, such as Animate Anyone and Champ. To me, the approach of directly concatenating reference latents and target noise latents spatially for a unified SD diffusion process is new. However, the \"inpainting motivation\" and spatial conditioning strategy are common in image-to-video generation [1] and multi-view 3D generation tasks [2, 3, 4]. Given that the quantitative improvements are minimal and there are insufficient qualitative comparisons—since the supplemental videos are solely produced by this paper—it is challenging to draw definitive conclusions about the effectiveness of the proposed method.\n\nRegarding the causal feature interaction strategy, it provides only slight improvements, as shown in Table 2 (PSNR: 18.64 vs. 18.59). Based on Figure 6, it seems that the causal feature interaction strategy may not be effective. In fact, it appears that the full model introduces artifacts in the connection region of the shoulder and neck compared to the model that does not utilize the causal feature interaction strategy.\n\n\n[1] CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.\n[3] One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion. CVPR'24\n[4] InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models\n[5] CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model. ECCV'24" }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. Explain more about the intuition from inpainting work.\n2. For the performance side, show more results to demonstrate that causal feature interaction does help." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Using the same denoising network for both reference feature extraction and target image generation reduces the training burden and ensures that the target and reference images reside in a consistent feature space.\n2. The quantitative results for video synthesis appear promising, demonstrating the SCD-V's effectiveness in maintaining appearance consistency across poses. More video results are preferred if possible." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a human image and video synthesis approach that frames the task as a spatially-conditioned inpainting problem, allowing reference appearance features to guide pose-compliant target generation within a unified denoising network. By using a shared single network with a causal feature interaction framework, the method effectively mitigates domain gaps, enhancing appearance consistency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The logic behind why inpainting is advantageous (Ln221-223) is unclear and requires further clarification. Simply framing the task as inpainting does not inherently address how it enhances appearance consistency.\n2. The proposed \"causal feature interaction\" lacks novelty. It is intuitive that target features should query information from the reference, while reference features should query only from themselves; this approach feels too trivial to be considered a novel contribution.\n3. The description of the method in Ln238-287 is overly redundant, especially regarding the use of self-attention in diffusion to achieve content consistency. This observation has already been well-documented in previous video generation research.\n4. There are performance concerns. In Table 1, the FID score is significantly higher than other methods, suggesting suboptimal quality. Furthermore, in Table 2, a straightforward spatial conditioning approach without causal feature interaction achieves a lower FID and FID-VID, along with a higher SSIM, which suggests that the main claimed contribution—\"causal feature interaction\"—does not improve results. In fact, pure spatial conditioning seems sufficient for content consistency. Additionally, Figure 6 shows that results \"without causal interaction\" are visually closer to the ground truth. Could the authors provide more video-format visual results to clarify?\n5. The paper has instances of careless writing (e.g., Ln261) and inconsistencies between titles and tables (e.g., Table 2), which detract from readability and clarity." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please see the 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 is well-written and easy to understand. Figure 3 demonstrates the motivation, and Figure 4 clearly explains the details of the proposed causal spatial condition. The proposed pipeline for human motion transfer can be easily extended to virtual try on human image editing, which further shows the effectiveness of the method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed a spatial conditioning strategy for human video animation and motion retargeting, building upon the self-attention mechanism proposed in the series of works with Reference-Net. The proposed strategy is efficient and lightweight compared to Reference-Net, and the causal feature interaction mechanism enhances the identity-preserving ability." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. As far as I understand, the proposed method should be quite efficient compared to previous works since there isn't any copied UNet structure. Is there any discussion or comparison of the efficiency, e.g., trainable parameters and inference time for a single batch?\n\n2. What's the difference between the proposed strategy and a \"trainable version\" of Reference-Only ControlNet, from [here](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)? I believe Reference-Only ControlNet also proposed a similar share-weight structure for appearance control. Any detailed discussion on the architecture design?\n\n3. Metric for video generation evaluation. I understand the authors follow previous works and adopt FVD as the video evaluation metric. However, this metric has recently been widely criticized by the community because of its inaccuracy in reflecting the overall quality. I wonder what the performance comparison would be if debiased FVD is used for evaluation. From [here](https://content-debiased-fvd.github.io/)\n\n4. Are there any side-by-side video visualization comparisons between this work and recent baselines? E.g. MagicPose, Champ? It would be better to judge the temporal consistency of the video quality.\n\n5. How does the model generalize to out-of-domain real human identities? E.g. Old people?\n\n6. The denoising network has been fine-tuned on real human datasets and 3500 self-collected dance videos only, but the identity preservation for cartoon-style images in Figure 11 and the supplementary video is quite good. Is there any explanation for this? Do the self-collected videos contain any cartoon characters?\n\nI'm more than **willing** to **raise** my score if my concerns are addressed." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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's qualitative results are inadequate and the image quality is poor. visual examples are insufficient to properly demonstrate the method's effectiveness. I believe there are some issues with the \"pose injection\" method. The authors should provide more experimental details and show additional generated results." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The techniques sound reasonable and the proposed method can enhance content consistency between the generated and reference images.\n- The graph is clear and the writing is easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a Self-Conditioned Diffusion (SCD) model designed to enhance consistency in human-centric image and video synthesis. By formulating the task as a spatially conditioned inpainting problem, the model employs a unified denoising network that minimizes domain gaps between reference and target images. The key innovations lie in two aspects: a causal feature interaction mechanism that maintains appearance consistency, and a two-stage generation process that separates reference appearance extraction from conditioned target generation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **No technical contribution.** The technical novelty is limited. The significance of this paper is not expounded sufficiently. The author needs to highlight this paper’s innovative contributions to prior-guided I2I/ I2V generation.\n2. **Overclaim and SOTA.** The experimental comparisons are outdated, comparing against older methods while claiming \"state-of-the-art\" status. The work overlooks recent 2024 publications and lacks quantitative evaluations against recent work. Authors should add necessary discussions and comparisons about some of the following: \"Controlnext\", \"MimicMotion\", \"Cinemo\", \"PoseCrafter(ECCV'24)\", \"Mimo\", \"X-portrait(SIGGRAPH'24)\", \"PoseAnimate(IJCAI'24)\", \"DynamiCrafter(ECCV'24)\", \"SparseCtrl(ECCV'24)\", \"LATENTMAN(CVPR'24)\" and \"Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis (CVPR'24)\"......" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024selfconditioned,\ntitle={Self-Conditioned Diffusion Model for Consistent Human Image and Video Synthesis},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1fC4ytCAgb},\nnote={under review}\n}" }, "abstract": { "value": "Consistent human-centric image and video synthesis aims to generate images or videos with new poses while preserving appearance consistency with a given reference image, which is crucial for low-cost visual content creation. Recent advancements based on diffusion models typically rely on separate networks for reference appearance feature extraction and target visual generation, leading to inconsistent domain gaps between references and targets. In this paper, we frame the task as a spatially-conditioned inpainting problem, where the target image is inpainted to maintain appearance consistency with the reference. This approach enables the reference features to guide the generation of pose-compliant targets within a unified denoising network, thereby mitigating domain gaps. Additionally, to better maintain the reference appearance information, we impose a causal feature interaction framework, in which reference features can only query from themselves, while target features can query appearance information from both the reference and the target.\nTo further enhance computational efficiency and flexibility, in practical implementation, we decompose the spatially-conditioned generation process into two stages: reference appearance extraction and conditioned target generation. Both stages share a single denoising network, with interactions restricted to self-attention layers. This proposed method ensures flexible control over the appearance of generated human images and videos. By fine-tuning existing base diffusion models on human video data, our method demonstrates strong generalization to unseen human identities and poses without requiring additional per-instance fine-tuning. Experimental results validate the effectiveness of our approach, showing competitive performance compared to existing methods for consistent human image and video synthesis." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Diffusion model", "human 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/248c4810ff2309ba9d7bd6f5d11065c68f90a899.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/0317bfa9d4e89dcb76ebf57ae1bca3b47fa00d87.zip" }, "title": { "value": "Self-Conditioned Diffusion Model for Consistent Human Image and Video Synthesis" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Is it possible to not employ the agent or multi-agent metaphor to formulate the proposal? What is the truth power of the proposal? Is it a compossible and multi-step paradigm of stochastic optimization based on 3 hand-crafted dimensions? \n2. Ine line 161, what is the loss function l? Also please explain more regarding the definition of the reward function. How is it different o related to the loss function of LLM auto-regression loss or other kinds of losses (if any other).\n3. In Algorithm 1, please clarify whether the sampling distribution of the data are different from iteration to iteration (line 3). \n4. In line 275, 373M model is used in ablation study. It is pretty small a model. The conclusions drawn from it might not be transferrable to LLMs of billions of parameters. Please justify the study." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "• A good analysis of how different aspects influence the performance of LLM training.\n• The experiment seems to demonstrate this kind of scoring of data points can help to improve the data deficiency and performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes to a mechanism to select data into the training process based on 3 main measure (quality, domain and topic) of the data. The 3 measures are adjusted dynamically and aggregating together to determine whether data point can be selected during the training process. A RL paradigm is employed to realize the proposal. Good performance is demonstrated in the provided experiments." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "• The connection to agent or multi-agent paradigm seems weak to me. It might not be necessary to formulate the problem and the solution via \"agent\" concept. A direct stochastic optimization formulation might provide more direct description and help audience better mastering what is the proposal." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": 4 }, "primary_area": null, "questions": { "value": "1. How does the proposed framework perform when scaling up to larger models (e.g., 10B+ parameters) and datasets (e.g., trillions of tokens)?\n2. Can you provide a more detailed analysis of the computational overhead introduced by the multi-agent system compared to baseline methods? \n3. What guidelines can be provided for selecting or designing agents for other data selection criteria? Is the framework flexible enough to incorporate new agents easily? How sensitive is the method to the choice of number and types of agents?\n4. How sensitive is the performance to the choice of reference tasks used for calculating rewards in the influence functions?\n5. Can you elaborate on how the dynamic adjustment of agent weights impacts the learning process over time? Are there scenarios where this adjustment could lead to suboptimal data selection?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Introducing a multi-agent framework to collaboratively select pretraining data is a novel idea that addresses inherent conflicts among existing methods.\n2. The empirical evaluation is extensive, comparing the proposed method against a wide range of baselines and demonstrating significant improvements.\n3. The paper clearly articulates the motivation, methodology, and findings, making it accessible to readers.\n4. Improving data efficiency in LLM pretraining is a critical challenge, and the proposed method offers a practical solution with demonstrable benefits." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel multi-agent collaborative data selection mechanism aimed at enhancing data efficiency during the pretraining of large language models (LLMs). Recognizing that existing data selection methods often operate independently and may conflict with one another, the authors propose a framework where each data selection method functions as an independent agent. An agent console dynamically integrates the information from all agents throughout the training process. The agents adjust their weights based on reward signals derived from the model's performance on reference tasks. The framework is designed to flexibly and robustly combine various data selection strategies, such as data quality scoring, topic diversity, and domain information. Extensive experiments demonstrate that this multi-agent approach significantly accelerates convergence in LLM training and achieves an average performance gain of up to 10.5% across multiple benchmarks compared to state-of-the-art methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While the experiments show promising results on models up to 1.3 billion parameters, it is unclear how the approach scales to larger models commonly used in practice.\n2. The choice of agents (quality, domain, topic) seems somewhat ad-hoc. A discussion on how to generalize the selection of agents or include other data selection criteria would strengthen the paper.\n3. While ablation studies are included, more detailed analysis on how each agent contributes to different types of tasks could provide deeper 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": 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": "-Figure 2 could be more clear to show that each agent has its own memory. Consider focusing only on the Domain Agent’s flow of work to make it easier to follow.\n\n-In Table 1, it would be best to bold the top results so it’s easier to see that your approach is indeed among the best.\n\n-Should this really be considered a multi-agent system or is this really a multi-step process? I don’t see any use of reasoning or decision making here. It seems at each step, each agent is systemically called/updated and each data point is labeled with a combination of scores from the “agents”. What is “agentic” about this? The approach is still valuable, just questioning whether it falls under “agents”.\n\n-Does your approach add significant additional latency to the pretraining stage? \n\n-Is your approach particularly valuable for small models?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "-Mixing data quality and data selection techniques is a challenging problem: they show in their case study that typical data curation techniques can conflict and naively combining them is not sufficient.\n\n-Unlike many off-the-shelf multi-agent systems, they are proposing optimization of each agent’s weights (stored in memory) based on reward signals from the model undergoing pretraining.\n\n-Results show that their multi-agent data selection produces the best performance. Ablations show the three agents in collaboration outperform all other permutations of the agents (with and without collaboration) and strongly outperform the setting with no agents at all. \n\nOriginality:\nI am not aware of another multi-agent approach for data selection in pretraining - so this appears to be a novel application of multi-agent systems.\n\nQuality:\nAll key components covered - clear literature review, motivation, experiment design, results. It would have been better if they made their contribution differentiations clear in the literature review - for ex, confirming if they are indeed the first multi-agent approach for data selection. And if not, how are they different.\n\nClarity: \nThe paper was generally well written and easy to read.\n\nSignificance:\nPretraining is the most critical and expensive operation for LLMs. To make the best use of your pretraining, optimizing the data is key." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a multi-agent approach to strategically select data for pretraining LLMs. The paper motivates the need for a multi-agent design by sharing a case study on the SlimPajama pretraining dataset. They illustrate that the most common data set considerations (and their corresponding metrics), including data quality, topic diversity, data impact, and data domain are not straightforward to jointly optimize. Therefore, they propose a multi-agent system, where each data selection method/metric is represented as an agent. Through this multi-agent approach, these methods can be mixed via multi-agent collaboration, forming a highly adaptive approach in data selection. They show that their multi-agent approach is effective: the data curated by their method leads to faster convergence in training and improved benchmark performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "-Figures can be improved, see my comments below.\n\n-Experiments only on 1B model. It would be interesting to see the impacts across more model sizes (smaller and larger) and model architectures to see which range of models really benefit from this. \n\n-This shows a lot of potential already, but the point would be very strongly proven if they could show comparison to other 1B model performances (DeepSeek, TinyLlama, etc.), showing that their approach yields superior models in general." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "* How does the definition of the agent used in this paper relates to the term agent used in other fields of AI? Such as multi-agent RL, or the type of work usually published in AAMAS? \n* The term \"console\" is usually applied to a component that is used by a human operator. Is the term agent console in the paper related to it?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* A case study validates the initial claim of the paper, that the quality, diversity and influence scores of data are not strongly correlated with each other.\n* Relatively extensive experiments were conducted with the training of a 1.3B LLAMA LLM." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors argue that training data selection is an important component in LLM training. The various techniques that had been proposed might be conflicting in their recommendations. The authors are proposing a technique in which the different data selection algorithms are considered independent agents, with an \"agent console\" integrating the recommendations. The approach enable the dynamic adjustment of contributions of the agents during the training process of the LLM. The SlimPajama dataset is used as the working example." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The fact that the diversity of topics, quality of material, and influence on the trained LLMs are different metrics is not a surprising observation - these are obviously very different things. A strong correlation between them would be the surprise. \n* The term \"agent\" has a relatively clear definition in the AI literature, as an autonomous entity, that takes actions in an environment, in the pursuit of a goal. The fact that the authors have to introduce a definition for the term \"agent\" and \"agent console\" and define them in terms of \"data selection method\" makes the paper difficult to follow. It doesn't seem that these \"agents\" are taking any actions, or have any autonomy. \n* In practice, the proposed approach appears to be a way to make a decision about what training data to include based on weighting three pre-existing metrics about the data sources. This decision process could have been easily written without introducing agent language. In fact, practitioners very likely are already making such judgements." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024multiagent,\ntitle={Multi-Agent Collaborative Data Selection for Efficient Language Model Pretraining},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1fwZJzGdKj},\nnote={under review}\n}" }, "abstract": { "value": "Efficient data selection is crucial to accelerate the pretraining of large language models (LLMs). While various methods have been proposed to enhance data efficiency, limited research has addressed the inherent conflicts between these approaches to achieve optimal data selection for LLM pretraining. To tackle this problem, we propose a novel multi-agent collaborative data selection mechanism. In this framework, each data selection method serves as an independent agent, and an agent console is designed to dynamically integrate the information from all agents throughout the LLM training process. We conduct extensive empirical studies to evaluate our multi-agent framework. The experimental results demonstrate that our approach significantly improves data efficiency, accelerates convergence in LLM training, and achieves an average performance gain of 10.5% across multiple language model benchmarks compared to the state-of-the-art 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": [ "Language Model Pretraining; Data-efficient Training; Data Selection" ] }, "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/0d707d791ba04c09351304d540e4ca38c482f4e1.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/4980270b11c6591913f450dff29957221288f46a.zip" }, "title": { "value": "Multi-Agent Collaborative Data Selection for Efficient Language Model Pretraining" }, "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": 2 }, "primary_area": null, "questions": { "value": "1. **Down View Clarification**: Could the authors clarify the colorless nature of the down view and its virtual camera position? Including a figure illustrating the virtual camera position could make this aspect clearer. Also can you explain why the down view looks colorless compare with top view?\n2. **Effect of S-PR on Generation**: The design of S-PR is unclear. Could the authors include a figure comparing the rendered images before and after applying S-PR? This would help illustrate S-PR’s impact on generation.\n3. **Focused Experimentation in Table 2**: The improvements mainly impact tasks needing precise top-down alignment (e.g., Insert Peg, Sort Shape). Given that most differences in Table 2 are within 1%, an experiment disabling both S-PR and down view could clarify other designs’ contributions. Additionally, statistical tests would help determine if small differences are meaningful. It will also be great if you can discuss the speed-accuracy trade-off applying these designs.\n4. **Baseline and Failure Analysis**: Real-world experiments would benefit from a baseline comparison. A typical baseline would be an RVT/RVT2 without designs introduced in the paper. It would also help if the authors could conduct a failure analysis to highlight potential improvement areas. If challenges arose in implementing real-world baselines, an explanation would be valuable.\n5. **Left View Performance Drop**: The results in Table 2 indicate a counterintuitive performance drop when incorporating a left view. A straightforward experiment replacing the right view with the left view (without adding extra views) could help isolate the cause. Based on this experiment, the authors can explore whether this drop is due to:\n - the presence of both left and right views introducing redundancy or conflicting information,\n - the left view alone, as opposed to the right view, negatively impacting performance,\n - or simply having an excess of views, which may complicate heatmap prediction?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. **Performance Improvement**: The proposed changes significantly enhance performance by mitigating occlusions from specific viewpoints, improving view flexibility.\n2. **Robust Experimentation**: The paper includes multiple experiments to verify the efficacy of each introduced method, which supports the validity of the approach." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes an improvement over the RVT/RVT2 method by introducing enhancements such as S-PR, S-MVT, and HSP. The updates effectively address the problem of view occlusion in RVT from certain angles, particularly the top view. Extensive experiments demonstrate impressive performance gains with these adjustments, validating their effectiveness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **Lack of Visual Illustration**: The concept of the down view is not entirely clear, especially concerning why it lacks color. A visual illustration showing the virtual camera position could enhance understanding.\n2. **S-PR Explanation**: It’s challenging to grasp exactly how S-PR contributes to generation. A comparative figure demonstrating results before and after applying S-PR would clarify this aspect.\n3. **Lack Real World Baseline**: The baseline experiment in real world is missing.\n4. **Unclear Contribution of Different Designs**: Most of the performance in Table 2 (S-RVT2) are within 1 point, which makes it unclear whether many of them are still useful." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "- It would be nice if the authors could have some discussions on the data efficiency of the proposed framework. As discussed in the Related Work section, both transformer deployment and imitation learning with high precision require substantial training data. In this paper, the sim experiments use 100 demonstrations per task, and the real experiments use 15-20 demonstrations per task. How does it compare to other methods?\n- Another very relevant question: For the real experiments, the paper states that \"the number of demonstrations for each task is determined by its complexity and the degree of variability in task configurations.\" How much will this affect the performances? Specifically, for the two tasks \"stack blocks\" and \"plug charger\", how would the model perform if there are only 15 demonstrations, as in the other two tasks?\n- I am curious, in Table 1 task Sweep to Dustpan, why is S-RVT success rate lower than RVT? Are there any specific features of this task that make it different from the others, or is it just a normal fluctuation in the measurement? (This is really just my curiosity. The overall experimental results look good to me, and a 10% success rate drop out of 25 tests here is acceptable.)" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The authors have conducted abundant experiments and ablation studies in both sim and real.\n- The experimental results look good. The proposed framework brings consistent improvements to RVT and RVT2 across different scenarios.\n- The paper is well-written. The concepts and intuitions are explained together with concrete examples, making it very easy to understand." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper addresses the limitations in previous virtual view-based methods, focusing on the occlusion problems and the resolution constraints. To resolve these problems, it proposes the Super Robot View Transformer (S-RVT) comprising of three modules: the Super Point Renderer (S-PR) that enhances the rendering process to mitigate occlusion artifacts, the Super-resolution Multi-View Transformer (S-MVT) that integrates superresolution\nto the output heatmaps, and the Hierarchical Sampling Policy (HSP) that samples multi-view heatmaps in 3D space to obtain accurate 3D poses. Experiments show that the proposed framework improves the performances of RVT and RVT2 in various setups." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- To my understanding, the paper is mainly addressing the uncertainty problems (in RVT or RVT-like robot learning frameworks): the aleatoric uncertainty is addressed by the virtual-view pointcloud rendering, and the epistemic uncertainty is addressed by the feature map superresolution. On one side, I like the intuitions discussed in the paper, on the other side, simply looking at the framework, the ways of resolving these problems look very straightforward, with a sequence of concrete engineering efforts. It would be good to have more concrete discussions, based on the method, on how RVT didn't address these uncertainties well and how the framework resolves these issues -- this can show better linkage between the high-level intuitions of the paper and the concrete steps in the method.\n- A concrete question following the previous question is about the pointcloud rendering: It is simply done by a 2D projection, but what is the quality of the projected virtual views? Does it have any requirements on the placement of the (real) camera? Specifically, in the ablation study, it shows that going from 4 virtual views to 5 views decreases performance. I think this implies that the virtual views are not all of good quality which helps the algorithm to figure out better policies." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- How is S-PR implemented in real-world scenarios?\n- What will happen if the resolution of input images is raised?\n- What's the performance of MVT in the real-world experiments?\n\nI will consider raising scores if my concerns are addressed in the rebuttal period." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "- This paper is well-written and easy to follow.\n- The proposed modifications on RVT are intuitive, and the RLbench experiments verify the effectiveness of S-MVT in simulator settings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents Super Robot View Transformer (S-RVT) -- a series of techniques to improve Robot View Transformer. It consists of 3 modules: the Super Point Renderer that mitigates occlusion artifacts, the Super-resolution Multi-View Transformer that performs superresolution to the output heatmaps, and the Hierarchical Sampling Policy that efficiently samples multi-view heatmaps in 3D space. The experiments suggest S-RVT obtains a consistent performance boost against RVT on the RLBench benchmark." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- I doubt the fundamental rationality of the Super Point Renderer (S-PR) and Super-resolution Multi-View Transformer (S-MVT) modules.\n - According to my understanding, S-PR renders the objects occluded by the robot. It would definitely be effective in the simulator, but how would that be made possible in the real world?\n - Meanwhile, S-MVT aims to perform super-resolution to the multi-view images. However, why not just enhance the resolution of RGB-D images in the beginning? D515 could capture depth photos in a resolution of up to 1024x768, but the RGB-D images used in the paper only have a resolution of 128x128.\n- While S-MVT is compared with MVT in the simulator setting, it is not compared with MVT in the real-world setting. I am particularly confused about how S-PR is implemented in real-world scenarios." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "1. In the paper: \"The 3D points are projected onto the 2D image plane by converting them into image coordinates using GPU accelerated matrix operations.\" Could the author give more details on the implementation and visualization of this?\n\n1. Why the MVT module can address the epistemic uncertainty? Please provide detailed information.\n\n2. In the HSP section, can the author provide some details about how HSP can solve the GPU memory overflow problem?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. This paper conducts sufficient experiments to evaluate the proposed framework on various robotic manipulation tasks with different baseline methods.\n2. The pictures in this paper are presented clearly.\n3. Related works are comprehensively reviewed." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed a framework for multi-task imitation learning with key-state-based methods for robotic manipulation learning, especially for high-precision tasks. The author claims their model addresses the epistemic uncertainty of the proposed framework. The framework, SRVT, consists of three modules: the Super Point Renderer (S-PR), the Super-resolution Multi-View Transformer (S-MVT), and the Hierarchical Sampling Policy (HSP). This paper shows both simulation and real-world experiment results to evaluate the framework." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The overall writing and flow of the paper need considerable improvement. The abstract, introduction, and related works sections are repetitive and convey similar concepts. Moreover, a paper should streamline these sections to provide a progressive understanding.\n\n2. This paper's primary claim that the proposed MVT module can advance epistemic uncertainty is not validated. To address such uncertainty, the paper must provide theoretical proofs, uncertainty analysis, and ablation studies. Some common methods, like conditional variable at risk, can be used for 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": 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": "* In line 257, the statement *\"S-MVT generates heatmaps with $sr$ times higher resolution\"* is unclear, as $sr$ is not introduced in the preceding paragraphs.\n* In line 260, should this result in $16^2$ patches instead of $16$ patches?" }, "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 addressing limitations in prior work, such as handling occlusion issues in the rendering process and overcoming resolution constraints in pose prediction, is valuable.\n* The authors perform extensive experiments, including comparisons with baseline models and ablation studies, to demonstrate the effectiveness of the proposed components.\n* They also conduct several real-world experiments and provide the video evidence." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Building on prior work, RVT and RVT-2, this study introduces S-RVT to address limitations like occlusion issues and resolution constraints. Specifically, it presents S-PR (Super Point Render) to enhance rendering and reduce occlusion artifacts, S-MVT (Super-resolution Multi-View Transformer) to integrate super-resolution to output heatmaps, and HSP (Hierarchical Sampling Policy) for accurate 3D pose estimation through a coarse-to-fine sampling approach. Experimental results show that S-RVT outperforms previous methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* It would be beneficial to clearly specify the differences between the proposed method and prior work, indicating which contributions are adopted from previous studies and which are newly introduced in this paper. For example, in Section 3.2, RVT-2 appears to have already implemented z-ordering and screen-space splatting techniques, yet this is not clarified here. Additionally, in Section 3.3, lines 255 to 263 (nearly half the paragraph) contain content similar to RVT and RVT-2, it would be helpful to focus more on the novel methods introduced in this work. Clearly distinguishing between techniques inherited from previous work and unique innovations would improve understanding and highlight the contributions of this study.\n* In Section 3.3, additional details on the upsampling process would be helpful for clarity. Could the authors expand on how the upsampling is implemented?\n* The authors are encouraged to provide further analysis of the experimental results:\n - In Table 1, S-RVT performs worse than RVT in tasks such as 'put in safe' and 'sweep to dustpan,' and S-RVT2 performs worse than RVT-2 in 'slide block' and 'sweep to dustpan.' Although these lower scores are acceptable for specific tasks, more in-depth analysis of why the proposed method underperforms in these cases would strengthen the findings.\n - In the ablation study, the impact of each component varies between S-RVT and S-RVT2. For instance, SPR is more critical for S-RVT2, whereas HSP has a greater influence on S-RVT. Additional analysis on why these components affect the models differently would offer valuable insights.\n* The authors are also encouraged to discuss failure cases of the proposed method." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024super,\ntitle={Super Robot View Transformer},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1g4s7ME93g},\nnote={under review}\n}" }, "abstract": { "value": "Learning a single model for multiple robotic manipulation tasks, particularly high-precision tasks, has been a long-standing challenge in robotics research due to uncertainties inherent in both the model and the data. These uncertainties, namely epistemic uncertainty arising from model limitations and aleatoric uncertainty stemming from data variability, hinder precise control.\nWhile the Robot View Transformer (RVT) improves performance by re-rendering point clouds from fixed viewpoints and processing structured 2D virtual images, it still suffers from occlusion artifacts in rendering and limited action precision due to resolution constraints.\nTo address these limitations, we propose the Super Robot View Transformer (S-RVT) framework, which integrates three novel components: the Super Point Renderer (S-PR), the Super-resolution Multi-View Transformer (S-MVT), and the Hierarchical Sampling Policy (HSP). The S-PR enhances the rendering process to mitigate occlusion artifacts, while the S-MVT integrates super-resolution to the output heatmaps, enabling finer-grained manipulation. The HSP efficiently samples multi-view heatmaps in 3D space to obtain accurate 3D poses.\nThese innovations collaboratively mitigate the challenges of occlusion and precision in manipulation tasks. Our experimental results demonstrate that S-RVT achieves a success rate of 87.8 \\% across 18 manipulation tasks, surpassing the state-of-the-art of 81.4 \\%. Notably, for high-precision manipulation tasks, S-RVT exhibits nearly a two-fold improvement over existing methods, underscoring its effectiveness in precise control scenarios. Our code and trained models will be released to support further research." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "robotic manipulation", "multi-task learning", "robot view 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/908210164cdd5f9b606d980de275c53864bd50a1.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/8b7a8883967f466a090a760f04b80492ebbede93.zip" }, "title": { "value": "Super Robot View Transformer" }, "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": 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": "- can authors discuss the work's limitations and potential impact on future work?\n- can authors discuss potential overlap with prior work notably [1] (see above)?\n- can authors adjust figure 1 with axis labels and explain why the number of samples (if I understand correctly) varies between epoch 1 and 5?" }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "- clear and well-written: the manuscript is well-written and easy to follow\n- relevant topic: the authors tackle an interesting finding (i.e., training with random labels leads to substantial performance increase) that is relevant to the community and connected to multiple popular topics like self-supervised representation learning as well as the emerging idea of a universal representation \n\n[1] Bojanowski, Piotr, and Armand Joulin. \"Unsupervised learning by predicting noise.\" International Conference on Machine Learning. PMLR, 2017.\n[2] Reizinger, Patrik, et al. \"Cross-Entropy Is All You Need To Invert the Data Generating Process.\" arXiv preprint arXiv:2410.21869 (2024).\n[3] Huh, Minyoung, et al. \"The platonic representation hypothesis.\" arXiv preprint arXiv:2405.07987 (2024)." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores the finding that training neural networks with random labels leads to substantial performance improvements in comparison to randomly initialised neural networks. Authors claim that these experimental findings (supported by empirical evidence & overlapping with prior work -- see below) highlight that the process of learning from data occurs independently of human-imposed structure and inform a novel perspective on the way neural networks work and do not discuss the work's limitations. Authors go on to proposing the use of random labels to fine-tuned pre-trained backbones to improve downstream generalisation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- strong overlap with non-cited work/lack of novelty: the authors centered their work around the observation that random labels offers substantial performance improvement which they claim is a novel finding (\"We completely removed the structure from the learning process by randomizing the class labels, and found that the model actually was able to learn from data despite the complete randomization and even performed better from a generalization perspective.\"). In fact, this observation has been presented and discussed in several works in the past, including [1], which is not cited by the authors.\n- soundness of claims: the paper makes bold claims about \"how neural networks learn\" and what drives this process (\"we present as provocative claim that the process of learning from data happens independently of human-imposed structures. To support this, we introduce the bold alternative hypothesis called the “Pan for Gold”. \"). These claims remain conjectures and hypothesis which are only supported by empirical evidence that the network learns from random labels which does not prove the author's \"pan for gold\" hypothesis. Additionally, authors further justify the relevance of their work by relying on GradCam visualisation, a method proven to be unreliable -- as also mentioned by authors.\n- confidence in empirical findings: while the paper is well-written and clear, there is a lack of polishing of figures and of empirical results which impedes clarity and well as confidence in empirical results (e.g., missing axis labels, randomly masked out portions of curves, single seed experiments, core findings in section one are conducted on two small scale datasets and a single architecture type).\n- missing sections: the authors omit important sections to their work including a related work section and a discussion of the paper's limitations.\n\n\n[1] Bojanowski, Piotr, and Armand Joulin. \"Unsupervised learning by predicting noise.\" International Conference on Machine Learning. PMLR, 2017." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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. **Theoretical Justification:** Can the authors provide a rigorous theoretical framework to support the \"Pan for Gold\" hypothesis? Specifically, how does SGD with unstructured labels in overparameterized models lead to meaningful generalization, and what are the underlying mechanisms?\n2. **Experimental Validation on Larger Datasets:** Have the authors considered testing the PUL algorithm on larger and more diverse datasets to validate the generality of their claims? Small-scale datasets may not capture the complexities of modern deep-learning tasks.\n3. **Comparative Analysis with Baselines:** How does the PUL algorithm perform compared to existing state-of-the-art methods in unsupervised domain adaptation and object discovery?\n4. **Clarity on Pan for Gold Hypothesis:** The hypothesis seems to conflate the effects of noise and regularization in SGD with meaningful learning from unstructured data. Can the authors clarify how their hypothesis differs from existing theories on overparameterization and implicit regularization?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "* **Challenging Conventional Wisdom:** The paper attempts to question the traditional beliefs about the necessity of structured labels and loss functions in deep learning, which is an interesting and bold endeavor.\n\n* **Novel Hypothesis Introduction:** The \"Pan for Gold\" hypothesis is a creative metaphor that could inspire new ways of thinking about generalization in deep learning.\n\n* **Exploration of Unstructured Labels:** Investigating the effects of training with unstructured labels is an intresting approach that could uncover overlooked aspects of model training dynamics." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces the \"Pan for Gold\" hypothesis, which challenges the traditional view that structured labels and well-defined loss functions are essential for deep learning models to learn meaningful representations and generalize well. The authors propose that generalization emerges naturally through the stochasticity inherent in SGD when training overparameterized models, even with unstructured (randomized) labels. They suggest that SGD acts like panning for gold, where valuable features are naturally sifted out from noise without relying on human-imposed structures.\nTo support this hypothesis, the authors conduct experiments where models are trained on datasets with completely randomized labels. Surprisingly, these models still learn meaningful features, as evidenced by improved performance over random initialization. They analyze this phenomenon using the NTK framework and observe a \"swing phenomenon,\" where model outputs fluctuate significantly during early training stages.\nBased on these observations, they introduce the PUL algorithm. They demonstrate PUL's effectiveness in tasks like unsupervised domain adaptation and object discovery. Additionally, they suggest that PUL mitigates issues like massive activation in vision transformers, aiding in model quantization." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* **Lack of Theoretical Rigor:** The paper makes strong claims without providing a solid theoretical foundation. The mathematical analysis is superficial and does not rigorously justify the \"Pan for Gold\" hypothesis or explain why unstructured labels should lead to better generalization.\n* **Insufficient Empirical Evidence:** The experimental evaluation is limited and inadequate to support the bold claims made. Experiments are conducted on small datasets like MNIST, CIFAR-10 and SVHN, which are not representative of modern large-scale tasks. The performance improvements reported are marginal and could be due to experimental noise.\n* **No Comparison with Baselines:** The paper fails to compare the proposed PUL algorithm with established baselines or state-of-the-art methods in the respective tasks. Without such comparisons, it's impossible to assess the significance of the results or attribute improvements to the proposed method.\n* **Overgeneralization of Findings:** The authors make sweeping generalizations about deep learning based on limited and specific experiments. The claim that generalization emerges naturally through SGD in overparameterized models trained with unstructured labels is not convincingly demonstrated.\nMethodological Issues: Key details about the experimental setup are missing or unclear, hindering reproducibility. For example, the process of assigning unstructured labels, hyperparameter settings, and specifics of the PUL algorithm are not adequately described.\n\nWeak Analysis of Results: The paper lacks a thorough analysis of the results. It does not explore alternative explanations for the observed phenomena or consider confounding factors. The interpretations often rely on anecdotal observations rather than rigorous investigation.\nAmbiguous Writing and Clarity Issues: The paper is difficult to follow in several sections due to ambiguous explanations and poor organization. Key concepts are not clearly defined, and the narrative lacks coherence, making it challenging to understand the proposed ideas fully." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Are the unstructured labels fixed during training or regenerated each epoch? Have you also experimented with changing labels at each epoch? This would be an important experiment as it could lead to completely different learning dynamics since the network cannot memorize stable image-label pairs.\n\n2. In Sections 4.1 and 4.2, how does the performance change with longer training periods? The paper only shows results with 2-3 epochs, but longer training analysis is necessary to understand the stability and effectiveness of the method.\n\n3. How was the optimal number of random classes determined in the PUL algorithm? Did you perform any experiments with different numbers of classes?" }, "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 presents a novel and interesting perspective on deep learning generalization by proposing a new hypothesis about the role of stochasticity in learning meaningful features\n2. The proposed methodology is remarkably simple yet demonstrates effectiveness, requiring only random labels and a few additional training steps\n3. The theoretical analysis through Neural Tangent Kernel provides mathematical insights into the learning dynamics and supports the main hypothesis\n4. The experimental results show significant performance improvements across various applications including domain adaptation and object discovery tasks, demonstrating the practical utility of the proposed method" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a new hypothesis about generalization in deep learning, suggesting it's not about learning structured patterns in data but rather a \"Pan for Gold\" process where SGD naturally filters useful features, and proposes the PUL algorithm utilizing random labels, demonstrating performance improvements in domain adaptation and object discovery tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper critically lacks essential experimental details needed for reproduction, including the specific method of generating unstructured labels, exact model architecture, hyperparameter settings, and detailed training procedures, making it difficult to validate the claims independently.\n\n2. The paper lacks clear explanation about whether unstructured labels remain fixed during training. Based on the paper's content, it appears that labels are fixed, in which case neural networks would inevitably learn visual features in the process of memorizing image-label pairs, as they need to recognize some visual patterns to distinguish between images even with random labels. This suggests that learning meaningful features might be a natural consequence of the memorization process rather than the proposed \"Pan for Gold\" hypothesis.\n\n3. The theoretical analysis is insufficient as the paper lacks in-depth discussion on why the \"Pan for Gold\" process leads to good generalization, focusing merely on describing phenomena without explaining the underlying mechanisms\n\n4. The experimental validation is limited, lacking analysis of performance with longer training epochs in Sections 4.1 and 4.2, missing ablation studies on the number of unstructured labels, and failing to provide sensitivity analysis for various hyperparameters." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Could you provide the definition of structure in this paper?\n2. How do you distinguish the impact of stochasticity with the impact of unsupervised/semi-supervised representation learning/model-inductive-bias/compression?\n3. In Table one, \"we applied transfer learning to the frozen encoder.\" Could you elaborate how is transfer learning carried out?\n\nMinor:\nIn Fig 2, \"red\" should be \"black\"" }, "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 well-written and presents an interesting analysis of generalization through stochasticity. I like the idea of decoupling the impact of human-imposed labels in generalization.\n\nThe paper carries out principled analysis on neural tangent kernel that demonstrates the swing phonemenon in the learning process. The visualizations of learning process through gradCAM and analysis on saliency maps are interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates the generalization of deep learning methods when faced with random labels. It argues that generalization is not about learning the structure of data (X, Y), but rather follows a stochastic process that initially fluctuates before converging to a stable function space, akin to \"panning for gold.\" Experiments on unsupervised domain adaptation show that using random labels with KL regularization outperforms the source-only baseline, which does not apply any adaptation. Exploratory analysis also shows that the proposed random labels reduce outliers in the attention map, resulting in a more balanced distribution that is more suitable for quantization." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Although I liked the exploration of stochasticity and its role in learning, I think the paper places too much emphasis on supervised data and stochasticity while neglecting other important factors in unsupervised and semi-supervised learning. The experiments are relatively weak because they rely on a supervised model trained with ground-truth data and KL regularization, which does not fully demonstrate the impact of stochasticity.\n\n**1. Definition of structure, the role of supervised labels, and considerations for unsupervised/semi-supervised learning**\n\nThe paper frequently mentions \"structure\" but does not provide a clear definition. It appears to me that \"structure\" refers to supervised class labels assigned by humans, and the paper argues that the \"structure\" itself is not the essence of the 'gold' result and that learning also happens with random labels.\n\nI disagree with the notion that \"the goal of deep learning is to learn from data according to structures defined by humans.\" I think the supervised signal is only one source of information that models use to learn. Other sources of information include data itself as used in unsupervised or self-supervised learning, information from a model decision boundary manifested through unlabeled data as in transductive and semi-supervised learning setups, and assumptions about the world such as convolution for image processing. The paper solely focuses on the structure from supervisory signals and ignores other sources of information, which also plays a crucial role in learning that could be attributed to stochasticity in this paper.\n\n**2. Experiments**\n\nThe experiments focus on domain adaptation where a supervised model has been trained on ground-truth labels in the source domain, and the goal is to adapt the model to a closely related target domain. This setup weakens the empirical results because the model is initialized with a learned representation from ground-truth labels and does not fully demonstrate the impact of stochasticity in a learning-from-scratch setup. Moreover, the model only performs small adjustments due to the constraint of KL regularization that penalizes the model for deviating from the source model. It is well-known that, in semi-supervised learning, a model could outperform the source-only baseline without any target labels. It is unclear if the improves is from stochasticity or semi-supervised learning (clustering assumption, KL, transduction through batchnorm)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": { "value": "The paper is horribly written and throws at the face of the reader, all throughout, ill-defined terms high level philosophical terms that I do not know what to make of." }, "flag_for_ethics_review": { "value": [ "No ethics review needed.", "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": 1 }, "primary_area": null, "questions": { "value": "see above" }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The paper presents the observation that training on data with random labels as an unsupervised per-training scheme can learn useful “features”, though the details of how are left very vague.\nI am not familiar enough with this area of literature to know how novel this claim is. To me it does not seem surprising that training on random labels leads to a better model initialisation than the no pertaining.\nUnfortunately the written quality of the paper is very low and thus it is difficult to determine if the paper really offers any strengths." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents the observation that training on data with random labels can learn useful “features”, and the author suggest doing this as an unsupervised per-training scheme, though the details of how are left very vague. Unfortunately the written quality of the paper is very low, it is difficult to determine much more than this." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I apologise if English is not the first language of the authors but the written quality of the paper is sadly unacceptable for ICLR. At the moment the paper is almost impossible to follow. While there are a lot of machine learning terms mention the sentences are so unspecific and vague, lacking in concrete definitions and jumping from place to place it makes assessing the ideas of the authors impossible for me. I do not know if LLM’s have been used in the writing of the paper, or in translating from another language but the final quality is just not good enough at the moment.\n\nThe issues with the write are as follows:\n\n1. Use of vague terms, which are not defined. For example “structure”, “function speed”, “Noisy features”, “Gold features”\n\n2. Vague handy wavey claims not back up with references: “This aligns with physical intuition, where relaxation of tension leads to the stabilization of the space, and high-energy regions, like artifacts, are naturally eliminated.”\n\n3. Reference to a “Panning through Unstructured Label (PUL) algorithm” which is never really defined, you have to try and glean what the algorithm is from passing comments.\n\n4. The details of the experiment are extremely vague. For example. The full text exampling an experiment is: “Table 5 presents the object discovery performance on various trained models on ResNet50, including ImageNet pretrained, DINO (Caron et al., 2021a), and ImageNet pretrained weights further trained using our method. As can be seen from the results, even with just a three epochs of training using unstructured labels, performance can be easily improved. ”\n\n5. Baselines for the experiments are not explained, the experiments jump from place to place with very little intuition and justification of why important choices were made We would like to recommend the paper is rewritten with a greater leave of specificity, intuition and detail before being resubmitted.\n\n\nOther.\n\nThe experiments are extremely limited, typically only considering a single model, data set with no mention of repeats or the training procedure\nBaselines for the experiments are extremely limited a single baseline is used, with little detail of what this baseline was and why it should lead to a fair comparison\nIn the conclusion the authors claim they introduce a “bold alternative hypothesis called the “Pan for Gold”,” but after reading the paper I’m am left wondering what this current hypothesis that “Pan for Gold” is mean to be an alternative too? This is never explained in any level of detail." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "This paper provides new insight on generalization through functional analysis and algorithms. The algorithm is applicable to wide area and sota in some areas" }, "_bibtex": { "value": "@misc{\nlee2024pan,\ntitle={Pan for gold},\nauthor={Junhoo Lee and Kyomin Hwang and Dongkwan Lee and Han Sangbum and Min Kyu KIM and Nojun Kwak},\nyear={2024},\nurl={https://openreview.net/forum?id=1gqR7yEqnP}\n}" }, "abstract": { "value": "Training a deep model is fundamentally about reducing loss, and we often believe that a ''good model'' is one that trained with a ''good loss.'' This paper investigates that belief. We show that even when learning with unstructured, randomized labels, models can still discover generalized features. We propose that generalization in deep learning is not about learning the structure of data through a well-structured loss, but rather a process akin to ''pan for gold,'' where gradient descent shakes through the function space, naturally stabilizing useful features. To support this, we present quantitative and qualitative experimental evidence, and introduce the Panning through Unstructured Label (PUL) algorithm. We demonstrate its effectiveness across various fields, showing improvements in unsupervised domain adaptation, state-of-the-art performance in object discovery, and its ability to mitigate massive attention issues. Finally, we offer a new interpretation of existing deep learning assumptions, challenging the conventional beliefs in the field." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": { "value": [ "~Junhoo_Lee2", "~Kyomin_Hwang1", "~Dongkwan_Lee1", "~Han_Sangbum1", "~Min_Kyu_KIM2", "~Nojun_Kwak1" ] }, "authors": { "value": [ "Junhoo Lee", "Kyomin Hwang", "Dongkwan Lee", "Han Sangbum", "Min Kyu KIM", "Nojun Kwak" ] }, "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": [ "Generalization", "Overparameterized Network", "functional analysis", "Domain Adaptation" ] }, "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": "lee|pan_for_gold" }, "pdf": { "value": "/pdf/0319d8978ae3a05fcc93747d94b335e773d1b564.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": "Pan for gold" }, "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": 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. Could you further explain why calculating document gradients with the base model and the query gradients with the fine-tuned model? Could this discrepancy cause any potential problems?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper tries to tackle an intellectually significant question: how do LLMs generalize reasoning abilities from pretraining data to solve completion questions? This exploration into the mechanics of LLM reasoning generalization is both timely and meaningful, given the increasing focus on interpretability and robustness in AI. \n2. The findings provide intuitive insights, showing that LLMs draw on a broad range of abstractly related documents when solving reasoning questions, as opposed to the more targeted document reliance seen in factual questions. This highlights the importance of procedural knowledge and coding data for reasoning tasks, an observation that aligns with broader intuitions about reasoning and learning in LLMs.\n3. A key technical strength lies in the revision and adaptation of EK-FAC influence functions. The authors refine this method to assess the influence on model accuracy, which is essential for examining how specific documents impact LLM performance in reasoning versus factual tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates the role of pretraining data in shaping large language models' (LLMs) abilities in reasoning tasks compared to factual question-answering. By analyzing two models of different sizes (7B and 35B parameters) across reasoning and factual queries, the authors aim to understand how LLMs generalize when tackling reasoning tasks and whether they rely on specific retrieval of information or broader procedural knowledge. The study applies influence functions to rank the most impactful pretraining documents for different queries, examining if reasoning draws from procedural patterns rather than specific facts.\n\nEmpirically, the study finds that reasoning tasks rely on a more distributed set of documents, often containing procedural content like code snippets or mathematical explanations, while factual questions frequently rely on specific documents containing direct answers. Code-based documents, in particular, emerge as influential for reasoning, likely due to their structured, step-by-step nature. Additionally, reasoning tasks across similar queries show correlated influence scores, suggesting a reliance on shared procedural knowledge. The larger 35B model also shows less variation in influence across documents, hinting at improved data efficiency. Together, these findings imply that LLMs approach reasoning by aggregating procedural knowledge rather than retrieving isolated factual data, shedding light on different generalization strategies in LLMs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The overall style resembles a blog post, presenting intriguing observations over a cohesive scientific narrative. For example, the conclusion/discussion section takes more than 1 page to explain everything again. The paper could either prioritize the revised EK-FAC function or convert the observations into some actionable strategies to improve LLMs. Additionally, reorganizing the paper to integrate findings more succinctly could create a more cohesive narrative.\n2. Although the paper acknowledges computational constraints, the scale of data and task complexity could be expanded to strengthen the conclusions. The study’s focus on basic arithmetic and simple mathematical queries limits its generalizability to broader reasoning tasks that are common in real-world applications. Also, the study examines only a subset (5 million documents) of the total pretraining data, which may exclude influential documents crucial to understanding the LLMs’ full generalization strategy.\n3. The paper predominantly examines positively influential documents, yet negatively influential documents could offer essential insights into reasoning limitations and biases. Understanding negative influences would allow the authors to identify pretraining data that hinders reasoning or introduces procedural noise, shedding light on inherent biases that might restrict generalization. Only focusing on the positively influential documents might bias our judgements towards cherry-picking conclusions." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "- Could you elaborate more on how you define \"procedural knowledge\" in the context of your findings? How does this relate to the concept of learning algorithms or routines within the training data?\n- Given the high influence of code documents, how might this skew the model's reasoning capabilities, especially in non-coding contexts?\n- With these insights, what are the potential adjustments or enhancements in training strategies for LLMs to improve their reasoning generalization?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper provides an important insight of LLMs, namely how models generalize beyond their training data, which is crucial for advancing reasoning capabilities of LLMs.\n- The use of influence functions to study generalization in LLMs offers a good perspective on how models might learn to reason.\n- The experiments are well-executed, and the analysis and explanation for drawing the findings are reasonable." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper investigates the generalization strategies employed by LLMs when performing reasoning tasks compared to factual recall. The authors examine the influence of pretraining data on two LLMs of different sizes (7B and 35B parameters) by using influence functions to rank documents based on their impact on the likelihood of model outputs for reasoning and factual questions. They find that for reasoning tasks, LLMs do not rely heavily on direct retrieval of answers from pretraining data but instead use a broader set of documents that contain procedural knowledge relevant to the task. This suggests that LLMs generalize by learning how to perform reasoning steps rather than memorizing specific solutions. In contrast, for factual questions, the influential documents often directly contain the answers. The authors also note the overrepresentation of code in influential documents for reasoning, indicating its importance in teaching procedural knowledge to the models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The study only looks at a subset of the pretraining data, which might not capture less frequent but highly influential documents. \n- Findings are based on two models from the same organization, potentially limiting the generalizability across different architectures or training regimes.\n- There's no cross-validation with other methods of understanding model behavior which could corroborate 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": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I have no serious questions for the authors, but if they have time:\n1. Can this methodology be applied to model false-positives? It would be interesting to explore how pretraining documents may relate to hallucinations in generative responses, given prior research which points to cases of memorization." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1. This paper presents a series of interesting and novel investigations into the influence of documents from pretraining in model responses. Most research in model interpretability is done by examining or modulating model parameters and activations, since it is usually computationally intractable to trace model responses back to pretraining samples; this is frontier research, and I was excited to read it.\n\n2. The paper presents insights into which documents are used to answer mathematical reasoning questions, and crucially provides comparisons between two models within the same family, and also to a secondary task in factual question answering. The latter comparison was especially useful and cleanly conveyed the points made: specifically, that factual responses often rely on a specific document, but evidence is shown that reasoning responses may draw on a breadth of documents, possibly aggregating heterogeneous information into one response.\n\n3. The experiments were extremely narrowly defined, but the authors caveat this early and often throughout the paper. Additionally, even in this narrowly scoped setting approximations must be made in order to be computationally tractable, and the authors honestly qualify discussions with reasonable alternate hypotheses and give sub-experiments to explore what is the most likely hypothesis. This kind of writing is very thoughtful and I appreciated that the authors made reasonable decisions and honestly qualified the claims, which were well supported." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper applies the EK-FAC influence function to LLMs in an investigation of which documents, from a representative sample of LLM pretraining data, are used by a given model to answer basic mathematical reasoning questions. The EK-FAC influence function is used to produce a score for a given triple (prompt, completion, document) and these scores provide a basis to rank documents as more or less useful in generating the completion for the given prompt. Due to intense computational requirements, this technique is applied on a small sample of 80 prompt/completion pairs, but in great detail, examining several hundred documents at the top of the ranking for each pair. Several key findings emerge, including that models employ documents for reasoning responses in a different manner than for factual responses, and that such mathematical reasoning responses often rely on documents describing verbal procedures or code." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. As mentioned above, the experiments were very narrowly scoped. Only 80 questions were analyzed in total, and this 80 was further broken down into smaller sub-groups. Moreover, the questions were very simple mathematical problems using small numbers, requiring only short reasoning hops, and not resulting in fractional-valued answers. The experiments were performed only on two models within one model family, and one model is not available publicly. The authors do note all of these things, and some (not all) of these decisions seem to be made due to computational constraints, which is understandable. However, it would have been nice if these experiments were at least reproduced on fully public models such as Llama.\n\n2. The description of EK-FAC was brief and not as clearly described as the later experiments and results, which were very clear. It would be nice to have a little more motivation about the individual components in the given formulas, since this methodology underlies all of the later experiments. Further, the discussion section at the end of the paper (sec 5) was very dense and a bit confusing. Maybe this could be restructured? The alternating hypotheses in the paragraph starting on L490 were particularly hard to follow.\n\n3. (This is a minor point) Some of the mystique surrounding \"reasoning\" in LLMs may be because as a field we have conflated many types of problems into one, in the fervor of \"AGI\". Though this paper often discusses general reasoning, it looks specifically at mathematical reasoning, and it could be made more clear that these studies are distinct from linguistic reasoning, logical reasoning, spatial, etc etc. Analyzing linguistic reasoning provenance would be fascinating using this method, but would require different 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. Why were these two specific LLMs chosen, instead of more widely used and capable models?\n2. Using both fine-tuned and base models in the same experiment could lead to unreliable results due to differences in parameter initialization, potentially affecting influence calculations.\n3. Since LLMs rely on embedded representations, even if keyword matching fails to find an answer, does it conclusively mean the document is not similar to the answer?\n4. Could examples of retrieved documents for reasoning tasks be provided to offer insights into how they influence the model's approach to reasoning?" }, "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 straightforward, well-explained, and includes sufficient detail, making it easily reproducible.\n2. This research addresses a crucial question: identifying what training data impacts LLM reasoning abilities, an area closely tied to model generalization and interpretability. It contributes to our understanding of LLMs.\n3. The paper presents intriguing findings, highlighting distinctions in how LLMs handle factual versus reasoning tasks. For instance, factual questions frequently retrieve specific information, while reasoning tasks benefit from procedural knowledge." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores the influence of specific pretraining data on the reasoning abilities of large language models (LLMs), focusing on how models rely on different types of documents when responding to reasoning versus factual queries. The paper applies influence functions to identify pretraining documents that impact performance on simple reasoning tasks. Results show that factual questions often depend on a smaller set of documents containing the answer, whereas reasoning questions are more influenced by documents with procedural knowledge." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The experimental setup is limited, potentially compromising the reliability of conclusions. Specifically: (1) only 80 queries were used for analysis, (2) the study included only three types of reasoning tasks, potentially limiting representation to other reasoning tasks, (3) there was no exploration of how different prompt formulations of the same query affect results, and (4) keyword-based methods for determining whether documents contain answers may be insufficiently accurate.\n2. The analysis may lack granularity, as it considers only each document’s influence on the overall completion without examining its impact on individual reasoning steps. This might affect the conclusions.\n3. While Appendix A.1 reports that influence scores are higher for certain documents, their similarity to random selections raises questions about whether influence functions reliably indicate actual influence." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We identify the data from a subset of pretraining data that is influential for downstream reasoning and factual questions for two LLMs of different sizes, and find evidence for a reasoning strategy that is unlike retrieval." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024what,\ntitle={What Kind of Pretraining Data Do Large Language Models Rely on When Doing Reasoning?},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1hQKHHUsMx},\nnote={under review}\n}" }, "abstract": { "value": "The capabilities and limitations of Large Language Models (LLMs) have been sketched out in great detail in recent years, providing an intriguing yet conflicting picture. On the one hand, LLMs demonstrate a general ability to solve problems. On the other hand, they show surprising reasoning gaps when compared to humans, casting doubt on the robustness of their generalisation strategies. The sheer volume of data used in the design of LLMs has precluded us from applying the method traditionally used to measure generalisation; train-test set separation. In this work, we study what kind of generalisation strategies LLMs employ when performing reasoning tasks by investigating the pretraining data they rely on. For two models of different sizes (7B and 35B) and 2.5B of their pretraining tokens, we identify what documents impact three simple mathematical reasoning tasks and contrast this to the data that are influential for answering factual questions. We find that, while the models rely on mostly distinct sets of data for each factual question, documents often have a similar influence on different reasoning questions with the same task, indicating the presence of procedural knowledge. We further find that the answers to the factual questions often show up in the most influential data. However, for the reasoning questions the answers usually do not show up as highly influential, nor do the answers to the intermediate reasoning steps. When we characterise the top portion of the ranking for the reasoning questions qualitatively, we find that the influential documents often contain procedural knowledge, like demonstrating how to obtain the solution using formulae or code. Our findings indicate that the generalisation strategy the model uses when doing reasoning is unlike retrieval, but more like a strategy using many documents doing a similar form of reasoning." }, "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; LLM; reasoning; pretraining data; influence functions; mathematical 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/75e06c79517fb2df91bfa5df65289a7fa272838b.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/9c07b9b53fa8813a4f85d6e07992d46fa6211b84.zip" }, "title": { "value": "What Kind of Pretraining Data Do Large Language Models Rely on When Doing 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": 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": "1. Is it correct to expect that the ELBO gap should converge to zero as the discretization becomes finer, or are there inherent limitations in the approach that cause the gap to saturate at a positive value? Clarifying this could help contextualize the observed results better.\n2. Are there any existing benchmarks or prior work that provide a comparable measure of ELBO gap performance for optimally trained diffusion samplers? How do the proposed methods stack up in this context?\n3. Can the authors provide more insight into why random placement of time steps works so (unexpectedly) well? Is there an intuitive or theoretical rationale for this observed behavior?\n4. In Theorem 3.4, there seems to be a potential issue as $\\vec μ_t$​ appears twice in the statement. Could this be a mistake, or is there a specific reasoning behind this repetition? Clarification would be helpful." }, "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 very well written and easy to follow, with clear exposition of the mathematical derivations and the empirical results.\n2. The experimental section is thorough and well designed, exploring the effects of different discretization strategies and their impact on performance in detail. The benchmarks used are diverse and represent a wide range of sampling challenges.\n3. The work provides strong empirical evidence that non-uniform time discretization (particularly random placement) improves training efficiency. This observation could be highly relevant for practitioners working with high-dimensional diffusion models. Furthermore, the identification of random time discretization as a performant strategy is novel and supported by robust experimental evidence.\n4. The paper effectively summarizes existing methods and objectives for diffusion sampling, offering a clear context for the proposed contributions and situating them within the broader body of work on diffusion models and sampling techniques." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates the training of diffusion samplers and neural stochastic differential equations (neural SDEs) by examining the connection between continuous-time objectives and their discrete-time counterparts. The authors establish that global objectives for discrete-time policies converge to path-space measure divergence objectives in the continuous-time limit, while local constraints asymptotically align with partial differential equations governing the time evolution of marginal densities. This theoretical grounding aims to bridge reinforcement learning (RL) objectives and stochastic control frameworks for diffusion processes. Empirically, the paper demonstrates that training with coarse, non-uniform time steps, particularly with random placements, can achieve substantial computational efficiency gains while retaining strong performance across a range of benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While the theoretical contributions are valuable and provide an interesting link between discrete-time and continuous-time objectives, they are not completely unexpected and partly already present in the literature.\n2. In the experimental results, it is noted that the ELBO gap does not converge to zero as the discretization becomes finer but instead appears to stabilize at a positive value. The authors do not give an explanation for this phenomenon. In particular, the lack of a \"benchmark\" makes difficult to connect these simulations to the numerical results presented in the first part of the paper above.\n3. The observed performance gains with randomly placed time steps are well supported by empirical results, but the paper does not provide a theoretical explanation for why this approach works so well. Offering more insight into this phenomenon would enhance the overall impact 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": 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 authors clarify why so much space is devoted to standard results? Would simplifying or condensing this content help highlight the unique contributions?\n\n2. Beyond applying existing convergence results, what novel techniques, if any, were introduced in proving Propositions 3.2, 3.3, and 3.4?\n\n3. Would more complex or realistic benchmarks alter the experimental outcomes, particularly in high-dimensional or non-Markovian sampling settings?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The approach of linking discrete-time policy objectives with continuous-time SDE training is a useful idea, albeit heavily reliant on established results.\n\n2. Authors show that this method potentially reduces computational costs for neural SDE training." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper examines training neural stochastic differential equations (SDEs) to sample from Boltzmann distributions without target samples. This work derives asymptotic equivalences by linking discrete-time policies to continuous-time diffusion. The approach is validated on sampling benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Firstly, I think the presentation of this work remains a major bottleneck for readers. Section 2 is preliminary, and it spans from pages 3 to 7. Such a lengthy preliminary section introduces well-known equations and results (e.g., equations (4)-(6) from GFlowNet papers, (9)-(15) from stochastic control and diffusion models, and (16), (17) as standard Euler-Maruyama discretizations). \nThese derivations, mostly grounded in existing work, dilute the contributions and add an undue burden for readers. Figures like Figure 3, which illustrate obvious points, seem unnecessary and further contribute to this issue. It is recommended to present additional informative and easy-to-follow diagrams in these sections.\n\n2. The primary theoretical contribution—showing asymptotic convergence from Euler-Maruyama discretization to continuous-time SDEs (Propositions 3.2, 3.3, 3.4)—seems not surprising. The convergence results are probably straightforward applications of established SDE theory, with little added insights or unique techniques. Without further exploration of new derivation techniques or distinctive theoretical angles, the contributions feel like direct applications of existing results.\n\n3. The experiments are conducted on standard synthetic benchmarks, such as Gaussian mixtures and low-dimensional toy distributions. To support this approach, it might be necessary to conduct higher-dimensional Bayesian inference tasks where the Boltzmann distribution is more untractable. Besides, the compared baselines exclude many recent models, such as flow-based generative models. \n\n3. While efficiency is demonstrated, additional benchmarks comparing computational costs with traditional methods in larger dimensions would be helpful for real-world applications." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "My main question is this: for a ML practitioner, how will the authors' results help?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper appears to be mathematically rigorous and experiments appear to give credence to the authors' work." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper discusses the relationship between continuous and discrete-time stochastic processes and their training. In particular, the main results give a series of propositions on how a discrete-time process can approximate a continuous time process. I have to say I had a hard time understanding the \"big picture\" of the authors' results." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I found the paper very difficult to read. The notation is dense, not all appears to be defined, some is non-standard and unclear and I found it a little tricky to understand exactly what the authors wanted to do. It may be that the authors have solved in interesting problem in a genuinely useful way but that was unclear from the paper. All but the very expert reader would, in my view, find the paper a difficult read. \n\nA few specific comments are:\n\n- Abstract could be more informative and precise\n- Introduction is quite meandering and I wasn't quite clear on exactly what the authors were trying to do.\n- Figures 1 and 2 were placed, in my view, quite early in the paper and were hard to interpret. They needed more textual description, or, considering where they were placed, needed \"dumbing down\" a little. \n- Equation (1) is somewhat standard but, for completeness, it would have been useful to know what \\sigma(t) is (I could guess). Equation (1) is similar to (9) apart from \\mu(t). I think the differences between the various forms of \\mu(t) needs to be explained in more detail.\n- It wasn't clear to me exactly what the reverse arrow meant in terms of policy e.g. the backwards arrow is used on \\pi(t) below equation (3) but without any definition as far as I can see. \n- I found Section 2 quite muddled with various different concepts introduced with not too much explanation. I realise there is a page limit, but it was bordering on the unpenetrable. \n- I didn't really understand how the Propositions in Section 3 ended up affecting the Results in Section 4. Perhaps I am dense, but it would be good if the authors could explain this better." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 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 the weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* The problem studied in this paper is well-motivated.\n\n* This paper presents extensive results in both theory and experiments.\n\n* The appendix provides a comprehensive complement to the main text." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores the connection between continuous-time SDEs and their discretization, particularly focusing on the influence of the chosen timestep. It demonstrates that using non-uniformly discretized time with fewer steps can achieve similar performance during inference. Theoretical results are provided to support this approach." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The theoretical results in Section 3 primarily focus on the convergence of the Euler-Maruyama method. Specifically, they show that convergence is ensured as the maximal step size approaches zero. However, these results do not explain why non-uniform discretization would generally be superior to uniform discretization. The advantage of non-uniform discretization—one of the main contributions of this paper—is demonstrated only through experiments\n\n* As previously mentioned, there seems to be a gap between the theoretical and empirical sections of this paper. After reading the introduction, I expected to see concrete theoretical results that justify the use of non-uniform discretization. However, simply showing that convergence is guaranteed as $\\Delta t$ approaches zero is unsurprising. The authors might consider adding more discussion on why uniform discretization is not always the optimal choice\n\n* It has been proven that the order of convergence is determined by the step size, and the Euler-Maruyama scheme with uniform discretization has been shown to achieve optimal performance in the general case (see 'Numerical Treatment of Stochastic Equations' by Rümelin, 1982). I wonder if the claim made in this paper contradicts that result.\n\nI would be willing to increase my rating if the authors are able to address my concerns." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We find theoretical connections between discrete-time and continuous-time training objectives for diffusion samplers and show their empirical implications for faster training." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024from,\ntitle={From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1hT2fsHbK9},\nnote={under review}\n}" }, "abstract": { "value": "We study the problem of training neural stochastic differential equations, or diffusion models, to sample from a Boltzmann distribution without access to target samples. Existing methods for training such models enforce time-reversal of the generative and noising processes, using either differentiable simulation or off-policy reinforcement learning (RL). We prove equivalences between families of objectives in the limit of infinitesimal discretization steps, linking entropic RL methods (GFlowNets) with continuous-time objects (partial differential equations and path space measures). We further show that an appropriate choice of coarse time discretization during training allows greatly improved sample efficiency and the use of time-local objectives, achieving competitive performance on standard sampling benchmarks with reduced computational cost." }, "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", "variational inference", "SDEs", "PDEs", "sampling", "stochastic processes", "GFlowNets" ] }, "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/0ad32a97a547aad5965a088d5e0b27a2ac9d410e.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": { "value": "/attachment/a976a40327ac91689205332763505867d7185932.zip" }, "title": { "value": "From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster 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": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "-- I might have missed it, but do at least a fraction of the experiments report some kind of human evaluation? I think to validate the method, at least some of it might be reasonable. While using another generative model (as a judge or to generate a calibration set) is popular, it still presents an incomplete analysis. \n\n-- The authors might also want to discuss the works on conditional language validity by Cherian https://arxiv.org/abs/2406.09714 and also the earlier work by Mohri and Hashimoto https://arxiv.org/abs/2402.10978. Further, there is also a literature on confidence scoring which is often used for fine-tuning and reducing hallucinations. e.g. Kuhn et al. https://arxiv.org/abs/2302.09664, Lin et al. https://arxiv.org/abs/2305.19187, Wang and Holmes https://web3.arxiv.org/abs/2406.05213. It would be useful to include a brief discussion of these and how conformal methods might be used to calibrate such scores. It would help to bridge the two somewhat separate lines of enquiry together." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "-- The paper is quite well-written and clear. Each of the steps are well-described and easy to follow. \n\n-- The method is novel along multiple axes: Wrappers generally make far more simplifying assumptions due to the intractability of conformal prediction directly in such settings; the admissibility control criteria and the connections to pareto methods are interesting and could provide straightforward avenues for future work.\n\n-- While still compute intensive (since multiple generations are required), it could be tuned based on available data for some domain." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This fairly well-explicated paper considers the following question of much recent interest: how could we obtain some semblance of guarantees for generative models' outputs i.e. in terms of factuality. This is related to the problem of hallucination control directly, since a method involving calibration for correctness could reduce hallucination if a suitable domain-specific calibration set were available. The approach of the paper is simple yet innovative. In a basic sense it does the following: In the first step, the method samples from the generative model conditioned on a fixed input. There is a calibration parameter that controls, based on a suitable non-conformity measure, that the generations contain at least one correct generation. Then, the generated set is pruned further using separate calibration parameters based on diversity and factuality considerations. Unlike some previous works, the sequential nature of the process permits the overall admissibility to be easily factorizable, permitting a direct application of conformal prediction proper. An interesting connection is also made to the pareto methods since there are multiple calibration parameters to be handled. The experiments report a general improvement datasets and are sufficient to demonstrate the applicability of the proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "-- There are now a bunch of papers on using conformal wrappers for filtering long form generations by dividing them into segments and then scoring. I think it would have been great to evaluate on such tasks as well, as generally QA type tasks are a bit too easy.\n\n-- Sampling multiple times and expecting a correct response can be quite compute-intensive." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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 paper defines admissibility as including at least one (semantically) correct answer in the prediction set and aims to minimize the prediction set size while ensuring this inclusion. This is achieved by a “sub-sampling” technique, sampling answers based on a quality score ranking. Can the proposed method generalize to a broader admissibility definition, such as including multiple correct answers (e.g., 5 out of 10) or maximizing the fraction of correct answers? How would this method perform compared to baselines if the goal were to optimize the fraction of acceptable answers within a fixed prediction set size?\n* How does the sequence of filtering stages (diversity vs. quality) impact performance? Why is diversity filtering prioritized in the proposed method?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* This paper presents an efficient approach for generating prediction sets with admissibility guarantees by using a sequential generation and greedy filtering strategy.\n* It reduces the number of admissibility checks during calibration compared to previous baselines, improving computational efficiency.\n* Experimental results support the method’s effectiveness in reducing both query counts and prediction set sizes to meet admissibility criteria." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents SCOPE-Gen, a sequential conformal prediction method designed to generate prediction sets that satisfy admissibility guarantees with high probability. The method operates in two main stages: a generation stage and a filtering stage. In the generation stage, i.i.d. samples are drawn from the generative model until a specified non-conformity measure (related to sample count or quality) surpasses a threshold set by calibration with ground-truth samples. In the filtering stage, the prediction set is refined in a greedy manner, optimizing for diversity and quality based on another threshold derived from calibration. To ensure admissibility, the approach leverages a Markov chain factorization for admissibility control, and calibration is conducted on independent, non-overlapping data subsets to enable this factorization. Experimental results demonstrate that SCOPE-Gen reduces both the number of queries to the admission function during calibration and the size of the prediction set needed to meet admissibility requirements, outperforming baseline methods like CLM." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The paper lacks a theoretical analysis detailing how effectively the proposed method reduces the required admissibility checks and prediction set size.\n* The sequential generation and filtering process may introduce additional computational costs by generating a large number of samples before the filtering stage.\n* The calibration process, which involves sample splitting for generation and each filtering stage, may require extra ground-truth samples to determine accurate threshold (lambda) values." }, "withdrawal_confirmation": null }, { "TLDR": null, "_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": "1. One of the motivations for the method is the possible reliance on human oracle and expenses related to querying it. In the end the experiment use a non-human validation function. Related questions:\n- Would not the human oracle make some of the assumptions invalid (e.g. the need for increasing update function)?\n- As some point you mention that multiple queries over the same example need to be executed - wouldn't the human validation bring even more noise into the whole process and invalidate some of your probabilistic conclusions?\n2. I do not understand equation (6). What level of quantile is this? What is the interpretation of the n-fracion in the right-hand side?\n3. Is there a way to independently post-evaluate that the experimental results are really conformal with the $\\alpha$ level you were trying to achieve. Or would you need to use your own calibration parameters? If it were possible, this would provide additional useful insight.\n4. Please address the concerns mentioned under Weaknesses." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper addresses an important problem of current stochastic generative models hallucinating non-factual responses. Formulating this problem within the risk-control framework can provide the mathematical means for addressing it. The experimental evaluation over the natural langauge tasks seems relevant." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a heuristic algorithm to filter predictions of generative model to achieve conformal admissability. It argues that previous techniques need to evaluate the admissability multiple times per instance during the calibration phase, which is impractical when the admissability is evaluated by a human oracle. In their setup, the admissability factorizes into Markov chain and thus es requires fewer queries to the admission function (e.g. human oracle). The paper presents the algorithm for the filtering heuristic as well as the necessary calibration algorithms using two filters based on diversity and quality functions of the generated examples. The experiments over natural language generative tasks and molecular generation reporting favorable metrics in particular in terms of reduction in number of queries and runtime." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I find the empirical evaluation very difficult to asses both, on its own as well as in comparison to previous methods. To understand the effects of various parts of the proposed algorithms, it would be beneficial to perform ablation studies that could provide more insights into the effects of its individual components (e.g. the update function, the coverage level $\\alpha$, etc.). \nI am not convinced about the benefits of the molecular example - when there is only one valid/admissable example, it seems to me that simple checking of the validity at the generation time for each generated specimen should be enough. I do not see the benefit of the proposed method in this setup. This also applies to the TriviaQA experiment.\nThe algorithm requires an independent calibration set which seems to be very difficult to obtain in practice. In the presented experiments either boils down to something very trivial (single example being the valid one) or relying on another model which itself may be of uncertain quality. Further, I see similar issue with the update and filter functions which seem difficult to formulate reasonable in realistic scenarios. For me these are major limitations of the method which shall be discussed. \n\nThe main text of the paper (section 9) spills over to page 11. As per the call for papers, there is a strict 10 page limit on the main text and the call suggests a desk reject in case of violations of this 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": 1 }, "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": "- Can the author clarify the results in Table 2 of the appendix? It appears that the performance gap between SCOPE and CLM is narrowing - can the authors explain why this might be happening?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Generally well-written; it’s clear that the manuscript is largely inspired (and adopted) from the setup in Quach et al 2024. Nevertheless, the authors detailed differences to Quach et al 2024 and highlight the efficiency of their method by leveraging sequential factorization." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This manuscript introduces a sequential conformal prediction method called SCOPE-Gen. SCOPE-Gen uses a sequential pruning approach to iteratively refine the prediction set, allowing for separate control over each factor in the Markov chain, and demonstrates a significant reduction in the number of admissibility evaluations required during calibration. The method has been experimentally validated in natural language generation and molecular graph extension tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- (minor) Fig 1 is slightly unclear — the caption should at least include some explanation of \\nu, which is not specified until Sec 3" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024conformal,\ntitle={Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=1i6lkavJ94},\nnote={under review}\n}" }, "abstract": { "value": "Generative models lack rigorous statistical guarantees with respect to their predictions. In this work, we propose Sequential Conformal Prediction for Generative Models (SCOPE-Gen), a sequential conformal prediction method producing prediction sets that satisfy a rigorous statistical guarantee called conformal admissibility control. This guarantee means that the prediction sets contain at least one admissible (or valid) example, with high probability. To this end, our method first samples an initial set of i.i.d. examples from a black box generative model. Then, this set is iteratively pruned via so-called greedy filters. As a consequence of the iterative generation procedure, admissibility of the final prediction set factorizes as a Markov chain, where each factor can be controlled separately, using conformal prediction. In comparison to prior work, our method demonstrates a large reduction in the number of admissibility evaluations during calibration. This is crucial e.g. in safety-critical applications, where these evaluations must be conducted manually by domain experts and are therefore costly and time consuming. We highlight the advantages of our method in terms of admissibility evaluations and cardinality of the prediction set through experiments in natural language generation and molecular graph extension tasks." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Conformal Prediction", "Generative Models", "Risk Control", "Active Learning", "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/7671aa4a190d0778e0e821656421711e15127489.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/b4a371dedafafb4dd0825d0f906c7a3a692b0196.zip" }, "title": { "value": "Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]