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Sign Operator for Coping with Heavy-Tailed Noise: High Probability Convergence Bounds with Extensions to Distributed Optimization and Comparison Oracle | Reject | Summary: ### **Motivation**
Heavy-tailed noise is a common issue in large-scale machine learning optimization, particularly for LLMs. Approaches like gradient clipping and normalization are common ways to mitigate this issue but require careful tuning or are simply not suitable for distributed settings. This paper expl... | Rebuttal 1:
Rebuttal: Dear Reviewer 5Sbn, thank you for your questions and positive evaluation of our paper.
**Claiming that an optimizer is better than AdamW.**
By saying this in abstract, we did not mean to suggest that the sign-based method is the best possible method for language model pretraining, but rather th... | Summary: In this paper, the authors consider the problem of minimizing a non convex function under two different oracle models. In the first case, the algorithm access to a noisy first order oracle obeying to an heavy-tailed distribution. In this model, authors analyze a number of version of the sign SGD method, and pr... | Rebuttal 1:
Rebuttal: Dear Reviewer 1isr, thank you for your valuable feedback.
We sincerely apologize for overlooking certain important works. We will address these gaps in a revision. Nevertheless, we would like to emphasize that our main results remain competitive with or outperform mentioned methods. In this case... | Summary: This paper investigates the robustness of a series of sign-based stochastic optimization methods for handling heavy-tailed (HT) noise in smooth non-convex functions. The authors argue that leveraging the sign of gradient estimates, without introducing additional hyperparameters, effectively addresses HT noise.... | Rebuttal 1:
Rebuttal: Dear Reviewer AScN, thank you for your time and comments and positive evaluation of our paper:
**(1) Expanding the evaluation with additional benchmarks could further enrich the findings and enhance their generalizability.**
Thank you for raising this point.
We complement our experiments with a ... | Summary: This paper studies optimization under heavy-tailed noise. The authors assume that the noise affecting gradient estimates and function evaluations has bounded $\kappa$-th moments for (1,2]. This assumption is common in many large-scale deep learning scenarios. Under this assumption, they develop sign-based meth... | Rebuttal 1:
Rebuttal: Dear Reviewer nio3, thank you for your feedback and positive evaluation of our paper.
**The biggest weakness of the paper is that it mainly integrates existing techniques and extends them under already established assumptions. The work does not introduce fundamentally new ideas, and its contribut... | null | null | null | null | null | null |
Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning | Accept (poster) | Summary: This paper propose to adopt the server-client momentum with variance reduction technique in the Async FL framework. By using VR technology, the proof eliminates the heterogeneity assumption while maintaining the same efficiency as sync FL such as SCAFFOLD. The experiments validate the algorithm's efficiency in... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the reviewer for recognizing our contributions and for the constructive comments. Our point-to-point responses to concerns on Weaknesses and Questions are given below.**
**Reply to Methods And Evaluation Criteria:**
1. Thank you for your comment. Our work advances async... | Summary: This paper studies asynchronous federated learning and proposes a novel momentum-driven asynchronous FL framework that eliminates the need for data heterogeneity bounds. The authors provide theoretical analysis and conduct experiments to verify the effectiveness of the proposed method.
Claims And Evidence: I ... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the reviewer for recognizing our contributions and for the constructive comments. Our point-to-point responses to concerns on Weaknesses and Questions are given below.**
**Reply to Methods And Evaluation Criteria:**
1. The asynchronicity of our method is reflected in th... | Summary: This paper addressed the data heterogeneity and staleness in asynchronous federated learning (AsycnFL). The authors propose MasFL that introduces the control variates into AsyncFL to stabilize the model updates during local training and global aggregation. Further, they normalize the momentum-averaged gradient... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the reviewer for recognizing our contributions and for the constructive comments. Our point-to-point responses to concerns on Weaknesses and Questions are given below.**
**Reply to Essential References Not Discussed:** We sincerely thank the reviewer for bringing this c... | Summary: Previous works on asynchronous federated learning put strong assumptions (like bounded gradient assumption) in order to get theoretical guarantees. However, these assumptions are usually not realistic. In this paper, the authors propose a new asynchronous FL algorithm which novelly combines with global and loc... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the reviewer for recognizing our contributions and for the constructive comments.**
**Response to Comments Or Suggestions:**
Thanks for the suggestion. We have already discussed the reference Yu et al. (2024) in the Related Work section of our original manuscript (see ... | null | null | null | null | null | null |
Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens | Accept (poster) | Summary: This paper focuses on the insufficiency of the LLM-based sequential recommendation tasks and proposes to enhance the tokenizers by introducing OOV tokens. A way of generating new tokens, namely META ID is proposed to characterize the users and items and provide token initializations for later finetuning proce... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive and encouraging feedback. We are glad that the reviewer finds our method **simple yet effective, the evaluation sound, and the overall contribution valuable**. Below, we address the concerns:
**Q1: On the finetuning strategy being overly simpli... | Summary: The paper proposes META ID, an Out-Of-Vocabulary (OOV) tokenization mechanism for improving user/item ID representation in LLM-based recommendation systems. While traditional methods struggle with token diversity and semantic conflicts in token representation, the proposed META ID OOV tokens, generated through... | Rebuttal 1:
Rebuttal: We thank the reviewer for the **thoughtful comments and valuable suggestions**. Below, we address each concern in detail. Overall, we emphasize that our method **compares fairly with LLM-based recommenders** using equivalent architectures, maintains **high efficiency**, and introduces **minimal co... | Summary: This paper introduces META ID, a framework that improves LLM-based recommender systems using out-of-vocabulary tokens. The authors demonstrate that in-vocabulary tokens lack diversity when representing users/items and propose constructing OOV tokens from meta-path features extracted from user-item interaction ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback. Below, we provide a point-by-point response addressing key concerns.
**Q1: Stability of DS.**
**A1**: Although diversity score (DS) involves KL divergence, it is computationally efficient in practice. We **avoid full pairwise comparisons (O... | null | null | null | null | null | null | null | null |
Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality | Accept (poster) | Summary: This paper proposes a technique to reduce the computational complexity of subgraph-GNNs, which have higher expressivity than 1-WL but are often too computationally expensive to be used in practice. In particular, the method, called HyMN, relies on sampling subgraphs based on some node centralities, which can a... | Rebuttal 1:
Rebuttal: The reviewer found the paper “*solid and impactful*”, the method as “*an important advance in the field*”, “*reasonable*”, “*effective*” and “*motivated*” by experimental results. They manuscript is also found “*well-written*".
We address comments below.
---
*“There seems to be a large discrepa... | Summary: The authors propose a centrality-based scheme for selecting expressive subgraphs for subgraph GNNs. Essentially, their idea is based on the observation that message passing themes and walks on graphs are the same thing, which makes using walk-based centralities a natural proxy for selecting nodes with high inf... | Rebuttal 1:
Rebuttal: We are very pleased to note the positive recommendation made by the reviewer. They have appreciated the proposed application of Network Science results to Deep Learning, and have found the paper “*extremely well written*”, with claims supported by “*clear arguments and empirical evidence*”.
The r... | Summary: This paper explores subgraph GNNs as a way to overcome the expressivity limitations of standard Message Passing Neural Networks (MPNNs). In a subgraph GNN, a graph is transformed into a "bag of subgraphs," where each subgraph is processed independently using an equivariant architecture, and the results are agg... | Rebuttal 1:
Rebuttal: *(W1) “The two experiments use very different types of graphs [...] it would be helpful to standardize.”*
We used TU datasets for perturbation analyses aligning with [1], but we agree standardization would be preferred. As suggested, we re-run them on the same ER graphs in the subgraph-count corr... | Summary: The paper proposes HyMN (Hybrid Marking Networks), a combination of walk-based structural encodings and centrality-based subgraph marking strategies. The goal is to design a subgraph GNN which is simultaneously effective (sampled bags tend to optimal) and efficient (few operations / no learnable components).
... | Rebuttal 1:
Rebuttal: We are very glad to note the reviewer found the paper “*extremely well-written*”, that it “*picks the right ideas and puts them together nicely*” and, importantly, that they believe the underpinning idea “*holds promise for spurring on further work in this area*”.
The reviewer shared the view tha... | null | null | null | null | null | null |
Improving Soft Unification with Knowledge Graph Embedding Methods | Accept (poster) | Summary: This paper presents the first integration of NTP and KGE, aiming to enhance the performance of NTP in terms of both effectiveness and efficiency. The experimental results demonstrate the synergy between these two lines of research.
Claims And Evidence: To some extent, yes. There are a few claims in the introd... | Rebuttal 1:
Rebuttal: > ***1. In the Introduction ... raises the question of whether this approach is suitable for such domains.***
Thank you for your insights! While LLMs also transform discrete symbols to continuous space, the difference is NeSy approaches incorporate more explicit reasoning priors in their framewor... | Summary: This paper proposes integrating Knowledge Graph Embedding methods into Neural Theorem Provers to address challenges in optimization and efficiency.
Claims And Evidence: The claims made in the paper are well-supported by evidence.
Methods And Evaluation Criteria: The proposed methods and evaluation criteria a... | Rebuttal 1:
Rebuttal: > ***The reasons for the accuracy drop in CTP3 and CTP4 on large-scale datasets, and propose corresponding improvements***
For CTP3, we recognize two issues that limits its performance particularly on large-scale dastasets, and propose two approaches to improve its performance. Recall for CTP3 we... | Summary: The paper proposes to integrate Knowledge Graph Embedding (KGE) methods with Neural Theorem Provers (NTPs) to enhance neuro-symbolic reasonings, and hence. The author proposes 4 ways to use KGEs and explain the methodology, the most intuitive variant seems to be use the kge at each proof step. Then the paper u... | Rebuttal 1:
Rebuttal: > ***The paper is dense with technical details as one can observe the paper uses a lot of spaces to talk about existing research and methods, make it less accessible to readers not familiar with the specific fields of NTPs or KGEs. In contrast, the newly proposed CTP may require more room for furt... | Summary: This paper investigates the integration of Neural Theorem Provers (NTPs) and Knowledge Graph Embeddings (KGEs) to enhance soft unification and reasoning efficiency. The paper systematically explores four strategies for integrating KGEs into NTPs:
CTP1: Uses KGE as an auxiliary loss to support NTP training.
CTP... | Rebuttal 1:
Rebuttal: > ***Complexity Analysis:***
Below we provide complexity analysis for the baseline CTP and the proposed variants. We particularly focus on the final step at each proof path during evaluation, since it is the computational bottleneck in the NTP framework.
Let $|\mathcal{E}|$ be the number of ent... | null | null | null | null | null | null |
VIP: Vision Instructed Pre-training for Robotic Manipulation | Accept (poster) | Summary: The authors propose a pretraining strategy for robotic manipulation in which all inputs are visual, with no language descriptions required.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: Yes
Experimental Designs Or Analyses: Yes
Supplementary Material: Yes
Relation To B... | Rebuttal 1:
Rebuttal: We believe the Reviewer has significant misunderstandings of this work. In the following, we address the concerns one by one using more precise explanations and sufficient experiments. The paper will be revised accordingly to avoid these misunderstandings.
## Q1: Insufficient support for vision i... | Summary: In this work, based on the observation that current policies cannot capture features from text instruction effectively, due to data scale, the authors propose a model named VIP which utilizes vision instructions to specify manipulation targets as an alternative. Specifically, the input of VIP is the current ob... | Rebuttal 1:
Rebuttal: We have addressed the concerns of the Reviewer one by one in the following. The paper will be revised accordingly.
## Q1: Experiments with more distraction.
As suggested by the Reviewer, we have added experiments with stronger distraction in both simulated and real robot environments. For simula... | Summary: This paper introduces a novel pretrained method designed for general robotic manipulation tasks. The authors argue that text-instructed policies often fail to effectively focus on target objects, and therefore, they propose integrating more interpretable features for pretraining visual-based policies. These fe... | Rebuttal 1:
Rebuttal: We have addressed the concerns of the Reviewer one by one in the following. The paper will be revised accordingly.
## Q1: Visualization details of the teaser figure.
In the teaser figure, we visualize the attention map between the action query $q_T$ at the last timestamp $T$ and image feature t... | Summary: This paper proposes VIP, a framework that uses visual signals as guidance for visual pre-training in robotic tasks. Instead of using language as the instruction (which is ambiguous), this work proposes using vision-instructed policy as an alternative. The vision-instructed policy uses visual cues to instruct t... | Rebuttal 1:
Rebuttal: We have addressed the concerns of the Reviewer one by one in the following. The paper will be revised accordingly.
## Q1: Ablation study in real-world experiments.
In the paper, we conduct ablation study mainly in simulation as the environment is easy to control, which benefits to ensuring fair ... | null | null | null | null | null | null |
Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation | Accept (poster) | Summary: This paper created a new math dataset sourced from the Art of Problem Solving. It designed a pipeline that includes (1) raw data collection, (2) math question detection, (3) question-answer extraction, (4) solution rewriting, and (5) data decontamination. Steps 1 to 3 involve the use of LLMs in the processing.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments. Below, we answer the main questions raised by the reviewer:
**Q1:** For question with 2 or 3 answers, how do you handle them?
**A1:** For the LiveAoPSBench evaluation set, we only retain questions with closed-form answers. If a solution provide ... | Summary: The paper introduces a scalable pipeline that leverages the Art of Problem Solving (AoPS) forum to construct two key resources for advancing Olympiad-level mathematical reasoning with LLMs. (1) AoPS-Instruct is a large-scale instruction-tuning dataset containing over 600k QA pairs, extracted and rewritten from... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. Below, we answer the main concerns raised by the reviewer:
**Q1:** The evaluation is limited to the LLMs with fewer than 7B parameters. To strengthen their claims, the authors should extend their experiments to larger-scale LLMs (exceeding 7B... | Summary: 1) The paper constructs a dataset for Olympiad-level mathematical reasoning with a large scale and diverse problems, which is significant for LLMs’ development in mathematical problem-solving.
2) Experiments demonstrate that the dataset can effectively improve LLM performance on benchmarks like MATH. Also, thi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. Below, we answer the main concerns raised by the reviewer:
**Q1:** No conflict resolution protocol for handling discreprancies in community-provided answers (e.g., voting mechanisms).
**A1:** For the LiveAoPSBench evaluation set, we only kee... | Summary: This paper introduces AoPS-Instruct, a dataset of 666K Olympiad-level math QA pairs, and LiveAoPSBench, a contamination-resistant benchmark, both sourced from the Art of Problem Solving (AoPS) forum. Using an automated pipeline, the authors extract and refine QA pairs, leveraging Qwen 2.5 72B to rewrite soluti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback.
**Q1:** In Table 2, the size of the datasets on which the models have been finetuned have not been specified. It would be important to ensure that the amount of data on which the models are finetuned is same across different datasets to ensur... | null | null | null | null | null | null |
Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models | Accept (poster) | Summary: The paper proposes Portable Reward Tuning (PRT), a fine-tuning pipeline to enable efficient and reusable fine-tuning across different foundation models (FMs). It is especially useful when the old FMs are replaced by new FMs with different pre-training dataset and even different model architectures. Previous fi... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. Here we focus on answering major concerns/questions due to space limitation. However, we sincerely appreciate other feedback and will reflect them to our revisions, as well as discussions below.
### Methods And Evaluation Criteria
> However, I believe the eva... | Summary: This paper proposes Portable Reward Tuning (PRT), a new fine-tuning paradigm that decouples the “reward” from the foundation model itself, thereby making it “portable” to other foundation models of the same architecture family (with shared label or token vocabulary). Overall, the method aims to reduce repeated... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We are really encouraged by the positive feedback to our research direction. Here we would like to address your concerns or questions.
> The authors only do a certain limited ablation around, e.g., the effect of different source vs. target architectures or tr... | Summary: This paper introduces a novel approach to reward tuning that can be transferred across model architectures. Instead of modifying a model parameters directly, the proposed approach trains an explicit reward model using the same objective as fine-tuning. At inference time, the reward model can be applied to any ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback, and finding our research direction promising. Here we would like to address the questions.
> There is a mismatch between algorithm 1 and the text in lines 171-206. Reward models are simply fine-tuned on a given task with cross entropy loss. However, algorithm... | null | null | null | null | null | null | null | null |
Nonparametric Identification of Latent Concepts | Accept (poster) | Summary: The paper proposes that when there is an unknown concept-to-class matrix, the concepts can be identified under certain assumptions on the structure of the matrix. Namely, it formalizes in which sense each concept needs to have characteristic classes it belongs to, or vice versa, how every class needs to have c... | Rebuttal 1:
Rebuttal: We are genuinely grateful for your insightful comments. In light of these, we have introduced **new experiments** (https://anonymous.4open.science/r/0-518C/rebuttal_new_results.pdf) and new discussions. Please find our detailed response below:
**Q1:** Strong assumptions / limited generative proce... | Summary: The paper studies the identifiability of latent concepts. The key assumption is that we observe class labels $c$ and observations $x$ which are mediated through concepts $z$, i.e., $c\to z\to x$ and they in addition allow for class independent features. Then they show that the difference in concepts for differ... | Rebuttal 1:
Rebuttal: We deeply appreciate your valuable insights. In light of these, we have carefully added several new discussions and conducted **new experiments** (https://anonymous.4open.science/r/0-518C/rebuttal_new_results.pdf). Please find our detailed responses below:
**Q1:** More clarifications on Eq. 5.
*... | Summary: This paper proposes a nonparametric framework for identifying latent concepts by leveraging structural diversity across observation classes, inspired by human cognitive mechanisms of learning through comparison. The authors establish theoretical guarantees for concept identifiability without relying on paramet... | Rebuttal 1:
Rebuttal: We are profoundly thankful for your insightful feedback. In light of it, we have included new discussions and conducted **new experiments** (https://anonymous.4open.science/r/0-518C/rebuttal_new_results.pdf). Please find our point-by-point responses below:
**Q1:** Thm. 1 in high-dimensional space... | Summary: The authors tackle the important problem of identifying latent classes from data. They claim that given some constraints, it should be possible to do given that different classes (e.g. different animals), will produce different sets of observable concepts (i.e. different shapes, colors, etc); and that as long ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback. Accordingly, we have included several new discussions in the updated version. Please see our point-by-point responses below:
**Q1:** More intuition on Theorem 2 and Figure 4.
**A1:** Thanks for the suggestion. We have added more discussion in t... | null | null | null | null | null | null |
Comparing Few to Rank Many: Active Human Preference Learning Using Randomized Frank-Wolfe Method | Accept (poster) | Summary: This paper considers an N-item-ranking problem with K-way comparisons, where K<<N. The goal is to determine the optimal K-subset selection strategy to minimize the worst-case ranking error. Previous approaches to this problem are computationally infeasible due to the exponentially large number to consider. To ... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for a positive review, and recognizing both benefits and shortcomings of our paper. Our rebuttal is below. We will incorporate all comments of the reviewer in the next version of our paper. If you have additional concerns, please reach out to us to discuss them.
We a... | Summary: This paper investigates how to select the optimal data points to learn the ground-truth reward model in RLHF. Specifically, compared to previous work [1], this paper extends the result from learning the ranking of $K$ actions from $K$-way feedback to learning the ranking of $N\geq K$ actions from $K$-way feedb... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for acknowledging the contributions of our work and putting it in the context of prior works. Our rebuttal is below. We focus on major issues and will incorporate all comments of the reviewer in the next version of our paper. If you have additional concerns, please re... | Summary: This paper focuses on the problem of RL using human ranking feedback. In particular, the goal is to learn the ranking of N items using K-way ranking feedback. To solve such a problem in an active learning fashion, the learner needs to first compute a design via Frank-Wolfe (FW), sample queries according to the... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for a positive review that discusses both the new aspects of our work and potential shortcomings. Our rebuttal is below. We focus on major issues and will incorporate all comments of the reviewer in the next version of our paper. If you have additional concerns, pleas... | Summary: This paper proposes an algorithm to rank N items from K-way comparisons (K<<N), formulated through a D-optimal design objective. The authors develop “DopeWolfe,” a randomized Frank-Wolfe variant with sparse and low-rank updates to avoid high complexity of naive approaches. They prove a sublinear convergence ra... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for detailed feedback, and recognizing that we solved the problem that we set out to solve well. Our rebuttal is below. We focus on major issues and will incorporate all comments of the reviewer in the next version of our paper. If you have additional concerns, please... | null | null | null | null | null | null |
The Limits of Tractable Marginalization | Accept (poster) | Summary: This paper defines three computation classes inclusing PM for tractable marginalization, PHM for Hamming marginalization and PVM for virtual evidence marginalization. The authors manage to show that class UFMAC is in all three classes mentioned before. Further, the authors show that PVM \subseteq PHM \subseteq... | Rebuttal 1:
Rebuttal: Thank you for your considered review.
On our paper’s relation to the conference theme, we recall that ICML has recently featured other papers entirely focused on the theory of tractable marginalization [0, ICML 2023; 1, ICML 2024] (with similarly focused papers appearing at closely related confe... | Summary: The paper "The Limits of Tractable Marginalization" explores the computational complexity of marginalization, a fundamental operation in probabilistic inference and formal verification.
The authors focus on the relationship between functions with tractable marginalization and their representation using unifo... | Rebuttal 1:
Rebuttal: Thank you for your considered review.
We see no questions or concerns in your review needing response. We nonetheless welcome any further discussion, e.g., as prompted by the other reviews. | Summary: This paper tackles the problem of characterizing the class of functions on which marginalization can be performed in polynomial time. Previous work describes a construction with polynomial size arithmetic circuits computing multilinear polynomials. The authors demonstrate that this construction is incomplete a... | Rebuttal 1:
Rebuttal: Thank you for your considered review.
We see no questions or concerns in your review needing response. We nonetheless welcome any further discussion, e.g., as prompted by the other reviews. | Summary: This paper proposes and studies UFMAC, a unifying arithmetic circuit formalism for representing functions that support tractable marginalization. It shows that UFMACs subsume prior such tractable classes, proves that all UFMACs support polynomial time marginalization, and also shows that the reverse isn't true... | Rebuttal 1:
Rebuttal: Thank you for your considered review. We are glad you found our work “neat,” “precise,” and “elegant,” even sometimes “refreshingly simple.” We provide answers to your questions below and welcome any further discussion.
[The “finally” in UFMAC] The choice to allow polynomial degree (i.e., the “Fi... | null | null | null | null | null | null |
Guided Zeroth-Order Methods for Stochastic Non-convex Problems with Decision-Dependent Distributions | Accept (poster) | Summary: This paper a new zero-order optimization algorithm for a new algorithm for a special optimization problem. The theoretical results show the proposed method converge to the stationary point and the step of steps to converge is also provided. The covariant matrix as estimated is a type of CMA-ES method (Eq. 6).
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the general appreciation of our work as well as constructive comments.
### Questions For Authors.
**1:** If the distribution $D(x)$ does not change significantly with respect to $x$, GZO-HS, which uses samples from past iterations, is likely to perform better.
This is b... | Summary: This work proposes new zero-th order methods for nonconvex performative prediction problem (i.e. optimization with decision-dependent distribution) which improve the theoretical sample complexity of the state of the arts when the function variation $G=\sup_{x,\xi}|f(x,\xi)|$ is large compared with the dimensio... | Rebuttal 1:
Rebuttal: We thank the reviewer for positive comments and constructive suggestions.
### Questions For Authors.
**(1):** Since we make no assumptions on $h$ except that it is normalized,
the sample complexities of our methods are derived in the worst-case scenario for $h$.
In this case, by letting $\alpha... | Summary: The authors explore zeroth-order methods to solve stochastic problems where the distribution of stochastic variables depends on the decision x. By incorporating partial information in the construction of gradient estimators, they demonstrate improved convergence rates compared to existing works.
Claims And Ev... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable and constructive feedback.
### Weakness.
> The presentation of Assumptions in section 4.1 is dense and difficult to follow, and the purpose of Assumption 4.3 needs clarification since it appears only in the appendix.
In response to your comment, we will add th... | Summary: This paper studies zeroth-order methods with partial gradient guidance for solving the performative prediction problem. Specifically, the proposed algorithm leverages the gradient information of the known function $f(x, \xi)$ to refine the update direction. The authors establish a rigorous worst-case convergen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive suggestion.
We answer each of the questions in the ''Weaknesses'' and ''Experimental Designs Or Analyses'' sections.
### Weaknesses.
1. Motivation.
The motivation for our study is clear, and we clarify the positioning of our methods in relation to existi... | null | null | null | null | null | null |
Compressed Image Generation with Denoising Diffusion Codebook Models | Accept (poster) | Summary: This paper introduces a novel approach called the Denoising Diffusion Codebook Model, which replaces the standard Gaussian noise sampling in the reverse diffusion process with a codeword from a predefined codebook. This method enables the development of new lossy image codecs and, more broadly, compressed imag... | Rebuttal 1:
Rebuttal: # Essential Reference
We thank the reviewer for highlighting the insightful work by Huan Liu et al. (ICLR 2022). It is compelling to address tasks such as compressed image restoration by formalizing them as a distribution shift (via optimal transport) with an informational bottleneck. We will incl... | Summary: This paper introduces Denoising Diffusion Codebook Models (DDCM), a approach that replaces standard Gaussian noise sampling in Denoising Diffusion Models (DDMs) with selections from fixed codebooks of i.i.d. Gaussian vectors. Despite using a discrete and finite noise representation, DDCM preserves the sample q... | Rebuttal 1:
Rebuttal: # DDCM Bitrate and File Size Clarification
Please note that the reported bitrate is precisely that of the compressed file size, as in traditional compression methods. Indeed, the mentioned logarithmic relationship $T\cdot log_{2}(K)$ between the codebook size $K$ and the number of sampling steps $... | Summary: This paper presents a novel approach, DDCM, to represent an image by DDPM procedure using a set of gaussian noise (codebook) indices. In other words, this paper shows that it is possible to "discretize" the "z" at every step of DDPM to approximate a high-quality diffusion procedure. DDCM can also be leveraged ... | Rebuttal 1:
Rebuttal: # Fig. 3 FID
Yes, the FID scale in Fig. 3-L is $10^1$. The FID of DDPM is 9.21 (black dashed line). Indeed, some prior works with the same DDM reported lower FID on ImageNet 256x256, when using 50k generated samples [R2]. However, we use only **10k generated samples** (see L171-L) to reduce comput... | null | null | null | null | null | null | null | null |
Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks | Accept (poster) | Summary: This paper is within the setting of learning physical systems from PDE equations. The authors propose a neural network architecture that outputs a divergence-free symmetric tensor (DSFT). By adding this constraint, the neural network is guaranteed to enforce conservation of mass and momentum. In multiple exper... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time reviewing the manuscript and their feedback that will contribute to improving the clarity of our work. Below we address the reviewer's concern:
**Methods and Evaluation Criteria**:
PINN and NCL were both trained using the full system (11)--(14), which includ... | Summary: In this paper, a method for learning divergence-free symmetric tensors is proposed. As neural network architectures that maintain conservation laws, a typical approach is learning divergence-free vector fields by using the exterior calculus. The proposed method is another approach based on a different geometr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and feedback. Below we address some of the raised points:
**Main Difference between NCL and RTNN:**
NCL enforces only the continuity (mass-conservation) equation, whereas our approach reformulates the conservation laws in flux form, constrainin... | Summary: This paper concerns preserving certain structure properties when using neural networks to solve partial differential equations. The method uses an inductive bias by enforcing a certain form of the tensor field approximation, and the final form is called "Riemann Tensor Neural Network" (RTNN) involves some non-... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and suggestions. We appreciate them and hope to address some of the questions below:
**Suggestions:**
We agree that including representative solution plots would help the reader infer the difficulty in PINNs solution. We also appreciate the sugges... | Summary: The paper proposes RTNNs, which can encodes the divergence-free constraints in neural networks within the PINN framework. The divergence-free constraint is satisfied by computing the hessian matrix of a feed-forward MLP network and combining with a special-designed basis. The method is evaluated on a range of ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and feedback. Below we address the reviewer's questions:
**Label Data Inclusion**
While we can incorporate labeled data to penalize discrepancies between predictions and observations, for this experiment we did not. We acknowledge the inconsis... | null | null | null | null | null | null |
An Augmentation-Aware Theory for Self-Supervised Contrastive Learning | Accept (poster) | Summary: This paper explores the role of data augmentation from the perspective of theoretical research. It for the first time propose an augmentation-aware error bound for self-supervised contrastive learning, showing that the supervised risk is bounded not only by the unsupervised risk, but also explicitly by a trade... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to Reviewer Do5D for appreciating the novelty of our theory. We address your concerns below.
---
**Weaknesses.**
**W1,W2,W3,W4,W7,W8.**
**A.** We will revise the typos as suggested.
**W5.** Mistakes in proof. (1) the decomposition at the beginning of Proof of T... | Summary: In this paper, the authors theoretically study how augmentations affect supervised risk, an unexplored area in self-supervised learning. Furthermore, they conduct some experiments to verify the theories.
Claims And Evidence: Yes, the claims are supported by evidence.
Methods And Evaluation Criteria: There is... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to Reviewer 6JLt for appreciating the significance and reliability of our findings. We address your concerns below.
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**Q1.** The error bound is not directly correlated with the final performance.
**A1.** We respectfully disagree. As shown in Section 4.2, we s... | Summary: Self-supervised contrastive learning effectively extracts representations from unlabeled data. Despite its success prompting theoretical studies, the impact of specific data augmentation techniques is still under-explored. To address this, the authors proposed an augmentation-aware error bound for self-supervi... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to Reviewer cGdD for appreciating the soundness of our theoretical formulations and results. We address your concerns below.
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**Q1.** Organization of Section 2.
**A1.** As suggested, we will make the discussions in Section 2.3 an independent section following... | Summary: This paper theoretically examines the role of data augmentation in contrastive learning. It demonstrates that supervised risk is bounded not only by unsupervised risk but also by a trade-off introduced by data augmentation. The analysis is further extended using Lipschitz continuity, providing insights into ho... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to Reviewer Xxrg for appreciating our practical theoretical assumptions and novel theoretical results. We address your concerns below.
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**Q1.** Empirical verification of Assumption 2.2.
**A1.** For each input image in the CIFAR100 dataset, we generate 100 dif... | null | null | null | null | null | null |
Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy | Accept (poster) | Summary: This paper introduces Cape, a DP prompt perturbation mechanism aimed at enhancing the privacy-utility trade-off in LLM-based inference services through context-aware utility function and bucketized sampling function. The proposal is motivative and provides promising solutions to the long-tail dilemma in privat... | Rebuttal 1:
Rebuttal: We thank the reviewer jdXq for the positive feedback on "The proposal is motivative and provides promising solutions". We hereby answer the specific questions below and provide detailed explanations.
> Paper polishment
Thanks for your suggestion. We acknowledge that on-device generation and acce... | Summary: The paper introduces Cape, a DP mechanism designed to protect user privacy when interacting with LLM inference services. The authors identify an issue in current LLM services: users need to submit their prompts in plaintext for inference, exposing sensitive information. The paper proposes a context-aware appro... | Rebuttal 1:
Rebuttal: We thank the reviewer pKo2 for the positive feedback on "achieves a better privacy-utility trade-off than existing approaches". We hereby answer the specific questions below.
> A more comprehensive evaluation for semantic privacy leakage and utility.
Thanks for your suggestions. For privacy eval... | Summary: The paper proposes a new approach to perturb the tokens in user prompts to preserve local differential privacy in an efficient and utility-friendly way. For this, the authors utilize a secondary model, called device model, to generate logits for each token and use exponential mechanism (weighted by these logit... | Rebuttal 1:
Rebuttal: We thank the reviewer Mrs5 for the positive feedback on "proposed approach would be useful to improve privacy preserving inference on LLMs", which is quite encouraging for us. We hereby answer the specific questions below and provide detailed explanations.
> Q1: Clarity of low efficiency of crypt... | null | null | null | null | null | null | null | null |
Learning Fused State Representations for Control from Multi-View Observations | Accept (poster) | Summary: The paper proposes a novel method called Multi-view Fusion State for Control (MFSC) to improve Multi-View Reinforcement Learning (MVRL). MFSC's main contribution is its integration of bisimulation metric learning into the MVRL framework, allowing for the extraction of task-relevant representations from multi-v... | Rebuttal 1:
Rebuttal: Thanks for your comments, and we will address your concerns in the following.
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**Potential Issues: hyperparameter tuning**
Thank you for your valuable feedback. For all baseline methods with publicly available implementations, we directly used their officially released code and default co... | Summary: The paper proposes a novel framework for Multi-View Reinforcement Learning (MVRL). The key contributions include:
- Integrating bisimulation metric learning into MVRL to extract task-relevant representations from multi-view observations.
- Introducing a multiview-based masking and latent reconstruction auxil... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for thoughtful and constructive feedback. We are pleased that our proposed framework for Multi-View Reinforcement Learning (MVRL), including the integration of bisimulation metric learning and masking-based latent reconstruction, was well-received. We appreciate the... | Summary: The paper proposes a novel framework for multi-view reinforcement learning to effectively learn task-relevant representations of the state from multi-view observations. The new framework not only incorporates the bisimulation metric which aligned representation with the task’s objectives but also add latent re... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their thoughtful evaluation, constructive feedback, and valuable suggestions, which have greatly contributed to improving the clarity, depth, and overall quality of our work. We will address your concerns in the following.
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**Q: Could you share the... | Summary: This paper addresses key challenges in multi-view reinforcement learning (MVRL), specifically redundancy in observations, distracting or irrelevant information, and robustness to missing views. To overcome these issues, the authors propose a framework, Multi-view Fusion State for Control (MFSC), integrating ta... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments, and we will address your concerns in the following.
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**W1:**
We noticed that the citation in Definition 3.1 was incorrect; the correct reference should be the $\pi$-bisimulation metric proposed by Castro et al.(2020)[1], rather than Castro et al.(2021)... | null | null | null | null | null | null |
Efficient and Separate Authentication Image Steganography Network | Accept (spotlight poster) | Summary: This paper presents a novel authentication-based image steganography framework (AIS) with separate invertible networks for authentication and hiding. The approach addresses critical challenges in multi-recipient security and large-capacity hiding. The experiments demonstrate significant improvements in stego/r... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments. We hope that the explanations on the questions can help you better understand our proposed method.
Q1: The comparison in Table 1 shows superior performance over baselines. However, recent diffusion-based steganography methods are not included. Altho... | Summary: This work advances adaptive steganography by redefining secure multi-user communication through the integration of invertible networks and dynamic authentication. A key innovation lies in decoupling authentication from secret data via distribution adaptation, a conceptual leap that reimagines information hidin... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments. We hope that the explanations on the questions can help you better understand our proposed method.
Q1: The dynamic lock-key module employs a "simplified UNet." Providing ablation studies on alternative architectures or clarifying the rationale for t... | Summary: The paper presents AIS, an authentication-based steganography framework that leverages invertible networks for distribution adaptation and parallel hiding. The two-stage design and focus on security and efficiency are commendable, and comprehensive experiments demonstrate the framework’s advantages. While the ... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments. We hope that the explanations on the questions can help you better understand our proposed method.
Q1: The distribution adaptation mechanism is central to reducing lock-induced artifacts. Could the latent distributions of locks and secrets before an... | Summary: This paper makes a substantial contribution to secure multi-recipient image steganography by addressing the critical yet under explored challenge of authentication. The proposed AIS framework elegantly integrates authentication through a novel two-stage architecture—IAN for lock-key generation and distribution... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments. We hope that the explanations on the questions can help you better understand our proposed method.
Q1: Could the authors clarify whether the dynamic lock-key generation strategy considers potential adversarial attacks on key generation?
A1: The sec... | null | null | null | null | null | null |
LLMs can see and hear without any training | Accept (poster) | Summary: The paper introduces Multimodal Iterative LLM Solver (MILS), a novel framework designed to provide multimodal capabilities, such as image, video, and audio captioning, without the need for task-specific training. The central innovation of MILS is its iterative optimization strategy, leveraging the inherent rea... | Rebuttal 1:
Rebuttal: We thank the reviewer for your insightful review and helpful comments. We address all concerns below:
## Clarification of the text representation in cross modal arithmetic
The representation is indeed the raw text. The novelty is in the inversion of the image into the caption itself. Using a cap... | Summary: This paper introduces MILS (Multimodal Iterative LLM Solver), which enables large language models (LLMs) to perform various multimodal tasks without any additional training. Through test-time optimization, it incrementally improves its outputs by leveraging two key modules (GENERATOR and the SCORER), by genera... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. We address all concerns below. In particular, we highlight the benefits of our iterative approach vs MeaCap, which is designed specifically for image captioning. We also clarify a possible misunderstanding regarding the use of large 405B LLM–excep... | Summary: The authors propose MILS: Multimodal Iterative LLM Solver, a simple method that claims to use the reasoning abilities of textual LLMs to have impressive zero-shot performance on multimodal tasks. At a high level, this involves a Generator model, which is either a text-LLM or a text-LLM chained to another syste... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review and their willingness to accept our paper. We are glad they found our method generalizable, claims well justified, and improved performance on a variety of different tasks. We address all concerns next:
## Investigation into the reasoning capabiliti... | Summary: This paper presents an iterative solver to enable pure language LLMs to "perceive" visual or audio signals through trial and error using discriminative scorers. Specifically, the authors construct a feedback loop between generators (LLMs) and discriminative ranker (e.g., SigLIP for image captions): first, the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review and willingness to accept the paper. We are glad you found our work novel, and claims supported by clear and convincing empirical evidence. We address all the remaining concerns below:
## For multimodal understanding, what is the strength or pote... | null | null | null | null | null | null |
EvFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams | Accept (poster) | Summary: This paper propose the EvFocus, the first framework designed to reconstruct sharp images from out-of-focus event streams, addressing the challenge of defocus blur, where existing event deblurring methods fail due to reduced spatial gradients and sparse event generation. The proposed approach integrates a tempo... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for the detailed and thoughtful feedback, and we carefully address the raised concerns below.**
---
### **Realistic 3D scenes and evaluation**
Thank you for pointing this out. We have conducted additional experiments on realistic 3D scenes with varying depth le... | Summary: This paper introduce a new network architecture for restoring all-in-focus grayscale video from defocused event-camera measurements. The method assumes a thin lens model and gaussian defocus blur. It is tested on simulated and real data. The proposed method outperforms existing methods quantitatively and quali... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for the encouraging feedback and constructive suggestions. We address the main points below.**
---
### **Additional baseline: event-to-video + image/video deblurring**
We appreciate this valuable suggestion. In the supplementary material (Fig. 11–12), we includ... | Summary: This paper proposes EvFocus, a novel architecture for reconstructing sharp images from defocused event streams. The key innovation lies in its temporal encoder, blur-aware dual-branch decoder, and re-defocus module, combined with a synthetic defocus event dataset for training. Experiments on synthetic and real... | Rebuttal 1:
Rebuttal: **We thank the reviewer for their thoughtful comments and constructive suggestions. We address each of the raised points below.**
---
### **Computational Efficiency**
We agree that runtime performance is important for real-world deployment. In response, we have conducted a **comprehensive runtim... | null | null | null | null | null | null | null | null |
A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs | Accept (poster) | Summary: This paper tackles the open problem of proving a rate optimal regret bound in average reward infinite horizon linear mdp with a computationally efficient algorithm. The problem it is solved successfully, using a double loop structure inspired by hong et al. 2025 and a deviation controlled mechanism to make sur... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review and suggestions for improving the paper. Here are our responses.
**Minor error in proof**. Thank you very much for catching this. We will replace $m_{t + 1}$ with $m_t$ for the equality to hold.
**Relation to previous work**. Thank you for the suggest... | Summary: This paper proposed an algorithm for infinite-horizon average-reward reinforcement learning with linear function approximation. The main problem need to be solved is the computation issue arises from minimize the value function in large state space. To address this, the paper proposed a new clipping technique,... | Rebuttal 1:
Rebuttal: Thank you for your review. Here is our response.
**Simulation in toy setting**. Thank you for the suggestion for running a simulation in a toy setting. We have confirmed that our algorithm runs in a *tabular setting* (i.e. linear MDP setting with feature vectors orthogonal) with average regret re... | Summary: This paper proposes a computationally efficient algorithm for infinite-horizon average reward linear MDPs. The paper seeks to improve upon the previously proposed approach $\gamma$-LSVI-UCB by Hong et al'25. The main contribution is that the algorithm proposed in Hong et al.'25 requires to iterate over all the... | Rebuttal 1:
Rebuttal: Thank you for your time for the review and for the suggestion for improving the paper.
**Q1: main technical novelties**. We will integrate the following summary in the introduction section and in Section 3.2 when introducing the method.
1. The main technical novelty lies in designing a clipped v... | Summary: In this paper, the authors have studied the reinforcement learning algorithm for linear MDPs in an infinite-horizon average-reward setting. Previous works approximate the average reward by the discounted one and employ a clipping-based value iteration method. However, it requires the computation of minimum of ... | Rebuttal 1:
Rebuttal: Thank you for your time for the detailed review. Your feedback will help us in improving our paper. Here are our responses to your questions.
**Q1: Computational issue \& Simulation**. You are correct to note that, in the large state space regime, the number of unique states visited over $T$ time... | null | null | null | null | null | null |
Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time | Accept (poster) | Summary: This paper proposes UAMD, a novel inference-time alignment approach for language models that allows satisfying multiple user-specified criteria through constrained decoding. The key insight is that for many criteria (like safety), meeting a threshold is sufficient rather than maximizing the reward. The method ... | Rebuttal 1:
Rebuttal: We thank you for the thoughtful review and address your concerns below.
> **Weakness 1:** The paper lacks ...
**Response:** **Computational efficiency analysis:** Thanks for the suggestion! Below is the inference time per prompt for various approaches using the setup in Evaluation-1 (main paper)... | Summary: This paper primarily focuses on aligning large language models (LLMs) at inference time (i.e., test time) without modifying their parameters. Prior work in inference-time alignment primarily aims to align LLMs towards a single objective defined through a reward model. Some studies have explored multi-objective... | Rebuttal 1:
Rebuttal: We thank you for the thoughtful review and address your concerns below.
> **Weakness 1:** The paper relies....
**Response to Weakness 1:**
**Evaluations using ArmoRM:** Thanks for your suggestion! We have conducted additional experiments using ArmoRM reward model [1] (RLHFlow/ArmoRM-Llama3-8B-v... | Summary: This paper proposes an inference-time alignment method UAMD for LLMs, which can consider user-specified perferences on different aspects based on transfer decoding. Specifically, UAMD is inspired by the bounded rationality theory which suggests that human decision-making follows the strategy of maximizing key ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We provided detailed responses as follows.
> **Weakness 1:** The theoretical properties of the proposed method are not empirically evaluated. It is unclear how well UAMD preserves multi-aspect preferences in practice.
**Response to Weakness 1:** We provi... | Summary: The paper proposes an alignment strategy for LLMs that does not maximize all rewards in the case of a multi-objective scenario; instead only key objectives important to the user are maximized while others only need to meet acceptable thresholds. This is formulated as a Langrangian optimization function, with ... | Rebuttal 1:
Rebuttal: **General Response:** We thank the reviewer for their thoughtful feedback and for recognizing the novelty and theoretical contributions of our work.
> **Weakness 1:** Evaluation is limited to scenarios with two objectives. It is unclear how well the method scales to more than two objectives.
**... | null | null | null | null | null | null |
Controlling Large Language Model with Latent Action | Accept (poster) | Summary: Existing LLMs often rely on token-level actions that may be overly large and inefficient. This paper proposes learning a compact and latent action space to improve controllability and exploration in RL. Specifically, the authors augment a latent action space with a pre-trained LLM to form a latent action langu... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's valuable feedback and recognition of our latent action approach.
**Q1:** Claims are not supported by clear evidence: Therefore, LAMP aims to decouple these semantics by assigning them to different and limited latent actions, enabling more efficient fine-tuni... | Summary: The paper presents Latent Action governed world Model from Pre-trained LLM (LAMP), a novel reinforcement learning (RL) approach designed to control Large Language Models (LLMs) by learning a compact, latent action space rather than relying on conventional token-level actions. LAMP leverages an inverse dynamics... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful feedback and positive recognition of the novelty of the perspective of RL from observation. We are grateful for the opportunity to address the reviewer's concerns and further clarify our methodology.
**Q1:** While the claims regarding improved co... | Summary: This paper proposes to learn a more compact latent "action" space for pretrained LLMs to improve controllability. This latent action space is learned by an autoencoder where the encoder is an inverse dynamics model p(a_t|x_0:t+1), the decoder is a transition model p(x_t+1|x_0:t, a_t), the conditional prior is ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for helpful feedback and positive assessment of the novelty in our latent action control approach. We address the reviewer's concern below, with key claims highlighted in bold for clarity.
**Q1:** In the ablation study, why did increasing the model size and the dat... | null | null | null | null | null | null | null | null |
Adaptive Constrained Optimization for Neural Vehicle Routing | Reject | Summary: This work proposes a instance-level adaptive constraint optimization framework to improve the feasibility satisfiability of learning methods for TSPs. The authors designed a dual variable-conditioned policy with two phase of learning. In the first phase, they consider varying values of the lagrange dual variab... | Rebuttal 1:
Rebuttal: Thanks for dedicating your time to review our work! We sincerely appreciate your acknowledgment of our contributions. Below are the detailed responses to your concerns. **Corresponding experimental results** can be found at [link](https://anonymous.4open.science/api/repo/4620_rebuttal-DDDF/file/ad... | Summary: This paper incorporates an instance-adaptative Lagrangian multiplier into the policy of the neural VRP solvers, aiming to enhance the performance of a most recent work PIP (Bi et al., 2024), which uses a fixed multiplier during training. Specifically, the multiplier varies among different instances and inputs ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and recognizing our contributions. Below please find our responses. **Corresponding experimental results** can be found at [link](https://anonymous.4open.science/api/repo/4620_rebuttal-DDDF/file/addtional_results_4620.pdf).
## **R1: Experiments with post-searc... | Summary: This paper extends the PIP framework (Bi et al., 2024) by allowing the assignment of distinct dual variables to accommodate varying constraint satisfaction difficulties across instances. To achieve this, the paper first proposes modifying the POMO network to incorporate dual variable information into the node ... | Rebuttal 1:
Rebuttal: Thanks for dedicating your time to review our work! We sincerely appreciate your recognition of our contributions to constrained combinatorial optimization. Detailed responses to your concerns are as follows. **Corresponding experimental results** can be found at [link](https://anonymous.4open.sci... | Summary: The paper introduces an instance-level adaptive constrained optimization method for neural vehicle routing. It builds on prior PIP-based approaches by assigning instance-specific dual variables instead of a uniform λ, aiming to better balance solution quality and constraint satisfaction across diverse instance... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work and recognizing our contributions. Below are detailed responses. **Corresponding experimental results** can be found at [link](https://anonymous.4open.science/api/repo/4620_rebuttal-DDDF/file/addtional_results_4620.pdf).
## **Response 1: Lacking ablation study**
... | null | null | null | null | null | null |
Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks | Accept (poster) | Summary: The paper proposes PACMAN, a novel method that learns a policy committee, ensuring that at least one near-optimal policy is included for each task with high probability. The proposed approach is evaluated on Half-Cheetah Velocity, Humanoid, and MetaWorld tasks, demonstrating superior performance compared to mu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and clarification questions!
>**Comment:** The method, especially the algorithm is not clear to me.
**Response**: The details of the algorithm are provided in Section 3.1. In summary, we cluster tasks into groups, where each group contains simila... | Summary: The paper introduces PACMAN, a novel framework for learning policy committees in multi-task Markov decision processes (MDPs) with diverse tasks. The approach includes clustering-based approach to create a representative cover over tasks and a gradient-based alternative to group tasks based on parametric repres... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and suggestions!
>**Comment:** The assumption of parametric task structure may limit generalizability.
Response: We agree that assuming that tasks are parametric is a limitation. However, we show that we can often use LLM embedding of natural lan... | Summary: The paper introduces a new learning paradigm, called policy committee learning effectively for solving multi-task RL. More specifically, a policy committee targets learning a set of policies maximizing the best-performing policy's discounted return for any given task sampled from a predefined task distribution... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and suggestions!
>**Comment:** Meta-World domain breaks the assumption made by the theoretical analysis that all the tasks share the same transition dynamics, as different tasks involve interacting with different objects, and therefore uses differ... | Summary: This paper introduces PACMAN, a novel framework and algorithmic approach for learning policy committees in multi-task reinforcement learning (MTRL) and meta-reinforcement learning (meta-RL) settings with diverse tasks. The key challenge addressed is the difficulty of generalizing effectively across diverse tas... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments!
>**Comment:** The reliance on a parametric task representation (or the ability to obtain one using LLMs) is a limitation, although a common one.
**Response**: Indeed, our method does require access to a parameterization for the tasks. We note, ... | null | null | null | null | null | null |
ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification | Accept (poster) | Summary: This paper introduces Refine via Intrinsic Self-Verification (ReVISE), an efficient framework that enables large language models (LLMs) to self-correct their outputs through self-verification. ReVISE allows LLMs to evaluate their reasoning processes and iteratively refine their outputs based on verification fe... | Rebuttal 1:
Rebuttal: Dear reviewer nE5C,
We sincerely appreciate your efforts and comments to improve the manuscript. We respond to your comment in what follows.
---
**[Q1] Would the proposed method benefit from multi-round self-correction?**
We have already investigated multi-round self-correction in Append... | Summary: The paper introduces DPO to fine-tune LLMs in two steps, which are self-verification and self-correction, and a special token [refine] is introduced. Simple and effective method.
Claims And Evidence: All claims made in the submission supported by clear and convincing evidence.
Methods And Evaluation Criteria... | Rebuttal 1:
Rebuttal: Dear reviewer Aquu,
We sincerely appreciate your efforts and comments to improve the manuscript. We respond to your comment in what follows.
---
**[W1] The datasets employed and experimental designs are not sufficient; the paper proposes to make demonstrations in mathematics and coding domains ... | Summary: This paper proposes a method to perform self-correction intrincically with two steps. First is a self-verifying stage, by leveraging SFT and DPO, the LLM will learn how to distiguish between correct reasoning and wrong reasoning. Second step is self-correction, when the LLM generates the refine token, then the... | Rebuttal 1:
Rebuttal: Dear reviewer cTTG,
We sincerely appreciate your efforts and comments to improve the manuscript. We respond to your comment in what follows.
---
**[W1] The diversity of the benchmarks is limited. It would be better to increase the range of task types here to fully verify the effectiveness of th... | null | null | null | null | null | null | null | null |
Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data | Accept (poster) | Summary: This paper investigates the compression efficiency of foundation models when accounting for parameter size. Through extensive experiments on 165GB of text, image, and audio data, the authors demonstrate that relatively small transformer models (millions of parameters) can outperform standard compression algori... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful assessment and constructive feedback. We are pleased that they think that our `topic is interesting and an important direction`, that our `experimental settings are comprehensive`, and that our `paper is well-written`.
**Could you also compare to state-of-... | Summary: The paper shows that small decoder-only transformers trained on *multimodal data* are effective data compressors. This happens when the modality of the data being compressed (text, audio, video) belongs to the training set. In this multimodal setup, the authors show that it is possible to achieve compression r... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough assessment of our work. We are pleased that they think that our `work addresses a relevant open question backed by a convincing set of experiments` with `methods and evaluation criteria that are appropriate and sound`, and that they consider our `paper well... | Summary: This paper studies byte language models as a means to compress data on multimodal data (text, audio, images). They show that by training small scale transformers data can be compressed better than with standard compression algorithms like gzip or domain-specific standards (JPEG). This is achieved without onli... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and constructive feedback. We are pleased that they think our `interesting findings will impact the future of compression via LMs`, our `results are rich with multiple ablation studies`, and our `paper is clearly written`.
**How do you ensure that y... | Summary: 1. This paper empirically examines the effectiveness of small pre-trained transformers (with millions of parameters) as multimodal data compressors for text, images, and audio.
2. Trained on 165GB of data per modality, these models achieve compression ratios that surpass both general-purpose and domain-specifi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and insightful comments. We are pleased that they think that our `paper presents a comprehensive empirical study`, which `leverages appropriate benchmarks`, and that our `writing is clear and well-structured`.
**What is the novelty over Delétang ... | null | null | null | null | null | null |
Partially Observable Reinforcement Learning with Memory Traces | Accept (poster) | Summary: This paper addresses the problem of history summarization in POMDPs. Specifically, it proposes a new method to summarize the history with memory traces, which inspired by eligibility traces, in place of the ubiquitous finite-length window. Memory traces induce a representation that accounts for the whole histo... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review! Below, we respond to your critical points and questions.
**Expanding the discussion on connections to prior work on POMDPs.**
We agree that our discussion on related works is relatively sparse. Based on your suggestions, as well as the other reviewers... | Summary: The paper studies learning with memory traces as an alternative to finite length history windows to solve POMDPs. Memory traces are exponential moving average of observations. In offline RL, when the forgetting factor \lambda < 0.5, they are shown to be equivalent to learning with windows in capacity and sampl... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review! Below, we respond to your critical points and questions.
> Larger experimental domains would make the paper more attractive.
Indeed, the environments we consider in our experiments are more illustrative than realistic. We intend the main contribution... | Summary: The authors propose memory traces as an alternative to fixed-window histories, or “frame stacking,” for addressing partial observability in RL. The concept is loosely related to eligibility traces, amounting to an exponential moving average of the observation stream, which is then fed to the agent as input for... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review of our paper! Below, we respond to your critical points and questions. Due to limited space, we cannot respond to every point, but we will do our best to incorporate all your suggestions in the final version.
**Proof of Theorem 5.8 and the choice of $\... | Summary: The authors propose a novel method for handling observations in reinforcement learning for partially observable systems.
Their method represents the history of observations with an exponential moving average. The authors analyze sample complexity bounds for offline on policy evaluation. The novel method is com... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review! Below, we respond to your critical points and questions.
> I think it would have been interesting to discuss the use of RNNs and transformers to represent the history of observations.
We are happy to include a brief discussion of the connections to R... | null | null | null | null | null | null |
LLaVA-ReID: Selective Multi-image Questioner for Interactive Person Re-Identification | Accept (poster) | Summary: This paper introduces a new task named interactive person re-identification (I-ReID), which aims to address the insufficient details in initial human descriptions. To support this task, the paper contributes a new dataset comprising multi-round dialogues generated using a customized approach with vision-langua... | Rebuttal 1:
Rebuttal: Thanks a lot for reviewing our paper and giving us valuable suggestions. We will answer the questions one by one.
> Q1: Visual hallucination in VLM captioning.
Thanks for your insightful comments. We acknowledge that GPT-like vision-language models (VLMs) are prone to hallucinations, particularl... | Summary: In this paper, a novel task is presented to address the limitations of traditional text-based ReID which rely on complete and one-time descriptions from witnesses. This work introduces interactive person re-identification (I-ReID) that employs multi-round question-answer dialogue to iteratively gather informat... | Rebuttal 1:
Rebuttal: We appreciate the insightful questions and will address your concerns in the following.
> Q1: Comparison of inference time and ablation study on the number of candidates.
We conduct these experiments on NVIDIA RTX 3090 GPUs. The inference time for the 5-round question generation of LLaVA-ReID an... | Summary: This paper introduces interactive person re-identification, which refines partial text queries via dialogue interactions to better suit real-world scenarios. The proposed LLaVA-ReID generates discriminative questions using images and dialogue history, enhanced by an image selector and a looking-forward supervi... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the novelty of our method. We will answer the questions one by one.
> Q1: Details about the ensemble.
In our integration of LLaVA-ReID with the existing T-ReID framework, we use the human-annotated captions in the T-ReID datasets as the initial queries. We first encod... | Summary: The paper introduces a new task for person re-identification, an interactive person re-identification (I-ReID) framework that iteratively refines incomplete or vague descriptions through multi-round dialogues. The paper proposed LLaVA-ReID, a selective multi-image questioner that leverages both visual and text... | Rebuttal 1:
Rebuttal: Thanks for your comments. We will answer your questions one by one in the following.
> Q1: Can you provide further analysis or experiments to assess the computational efficiency of your candidate selection module compared to baseline methods? (As well as the question generation time)
We conduct ... | null | null | null | null | null | null |
Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift | Accept (poster) | Summary: The paper introduces Non-stationary Direct Preference Optimization (NS-DPO) for preference learning on non-stationary offline datasets. With an assumption on the upper bound of preference drift and log-linear policies, the paper also shows that the method achieves a sample complexity of $O(n^{-1/4})$, or $O(n... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and recognising the precision of our theoretical analysis. We address the questions and suggestions of the reviewer as follows.
## Response to the questions
__*(1) “For the gradual drift experiments, were the datasets constructed such that the model sees incr... | Summary: This paper discusses a new approach called NS-DPO to address the issue of temporal preference drift in LLMs. Current LLM preference optimization algorithms do not account for changes in preferences over time, leading to misalignment. NS-DPO models time-dependent reward functions using a Dynamic Bradley-Terry m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and recognising the intriguing nature of this problem. We believe this will become a far more prevalent problem as LLM providers gather bespoke datasets over longer time frames and are keen to raise this within the LLM research community. As such, we are glad t... | Summary: This paper addresses the issue of temporal preference drift in training large language models (LLMs) using human feedback. The authors propose Non-Stationary Direct Preference Optimization (NS-DPO), which incorporates a Dynamic Bradley-Terry model to account for time-dependent reward functions. This method int... | Rebuttal 1:
Rebuttal: We thank the reviewer for their response and recognising that our conclusions are well supported by both theoretical evidence and extensive experiments that show NS-DPO’s effectiveness in handling non-stationary preferences. We think this is a super important problem, unaddressed within the litera... | null | null | null | null | null | null | null | null |
No Soundness in the Real World: On the Challenges of the Verification of Deployed Neural Networks | Accept (spotlight poster) | Summary: The paper provides a detailed theoretical and empirical analysis on the soundness of neural network verifiers. The paper demonstrates that a number of State of the Art verifiers can produce misleading or even wrong results when considering the evaluation of NNs on modern hardware.
## update after rebuttal
I s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough and positive review and the many useful suggestions and pointers! We will incorporate these in the paper. While we agree with all of the comments, we elaborate on some of the specific points.
**Proof of Proposition 5.5.** Indeed, if the number of bits of the... | Summary: The paper discusses a crucial problem with neural network verification: The networks are evaluated not as pure mathematical functions, but on real-world hardware that depends on specific floating point precision and computation orderings. However, this fact is often not taken into account by NN verification to... | Rebuttal 1:
Rebuttal: We are happy to see that the reviewer considers the paper original and of significance. Let us address the issues raised.
**Issue 1: The main point is not novel.** The reviewer correctly states that floating point issues have been shown to fool certain approaches to verification. However, some ve... | Summary: This paper studies the problem of the gap between theoretical soundness and practical soundness of neural network verification, which is commonly seen in the deployment of neural networks. It also proposes adversarial networks based on such characteristics to fool the verifiers to compromise soundness. Experim... | Rebuttal 1:
Rebuttal: We are pleased to read that the reviewer finds the topic interesting. Let us discuss the main issues raised here.
**Issue 1: The discovered issues are not surprising.** Please refer to our answer to reviewer *N1Xq* that covers the same issue in detail.
**Issue 2: In symbolic methods, the floati... | Summary: The paper discusses the soundness of neural network verifiers. In particular, it exploits the order of floating point operations which may lead to unsound bounds computed by a neural network verifier.
Claims And Evidence: The paper wants to demonstrate the most current neural network verifiers cannot fully ha... | Rebuttal 1:
Rebuttal: We are encouraged by the fact that the reviewer considers the paper interesting and the topic important.
Before moving on to addressing the specific issues raised, let us recall that our specific interest lies in provably sound verification (a motivation shared by a sizable community) as opposed... | null | null | null | null | null | null |
Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations | Accept (spotlight poster) | Summary: This paper proposes to use the intermediate features of video diffusion model as a visual encoder for generalizable robot action prediction. Firstly, a pre-trained video generation model is fine-tuned on robotic dataset. Then, the latent features of the first denoising step are aggregated for down-steam action... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our paper! Thank you for your support in our work!
**Q1: What's the percentage of each task category in the training and testing set?**
ANS: Thank you for the questions! The specific number of evaluated trajectories can be found in Appen... | Summary: This paper introduce Video Prediction Policy(VPP), a robotics framework that leverages video diffusion model to capture the dynamic presentation vital to policy training. VPP consists of a two-stage process: i) a general video model is fine-tuned into a text-guided video prediction (TVP) model using large-scal... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Thank you for your support on our work!
**Q1: Regarding the data efficiency of VPP model**
ANS: Thank you for your constructive question! In Table 1 of the original paper, we conducted experiments on the CALVIN benchmark usin... | Summary: The paper is about fine-tuning a pretrained image to video diffusion model on video-caption datasets that focus on object manipulations. The image to video model is turned into an image plus text to video model through the fine-tuning. This diffusion model is then used as a feature extractor (no iterative de-n... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Based on your review, we added a detailed discussion and additional experiments.
**Q1: About the details on method: what diffusion time-step is used to extract TVP features during inference?**
Thank you for your question. In... | Summary: The paper introduces the Video Prediction Policy (VPP), a versatile robotic policy that enhances robot control by utilizing predictive visual representations generated by text-guided video prediction models (TVPs). VPP employs a two-stage methodology: first, fine-tuning a text-guided video prediction model on ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Based on your review, we added detailed discussions and additional experiments:
**Q1: Control frequency comparisons to UniPi**
ANS: We are afraid misunderstandings exist on the UniPi work. In page 5, sec 3.2 of the original ... | null | null | null | null | null | null |
Speculative Prefill: Turbocharging TTFT with Lightweight and Training-Free Token Importance Estimation | Accept (poster) | Summary: SpecPrefill proposes a prompt token pruning technique using speculation by a small model before forwarding the large model. This method is especially beneficial when the token length is medium (latency of attention and MLP are similar, so sparse attention methods do not do a good job) because it reduces the nu... | Rebuttal 1:
Rebuttal: ### Summary
We sincerely thank the reviewer for the effort in providing constructive comments. We addressed all the feedback from the reviewer, and believe that with these changes, the results in our paper become significantly stronger (added experiment result numbers in reviewer USDK’s rebuttal).... | Summary: This paper introduces Speculative Prefill, an innovative training-free framework that elegantly addresses efficiency challenges in language model inference. By leveraging a lightweight model to speculate on important tokens based on context, the approach impressively enhances time-to-first-token performance. T... | Rebuttal 1:
Rebuttal: ### Summary
We thank the reviewer for the constructive and insightful feedback! We addressed all feedback and believe that incorporating all comments makes this paper significantly stronger:
### Per-feedback detailed response
> It would be interesting to see more exploration of how it compares w... | Summary: This paper propose SpecPrefill, that identifies the important tokens during the pre-fill stage via accumulating the attention scores in a 8-steps look-ahead window, calculated with a small model as the speculator. This approach can achieves a speedup of about 3x and also maintains a comparable performance comp... | Rebuttal 1:
Rebuttal: ### Summary
We sincerely thank the reviewer for the time and constructive feedback! We addressed all feedback and believe that it makes this paper significantly stronger (**added experiment result numbers in reviewer USDK’s rebuttal**). Specifically, we addressed the feedback by:
1. We moved the... | null | null | null | null | null | null | null | null |
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors | Accept (poster) | Summary: This paper presents a mostly-theoretical study of four sources of error in diffusion sampling, in the special case of multivariate Gaussian distributions. First they derive exact solutions for the diffusion reverse SDE and ODE for a Gaussian, and then apply these to study the initialization, discretization, tr... | Rebuttal 1:
Rebuttal: We sincerely thank you for your feedback.
> L129 “sensible” = “sensitive”?
Thank you for noticing this typo, which we have corrected.
> L148: Assumption 1: I am curious why you need to assume a centered (mean = 0) Gaussian? Could the results be extended to arbitrary mean? (Are you not doing th... | Summary: 1. Under the assumption of a Gaussian data distribution, the authors derive exact solutions for both the backward stochastic differential equation (BSDE) and the probability flow ordinary differential equation (ODE) in the VP-SDE diffusion model.
2. This paper systematically analyzes the convergence properties... | Rebuttal 1:
Rebuttal: We sincerely thank you for your feedback.
## Answers to questions
> I am curious about whether there truly have been no prior works exploring the explicit solutions of the VP-SDE and PF-ODE in the Gaussian data setting, as their solutions are not particularly complex and can essentially be found ... | Summary: The paper diffusion-based generative models assuming that the data distribution is a known Gaussian distribution. Since the data distribution is Gaussian the diffusion process score is linear and known in closed form, which allows the authors to:
1. Solve in closed form the reverse (noise → data) SDE and ODE... | Rebuttal 1:
Rebuttal: We sincerely thank you for your feedback.
> The experiment in section 5 however does not highlight the impact of score function approximation error on the distribution metrics reported. The authors study the effect of sampling parameters on the distribution metrics, not the effect of higher or ... | Summary: The article studies different types of errors when implementing diffusion models (DM) by constructing a Gaussian DM, for which the solution is known in closed form, and then measuring the errors between the exact and approximated solutions using the Wasserstein distance. They analyse the sensitivity of the err... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful and positive feedback. We are pleased that the you find our work a meaningful contribution to the theoretical understanding of diffusion models (DMs) and recognize its relevance within the broader literature. Below, we address your specific questions and ... | null | null | null | null | null | null |
A Meta-learner for Heterogeneous Effects in Difference-in-Differences | Accept (poster) | Summary: The paper introduces novel orthogonal meta-learners for estimating the conditional average treatment effect of the treated (CATT) in a difference-in differences (DID) setting. The main contributions include the proposed orthogonal loss, theoretical guarantees in form of doubly robust rates, and extensions to g... | Rebuttal 1:
Rebuttal: Thank you for the helpful reviews! The result of Proposition 2.3 follows as a simple extension of the ATT identification strategy under conditional parallel trends. The reviewer is right that we should not claim this as a result of this work. We will reference for instance, in the preamble of the ... | Summary: The paper proposes a doubly robust metalearner for the Conditional Average Treatment Effect on the Treated for panel data. The method is targeted for binary
treatments and builds upon the parallel trends assumptions common in Difference-in-Differences settings.
Furthermore, the paper proposes
a robust meta-le... | Rebuttal 1:
Rebuttal: Thank you for the helpful reviews! We apologize for any typos and will definitely fix them for the camera-ready version. As pointed out by many reviewers, we were not able to include a detailed discussion and literature review sections in the initial submission due to length constraints. In the ca... | Summary: The paper develops a Neyman-orthogonal meta learner for estimating a conditional average treatment effect on the treated (CATT) in the framework of difference-in-differences (DiD). This framework casts the problem of CATT estimation as a convex risk minimisation that involves auxiliary (nuisance) models. The a... | Rebuttal 1:
Rebuttal: Thank you for the helpful reviews! We will fix the inconsistent notations and restructure the paper for the camera-ready version. We introduced “auxiliary models” in the abstract to also accommodate for audiences that are not very familiar with the field. That being said, given the target audience... | null | null | null | null | null | null | null | null |
CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization | Accept (poster) | Summary: The authors explore the prompt tuning problem of vision-language models and propose a training method, CoCoA-Mix, to improve generalization and specialization simultaneously.
CoCoA-Mix consists of a confusion-aware loss to enhance specialization for confusing classes and a confidence-aware temperature strategy... | Rebuttal 1:
Rebuttal: ## `Theoretical Claims`
In our paper, we refer to Eq. 12 in [1], which relates distributional shift to generalization performance:
$$
l\_\\text{test} \\leq l\_\\text{train} + \\frac{M}{\\sqrt{2}} \\sqrt{\\text{KL} [p\_T(z)|p\_S(z)] +\\mathbb{E}\_{p\_T(x)}\\left[ \\text{KL}[p\_T(y|x)|p\_S(y|x)] \\r... | Summary: This paper proposed a series of techniques to tackle the problem of improving specialization in prompt tuning, including confusion-aware loss, mixture-model using confidence-aware temperature. Extensive experiments are conducted to show the performance of the proposed method.
Claims And Evidence: Yes. The pap... | Rebuttal 1:
Rebuttal: ## `Other Strengths And Weaknesses`
First, the "CoA-Loss" in ***Table 1*** corresponds to a naive ensemble model with a fixed $\\pi=\\{0.5,0.5\\}$. This naive mixture prediction can perform well when the equal weighting is near-optimal for a given dataset. In contrast, CoCoA-Mix optimizes CoA-Tem... | Summary: This paper addresses the challenge of improving both specialization and generalization in prompt tuning for vision-language models. It proposes a confusion-aware loss (CoA-loss) that refines decision boundaries between confusing classes, enhancing specialization. Additionally, they introduce a confidence-aware... | Rebuttal 1:
Rebuttal: ## `W-1`
We agree that Remark 3.3 should state $\\epsilon\_T(\\hat{p}\_t^\\pi)=\\min\_i\\epsilon\_T(\\hat{p}\_{t\_i})$, not $\leq$. However, our main claim regarding the simultaneous improvement of specialization and generalization **relies on Theorem 3.2 and our method, not Remark 3.3**.
The ... | null | null | null | null | null | null | null | null |
AdvAgent: Controllable Blackbox Red-teaming on Web Agents | Accept (poster) | Summary: The paper introduces AdvAgent, a black-box red-teaming framework that uses RL to optimize adversarial prompts, injecting them into HTML to mislead web agents. It achieves high attack success rates on GPT-4V and Gemini 1.5, revealing the limitations of prompt-based defenses.
Claims And Evidence: Yes.
Methods ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive feedback. Below, we address each of the concerns raised:
> Q1: More clarifications on the usage of DPO optimization in AdvAgent.
Thank you for the interesting question! We'd like to clarify that our reinforcement learning setu... | Summary: This paper introduces AdvAgent, a black-box red-teaming framework designed to systematically uncover vulnerabilities in foundation model-based web agents, which are increasingly used to automate complex tasks but also pose significant security risks. The key contribution of AdvAgent is its use of a reinforceme... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment and thoughtful feedback. We appreciate your recognition of the novelty and effectiveness of AdvAgent, and your acknowledgment that the proposed RL-based framework is reasonable and promising. Below, we respond to your main concern regarding gener... | Summary: The paper introduces AdvAgent, a black-box red-teaming framework designed to red team web agents against prompt injection attacks.These agents, while enhancing productivity, pose security risks due to their autonomous decision-making capabilities. The method first starts by collecting the dataset of successful... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback. We appreciate the recognition of the novelty and importance of our work in red-teaming web agents. We address the specific concerns and suggestions below:
> Q1: Generality of AdvAgent and experiments on additional web agents.
... | null | null | null | null | null | null | null | null |
Tilted Sharpness-Aware Minimization | Accept (poster) | Summary: This paper introduces Tilted Sharpness-Aware Minimization (TSAM), a novel extension of Sharpness-Aware Minimization (SAM) designed to further enhance generalization in deep learning models. While SAM aims to minimize the worst-case local solutions, it overlooks many neighboring regions that may also contribute... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable comments. We hope our response below can address the reviewer's concerns.
**[more experiment results]** We appreciate the reviewer’s suggestions for adding more baselines. We have cited the papers mentioned by the reviewer in related work. We did no... | Summary: Authors propose Tilted SAM (TSAM) that builds upon SAM in order to smooth out the optimization process using exponential tilting. Unlike SAM, which focuses on the worst-case loss within a neighborhood, TSAM reweights local solutions based on their loss values, favoring flatter minima. Authors claim that this ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable comments.
**[expensive computation]** We would like to clarify that the runtime numbers in Table 5 are reported for the same number of iterations of all methods (to illustrate the per-iteration cost), while the final test performance in the experime... | Summary: The authors propose TSAM, which is a version of SAM where, instead of taking a max over the loss around a point in parameter space during training, the authors propose to take a weighted average. Since the method is not tractable for weight functions, the authors develop an approximation by sampling. It is the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and positive assessment of our work! We would like to address the remaining questions/concerns as follows.
**[experimental improvements]** We observe statistically significant improvements of TSAM compared with the baselines. In terms of the test loss, TSAM outp... | Summary: This paper proposes Tilted SAM (TSAM) as a smoothed version of SAM using exponential tilting. Its smoothness enbles an easier optimization and a better generalization.
Claims And Evidence: All the claims are clear to me except one:
"Empirically, TSAM arrives at flatter local minima and results in superior te... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback.
**[connections between flat local minima and improved generalization]** Note that we do not intend to claim ‘flat local minima leads to a better generalization in all cases’ by claiming “TSAM arrives at flatter local minima and results i... | null | null | null | null | null | null |
Vision-Language Model Selection and Reuse for Downstream Adaptation | Accept (poster) | Summary: The paper deals with Model Label Learning (MLL) to select and reuse pre-trained VLMs for specific downstream tasks. It addresses the challenge of choosing the best VLMs from numerous options, each with varying performance on different tasks and different classes. The MLL approach includes model labeling, selec... | Summary: This paper presents a framework for organizing models that facilitates the storage, labeling, and reuse of vision-language models (VLMs). This system enhances overall performance compared to using a single VLM. A model labeling process is introduced to precisely describe the functionality of each VLM, enabling... | Summary: The paper explores a practical VLM reuse problem and proposes Model Label Learning (MLL), an efficient approach for selecting and reusing pre-trained Vision-Language Models (VLMs) for downstream tasks. The framework comprises three modules: (1) Model Labeling, which assigns labels to VLMs based on their capabi... | Summary: This paper introduces Model Label Learning (MLL) for selecting and reusing pre-trained VLMs for downstream tasks. This method aims to address the challenge of choosing the best VLM from a growing hub, given their diverse performance across tasks and primarily it is impractical to evaluate them exhaustively. Th... | null | null | null | null | null | null | ||||
Collapse or Thrive: Perils and Promises of Synthetic Data in a Self-Generating World | Accept (poster) | Summary: This paper investigates model collapse under different data regimes: 1) when we train the model on the latest synthetic data (the setting used in Shumailov et al.) 2) when we accumulate the synthetic data at each iteration (keeping the original data in the first iteration) 3) when we accumulate and subsample t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and recommendation. We appreciate your recognition that our paper is well-written and that our claims are properly validated.
### Theoretical Results for Accumulate-Subsample
You raise an excellent question. The accumulate-subsample workflow presents signific... | Summary: This paper examines the phenomenon of model collapse in generative learning, where models are trained on data that includes synthetic generations from previous iterations. The authors investigate three training paradigms - Replace, Accumulate, and Accumulate-Subsample - and find that Replace leads to model col... | Rebuttal 1:
Rebuttal: ### Definitions of Model Collapse
You raise an excellent point. A recent review (https://arxiv.org/abs/2503.03150) identifies multiple definitions of model collapse. We focused on test loss divergence as it addresses an existential threat: all future generative models becoming useless. We'll expa... | Summary: The paper investigates model collapse (a phenomenon observed when generative models are trained with output generated from such models) in 3 different settings (replace, accumulate, and accumulate-subsample). They find that model collapse occurs in the replace and the accumulate-subsample setting (but slower).... | Rebuttal 1:
Rebuttal: ### On Novelty
> My biggest concern with the paper is its novelty. It builds heavily on Gerstgrasser et al. and section 2 (a large part of the paper) only provides new results for different kinds of generative models with equal conclusions.
We disagree with this characterization. Our Section 2 i... | Summary: The manuscript studies three ways of using synthetic data, both empirically and theoretically.
The manuscript starts by examining Gerstgrasser et al. 2024's two claims with the proposed three generative modeling settings in section 2, where some well-established settings and tasks are directly used, making t... | Rebuttal 1:
Rebuttal: While we appreciate your review, you are mistaken about your claim that we primarily replicated experiments from other works, and the questions that you asked were largely answered in the paper. To explain line-by-line.
> It seems like the manuscript just reproduces these experiments [from Bertra... | null | null | null | null | null | null |
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces | Accept (poster) | Summary: The paper addresses intersectional fairness in reinforcement learning (RL) with exponentially many constraints arising from overlapping groups. The authors propose oracle-efficient algorithms for three settings: 1) tabular MDPs, 2) large MDPs with structured groups (via separator sets), and 3) general groups (... | Rebuttal 1:
Rebuttal: Dear Reviewer Gqfm,
Thank you for your positive assessment of our work.
**Empirical Scope**
As we highlight in Section 5 on Future Work, we completely agree that moving towards more realistic scenarios with these algorithms poses an interesting future direction for our work. However, the curre... | Summary: This paper investigates an interesting problem of intersectional fairness in RL. Unlike standard fairness approaches in RL, this work formulates fairness as a multi-objective optimization problem. Specifically, the goal is to optimize fairness and efficiency by maximizing the utility of the least advantaged gr... | Rebuttal 1:
Rebuttal: Dear Reviewer zwEM,
Thank you for acknowledging the significant theoretical contribution of our work and the detailed feedback on our submission.
**Individual Fairness** The notion of fairness we consider represents a middle ground between fully statistical notions of fairness and fully individu... | Summary: This paper studies a constrained RL problem in the episodic setting that generalizes the problem of minimax group fairness, allowing for exponentially many group fairness constraints due to the intersection of grouping functions. The authors focus on developing oracle-efficient algorithms for solving this prob... | Rebuttal 1:
Rebuttal: Dear Reviewer sNea,
Thank you for your positive assessment of our work and your feedback.
**Additional Experimental Simulations** The experimental study that we
provide is used to highlight the applicability of our FairFictRL algorithm. Fictitious Play is easy to implement in practice because it... | Summary: This paper explores the problem of intersectional fairness in reinforcement learning (RL) with large state and constraint spaces, proposing several oracle-efficient algorithms to optimize multiple objectives simultaneously while ensuring fairness across intersecting demographic groups. The authors provide theo... | Rebuttal 1:
Rebuttal: Dear Reviewer xri7,
We appreciate the positive assessment of our work.
**Deep RL experiments** As we highlight in Section 5 on Future Work, we completely agree that deep learning poses an interesting future direction for this type of work. However, the current manuscript is largely a theoretic... | null | null | null | null | null | null |
Efficient Noise Calculation in Deep Learning-based MRI Reconstructions | Accept (poster) | Summary: The authors propose a scheme for efficient voxel-wise noise estimation for Deep-learning based accelerated MRI algorithms. The method builds upon estimating the noise covariance from the DL-models Jacobian by using an unbiased estimator for the diagonal elements (of the cov-matrix) via the proposed Jacobian sk... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful comments and recognition of our extensive experimentation across diverse DL reconstruction methods and MR imaging scenarios. Below we explicitly address each concern, highlighting the relevance and impact of our approach in downstream medical imaging via ext... | Summary: The authors have developed a computational- and memory-efficient estimator of the voxel-wise variance in MRI reconstruction for uncertainty quantification in the reconstruction. The method is evaluated on MRI data and compared to Monte Carlo-simulations. The method is simple and easy to understand and computat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. Thanks to the reviewer for thorough examination of the manuscript, we implemented all suggested corrections (typos, cross-refs, terminology) and cleaned up the references. We also added an appendix overview outlining its structure for improved reada... | Summary: Authors propose a technique to calculate voxel-wise variance for quantifying uncertainty that stems from acquisition noise in accelerated MRI reconstructions. Authors propose to estimate the noise covariance using an approximation to the Jacobian of the neural network. The approximation is done through an unbi... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review, and valuable feedback which we believe significantly enhanced our work. We addressed your valuable suggestions, and clarifed of our novelty and contributions.
## 1. Novelty and Technical Contributions
We respectfully disagree with the assertion that our paper... | null | null | null | null | null | null | null | null |
NeuralCohort: Cohort-aware Neural Representation Learning for Healthcare Analytics | Accept (poster) | Summary: The paper proposes NeuralCohort, a cohort-aware neural representation learning method for healthcare analytics. It introduces two modules: (1) a Pre-context Cohort Synthesis Module to derive fine-grained cohorts via pseudo patient similarity, and (2) a Biscale Cohort Learning Module to integrate intra- and int... | Rebuttal 1:
Rebuttal: Thanks for the detailed reviews. This is the link to our supplemented results: https://anonymous.4open.science/r/ICML2025-93F9.
Q1: Lack validation of fine-grained cohorts. No qualitative analysis of cohort granularity or interpretability; visualizations lack clinical context.
In practice, clini... | Summary: This paper introduces the NeuralCohort framework, a novel method for neural cohort generation and selection based on two distinct strategies that model and generate (local) intra- and (global) inter-cohort information using a pre-context synthesis module employing pseudo similarity and a biscale cohort learnin... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments. This is the link to our supplemented results: https://anonymous.4open.science/r/ICML2025-93F9.
Q1: The possibilities of techniques. How would SimCLR help?
Key components like reverse-time attention and mutual information have been validated in prior work (... | Summary: This paper proposes NeuralCohort, a cohort-aware neural representation learning method for healthcare analytics. The approach segments patients into fine-grained cohorts and captures both intra- and inter-cohort information through a two-module paradigm.
Claims And Evidence: The paper presents a well-supporte... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback. This is the link to our supplemented results: https://anonymous.4open.science/r/ICML2025-93F9.
Q1: Lack clear evidence on how cohort insights translate into clinical interventions.
Please refer to Appx. K for detailed analysis. To be more specific, Cohort ... | Summary: The paper proposes NeuralCohort -- a cohort-aware neural representation learning method designed to improve electronic health record (EHR) analysis. It addresses the challenges of fine-grained cohort segmentation and effectively utilizes both intra- and inter-cohort information. By incorporating the Pre-contex... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. We sincerely appreciate your recognition of NeuralCohort as a cohort-aware neural representation learning framework that effectively addresses the challenges of fine-grained cohort segmentation and the modeling of both intra- and inter-cohort r... | null | null | null | null | null | null |
An in depth look at the Procrustes-Wasserstein distance: properties and barycenters | Accept (poster) | Summary: This paper defines a quotient space of discrtete measures over which PW is a distance and provides an estimation algorithm for the PW barycenter. This paper then shows that one of the main advantages of PW is its capability to produce very faithful barycenters in particular conditions. Experiments have verifie... | Rebuttal 1:
Rebuttal: First of all, thanks for the kind remarks and for reviewing the paper and checking the supplementary. We provide in the following the answer to the specific questions.
> 1-2. Figure 1 lacks more explanation. The caption shows: OT barycenters using (a) Free Wasserstein (b) Gromov-Wasserstein with ... | Summary: The paper proposes a method for aligning and matching point clouds based on Optimal Transport using the Procrustes-Wasserstein distance (PW), allowing the alignment to be calculated by also taking pose transformations into account.
The paper shows that PW is indeed a distance in the space of discrete probabi... | Rebuttal 1:
Rebuttal: First of all, thank you very much for the careful reading of the paper, as well as for the analysis and comments, they are truly appreciated. We have addressed all the grammatical errors identified in the text, and the corrections will be included in the final version of the paper. We also appreci... | Summary: This paper extends the Wasserstein barycenter to the Procrustes-Wasserstein (PW) barycenter, offering a novel approach to computing representative shapes from a collection of point clouds. Additionally, it provides a proof that the Procrustes-Wasserstein distance satisfies the properties of a valid distance me... | Rebuttal 1:
Rebuttal: Thanks for your kind remarks as well as for reviewing the paper and checking the correctness of the main proof.
> 1. What is the convergence behavior of the proposed Procrustes-Wasserstein (PW) barycenter?
Inspecting the convergence properties of the PW barycenters is a crucial homework and a ve... | Summary: The authors propose that in the quotient space of the discrete measures over the rigid transformation equivalence, the Procrustes Wasserstein is a metric. To calculate the Procrustes Wasserstein distance, they introduce several initialization methods, i.e. Euc-GW, Geo-GW, Fiedler-W and UPCA-W. The barycenter p... | Rebuttal 1:
Rebuttal: Thanks for reviewing the paper and checking the proofs. We are quite disappointed by the gap between the assigned score (reject) and a review which sounds quite positive. We would appreciate further explanations on the concerns raised about lack of novelty: PW barycenters are new. However, we pro... | null | null | null | null | null | null |
Organize the Web: Constructing Domains Enhances Pre-Training Data Curation | Accept (poster) | Summary: This paper presents WebOrganizer, a method to construct, and annotate two sets of explainable and orthogonal domains (24 topics and 24 formats) with LLMs (e.g., Llama-3.1-405B-Instruct). The annotations are then approximated by a smaller (140M) model, making it scale efficiently to large corpora. It then emplo... | Rebuttal 1:
Rebuttal: Thank you for your review and for engaging closely with our work! We are glad you found that our methodology is overall sound and that our claims are supported by clear evidence. However, you raised several points with respect to evaluation and novelty, which we hope to address below.
> Comparing... | Summary: The paper introduces WebOrganizer, a framework to categorize pre-training data for LMs using topics and formats. The labels are annotated by a pretrained LM. Experiments show that (1) reorganizing data according to these domain classifications significantly improves downstream performance, (2) integrating WebO... | Rebuttal 1:
Rebuttal: Thank you for your kind review! We are glad you found the claims in the paper clear and supported by convincing experiments!
> Assumption of selecting topics and formats independently
We don’t mean to make an assumption in the theoretical sense, but we simply propose to form a mixture distributi... | Summary: This paper tackles two main challenges: first is to separate the huge pre-training corpus into groups of domains and styles, and the second part is to compute the optimum mixing/reweighting strategy to combine these datasets to achieve targeted improvements in few specific metrics like MMLU and HellsSwag. They... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper! We are glad that you highlighted our extensive experiments and analyses, as well as the scalability and practical value of our proposed approach. We would like to briefly address two of your points.
> Reliance on RegMix
In the paper, we clearly acknowledge... | Summary: The authors propose a framework for organizing domains from unstructured web corpora along dimensions of topic and format. They use a human-in-the-loop procedure to design domain taxonomies and train a small language model to automatically categorize documents in a pre-training corpus, a 200B token subset of C... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and for regarding our paper as a solid contribution to the field with strong empirical results. We are especially grateful for the detailed suggestions with respect to the typos and clarity in Section 5, and the many interesting and relevant citations. We will im... | null | null | null | null | null | null |
Robust Multi-Agent Reinforcement Learning with Stochastic Adversary | Accept (poster) | Summary: This paper proposes a soft-policy-based MARL observation adversary consisting of a director module and an attack generator. The convergence guarantee of the director module is provided. Experiments on SMAC and CAV benchmarks demonstrate the effectiveness of the proposed method.
## update after rebuttal
My co... | Rebuttal 1:
Rebuttal: Thank you for your insightful suggestion. We have added RAP and ROMANCE as benchmark methods in the 8m VDN-based setting, as shown in Table 7 (https://anonymous.4open.science/r/icml25-9974-8E33/9974.pdf).
Q1: Essential References Not Discussed
We plan to include a discussion of the references in... | Summary: The paper proposes replacing workst-case adversary to stochastic adversary to improve robustness in multi-agent reinforcement learning (MARL). The proposed adversary model consists of a director and an actor where the former predicts a direction of manipulation and the latter translates this action into a mani... | Rebuttal 1:
Rebuttal: Thank you for your question. Based on your suggestion, we have provided additional experimental results, including ablation studies on $\alpha$ (Fig. 1) and continuous control tasks (Table 1), available at the following link: https://anonymous.4open.science/r/icml25-9974-8E33/9974.pdf
Q1: Some un... | Summary: This paper proposes Adversarial Training with Stochastic Adversary (ATSA) to fortify the robustness of models trained by multi-agent reinforcement learning. It addresses the overfitting problem of existing methods by training the proposed adversary online alongside the protagonist agent. ATSA implements an SDo... | Rebuttal 1:
Rebuttal: We have added additional experiments on the MPE environment (Table 1), the QTRAN (Table 4) and non-deterministic policy (Table 2), ERNIE (Table 5), and results for Q5 (Table 6). Please refer to https://anonymous.4open.science/r/icml25-9974-8E33/9974.pdf
Q1: MPE and QTRAN.
As suggested, we have e... | Summary: This paper proposes Adversarial Training with Stochastic Adversary (ATSA) for training Multi-Agent Reinforcement Learning (MARL) models, where the adversary is trained simultaneously with the protagonist agent. ATSA reduces the models' sensitivity to perturbations in observations, while addressing issues with ... | Rebuttal 1:
Rebuttal: Thank you for the suggestion. Additional results—including continuous (Table 1), non-deterministic (Table 2) cases, and further details for Q5 (Tables 1 and 3)—are available at the following link: https://anonymous.4open.science/r/icml25-9974-8E33/9974.pdf
Q1: Deterministic and discrete protagoni... | null | null | null | null | null | null |
Test-Time Training Provably Improves Transformers as In-context Learners | Accept (poster) | Summary: The paper investigates the theoretical and empirical advantages of Test-Time Training (TTT) for improving transformers as in-context learners. Authors develop a theoretical framework to characterize how a single-step gradient update at test time enhances in-context learning. The authors provide a rigorous theo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their favorable evaluation of our work and for the helpful feedback. We now reply to each of the points raised below.
> **Experimental Designs Or Analyses.** Yes, the experimental design is valid ... Potential limitations are the narrow focus on TabPFN and the need for a... | Summary: This paper examines the impact of test-time training (TTT) on the performance of a single-layer linear attention model (without an MLP) after a single gradient step during test-time fine-tuning. The authors focus on the problem of in-context linear regression and characterize the performance improvement achiev... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and positive evaluation of our work. We address the weaknesses and questions brought up below.
> **Experimental Designs Or Analyses.** The experiments are reasonable; however, to support the claim that this theory provides insight into broader in-co... | Summary: This paper investigates how Test-Time Training (TTT) affects transformer models' in-context learning capabilities. The authors formulate this as a two-stage problem: a model is first trained on a pre-training dataset, then further trained on test data using single-step gradient descent. They provide theoretica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed evaluation. We reply to each of the points below.
> **Theoretical claims**. The mathematical proofs contain imprecise statements. ...
**Response:** Thank you for pointing this out. The approximations in Theorems 4.2, 5.3, and Corollary 4.5 stem from ignor... | Summary: In-context learning and Test-time Training (TTT) are two ways of enhancing the predictive power of pretrained models on new tasks at test time. In-context learning involves incorporating demonstrations from the task into the prompt context. TTT involves light finetuning of the model on data related to the test... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive assessment of our work and their detailed feedback. We now address each point below.
> **Weakness.** While the theory presented concretely highlights the effect of various factors on TTT, most results do not seem particularly surprising. ... theory does not p... | null | null | null | null | null | null |
On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents | Accept (poster) | Summary: The paper explores how well multi-agent systems based on large language models (LLMs) can handle errors introduced by faulty agents. It specifically looks at resilience across tasks like writing code, solving math problems, translating text, and evaluating written text. To test this, it uses two methods: AUTOT... | Rebuttal 1:
Rebuttal: We deeply appreciate reviewer 3cYX’s time for reviewing and your insightful comments. We are particularly encouraged that you find that the experiments and analyses are sound and rigorous. We address your concerns one by one:
## **[Design & Analysis] [W1]** Advanced methods like Graph-based frame... | Summary: The paper investigates the resilience of different multi-agent architectures to faulty agents. Two approaches, autotransform, which transforms the system prompt of the agent into a malicious one, and autoinject, which takes the outputs of other agents and intentionally injects specific errors, have been propos... | Rebuttal 1:
Rebuttal: We deeply thank reviewer iwu4 for reviewing and appreciate the suggestions. We are encouraged that you find our paper well-written and sufficiently motivated! We address your concerns one by one:
## **[Claim & Evidence 1] [Broader Literature] [Reference] [W1]** Missing papers.
Thanks for providi... | Summary: This paper investigates the resilience of large language model (LLM)-based multi-agent systems against faulty or malicious agents. It compares different system architectures—Linear, Flat, and Hierarchical—across tasks like code generation, math problem-solving, translation, and text evaluation, finding hierarc... | Rebuttal 1:
Rebuttal: We deeply thank reviewer xvqz for your time and effort in reviewing our work, and your invaluable comments that further enrich our paper. We are particularly encouraged that you find our claims well-supported, our methods & evaluations reasonable, and by your recognition of the importance and nove... | Summary: This paper explores the resilience of multi-agent collaboration by introducing faulty agents and errors. It embarks on an empirical approach to examine performance drops in different scenarios / multi-agent system structures. The authors introduce AUTOTRANSFORM and AUTOINJECT algorithms for creating faulty age... | Rebuttal 1:
Rebuttal: We deeply appreciate reviewer LJmt’s time for reviewing and providing valuable suggestions. We are encouraged that you find our experiments comprehensive and well-executed, methods reasonable, and conclusions helpful for the community. We address your concerns here:
## **[W1]** Math definition on... | null | null | null | null | null | null |
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting | Accept (spotlight poster) | Summary: This paper studies privacy amplification with structured subsampling, for applications in DP-SGD training on forecasting models. The sampling considered works by first selecting a subset of time series (top-level sample), then one or more contiguous subsequences per sample (bottom-level sample), and finally sp... | Rebuttal 1:
Rebuttal: Thank you for your review and great suggestions for further expanding our experimental evaluation!
Please note that we cannot update the manuscript during this phase, but will integrate your suggestions as soon as possible.
## Additional experiments/baselines
### Effect of subsampling paramete... | Summary: This paper investigates the problem of training forecasting models (specifically those that leverage the temporal structure directly in their architecture) on both univariate and multivariate time series data. As is present most prior work on DP-SGD, amplificaton is a key result necessary for achieving realist... | Rebuttal 1:
Rebuttal: Thank you for your review!
Please note that we cannot update the manuscript during the current rebuttal phase, but will integrate your feedback as soon as possible.
### Motivation for Section 5.2
Upon re-reading the section, we agree that we could have done a better job of explaining what exactly... | Summary: This paper studies privacy amplification under subsampling when working with forecasting models on time series data. In these cases, the dataset usually consists of sequences, and subsampling occurs on multiple levels: top-level sampling chooses a subset of sequences, bottom-level sampling chooses a subsequenc... | Rebuttal 1:
Rebuttal: Thank you for your review and great questions!
Please note that we cannot update the manuscript during the current rebuttal phase, but will integrate your feedback as soon as possible.
### Sampling choices/parameters for model training
Thank you for pointing out this ommission. **We will of cours... | null | null | null | null | null | null | null | null |
How Much Can Transfer? BRIDGE: Bounded Multi-Domain Graph Foundation Model with Generalization Guarantees | Accept (poster) | Summary: The authors argue that while the "pretrain-then-prompt" framework has been extensively studied in other fields, its application in the graph domain remains underexplored, particularly from a theoretical perspective.
To address this gap, this paper introduces BRIDGE, a pretraining and prompt learning framework ... | Rebuttal 1:
Rebuttal: We sincerely thank for the positive evaluation regarding its theoretical foundation for constructing GFMs. Response:
**Q1: Why work well in zero-/one-shot but not in two-/three-shot.**
**A1:** Thank you for the insightful question.
- **0-shot limited for text-attributed graphs:** Knowledge tran... | Summary: BRIDGE is a bounded multi-domain graph pre-training and prompt learning framework that enhances knowledge transfer in graph foundation models. It integrates domain-invariant alignment, a lightweight MoE for selective knowledge transfer, and a graph spectral-based generalization bound. Experiments show state-of... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and positive feedback. We appreciate your recognition of BRIDGE’s contributions and address your comments and suggestions point by point below.
**Q1: The scalability and computational efficiency.**
**A1:** Thank you. For scalability, we address this in **Revi... | Summary: This paper introduces BRIDGE, a graph foundation model framework designed for multi-domain knowledge transfer using domain-invariant feature aligners, a MoE prompt initialization, and a theoretical generalization bound. It addresses feature heterogeneity across domains, efficient adaptation via prompt tuning, ... | Rebuttal 1:
Rebuttal: Thank you for your insightful and constructive review, and for recognizing the contributions of BRIDGE. We address your questions briefly below.
**Q1: The empirical validation of the generalization bound.**
**A1:** To empirically validate the effectiveness of the bound, we conducted specific abl... | null | null | null | null | null | null | null | null |
What Do Learning Dynamics Reveal About Generalization in LLM Mathematical Reasoning? | Accept (poster) | Summary: The paper introduces a novel metric called "pre-memorization train accuracy" that predicts how well large language models will generalize during reasoning tasks. The key insight is examining model performance on training examples before they are memorized verbatim. The authors show this metric strongly correla... | Rebuttal 1:
Rebuttal: We thank the reviewer for the nice feedback! We answer the questions below.
> How sensitive is the pre-memorization accuracy metric to the choice of memorization threshold?
In Fig, 9, we provide some some analysis on the sensitivity of the predictive power (R^2) of our metric to the choice of ... | Summary: This paper focuses on LLM reasoning tasks and proposes a metric called **per-memorization train accuracy**, which is the accuracy of model samples on training queries before they begin to copy the exact reasoning steps from the training set. The authors show that the proposed metric is predictive of the **test... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback! In the following rebuttal, we first provide a detailed description of how our metric is calculated and used in practice. Next, we address the concerns about the scope of our experiments. Finally, we provide clarifications for our experimental setup. We will ... | Summary: The paper studies how LLMs generalize in reasoning tasks and introduces "pre-memorization train accuracy" as a metric that predicts test accuracy. The key idea is that models first learn correct reasoning patterns before they start memorizing training examples, and this early accuracy is strongly correlated wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the nice feedback! We answer the questions below:
> How does pre-memorization accuracy behave in one-epoch fine-tuning? Many LLMs are fine-tuned in a single pass. Does the metric still hold if computed over early training steps?
Reasoning data tend to be rare, so it is ... | Summary: This paper investigates how the learning dynamics of large language models (LLMs) during finetuning influence their generalization on reasoning tasks. The key contribution is the introduction of pre-memorization train accuracy, defined as the highest accuracy a model achieves on a training example before it be... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback! In this rebuttal we provide a more detailed discussion of the implications of our experimental findings, and provide additional explanations for our experimental design.
> The authors need a deeper analysis and discussion of pre-memorization accuracy.
... | null | null | null | null | null | null |
On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning | Accept (poster) | Summary: The paper presents a FTRL variant for dynamic regret with optimism in a compact domain. The paper relies on an adaptive correction $g_t^I$ to correct stored gradient states $\sum_t g_t$, similar as an ``adaptive restart'' of FTRL whenever is desired. This general development yields to regret bound when path le... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read the paper and providing the feedback
**Weaknesses**.
We appreciate the reviewer’s recognition of the clarity of the FTRL-based analysis for dynamic comparators. We understand the concern regarding its broader impact, given that the minimax dynamic regret boun... | Summary: This paper investigates the Follow-the-Regularized-Leader method (FTRL) under the Online Convex Optimization (OCO) framework for dynamic regret minimization. Specifically, the authors propose a series of FTRL-based methods, deliver novel analysis and establish new dynamic regret bound, which can recover existi... | Rebuttal 1:
Rebuttal: Many thanks for your review and feedback.
**References**.
We selected Chen et al. (2024) from the SEA thread as it was the first to study SEA under the dynamic regret metric. That said, we will update the related work with the suggested background references (SEA \& others) so as to better reflec... | Summary: This work revisits the Follow-The-Regularized-Leader (FTRL) framework and explores how to utilize FTRL to derive dynamic regret bounds. The key finding in this paper is to predict with the modified loss $f_t(x) + I_{X}(x)$ rather than $f_t(x)$, where $I_X(x)$ is the indicator function. When the unprojected dec... | Rebuttal 1:
Rebuttal: Thank you for reading the paper and providing feedback.
**Claims \& missing reference**.
We agree that it is important to discuss carefully Ahn et al. “Adam is FTRL in …”; we indeed cite this paper and mention that it uses an FTRL variant that attenuates history to obtain discounted regret bounds... | Summary: The paper presents a new optimistic algorithm, Follow-The-Pruned-Leader (FPRL) that aims to achieve dynamic regret in $O(\sqrt{P_TE_T})$, where $E_T$ measures the prediction error. The key insight is to avoid simply stacking previous gradients as they make the standard Follow-the-Regularized-Leader (FTRL) less... | Rebuttal 1:
Rebuttal: Thanks for taking the time to read the paper and provide the feedback.
**Claims \& evidence**
$258R$: Indeed, these details were omitted from the main text due to space constraints and accidentally left out of the appendix. We apologize for this oversight. Briefly, the claim is that the term $H_... | null | null | null | null | null | null |
Be Confident: Uncovering Overfitting in MLLM Multi-Task Tuning | Accept (poster) | Summary: This paper proposes Noise Resilient Confidence Alignment (NRCA) to reduce overfitting in open-response tasks during multi-task fine-tuning of Multimodal Large Language Models NRCA enhances performance on tasks like image captioning and visual question answering, outperforming traditional fine-tuning methods. E... | Rebuttal 1:
Rebuttal: Dear Reviewer XwCL:
Thank you very much for your valuable comments and constructive feedback. Below, we carefully address each of your concerns point-by-point, providing detailed explanations and additional evidence to clarify our approach and validate its effectiveness.
**Q1: Limitation Discuss... | Summary: In this work, the author focuses on Multimodal Large Language Models tuning. Specifically, the authors propose a method called Noise Resilient Confidence Alignment (NRCA) that aims to alleviate the issue of overfitting, particularly in open-response tasks during multi-tasks tuning. approach emphasizes maintain... | Rebuttal 1:
Rebuttal: Dear Reviewer SJh5:
Thank you for your thoughtful review and for raising key concerns regarding our work. We aim to address your concerns in our detailed responses below, hoping to provide clarity and demonstrate the effectiveness of our proposed approach.
**Q1: Conceptual Discussion** (Other St... | Summary: This paper introduces "Noise Resilient Confidence Alignment" (NRCA), a method to address overfitting in multi-task fine-tuning of Multimodal Large Language Models (MLLMs). The authors observe that while fine-tuning MLLMs on multiple tasks, performance on open-response tasks (like image captioning) degrades ove... | Rebuttal 1:
Rebuttal: Dear Reviewer ELAr:
We sincerely thank you for your valuable feedback and hope our responses adequately address your concerns and merit a score revision.
**Q1: Theoretical Claims of NRCA and Its Connection to Visual Representation**
A1: Denote $I$ and $\tilde{I}$ as the average confidence over ... | Summary: In the reviewed paper, the authors identify overfitting in open-response tasks as a significant challenge in multi-task multimodal large language model (MLLM) fine-tuning. They propose a novel method called NRCA, which aligns prediction confidences between noisy and normal visual inputs to improve visual repre... | Rebuttal 1:
Rebuttal: Dear Reviewer H4RG:
Thank you very much for your affirmation of our work, as well as the insightful concerns and questions you have raised. We have carefully considered each comment and provided responses.
**Q1: Multi-Task Specialization Definition** (Claims And Evidence)
A1: Multi-Task Special... | null | null | null | null | null | null |
Benchmarking Abstract and Reasoning Abilities Through A Theoretical Perspective | Accept (poster) | Summary: This paper introduces a rigorously designed and theoretically grounded benchmark for a crucial yet underexplored area: the evaluation of abstract reasoning in Large Language Models (LLMs). The work establishes a clear mathematical framework that defines abstract reasoning as the ability to extract invariant pa... | Rebuttal 1:
Rebuttal: ### 1. Task Complexity Expansion
We agree that further expanding task complexity is essential. In future work, we will:
- Introduce multi-step reasoning tasks that require chaining hypotheses, intermediate conclusions, and final integration.
- Incorporate hierarchical rules with nested, conditio... | Summary: This work build a benchmark of arithmetic computation tasks that targets at the abstract reasoning abilities of large language models, and finds out that the power existing large language models relies on the task domains.
Claims And Evidence: See below
Methods And Evaluation Criteria: The benchmark provides... | Rebuttal 1:
Rebuttal: ## 1. Concrete Instances vs. Abstract Features
Our paper distinguishes concrete instances (C)—detailed input strings containing surface-level information—from abstract features (A), which capture only the essential properties required for reasoning. For example, a concrete description of a dog ma... | Summary: This paper presents a theoretically grounded benchmark to evaluate abstract reasoning in Large Language Models (LLMs). It defines abstract reasoning as extracting essential patterns and applying consistent rules to these patterns. Two metrics, Γ (Abstract Reasoning Score) and ∆ (Memory Dependence Score), are i... | Rebuttal 1:
Rebuttal: ## 1. Relevant References
Thank you for highlighting these references. Chollet (2019) is already cited (see Introduction). In the revised version, we have added:
- **Garcez (2020), *Neurosymbolic AI: The 3rd Wave***
This work highlights the limits of connectionist methods and supports our di... | Summary: The goal of the paper is to evaluate the abstract reasoning capabilities of LLMs. The paper points out flaws with two existing benchmarking paradigms: The symbolic reasoning benchmarks like GSM8K risk memorization since the models could be (inadvertently) trained on these benchmarks. The visual abstract benchm... | Rebuttal 1:
Rebuttal: ### 1. Test Set Sampling and Duplicates
1. **Test Set Distribution:**
Uniform distribution.
2. **Real-World Distribution and Correlation:**
No, it is not sampled from a real-world task-based distribution such as SAT problems. However, our benchmark shows strong correlation with oth... | null | null | null | null | null | null |
Runtime Analysis of Evolutionary NAS for Multiclass Classification | Accept (poster) | Summary: This study presents a theoretical analysis of the runtime of the ENAS algorithm in solving multiclass classification problems. The authors first introduce a benchmark problem and then propose a two-level search space. Based on this design, the authors analyze the upper and lower bounds on the expected runtime ... | Rebuttal 1:
Rebuttal: Many thanks for your valuable comments of our work. Below, we would like to take this opportunity to respond to your concerns.
>1. Are the ENAS algorithms widely used in practice are (1+1)-ENAS? In other words, can this finding well support the widely used ENAS algorithms in practice?
(1) In pra... | Summary: This paper introduces a novel runtime analysis framework for evolutionary neural architecture search (ENAS). Compared with the previous studies, which focus on the binary classification problem, the runtime analysis focuses on the multiclass classification problem in this work. Specifically, this study first i... | Rebuttal 1:
Rebuttal: Many thanks for your recognition and comments of our work. Below, we would like to take this opportunity to address your concerns.
>1. The authors state that the expected runtime to find the target solution is $O(rM\ln(rM))$. However, it is suggested to discuss how this conclusion contributes to ... | Summary: This paper investigates the runtime analysis of Evolutionary Neural Architecture Search for multiclass classification problems. The core of the research is the proposal of a multiclass classification benchmark problem and the design of a two-level search space based on this problem. The authors then analyze th... | Rebuttal 1:
Rebuttal: Your detailed comments are much appreciated, and we will revise the manuscript accordingly. Below, we address your questions.
>1.How does the proposed runtime analysis scale with increasing problem complexity (e.g., higher-dimensional input spaces or larger numbers of classes)? Are there any limi... | Summary: The paper delves into the runtime analysis of ENAS for multiclass classification，It introduces a new benchmark problem, MCC, designed to simulate multiclass classification tasks, and formulates a fitness function to evaluate neural architectures' performance on this problem. The authors also design a two-level... | Rebuttal 1:
Rebuttal: Many thanks for your recognition and encouraging comments of our work. Below, we take the opportunity to respond to your concerns.
>1.The proposed MCC benchmark problem, while theoretically sound, is highly simplified and may not reflect the complexity of real-world multiclass classification task... | null | null | null | null | null | null |
Discrete Markov Probabilistic Models: An Improved Discrete Score-Based Framework with sharp convergence bounds under minimal assumptions | Accept (poster) | Summary: The authors propose the Discrete Markov Probabilistic Model (DMPM), a novel algorithm for generative modeling on the binary data space (bits). The generative model is based on a continuous-time Markov Chain (CTMC) framework with forward processes, which “noisses” the data, and backward processes, which “denois... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful remarks and the opportunity to clarify the significance of our theoretical results. Our primary aim is to establish convergence guarantees for discrete generative models, analogous to those in continuous diffusion. This led us to a discrete score function... | Summary: This paper introduces Discrete Markov Probabilistic Models (DMPMs), a novel framework for discrete data generation based on continuous-time Markov chains (CTMCs). The forward noising process follows a Poissonian clock that flips bits randomly, while the reverse process reconstructs the data via an estimated di... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very positive feedback, and for finding our paper an interesting contribution to discrete diffusion models from both theoretical and methodological perspectives.
> I wonder if the DMPM can be generalized to systems where each particle has more than two potential st... | Summary: Score-based diffusion models are becoming one of the most promising non-adversarial and easy-to-implement data distribution reconstruction techniques. The idea is to define a forward noise process that gradually degrades the training data until it is transformed into a simple distribution that is easy to sampl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, which will help improve the presentation of our contributions. We hope that we have addressed all of their questions in the responses below. Given their positive feedback—particularly their appreciation of the "very impressive" theoretical contributions—we... | Summary: This paper introduces the Discrete Markov Probabilistic Model (DMPM), a uniform noising/denoising algorithm for discrete data generation. The authors establish theoretical convergence bounds under minimal assumptions and validate the effectiveness of their method empirically on both low-dimensional Bernoulli d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback, especially regarding the strength of our theoretical contributions. Due to space constraints, we address only the most critical concerns, but we welcome further discussion if any important points remain unaddressed.
> Additional large-scale exper... | null | null | null | null | null | null |
Probing Visual Language Priors in VLMs | Accept (poster) | Summary: This paper investigates visual language priors in vision-language models , analyzing how these models rely on textual biases rather than true visual reasoning. The authors introduce ViLP, a benchmark designed to expose such priors by presenting models with out-of-distribution images and distractor text-based ... | Rebuttal 1:
Rebuttal: We greatly appreciate the detailed and insightful feedback. Below, we have carefully addressed the opportunities for improvement you highlighted.
---
> Failure case analysis
We sincerely appreciate your suggestions and will include an in-depth failure case analysis section in revision.
We obser... | Summary: This paper introduces ViLP, a benchmark to investigate how VLMs relies heavily on textual priors, and ignore visual inputs. ViLP consists of 300 questions (each including a distractor fact), and three image-answer pairs (a prior answer and two test answers). With extensive experiments, the authors show that ex... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer j6Xe QTDS’s thoughtful and encouraging feedback. Due to constraints in time and computational resources, we have prioritized addressing your question and will incorporate the suggested additional improvements in future revisions.
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> Why is it necessary to fine-... | Summary: The paper presents an investigation into the over-reliance of vision-language priors in existing Vision-Language Models (VLMs). To enable it, the paper presents ViLP, a carefully designed benchmark which consists of Prior Answer that can be directly inferred from the question, and Test Answers which rely on vi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the positive review provided by Reviewer 1kEP, and are grateful that Reviewer 1kEP “likes the paper structure”, and “convinced with the methodology adopted in the paper”, believes “the ViLP dataset is well motivated”, and “the results are promising as well”.
Below, we pr... | Summary: This paper investigated the problem of over-reliance on visual language priors of Vision-Language Models (VLMs) instead of visual reasoning. To study this problem, this work further proposed a benchmark ViLP containing 900 question-image-answer triplets, covering both prior answers and test answers, revealing ... | Rebuttal 1:
Rebuttal: We greatly appreciate the constructive feedback from Reviewer 4vBy. We seriously address your comments below.
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> Regarding the style of images.
We appreciate this valuable point and acknowledge that some generated images appear cartoon-like or synthetic, due to the image generation tools se... | null | null | null | null | null | null |
Asymmetric Decision-Making in Online Knowledge Distillation: Unifying Consensus and Divergence | Accept (poster) | Summary: This article introduces Asymmetric Decision-Making (ADM), an online knowledge distillation (OKD) method that enhances both teacher and student models through consensus and divergence learning. Unlike other traditional KD methods this method names as ADM actively refines the teacher while improving the student,... | Rebuttal 1:
Rebuttal: **Q1: lack of rigorous theoretical justification**
**A1:** Firstly, we assert that the proposed ADM method adheres to theoretical derivations. To substantiate this claim, we provide additional theoretical support:
# Information-Theoretic Analysis of Asymmetric Decision-Making (ADM)
## 1. Notatio... | Summary: This paper proposes an Asymmetric Decision-Making (ADM) approach for online knowledge distillation that adaptively fosters consensus learning for students while continuously encouraging teachers to explore harder features, thereby boosting performance on tasks like classification, semantic segmentation, and di... | Rebuttal 1:
Rebuttal: **Q1: gap between the motivation of the proposed method and its findings**
**A1:** The function of $L_{di}$ is to extract knowledge from unexplored foreground regions within the background areas, rather than merely utilizing background information. These regions present a challenge to the model, ... | Summary: This paper addresses online knowledge distillation (OKD), a single-stage method where teacher and student models learn simultaneously. The authors focus on improving OKD performance by exploring intermediate feature alignment. Specifically, they propose an Asymmetric Decision-Making (ADM) strategy to unify con... | Summary: This paper proposes an online knowledge distillation method to build a student model by knowledge transfer from larger teachers which are trained together with the student. The key assumption is that the student and teachers are more likely to share similar features in foreground rather than background areas. ... | Rebuttal 1:
Rebuttal: **Q1: the effects of consensus learning need to be further clarified**
**A1:** Applying $L_{co}$ to the student model does not imply that the performance improvement of the student model is primarily due to $L_{co}$. As evidenced in Table 13, when $L_{co}$ is used alone, the teacher model's perf... | null | null | null | null | null | null | |
DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers | Accept (poster) | Summary: The paper introduces Dynamic Sequence Parallelism (DSP) as a novel approach to sequence parallelism for multi-dimensional transformers.
It addresses the limitations of embedded sequence parallelism, which shards along a single sequence dimension and incurs significant communication overhead.
DSP dynamically sw... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer o73Q for the valuable questions and comments. For the concerns and questions, here are our responses:
**Q1: Performance gains are not convincingly demonstrated for long sequences**
**A1**: We thank the reviewer for this important question and have conducted additi... | Summary: This paper presents DSP (Dynamic Sequence Parallelism) for scaling multi-dimensional transformers. The solution adaptively switches parallel dimensions by reshuffling data with all-to-all communication between multiple GPUs. The evaluation results demonstrate that parallelizing across sequence dimensions can r... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer qVhs for the valuable questions and comments, especially about the evalutions which are indeed not detailed enough.
**We have conducted the following experiments to make our evalutions more comprehensive and have added them to the experiment section or appendix or... | Summary: The paper introduces a new method, called Dynamic Sequence Parallelism (DSP), to scale multi-dimensional transformers efficiently by dynamically switching the parallel dimension at different computation stages. Unlike existing sequence parallelism techniques (e.g., Megatron-LM, Megatron-SP, DeepSpeed-Ulysses, ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer Ux6h for the valuable questions and comments. For the concerns and questions, here are our responses.
**Q1: The experiments only use H100 GPUs, leaving performance on other GPUs unknown.**
**A1:** Thanks for pointing out this! We test the performance on A100 GPUs.... | Summary: The authors propose dynamic sequenced parallel (DSP), a model sharding scheme for multi-dimensional transformers. M-D transformers have two or more sequence dimensions unlike just one for the regular transformers. Existing sequence-parallel sharding does not account for this and are sub-optimal. DSP works by a... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer 4hHH for the valuable comments and acknowledgement of our work. | null | null | null | null | null | null |
Scaling Test-Time Compute Without Verification or RL is Suboptimal | Accept (spotlight poster) | Summary: The paper theoretically studies inference time scaling by modeling LLM generation as a Markov process with horizon $H$ and comparing verifier-based (VB) methods to verifier-free (VF) methods. For binary and nondecreasing rewards, and under a heterogeneity assumption of the base model search trace solutions, it... | Rebuttal 1:
Rebuttal: Thank you for the review and a positive assessment of our work! To address your remaining concerns, we clarify the data annotations for VB and VF methods and show that our analysis can be extended to settings beyond binary rewards, while proving a similar separation between VB and VF methods. We a... | Summary: This paper studies the performance of two prevalent methods which are named as verifier-based (VB) and verifier-free (VF) methods in terms of scaling test-time compute. VB methods utilize a verifier or reward signals to improve a policy while VF approaches use expert data to supervise the policy training. They... | Rebuttal 1:
Rebuttal: Thank you for the feedback! We believe that many concerns perhaps stem from a misunderstanding of the setup. We study the efficacy of different fine-tuning algorithms to train LLMs to attain better performance as they utilize more test-time compute, measured in terms of the token length. Verifier-... | Summary: - This paper seeks to understand the best way to finetune LLMs that leads to performance that is most scalable in terms of test-time compute
- These are split into VF methods that don't use a verifier (eg SFT on expert trajectories) and VB methods that do (eg RL on 1/0 rewards)
- The focus is on theoretical re... | Rebuttal 1:
Rebuttal: Thank you for the review and a positive assessment of our work! To address your concerns, we added an experiment for the didactic setup where we show an increasing performance gap between VF and VB methods, as we scale both n (the number of prompts) and H (the output length), matching our results ... | null | null | null | null | null | null | null | null |
Preference-CFR: Beyond Nash Equilibrium for Better Game Strategies | Accept (poster) | Summary: The paper introduces Preference-CFR (Pref-CFR), an extension of Counterfactual Regret Minimization (CFR) aimed at generating diverse strategies in extensive-form games. Traditional CFR-based approaches focus on solving for Nash Equilibria (NE), which prioritize optimality under worst-case conditions but lack s... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We summarize some of the questions and answer them. If there are still questions or new questions arise, please feel free to discuss further.
---
## How sensitive are the results to the heuristic tuning of $δ$ and $β$, particularly in large-scale games?
Conducting e... | Summary: This paper tackles a key limitation of standard CFR-based methods. Typically, these methods converge to a single Nash equilibrium (NE) and struggle to accommodate risk preferences or different playing styles. To address this, the authors propose Preference-CFR (Pref-CFR), which introduces two key parameters at... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will first answer your questions and comments in order, and then summarize and answer some questions from previous sessions. If there are still questions or new questions arise, please feel free to discuss further.
---
## Questions 1
Our algorithm adopts a simila... | Summary: This paper introduces **Preference Counterfactual Regret Minimization (Pref-CFR)**, an extension of **Counterfactual Regret Minimization (CFR)**, designed to incorporate **strategy diversity** and **playstyle customization** in game AI. While standard CFR focuses on computing **Nash Equilibrium (NE)**, the aut... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. The strengths of the paper were perfectly understood, so we will only address weaknesses and questions below.
---
## Comparisons to state-of-the-art AI
Our algorithm is not aimed at defeating top-level AI, so we didn't conduct this experiment at the start. Ho... | Summary: This paper proposes a method for finding approximate equilibrium strategies in extensive-form games and claims to achieve strategies that balance “style” and “diversity” with playing strength. The authors present theoretical ideas in the context of two-player, zero-sum (2P0S) games (e.g., Kuhn poker), and also... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We first answer your questions and comments in order (due to word limit, the original text is not quoted), and then we summarize some of the Experiments questions and answer them. If there are still questions or new questions arise, please feel free to discuss further.... | null | null | null | null | null | null |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs | Accept (poster) | Summary: Regarding the limitation that conventional LLM fairness metrics ignore the impact of model uncertainty on biases, this work proposes an uncertainty-aware fairness metric UCerf. Additionally, to tackle the shortcomings of current datasets, the authors introduce a gender-occupation fairness assessment dataset, S... | Rebuttal 1:
Rebuttal: **We thank the reviewer for highlighting important points about the motivation and interpretation of UCerF, and the evaluation results.**
## Q1 Explanation regarding discuss around Fig.1
We thank the reviewer for pointing out a potential confusion in the paper. Please refer to the second paragrap... | Summary: Recent large language models were trained on vast scales of data to achieve stellar performance. However, these models also suffer from the biases present in their training data, creating fairness concerns across sensitive attributes. This work examines conventional fairness metrics in the context of large lan... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the thoughtful feedback and for recognizing the novelty and contribution of our fairness evaluation framework and the clarity of our design rationale.**
## Q1 Additional Group Fairness Metrics
We thank the reviewer for the suggestion to further support our claims. We a... | Summary: This paper observes that conventional accuracy-based fairness metrics overlook disparities in prediction uncertainty across demographic groups. Moreover, existing gender-occupation bias datasets are insufficient for evaluating modern large language models (LLMs), which have strong semantic understanding capabi... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the constructive feedback and for acknowledging the significance of fairness evaluation through the lens of uncertainty and the contribution of SynthBias to the fairness research community.**
## Q1 Motivation for a combined correctness-uncertainty metric
We appreciate ... | Summary: The paper proposes a novel fairness metric, UCerF, which takes into account not only the model's predictions but also its uncertainty. In addition to the metric, authors propose a new synthetic dataset (SynthBias) for fairness evaluation of LLM on co-reference resolution task. Finally, the authors combine the ... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the thoughtful feedback and recognition of the novelty and relevance of our proposed UCerF metric and SynthBias dataset.**
## Q1 Use of other uncertainty quantification estimators
Please refer to reviewer 6e3v Q2 for response.
## Q2 Limited dataset in evaluations
We app... | null | null | null | null | null | null |
Polynomial-Time Approximability of Constrained Reinforcement Learning | Accept (poster) | Summary: The authors study the computational complexity of constrained MDPs.
They show some novel results regarding finding approximately optimal policies.
In particular, they studied the question of whether polynomial time approximation algorithms exist for many of the classic formulations studied in the CRL literatu... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We will update the paper to clarify the points the reviewer made. To address your questions,
**[Page 4]** This is meant to be s' as is. Note, this equation corresponds to the usual policy evaluation equations for a deterministic policy. Here, we are assum... | Summary: The paper develops approximation algorithms for constrained MDP problems. The problem formulation in the paper can incorporate a broad class of recursively computable constraints, including almost-sure, chance, expectation, and their anytime variants. For this general problem formulation, the paper develops a ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! Please see our general rebuttal above in Reviewer htBq's rebuttal section, which addresses Weaknesses 2-4 and other points the reviewer brought up. In addition to those points, we address several points below.
**[Weakness 1]** In our paragraph "Past Work"... | Summary: The submission provides a novel generic bicriteria approximation algorithm that covers a broad range of Constrained Markov Decision Processes (CMDPs).
Claims And Evidence: Yes, with the exception of the malformed definitions and anecdotal experimental setup (see "Other Strengths and Weaknesses").
Methods And... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! Please see our general rebuttal above in Reviewer htBq's rebuttal section.
**[Formalization]** In addition, we address the claim that our problem is not properly formalized. As our work is within the domain of CRL, we assume some basic familiarity with C... | Summary: In this paper, they study polynomial algorithm for a generalized constrained MDP (CMDP).
In particular, they formulate a class of CMDPs whose constraints have generalized Bellman-recursive structures, and
construct polynomial algorithms for it via state/action augmentation and budget/state discretization.
More... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We intend to revise the paper with your notational suggestions in mind. Please see our general rebuttal below, which addresses your concerns about our title, the significance of our results, and the role of our knapsack example:
--- *General Rebuttal* ---... | null | null | null | null | null | null |
Regularized Langevin Dynamics for Combinatorial Optimization | Accept (poster) | Summary: The paper introduces Regularized Langevin Dynamics, a novel sampling-based method for improving solutions to CO problems through LD. RLD incorporates regularization to control the distance of solution updates, enhancing traditional heuristics and neural network models by mitigating the issue of local optima. T... | Rebuttal 1:
Rebuttal: > However, it seems to lack theoretical evidence
>Is it possible to apply regularization to the solution updates only when detecting local optima?
Thank you for your insightful question. Given the short time period, here we simply outline some perspective for the theoretical analysis.
When not... | Summary: *Regularized Langevin Dynamics for Combinatorial Optimization* proposes Regularized Langevin Dynamics (RLD), a sampling framework for combinatorial optimization (CO). The authors note that discrete Langevin dynamics (LD) has limitations in exploration when applied to CO. RLD addresses this by enforcing an expe... | Rebuttal 1:
Rebuttal: We sincerely thank you for your positive feedback, here are the response to your concerns.
>Is there any advantage of RLNN compared to RLSA? It seems it falls behind of RLSA on both decision quality and inference time.
Recall that our RLNN is designed to address a limitation of RLSA, i.e., its r... | Summary: This paper introduces Regularized Langevin Dynamics (RLD), an approach inspired by normalized gradient descent to enhance combinatorial optimization (CO) methods. The authors develop two specific algorithms: Regularized Langevin Simulated Annealing (RLSA), which incorporates simulated annealing, and Regularize... | Rebuttal 1:
Rebuttal: > There are concerns regarding experimental fairness, particularly in the comparison of iSCO and RLSA… The experimental section in the iSCO paper reports DIMES achieving a size of 42.06 on ER-[700–800] in 12.01 minutes and 332.80 on ER-[9000–11000] in 12.51 minutes, which aligns with previously re... | Summary: This paper proposes improvements to existing diffusion-based amortized neural samplers and discrete Langevin dynamics samplers for combinatorial optimization, introducing simple regularization techniques aimed at mitigating issues with local minima in discrete spaces. The key claim is that avoiding local minim... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful comments and constructive suggestions.
> the novelty appears somewhat incremental—essentially amounting to relatively simple adjustments to existing techniques
We see your point but would like to highlight that our approach addresses a fundamen... | null | null | null | null | null | null |
Flexible, Efficient, and Stable Adversarial Attacks on Machine Unlearning | Accept (poster) | Summary: The paper presents the Dynamic Delayed Poisoning Attack (DDPA), a novel adversarial framework for machine unlearning (MU) that overcomes three key limitations of existing MU attacks: inflexibility due to predefined targets, inefficiency in handling multiple requests, and instability from non-convex loss functi... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the helpful and constructive comments. We have tried our best to address your concerns. We will include all the analyses, duscussions, and experimental results in this rebuttal into the submission. Due to the limit of 5000 characters, if anything remains unc... | Summary: This work addresses adversarial attacks on machine unlearning (MU), focusing on target-agnostic attacks that can target arbitrary parameters upon request. It allows quick responses to multiple MU attack requests after deployment, while maintaining attack stability. First, the authors use a convex polyhedral ap... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the helpful and constructive comments. We have tried our best to address your concerns. We will include all the analyses, duscussions, and experimental results in this rebuttal into the submission. Due to the limit of 5000 characters, if anything remains unc... | Summary: This paper proposes a novel adversarial attack framework for machine unlearning (MU) called Dynamic Delayed Poisoning Attack (DDPA). The key contributions include:
1. Target-agnostic attack flexibility: By leveraging thrust vector control (from aerospace engineering) and simplex geometry, DDPA dynamically ma... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the helpful and constructive comments. We have tried our best to address your concerns. We will include all the analyses, duscussions, and experimental results in this rebuttal into the submission. Due to the limit of 5000 characters, if anything remains unc... | Summary: This paper introduces a new adversarial attack framework specifically designed for machine unlearning systems. The central contribution is the Dynamic Delayed Poisoning Attack (DDPA) method, which addresses the limitations of previous approaches by being target-agnostic, enabling efficient handling of multiple... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the helpful and constructive comments. We have tried our best to address your concerns. We will include all the analyses, duscussions, and experimental results in this rebuttal into the submission. Due to the limit of 5000 characters, if anything remains unc... | null | null | null | null | null | null |
Tensor-Var: Efficient Four-Dimensional Variational Data Assimilation | Accept (poster) | Summary: The paper introduces Tensor-Var, a novel framework for variational data assimilation, which integrates kernel conditional mean embedding (CME) with four-dimensional variational assimilation (4D-Var). Traditional 4D-Var methods are computationally expensive and struggle with nonlinear dynamics and imperfect sta... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer wFvT for these insightful questions. Below, we address each point in detail.
- **Choice of kernel functions**
Thank you for raising the question about choices of kernel functions in Tensor-Var. We would like to clarify that we have included an ablation study in ... | Summary: This paper discusses Four-Dimensional Variational Data Assimilation (4D-Var), which is widely used in weather forecasting and dynamic system state estimation. Traditional methods struggle to properly model the nonlinear relationships between observational data and numerical models. The authors propose a novel ... | Rebuttal 1:
Rebuttal: We greatly appreciate reviewer's constructive feedback and recognition of the strengths of our work. Below, we address each of the questions in detail.
- **Compare with sota ML-based DA methods.**
We thank reviewer for raising the questions regarding the comparison with recent ML-based meth... | Summary: This paper proposes Tensor-Var, a framework combining kernel conditional mean embedding (CME) with 4D-Var to linearize nonlinear dynamics and enable convex optimization in a learned feature space. It addresses the high computational costs and theoretical limitations of traditional 4D-Var and deep learning hybr... | Rebuttal 1:
Rebuttal: We greatly appreciate valuable and constructive suggestions from Reviewer dYtv. Your efforts have significantly contributed to improving the quality of our paper. Below, we provide detailed responses to each of your comments.
- **Higher-resolution experiment**
We would like to clarify that t... | Summary: This paper introduces 4d Tensor-Var, a framework for performing data assimilation with learnable features. Building upon CME to parameterize the state space model, the authors propose two versions of constructing the features space.
Claims And Evidence: I would recommend reorganizing the presentation: current... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer Qu2S for your thoughtful review and constructive feedback on our manuscript. Your comments have been invaluable in improving the clarity and presentation of our work. Below, we address each of your comments.
- **Reorganize the method section**
Thanks reviewer’s ... | null | null | null | null | null | null |
A Machine Learning Approach to Duality in Statistical Physics | Accept (poster) | Summary: This paper show cases a machine learning approach to find dual models in statistical physics. The authors outline a training procedure to find an ML model that can match the observables estimated from two statistical physics systems. If such a match is found for two different Hamiltonians then that points to t... | Rebuttal 1:
Rebuttal: ## **Stronger evidence of approximate duality**
We thank the reviewer for the suggestion and agree that additional evidence strengthens our claims. We now provide such evidence, showing that a broad set of moments—including correlation length—is accurately matched across approximate duals. This c... | Summary: A methodology is developed for automatically finding duality transformations
on lattice gauge theory models (actually simple 2D Ising models are considered
in this paper as a proof of concept). This duality transformation is an important tool in physics as it
can allows one to access physical properties of sy... | Rebuttal 1:
Rebuttal: ## **Code Availability & Moments & Reproducibility**
We would like to kindly remind the referee that a full working code was provided in the supplementary material. Feature computation is handled by `src.utils_ising.generate_masks` (which generates 13 link product masks) and `src.utils_ising.feat... | Summary: This paper develops a machine learning method to find dualities in statistical physics models. The authors turn duality discovery into an optimization problem by using neural networks to map between original and dual models. They create a loss function that matches correlation functions between the two descrip... | Rebuttal 1:
Rebuttal: We thank the referee for their careful reading and interesting questions. We believe each of their three questions are interesting starting points for future research, and our more precise responses are below:
1. **How would the method work for systems with continuous symmetries where there are ... | null | null | null | null | null | null | null | null |
Constrained Optimization From a Control Perspective via Feedback Linearization | Reject | Summary: The paper studies feedback linearization to solve nonconvex optimization problems in which both the objective function and the constraints are nonconvex.
## update after rebuttal
No additional updates
Claims And Evidence: The paper lacks in rigorous definition of key mathematical entities and assumptions nec... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the comments and suggestions. We have **revised our paper** accordingly (see https://anonfile.io/f/wvtpAhSf). Below, we briefly summarize the reviewer's concerns and then address them one by one:
1. **Lack of Clarity in Assumptions and Paper Structure:** *The r... | Summary: The manuscript proposes a new perspective on analyzing first-order algorithms in constrained optimization that is rooted in the control-theoretic notion of feedback linearization. The manuscript is overall well written and the main innovation is presented well. There are also interesting numerical examples tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We have incorporated the suggestions into the **revised manuscript** (see https://anonfile.io/f/wvtpAhSf). We summarize and address the reviewer’s concerns as follows:
1. **Feedback linearization perspective:** *How does the feedback linearizat... | Summary: The paper develops the view of optimization methods as (optimal) feedback control of ODEs, which is some ways is the original view in the Soviet literature, but has been rediscovered only in the past decade in the Western machine learning, with the work of Andrew Packard, Bin Hu and others. Among others, it al... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for carefully reading our paper and providing constructive feedback, as well as for their positive assessment of our work and its positioning within the broader literature on optimization and control. We are also grateful for the reviewer’s detailed suggestions on t... | Summary: The paper develops a theoretical foundation for using feedback linearization (FL) from control theory to address constrained optimization problems, proving global convergence rates, extending FL methods to inequality constraints, relating FL to Sequential Quadratic Programming (SQP), and introducing a novel mo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the comments and suggestions! We have **revised our paper** (see https://anonfile.io/f/DEcRCSxS) accordingly. We briefly summarize the reviewer’s main concerns as follows and address them one by one:
1. **Missing Discussion on First-Order Methods:** *The review... | null | null | null | null | null | null |
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning | Accept (poster) | Summary: This paper proposes a keypoint and descriptor detection algorithm, called TimePoint, for time-series alignment. The authors use a deep learning approach to train a model on synthetically-generated data for detecting keypoints and descriptors. The correspondences learned by their model is passed into a DTW algo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words and suggestions.
# Q1 - The scope of the proposed method
We appreciate the reviewer's concern regarding the scope of the proposed method. Time Series Alignment (TSA) has been a key research topic of time series analysis and machine learning in general.... | Summary: The paper presents TimePoint, a self-supervised method for accelerating Dynamic Time Warping (DTW) in time series alignment by leveraging keypoint detection and descriptor learning from synthetic data. The main findings demonstrate that TimePoint significantly outperforms traditional DTW in terms of speed and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words and comprehensive response and suggestions.
# Q1 - TP vs. SOTA time series alignment: robustness to noise and temporal distortions.
TP was evaluated on 100+ datasets of the UCR archive. These datasets significantly vary in terms of noise and temporal d... | Summary: This paper introduces TimePoint, a self-supervised framework for accelerating DTW-based time series alignment by learning keypoints and descriptors. TimePoint leverages 1D diffeomorphisms to model nonlinear temporal distortions, combined with fully convolutional and wavelet-based architectures to extract multi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words and suggestions.
# Q1 - It is recommended to conduct experiments under the real-world dataset like PTB [1]
Agreed, but this was already done in the paper. Please note that while TP was trained on synthetic data, all of the reported experiments were condu... | Summary: This paper proposes a self-supervised method that dramatically accelerates DTW-based alignment while typically improving alignment accuracy by learning keypoints and descriptors from synthetic data.
Claims And Evidence: The claims are clear and convincing except one concern.
While CPA is an effective method ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words and comprehensive response and suggestions.
# Q1 - Non-stationary time series (NSTS)
This is an excellent point. A single CPAB warp implies a stationary velocity field, while the CPAB prior restricts the warps to avoid unrealistic distortions. Three fa... | null | null | null | null | null | null |
InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference | Accept (poster) | Summary: This paper emphasizes the reason why unsupervised gene regulatory network inference lags in supervised ones is that it did not well utilize the prior knowledge. They followed the framework proposed by DeepSEM and proposed infoSEM to involve prior knowledge provided by BioBERT and logit probability. In addition... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments. Below, we address their questions.
**1. Do Functionally Similar Genes Share Causal Relationships?**: The assumption that functionally similar genes share causal relationships is well supported by biological literature. Evolutionary principles su... | Summary: This paper introduces a method that incorporates gene embeddings from pretrained language models or known gene-gene relationships into the existing DeepSEM framework, resulting in two models: InfoSEM-B and InfoSEM-BC. The authors provide a detailed discussion on how to integrate the interaction matrix $Y$ and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive comments. We appreciate the recognition of our new benchmark and the value of integrating external priors for GRN inference. We address the reviewer’s questions below and will include them in the camera-ready version.
**1. External Prior... | Summary: Summary
This study proposes infoSEM, a deep generative model with informative priors for gene regulatory network (GRN) inference. By integrating text-based gene embeddings as biological priors, it addresses the critical challenge of GRN reconstruction without ground-truth interaction labels. The authors also ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and acknowledgment of our real-world benchmark as a key advancement in GRN inference, along with their praise for our theoretical clarity. Below, we address their questions and provide additional results, which we will include in the camera-ready version.
**1... | Summary: The paper introduces InfoSEM, an unsupervised generative model for Gene Regulatory Network (GRN) inference that leverages textual gene embeddings as informative priors. The model can also integrate ground truth (GT) labels when available, avoiding biases and enhancing performance. The authors propose a new ben... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable feedback and acknowledgment of our work’s novelty and rigor. We are happy that our analysis of biases in supervised GRN benchmarks and InfoSEM’s performance on the unseen-gene benchmark were well received. Below, we address the reviewer’s questions and provide... | null | null | null | null | null | null |
Pixel2Feature Attack (P2FA): Rethinking the Perturbed Space to Enhance Adversarial Transferability | Accept (poster) | Summary: This paper introduces Pixel2Feature Attack (P2FA), a novel approach aimed at enhancing the transferability of adversarial examples in black-box attacks. The main point of the paper is to address the inefficiency of existing feature-level attacks, which tend to perturb features multiple times in pixel space, le... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their valuable feedback, and we will address the following issues in our response. (If the latex formula is not rendered, please refresh the page.)
# Q1: Explanation of Feature Importance Assessment Methods
In Sec. A.1 of the appendix, we provided a brief intr... | Summary: This paper theoretically analyzes that existing multi-feature-based attack methods are essentially equivalent to perturbing features once. Correspondingly, a P2FA is proposed to perturb the feature spaces multiple times, by shiting the perturb space from pixel to feature. Extensive experiments were conducted t... | Rebuttal 1:
Rebuttal: # Q1: Lack of Transformer-based models
You rightly pointed out that Transformer-based models dominate contemporary research, and there is a legitimate interest in understanding how our proposed attack performs against these more advanced architectures.
In response to your comment, we have follow... | Summary: In this paper, the authors propose Pixel2Feature Attack (P2FA) to enhance the transferability of feature-based attack across different DNN models.
To enhance the efficiency, the proposed P2FA shifts the disturbance space from the pixel space to the feature space. Specifically, P2FA perturbs feature maps withi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, which we address below. (If the latex formula is not rendered, please refresh the page.)
# Q1: Theoretical Proof
We appreciate the reviewer spotting an error in Eq. (11). We omitted the constraint on $x^{adv}$. Next, we will focus on rigorousl... | null | null | null | null | null | null | null | null |
In-Context Deep Learning via Transformer Models | Accept (poster) | Summary: This paper studies the representation power of the transformer model in performing in-context learning (ICL). In particular, this paper focuses on the implementation of in-context gradient descent with respect a general deep neural network. The proposed construction is flexible that it can either use a ReLU at... | Rebuttal 1:
Rebuttal: **Thanks for your detailed review. We have revised our draft (changes marked in BLUE) in this [anonymous Dropbox folder](https://www.dropbox.com/scl/fo/cv5unyj4vew76op0xjftj/ALWvpjkaQ-_83Je7h0APsgs?rlkey=z80f15tgc59a3zl2m7owaklbe&st=05cr7uzt&dl=0).**
> **C1**: Reconstruction of the Manuscript.
*... | Summary: This paper shows that one can construct weights of a ReLU-activation transformer that can simulate L steps of gradient descent on an N-layer ReLU network using in-context examples.
## update after rebuttal
While I have concerns with clarity and usefulness of the construction, I appreciate the author's honest... | Rebuttal 1:
Rebuttal: **Thanks for your detailed review. We have revised our draft and addressed all concerns. The revised version (changes marked in BLUE) is available in this [anonymous Dropbox folder](https://www.dropbox.com/scl/fo/cv5unyj4vew76op0xjftj/ALWvpjkaQ-_83Je7h0APsgs?rlkey=z80f15tgc59a3zl2m7owaklbe&st=05cr... | Summary: The paper introduces an approach that harnesses the transformer's ability to emulate the in-context learning for training process of deep models. Its key contribution is the demonstration of a practical instance where a transformer is used to simulate the training process of a deep neural network. Furthermore,... | Rebuttal 1:
Rebuttal: Thank you for your review! We greatly appreciate your attention to detail and recognition of our theoretical and experimental contributions! Your constructive comments and encouraging words are also highly appreciated! | Summary: This paper studies the expressive power of transformer models to simulate gradient descent on other architectures like N layer feedforward networks using in-context learning. The authors corroborate their study with experiments on synthetic datasets that show that the in-context learning performance of Transfo... | Rebuttal 1:
Rebuttal: > **Q1**: How would the bound vary if the transformer's size is allowed to increase beyond (2N+4)L? Can it be made polynomial, by making the transformer size grow exponential?
**Response:**
Thank you for your question. The bound does not change if the transformer's size exceeds $(2N+4)L$, and th... | null | null | null | null | null | null |
Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs | Reject | Summary: The paper propose an interesting phenomenon termed "underthinking" for LLM reasoning -- models frequently switch between different reasoning strategies without sufficiently exploring promising paths, leading to inadequate reasoning depth. Authors introduce a novel metric to quantify underthinking by measuring ... | Rebuttal 1:
Rebuttal: > Q1: Reliability of using LLMs to assess the correctness of intermediate reasoning steps.
A1: We fully understand your concerns about the reliability of using LLMs to assess the correctness of intermediate reasoning steps, particularly the dependency on model capabilities and prompt sensitivity.... | Summary: This paper introduces a novel investigation into the phenomenon of 'underthinking' in large language models (LLMs), specifically those designed for complex reasoning tasks, such as the 'o1-like' models. The authors define underthinking as the premature abandonment of promising reasoning paths, leading to subop... | Rebuttal 1:
Rebuttal: > Q1: More experimental results & analyses.
A1: Thank you for your insightful feedback and constructive suggestions. We have carefully addressed your points in the revised manuscript and conducted additional experiments to substantiate our claims. Below, we summarize our improvements with respect... | Summary: This paper investigates strategies to leverage decoding-time interventions for improving reasoning depth and accuracy in o1-like LLMs. The authors propose a thought switching penalty TIP decoding strategy that discourages frequent and premature switching between reasoning paths to mitigate underthinking; then,... | Rebuttal 1:
Rebuttal: > Q1: More insights about how TIP works and the generality of TIP.
A1: Thank you for your insightful questions and constructive feedback. We fully agree that more in-depth insights into *why* TIP enhances models' reasoning depth and accuracy would significantly reinforce our method.
To further c... | Summary: This paper introduces and investigates the phenomenon of "underthinking" in advanced "o1-like" large language models, characterized by their tendency to frequently switch between reasoning thoughts without sufficiently exploring promising paths, particularly on complex problems. The authors empirically demonst... | Rebuttal 1:
Rebuttal: Thank you for the valuable suggestions, which helped clarify and strengthen the paper.
> Q1: Provide the code for reproduction.
A1: We will make our code publicly available soon.
---
> Q2: The term "o1-like models" isn't precisely defined.
A2: Thank you for pointing this out. We acknowledge t... | null | null | null | null | null | null |
Function-to-Style Guidance of LLMs for Code Translation | Accept (poster) | Summary: This paper studies code translation. Different from previous settings where researchers only care about the functional correctness of translated codes, the authors in this paper also care the functional consistency. They propose a method to handle this by splitting it into a two-stage training framework, funct... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's insightful and constructive feedback, and we have carefully addressed each point in our response to resolve your concerns.
If our response has satisfactorily addressed your questions, we kindly request your consideration of raising the score (currently Rating: ... | Summary: This paper proposes F2STrans, an approach for improving code-to-code translation by optimizing large language models (LLMs). The main idea of this paper is to provide fine-tuning processes that instruct knowledge on good code and bad code in terms of two criteria: Functional Learning and Style Learning. For fu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments.
>**Q1:**
Further study is needed to analyze the case when LLM judges provide low-quality output.
**A1:**
We observe that LLM Judge is prone to errors when evaluating source code that is either lengthy or contains numerous built-in ... | Summary: Previous work in code translation has focused on improving the performance of LLM-based code translation by focusing on multilingual training and various test-time inference strategies. In this paper, the authors hypothesize that optimizing for program correctness and program readability can improve the perfor... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our work and providing valuable feedback.
We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns.
If so, we would deeply appreciate it if you could raise your score (currently Rating: 2: Weak r... | Summary: Authors present F2STRANS, a novel function-to-style guiding paradigm that enhances the code translation performance of smaller LLMs by addressing functional correctness and stylistic readability.
Authors propose a 2-stage learning approach:
1) The functional learning stage is based on instruction fine tunin... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments.
We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns.
If our response meets your expectations, we would greatly appreciate it if you could consider raising your score (curre... | null | null | null | null | null | null |
Language Models May Verbatim Complete Text They Were Not Explicitly Trained On | Accept (spotlight poster) | Summary: This work shows that current LLMs are able to generalize from their training dataset in a way that completes unseen (measured by n-gram overlap) data samples during inference time, thus challenging the use of n-gram-based metrics for a wide range of fields such as memorization, contamination, poisoning, and da... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive assessment of our work! We’re glad the reviewer finds the paper enjoyable to read, the experiments well-designed, and the claims important for the community. We hope to address the comments below and would appreciate the reviewer’s consideration.
> [Weakness]... | Summary: This paper illustrates numerous challenges with the existing n-gram definition of membership in the LLM privacy community. They show that LLMs can output n-grams even if they have all been removed from the training data. They then show that the n-gram definition can be gamed by constructing training datasets w... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our work! We’re glad that the reviewer finds the work rigorous, well-written, and of broad interest to the community. We hope to address comments below, and would appreciate the reviewer’s consideration.
> It might be useful to run the same exp... | Summary: The authors present a study on the ability of LLM to generate and complete verbatim text, which they were not explicitly exposed to during training. They start by challenging the n-gram overlap membership, showing how redacting samples filtered using this criterion does not hinder LLMs' capability to generate ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and effort! We understand & address the concerns below and would appreciate the reviewer’s consideration.
> [Claims & evidence] less aligned with the conclusions on unlearning
We wish to clarify that:
- Unlearning serves many goals, one is output suppression [1]... | Summary: This paper demonstrates LLMs may generate verbatim versions of text that is not included in their training data *as measured by n-gram membership tests*. After demonstrating this, they examine some possible reasons why this may be and show that it is possible to adversarially inject samples into training data ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive assessment of our work! We’re glad that the reviewer finds the paper well-motivated, well-written, and important to the community. We address the comments below.
> [Weakness 1]: Only GPT-2 models are considered, due to training feasibility. While understandab... | null | null | null | null | null | null |
Near-optimal Sketchy Natural Gradients for Physics-Informed Neural Networks | Accept (poster) | Summary: This paper proposes SNGD, a sketched version of natural gradient descent (NGD), for training PINNs. SNGD uses sketching to scale a previous optimizer, ENGD, to larger neural networks for PINN training. The authors evaluate SNGD against Adam, BFGS, and NEGD on several benchmark problems and demonstrate that SNG... | Rebuttal 1:
Rebuttal: We thank the reviewer for the many helpful suggestions, for their careful and detailed comments and for the positive comment about the novelty and utility of the work.
**Claims And Evidence:**
- If we understand your comment correctly, we believe that our observation about rank deficiency is co... | Summary: This work improves the computational efficiency and estimation accuracy of NEGD by leveraging the structural properties of the Gram matrix and introducing the classical RSVD method. It effectively addresses the high storage and computational costs associated with the Gram matrix while also enhancing the neural... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful and detailed suggestions and comments and the time and effort they put into reviewing our manuscript.
**Experimental Designs Or Analyses**
We will add comparisons with ADAM to 5.2-5.4.
At the end of section 5, we specify how $p$ is chosen and set the to... | Summary: The manuscript discusses the application of randomized numerical linear algebra to scale natural gradient methods for the training of physics informed neural networks (PINNs). More precisely, a randomized eigensolver is employed to solve the linear system in the natural gradient algorithm at every step. The au... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our manuscript and for their helpful suggestions, comments and questions. We greatly appreciate their positive assessment of our work.
**Relation To Broader Scientific Literature:**
We thank the reviewer for the suggestion and have included a ... | Summary: This paper introduces a novel natural gradient descent method based on sketching. Instead of computing the search direction of the natural gradient exactly, the paper uses the sketching technique to compress the large Gram matrix into a smaller one with a random Gaussian matrix. The algorithm is interesting an... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to carefully review our manuscript and appreciate the positive review of our work.
**Questions For Authors:**
The proposed algorithm is, indeed, not limited to training PINNs. We began the paper by focusing specifically on improving the training of PINNs... | null | null | null | null | null | null |
Beyond Message Passing: Neural Graph Pattern Machine | Accept (poster) | Summary: ## Update after rebuttal
The authors have solved most of my concerns. I decide to maintain my score.
Graph neural networks struggle to capture essential substructures, such as triangles in social networks or benzene rings in molecular graphs, due to their reliance on message passing. To address this limitatio... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. We appreciate your constructive suggestions regarding related work and theoretical clarification, and we address each of these points in detail below.
> A detailed comparison to RUM.
>
**Motivation**: RUM is designed to jointly address expressiveness, over-s... | Summary: The paper introduces the Neural Graph Pattern Machine (GPM), a framework designed to enhance the expressiveness of graph learning models by directly learning from graph patterns. Traditional Graph Neural Networks (GNNs) rely on message passing to aggregate information from local neighborhoods, which can limit ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and encouraging review. We’re glad the reviewer appreciated our contributions in expressiveness, scalability, and interpretability. We also appreciate the constructive suggestions regarding additional experiments, and we address each point in detail below.
> GPM has t... | Summary: This paper proposes GPM, a graph transformer based on randomly sampling walks in the graph. GPM achieves strong empirical results across a variety of datasets and types of tasks. Furthermore, by sampling a large number of paths (or very long paths) GPM achieves high expressivity.
## update after rebuttal
The ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of the strengths of our method and experimental evaluation. While we take your concerns about the theoretical aspects seriously, we would like to emphasize that the primary contribution of our work lies in the **model design and empirical success**; the the... | Summary: This paper introduces a framework for graph learning named Neural Graph Pattern Machine (GPM). It aims to directly capture substructure patterns instead of relying on message-passing mechanisms. The model leverages random walks to extract graph patterns, and then convert them into semantic paths and anonymous ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed and insightful feedback, as well as for recognizing our contributions in methodology, theoretical insights, and empirical performance. We particularly appreciate the constructive suggestions regarding theoretical clarity, experimental completeness... | null | null | null | null | null | null |
Ladder-Residual: Parallelism-Aware Architecture for Accelerating Large Model Inference with Communication Overlapping | Accept (poster) | Summary: This paper proposes a communication-friendly Transformer layout, Ladder Residual, to accelerate tensor-parallel training. Ladder Residual enables straightforward computation-communication overlapping compared with vanilla sequential or parallel layout. It achieves 29% end-to-end speedup in TP8 training. Ladde... | Rebuttal 1:
Rebuttal: Thanks for your feedback on our paper! Below we provide a few clarifications on how our experiments demonstrate practical gain from ladder-residual and its compatibility with other parallelism.
> In the paper, some experiments are conducted under TP16, where inter-node communication cost is stron... | Summary: This paper proposes Ladder-Residual, which modifies the residual connection in Transformers such that the i-th block reads from the (i−2)-th block's result instead of the (i−1)-th. This interleaved schedule allows direct overlap between the Transformer block's computation and the subsequent all-reduce communic... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback and the questions. Below we discuss how our paper is novel, why it’s compatible with other parallelism, address the concern on scaling, and provide analysis on the change of activations.
We also thank the reviewer for the suggestions on the presentation. We will i... | Summary: This paper proposes Ladder Residual, an alternative to the transformer architecture that breaks the communication-computation dependency in conventional parallelism patterns, in order to accelerate the inference, at the cost of accuracy degradation.
Claims And Evidence: I have some doubts about the claim "We ... | Rebuttal 1:
Rebuttal: Thanks for your time and the feedback! We want to clarify that in a lossy efficient method (where a more efficient architecture is proposed to approximate the original one), trading accuracy for efficiency is common and we provide a good trade-off. It’s difficult to have one-size-fits-all as some ... | null | null | null | null | null | null | null | null |
The Price of Linear Time: Error Analysis of Structured Kernel Interpolation | Accept (poster) | Summary: The authors provide a theoretical treatment of structured kernel interpolation in Gaussian processes, particularly focusing on cubic convolutional interpolation methods. They provide a comprehensive characterization of error and complexity. Their main results focus on the interplay between the number of induci... | Rebuttal 1:
Rebuttal: Thank you for your support and positive assessment of our work as a valuable contribution! We are glad that you found the proofs straightforward and the work well-positioned. Regarding your points:
* **Clarifying the SKI Implementation (Sec 3.2 / 3.3 Connection):** We will make the connection betw... | Summary: For a fixed kernel, non-zero noise-variance parameter, and spatial dimension, the authors give an asymptotic bound on the number of inducing points needed to reach some accuracy threshold with the SKI approximation as a function of the number of data points. Upper bounds on error in the kernel matrix, the log... | Rebuttal 1:
Rebuttal: Thank you for your thorough and critical reading of our manuscript. We appreciate the detailed feedback, particularly regarding the assumption about kernel derivatives.
**Regarding the critique of Assumption 5.3:** You are absolutely correct. Our claim that the partial derivative of the kernel $k... | Summary: In this paper, the authors prove several bounds for quantities of interest when using the structured kernel interpolation approximation of Gaussian processes. These bounds include element-wise errors in the kernel matrix, the spectral norm of the difference between the approximated and true Gram matrices, the ... | Rebuttal 1:
Rebuttal: Thanks for your helpful review and for recognizing the paper's comprehensive scope. We acknowledge the concern regarding the density of the paper due to proofs being in the appendix. In the next version, we will incorporate additional proof intuitions or summaries of the key steps for our main the... | Summary: This paper presents a theoretical analysis for the structured kernel interpolation (SKI, Wilson and Nickisch, 2015), a popular method for scaling Gaussian processes (GPs). The paper proves the error bounds for the SKI gram matrix and examine its effect on GP hyperparameter estimation and posterior inference. C... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful suggestions! We are encouraged that you found the theoretical claims rigorous and the proofs correct.
We will address the clarity issues raised:
* We will explicitly define $k=1,\ldots,K$ as the iterations when we first use it in Section 5.1.
* ... | null | null | null | null | null | null |
Generalizing Causal Effects from Randomized Controlled Trials to Target Populations across Diverse Environments | Accept (poster) | Summary: This paper studies the identification and estimation problem in generalizing treatment effects from an RCT to an observational dataset. Instead of assuming all relevant separate set is observed, it is assumed that part of them are only observed in one dataset. The identification is made possible by introducing... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s great efforts and insightful comments to improve our manuscript. In below, we address these concerns point by point.
> **[Q1]**
Regarding the testability of Assumption 3.2, we would like to clarify the following:
1. Assumption 3.2(1), i.e., $Z\notindep X^m... | Summary: This paper deals with generalizing treatment effects estimated from RCTs to different environments where there exists environmental shifts. Existing methods assume that covariates common to both source and target datasets contain the separating set, which is often violated in real-world.
The authors propose a... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s great efforts and insightful comments to improve our manuscript. In below, we address these concerns point by point.
> **[Claims And Evidence 1]**
First, we would like to clarify that **the method we propose for automatically selecting shadow variables is t... | Summary: This paper studies the problem of generalizing RCTs under environment shifts, particularly shifts in the distribution and quantity of covariates. It relaxes the assumption in the prior literature where the separating set (variables that simultaneously affect treatment effect heterogeneity and environmental shi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s great efforts and insightful comments to improve our manuscript. In below, we address these concerns point by point.
> **[Other Comments Or Suggestions]**
Thank you for your valuable suggestions. We will carefully revise our manuscript based on your recomme... | null | null | null | null | null | null | null | null |
Multi-Session Budget Optimization for Forward Auction-based Federated Learning | Accept (poster) | Summary: This paper explores the design of bidding strategies for data consumers in auction-based federated learning (AFL), involving three key stakeholders: (1) data owners, who are willing to share their potentially sensitive data in exchange for appropriate compensation; (2) data consumers, who require data to train... | Rebuttal 1:
Rebuttal: Thank you for your insightful and encouraging feedback. Below, we provide detailed, point-by-point responses to the key questions raised in the comments.
>W1. More motivation of why multi-session auction scenario is realistic seems to be helpful.
Practical federated learning environments typic... | Summary: This paper introduces MBOS-AFL, a hierarchical reinforcement learning-based strategy for multi-session budget optimization in forward auction-based federated learning (AFL). The key idea is to enable data consumers (DCs) to dynamically allocate budgets across multiple FL training sessions (via an inter-session... | Rebuttal 1:
Rebuttal: Thank you for your encouraging and insightful feedback. Below, we provide point-by-point explanations to key questions raised in the comments.
>W1): The system uses the SPSB auction mechanism, why is not bidding the value truthfully optimal? That is the main promise of this type of auction.
In t... | Summary: The authors deal with the multi-session budget allocation problem for data consumers in auction-based federated learning and propose the MBOS-AFL by introducing a a hierarchical RL framework.
Claims And Evidence: claims are well-supported.
Methods And Evaluation Criteria: The method is novel and appropriate ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful suggestions and valuable feedback.
>1. The selected baselines are outdated to some extent. It is suggested to incorporate more recent ones like [1, 2] with the same setting to help position the proposed method. In addition, Can you compare your bidding stra... | null | null | null | null | null | null | null | null |
Language Models as Implicit Tree Search | Accept (poster) | Summary: This submission proposes a novel preference optimization method for LLMs, grounded in the theoretical insight that LLMs can be viewed as implicit tree search, combining direct performance optimization with Monte Carlo Tree Search (MCTS).
Claims And Evidence: No
Methods And Evaluation Criteria: Yes
Theoretic... | Rebuttal 1:
Rebuttal: Thanks for your comments and suggestions.
**Q1. The concept of Implict Tree search (ITS) is not clearly stated in the main part, and authors should explain what the difference between explicit and implicit tree search and what the advantages of implicit tree search in the introduction section**
... | Summary: This paper introduces a novel preference optimization framework called Implicit Tree Search Preference Optimization (IT-PO) that addresses the reasoning limitations of existing LM alignment methods like Direct Preference Optimization (DPO). The key innovation is incorporating a second language model policy tha... | Rebuttal 1:
Rebuttal: Thanks for your comments and suggestions.
**Q1. The limitation due to the demand of two language models.**
**R1**: It is a good question since the requirement of LLM $\theta$ and $\varphi$ is inevitable in IT-PO. Despite so, the implementation can partially mitigate the limitation. First, despit... | Summary: The paper introduces a novel approach to preference optimization for LLM by incorporating Implicit Tree Search, drawing inspiration from Monte Carlo Tree Search and AlphaZero-like algorithms. The key contribution is an alternative preference optimization framework that allows LMs to implicitly execute a tree s... | Rebuttal 1:
Rebuttal: Thanks for your comments and concerns. We attempt to address the concerns by discussing the writing quality and the causes of marginal performance gain.
**Q1: Writing quality.**
**R1**: We response from three aspects.
**First**, we appreciate the critique to phrases and grammar, and commit that... | Summary: The paper proposes "Implicit Tree Search" (ITS), a stochastic approach that reformulates Monte Carlo Tree Search (MCTS) as a policy optimization problem. Unlike traditional MCTS methods that rely on discrete visit counts for exploration, ITS uses a continuous policy with reversed KL-divergence constraints.
Cl... | Rebuttal 1:
Rebuttal: Thanks for your comments and suggestions.
**Q1. More implementation details behind theory ("they sometimes gloss...more detailed explanation").**
**R1**: Despite Theorems 4.2, 4.3 containing numerous variables, most are derived from the data rather than serving as hyperparameters. Thus, IT-PO ho... | null | null | null | null | null | null |
Automated Red Teaming with GOAT: the Generative Offensive Agent Tester | Accept (poster) | Summary: The authors introduce a new method for an important scenario of multiturn red teaming. The authors propose to use a helpful LLM agent system to dynamically use and combine existing jailbreaking methods to adaptively attack target models. Experiments show its superior performance and efficiency over baseline me... | Rebuttal 1:
Rebuttal: Thank you for your comments! We would like to point out that the novelty of our work lies in automating manually discovered red teaming strategies. We achieve very high attack success rates with a lower computational budget than the most comparable work. We do this by following an established eval... | Summary: The paper introduces GOAT (Generative Offensive Agent Tester), an automated multi-turn red teaming framework to assess how large language models (LLMs) respond to adversarial prompts. Unlike single-prompt attacks, GOAT uses a separate “attacker model” that dynamically applies various prompt-level “jailbreaking... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and constructive feedback!
# Response to Concerns and Questions
We agree with the reviewer that some additional experiments are warranted and we have expanded to cover additional attacker models (deepseek-r1), target models (deepseek-r1 and o1), additiona... | Summary: This paper introduces a novel automated red teaming approach (GOAT) for conversational AI systems. GOAT uses recent reasoning capabilities of advanced LLMs equipped with a set of tools, ie. attack models (here, multiple adversarial prompt strategies). In a multi-turn conversation with the target model, GOAT se... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thorough review and suggestions that help us make the paper stronger! We agree with the reviewer about the suggestions for better clarity and readability and will fix these in a potential camera-ready submission!
# The attacker model
First of all, we apologize as we... | Summary: The paper introduces GOAT (Generative Offensive Agent Tester), an automated red teaming system designed to identify vulnerabilities in LLMs. GOAT simulates adversarial conversations by leveraging various known prompting techniques to jailbreak LLMs. The key innovation is its multi-turn conversational approach ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful engagement with the work! We believe the questions and concerns raised will help strengthen the paper.
# Q1 Clarifying the attacker model used
We apologize for the omission and we will certainly explain this in a potential final revision! For the experi... | null | null | null | null | null | null |
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning | Accept (poster) | Summary: This work studies the task of Multi-Label Class Incremental Learning (MLCIL), where a model is tasked to incrementally learn to assign multiple labels for each image during each incremental session. The goal is to not forget previously leanred classes while learning novel classes. This task is an extension of ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and constructive suggestions. Below, we provide a point-by-point response to your questions and concerns.
---
**Q1**: Ambiguous confidence scores and refinement mechanisms in the PL module.
---
1. **Ambiguous confidence scores**, To clarify this issue, we v... | Summary: This paper focuses on the multi-label class-incremental continual learning (ML-CICL) task. To address the challenges of missing historical labels and class imbalance in this task, the authors propose an exemplar-free L3A method. Specifically, L3A utilizes a pretrained language model (PLM) to supplement histori... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and insightful suggestions. Below are our point-to-point responses:
---
**W1**: Comparison between WAC and learnable parameter-based methods.
---
To clarify, the Weighted Analytic Classifier (WAC) is a recursive analytical learning method, where we addres... | Summary: This paper proposes Label-Augmented Analytic Adaptation (L3A), an exemplar-free approach without storing past samples, for Multi-Label Class Incremental Learning (MLCIL). It integrates two key modules: the pseudo-label (PL) module implements label augmentation by generating pseudo-labels for current phase samp... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and constructive suggestions. Below we provide point-by-point responses to your comments:
---
**W1 & Q1**: Uniqueness of challenges in MLCIL problem.
---
Following prior research [1,2], we consider label absence as a unique challenge in MLCIL. Unlike single... | Summary: This paper addresses the challenges of multi-label class-incremental learning (MLCIL), specifically label absence, class imbalance, and privacy constraints. The proposed method, L3A, introduces two key modules: 1) Pseudo-Label (PL) Module: Generates pseudo-labels for historical classes using the previous class... | Rebuttal 1:
Rebuttal: Thanks for your review and valuable comments. Here we address your concerns individually as follows:
---
**W1**: Recursive updates in Eq. 13 may be computationally intensive.
---
The computational cost of our method primarily comes from the matrix inversion in Eq. 13 with the complexity of $\mat... | null | null | null | null | null | null |
Dynamic Sparse Training of Diagonally Sparse Networks | Accept (poster) | Summary: This paper introduces DynaDiag, a structured Dynamic Sparse Training (DST) method that enforces a diagonal sparsity pattern in neural networks. The method aims to overcome the inefficiencies of unstructured sparsity, which struggles to translate into hardware acceleration. The authors propose a custom CUDA ker... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and feedback, especially about grounding our work in theoretical foundations. We address the questions and comments below:
## LoRA-FA Finetuning
We used LoRA-FA (Sec 4.3.1) primarily to interpret the performance gap between RigL and DynaDiag by introducin... | Summary: This paper introduces DynaDiag, a novel structured Dynamic Sparse Training (DST) method based on the composition of diagonal matrices. This parametrization is then transformed into the BCSR format to enable actual GPU acceleration. The proposed model is evaluated by training from scratch vision VIT and MLP-Mix... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and positive comments. We are glad to hear that the
reviewer appreciates the novelty of our work. We address the questions and comments below:
## BCSR Transformation and Contribution (Q1)
For acceleration on GPUs, a diagonal matrix is converted to blocks (... | Summary: The paper introduces DynaDiag, a novel structured Dynamic Sparse Training (DST) method that enforces diagonal sparsity to improve both computational efficiency and model accuracy. Unlike unstructured DST methods, which often fail to achieve hardware acceleration despite high sparsity ratios, DynaDiag maintains... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and constructive feedback, which has significantly helped enhance our work. Below, we concisely address each comment:
## Comparison with SET, MEST, CHT, CHTs Methods (Response to Q1, Q4, Q7)
Due to time constraints, we chose the above four of the five met... | null | null | null | null | null | null | null | null |
AssistanceZero: Scalably Solving Assistance Games | Accept (poster) | Summary: The paper proposes AssistantZero, a method to learn cooperative assistants where the reward function of the human player is unknown. The paper presents a new environment, MBAG-- a 3D grid with many possible reward functions and a variety of ways to help the human in the loop. In contrast to using a PPO, Assist... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and helpful feedback. Below are responses to individual points and questions:
* **Complexity of environment:** While the reviewer refers to MBAG as a toy setting, we argue it is substantially more complex than prior environments in assistance games,... | Summary: The paper proposes using assistance games instead of RLHF to train AI assistants. The advantages of assistive games are that 1) they make AI and humans collaborate to accomplish tasks instead of AI just trying to get good ratings from humans (by potentially fooling humans); 2) AI reasons with uncertainty about... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and helpful suggestions. Below are responses to individual questions.
## Responses to questions
**1\. The paper assumes access to $\hat{p}_t(\theta)$ in Line 294\. How can this be available for general human tasks?**
We believe the reviewer is as... | Summary: This paper applies assistance games—where the assistant must infer the user’s hidden goal—to a challenging Minecraft building task with over $10^{400}$ possible goals. Their new AssistanceZero algorithm extends AlphaZero to partial observability by predicting both the human’s actions and the reward parameters ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and are glad they found the paper proposes a “novel approach” and that our MBAG environment is “also a strong contribution.” We have responded to individual points and questions below.
## Small user study
**“The user study is fairly small (only 16 ... | null | null | null | null | null | null | null | null |
Learning Survival Distributions with the Asymmetric Laplace Distribution | Accept (poster) | Summary: The paper introduces a novel parametric survival analysis framework based on the Asymmetric Laplace Distribution (ALD). By formulating the survival problem through ALD, the authors derive closed-form expressions for key distributional summaries (e.g., mean, median, mode, and quantiles) and propose an architect... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comprehensive evaluation and insightful comments.
We also appreciate the detailed review of our theory, experiments, and connections to the broader literature.
Below we respond to the specific points raised:
**R1[Claims And Evidence, Other Strengths And Weaknesse... | Summary: The paper proposes a parametric survival analysis method based on asymmetric Laplace distribution. It enables predicting continuous distribution-based predictions, unlike existing discretized nonparametric methods.
Claims And Evidence: The paper mentions several limitations of the existing methods 40-54, such... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful reading and constructive feedback.
We added four more baselines shown in Table 2 [https://anonymous.4open.science/r/ICML25/Fig1.png ] and more comparisons (See the reply for **Reviewer rnhn**) will be added.
Below, we will address your concerns.
**R1[Claim... | Summary: This paper proposes a parametric survival analysis model which uses asymmetric Laplace distributions (ALDs) to represent survival distributions, where the non-linear dependence of ALD’s parameters on static covariates is modeled by neural networks. The experiments on synthetic and real-world data confirmed the... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback, and for generously suggesting many references that we had not previously considered.
We added 4 more baselines and the results are available in Table 2 [https://anonymous.4open.science/r/ICML25/Fig1.png ].
Below, we provide a detailed discus... | Summary: - Authors introduce a parametric survival analysis model, which utilises the Asymmetric Laplace Distribution in the Quantile Regression inspired loss function.
- Inspired by the mean absolute deviation loss function, which models the hyperplane with median distance from all points, the quantile regression is... | Rebuttal 1:
Rebuttal: Thank you for your constructive and insightful feedback.
We appreciate the time and effort spent evaluating our work and will address the concerns and questions below.
**R1[Other Comments Or Suggestions]:**
In response, we would like to emphasize that our method introduces a novel but simple los... | null | null | null | null | null | null |
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training | Accept (poster) | Summary: This paper proposes to find optimal settings of warmup-stable-decay (wsd) learning rate (LR) scheduler for training large language models (LLMs) from a theoretical inspiration. The wsd scheduler is a piecewise function by training iterations, the first part is a constant function (named based LR) and the secon... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and suggestions, and address each question below in detail. We hope that after clarifying all concerns we can improve the reviewer's rating of our submission.
* *First, training loss is not an important criterion for LLMs*: For all LLM experiments in our p... | Summary: This paper studies learning-rate schedules in large model training, by bridging a new theoretical convergence analysis to empirical observations.
In particular, the first contribution (observation) is that for two popular schedules (cosine and wsd), the empirical loss curves of large model training, which is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the theoretical and empirical contributions of our paper. We are delighted that the reviewer considers the paper to be interesting to the optimization and ML community. Regarding the questions:
* Convergence rate of cosine: unfortunately, due to the form of... | Summary: This paper establishes a novel connection between empirical learning-rate schedules (e.g., cosine, WSD) used in LLM training and theoretical bounds on the loss at the final iterate of SGD in a non-smooth stochastic convex setting. Through empirical studies on Llama-style transformers, the paper demonstrates th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback, especially for the assessment that our experimental results are strongly supporting the claims/contributions.
* We ran additional experiments with SGD on Imagenet as suggested by the reviewer (see details here: https://anonymous.4open.science/r/... | Summary: This work demonstrates that several empirical observations align with the last-iterate sub-optimality gap in convex optimization. Furthermore, the authors show that adjusting the learning rate in continual learning—an approach that theoretically improves this sub-optimality gap—also enhances real-world trainin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and detailed feedback. We address all questions in detail below. We hope that this will clarify all concerns, and allows for a higher scoring of our submission.
* *limited empirical evaluation, one real dataset:* We agree that it is beneficial to ... | null | null | null | null | null | null |
DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion Model | Accept (poster) | Summary: The paper proposes a new method, named DragLoRA, for drag-based image editing using a pre-trained Stable Diffusion model. Specifically, the paper proposes two novel steps: DragLoRA online optimization (DOO) and Input Latent Feature Adaptation (ILFA). In DOO, instead of optimizing the latent representation like... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful and constructive feedback. We appreciate the depth of your analysis and the valuable suggestions you provided. Below, we address your concerns point by point:
### **1. Difference from the Original DDS Loss.**
In the original DDS paper, $\nabla_{\theta} L_{... | Summary: The authors presented the DragLoRA method for efficient and more accurate drag-editing, which is specified by a mask of the area to be edited and pairs of points specifying the direction of shift editing. The method is based on the use of LoRA adaptors for simultaneous gradual shifting towards target points, b... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and constructive feedback. Below are our point-by-point responses:
### **1. Runtime Comparison Fairness.**
Our reported time excludes the offline LoRA finetuning time for reconstruction (~48s per image over 80 steps on NVIDIA 4090 GPU), which applies to **all me... | Summary: This paper introduces the DragLoRA framework to enhance point tracking in drag-based image editing, thereby improving editing precision. It proposes a DDS loss combined with drag loss, along with a cyclic denoise-renoise process to maintain semantic fidelity with the source image. Additionally, an adaptive opt... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We address your concerns below:
### **1. Test data details.**
We mainly conduct our experiments on DragBench and Drag100, which are proposed by DragDiffusion and GoodDrag. The qualitative comparisons in Fig.4. and Fig.6 are mainly based on these two test se... | Summary: This paper introduces DragLoRA, a framework that integrates LoRA into drag-based editing. Instead of optimizing the input feature obtained from DDIM inversion, the paper claims to improve accuracy and efficiency by utilizing lora adaptation. A denoising score distillation loss is proposed to align the outputs ... | Rebuttal 1:
Rebuttal: Thank you for your feedback regarding the clarity of our Preliminaries section and symbolic expressions. We appreciate the opportunity to clarify our approach.
### **More details about drag-based image editing, particularly its motion supervision and point tracking.**
Drag aims to make the seman... | null | null | null | null | null | null |
Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule | Accept (poster) | Summary: This paper focuses on modeling twisted probability path of multimodalities in structure-based drug design.
It analyzes the theoretical link between noise schedules and VLB in multimodality probabilistic modeling and further proposes a VLB-optimal scheduling strategy to address this bottleneck.
By integrating ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the careful reading and insightful feedback. Below, we address each question and concern to improve the clarity and completeness of our work, as well as demonstrate its generality.
## Questions
**Q1: Integration with More Frameworks**
> Why not integrate the p... | Summary: The present paper is concerned about structure-based drug design (SBDD) using a Bayesian Flow Network (BFN). One of the issues when applying BFN to SBDD is that the model has to generate a molecular graph, which is a discrete object, as well as its 3D structure, which is a continuous object. A numerical exampl... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough reading of our manuscript and the positive evaluation. We are pleased that the reviewer recognized the key challenges and contributions of our work on deriving optimal schedules for structure-based drug design, and we certainly welcome further discu... | Summary: This paper presents a novel method for finding optimal noise scheduling in structure-based generative models. Typically, deep generative networks used for structure-based drug design generate molecules directly within the binding pocket of a protein, allowing the problem to be defined as 3D molecular graph gen... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough evaluation and valuable feedback. Below, we address each point raised to improve the clarity, reproducibility, and depth of our work.
## Questions
**Q1: Architectural Changes**
The backbone of MolPilot aligns with DecompDiff, where we only replace... | null | null | null | null | null | null | null | null |
Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting | Accept (poster) | Summary: This paper proposes Time-VLM, a multimodal framework using pre-trained VLMs for time series forecasting. It introduces VAL, RAL, and TAL modules to consider the information from three different views. Experimental results show that it achieves good performance with high efficiency. It contributes to a possible... | Rebuttal 1:
Rebuttal: > **Q1: Add the latest references related to multi-modal time series**
>
**A1:** Thank you for your reminder. Our manuscript now includes two recent comprehensive surveys on multi-modal time series analysis [1,2], which we have integrated into both the **Introduction** and **Related Work** secti... | Summary: Time-VLM is a groundbreaking framework that leverages pre-trained VLMs to unify temporal, visual, and textual data for time series forecasting. Key innovations include adaptive time-series-to-image conversion (VAL), memory-enhanced temporal modeling (RAL), and contextual prompt generation (TAL). The experiment... | Rebuttal 1:
Rebuttal: > **Q1: Scalability: Exploring larger VLMs (e.g., LLaVA, GPT-4V) could enrich textual context and further improve performance, offering a promising avenue for future work.**
>
**A1:** Thank you for your suggestion. We empirically evaluated VLMs across different scales, ranging from smaller archi... | Summary: This paper proposes Time-VLM, a multimodal framework that leverages vision-language models to encode temporal, "visual" and textual modalities for enhanced time series forecasting. Specifically, RAL encodes and saves time series into a memory bank for further interaction with multi-modal embedding. VAL encodes... | Rebuttal 1:
Rebuttal: Thank you for recognizing the method design, complete evaluation and unique innovation of our paper. Below are our responses to your major questions.
> **Q1: Textual Encoder Limitations Analysis**
>
**A1:** We systematically investigated the limitations of textual encoders in VLMs through three... | Summary: The paper introduces Time-VLM, a multimodal time series forecasting framework that leverages pre-trained Vision-Language Models (VLMs) to integrate temporal, visual, and textual information. By combining retrieval-augmented learning, vision-based encoding, and text-based contextualization, Time-VLM enhances fo... | Rebuttal 1:
Rebuttal: Thank you for recognizing the clear motivation, method design, and rigorous evaluation of our paper. Below we endeavor to address your questions.
> **Q1: Comparison with Time Series Foundation Models**
>
**A1:** We appreciate your suggestion. However, Time-VLM fundamentally **differs** from fo... | null | null | null | null | null | null |
Efficiently Access Diffusion Fisher: Within the Outer Product Span Space | Accept (poster) | Summary: The diffusion Fisher information matrix (or just 'diffusion Fisher') provides useful quantitative information about the sensitivity of diffusion model log-likelihoods to small changes in state, and can be exploited to (among other things) evaluate sample likelihoods and guide diffusion models to generate highe... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We will answer your questions one by one regarding these suggestions/questions.
> **Answer to Suggestion 1:** The authors' evaluations are easy to understand and clear, although it would be helpful if more examples (along the lines of Figs 2 and 4) we... | Summary: This paper introduces a novel formulation of the diffusion Fisher (DF) information in diffusion models by expressing it as a weighted sum of outer products of the score function and initial data, thereby revealing that DF lies in a space spanned by specific outer-product bases dependent solely on the initial d... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We will answer your concerns/questions one by one.
> **Concern 1:** I have a problem with the paper's main claim that JVP cannot be calculated effectively, they claim that the time complexity is $O(d^2)$...
Please allow me to humbly clarify that we didn't c... | Summary: This paper addresses the challenge of efficiently accessing the diffusion Fisher information (DF) in diffusion models (DMs). Based on the analytical formulation of the diffusion Fisher, the authors propose two novel algorithms: DF Trace Matching (DF-TM) for efficiently estimating the trace of the DF, and DF En... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We will answer your weaknesses/questions one by one.
> **Weaknesses 1:** The bound in Proposition 5 is vacuous when t is small...
It is true that as $t$ approaches $0$, the bound in Proposition 5 will be ill-defined and blow up due to division-by-zero. This ... | null | null | null | null | null | null | null | null |
IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models | Accept (poster) | Summary: This work presents an image restoration framework IRBridge. IRBridge connects the bridge model with the diffusion process of the generative diffusion model by introducing a transition equation, thereby enabling direct utilization of the pre-trained generative model for image inpainting. This methods transforms... | Rebuttal 1:
Rebuttal: We are grateful for your positive review! We will carefully address your concerns below.
**Q1: Empirical selection of hyperparameters**
We acknowledge that this is a limitation of IRBridge, and we offer two key insights.
+ As shown in Appendix C, while different choices of timesteps can signifi... | Summary: This paper argues that the existing diffusion-based restoration methods are based on the standard diffusion process and cannot intuitively simulate the transition from low-quality images to high-quality images. To solve this problem, the bridge model is used. Subsequently, the paper extends the bridge model to... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful review and valuable comments! We will carefully address your concerns below.
**Q1: Insufficient comparative experiments.**
We have added a [QUANTITATIVE COMPARISON](https://anonymous.4open.science/r/IRBridge-4181/asserts/table1.md) between IRBridge and the... | Summary: This paper introduces a new approach for leveraging pre-trained generative diffusion models in image restoration bridges. Traditional image restoration bridge models require training from scratch for each degradation type, making them computationally expensive. This work aims to eliminate that requirement by i... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments! We will address your concerns point by point.
**Q1: Evaluations on real-world scenarios.**
We present the [EVALUATION RESULTS](https://anonymous.4open.science/r/IRBridge-4181/asserts/table2.md) on real-world datasets (RealRain-1K for deraining, R... | Summary: Traditional image restoration bridge models require training from scratch for each degradation type, limiting efficiency and generalization. Meanwhile, pretrained generative diffusion models are underutilized due to mismatched intermediate states between generative and restorative diffusion processes. This wor... | Rebuttal 1:
Rebuttal: We are grateful for your valuable comments, which are helpful for improving our manuscripts. Below, we will address your concerns point by point.
**Q1: Test image resolution and supported resolution.**
To ensure a fair comparison, we conducted all comparison experiments at a 512×512 resolution i... | null | null | null | null | null | null |
Compression for Better: A General and Loss-Driven Compression Framework | Reject | Summary: This paper introduces LLC, a LossLess model Compression framework that specifies the permissible error range for lossless model compression through higher-order error bound analysis. LLC is applied to both quantization and decomposition, achieving notable compression results without compromising performance.
... | Rebuttal 1:
Rebuttal: Q1: About convolution representation.
A1:
1. The convolution layer can be expressed in the first-order differential form[2]. This first-order approximation is applicable to any differentiable operation unit (including conv layers, etc.), and its effectiveness comes from the differentiability assu... | Summary: This paper proposes a general theoretical framework to achieve lossless compression. The paper uses a loss-driven framework to specify the error range each layer's weight and activation can tolerate. A model compression scheme such as quantization or decomposition can therefore be searched within the error ran... | Rebuttal 1:
Rebuttal: Q1: About Smoothness Condition.
A1: Except at the zero point, ReLU is smooth and differentiable in most regions; its non-differentiability occurs only when the activation value is exactly zero. Since real-world datasets (e.g., ImageNet) typically feature continuous distributions, the activations ... | Summary: This paper presents a novel theoretical framework, LossLess Compression (LLC), which provides a principled approach to model compression while ensuring that the model’s loss remains unchanged or even decreases after compression. Through extensive experimentation, LLC demonstrates its effectiveness in achieving... | Rebuttal 1:
Rebuttal: Q1:About when LLC cannot reduce loss
A1:Regarding the limitations of LLC, we emphasize that the loss reduction achieved by LLC is not exaggerated but based on strictly defined mathematical conditions. This sentence has been mentioned in the advantages of the paper you commented on: LLC has clear ... | Summary: The paper proposes a general model compression framework named LossLess Compression theoretical framework(LLC), which focuses on reducing the model loss for better model performance. By considering quantization and decomposition as adding noise to the model weights and activations, the loss introduced by model... | Rebuttal 1:
Rebuttal: Q1: About the evaluation metrics in Table 3.
A1:
First, in Table 3, the decrease in loss value represents the decrease in cross entropy. Cross entropy and accuracy represent different evaluation methods. Compared with accuracy, cross entropy can compare the closeness between the probability dist... | null | null | null | null | null | null |
Splitting & Integrating: Out-of-Distribution Detection via Adversarial Gradient Attribution | Accept (poster) | Summary: The authors propose a post-hoc detector, which is an interesting approach, especially given that post-hoc methods generally struggle with robustness against adversarial examples. Their lightweight architecture allows for easy retraining and is practical for deployment.
In particular, their method involves spl... | Rebuttal 1:
Rebuttal: **Theoretical Claims:**
We appreciate the reviewer's suggestions. Our mathematical proofs are all grounded in theoretical foundations. Regarding the reviewer's concern about "OOD samples being overconfident in prediction," we have cited (Nguyen et al., 2015; Hein et al., 2019) on lines 161-164 in... | Summary: This paper addresses the challenge of out-of-distribution (OOD) detection in deep learning by proposing S & I, a novel method based on layer Splitting and gradient Integration via Adversarial Gradient Attribution. While existing gradient-based methods struggle to distinguish OOD samples due to non-zero gradien... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable suggestions. We provide the following responses to the questions in “**Questions For Authors**”.
Q1: We would like to clarify that the insignificant improvement of our method on CIFAR100 does not mean limited effect, but means that our method can achieve the... | Summary: This paper proposes a method called S&I (Splitting and Integration) for improving out-of-distribution (OOD) detection in deep neural networks. S&I introduces two components: (1) layer splitting, which decomposes intermediate layers of the network to iteratively update adversarial examples and reduce gradient i... | Rebuttal 1:
Rebuttal: **Other Strengths And Weaknesses**
Weakness1：We thank the reviewer for the valuable comment. We would like to clarify that the insignificant improvement of our method on CIFAR100 does not mean limited effect, but means that our method can achieve the same or even slightly better performance than ... | Summary: This paper proposes S & I (Splitting and Integrating), a gradient-based out-of-distribution (OOD) detection method that builds on gradient attribution techniques by leveraging adversarial attacks to refine feature explanations. The core idea is to split neural network layers and iteratively integrate attributi... | Rebuttal 1:
Rebuttal: **Claims And Evidence:**
1. We sincerely state that in Section B of the supplementary materials, we have provided ablation experiments for the adversarial attack module and the layer splitting module. By comparing Table 4 and Table 5, it can be demonstrated that the adversarial attack module play... | null | null | null | null | null | null |
The Devil Is in the Details: Tackling Unimodal Spurious Correlations for Generalizable Multimodal Reward Models | Accept (poster) | Summary: The paper addresses the challenge of unimodal spurious correlations in Multimodal Reward Models (MM-RMs), particularly how these models fail to generalize to out-of-distribution (o.o.d.) data. These spurious correlations occur when models over-rely on text-only features, which hold in the training distributio... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the strengths of our work. We address your concerns as follows.
**C1: More detailed analysis on the theoretical aspects of why the proposed approach outperforms other methods.**
**C2: Justify the use of SFC as a meaningful metric for measuring the failure of uni... | Summary: This paper highlights how unimodal spurious correlations reduce generalization in multimodal reward models. In cross-distribution tests, MM-RMs trained on large, seemingly robust datasets still fail to generalize in unseen environments, primarily because they exploit textual cues rather than genuinely integrat... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the strengths of our work. We address your concerns as follows.
**C1: While the paper presents a creative fix, it does not introduce new core ML techniques or theory.**
**C2: From a rigorous ML research perspective, it may be viewed more as an engineering improv... | Summary: This paper reveals that multimodal reward models (MM-RMs) are struggling to address out-of-distribution (OOD) input queries and identify the unimodal spurious correlation (text-only reliance behavior of MM-RMs) as the main cause of this issue. The authors provide some hypotheses behind this issue and propose t... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the strengths of our work. We address your concerns as follows.
**C1: Although the authors provide rich analysis to strengthen the motivation and rationale behind the method development, one concern is that they only validate their approach with InternVL2-8B. It ... | Summary: The paper proposes to improve the generalization of Multimodal Reward Models (MM-RMs) by addressing the issue of unimodal spurious correlations. It introduces a Shortcut-aware MM-RM learning algorithm that dynamically reweights training samples to emphasize multimodal understanding, reducing reliance on text-o... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the strengths of our work. We address your concerns as follows.
**C1: It would be more demonstrative if the authors could conduct evaluations on more benchmarks like MMMU and MMStar.**
A1: Thank you for your concern about benchmark evaluation. We clarify that ou... | null | null | null | null | null | null |
Otter: Generating Tests from Issues to Validate SWE Patches | Accept (poster) | Summary: This paper introduces a novel code agent specifically designed to generate test cases for real-world repositories, comprising a localizer, action planner and generator. A variant of the agent executes all but one of these components and picks the best results using some heuristics and execution feedback. The r... | Rebuttal 1:
Rebuttal: a) Reporting Coverage: We will add a coverage column to Table 1.
| Model | Approach | Coverage |
|-|-|-|
| Mistral | Zero-shot | 60.6 |
| | Otter | 70.5 |
| | Otter++ | 70.4 |
| GPT-4o | Zero-shot | 60.0 |
| | Otter ... | Summary: This paper introduces Otter, a system for generating tests from issue descriptions to validate software engineering (SWE) patches. Unlike prior work that focuses on generating tests for existing code, Otter addresses the scenario where a code patch does not yet exist, supporting test-driven development (TDD) a... | Rebuttal 1:
Rebuttal: a) Limited novelty: Our approach introduces self-reflective action planning for generating bug-reproduction tests, which is a novel application in this problem setting. Our approach also incorporates heterogeneous prompting for multi-sampling, which is shown to be better than multi-sampling at hig... | Summary: The study describes a system to produce tests from code issues by leveraging LLMs with rule-based analysis and multi-step process with feedback. In addition, the study provides an associated benchmark derived from the SWE-bench Verified benchmark for evaluating tests generated from issues.
Claims And Evidence... | Rebuttal 1:
Rebuttal: a) Issue with Sympy Coverage: We use the Python coverage package in the TDD-Bench-Verified benchmark, which works well with Pytest. However, the Sympy project does not use Pytest for testing. Additionally, the coverage package may fail due to dependency issues. In Sympy, we were able to generate t... | Summary: The paper introduces Otter, an LLM-based solution that generates unit tests directly from issue descriptions before a code patch is written. This approach supports test-driven development scenario and reduces LLMs overfitting of unit tests to the specific focal code. Otter employs a self-reflective action plan... | Rebuttal 1:
Rebuttal: a) Alternative Localization Strategies: Thank you for your suggestion. To further assess the impact of localization, we re-ran Otter using oracle golden localizations for both focal and test functions. We did not observe any significant performance gain (32.5% vs 31.4% with GPT-4o) in the fail-to-... | null | null | null | null | null | null |
Geometric Resampling in Nearly Linear Time for Follow-the-Perturbed-Leader with Best-of-Both-Worlds Guarantee in Bandit Problems | Accept (poster) | Summary: The paper proposed some useful variants of the Geometric Resampling (GR) algorithm called Conditional Geometric Resampling (CGR) and those algorithms improve the sample complexity from O(K^2) to O(Klog(K)) in each round while keeping the BOBW guarantee for a certain perturbation distribution. The experiment re... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We have addressed your question and comment below.
**Q1. Are there any disadvantages of CGR to the other algorithms (such as Tsallis-INF)?**
**A1.**
Generally, we believe that CGR has no disadvantages over the conventional GR, as it is intuiti... | Summary: This paper studies FTPL for MAB problem. The authors first propose a general receipe for designing loss estimate procedure. Then they also give several concrete variants under this framework and shows that they enjoy certain improved time complexity while maintain the optimal BOBW guarantee. These theoretical ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We have addressed your question below.
**Q1. Any intuitions for why the new proposed scheme would give better empirical regret?**
**A1.**
Generally, the variance of the estimator $\widehat{w_{t,I_t}^{-1}}$ introduces an additional regret term i... | Summary: The paper proposes a new estimation procedure based on conditional resampling to improve both theoretically and empirically the sample efficiency of FTPL while maintaining the regret guarantees.
Claims And Evidence: Claims are supported by clear evidence.
Methods And Evaluation Criteria: Evaluation criteria ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We have addressed your question below.
**Q1. How would the analysis change in the proposed sampling approach when extending from bandits to semi-bandits?**
**A1.**
The analysis would not change so much if we are just interested in extending ou... | Summary: The paper proposes Conditional Geometric Resampling (CGR) to improve the computational efficiency of the Follow-the-Perturbed-Leader (FTPL) algorithm in the multi-armed bandit problem.
By introducing a carefully selected, necessary stopping condition in the resampling process, CGR reduces the expected computat... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback on our work. We have addressed your questions and comments below.
**Q1. What is the main technical difficulty in designing a variant of GR that is a sort of counterpart of Tsallis-INF with the Reduced-Variance estimator, especially compared... | null | null | null | null | null | null |
Compositional Risk Minimization | Accept (poster) | Summary: This paper addresses compositional generalization by tackling compositional shift, where test data contains unseen combinations of attributes. The authors propose Compositional Risk Minimization (CRM), using additive energy distributions to model attributes and providing an alternative to empirical risk minimi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and insightful feedback! We now address the concerns raised by the reviewer ahead.
**1. Limited empirical comparisons against existing baseline methods**
We have done extensive benchmarking of CRM with 6 widely used baselines in the literature of subpopul... | Summary: This paper proposes compositional risk minimization (CRM), an approach to compositional generalization that is based on additive energy distributions. The intuition is to train an energy-based classifer on the training set, then modify it to account for known bias between the observed training and test distrib... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and insightful feedback! We will fix the typo in the caption of Figure 1, thanks for pointing this. We now address the concerns raised by the reviewer ahead.
**My main concern is whether the additive energy distribution assumption is realistic (beyond the ... | Summary: This paper introduces a method for addressing compositional shifts in discriminative tasks. The authors propose a theoretical framework built on additive energy distributions, where each energy term represents an attribute. They introduce the discrete affine hull concept to characterize extrapolation capabilit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and insightful feedback! We are glad they appreciate the technical soundness of our work, on both the theoretical and empirical front. We now address the concerns raised by them.
> Additive Energy Distribution (AED) Limitations
We emphasize that the bench... | Summary: This paper addresses the compositional shifts, a hard type of sub-population shifts, and proposes compositional risk minimization. The method is well-motivated and some theoretical analyses are provided. Results on the sub-population shift benchmark are shown to support the proposed method.
Claims And Evidenc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and insightful feedback! We now address the concerns raised by the reviewer ahead.
**1. The analysis and proposed algorithm are designed to handle multiple attributes, with the theoretical advantages being most relevant for this multi-attribute context. Ho... | null | null | null | null | null | null |
Continuous Visual Autoregressive Generation via Score Maximization | Accept (poster) | Summary: This paper presents a new continuous visual autoregressive generative framework, which prevents the information loss caused by vector quantization. This framework takes energy score as the training objectives, which is likelihood-free and easy to make probabilistic predictions in the continuous space. In addit... | Rebuttal 1:
Rebuttal: We sincerely appreciate the Reviewer's time and efforts in reviewing our work. We provide discussions about the concerns as follows.
> In Table 1, it is essential to show the results of MAR and GIVT. They are two important baselines to compare.
We fully agree with the Reviewer and will include c... | Summary: This paper introduces a continuous visual autoregressive framework EAR. The approach is grounded in strictly proper scoring rules, which provide a statistical basis for evaluating generative models, and primarily utilizes an energy score-based training objective to handle continuous data without requiring like... | Rebuttal 1:
Rebuttal: We sincerely appreciate the Reviewer's time and efforts in reviewing our work. We provide discussions about the concerns as follows.
> While the paper mentions that “GIVT is confined to the pre-defined family of Gaussian mixtures,” this is not necessarily a drawback. Instead, it may be the result... | Summary: This paper introduce energy-based autoregressive to train a continuous autoregressive models. The continuous model bypasses the traditional approach of using discrete representation to train an autoregressive model, therefore reduce the information loss during discrete quantization. The experiment shows the ef... | Rebuttal 1:
Rebuttal: We sincerely appreciate the Reviewer's time and efforts in reviewing our work. We provide discussions about the concerns as follows.
> The ablation for masked autoregressive should be provided with causal and full attention like setting in MAR.
We thank the Reviewer for raising this concern. We ... | null | null | null | null | null | null | null | null |
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