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Retraining-free Merging of Sparse MoE via Hierarchical Clustering | Accept (poster) | Summary: This paper introduces HC-SMoE, a retraining-free framework for merging experts in Sparsely Activated Mixture-of-Experts (SMoE) models via hierarchical clustering. The key idea is to group experts based on their output similarities over a calibration dataset, followed by frequency-weighted merging to reduce mod... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable feedback and effort spent on the review, and would like to respond to the reviewer’s questions as follows.
**Q1.** Theoretical Gaps: No formal analysis of clustering quality or merging stability.
**Response:**
We appreciate the reviewer's suggestion regardi... | Summary: The paper presents HC-SMoE, a new framework for reducing SMoE model parameters that doesn't require retraining and works across different tasks. HC-SMoE uses hierarchical clustering on expert outputs and frequency-weighted merging, which offers two main benefits over previous approaches. First, it uses iterati... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable feedback and effort spent on the review, and would like to respond to the reviewer’s questions as follows.
**Q1.** It was not entirely clear to me why Li et al. (2024) proposal is one-shot and why HC is iterative.
**Response:**
The fundamental distinction b... | Summary: This paper proposes a simple and effective task-agnostic method named HC-SMoE to merge the experts in pre-trained mixture-of-expert models. HC-SMoE first obtains expert outputs on a calibration dataset, and then conducts a hierarchical clustering of the experts based on these outputs. The experts inside a clus... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable feedback and effort spent on the review, and would like to respond to the reviewer’s questions as follows.
---
**Q1.** Please consider adding baseline results to see if HC-SMoE still outperforms the baselines when the calibration dataset and the evaluation d... | Summary: This paper proposes an untrained sparse expert merging strategy, HC-SMoE, which reduces the parameters of Sparsely activated Mixture of Experts (SMoE) models through expert merging. The clustering strategy adopts hierarchical clustering based on expert output similarity to progressively group experts, while t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable feedback and effort spent on the review, and would like to respond to the reviewer’s questions as follows.
**Q1.** Theoretical analysis is still lacking: the theoretical advantages of hierarchical clustering are described vaguely.
**Response:**
Please refer ... | null | null | null | null | null | null |
MCU: An Evaluation Framework for Open-Ended Game Agents | Accept (spotlight poster) | Summary: This paper presents Minecraft Universe (MCU), a novel evaluation framework designed to benchmark open-ended AI agents in Minecraft. The authors develop a system with three main innovations: a large-scale collection of atomic tasks spanning from combining diverse categories and subcategories; an LLM-based task ... | Rebuttal 1:
Rebuttal: > LLM diversity on task generation may be limited
Thank you for this thoughtful observation. To mitigate prompt-induced bias and encourage diversity in LLM-generated configurations, we explicitly design our prompts to promote variability in initialization elements such as biome, weather, and play... | Summary: MCU proposes a scalable benchmark for open-ended game agents in Minecraft. It introduces 3,452 atomic tasks, spanning 11 categories and 41 subcategories, that can be dynamically composed into complex challenges. Using an LLM-based task configuration generator, the framework creates diverse, realistic scenarios... | Rebuttal 1:
Rebuttal: Due to character limitations, we regret that we can only provide a simplified version of the response below:
> What was the rationale behind selecting these six specific evaluation criteria?
>
> Some evaluation criteria may not translate well across all atomic task categories.
>
> It is not clea... | Summary: Minecraft Universe (MCU) introduces an advanced evaluation framework for AI agents in Minecraft. The authors build on a history of environments and datasets for Minecraft agents (e.g. MineStudio, MineDojo), to provide a polished evaluation framework with a large diversity of high-quality tasks and a novel auto... | Rebuttal 1:
Rebuttal: > The correlation metrics on absolute ratings are hard to interpret in isolation (how do I interpret a correlation of 0.71?), unless e.g. you show that the correlation between the auto-eval and human ratings is close to the correlation between humans and other humans.
We appreciate this point and... | Summary: The paper introduces Minecraft Universe (MCU), a framework that improves evaluation for agents playing Minecraft. MCU includes over 3K composable atomic facts, an LLM-based generator that generates complex tasks by composing the atomic facts, and an automatic evaluation method with a VLM. The paper shows the a... | Rebuttal 1:
Rebuttal: > My main concern regards the quality of the generated data. (How many errors are detected?) quantifying the advantages in Fig.2 (proportion of open-ended tasks, etc.).
Thank you for raising this important point.
To evaluate the quality of the generated data, we randomly sampled 100 atomic tasks... | null | null | null | null | null | null |
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning | Accept (poster) | Summary: This paper studies an interesting and important question, which is about the use of hypernetworks to efficiently approximate the Pareto Front. The proposed approach, PHN-HVVS, addresses multi-objective optimization (MOO) tasks in machine learning by novelly designing a novel loss function and sampling rays fro... | Rebuttal 1:
Rebuttal: **Comments 1**. Eq. (14) defines the distance metric between the solution and the preference vector along the given direction. Although it is correct, the authors can explain this equation a bit more.
**Response**. Thanks for your suggestions. In the final version of this paper, we will formally... | Summary: The paper proposes a novel sampling approach for pareto front learning and federated learning. The main idea is to use genetic algorithms to sample in a way that covers the space better. They then experiment on several MOO and FL benchmarks.
Claims And Evidence: There are some issues with the evidence in this... | Rebuttal 1:
Rebuttal: **Notes**: All the tables can be found at https://anonymous.4open.science/r/icml_rebuttal-E75A/rebuttal.pdf.
**Response to Claims and Evidence**:
In the context of this paper, a special metric, namely Maximum Spread (MS), can play a role similar to the standard deviation to better evaluate the... | Summary: This paper proposes a method for sampling reference points (rays) from the unit simplex based on Voronoi tessellation and a genetic algorithm. Furthermore, the addition of the Hypervolume (HV) indicator to the objective function of the PHN further improves performance. The algorithm's capabilities are evaluate... | Rebuttal 1:
Rebuttal: **Notes**: All the tables and figures can be found at https://anonymous.4open.science/r/icml_rebuttal-E75A/rebuttal.pdf.
**Response to W1 \& W2**: We appreciated your insightful questions, which ever motivated us to develop the solution of this paper.
Specifically, there is a mapping of rays t... | null | null | null | null | null | null | null | null |
LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models | Accept (poster) | Summary: This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for LLMs, together with an open-source research framework. The proposed benchmark consists of 3 Interactive Dialogue tasks and 5 RL Capability tests, which require multiple rounds of language interaction and cover a range of tasks in ope... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and questions. We would like to answer the questions in your review as follows:
1. "How does the dialogue tasks in the proposed benchmark differ from classical multi-turn task-oriented dialog dataset, such as MultiWOZ?"
The goal of our paper is to presen... | Summary: This paper introduces LMRL-Gym, a benchmark framework for evaluating multi-turn reinforcement learning with language models, consisting of 8 tasks divided into interactive dialogue tasks and RL capability tests. The framework includes implementations of several baseline methods (PPO, ILQL, behavior cloning) an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions for related works. We will be sure to the cite the works that you have noted, and move material from our paper to the Appendix / supplementary section. To address the questions in your review, we would like to provide responses to the follow... | Summary: This paper introduces LMRL-Gym, a benchmark for evaluating reinforcement learning algorithms for multi-turn generation of large language models (LLMs). LMRL-Gym provides 3 interactive dialogue tasks and 5 RL capability tasks. More specifically, interactive dialogue tasks include 20Qs (Twenty Questions), Guess ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions for clarity on the work. We will revise our figure and term accordingly as per your suggestion. We've addressed the main questions raised in your review by: (1) providing an extensive literature review of other popular benchmark papers (2) c... | Summary: The authors present 8 tasks to evaluate and build on the multi-turn capabilities of LLMs using RL. 3 tasks are interactive dialogue tasks - teaching persuasion and gather information. 5 tasks are core RL capability tasks - teaching strategic decision making, credit assignment, trajectory stitching in partially... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We've addressed the main questions raised in your review by: (1) providing a justification for our choice of models (2) clarifying why we chose to have both interactive dialog tasks as well as text game tasks (3) answering your question regarding CoT for G... | null | null | null | null | null | null |
ExtPose: Robust and Coherent Pose Estimation by Extending ViTs | Accept (poster) | Summary: This paper proposes a ViT-based model, ExtPose, for 3D single-human pose estimation. It can handle both image inputs and video inputs. Besides, it can utilize strong 2D HPE models (e.g. ViTPose) to enhance the 3D meshes. The overall performance is great - it achieves remarkable error reduction on existing benc... | Rebuttal 1:
Rebuttal: We express our sincere appreciation for the helpful reviews and tackle the concerns below:
**[Design 1] Accuracy-efficiency tradeoff curve?**
**A:** Thanks for this suggestion. Table 9 in our submission already follows Xu et al. (2022) and Wang et al. (2024) to demonstrate computational efficien... | Summary: The authors propose ExtPose: a robust and Coherent pose estimation by refining a ViT-based HPE. Several contributions are proposed in this paper: 1) 2D pose and image information are combined are combined in a ViT model, 2) Temporal context is integrated. The resulting model is compared to the SOTA models usin... | Rebuttal 1:
Rebuttal: We are grateful for the reviewers' valuable efforts and their recognition of our work! If you have any other questions, we are always here, and you are welcome to discuss with us. | Summary: The paper proposes a 3D pose estimation algorithm that simultaneously considers 2D pose information and temporal information. Through parameter sharing and multimodal data alignment strategies, the algorithm is able to accurately estimate 3D pose. The authors validate their proposed approach through extensive ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's constructive comments and address the concerns below:
**[Strengths & Weaknesses 1/Exp. 1] The effect of noise in 2D pose detection?**
**A:** Thanks for your great insight in studying the effect of 2D pose quality on performance!
- Firstly, in Fig. 6, we **q... | Summary: This work presents ExtPose, a ViT-based framework that extends a pre-trained ViT backbone to better handle image alignment and temporal coherence by introducing the following: 2D skeleton images as additional input, cross-modality interaction, and cross-frame interaction. Even though the proposed methods can b... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable feedback and address the concerns below:
**[Method 1] Pose-pose attention mask in Eq. (11)?**
**A:** Really thanks for your careful inspection! There is indeed a typo in Eq. (11). As described in the text Ls. 223-229 right above Eq. (11), the lowe... | null | null | null | null | null | null |
Active Fine-Tuning of Multi-Task Policies | Accept (poster) | Summary: The paper tries to tackle the problem of maximizing the multi-task performance of a pre-trained policy with minimal additional demonstrations via active learning. The proposed algorithm builds upon existing active learning approaches in non-sequential domain. AMF selects queries that maximizes the expected inf... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thorough evaluation of our submission, and the insightful comments.
**Additional baselines and prior work**
We thank the reviewer for suggesting these two additional works. We found them to be very relevant to this direction, but not directly applicabl... | Summary: This paper investigates an active multi-task fine-tuning scheme, which adaptively selects the task to be demonstrated for sample-efficient fine-tuning of multi-task behavioral cloning policies. The authors provide a practical version of the proposed method and highly the efficacy of the proposed method through... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for thoroughly reviewing our work. We are happy to address each comment below.
**Comparison with other methods in the domain**
> It would be great if the authors could include comparisons with other methods in the domain
To the best of our knowledge, the probl... | Summary: Pretrained generalist policies are becoming popular in the robot learning field for the gained capabilities by large-scale training. Nevertheless, deploying such policies in a zero-shot manner is still lacking. Hence, adaptation of generalist policies is a must to utilize the acquired representations and skill... | Rebuttal 1:
Rebuttal: We would like to thank the author for their thorough review and positive evaluation of our work. We are happy to provide an answer to each question and comment.
**MT10/50**
> I would have expected to evaluate the proposed approach on the standard Metaworld scenarios (MT10 and MT50).
The evaluati... | Summary: *Note: I previously reviewed this paper during an earlier submission cycle. While I acknowledge the authors' efforts to enhance the manuscript, several concerns I raised earlier remain inadequately addressed in this revision. Thus, I incorporated some parts of my prior review, and adjusted the content accordin... | Rebuttal 1:
Rebuttal: We thank the reviewer for a (second) evaluation of our submission. We will address each comment individually.
**Additional environments/policy architectures**
> Future evaluations would benefit from testing on more challenging robotic benchmarks
We understand the significance of more challengin... | null | null | null | null | null | null |
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation | Accept (poster) | Summary: This paper presents a novel proxy-centric framework for addressing the alignment challenge in RAG systems. The key innovation lies in its introduction of a lightweight multi-agent system that mediates between retrievers and LLMs without requiring modifications to either component. The framework is inspired by ... | Rebuttal 1:
Rebuttal: Dear Reviewer stM2,
Thank you for your thoughtful review and constructive suggestions. We particularly appreciate your recommendations about extending our evaluation to more challenging benchmarks and exploring alternative training strategies. These insights will help strengthen our work. We woul... | Summary: This paper proposes a proxy-centric framework that enhances communication between retrievers and Large Language Models (LLMs) through a lightweight multi-agent system named C-3PO. Unlike the vanilla RAG framework, the proposed framework incorporates multiple specialized LLM agents to manage different stages of... | Rebuttal 1:
Rebuttal: Dear Reviewer Tndf,
Thank you for your thorough and constructive review. We would like to address each of your concerns in detail:
> W1
Thank you for raising this important issue about the framework's generalizability. We would like to clarify several aspects:
1. Design Philosophy:
- Our proxy-... | Summary: The paper proposes C-3PO, a plug-and-play multi-agent system used to enhance the alignment of retrievers and LLMs in RAG systems. Specifically, C-3PO consists of three LLM agents: a reasoning router designed to determine the reasoning strategy for a specific question, an information filter agent used to identi... | Rebuttal 1:
Rebuttal: Dear Reviewer TgQU,
Thank you for your thorough and constructive review of our paper. We would like to address each of your concerns in detail:
> W1
We appreciate your concern about computational efficiency. We would like to clarify that our tree-structured rollout **does not introduce addition... | Summary: The paper proposes C-3PO, which introduces a multi-agent system that optimizes retrieval, query generation, and information filtering. It uses multi-agent reinforcement learning (MARL) with tree-structured rollout and Monte Carlo credit assignment. Experiments show that C-3PO significantly enhances RAG perfor... | Rebuttal 1:
Rebuttal: Dear Reviewer B7TK
We sincerely appreciate your thorough review. We have carefully addressed each of your concerns below:
> Issue 1 & W3
We appreciate the mentioned related works, and would like to clarify several important points:
1. First, we acknowledge the importance of these works. We will ... | null | null | null | null | null | null |
Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation | Accept (poster) | Summary: The paper studies the bipartite ranking problem in a multi-label setting, comparing two approaches: one based on loss aggregation and the other on label aggregation. It shows that loss aggregation can result in label dictatorship.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Cla... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful questions. We clarify below:
**1. Regarding Interpretation of Prop 6.1 / Loss Aggregation Weighting & Practical Example:**
> Re: Prop 6.1 & loss weighting. Why problematic if $\pi^{(k)}\approx 0.5$? Isn't less weight on noisy labels reasonable? ... | Summary: The paper formulates a new problem, where each instance is associated with multiple labels and the goal is to find a ranking for these labels. To deal with this problem, the paper derives the Bayes-optimal solvers for two commonly used loss functions loss aggregation and label aggregation and proves pareto-opt... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful questions. We address the specific points you raised below.
**1. Regarding Theory Guiding Design:**
> "...nor does it explain how the theoretical results guide the design of the method."
> "How can the theoretical results guide the design of th... | Summary: This paper investigates the problem of bipartite ranking with multiple binary labels, comparing two approaches: loss aggregation and label aggregation. The authors provide a theoretical analysis of the Bayes-optimal solutions for both methods and empirically validate their findings. Extensive experiments have ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful questions. We address the specific points you raised below.
**1. Anti-correlated Labels:**
> "test label aggregation … with anti-correlated labels"
Thank you for this insightful question. We show below that the optimal scorer *does* depend o... | null | null | null | null | null | null | null | null |
Olica: Efficient Structured Pruning of Large Language Models without Retraining | Accept (poster) | Summary: This work proposes using PCA to compress the matrix product in MHA with a fast computation approach for LLM compression. Additionally, to address errors caused by pruning, a reconstruction method based on ridge regression is introduced for FFN. The experiments cover LLaMA-based models.
Claims And Evidence: I ... | Rebuttal 1:
Rebuttal: We really appreciate the time and efforts you extended in reviewing our paper. Below please find our responses regarding your concerns.
**Q1**: SVD and AWSVD treat each of $W_v$ and $W_o$ independently, which might provide more accurate estimates than treating the product of the two matrices, $W... | Summary: This paper proposes Olica, a structured pruning method for large language models that eliminates the need for retraining. The approach introduces Orthogonal Neuron Decomposition to compress the multi-head attention layer using PCA-based factorization and Linear Calibration to mitigate pruning-induced errors in... | Rebuttal 1:
Rebuttal: We greatly thank you for the detailed reviews and helpful suggestions. We reply point-by-point here.
**Q1:** The claim of "no retraining" is misleading.
**A1:** Thanks for your question. In deep learning, "training'' or "fine-tuning'' generally require a series of forward and backward processe... | Summary: Olica is a retraining-free structured pruning framework for Large Language Models (LLMs), with orthogonal decomposition and linear calibration. It unifies MHA matrix products and applies PCA to preserve essential information while compressing the model. A fast decomposition method reduces PCA complexity, and a... | Rebuttal 1:
Rebuttal: Thank you for providing the insightful comments. We will try our best to address your concerns as follows.
**Q1:** The key observation of this article is that MHA depends on $W_{q_i}W_{k_i}^{\top}$, which is not true on llama, because of RoPE. However, all experiments in this article are done on ... | Summary: This paper proposed a new structured pruning method by treating the matrix products WqWk and also WvWo as unified entities and applying PCA, and pruning the unimportant information. They also pruned the FFN layer and introduced a linear calibration method to reconstruct the residual error with two low-rank mat... | Rebuttal 1:
Rebuttal: Many thanks for your time and efforts in reviewing our paper. We will fully address your concerns below.
**Q1:** The product of $W_{q_i}W_{k_i}^{\top}$ has already shown the low-rank property since $d_h$ is smaller than $d$. Based on this, are you trying to reduce $d_h$ into an even smaller $r$ ... | null | null | null | null | null | null |
Secant Line Search for Frank-Wolfe Algorithms | Accept (poster) | Summary: This paper introduces a new step-size strategy, the Secant Line Search (SLS), to optimize the Frank-Wolfe (FW) algorithm. SLS leverages the secant method to solve the line search problem, which reduces the computational cost compared to traditional methods. Theoretical guarantees for SLS’s convergence are prov... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and questions.
> The paper lacks a detailed complexity analysis for the proposed methods.
We provide an analysis of the local superlinear convergence rate of the secant method and of its global convergence under suitable assumptions, which is an improveme... | Summary: This paper proposes Secant Line Search (SLS), a new step size strategy for Frank-Wolfe algorithms, by posing line search as root finding and using the secant method to solve it. The method is simple and easy to implement. The same principle can seemingly be applied in line search for algorithms other than Fra... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and questions on the paper.
We agree that using root finding methods for line search is not new and in fact many line search methods, such as e.g., bisection line search is of that type. However, (Quasi-)Newton methods, secant methods, etc are traditionall... | Summary: The paper introduces a novel step-size strategy for Frank-Wolfe (FW) algorithms called Secant Line Search (SLS), which utilizes the secant method to determine step sizes efficiently. SLS requires only function and gradient evaluations, making it computationally less expensive while adapting to the local smoot... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and questions.
> Limited theoretical novelty. The convergence guarantee is derived by directly combining the existing convergence results of the Frank-Wolfe method and the secant method.
The theoretical novelty is indeed not the central element here, howe... | Summary: This paper suggests that in Frank-Wolfe algorithm, one can use the Secant Method to set the step size for performance improvement.
Claims And Evidence: Line 159, left column: $S(x,y) = S(y,x)$ is not true.
Proof of lemma 2.1 is dubious: by what monotonicity can one claim that $\frac{\Delta(x,a)}{\Delta(x,y)}... | Rebuttal 1:
Rebuttal: > Line 159, left column: $S(x,y) = S(y,x)$ is not true.
$ S(x, y) = S(y, x) $ holds. Writing the definition and multiplying by $\Delta(x, y) = \Delta(y, x)$ and reorganizing, we obtain that the expression is equivalent to $ (x-y)\Delta(x,y) = \phi(x) - \phi(y) $, which clearly holds true by defin... | null | null | null | null | null | null |
Theoretical Analysis of Contrastive Learning in Vision-Language Model Pretraining: The Role of Synthetic Text Captions for Feature Alignment | Reject | Summary: The paper considers the theoretical analysis of the contrastive learning (of image-text pairs) in VLM pre-training. In particular, the paper considers the training dynamics (with potentially noisy and low-quality data), nonlinear activation (ReLU in the one-hidden-layer model), zero-shot generation of VLM, and... | Rebuttal 1:
Rebuttal: # Reviewer yyfG
We thank the reviewer for the valuable time in the evaluation.
## Absence of Image-Text Pairs and Caption-Quality Metrics
Although one of the contributions of this paper is the theoretical demonstration that a well-designed recaptioning process can yield high-quality data, the goa... | Summary: This paper theoretically analyzes the issue of spurious correlations in Vision-Language Models (VLMs) trained via contrastive learning. It mathematically demonstrates that using synthetic text captions can enhance feature alignment and improve zero-shot performance by reducing these correlations. The key contr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the evaluation.
## General Response 3: New experiments
### Quantitative Results on Silhouette Score
We agree that t-SNE visualization does not provide statistical evidence for the separation quality between different methods. **To address this limitation, we adopt the Sil... | Summary: This paper provides a comprehensive theoretical overview of VLM training dynamics, establishing theoretically why training VLMs with synthetically generated text captions might bring improved downstream performance on zero-shot classification tasks. The paper conducts its analysis with one-hidden-layer neural ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable time in the evaluation.
## General Response
### GR1: Clarification of modality misalignment
- **Our feature misalignment model includes both spurious correlation and less informativeness in the raw text.** The latter means that synthetic text adds relevant fe... | Summary: This paper presents the first theoretical analysis of the training dynamics of vision-language models (VLMs) with nonlinear activation functions and provides a theoretical justification for the effectiveness of synthetic text captions in improving pre-training performance. Specifically, the authors analyze the... | Rebuttal 1:
Rebuttal: ### **We STRONGLY recommend the reviewer to first read the General Responses 1 and 2 provided in Reviewer iZBY's rebuttal because of the space limit.**
We thank the reviewer for the valuable time in the evaluation.
## Oversimplified model architecture and loss function
- The training dynamics ana... | null | null | null | null | null | null |
DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy | Accept (poster) | Summary: This work proposes to fine-tune LLM with a small amount of data to achieve strong performance in Diplomacy. They propose to factorize the combinatorial joint action space into manageable subspaces in an autoregressive manner, and derive a corresponding learning objective for the factorized actions to fine-tune... | Rebuttal 1:
Rebuttal: **Q1. Explain the definition of the original joint Q-value.**
A1. In the context of Diplomacy, we define the joint Q-value as the **expected cumulative reward** a player receives after executing a given joint action from the current state [1]. When applying **iterative equilibrium search methods*... | Summary: This paper introduces DipLLM, a fine-tuned Large Language Model (LLM) designed for strategic decision-making in the game of Diplomacy. The authors argue that traditional equilibrium search methods require substantial computational resources, whereas fine-tuning an LLM can yield superior performance with signif... | Rebuttal 1:
Rebuttal: Detailed tables are available at https://sites.google.com/view/dipllm.
**Q1. The approach still depends on externally generated datasets.**
A1. Our approach relies on externally generated data to enable efficient data collection and significantly accelerate LLM training. While self-play is a via... | Summary: DipLLM is a fine-tuned LLM designed to play the complex multiplayer game Diplomacy. DipLLM leverages an autoregressive factorization framework to simplify multi-unit action assignments into unit-level decisions. By fine-tuning with only 1.5% of the data needed by the state-of-the-art Cicero model, DipLLM achie... | Rebuttal 1:
Rebuttal: Detailed tables are available at https://sites.google.com/view/dipllm.
**Q1. Why define rewards only for joint actions but not for single actions?**
A1. **This setup follows prior work** on Diplomacy [1], where a player's action is the decisions of all units, with rewards based on the resulting ... | Summary: This paper introduces DipLLM, a fine-tuned Large Language Model (LLM) aimed at learning equilibrium policies for the no-press variant of the game of Diplomacy. The authors propose an autoregressive factorization framework to break down multi-unit action selection into smaller, sequential decisions, thereby mit... | Rebuttal 1:
Rebuttal: Detailed tables are available at https://sites.google.com/view/dipllm.
**Q1. How crucial is base LLM knowledge for Diplomacy? Would a smaller, specialized model with the same data still underperform?**
A1. Our experiments show that **general knowledge in base LLMs is crucial** for success in Dip... | null | null | null | null | null | null |
Do Multiple Instance Learning Models Transfer? | Accept (spotlight poster) | Summary: The paper presents the first comprehensive investigation into the transfer learning capabilities of MIL models in computational pathology. It evaluates 11 different MIL architectures pretrained on diverse pan‐cancer tasks (i.e., PC-108 and PC-43) across 19 downstream tasks, including cancer subtyping, grading,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback on areas to improve rigor and insights from our results.
**Q1. Benchmarks and statistical measures**
***
We have added benchmarks with finetuned GigaPath (Q1 of BdAq), as well as using CHIEF pre-training on the PC dataset (Q1 of BdAq). We show std... | Summary: This paper investigates transfer learning in MIL models for computational pathology. The authors test 11 MIL models across 19 pretraining tasks, showing that finetuning pretrained models significantly outperforms training from scratch, despite domain differences. Pan-cancer pretraining enables consistent gener... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable suggestions to further explore slide foundation models and clarify contributions. We provide our response below.
**Q1. Retraining CHIEF**
***
We train a new model (PC-CHIEF) on the PC dataset using CHIEF's training recipe, comprised of supervised contrasti... | Summary: The paper investigates the transfer learning capabilities of Multiple Instance Learning (MIL) models in computational pathology, evaluating 11 MIL models across 19 tasks. It finds that pretrained MIL models consistently outperform those initialized randomly, with pan-cancer pretraining tasks (such as PC-108 an... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and for sharing your enthusiasm on the robust empirical evidence and strong performance of transferring MIL models. We have sought to address nearly all suggestions in additional experiments. Further details are provided below.
**Q1. Pretraining strategies**
*... | Summary: This work explores the transferability of multiple instance learning in computational pathology. A variety of experiments are conducted to investigate how various factors affect the transferability, filling the gap of CPath community.
Claims And Evidence: Some experimental results need further justification.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for their extensive suggestions on further improving the rigor and insights from our work.
**Q1. Model Size**
***
We add larger ABMIL models (7M and 9M parameters) and Transformers at comparable sizes, finding:
1) ABMIL performance plateaus in the 5-7M range, with a notable ... | null | null | null | null | null | null |
ELoRA: Low-Rank Adaptation for Equivariant GNNs | Accept (poster) | Summary: The paper introduces Equivariant Low-Rank Adaptation, a parameter-efficient fine-tuning method for pre-trained equivariant Graph Neural Networks used in interatomic potential modeling. Unlike existing fine-tuning approaches that break equivariance, ELoRA employs a path-dependent low-rank decomposition to updat... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments and suggestions.
**Q1: The meaning of $K$.**
A1: $K$ means the number of channels. $K_0^1$ means the number of channels of the first tensor when $l=0$.
**Q2: The meaning of the superscript of $K$.**
A2: The superscript of $K$ does not represent a power. The ... | Summary: The paper presents *ELoRA*, a novel method for fine-tuning equivariant GNNs that preserves the essential equivariance property, addressing limitations of traditional fine-tuning approaches. ELoRA demonstrates significant improvements in model performance. The method employs a path-dependent weight update decom... | Rebuttal 1:
Rebuttal: Thank you for your feedback and we will correct all the typos, improve the writing and enhance the readability according to your comments. We will polish the describtion of Figure1 and add reference link for the proved propositions.
**Q1: The novelty of ELoRA.**
A1: We propose a PEFT method for ... | Summary: Existing parameter-efficient fine-tuning (PEFT) methods are not suitable for equivariant GNNs. To that end, the authors propose a novel method for equivariant low-rank adaptation for finetuning equivariant GNNs. Specifically, the authors propose a path-dependent low-rank adaptation for the tensor product weigh... | Rebuttal 1:
Rebuttal: Many thanks to your questions and suggestions.
**Q1: The fairness in using only 50 samples in rMD17 experiments.**
A1: In practical downstream applications, fine-tuning methods are often expected to perform well with limited first-principles training data, as DFT calculations are computationall... | Summary: The paper introduces a variant of LoRA for finetuning geometric graph neural networks that use spherical harmonics. The idea is to consider the main model parameters that appear in path-dependent tensor-product using Clebsch-Gordon in these SO(3) equivariant models, and provide path-dependent low-rank adaptati... | Rebuttal 1:
Rebuttal: We thank the reviewer for the professional and valuable comments.
**Q1: The analysis on SVD decomposition of weight matrices.**
A1: We apologize for the analysis of the SVD decomposition, which could be misleading. Section 3 serves as a transitional part, aiming to convey the necessity of fine-t... | null | null | null | null | null | null |
iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection | Accept (poster) | Summary: This paper proposes a novel framework, instance Decoupled Prompt Attention (iDPA), for incremental object detection in medical images. This task is challenging due to the strong coupling between foreground-background features and the large domain gap between natural and medical images. This work proposes insta... | Rebuttal 1:
Rebuttal: We appreciate your detailed feedback and suggestions for improvement. We treasure the opportunity to address your concerns and improve our work.
## Weakness 1: Domain Shift Robustness with Bounding Box-only Features
Thank you for this important observation. The paper focuses emphasizes instance-... | Summary: This paper aims to tackle the challenge of incremental medical object detection, which adapts to emerging medical concepts and retains prior knowledge. The authors claim that existing works are only designed for classification and fail to capture fine-grained information for detection tasks, which are mainly l... | Rebuttal 1:
Rebuttal: **Question 1: Conceptual Gap Between Medical and Natural Domains**
We appreciate the reviewers' feedback and would like to clarify the differences between the medical and natural domains, as well as the limitations of existing methods in medical object detection. Previous studies, such as those b... | Summary: This paper proposes a novel incremental medical object detection framework called iDPA, which is composed of an instance-level prompt generation (IPG) and a decoupled prompt attention (DPA) module. Comparing existing methods, the instance-level prompt generation provides learnable prompts with fine-grained tas... | Rebuttal 1:
Rebuttal: **Question 1: Significance of Incremental Learning in Full-Data Settings**
Incremental learning methods like iDPA offer significant advantages in medical settings where models must be deployed incrementally due to regulatory, ethical, or resource constraints. Retraining large models on full datas... | Summary: This paper proposes iDPA (Instance Decoupled Prompt Attention), a novel framework for Incremental Medical Object Detection (IMOD). The primary motivation is that existing prompt-based continual learning methods, while effective for classification tasks, struggle with object detection due to the need for fine-g... | Rebuttal 1:
Rebuttal: Thank you for the detailed and constructive feedback! We treasure the opportunity to address your concerns and improve our work.
# 1. Mathematical Justification for DPA superiority
We appreciate the reviewer’s feedback. DPA enhances prompt learning by separating the attention mechanism into disti... | null | null | null | null | null | null |
Sample-Optimal Agnostic Boosting with Unlabeled Data | Accept (poster) | Summary: This work proposes an agnostic boosting algorithm that seeks to improve sample complexity by incorporating unlabeled data. The central idea is to design a potential function whose gradient can be split into two distinct parts—one that depends only on the model’s output (reflecting the feature information) and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
**Runtime overhead over PAB:** We remark that both in theory and in practice, our algorithm is no slower than the PAB algorithm of Kanade et al. To achieve $\varepsilon$-excess error, the PAB algorithm performs $1/\gamma^2 \varepsilon^2$ rounds of boostin... | Summary: This paper explores how unlabeled samples can be useful in the task of agnostic boosting, in which access to a weak learner must be leveraged to construct a more accurate learning algorithm. The task of agnostic boosting has previously been considered only in settings with access to labeled samples, and the st... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and thorough review. We will execute all the suggested editorial suggestions upon revision.
**Experimental setup:** Each dataset is split into a labeled sample pool and an unlabeled sample pool; the labels for the unlabeled sample pool are disca... | Summary: The main contribution of this paper is presenting a new agnostic boosting algorithm. In particular, this algorithm is computationally efficient assuming an oracle access to weak learners and improves the sample complexity of previous algorithms in a certain way. Moreover, the authors demonstrated an applicatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
**Statistical Gains over Realizable-Agnostic Reduction:** At the outset, we find this to be a loaded question (i.e., one that in our view has an unfair presupposition built in). If one were to discard computational constraints, one can perform ERM on the ... | Summary: This work designs boosting algorithms in the agnostic setting. Their main contribution are novel algorithms in a previously unexplored direction: Can unlabeled samples reduce the number of labeled samples required for boosting? This paper gives a positive result by providing several algorithms that achieve dif... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. Both the suggestions – introducing $\gamma$ early and defining the VC dimension – are well noted, and we will execute these upon revision. | null | null | null | null | null | null |
Energy-Based Flow Matching for Generating 3D Molecular Structure | Accept (poster) | Summary: This paper proposes an enhanced flow matching framework, improving the standard setup from the energy-based perspective.
It provides a specific instance called IDFlow, which employs the reconstruction error as the energy function during training and then iteratively predicts and refines the sample.
Extensive e... | Rebuttal 1:
Rebuttal: Thank you for the effort to review our work! Here is our answer for addressing your concerns and a link to additional figures.
https://anonymous.4open.science/r/ICML2025R-F85D/
>Since the definition and goal of Eq.13 and Eq.18 are obviously different, why can the proposed method be interpreted f... | Summary: The authors propose an energy-based perspective of flow matching for the purpose of improving the quality of 3D structures predicted by generative models. This perspective leads to a modified training procedure called Idempotent Flow Map Training, which trains a network to produce predictions by iterative refi... | Rebuttal 1:
Rebuttal: Thank you for the effort to review our work! Here is our answer for addressing your concerns and a link to additional figures.
https://anonymous.4open.science/r/ICML2025R-F85D/
>Can you include Figure 4 but with wall-clock time as the x-axis?...
Thanks for pointing out the increased throughput ... | Summary: This paper introduces a new method to train flow matching models. They want to sample from an energy function and this comes with very interesting outcomes. By using an energy function based on a squared euclidean distance, they realize that it boils down to train an indepotent map. To make the training effici... | Rebuttal 1:
Rebuttal: Thank you for the effort to review our work! Here is our answer for addressing your concerns.
>The paper is not always well written. I got a little lost in the paragraph Energy relaxation, confidence model and the EBMs. Maybe the author can rewrite it.
Thanks for pointing out the writing about t... | Summary: This paper proposes to enhance the flow-matching framework with an energy-based perspective to learn iterative mapping. Such an idempotent mapping, as demonstrated theoretically in the paper, has better stability during generation. Experiments on protein docking and generation demonstrate better generation qua... | Rebuttal 1:
Rebuttal: Thank you for the effort to review our work! Here is our answer for addressing your concerns.
>For the comparison of time, the authors tried to unfairly compare models with different numbers of sampling steps.
We want to clarify that our comparison is fair. HarmonicFlow adopts 20 steps (equivale... | null | null | null | null | null | null |
Multivariate Conformal Prediction using Optimal Transport | Reject | Summary: This paper proposes a new conformal score function for a multivariate response paired with a Euclidean predictor. The idea behind the score is to use a functional of optimal transport from the d-dimensional score to the uniform distribution. The marginal coverage of the proposed score is guaranteed. The numeri... | Rebuttal 1:
Rebuttal: Many thanks for your detailed review and for the many kind suggestions to improve our work.
>**In conformal prediction, conditional coverage is more important than marginal validity ... but I believe only marginal validity alone may not be sufficient for publication in a top conference like ICML.... | Summary: The submission proposes to use optimal transport for multivariate conformalized quantile regression. Intuitively, the proposed method first finds the optimal transport map between the unknown data distribution and the uniform ball. Constructing quantile regions in this space is preferable because the problem b... | Rebuttal 1:
Rebuttal: Many thanks for your thoughtful feedback, precise description of shortcomings, and suggestion to add references and baselines.
>**Presentation is rushed, which thwarts clarity**
We apologize for this. We decided to submit based on the publication of a concurrent submission with a similar idea in... | Summary: This paper introduces a conformal prediction method that constructs quantile regions for multivariate conformity scores using optimal transport. The authors provide finite-sample guarantees for both the exact optimal transport map and its more computationally efficient approximations.
Claims And Evidence: Yes... | Rebuttal 1:
Rebuttal: Many thanks for your detailed and insightful review.
> **conditional coverage are relevant metrics**
We agree and now include conditional coverage metrics. We also implement a localized variant of `OTCP` where the data is partitioned in the feature space using $k$-means ($k=5$ or $10$) and a sep... | null | null | null | null | null | null | null | null |
Integration-free Kernels for Equivariant Gaussian Process Modelling | Accept (poster) | Summary: This paper introduces a novel class of integration-free equivariant kernels for Gaussian processes (GPs), addressing the computational inefficiency of traditional equivariant kernels that require group integrations. The key idea leverages fundamental regions to project inputs into a representative subset, enab... | Rebuttal 1:
Rebuttal: We would like to thank the referee very much for taking the time reviewing our work, highlighting the underlying rationale and the obtained numerical benefits within a probabilistic prediction and evaluation framework, and pointing out further aspects that will deepen our work. Two specific direct... | Summary: This paper introduces the group-theoretic notion of fundamental regions and proposes a feasible method to construct kernels for equivariant functions. The proposed method is free of integration operations and much faster than conventional methods. Experiments on synthetic and real-world data confirmed the mode... | Rebuttal 1:
Rebuttal: We would like to thank the referee for taking the time to review our work and the constructive comments that will truly help us improving the paper. We really appreciate the suggestion to open on other physical knowledge that can be incorporated in GP models and kernel methods. We discovered the s... | Summary: The paper introduces an integration-free approach to constructing equivariant kernels, leveraging the concept of fundamental domains of the action. The authors demonstrate the effectiveness of their method through one synthetic example and two real-world applications, highlighting its practicality and potentia... | Rebuttal 1:
Rebuttal: We warmly thank the referee for taking the time to check our results and for the very encouraging positive evaluation. We will of course fix the citation command and precise the closure notation. We hope that the referee will also appreciate the overall discussion and our efforts to further improv... | Summary: This paper introduces integration-free equivariant kernels to avoid computationally expensive integration. The method is claimed to be computationally efficient while preserving equivariance. Applications in velocity fields and molecular dipole moments are used to demonstrate effectiveness. While promising, th... | Rebuttal 1:
Rebuttal: We warmly thank the referee for taking the time to review our work and the stimulating comments. The question and comments pertaining to scaling to higher-dimensional equivariant problems call for a distinction between group order, input dimensions, and training set size (in particular). The state... | null | null | null | null | null | null |
Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning | Accept (poster) | Summary: This paper improves the reasoning capabilities of Large Language Models (LLMs) by integrating discrete latent tokens (obtained using VQ-VAE) into the reasoning process. The authors propose a hybrid reasoning representation that partially replaces textual chain-of-thought (CoT) tokens with latent tokens, reduci... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment and reply below.
**#1**
> The experimental designs are overall sound. However, I have one question regarding Section 4.2.2, where you mention selecting the learning rate based on the lowest validation error. However, the MetaMathQA dataset only provides a tra... | Summary: This paper proposes a method for fine-tuning LLMs to use new discrete latent tokens for efficient reasoning, often matching or exceeding chain-of-thought performance without using as many tokens. The approach leverages a VQ-VAE to learn to compress chain-of-thoughts into a set of discrete latent codes which an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment and reply below.
**#1**.
> I could not find the final training and testing loss of the VQ-VAE. Also, some ablation on the codebook size or the chunk size would be useful.
Yes, the training loss and the testing loss of the VQ-VAE is 1.21 and 1.25, respectivel... | Summary: This paper proposes a novel method—“Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning”—that aims to improve the efficiency and performance of large language models (LLMs) on reasoning tasks. The key idea is to compress the chain-of-thought (CoT) by partially replacing the earl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment and reply below.
**#1**.
> In the paper, the codebook size also need to be tuned, making the proposed method problem-dependent
We would like to clarify that the codebook size is not problem dependent and the model performance remains robust across different... | null | null | null | null | null | null | null | null |
On the Benefits of Active Data Collection in Operator Learning | Accept (spotlight poster) | Summary: This paper studies the approximating solution operator of the PDE using active learning queries given the kernal. The paper includes an upperbound for an active learning setting that converges to an irreducible error with a larger number of queries. Further, the authors show a lower bound for passive learning.... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting that our work provided rigorous proofs and numerical experiments.
* **“I wonder if the authors can discuss what other types of queries they think might be interesting for PDE approximation if they have any in mind.”**
One natural direction would be to explore a ... | Summary: The authors study active learning of linear operator motivated by the context of approximating the solution operator of linear PDEs, a setting where the user may "manufacture" a small number of queries to give to a PDE oracle in order to approximate the underlying linear solution operator.
In slightly more de... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address their concerns below.
* **``On the assumption of a known covariance kernel $K$:"** We agree that assuming knowledge of the kernel $K$ may seem unnatural from the perspective of classical statistical learning. However, in operator l... | Summary: The authors study the problem of learning a bounded, linear operator through active learning with the assumption that the input functions are drawn from a mean 0 distribution with a known continuous covariance kernel, $K$.
Their main contribution is a deterministic strategy, which involves solving the Fredho... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comment and noting that our work provides strong theoretical guarantees with empirical evidence. We address reviewer's concern below.
* **``While this is a theoretical study, it would be an interesting next step to see their method on a broader range ... | Summary: The authors consider active data collection in operator learning with distributions, induced by a stochastic process, over function spaces. They obtained an upper bound for active data collection by spectral techniques and a lower bound for passive data collection, which shows the benefit of the active approac... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and for noting that the topic and the problem setting in the paper are intriguing. Below we respond to the main concerns and questions.
* **On the $L^2$ norm and associated probability measure:**
We thank the reviewer for pointing this out. As ... | Summary: This paper proposes an active learning method to learn bounded linear operators from data. This method selects input functions based on the eigenfunctions of the covariance kernel, leading to faster convergence rates. The paper establishes minimax optimal error bounds, showing that active learning can outperfo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging and positive assessment, and for recognizing that our work offers significant insights by establishing minimax-optimal error bounds and clarifying when active learning is beneficial. We address the reviewer’s questions below.
* **``Q1: Could FNO benef... | Summary: This paper is in the general area of using AI for PDE. Its goal is to minimize the input-output pairs needed to train such an AI model. The paper proves a new bound on the sample complexity. The results show that the proposed method have arbitrarily fast error convergence rates with sufficiently rapid eigenval... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and for recognizing that the overall direction of our work is interesting and potentially impactful. Below, we address the reviewer’s questions and concerns.
* We agree with the reviewer that, in its current form, the scope of our work does not ... | null | null |
Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism | Accept (poster) | Summary: This paper seeks to investigate why SSMs underperform Transformers on on retrieval tasks. The paper identifies a "Gather-and-Aggregate" (G&A) mechanism that emerges in Transformers and SSMs (though with some differences). The authors find that Transformers and SSMs concentrate this G&A mechanism in just a few... | Rebuttal 1:
Rebuttal: We’re glad the reviewer found our analysis of the Gather-and-Aggregate (G&A) mechanism compelling—both in its development within SSMs and its implications for hybrid models. Their main concern centers on the need for stronger empirical support and broader evaluations. We respond to each of these c... | Summary: I am not an expert in transformers and SSMs.
The authors reverse engineer language models to show that retrieval capabilities are supported by distinct parts of the networks compared to overall knowledge.
By a systematic lesioning of layers, they identify that (at least) two layers are needed to support retrie... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive remarks. We’re glad the conceptual separation between knowledge and retrieval, along with the identification of the elements driving retrieval, was found to be valuable.
If any questions arise, we would be happy to address them. | Summary: This paper investigates the performance gap between Transformer and State-Space Model (SSM) language models, focusing on retrieval capabilities. The authors identify a "Gather-and-Aggregate" (G&A) mechanism that emerges in both architectures but is implemented more effectively in Transformers. This two-part me... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s recognition of the novelty in our approach to architectural differences and hybrid experiments. Their comments raise valuable questions about performance gaps, generality across models and tasks, and the role of other components. We address each point below.
> There ... | Summary: **Updates after rebuttal: I increased my score in light of authors' rebuttal (particularly on the distillation procedure)**
*I appreciate the authors efforts in explaining and demonstrating how their distillation recipe can be used to distill hybrid models from scratch, motivated from the Gather-and-Aggregate... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback on our approach to identifying retrieval heads in language models and their relevance to hybrid architectures. Below, we address the reviewer’s concerns about terminology, empirical support, and methodological clarity.
> It is well known empirically ... | null | null | null | null | null | null |
Learning Vision and Language Concepts for Controllable Image Generation | Accept (poster) | Summary: This paper explores the theoretical foundations of concept learning for aligning atomic vision and language concepts, with applications in controllable text-to-image (T2I) generation. The authors formulate concept learning as a latent variable identification problem and propose a novel theoretical framework th... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback. Please see our responses below and our uploaded results at https://anonymous.4open.science/r/ICML2025-F636/rebuttal.pdf.
**1. Human evaluations.**
Thank you for the nice suggestion. We have added the human evaluation results to the uploaded Figure 7. The huma... | Summary: This paper explores concept learning by extracting interpretable "atomic concepts" from multimodal data (images and text) to support tasks like text-to-image (T2I) generation. It frames concept learning as a latent variable identification problem within a graphical model, establishing conditions for component-... | Rebuttal 1:
Rebuttal: We are grateful for your thorough assessment. Please find the responses below and our uploaded results at https://anonymous.4open.science/r/ICML2025-F636/rebuttal.pdf.
**1. Detailed ablation analyses.**
Thank you for your constructive feedback. We have added evaluations of the loss terms in the ... | Summary: This paper introduces an Identification Theory for identifying atomic multimodal concepts. Leveraging this theory, the authors apply the method to controllable text-to-image generation. Both qualitative and quantitative evaluations have been conducted to assess the effectiveness of the proposed approach.
Clai... | Rebuttal 1:
Rebuttal: We appreciate your valuable time and efforts dedicated to reviewing our work. Please find our responses below and the uploaded results at https://anonymous.4open.science/r/ICML2025-F636/rebuttal.pdf.
**1. “It is recommended to incorporate additional metrics that specifically assess disentanglemen... | Summary: - This paper addresses the problem of learning atomic multimodal concepts by proving under certain nonparametric assumptions, it is possible to component-wise identify each textual concept and each visual concept.
- Guided by this theory, they propose ConceptAligner, a T2I model that explicitly learns discre... | Rebuttal 1:
Rebuttal: Thank you for the time dedicated to reviewing our paper, the insightful comments, and valuable feedback. Please see our point-by-point responses below and the uploaded results at https://anonymous.4open.science/r/ICML2025-F636/rebuttal.pdf.
**1. Experiments on various domains.**
Thank you for y... | null | null | null | null | null | null |
Stay Hungry, Keep Learning: Sustainable Plasticity for Deep Reinforcement Learning | Accept (poster) | Summary: This paper proposes Plastic PPO (P3O) to address the plasticity loss in online RL. The key idea of P3O is the combination of cyclic neuron reset and inner distillation for policy network, which better balances the plasticity recovery and knowledge retention. The proposed methods are evaluated in MuJoCo and fou... | Summary: The authors introduce the concept of neuron regeneration and propose a framework named Sustainable Backup Propagation(SBP) that maintains plasticity in neural networks through this neuron regeneration process. The SBP framework achieves network neuron regeneration through two key procedures: cycle reset and in... | Summary: This paper introduces Sustainable Backup Propagation (SBP), a framework designed to maintain neural network plasticity while preserving learned knowledge. SBP employs neuron regeneration through cycle reset and inner distillation and is integrated into Proximal Policy Optimization (PPO), leading to the develop... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' insightful comments and suggestions. Below are our detailed responses to the questions raised.
## 1. Reply to Questions 1
Details about the off-policy (SAC + SBP) experiments are included in Appendix A.4. The results demonstrate performance improvements, and the plast... | Summary: The paper tackles the problem of plasticity in on-policy reinforcement learning. Combining techniques of weight reseting and distillation, the authors propose a technique that the authors call "Sustainable Backup Propagation" (SBP). In SBP, some percentage of neurons are reinitialized every $n$ step. To mitiga... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' feedback and insightful comments. Below are our detailed responses to the questions and concerns raised.
## 1. Reply to Questions 1
We believe the proposed method enhances learning efficiency in massively parallel setups. SBP acts as a flexible plug-in that integrates... | Summary: The paper presents a new way of increasing plasticity in neural networks used in reinforcement learning. The main idea is to reset some of the neurons, while using a distillation strategy that maintains the "knowledge" of the reset neurons by the rest of the network. The method is considered in the context of ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' feedback and insights. Below are our detailed responses to the questions and comments raised.
## 1. Reply to Questions 1
Figure 1 demonstrates that primacy bias also exists in PPO. More training epochs lead to higher data fitting, but fitting the early data ... | Summary: This paper proposes a new Sustainable Backup Propagation(SBP) framework to maintain plasticity in Deep RL. SBP combines knowledge distillation with cyclical resetting of neurons. Results show that when SBP is combined with PPO, it results in much better performance and stability than PPO.
Claims And Evidence:... | Summary: This paper addresses the loss of plasticity in deep reinforcement learning (DRL) models, which is a phenomenon where neural networks become less adaptable over time as they learn different task distributions. The authors explain that phenomena like primacy bias (overweighting early experiences) and dead neuron... | ||||
Discovering Spoofing Attempts on Language Model Watermarks | Accept (poster) | Summary: This paper proposes a statistical test for identifying spoofing attacks against sampling-based LLM watermarks. The method is based on the intuition that the frequency of watermark violation conditioned different n-grams in spoofed texts would be different with that in actual watermarked texts. The result shows... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and address their individual questions below. We have attached one additional figure [here](https://drive.google.com/file/d/1Rh3JLVob1UuV2-ElQ7_QgnDNi2CZ-mvb/view).
**Q1: Can the current spoofing methods be *easily* adapted to bypass the proposed d... | Summary: One use of watermarking schemes for generative models is for attribution. In recent years there have been several "spoofing" attacks on watermarking schemes which can make a certain piece of text to appear as if it was generated by a certain model by "copying" the watermark associated with that model. This pap... | Rebuttal 1:
Rebuttal: $\newcommand{\D}{\mathcal{D}}$$\newcommand{\T}{\tilde{\D}}$We thank the reviewer for their helpful feedback and address their individual questions below.
**Q1: Does the proposed method require a good estimate of the spoofer's training corpus in order to be applicable?**.
A reasonable estimate is... | Summary: This paper investigates whether learning-based attacks that attempt to spoof watermarking schemes in language models leave detectable artifacts in the generated text. The authors propose a statistical method to distinguish genuinely watermarked text from spoofed text by modeling the relationship between the ob... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and address their individual questions below. We have attached two additional figures [here](https://drive.google.com/file/d/1-bFmwwrN_kGELtsuhMUTbU4gOBB-UBru/view).
**Q1: Can the authors explicitly analyze the distribution shift between Reprompti... | null | null | null | null | null | null | null | null |
Training Large Language Models to Reason Efficiently | Reject | Summary: The paper proposes an approach to finetune reasoning models to reduce unnecessary reasoning steps while preserving accuracy. The approach penalizes excessive reasoning steps while ensuring the model still arrives at correct answers. Experimental results show that the proposed RL achieves up to 50% token reduct... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback. We would like to address their main concerns and clarify a few points raised in the review.
We acknowledge the concern about computational cost. However, we believe it is important to distinguish between training and deploymen... | Summary: This paper presents a simple but effective way to reduce the reasoning length of o1/r1 like RL-based reasoning models without any inductive bias. The method is rather clear and simple but effective.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Classic RL based reasoning evaluations: GSM8K, MATH... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and encouraging feedback. We're glad the clarity, simplicity, and effectiveness of our method came through, and appreciate your recognition of its generalizability.
Should the reviewer have any further questions, we would be happy to discuss them. | Summary: The paper proposes a reinforcement learning approach that trains models to dynamically allocate inference-time computation based on task difficulty. By incorporating a length penalty into the reward function—with a tunable hyperparameter α—the method encourages the model to produce correct answers with shorter... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful feedback. We appreciate that the reviewer recognized the practical relevance of our work and the simplicity of our proposed solution.
**[1] Evaluating on non-math tasks.**
We appreciate the suggestion to evaluate the method on tasks beyond mat... | Summary: * The paper proposes a training procedure to find reasonable trade-offs of accuracy-compute to solve a reasoning problem.
* "accuracy" in terms of mathematical reasoning abilities (e.g., GSM8K benchmark)
* "compute" in terms of average inference-time tokens with CoT required to answer the question
* The cr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough and insightful comments, which have greatly helped us improve the clarity and rigor of our manuscript.
Below, we address the reviewer’s major concerns:
[1] **Missing baseline**:
We appreciate the reviewer highlighting the missing baseline. However, both th... | null | null | null | null | null | null |
MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models | Accept (spotlight poster) | Summary: This paper presents a novel benchmark dataset called MapEval constructed based on Google Maps for various map-based geospatial reasoning question answering. The dataset consists of three components: MapEval-Textual, MapEval-Visual, and MapEval-API which correspond to different types of geospatial questions tha... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the applicability, effectiveness of our proposed benchmarking task, and comprehensive experiments in our work!
> Q1: Can you describe the way how you select the geographic questions? Many QA benchmark works start at collecting important question sets such as Hotpo... | Summary: This paper introduces a geospatial benchmark called MapEval. It covers textual, visual and API-related tasks, and evaluates a set of close-source and open-source LLMs and VLMs. The results highlight the gap between close-source and open-source models and between current foundation models and humans, suggesting... | Rebuttal 1:
Rebuttal: > Q1: The benchmark uses Google Map as the source. Can it be extended to other map services, or other modalities (e.g. Satellite images in Google Map)?
Yes, the benchmark can be extended to other map services. The latest MapQaTor update integrates OpenStreetMap, Mapbox, and TomTom, broadening app... | Summary: The paper introduces MapEval, a benchmark designed to evaluate the geospatial reasoning capabilities of foundation models across textual, API-based, and visual tasks. It comprises 700 multiple-choice questions covering spatial relationships, navigation, travel planning, and map interactions across 180 cities a... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the applicability, effectiveness of our proposed benchmarking task, and comprehensive experiments in our work!
> W1: Despite the proposed benchmark evaluation dataset, its size (700 questions) remains relatively small compared to other LLM benchmark evaluation da... | Summary: The authors introduce a benchmark designed to assess the map-based reasoning capabilities of foundation models. This benchmark consists of 700 multiple-choice questions covering locations, including 180 cities and 54 countries across tasks such as processing spatial relationships, navigation, travel planning, ... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the applicability, effectiveness of our proposed benchmarking task, and comprehensive experiments in our work!
> W1: One potential limitation is that the benchmark focuses on multiple-choice questions, which may not fully capture the open-ended nature of real-worl... | null | null | null | null | null | null |
Prompt-based Depth Pruning of Large Language Models | Accept (poster) | Summary: This paper propose prompt-based depth pruning of Large Language Models, where given a prompt, a router is trained to select a best set of LLMs layers, and other layers will be pruned.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: NA
Experimental Designs Or Analyses: No i... | Rebuttal 1:
Rebuttal: Dear reviewer 9fAo,
We appreciate your positive evaluation on our paper, acknowledging the practical advantages of our method. We respond to the concerns you raise in what follows.
---
### **It would be better to show the training convergence analysis of the router.**
Following your suggestion... | Summary: This paper propose a input-dependent depth pruning method for LLMs. Unlike existing static model pruning methods where the LLM is pruned into a small subnetwork and apply it for all testing examples, this paper considers select differently pruned sub-networks for inference depends on the task that the testing ... | Rebuttal 1:
Rebuttal: Dear reviewer KB85,
Thank you for your constructive and thoughtful feedback. In what follows, we address the concerns you raised one-by-one.
---
### **Missing paper on dynamic model pruning [1]**.
Thank you for the pointer to this concurrent work. The paper [1] missed our attention, as... | Summary: This paper introduces Prompt-routed Dynamic Depth Pruning (PuDDing), a method for dynamically pruning transformer blocks in LLMs based on the input prompt. The core motivation is that the importance of transformer layers is task-dependent, making static pruning suboptimal. To address this, PuDDing trains a lig... | Rebuttal 1:
Rebuttal: Dear reviewer r1RD,
Thank you for your constructive feedback. In what follows, we respond to the points raised by the reviewer one-by-one.
---
### **No Real-World Deployment Results (...) Missing mobile/edge device benchmarks weakens the practical relevance of the method.**
Thank you for thi... | Summary: This paper introduces PuDDing, a method to reduce LLM inference costs by skipping transformer layers on a per-input basis. The motivation is that different tasks or queries may not require all layers of a deep model. PuDDing consists of two main components: (1) a procedure to generate a small set of candidate ... | Rebuttal 1:
Rebuttal: Dear reviewer 2qCi,
Thank you for your insightful comments and suggestions. In what follows, we respond to the points raised by the reviewer one-by-one.
---
### **The scope of the task is too narrow, all being the commonsense reasoning tasks**.
**TL;DR. We have already evaluated PuDDing on M... | null | null | null | null | null | null |
Joker: Joint Optimization Framework for Lightweight Kernel Machines | Accept (poster) | Summary: This paper proposes a novel algorithm for extremely large-scale kernel machines. Its main contribution lies in Theorem 2.1, which reformulates the objective function in the dual problem of kernel methods into a form based on decoupled conjugate functions. This ensures convexity and strong duality, making it po... | Rebuttal 1:
Rebuttal: ## Weak. 1: "Exact" and "Inexact"
In the context of this paper, the "inexact" and "exact" are not about the approximation of eq.(7),
which has been stated in Section 1.2.
"Exact" refers to solving the problem eq.(1) without approximating the kernel function $K(\cdot,\cdot)$, and these methods usua... | Summary: The authors propose a general optimization scheme for kernel machines. Similar to Teo et al. (2009), conjugate loss functions allow for unified representations that are solved with block coordinate descent in dual space (Sorensen, 1982). The authors report on empirical results showing that their optimizer is m... | Rebuttal 1:
Rebuttal: # Questions
## Q1. Convergence
The linear convergence rate of the proposed DBCD-TR can be proven using Polyak-Lojasiewicz condition.
However, it is a loose bound and does not highlight the advantage of DBCD-TR.
The tighter bound (a reasonable guess is a superlinear rate) is a challenging work, con... | Summary: This paper explores a joint optimization framework for diverse kernel models, including KRR, logistic regression, and support vector machines.
The authors employed a dual block coordinate descent method with trust region (DBCD-TR) and kernel approximation with randomized features to solve the proposed model, w... | Rebuttal 1:
Rebuttal: ## Q1. bias term.
It is for simplicity and keeping consistency with the recent literature of kernel methods, where they also do not consider it.
A simple way to include the bias is to append a constant after $\varphi(x)$.
However, adding this term generally has no impact on the final performance,
... | Summary: The paper proposes Joker, a novel optimization scheme that aims at scaling kernel methods beyond current computational limitations. It is versatile and can handle several objective functions in a similar manner. The core idea of Joker is to solve the dual problem with a block coordinate descent with trust regi... | Rebuttal 1:
Rebuttal: ## Weak. 1: precise writing.
We appreciate your suggestions to make the expression more precise.
The following are our responses and the plan of revision.
(line 94): Your expression is rigorous. We should add that $K$ satisfying Mercer condition is positive definite (Mercer condition is equivalen... | null | null | null | null | null | null |
The Jailbreak Tax: How Useful are Your Jailbreak Outputs? | Accept (spotlight poster) | Summary: - The paper introduces the concept of "jailbreak tax" - the degradation in model performance/utility when bypassing safety guardrails in LLMs.
- Key innovation: Rather than evaluating jailbreaks on harmful tasks (which are hard to assess objectively), they evaluate on benign tasks with known ground truth (mat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We clarify the questions below:
## Real world safety content and alignment
> The alignment methods may not precisely mirror real-world safety alignment in commercial models.
In EvilMath, by rewording questions to contain dangerous terms such ... | Summary: This paper proposes benchmarks to evaluate the performance of jailbroken large language models to beyond just bypassing refusal. I quantifies the jailbreak tax, which is the performance of a model when it is jailbroken relative to the unaligned version of the model. The paper analyzes how factors such as the j... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. We clarify the questions as follows:
## Correlation between the jailbreak’s success rate and its impact on model utility
> What does each point in the figures represent?
The different points with the same shape represent the same jailbreak meth... | Summary: This paper questions whether jailbreak attacks on LLMs actually generate useful outputs, e.g., does a recipe of a bomb made by LLMs really make a bomb? This question leads to a new metric called Jailbreak Tax—the performance drop after bypassing safety mechanisms. To this end, the author considered verifiable ... | Rebuttal 1:
Rebuttal: We are glad that the reviewer finds our findings interesting and novel, and thinks they should be shared with the community.
We thank the reviewer for the insightful feedback, we carefully considered the concerns and addressed them below.
## Realistic safety examples
> It will be very interesti... | null | null | null | null | null | null | null | null |
MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces | Accept (poster) | Summary: This paper tackles the problem of learning LLM agents in large goal spaces. This paper considers the situation where an LLM maximizes the expected success probability over the huge number of goals. To train the LLM more efficiently, the paper proposes a goal selector that chooses the best goal to achieve based... | Rebuttal 1:
Rebuttal: We thank reviewer pnae for their detailed reading of our manuscript, finding our method reasonable and our experiments well-designed and supporting our conclusions. We now address the reviewer’s concerns.
## Novelty / contribution concerns
We acknowledge the reviewer’s concerns regarding the simp... | Summary: This paper presents a framework for improving competence and learning progress (LP) estimation, used for the goal section of LLM agents in very large (even infinite) evolving goal spaces. The proposed method leverages the semantic relationships between goals and an LLM’s internal semantic knowledge to improve ... | Rebuttal 1:
Rebuttal: We thank reviewer 3cqQ for their detailed review of our paper, finding our claims well supported by the experiments, the experimental protocol well-designed, and highlighting the interestingness of our section on automatic curriculum learning and our analysis of the LLM’s embedding space. We now a... | Summary: A key challenge in learning progress prediction is modeling one’s own competence in a computationally feasible and generalizable way. The paper introduces MAGELLAN, a metacognitive framework that enables LLM agents to learn to predict their competence and LP online. MAGELLAN captures semantic relationships bet... | Rebuttal 1:
Rebuttal: We thank reviewer s63G for their in-depth review and comments on our work, finding the claims clearly supported by our experiments, as well as highlighting the novelty of our method. We now provide answers to the comments and questions asked by the reviewer.
## Table 1
We acknowledge that Table 1... | Summary: The paper introduces MAGELLAN—a metacognitive module that enables autotelic LLM agents to estimate their own learning progress (LP) over large, discrete, and evolving goal spaces. The approach leverages the inherent semantic understanding of an LLM to learn a goal‐conditioned competence estimator that generali... | Rebuttal 1:
Rebuttal: We thank reviewer 9oss for their thorough feedback, finding our approach using an LLM to estimate LP innovative, highlighting the comprehensiveness of our experimental evaluation and acknowledging the effectiveness of MAGELLAN. In the next paragraphs, we answer reviewer 9oss’ concerns.
## Single ... | null | null | null | null | null | null |
Fully Dynamic Embedding into $\ell_p$ Spaces | Accept (poster) | Summary: The Authors presented an algorithm to embed dynamic weighted graph to $\\ell\_p$ space, achieving $O(\\log(n))^{2q} O (\\log (nW))^{q-1}$ expected distortion with $O(m^{1/q + o(1)})$ update time and $O(q \\log (n) \\log (n W))$ query time.
## update after rebuttal
First, I appreciate the authors' sincere resp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed reading of our paper.
We are glad that the reviewer found our theoretical analysis "interesting" and "elegant".
We address the reviewer's concerns and comments below.
> how the paper contributes to the ICML community ... require rewriting ..
We have discu... | Summary: This paper studies the problem of maintaining a low-distortion embedding from the shortest path metric on a graph into $\ell_p$ metric, where the graph undergoes edge insertions and deletions. Given a parameter $q$, the paper presents an algorithm that dynamically maintains an embedding that is non-contractive... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our paper.
We are glad that the reviewer believes our "expositions are clear" and
that "the ideas of maintaining edge-dominant trees and randomly perturbing the edge weights are interesting".
We address the reviewer's concerns and comments below.... | Summary: The paper presents a fully dynamic algorithm for embedding graph metrics into ℓp spaces, supporting edge insertions and deletions. The algorithm achieves low expected distortion, non-contractivity, and efficient query and update times. Key results include maintaining low-distortion embeddings with O(log(n)) ex... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive review. We appreciate the positive assessment that our results
are "significant contributions to the field" and that "The clarity and precision of the theoretical analysis are well".
Below, we respond to the reviewer’s specific concerns:... | Summary: This paper is about dynamic maintenance of Bourgain embeddings (a.k.a. Embedding metric spaces into low-dimensional l_p spaces) with low distortion for undirected graphs with polynomial bounded lengths that undergo edge insertions and deletions. The main result is dynamic Bourgain embedding for graphs that ach... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and detailed review. We are particularly grateful for the positive assessment that the paper is "well written" and that the problem is "of fundamental importance to many communities." We're also glad that the reviewer appreciated the elegance and care in ... | null | null | null | null | null | null |
Optimal Algorithm for Max-Min Fair Bandit | Accept (poster) | Summary: The authors study the max-min fair bandit optimization problem where the objective is to maximize the minimum reward achieved in a multi-player multi armed bandit instance. This paper designs a decentralized fair elimination algorithm that achieves an improved regret bound of $O((N^2+K)log(T)/\Delta)$. They pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable and detailed comments. Please see our response below.
Q1. Do you assume knowledge of $\Delta$ in the collision based communication?}
In the collision-based communication discussed in Remark 1, we assume the algorithm knows the order of $\Delta$ such that t... | Summary: This paper studies the multi-player multi-armed bandit problem in a heterogeneous setting with collisions, focusing on max-min fairness. Instead of maximizing total rewards, the goal is to maximize the reward of the player who receives the lowest reward, ensuring fairness. The contributions are as follows: (i)... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable and detailed comments. Please see our response below.
Q1. It is unclear how to explore the remaining $K-N$ arms when $K-N< N$. How can be constructed without causing collisions?
When $K-N < N$, we can also construct $K-N$ matchings for those remaining $K-N... | Summary: The authors consider the multi-player multi-armed bandit setting, where $N$ players each choose one of $K$ arms in a cooperative but decentralized manner. The authors propose a new algorithm for this setting with optimal regret, and the authors include the factors of $N,K$ and $\Delta$ in the regret bound. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable and detailed comments. Please see our response below.
Q1. How much broader impact this work will have on either the bandits or fairness literature?
We emphasize that the optimal exploration design in Algorithm 2 holds applicability beyond the current conte... | Summary: This paper studies the learning problem of Multi-player multi-armed bandits. The reward model is heterogeneous. Also, if two distinct players choose the same arm, both players receive zero reward. The goal is to minimize max-min regret. This framework is interesting and useful for important real-world applicat... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable and detailed comments. Please see our response below.
Q1. It is nice to have minimax regret upper/lower bound.
We are grateful to the reviewer for highlighting the significance of deriving the minimax regret bound. This bound is crucial as it showcases the... | null | null | null | null | null | null |
HPS: Hard Preference Sampling for Human Preference Alignment | Accept (poster) | Summary: The paper introduces Hard Preference Sampling (HPS), a framework for aligning large language models with human preferences. Traditional methods face challenges with harmful content, inefficient use of dispreferred responses, and high computational costs. HPS addresses these issues through a training loss that ... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments! We provide our response and hope our response addresses your concerns. We also look forward to the subsequent discussion which may further help solve the current issues.
**1) Our revision will discuss alignment methods like SLiC-HF (arXiv:2305.10425) and LiP... | Summary: This paper propose a novel HPS method to prioritize the most preferred response while rejecting all other responses.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: Yes, I checked the sampling complexity and reward margin analysis.
Experimental Designs Or Analyses: The a... | Rebuttal 1:
Rebuttal: Thank you for the insightful and positive comments! In the following, we provide our point-by-point response and hope our response helps address your concerns. We also look forward to the subsequent discussion which may further help solve the current issues.
**Our HPS method can also be extended ... | Summary: The paper introduces **Hard Preference Sampling (HPS)**, a framework for aligning Large Language Models (LLMs) with human preferences. It addresses issues in existing methods (PlackettLuce and Bradley-Terry models) by prioritizing preferred responses, explicitly rejecting dispreferred/harmful ones, and focusin... | Rebuttal 1:
Rebuttal: Thank you for the insightful and valuable comments! In the following, we provide our point-by-point response and hope our response helps address your concerns. We also look forward to the subsequent discussion which may further help solve the current issues.
**1) PL simplifies to BT when $n=2$, b... | Summary: This work introduces **Hard Preference Sampling**, a framework for aligning large language models to human preferences. HPS introduces a training loss that adaptively penalizes dispreferred responses, and focuses on “hard” dispreferred responses, i.e. responses that are similar to preferred responses to increa... | Rebuttal 1:
Rebuttal: Thank you for the insightful and positive comments! We provide our response and hope our response addresses your concerns. We also look forward to the subsequent discussion which may further help solve the current issues.
**1) For quality-weighted BT**, we could not find prior work directly relat... | Summary: This paper adapts the concept of hard negative sampling, which was previously employed in metric learning and contrastive learning settings, to preference alignment. Hard Preference Sampling (HPS) framework reconsiders the loss function derived by incorporating reward into the Plackett-Luce (PL) model and use... | Rebuttal 1:
Rebuttal: Thank you for the insightful and positive comments! In the following, we provide our point-by-point response and hope our response helps address your concerns. We also look forward to the subsequent discussion which may further help solve the current issues.
**1) For hard negative sampling**, thi... | null | null | null | null |
On the Generalization Ability of Next-Token-Prediction Pretraining | Accept (poster) | Summary: The authors give a generalization error bound for decoder-only transformer language models trained with next-token prediction objective. The bound is a function of the number of training sequences $N$, number tokens $m$ per such sequence, number of model parameters, etc. Within a sequence, they assume that tok... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions. We have carefully considered them and have added supplementary explanations in the relevant sections of the paper. Additionally, we have incorporated the suggested references into our paper. Below are our responses:
**A1:** This is indeed a si... | Summary: This paper presents new Theoretical results to study the generalization of decoder-only transformer based LLM models. The paper revolves around using a Rademacher complexity argument to bound the generalization risk of a multi layer transformer model with fixed position encoding used for tokens. This work focu... | Rebuttal 1:
Rebuttal: First, thank you very much for your recognition and support of our work. We have incorporated your valuable suggestions by adding a comparative discussion of related past and recent work in the appendix and have made every effort to supplement our experiments. Additionally, we are more than willin... | Summary: This work derives new bounds on the generalization power of multi-layer multi-head transformer models pertained through Next-Token-Prediction mechanism. The first theorem in the paper shows that the generalization error is bounded by the Rademacher complexity of the class of $\mathcal{G}(\mathcal{H})$ (where $... | Rebuttal 1:
Rebuttal: We are delighted to receive your recognition and support for our work, and we appreciate your careful attention to details we might have overlooked. We have made corrections to the relevant sections of the paper based on your suggestions. Additionally, we plan to release the code upon acceptance o... | Summary: This paper investigates the generalization properties of Next-Token Prediction (NTP) pre-training. The derived generalization bounds highlight the influence of key model parameters, such as the number of token sequences and the maximum sequence length. Specifically, the contributions include: establishing a Ra... | Rebuttal 1:
Rebuttal: Thank you for your recognition and support of our work. Below, we provide a detailed comparative analysis of our work with the two most relevant previous studies, [1] and [2]. We sincerely hope this will address your concerns. All of the following content has been added to the discussion section o... | null | null | null | null | null | null |
InfAlign: Inference-aware language model alignment | Accept (poster) | Summary: This paper explores a novel problem in LLM alignment **considering inference-time procedures**. More specifically, it aims to maximize the reward given a fixed LLM and an inference-time procedure, using reinforcement learning (RL). The focus is on Best-of-N as the inference-time procedure, while also providing... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty and sufficiency of the provided evidence in our work. Below we address the reviewer’s concerns. We provide additional experimental results at https://drive.google.com/file/d/1pe4kZeu7JkW0e-o5QtQFwpnvkZ4xVI9s
***Q1: However, the evaluations seem ... | Summary: This paper proposes the inference-aware alignment (InfAlign) framework to optimize model's inference-time win rate when various decoding strategies $\mathcal T$, e.g. Best-of-N, are applied.
The authors solve the KL-regularized win-rate maximization problem using an equivalent KL reward-maximization problem, w... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the theoretical soundness of our work. Below we address the reviewer’s concerns. We provide additional experimental results at https://drive.google.com/file/d/1pe4kZeu7JkW0e-o5QtQFwpnvkZ4xVI9s
***Q1: The authors claim that training using calibrated reward ... | Summary: The paper proposes a new alignment method based on RL to optimize the Best-of-N and Worst-of-N performance of language models. They define the alignment problem as optimizing the win rate against the reference policy minus the KL penalty. To solve the alignment problem under some inference-time procedure, they... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading and comments. Below we address the reviewer’s questions in detail. We provide additional experimental results at https://drive.google.com/file/d/1pe4kZeu7JkW0e-o5QtQFwpnvkZ4xVI9s
***Q1: I am skeptical of the problem setting. The reward in definition ... | Summary: The paper introduces a new problem called InfAlign and proposes a method called InfAlign-CTRL to solve it. Their theoretical properties are investigated. Their main claims are (1) InfAlign-CTRL with no inference procedure improves the standard win rate due to the reward calibration, and (2) InfAlign-CTRL with ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of proposed method and theoretical investigations. Below we address the reviewer’s concerns and provide additional experimental results: https://drive.google.com/file/d/1pe4kZeu7JkW0e-o5QtQFwpnvkZ4xVI9s
***Q1: …the assumption that both the win-r... | null | null | null | null | null | null |
CFPT: Empowering Time Series Forecasting through Cross-Frequency Interaction and Periodic-Aware Timestamp Modeling | Accept (poster) | Summary: The paper introduces CFPT method including two branches to address two key limitations in existing methods: inadequate modeling of interactions between different frequency components and insufficient exploitation of timestamp periodicity. The CFI branch processes signals in the frequency domain and captures in... | Rebuttal 1:
Rebuttal: Many thanks for your review and precious comments and advises. Specific responses are presented below:
**Response to Question 1:**
Thank you for your valuable comments. We did notice that the text in Figure 5 appears too small. In the revised version, we will increase the font size and enhance t... | Summary: This paper addresses the challenge of long-term time series forecasting by introducing CFPT, a method that integrates frequency component analysis with timestamp pattern recognition. The key innovation lies in modeling cross-frequency interactions while simultaneously capturing periodic characteristics in time... | Rebuttal 1:
Rebuttal: Many thanks for your review and precious comments and advises. Specific responses are presented below:
**Response to Question 1:**
Thank you for your valuable comment. We will clarify that we implement the DFT using FFT algorithms in our work. Specifically, we will add the following statement a... | Summary: This paper targets the problem of long-term time series forecasting with attention to frequency components and timestamp patterns. The proposed method is based on two observations: the importance of different frequency components varies across scenarios and their interactions may impact forecasting accuracy, a... | Rebuttal 1:
Rebuttal: Many thanks for your review and precious comments and advises. Specific responses are presented below:
**Response to Question 1:**
Thank you for your valuable comments. In the revised version, we will update Figure 3 by carefully adjusting the layout to make the expression of Figure 3 more aesth... | Summary: This article investigates time series tasks and proposes a method called CFPT. Its main idea is to improve prediction results by capturing the complex relationships between different frequency components. The paper is written very clearly and the proposed modules have good motivation.
## Update after rebuttal... | Rebuttal 1:
Rebuttal: Many thanks for your review and precious comments and advises. Specific responses are presented below:
**Response to Question 1:**
Thank you for suggesting a more comprehensive introduction to Section 5. We will add the following sentence at the end of the opening paragraph: "Furthermore, we per... | null | null | null | null | null | null |
Redundancy Undermines the Trustworthiness of Self-Interpretable GNNs | Accept (poster) | Summary: The paper tackles the fundamental challenge of verifying whether explanations extracted by self-interpretable models, which are considered to be more trustworthy by design, are so.
The authors highlight an issue with current approaches by providing the intuitive example (Fig. 1) of how simply changing the rand... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. Below we address your concerns.
(1) Overstatement of the First Systematic Investigation
We have addressed this in our response to Reviewer 6xUG, where we acknowledge the mistake and clarify our intent.
(2) About Evaluation Metrics
Faber et al. (2021) argue ag... | Summary: The paper aims to systematically investigate trustworthiness of explanations provided by self-Interpretable GNNs. That is, GNNs that simultaneously act as classifiers and explainers. Such GNNs highlight a subgraph as an explanation for a given graph. They provide a brief taxonomy of different self-Interpretab... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. Below we address your concerns.
(1) Robustness of Self-Interpretable GNNs to Spurious Correlations
We have addressed this in our response to Reviewer 6xUG, where we acknowledge the mistake and clarify our point.
(2) Graph-Level Analysis of EE (Theoretical)
Th... | Summary: The authors study the (lack of) reliability of GNN explanations, focusing on self-explainable architectures, which promise precisely to output more reliable explanations. They notice that these models however produce unstable explanations, or more specifically that their explanations vary even substantially b... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. Below we address your concerns.
**Constructive Feedback We Appreciate and Will Revise:**
(1) Overstatement of the First Systematic Investigation
Our intent was to emphasize that we are the first to investigate the inconsistency and its associated inaccuracy -... | Summary: This paper investigates the inconsistency in explanations generated by self-interpretable GNNs. It identifies redundancy—caused by weak conciseness constraints—as the root cause of explanation inconsistency, which in turn reduces trustworthiness. The paper argues that redundancy is difficult to eliminate compl... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work. We appreciate the references you suggested and will discuss them in the revised manuscript. Your recognition of our work truly means a lot to us. | null | null | null | null | null | null |
Contextual Online Decision Making with Infinite-Dimensional Functional Regression | Accept (poster) | Summary: This paper consider a contextual decision making problem where the context space is infinite but the decision set is finite. Such kind of formulation applies ,for example, in the contextual Multi-Armed Bandit model. The authors focuses on learning infinite CDF functions, that define the distribution over the d... | Rebuttal 1:
Rebuttal: Thank you for your questions. We answer your questions in order.
**RE:oracle ineq** **Oracle inequality** provides an upper bound on the performance of the learning algorithm compared to an ideal benchmark ("oracle"), the best function in a reference class. A typical form is:
$L(\hat{f}) \leq L(... | Summary: This paper proposes a general framework for contextual online decision-making problems. The unique challenge about the general setting lies in the estimation the ground-truth distribution $F^*$ which is a function itself, and learning the distribution becomes an infinite-dimensional functional regression probl... | Rebuttal 1:
Rebuttal: Thank you for your kind questions, and we would now like to answer your questions in order.
**RE: Model Assumption 2.1** Thank you for your question. In machine learning, it is a common assumption to assume that the underlying true model lies in some known model class. This assumption is usually ... | Summary: The paper proposes a unified framework for contextual online decision-making using infinite-dimensional functional regression. It introduces an efficient regression oracle to estimate context-dependent CDFs, enabling sublinear regret across tasks. The authors establish a regret bound linked to the eigendecay r... | Rebuttal 1:
Rebuttal: Thank you for your kind remarks and questions, and we would now like to answer your questions in order.
**RE: Minimax bound**
Thank you for your question. In the conclusion part, we actually point out that investigating the minimax lower bound of the regret with respect to the eigendecay rate is... | Summary: This paper studies the contextual bandits setting, in which it develops a novel method --- with broad applicability --- for making decisions whose utility is allowed to be any (square-integrable) function of entire (contextual) distributions associated to each arm/action. This flexible definition of rewards is... | Rebuttal 1:
Rebuttal: Thank you for your remarks and questions. We would now like to answer your questions in order.
**RE: Squared-Integrability**
Thank you very much for your question! We point out that in many online learning settings such as finite-dimensional linear bandits [85, Abbasi-Yadkori, Yasin et al. (2011... | null | null | null | null | null | null |
Modular Duality in Deep Learning | Accept (poster) | Summary: The contributions of this paper could be summarised as follows:
* it combines the notion of dualization/steepest descent with the notion of a modular norm (a max-of-norms aggregation of norms tailored to each single module). This goes beyond previous works on steepest descent that only consider a l1-type aggre... | Rebuttal 1:
Rebuttal: Dear Reviewer BSBb, thank you for your contributions to the conference. We are grateful for your constructive and thorough review of our paper. We hope to provide useful responses to your questions!
First, we ran new experiments to address your questions about the long-term performance of duality... | Summary: The paper introduces a recursive procedure called modular dualization for constructing duality maps in general neural architectures. This method unifies two important optimization techniques—maximal update parameterization and Shampoo—by demonstrating that both are partial approximations of a single duality ma... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort reviewing for ICML.
First, we point out that the **optimizer anthology [3]** is a non-archival workshop paper, and therefore a conference submission on the same topic is in accordance with ICML policy. But even given this, our work goes beyond the a... | Summary: This paper proposes a recipe for neural network design and optimization via "modular dualization". A module consists of a forward pass operation, "mass" and "sensitivity" parameters, and a *norm* associated with the weight space. This design allows for concatenation and composition of modules. The key insight ... | Rebuttal 1:
Rebuttal: Dear reviewer NWmj, we are sincerely grateful for your time and effort reviewing for ICML. We also really appreciate your thorough and positive review of our work.
Given your comments that *“the main ideas in this paper are appealingly simple to read and understand”* and also *“likely to have imm... | null | null | null | null | null | null | null | null |
Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models | Accept (poster) | Summary: This manuscript presents a novel testable condition for identifying valid instrumental variable sets within Additive Nonlinear, Constant Effects Models using observational data. The proposed Cross Auxiliary-based Independent Test (CAT) condition is shown to be both necessary and sufficient under mild assumptio... | Rebuttal 1:
Rebuttal: We appreciate your inspiring positive feedback and suggestions. Please see below for our responses to your specific comments.
> **W1.** The article focuses on the constant effects model. However, this is not a weakness per se, nor do I believe it constitutes a reason for rejection.
**A1:** **we... | Summary: This paper studies the testability of instrumental variables (IV), or in other words, helps researchers find the correct set of IVs using observational data.
For this purpose, most existing methods assume a simple linear model or discrete treatment variables, and still, the exclusion restriction condition (C2... | Rebuttal 1:
Rebuttal: We appreciate your comments and suggestions, and we hope the following response addresses your concerns.
> **W1.** The linear assumption from treatment X to effect Y is still strong...Under this assumptions, actually the core condition (CAT condition) is a direct consequence of existing condition... | Summary: This paper addresses the challenge of selecting instrumental variables (IVs) for causal inference in the Additive Nonlinear, Constant Effects (ANICE) model.
Unlike traditional methods that assume linearity, the proposed approach generalizes IV selection to nonlinear settings, making it applicable to real-wor... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the significance of our theoretical contributions and the novel of the algorithm. We hope the following response addresses your concerns.
> **W1.** The Auxiliary Variable...using Eq.(5).
**A1.** Yes, the auxiliary variable can be viewed as a pseudo-residual. We would ... | Summary: This work proposes a method for identifying valid instrumental variables from observational data under the additive nonlinear model with constant effects. The authors introduce a new testable condition that is necessary and sufficient for selecting a valid IV set (the CAT condition). The proposed algorithm lev... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments. Please find our responses below.
> **S1.** time complexity analysis
**A1:** Let $n$ denote the sample size, $m=|\mathbf{Z}|$, and $p=|\mathbf{W}|$. The time complexity of our algorithm consists of three components:
1. Caculate the covariates' residual: $\mat... | null | null | null | null | null | null |
From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning | Accept (poster) | Summary: The paper addresses the problem of Bayesian learning with a wide two-layer linear network. It develops a formalism which bridges between prior works, and in particular covers in unified manner the rich (mean-field) and lazy (standard parameterization) regime. The analysis consists in rephrasing the problem as ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their overall positive feedback and are confident to address the raised points below.
**Application to real-world data sets**
The theory indeed applies to arbitrary data, since it only requires the input kernel $C^{(xx)} = \frac{g_v}{D} X X^{\mathsf{T}}$ without specifi... | Summary: This paper aims to provide an account of feature learning in linear Bayesian neural networks that bridges the gap between results emphasizing isotropic rescaling and anisotropic reshaping of kernels.
## Update after rebuttal
After the authors' rebuttal, I weakly recommend acceptance.
Claims And Evidence: Mo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
**Claims and Evidence**
We will add experiments on MNIST in a revision (see [Fig. 1 in Supplement](https://drive.google.com/file/d/1i2bhJbJYuJIvyNj12eWLjyAhrRlmCf9I/view?usp=sharing)). We also extend the formalism to non-linear activations and include ex... | Summary: This paper introduces a unifying theoretical framework which connects the kernel rescaling approach with the adaptive kernel approach in the Bayesian setup. The authors demonstrate that both of these approaches can be derived from the same starting point (the network’s posterior) but differ in the choice of or... | Rebuttal 1:
Rebuttal: We thank the referee for their positive evaluation and for the precise summary. In a revision, we will address the three mentioned weaknesses:
1. Our work aims at a feature learning theory from first principles. We hope that in the long term this theory can be leveraged to build a theoretical fou... | null | null | null | null | null | null | null | null |
R.I.P.: Better Models by Survival of the Fittest Prompts | Accept (poster) | Summary: The paper proposes a recipe for the prompt selection problem in the pairwise preference optimization setting in RLHF called RIP filtering. The proposed method utilizes an external reward model to grade multiple completions of the candidate model and use several metrics of this random completion set as a guidel... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our contributions.
1. >effectiveness of RIP if reward models are of smaller size, and from a different model family
Thank you for this insightful feedback. To address the reviewer’s question, we select a lightweight non-Llama-based reward model “Ray2333/GR... | Summary: This paper introduces a method for filtering prompts used for preference-tuning (in this case, DPO), RIP. The method simply filters preferences based on reward, output length, and gap between chosen and rejected responses. Experiments training on datasets filtered by this method shows improvements in llm-as-a-... | Rebuttal 1:
Rebuttal: 1. >One caveat is that the claims are very much scoped to alignment performance on llm-judge benchmarks, which is a very particular domain.
Thank you for bringing this to our attention. We acknowledge that in our draft, we demonstrated the effectiveness of RIP on alignment performances in general... | Summary: The paper introduces a novel data curation method called Rejecting Instruction Preferences (RIP) designed to improve the quality of training data for large language models. The core idea is to filter out low-quality prompts by examining paired model responses. Experimental evaluations on benchmarks such as Alp... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting our strength on the novelty of RIP filtering and its significant improvements as compared to traditional prompt-based filtering methods.
> 1. Could the authors provide a more detailed explanation regarding why the rejected response length is chosen as a fil... | Summary: The authors propose RIP, a data filtering method that leverages 3 criteria (rejected response length, rejected response reward, and reward gap) in assessing prompt quality in the context of preference fine-tuning. They find benchmark improvements of ~10% compared against non-filtered DPO. Furthermore, the auth... | Rebuttal 1:
Rebuttal: 1. >Lack of ablations and clarity surrounding the effectiveness of each of the 3 criteria individually.
Due to the paper's length constraints, we have included an ablation study in the appendix (Table 19 and 20, line 990). In this study, we conducted data filtering experiments using each criterio... | null | null | null | null | null | null |
Improving Multimodal Learning Balance and Sufficiency through Data Remixing | Accept (poster) | Summary: This paper introduces a method called Data Remixing to alleviate modality laziness and modality clash, which guarantees both sufficiency and multimodal balance. The authors demonstrate that batch-level gradient direction conflicts lead to modality imbalance. Firstly, the authors divide the samples into K subse... | Rebuttal 1:
Rebuttal: **Weakness1:** There are no concrete measurements to demonstrate the efficiency of Remix.
**Response:**
+ Our method focuses on variations at the data level and we have reported **size of training set** in Table 2 of the main text to prove the efficiency.
+ We measure the **training time** of 4 ... | Summary: The authors address the problems of modality laziness and cross-modal clash in multimodal joint learning at the same time. They propose a method by remixing the original multi-modal input pairs, which involves decoupling multimodal data into unimodal subsets and selecting difficult samples for each modality to... | Rebuttal 1:
Rebuttal: **Weakness1:** The applicability of the Remix method on three or even more modalities.
**Response:**
+ **Our method is not limited by the number of modalities.** As the number of modalities increases, our method remains applicable by simply **retaining the modality with the lowest KL divergence... | Summary: This paper mainly proposes to combat with the issues of modality laziness and modality clash. Both issues happen when multimodal models prioritize learning from the strong modality and the batch gradient can be interfered across modalities. The authors propose the Data Remixing method to solve such problems. S... | Rebuttal 1:
Rebuttal: **Weakness1:** Modality Clash and Modality Imbalance
**Response:**
+ In summary, **Modality Imbalance** is **unidirectional**, while **Modality Clash** is **bidirectional**. **Modality Imbalance** refers to a scenario where a strong modality dictates the learning process, preventing **other mod... | Summary: The paper tackles the problem of multimodal learning and specifically how to make all the modalities to contribute equally to the training objectives. The authors suggest multiple steps to alleviate the issue, including decoupling
multimodal data and filtering hard samples for each modality to mitigate modalit... | Rebuttal 1:
Rebuttal: **Weakness1:** The method and experiments are limited to certain modalities.
**Response:**
+ **Our method is not restricted to specific modalities**. In our theoretical analysis, we make no prior assumptions about modality properties, ensuring its general applicability. Meanwhile, the key steps... | null | null | null | null | null | null |
NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel | Accept (poster) | Summary: The paper proposes NTK-DFL, a decentralized federated learning method that uses the neural tangent kernel (NTK) to mitigate the effect of data heterogeneity in the clients. The authors prove the convergence guarantee of the proposed method and show that it outperforms existing methods such as DFedAvg in the nu... | Rebuttal 1:
Rebuttal: ## Responses to Reviewer Zq37
1. **"The proposed method incurs a high communication cost because clients send Jacobian matrices to their neighbors. This is undesirable since communication cost is often a bottleneck in decentralized federated learning."**
We acknowledge the reviewer’s concern reg... | Summary: This paper studies decentralized federated learning and leverages the neural tangent kernel to improve performance and convergence under heterogeneous settings. The proposed method is evaluated on three public datasets and shows improved performance.
Claims And Evidence: The claims are supported by method des... | Rebuttal 1:
Rebuttal: ## Responses to Reviewer Qo5R
1. **"More recent literature should be included in the discussion."**
We thank the reviewer for this suggestion. In the originally submitted manuscript, we had included comparisons with DFedSAM (Shi et al., 2023), which is, to our knowledge, the most recent, relevan... | Summary: The paper proposes to integrate NTK training in DFL. Numerical results show NTK-DFL achieves higher accuracy under the same level of communication round.
Claims And Evidence: The claims are well supported.
Methods And Evaluation Criteria: The benchmark datasets and the algorithms are relevant for comparison.... | Rebuttal 1:
Rebuttal: ## Responses to Reviewer oV9C
1. **"It is hard to imagine a scenario where [the NTK-DFL regime] has any benefit in terms of communication overhead."**
Please see ***Response 1 to Reviewer Zq37*** for a detailed discussion.
2. **“Authors should show acc vs. communication bit curve instead of acc ... | Summary: The paper proposes NTK-DFL, a decentralized federated learning method that uses the Neural Tangent Kernel (NTK) for weight evolution, replacing SGD with Jacobian-based updates. It integrates per-round parameter averaging and final model averaging to address statistical heterogeneity. Experiments show the impro... | Rebuttal 1:
Rebuttal: ## Responses to Reviewer cjuK
1. **“NTK-based approaches have explicit constraints in modeling neural networks (linear layers), which makes the proposed method hard to incorporate into modern architectures (CNN, Transformers).”**
We appreciate this important point. Indeed, our current fully-conn... | null | null | null | null | null | null |
Towards Attributions of Input Variables in a Coalition | Accept (poster) | Summary: This paper studies the partitioning of input variables in feature attribution methods. The central issue is that existing attribution methods compute importance scores of single features or predefined partitions, but they are not very good at attribution for meaningful coalitions of variables. The paper identi... | Rebuttal 1:
Rebuttal: Thank you very much for your great efforts in reviewing this paper. We would like to answer all your concerns. **Please let us know if you have further questions or if you are not satisfied with the current responses.**
**Q1: “No time complexity or scalability analysis.”**
> The experiments invol... | Summary: This paper proposes a new perspective on common attribution methods such as Shapely values and Banzhaf values. The paper does so in a quite theoretical way illustrating that one can reformulate the computation of these attribution methods in terms of "AND" and "OR" interactions. The AND interactions are "hot" ... | Rebuttal 1:
Rebuttal: Thank you for your comments. **Please feel free to contact us if you have any further concerns as soon as possible.**
---
**Q1: Ask for the significant value of our method. Why not focus on computational efficiency or design new methods?**
> “I do not understand how … community”
> “I think tha... | Summary: This paper attempts to provide insights into an issue in attributions. The issue is when one computes an attribution method, such as the Shapley value, for a coalition of inputs, it does not equal the sum of the individual input values when they are attributed to separately. This effect is explained by analyzi... | Rebuttal 1:
Rebuttal: Thank you very much for your appreciation of this work. We would like to answer all your concerns. Please let us know if you have further questions or if you are not satisfied with the current responses. Thanks a lot.
---
**Q1: In some experiments, it is not clear how faithfulness is being measu... | null | null | null | null | null | null | null | null |
Masked Generative Nested Transformers with Decode Time Scaling | Accept (poster) | Summary: Recent advances in visual generation have improved content quality but face challenges in computational efficiency during inference. Many algorithms require multiple passes over a transformer model, keeping a consistent model size that leads to high computational costs. This work proposes two strategies to add... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and constructive reviews. We are glad to see that the reviewer appreciates the effectiveness of decode-time model scaling and caching, to significantly reduce computational costs in visual generation while maintaining competitive performance and demons... | Summary: This paper introduces Nested Transformers for efficient image and video generation. The method progressively increases the model size during decoding to reduce computational costs in the early steps. Additionally, KV-caching is employed across decoding steps to further enhance efficiency. Experiments are condu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and constructive reviews. We are glad to see that the reviewer found this to be an efficient approach for image and video generation, experimentally demonstrating reduced computational costs while maintaining generation quality and offering an unexplor... | Summary: This paper proposed a promising efficient approach for image/video generation. Specifically, this work introduces the concept of model size scheduling during the generation process to significantly reduce compute requirements. It demonstrates KV cache also works for parallel decoding. They used nested modeling... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and constructive reviews. We are glad to see that the reviewer acknowledged the work to be a promising and efficient approach for image/video generation by introducing model size scheduling and demonstrating the effectiveness of KV caching and nested m... | Summary: The paper introduces MaGNeTS, a approach to improving the efficiency of visual generative models by dynamically scaling model size during decoding.
Claims And Evidence: The claims made in the submission are largely supported by clear and convincing evidence, particularly through extensive experiments on Image... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and constructive reviews. We are happy to hear that the reviewer acknowledged the novelty of the dynamic compute allocation approach in our work, which helps to reduce redundant computation, and experimental evidence supporting the claims. We answer th... | null | null | null | null | null | null |
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees | Accept (poster) | Summary: This paper studies adversarial robustness in the L2D paradigm. Based on rigorous theoretical results, they propose a novel method called SARD to improve the adversarial robustness of L2D models.
Concretely, this paper first presents untargeted and targeted attacks on the L2D, based on optimization on the comm... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their encouraging and constructive feedback. We appreciate their recognition of the rigor of our theoretical contributions and the strength of our experimental results in supporting our claims. We also acknowledge and value their observation that, to the best of... | Summary: This paper addresses adversarial robustness in two-stage Learning-to-Defer (L2D) frameworks by introducing two new attack strategies: untargeted attacks, which disrupt agent allocation, and targeted attacks, which redirect queries to specific agents. To counter these attacks, the authors propose SARD, a robust... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful evaluation and appreciate the recognition of our experiments and theoretical claims. In the following, we clarify our motivations, provide justification for our design choices (e.g., the multi-task emphasis in Section 3), and highlight the technical chall... | Summary: This paper identifies that Learning-to-Defer frameworks are vulnerable to adversarial attacks and introduces two attack strategies: untargeted attacks that disrupt allocation and targeted attacks that redirect queries to specific agents. The authors propose SARD, a robust algorithm with theoretical guarantees ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive feedback. We are grateful for your recognition of the rigor in our theoretical contributions and the strength of our empirical validation.
> The computational complexity [...] practical deployment considerations.
Thank you fo... | Summary: This paper investigates the two-stage learning to defer (L2D) frameworks under adversarial attacks. The authors introduce two novel attacks: untargeted and targeted, that exploit structural weaknesses in L2D systems. Then the authors propose the SARD algorithm, a robust, convex deferral mechanism that is both ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback. We are glad that they
found our contributions meaningful, and we appreciate their recognition of the soundness
of both our theoretical and empirical results.
> In the experiments, [...] with real-world expert predictions (e.g.,... | null | null | null | null | null | null |
CoastalBench: A Decade-Long High-Resolution Dataset to Emulate Complex Coastal Processes | Accept (poster) | Summary: This paper mainly focuses on the dataset construction of simulating coastal processes via the Regional Ocean Modeling System (ROMS), considering ocean, meteorological, river and static variables. The work builds a ViT-based (Vision Transformer) network to use the dataset for coastal ocean variable prediction.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and hope our responses clarify the concerns.
**Related Models:** Thank you for raising this important point. We agree that baselines are important. To our knowledge, no existing deep learning method is specifically designed for complex regional co... | Summary: This paper provides a large-scale, high-resolution coastal simulation dataset to train and evaluate deep learning models. The dataset contains various oceanography variables alongside external atmospheric and river forcings. Then, the author proposes a customized ViT model that takes initial and boundary condi... | Rebuttal 1:
Rebuttal: Thank you for your comments and thoughtful questions. Below are our responses:
**Comparison with existing datasets:** We include a comparison table of representative works on regional coastal ocean modeling, highlighting that existing datasets are generally smaller in scale and focus on simpler p... | Summary: This paper introduces a decade-long, high-resolution dataset for modeling complex coastal processes in the area of Charlotte Harbor, Florida, USA. The dataset is generated using a validated numerical model, ROMS. A flexible ViT model is designed to ingest a multitude of diverse data sources (e.g. initial, bou... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable review; it is crucial for improving the quality of our manuscript.
**Motivation and applications:** Our problem setting is motivated by the need to efficiently emulate complex coastal processes for practical applications. High-resolution numerical models such... | null | null | null | null | null | null | null | null |
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models | Accept (poster) | Summary: This paper introduces RoSTE, a method that combines rotation-based transformation with Quantization-Aware Training to improve the efficiency of the SFT process.
## update after rebuttal
Thanks for providing the additional results. I will raise my score accordingly. Please be sure to include these experimenta... | Rebuttal 1:
Rebuttal: *Table B: Training Time and Training Memory Consumption. Our server of 8 $\times$ A100 has a total GPU memory of 320GB.*
| Model| Method| Total Training Time (h) | Peak Memory (GB) |
|-|-|-|-|
| Qwen 2.5 7B | SFT| 2.1| 300|
|| GPTQ| 2.1| 0|
|| QuaRot| 2.1 | 0|
|| SpinQuant| 3.4| 263|
|| LoRA| 0.... | Summary: This paper aims to combine quantization-aware SFT and rotation strategy. This work is the first to leverage rotation-based quantization in QA-SFT.
The authors propose a bilevel optimization formulation – upper level subproblem for optimizing weight matrices and lower level subproblem for selecting rotation ma... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for acknowledging the strength of our approach. Below we summarize our response to your concerns.
> it is worth noting that the theoretical claims depend on certain assumptions (such as the interpolation condition and specific properties of the Gram matrix) th... | Summary: RoSTE introduces a novel Quantization-Aware Supervised Fine-Tuning (QA-SFT) approach for large language models (LLMs), addressing the inefficiencies of traditional post-training quantization (PTQ) and the high computational cost of quantization-aware training (QAT). The proposed Rotated Straight-Through Estima... | Rebuttal 1:
Rebuttal: *Table A.1: Results on Pythia 1B model.*
| Bit-width | Method| ROUGE-1 | ROUGE-2 | ROUGE-L | ROUGE-LSum | ROUGE (Avg.) |
|-|-|-|-|-|-|-|
| **FP16**| Base| 22.40| 5.73| 17.35| 17.59| 15.77|
|| SFT| 32.80| 11.84| 25.49| 25.50| 23.91|
| **W4A4KV4**| **RoSTE** | **31.80** | **11.03** | **24.71** | **... | Summary: This paper introduces RoSTE, a method for quantization-aware SFT of LLMs. RoSTE aims to jointly optimize model weights and rotation matrices during fine-tuning, enabling efficient 4-bit quantization of weights, activations, and KV caches in a single training phase. This integration contrasts with the approache... | Rebuttal 1:
Rebuttal: > Memory Efficiency: ...
We agree partially with your points. While RoSTE maybe less memory efficient than methods such as QLoRA, we observe that RoSTE has a similar training cost in compute and memory usage as full-param SFT (cf. Table B in response to Rev. mQhc). The overhead with adaptive rota... | null | null | null | null | null | null |
ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation | Accept (spotlight poster) | Summary: The paper addresses a common limitation in existing generative models, where actions are tokenized independently. To resolve this issue, the paper introduces ActionPiece, a novel method that explicitly incorporates contextual information when tokenizing action sequences. Experimental results on public datasets... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful suggestions! Below, we address the questions listed under "Questions for Authors", followed by further discussion on related topics.
**Q1: Why are token pairs between adjacent actions assigned lower weights?**
**A1:** First, we'd like to clarify that to... | Summary: This paper introduces ActionPiece, a novel tokenization method for generative recommendation systems that incorporates context when tokenizing user actions. Unlike existing approaches (RQ-VAE, etc.) that tokenize each action independently, ActionPiece represents actions as unordered feature sets and builds a v... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback! Below, we first provide a detailed example of learned vocabulary, followed by clarifications regarding the experiments and method design.
**Q1: Example of learned vocabularies**
**A1:** As detailed in Section E, we use vector-quantized (VQ) tokens... | Summary: this paper proposed ActionPiece, a tokenization strategy for generative recommendation systems. the main idea of ActionPiece can be summarized as following: after collecting all features of each action set, the authors proposed to reconstruct the user historical action sequences by i) vocabulary construction: ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We appreciate your recognition of the intuition, simplicity, and effectiveness of ActionPiece tokenization technique.
**Q1: On experiments with industrial-scale datasets or online A/B testing**
**A1:** We acknowledge t... | Summary: This paper introduces ActionPiece, a context-aware action sequence tokenization method for Generative Recommendation (GR). The main contributions are as follows: (1)Context-aware tokenization, which represents user action sequences as sequences of unordered feature sets and then merges frequently co-occurrin... | Rebuttal 1:
Rebuttal: Thank you very much for your time and thoughtful feedback. We greatly appreciate your constructive and insightful suggestions. Below, we address your concerns regarding the experiments, followed by additional discussions.
**Q1: Inconsistent experimental settings**
**A1:** We included results on ... | null | null | null | null | null | null |
On the Learnability of Distribution Classes with Adaptive Adversaries | Accept (poster) | Summary: In *robust distribution learning*, the goal is to learn a distribution p in some known class C given samples from p. The goal is to learn p up to some small total variation distance -- that is, find some hypothesis distribution q such that TV(p,q) is small. The twist is that a small fraction of the samples you... | Rebuttal 1:
Rebuttal: Thank you for the positive review! We are happy to hear that the reviewer appreciates the high quality of our writing and the significance of the open problem we resolve. | Summary: Post rebuttal: The authors addressed my main concerns, and so I have raised my score by one point.
--
This paper studies the question of whether learnability, or realizable learnability, in the PAC sense implies learnability against an *adaptive adversary*. There are multiple notions of adaptive adversaries, a... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review! We are glad to hear the reviewer appreciates the significance of our results (c.f. “if new and correct, are significant and worth publishing in a top tier ML venue”).
We acknowledge fair criticisms made regarding writing. Indeed, the main critique of the pape... | Summary: The paper investigates the problem of learning distributions under an adaptive adversary, i.e., adversary has full knowledge of sample and can apply noise to the samples before passing it to the learner.
Claims And Evidence: Main result (MR): There is a class of distributions C that is not learnable in the re... | Rebuttal 1:
Rebuttal: Thank you for the positive review! We are glad that the reviewer found the ideas in the paper quite interesting and the problem studied to be quite relevant.
>...the theorems are clearly written and intution is relatively easy to fallow. Although, more focus on natural language explanations or a ... | null | null | null | null | null | null | null | null |
A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning | Accept (poster) | Summary: This paper addresses the challenge of long-tailed semi-supervised learning (SSL) by proposing a framework that automatically integrates multiple expert knowledge to generate high-quality pseudo-labels, thereby improving SSL performance in imbalanced data settings. The authors analyze three types of long-tailed... | Rebuttal 1:
Rebuttal: #### **References:**
We appreciate the reviewer's suggestion and confirm that the highlighted literature is relevant to our study, and we will include citations in the revised manuscript.
#### **Realistic cases (Weaknesses 1):**
In the medical field, when collecting information from various patien... | Summary: In response to the problem of distribution mismatch between labeled and unlabeled data in long-tail semi-supervised learning (LTSSL), current methods refer to experts to model various unlabeled data, but various experts cannot match long-tail pseudo-labeled data well. This paper proposes a dynamic expert alloc... | Rebuttal 1:
Rebuttal: #### **More experiments (Experimental Design 1):**
Following recent works like CPE and BaCon, we conducted experiments across three datasets (CIFAR-10-LT, STL-10-LT, and SVHN-LT), each evaluated under two different imbalance ratios (150 and 200). As suggested, we have extended our evaluation to in... | Summary: This paper proposes Meta-Expert, a semi-supervised learning method tackling the long-trained problem. By investigating the effectiveness of assiging different experts regarding the class membership, the model applies a dynamic expert assignment model to learn to assign the soft weight for three expert models. ... | Rebuttal 1:
Rebuttal: #### **Questions 1 (Aligned experimental setting):**
Our work focuses on advancing long-tailed semi-supervised learning, thus primarily investigating settings with higher imbalance ratios (the more challenging scenarios) compared to recent works. As suggested, we have conducted supplementary exper... | Summary: This paper introduces Meta-Expert, a framework designed for long-tailed semi-supervised learning. Specifically, Meta-Expert includes a Dynamic Expert Assignment module, which predicts the class membership of a sample. A Multi-Depth Feature Fusion module, which integrates features from different depths to mitig... | Rebuttal 1:
Rebuttal: #### **References:**
We appreciate the reviewer's suggestion and confirm that the highlighted literature is relevant to our study, and we will include citations in the revised manuscript.
#### **Properties of different layers (Question & Weakness1):**
In deep networks, shallow layers capture local... | null | null | null | null | null | null |
PiD: Generalized AI-Generated Images Detection with Pixelwise Decomposition Residuals | Accept (poster) | Summary: In this paper, the authors propose extracting the “residual” of images to detect AIGC images. Specifically, the residual refers to artifacts introduced at the low-level visual features due to the generative model’s excessive focus on semantic content during the image generation process. These artifacts serve a... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 4ooD for the thoughtful comments. The responses to the questions are as follows.
> *Q1: Why do these residuals inherently capture the artifacts?*
**A:** Thanks.
- Generally, the residual for an image input $x$ has the form $R(x) = x - \Phi(x)$ as described in the pap... | Summary: This paper proposes Pixelwise Decomposition Residuals (PiD) to distinguish real images from synthetic ones. Based on the hypothesis that generative algorithms often overlook low-level signals, the authors decompose synthetic images in the RGB domain. Specifically, they convert images to the YUV color space, ap... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 45ju for the detailed comments. The responses to the questions are as follows.
> *Q1: Computational efficiency.*
**A:** Thanks. We compare the computation cost of our method and previous methods. The inference time is only slightly slower than the baseline model, whil... | Summary: In this paper, a new discriminant method PID is proposed to detect whether an image is generated by a generative model. This paper explores the impact of noise residual distribution on the discriminant model and believes that the noise space of the image can be represented by color space transformation. By tra... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer uAyf for the constructive comments, and the responses are as follows.
> *Q1: Include real image datasets of different distributions.*
**A:** Thanks. The real image distribution is an important factor during testing. The test datasets UniversalFakeDetect and Self-Synth... | Summary: This paper focuses on Generalized AI-generated image detection via learning low-level signals (residual components) from image compression. To achieve this, the authors map the pixel vector to another color space (e.g., YUV), quantize the vector, and map back to the RGB space. Afterward, the quantization loss ... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer jvUK for the acknowledgement and constructive feedback. The response is as follows.
> *Q1: Computation efficiency.*
**A:** Thanks for the advice. To prove the computation efficiency of the proposed method, we compute the inference time (single forward pass) and the si... | Summary: This paper proposes a novel framework for detecting AI-generated images (AIGIs) based on pixel-wise image residuals. Residuals are extracted from the quantization error of color space transform and used to train a binary classification model. The results demonstrate promising performance in detecting AIGIs.
C... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 6H4b for the thoughtful and constructive feedback. The response is as follows.
> *Q1: Performance on related datasets.*
**A:** Thanks for the advice. We conducted the perturbation experiments on the GenImage dataset (SDv1.4 as an in-domain test set and other models... | null | null | null | null |
Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems | Accept (poster) | Summary: This paper proposes an explicit modeling approach for solving multi-solid problems, incorporating factors influencing solid deformation through specialized modules. The authors design a novel transformer-based architecture to achieve this, demonstrating state-of-the-art performance across multiple datasets.
C... | Rebuttal 1:
Rebuttal: Sincerely thanks for insightful comments.
> The experimental evaluation could be strengthened by including scaling studies to demonstrate the method's scalability.
Thanks for the insightful suggestion. We conducted a series of scaling experiments to analyze the model’s behavior under varying con... | Summary: The paper explicitly models the contact constraints and loads in multi-solid systems, using a Transformer-based framework.
- The system contains three types of objects, deformable solids, rigid solids, and forces. Instead of treating each point as a token, the paper proposes to incorporate the mesh edges into... | Rebuttal 1:
Rebuttal: Sincerely thanks for insightful comments.
> How to get all the solids pairs that are likely to contact?
In most cases, such as the stamping and grasping scenarios discussed in the paper, the solid pairs that are likely to come into contact are **known a priori** based on the deterministic nature... | Summary: This paper presents a transformer-based framework for explicitly modeling multi-solid interactions. The approach differs from implicit approaches that merge solids into a unified PDE or use graph-based message passing, and presents an explicit modeling one that structures interactions through a deformation tri... | Rebuttal 1:
Rebuttal: Sincerely thanks for insightful comments.
> Weakness: enforcing physical correctness
We first emphasize that the “explicit modeling” we defined **lies not in the explicit PDE constraints used in PINNs or hybrid models**, but rather in **how the model structure leverages and organizes input infor... | Summary: This paper focuses on multi-solid tasks and proposes Transformer-based model to deal with the interactions between objects.
To better handle the interactions, the paper proposes to explicitly model the external forces and contact interactions, whose hidden representations are combined with the objects’ embeddi... | Rebuttal 1:
Rebuttal: Sincerely thanks for insightful comments.
> Equations 4, 9
Thanks for pointing this out—Equations (4) and (9) do contain errors, and we apologize for the typos. We give a mathematically correct formulation here:
$$
z^\xi_j=\frac{\sum_{i=1}^{N^\alpha} w^\alpha_{i,j}x^\alpha_i + \sum_{i=1}^{N^\be... | Summary: This paper presents the Unisoma focusing on the PDE solving of multi-solid systems. Different from previous methods, Unisoma proposes to embed the solid type (rigid or deformable) and load information into the model for explicit modeling of multi-solid interactions. Technically, Unisoma employs the slice opera... | Rebuttal 1:
Rebuttal: Sincerely thanks for insightful comments.
> About the convolution and local information in Transolver
From the paper and official code of Transolver, the conv is applied only to **structured meshes or uniform grids** (Section 3.1 in Transolver); for **irregular meshes**—which are the focus of us... | null | null | null | null |
Slimming the Fat-Tail: Morphing-Flow for Adaptive Time Series Modeling | Accept (poster) | Summary: This paper tries to address the challenge of forecasting temporal sequences characterized by non-stationarity and leptokurtic (fat-tailed) distributions. The proposed Morphing-Flow (MoF) framework innovatively integrates a spline-based transform layer (“Flow”) with a test-time-trained adaptation method (“Morph... | Rebuttal 1:
Rebuttal: Thank you for raising this concern.
---
> **Question:**
> *Regarding baseline performances*
Prior works, such as iTransformer and PatchTST, adopt **different hyperparameters for different datasets**, as summarized in the table below. This practice makes it difficult to disentangle performance... | Summary: This paper proposes Morphing-Flow, a spline transformation module coupled with test-time adaptation, to counter fat-tailed distributions and distribution shifts.
Claims And Evidence: The paper claimed that fat-tailed distributions have negative effects on model convergence. This claim is supported by some emp... | Rebuttal 1:
Rebuttal: Thank you for your comments. Please kindly find our response below.
---
> **Comment 1:**
> *Regarding the contribution*
As shown in Fig.1, while stabilizing methods normalize data, they often expose heavier tails(often exceeding those in our synthetic setups) in real datasets—outliers that dis... | Summary: The work introduces Morphing-Flow (MoF), a framework to address the challenges of fat-tailed distributions in time series forecasting through adaptive normalization and test-time adaptation. MoF combines a spline-based Flow layer for distribution normalization and a Morph module for dynamic adaptation, achievi... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
---
> **Suggestions 1:**
> *Section 2: The term "excess kurtosis" is used without defining it.*
Thank you for the helpful comment.
A definition of *"excess kurtosis"* was included in Appendix C.2: it measures the tailedness of a distribution relative to a Gaussi... | null | null | null | null | null | null | null | null |
FedECADO: A Dynamical System Model of Federated Learning | Accept (poster) | Summary: This work addresses the inherent challenges of heterogeneous data distributions and computational resource disparities in FL by introducing FedECADO, a novel algorithm inspired by a continuous-time ODE theoretical framework for understanding federated optimization process. Extensive empirical studies have demo... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and hope the following addresses their concerns.
---
## **Continuous-time ODE**
The key innovation of FedECADO over the framework proposed by Agarwal and Pileggi (2023) is its introduction of circuit- and simulation-based techniques to add... | Summary: This paper considered the federated learning problem, and focused on addresses the challenges from heterogeneous data distributions and computational workloads. To address these challenges, this paper proposed FedECADO, which is the first algorithm that leverages the idea of a dynamical system representation o... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments.
---
## **Hessian Approximation**
In federated learning with non-IID data distribution, each client’s local loss function is a function of its unique dataset. The Hessian captures the curvature of each loss function at the client’s speci... | Summary: This paper proposes a federated variant of ECADO (Agarwal and Pileggi, 2023). ECADO is an equivalent circuit approach to distributed optimization. ECADO consists in reconstructing a distributed optimization problem in terms of circuit principles, and finding the critical points of the equivalent circuit model... | Rebuttal 1:
Rebuttal: Thank you for your comments.
---
## **Novelty of FedECADO**
Regarding novelty, FedECADO is inspired by distributed circuit simulation, where sub-circuits are independently simulated and recombined using waveform relaxation (White and Sangiovanni-Vincentelli, 2012). Our approach extends wavefor... | Summary: This paper explores the interpretation of federated learning in dynamical systems and adapts the ECADO algorithm to federated learning, proposing the FedECADO algorithm. It uses a physical equivalent circuit model to explain the federated learning process and targets optimizations for non-IID data and client a... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments.
---
## **Novelty of FedECADO**
FedECADO builds on the distributed optimization method, ECADO, and introduces key innovations (multi-rate integration and chord-based modeling) to address the unique challenges in federated learning. Unlike... | null | null | null | null | null | null |
Learning from Sample Stability for Deep Clustering | Accept (poster) | Summary: A deep clustering method based on the idea that unstable points, whose representations change a lot each epoch, are more likely to be inaccurately clustered. The main proposals are a loss function to encourage representation stability, and the exclusion of unstable points from training.
Claims And Evidence: T... | Rebuttal 1:
Rebuttal: > **On main results (Claims And Evidence)**
We compared four state-of-the-art (SOTA), including IDFD, ProPos, CoNR, DMICC, the baseline BYOL by reproducing them, with the same experimental setup as ours, including backbone, batch size, number of epochs, etc. These experimental settings can signi... | Summary: This paper proposes a deep clustering method by identifying hard samples based on their stability during the training. By taking the sample stability into consideration, the proposed method improves instance-level representation learning and cluster-level grouping, leading to superior clustering results on fiv... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of this work. We highly value the insightful comments you have provided, and below we offer our responses.
> **Essential references not discussed**
Thank you for providing the two recent related articles. After careful reading, we believe that these two papers ar... | Summary: This article introduces LFSS, a novel deep clustering method that leverages sample stability, which is measured as the cosine similarity between representations across consecutive training epochs as a supervisory signal. The authors motivate the approach by showing that samples with unstable representations te... | Rebuttal 1:
Rebuttal: Thank you for your important questions. Below is our response:
> **Hyperparameter Sensitivity (Weakness 1)**
Actually, we provided an analysis of the sensitivity of four hyperparameters on CIFAR-10 in Appendix E. Here, we further provide a related analysis on three datasets: CIFAR-10, CIFAR-20, a... | Summary: This work introduces a novel sample stability, which is strongly tied to misprediction and memorization difficulty. By leveraging stability as a supervision signal, the proposed LFSS method outperforms state-of-the-art approaches on multiple benchmarks.
## update after rebuttal
I read the rebuttal, which add... | Rebuttal 1:
Rebuttal: Thank you for your careful review and constructive comments. Below is our response:
> **Changes in sample stability during training (Weakness 1)**
Thank you for your valuable advice. We conducted experiments to investigate the changes in sample stability as training progresses. The experiments w... | null | null | null | null | null | null |
MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents | Accept (poster) | Summary: This paper proposes MELON, a novel defense against indirect prompt injection attacks. MELON detects such attacks by re-executing tool calls with masking and identifying malicious behavior through similarity comparison. Comprehensive evaluations on AgentDojo demonstrate its effectiveness.
# Update after rebutt... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and constructive comments!
## A1. Comparisons between MELON and LLM Detector
We would like to respectfully point out that MELON is still better than LLM Detector, and it does not dilute our contribution. The reasons are four-fold.
1. **Robustness against s... | Summary: This paper introduces MELON (Masked re-Execution and TooL comparisON), a novel defense mechanism against indirect prompt injection (IPI) attacks on LLM agents. In IPI attacks, malicious actors embed harmful instructions in external resources (like websites or databases) that agents retrieve during task executi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and constructive comments!
## A1. Adaptive attacks
We tested MELON against two adaptive attacks following reviewer dKcG's suggestions:
1. **Obfuscation Attack**: This involves inserting random information before and after the malicious prompt. For example... | Summary: This paper introduces MELON (Masked re-Execution and TooL comparisON), a novel defense method against indirect prompt injection (IPI) attacks targeting LLM agents. MELON is based on the observation that under successful IPI attacks, agent actions become less dependent on user input and more reliant on malicio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments!
## A1. Computational cost
As discussed in Sec 5, the concern about the computational overhead can be mitigated by applying the KV cache to the previous prompts and tool contents. We estimate that this optimization can reduce the overhead by 70%... | Summary: The paper introduces MELON, a novel defense mechanism against Indirect Prompt Injection (IPI) attacks on LLM agents. MELON re-executes the agent's trajectory with a masked user prompt, replacing the original task with a task-neutral prompt. If the actions generated in the original and masked executions are sim... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and constructive feedback!
## A1. Stronger LLM Detector baseline against stronger attacks
Following the reviewer's comments, we conducted new experiments with the LLM Detector that explicitly outputs the reasoning process before providing the final answer a... | null | null | null | null | null | null |
Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data | Accept (poster) | Summary: This paper presents a theoretical and algorithmic framework for addressing the class imbalance problem in machine learning, particularly in multi-class settings with long-tailed distributions. The authors introduce a novel class-imbalanced margin loss function for both binary and multi-class classification, pr... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review. We will take your suggestions into account when preparing the final version. Please find responses to your specific questions below.
**1. Essential References Not Discussed: The key contribution ... However, [1] has extended Cao's analysis to multiclass scen... | Summary: The first main result is a consistency bound for a class-imbalanced margin loss when the hypothesis set is complete. The second result is a margin-based generalization bound for imbalanced binary classification in terms of Rademacher complexity. The last result is a bound for the Rademacher complexity when the... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**1. Methods And Evaluation Criteria: The methods used for the theoretical study in the paper are based on functional analys... | Summary: This paper introduces a novel theoretical framework for analyzing generalization in imbalanced classification. It proposes a new class-imbalanced margin loss function for both binary and multi-class settings, proves its strong $\mathcal{H}$-consistency, and derives corresponding learning guarantees based on em... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**1. Essential References Not Discussed: The paper cites key literature in the field of imbalanced learning, including data ... | Summary: The paper addresses the challenge of class imbalance in machine learning, particularly in multi-class problems with long-tailed distributions. The authors propose a novel theoretical framework for analyzing generalization in imbalanced classification, introducing a class-imbalanced margin loss function for bot... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**1. Weaknesses 1: The experimental results presented in the paper (including the results of the comparison methods) are sig... | null | null | null | null | null | null |
ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics | Accept (poster) | Summary: The paper proposes incorporating user demonstrations into LLM-based reward design methods in robotics. Their proposed approach is a direct contender of EUREKA. The main motivation of this work is that language can be ambiguous for task requirement specification and hence using user demonstrations is a good way... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and for recognizing the value of combining demonstrations with language for reward design, as well as ELEMENTAL’s improved performance. All updated tables and figures are included in https://shorturl.at/YHEDU (referred to as Response Table and Respon... | Summary: In this paper, the authors propose a framework that combines natural language guidance with visual user demonstration to align robot behavior. Using inverse RL and iterative self-reflection, ELEMENTAL improves task success by 41.3% over previous methods in out-of-distribution tasks.
In the first stage, featur... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and for recognizing ELEMENTAL’s contribution in automating not only reward design but also feature construction. We are glad the reviewer found the paper clear and the method well-motivated. All updated tables and figures are included in https://sh... | Summary: This paper introduces ELEMENTAL, a framework for reward design in robotics that integrates vision-language models (VLMs) with an inverse reinforcement learning (IRL) backbone. The authors aim to address the shortcomings of purely language-based reward engineering, particularly the difficulty of specifying nuan... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We are glad that the reviewer found our integration of VLMs with IRL to be a compelling combination and appreciated our empirical comparisons and ablation studies. All updated tables and figures are included in https://shorturl.at/YHEDU (referred to... | Summary: The paper introduces ELEMENTAL, which combines VLMs with Learning from Demonstration (LfD) to address challenges in reward design for robotic tasks. ELEMENTAL leverages visual demonstrations and natural language descriptions to generate task-relevant feature functions, which are optimized through an enhanced M... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and for highlighting the strengths of our IRL-VLM integration, self-reflection mechanism, and experimental results. In response, we have added real-world user study results, runtime analysis, and experiments using OpenAI o1 model. All updated tab... | Summary: The paper proposes an approach to inverse reinforcement learning (IRL) that uses the knowledge of a VLM to construct code the computes state features from the environment. These features are then used with MaxEnt IRL, and iteratively refined online to match the demonstration trajectories. Experiments show bett... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback, and for recognizing that ELEMENTAL presents a novel approach to resolving reward ambiguity in IRL by using VLM knowledge as a semantic prior. In response to the reviewer’s comments, we have added new experiments, expanded statisti... | null | null | null | null |
Oscillations Make Neural Networks Robust to Quantization | Reject | Summary: The paper investigates the role of oscillations in Quantization Aware Training (QAT) for neural networks. Traditionally, oscillations in QAT, caused by the Straight-Through Estimator (STE), are considered undesirable artifacts. However, this paper presents a different perspective by proposing that these oscill... | Rebuttal 1:
Rebuttal: Thank you for the careful reading of our manuscript, your helpful comments and suggestions of relevant literature.
* Thank you for the suggestion on exploring the quantization performance for different $\lambda$ values. We have now expanded the analysis in A.2 to span a larger range of $\lambda$.... | Summary: This work researches the oscillation effect during quantization-aware training (QAT) from a novel perspective. While most previous work identifies oscillation as a negative effect and tries to minimize it during QAT, the author of this work focuses more on the beneficial influence of preserving model performan... | Rebuttal 1:
Rebuttal: Thank you for the careful reading of our manuscript, your helpful comments and pointers to relevant literature.
We agree that the theoretical analyses in Section 4 are straightforward. At the same time, they provide essential intuition and motivation for the empirical results because they give an... | Summary: The paper uses a linear model to explain the mechanism of weight oscillation during quantization-aware training (QAT). It discovers that the oscillation is because the loss function with quantized weights encourages the latent weights to cluster around the edge of quantization buckets, not the center. The pape... | Rebuttal 1:
Rebuttal: Thank you for the careful reading of our manuscript and your insightful comments, and for this characterisation of our work: "The analysis of QAT's encouraging latent weights to be clustered around bucket edge is already interesting enough to be shown to the public."
Regarding point (1), we agree... | Summary: This paper challenges the traditional view of oscillations in QAT as undesirable, arguing they can enhance robustness. Through theoretical analysis of linear models, the authors decompose the QAT loss gradient into the original full-precision component and an oscillation-inducing term. Then they introduced Osc... | Rebuttal 1:
Rebuttal: Thank you for your thorough and careful reading of our manuscript.
We are pleased to read that you found our paper to be "well-written and has good theoretical proof" and about your assessment that the "experimental designs and analyses are sound and convincing".
However, we respectfully disagre... | null | null | null | null | null | null |
Galileo: Learning Global & Local Features of Many Remote Sensing Modalities | Accept (poster) | Summary: This work introduces a vision foundation model for remote sensing data based on self-supervised learning. The proposed method features two key technical designs: 1) A flexible encoder architecture that supports space-time, spatial, temporal, and static data. 2)A new training objectives that incorporate both gl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback, for your attention to detail and for acknowledging the strengths of our submission.
### 1. Technical Novelty
Our work introduces several technical innovations for self-supervised learning in general and pretraining models for remote sensing speci... | Summary: The paper proposes a multimodal geospatial foundation model called Galileo. The authors also propose a new joint dataset combining various modalities with temporal, spatial, and spatiotemporal variations. As the architecture is ViT based the authors also provide methods for generating patches for diverse resol... | Rebuttal 1:
Rebuttal: Thank you for your review, and your detailed questions.
## Weaknesses
### 1. Evaluation on only Sentinel 2 tasks
We agree that benchmark datasets over-represent Sentinel-2 based tasks, providing few opportunities to test the value of Galileo’s many modalities that reflect the wide diversity of ... | Summary: This paper proposes the "Galileo" family of pre-trained remote sensing models, which aims to learn both global and local features to cope with the multimodal, variable input size, and large-scale span characteristics of remote sensing data. The authors improve the ViT architecture to enable the model to flexib... | Rebuttal 1:
Rebuttal: Thank you for your thorough review; we are glad you recognize the meaningful challenges we address with Galileo, since this was one of the primary objectives of this work.
### Balancing the losses
We combine the global and local objectives via a simple average (Section 2.2.3), and demonstrate via... | Summary: This paper introduces *Galileo*, a family of pretrained ViTs that flexibly encode multi-source Earth observation (EO) data of varying spatial and temporal scales for various downstream tasks. To address limitations in existing pretrained EO "foundation models", *Galileo* uses a self-supervised learning (SSL) r... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and excellent suggestions.
## Claims and Evidence
### 1. Flexible input shapes
Fig 3 and Tab 11 show Galileo performs well across varying patch sizes. Tabs 3 and 5 show Galileo takes both pixel timeseries and images. We also run MADOS segmentation with a smaller ... | null | null | null | null | null | null |
Flow Matching for Denoised Social Recommendation | Accept (poster) | Summary: 1. While there have been many prior works on generative recommendation systems, few have explored the direction of noise. This paper addresses the challenges posed by noisy social networks.
2. It provides a detailed theoretical explanation of the advantages of the flow-matching model, particularly in comparis... | Rebuttal 1:
Rebuttal: **1. Differences between redundancy and errors**
Thanks! Redundancy refers to similar suggestions that do not provide additional value. Errors, on the other hand, represent inaccurate or flawed information, which could arise from noisy data sources, incorrect predictions, or misclassifications. Wh... | Summary: The paper introduces a generative model for social recommendation systems, using flow-matching to efficiently handle noise in social networks while preserving relational structures. It provides a thorough theoretical analysis and experiments demonstrating its superiority, fast convergence, and better fitting p... | Rebuttal 1:
Rebuttal: **1.Consistent with the denoising objective ?**
Thanks! Our denoising process aligns with the general denoising objective in diffusion models. While typical diffusion models use stochastic noise processes, our method is based on ODEs, achieving a more direct reconstruction objective.
**2.Types of... | Summary: The study presents Recflow, a flow-matching model for social recommendation systems, which addresses challenges in traditional recommendation methods, especially in noisy social networks. The key issue with many graph-based approaches is their inability to handle noisy edges in social graphs, which can degrade... | Rebuttal 1:
Rebuttal: **1. More theoretical foundation and mathematical descriptions.**
Thanks, and we agree. Following most works [1][2], we also define the isotropy as noise obeys the standard normal distribution N(0,1), which leads to the uniform properties in all directions. While, anisotropy refers to noise distr... | Summary: This paper introduces RecFlow, a social recommendation system using flow matching to handle noise in social graphs. Unlike traditional diffusion models that assume isotropic noise, RecFlow better captures the anisotropic nature of social data by learning velocity fields that preserve structural relationships. ... | Rebuttal 1:
Rebuttal: **1. It is unclear whether it addresses label errors, outdated connections, or other types of noise.**
The noise we primarily address is graph-based noise, where social edges may represent outdated or misleading connections, potentially degrading the quality of recommendations. Regarding the type ... | null | null | null | null | null | null |
The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training | Accept (poster) | Summary: The paper studies how bidirectional and autoregressive training objectives influence the structure of the query-key matrix $W_{qk}$ in self-attention. The results show that bidirectional training induces symmetric structures in $W_{qk}$, whereas autoregressive training
results in matrices characterized by dire... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough and positive review, as well as the valuable suggestions and questions.
First, we agree with the reviewer that we could have utilized the available manuscript space more effectively. In response to suggestions from Reviewers HwPP and JKhN, we will revise Se... | Summary: The paper investigates the inherent structures within self-attention mechanisms, focusing on symmetry, directionality, and emergent dynamics in Transformer training. The authors provide a mathematical framework for analyzing self-attention weight matrices and examine how different training objectives, namely b... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful, detailed, and valuable input. First, we address the weaknesses raised by the reviewer:
1. We acknowledge that we did not evaluate real-world deployments, as our goal was to analyze the structures that emerge in self-attention matrices during pretraining. How... | Summary: This work investigates the training process of Transformers, revealing a structured pattern of the attention weights' update as a linear combination of rank-1 matrices. Based on this, it is demonstrated that bidirectional training (encoder-only) induces symmetry in weight matrices, while autoregressive trainin... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive feedback provided.
Below, we address the reviewer’s questions, along with the related concerns highlighted as potential weaknesses.
1. Proposition 2.1 aimed to show that accurate token prediction depends on learning an effective bilinear f... | Summary: In this paper the authors focus on the structure of the attention matrix used in Transformers and in particular the effect of the training strategy on the overall structure inherited by the same. Showcasing that autoregressive training leads to directional matrices, whereas bidirectional training induces symm... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful review, for going through the proofs in the Appendix, and for the detailed feedback to improve clarity. While we appreciate the concerns raised, we respectfully disagree with the claim that the manuscript lacks the necessary mathematical rigor for its claims a... | null | null | null | null | null | null |
Exploring Representations and Interventions in Time Series Foundation Models | Accept (poster) | Summary: The paper delves into learned representations of time series foundation models. The authors evaluate the similarity of representations using CKA, revealing that larger models can learn redundant patterns. They propose a block-wise layer pruning strategy to reduce the feature dimensionality while keeping the pe... | Rebuttal 1:
Rebuttal: Dear Reviewer tr3o,
Thank you for your thoughtful and detailed review of our paper. We appreciate that you found our "analysis of representation similarity, pruning effectiveness, and concept steering to be detailed and that the claims made in the paper are generally well-supported."
We have ad... | Summary: The paper performs analyses into 3 time series foundation models, Chronos, Moirai, and MOMENT. Using concepts from the interpretability literature, the paper studies i) representation similarity across layers, ii) identification of human interpretable concepts, and iii) model intervention. Via experiments, the... | Rebuttal 1:
Rebuttal: Dear Reviewer Lqam,
Thank you so much for your time! We are glad that you found that our paper "very nicely sets up the experimental design to support their [our] claims, is comprehensive in exploring 3 different models, and presents clear and convincing evidence regarding representational simila... | Summary: This paper investigates the internal workings of time series foundation models by analyzing their learned representations. It reveals that these models exhibit block-like redundancy across layers, which can be exploited through block-wise pruning to reduce model size and improve inference speed without comprom... | Rebuttal 1:
Rebuttal: Dear Reviewer i5c5,
Thank you for reviewing our paper. We appreciate your recognition of our work's contributions regarding model redundancy patterns, block-wise pruning, and interpretable latent space concepts.
Given your "Weak accept" recommendation, we'd be grateful if you could let us know h... | null | null | null | null | null | null | null | null |
Neutral residues: revisiting adapters for model extension | Accept (poster) | Summary: This paper presents Neutral Residues, a method for extending large language models (LLMs) to new domains while mitigating catastrophic forgetting. The proposed method builds upon adapter-based techniques (to add extra capacity to the model), introducing architectural modifications with parallel gated adapters,... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their feedback on our paper.
1. **Only the English-pretrained model is used. Also, the target languages should be more diverse with the language taxonomy.**
Our experiments are also conducted on Gemma, which was trained on a small amount of multiling... | Summary: Extending a pre-trained large language model (LLM) to a new domain/language is challenging. It is known that such model extension often encounter a trade-off, between performing well on the new domain/language vs degrading performance on the original domain/language. This paper addresses such the problem by re... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their feedback on our paper.
1. **Have you ever tried out any multilingual settings including more than 2 languages in total?**
We conducted new experiments by adding French, Danish, Hungarian, and Slovak simultaneously to Gemma 2B. The training is ... | Summary: This paper addresses the challenge of extending a pretrained large language model to a new domain (e.g., a new language) without catastrophic forgetting of the original domain. The authors propose “neutral residues,” a method that adds adapter layers to the model and trains them such that their outputs are nea... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their feedback on our paper.
1. **Limited Experiment Scope: The experiments are mostly on adapting to one new language.**
Exploring sequentially adding multiple domains would be interesting, but due to time constraints, we leave it for future work. ... | Summary: Authors explore a set of techniques and strategies for reducing the compute needed to train an already trained LLM for a new task or language. These include the ratio of the training data to the pretraining data, the architecture of the newly added modules (ffd or multi-head attention), the way that they are a... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their feedback on our paper.
1. **To my understanding, all the techniques and strategies discussed in the paper are drawn from previous studies**.
Taken individually, some techniques used in our paper were indeed proposed in previous work. However, ... | null | null | null | null | null | null |
Algorithmic Recourse for Long-Term Improvement | Accept (poster) | Summary: Existing work in improvement-oriented algorithmic recourse assumes access to an accurate underlying model of whether or not a user taking an action improves their outcome. The paper proposes to overcome this limitation by using a bandit algorithm to learn a more accurate improvement model over time based on de... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable and thoughtful feedback. We will reflect all of them in our final version. In the following, we will respond to the key comments and questions raised by the reviewer.
---
> What specifically is meant by "long-term perspective"? The phrase comes u... | Summary: This paper proposes an online algorithmic recourse setting where an unknown oracle returns the real-world outcome for a given input. The authors introduce bandit algorithms to address this problem. Experiments demonstrate that their proposed methods outperform existing recourse approaches.
Claims And Evidence... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable and thoughtful feedback. We will reflect all of them in our final version. In the following, we will respond to the key comments and questions raised by the reviewer.
---
> The main concern I have is the real-world application (i.e., why should w... | Summary: This paper is about algorithmic recourse: it aims to help individuals take actions to change unfavorable predictions made by machine learning models (like getting a loan rejection changed to approval). The issue that this paper tries to resolve is that many current methods only focus on changing the prediction... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable and thoughtful feedback. We will reflect all of them in our final version. In the following, we will respond to the key comments and questions raised by the reviewer.
---
> **Methods And Evaluation Criteria**
> No. The critical weak point of this... | Summary: The paper studies Algorithmic Recourse (AR), i.e., providing a recourse action $a$ to individuals $x$ to improve so that their classification changes from $h(x)=0$ to $h(x+a)=1$ or “h-valid.” The authors frame their problem in the “improvement” setting (König et al.), where the goal is also to improve classif... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable and thoughtful feedback. We will reflect all of them in our final version. In the following, we will respond to the key comments and questions raised by the reviewer.
---
> The Bernoulli reward model in equation (2) needs more description. Can yo... | null | null | null | null | null | null |
Enhancing Ligand Validity and Affinity in Structure-Based Drug Design with Multi-Reward Optimization | Accept (poster) | Summary: This paper proposes a multi-reward optimization framework to enhance ligand validity and binding affinity in structure-based drug design (SBDD). By integrating Direct Preference Optimization (DPO) with Bayesian Flow Networks (BFNs), the authors achieve joint optimization of multiple objectives, such as binding... | Rebuttal 1:
Rebuttal: ## [R3] Reviewer rt1G
We sincerely appreciate your positive and constructive feedback. Below, we address the comments and questions raised. Due to the word limit, additional experimental results can be found in [anonymous pdf link](https://anonymous.4open.science/r/anonymouspdf-718B/anonymous%20r... | Summary: This paper introduces a multi-reward optimization framework for structure-based drug design, integrating Bayesian Flow Networks (BFNs) with Direct Preference Optimization (DPO). The method aims to simultaneously optimize ligand binding affinity, synthetic accessibility, and conformational stability. By incorpo... | Rebuttal 1:
Rebuttal: We deeply appreciate your thoughtful comments and positive feedback. Here, we address the comments and questions mentioned.
---
**W1) The model does not outperform baselines in ligand-protein clash detection, which may restrict practical applications.**
We acknowledge the reviewer’s concern reg... | Summary: This paper introduces a multi-reward optimization framework for structure-based drug design, addressing the challenge of generating ligand molecules with multiple desired properties like binding affinity, validity, and drug-likeness. It fine-tunes generative models for these attributes together, using direct ... | Rebuttal 1:
Rebuttal: We deeply appreciate your efforts and positive feedback. Here, we address the comments and questions mentioned.
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**W1) Lack of computation complexity analysis against diffusion baselines. might be complex given the complexity of Bayesian Flow Networks (BFNs) and multi-reward optimization**
... | null | null | null | null | null | null | null | null |
Permutation-Free High-Order Interaction Tests | Accept (poster) | Summary: This paper introduces permutation-free kernel-based tests for detecting high-order interactions in multivariate data. The proposed methods, xdHSIC, xLI, and xSI, leverage V-statistics and cross-centring to achieve a standard normal null distribution, eliminating computationally intensive permutations. Empirica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. Below we discuss the concerns raised by the reviewer.
**Re: iid assumption:** We have added a discussion of how our method might be applicable to time series in the response to Reviewer vR6R. In summary, our method is still applicable if multiple iid reali... | Summary: The paper studies testing for independence across many variables. The Streitberg interaction is a way to test for any factorization of a joint distribution. While it had been kernelized before, now it is kernelized and centered with sample splitting, in a way that avoids permutations.
Claims And Evidence: I f... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive review and recognising our effort of making the technical literature accessible. We have corrected the typo in our revision and address your concerns below.
**Re: formal statements of normality:** The normality results follow directly from our construct... | Summary: This paper proposes a set of permutation-free high-order interaction tests, addressing the computational inefficiency of existing permutation-based kernel hypothesis tests. It uses cross-centering techniques to derive test statistics that follow a standard normal distribution under the null hypothesis. The aut... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review. Below we refer to Figure S1, S2 & S3 in the link: https://imgur.com/a/uju1pHq.
**Re: iid assumption:** As the reviewers correctly pointed out, our methods depend on the data being iid and indeed in many application areas, the nature of the data is ... | Summary: This paper introduces permutation-free high-order interaction tests for joint independence and partial factorization of d variables. Traditional kernel-based hypothesis tests, such as HSIC and its extensions (e.g., dHSIC), rely on computationally expensive permutation-based null approximations. The authors pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their reviews. Below we refer to the Figure S1, S2 & S3 in the link: https://imgur.com/a/uju1pHq.
**Re: SCMs with diverse nonlinear forms and alternative kernels:** For the example in Fig. 5, we have added three new analyses where the V-structures are encoded by sinc, lo... | null | null | null | null | null | null |
Functional Alignment Can Mislead: Examining Model Stitching | Accept (spotlight poster) | Summary: The authors investigate the suitability of model stitching as a tool for analyzing the informational content of feature representations. Specifically, the authors show that models trained for different tasks (which arguably encode different information in their representations) could be stitched together to ac... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for stating their understanding of the paper and for the time they dedicated to structure the evaluation of our paper.
## Questions For Authors
* Q1: The reviewer has misunderstood our methodology. While point 1 is correct, points 2--5 are incorrect.
* 2&3: w... | Summary: This work conducts an empirical evaluation of when functional alignment is not an indicator of the semantic similarity of the learned features. Functional alignment between models $A$ and $B$ is measured by the performance of a “stitched” model where an affine layer connects the first $l$ layers of $A$ and the... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for the positive comments.
## Experimental Design or Analyses:
> One potential concern is that no hyperparameter tuning was done for the sender or receiver models on the real-world data (although many experiments use pretrained ImageNet model... | Summary: This paper suggests rethinking the use of model stitching as a representation comparison tool. Model stitching is a functional approach to comparing representations of two models. Essentially one glues the first $k$ layers of one model with the last $\ell$ layers of another model with 1-2 *stitching* layers. T... | Rebuttal 1:
Rebuttal: Thank you for the useful suggestions and detailed review.
## Claims and Evidence
We agree that the paper can stand on its own without the broader statements about the functional perspective. We will restrict these to the discussion section. Would this fully address the concern?
## Methods and E... | Summary: This paper contributes to the representation alignment field. The core message is that the functional similarity of representation spaces, measured by stitching performance, is not correlated with information content. This is shown empirically through well-controlled settings (where the variation factors are k... | Rebuttal 1:
Rebuttal: Thank you for the useful recommendations, questions, and very constructive review.
## Experimental Design or Analyses
> Even after reading the corresponding section in the supplementary material, I had trouble understanding the "Embedding Mapping" procedure for AE stitching (Section 6). My unde... | null | null | null | null | null | null |
Vintix: Action Model via In-Context Reinforcement Learning | Accept (poster) | Summary: The paper introduces Vintix, a cross-domain action model capable of in-context reinforcement learning (ICRL).
The key contributions include:
(1) Continuous Noise Distillation, extending existing work to continuous action spaces;
(2) a cross-domain dataset spanning 87 tasks across 4 environments (Meta-Wor... | Rebuttal 1:
Rebuttal: Thank you for your review. Based on your feedback, we have identified these key points:
1. **How does Vintix compare to the latest action models?**
2. **Does Vintix learn to adapt or does it mimic expert through task identification?**
3. **How does the quality of dataset impact performance?**
4. ... | Summary: This paper explores the potential of In-Context Reinforcement Learning (ICRL) for developing generalist agents capable of learning and adapting through trial-and-error interactions at inference time. The authors present Vintix, a fixed, cross-domain action model that leverages the Algorithm Distillation (AD) f... | Rebuttal 1:
Rebuttal: Thank you for the review. Based on your feedback, we believe the central point of discussion is the practicality — in a broad, real-world sense — of the proposed approach. In particular, we address the following raised topics:
- **Do the selected domains represent a broad spectrum of challenging,... | Summary: This work explores In-Context Reinforcement Learning (ICRL) as a method for developing generalist agents that can learn through trial-and-error during inference. The proposed approach is built on Algorithm Distillation (AD), a prior in-context RL work. Specifically, based on AD, the authors adopt continuous no... | Rebuttal 1:
Rebuttal: Thank you for the time and effort you devoted to reviewing our paper. We have identified the following key issues for further discussion:
**Is it possible to include experiments comparing the proposed approach with vanilla AD to provide stronger evidence?**
Vintix diverges from standard AD throu... | Summary: This paper proposes a method to train a general ICRL-capable agent following a version of Algorithm distillation (aka noise distillation) across four environment suites (aka domains). Their model architecture (like JAT) makes a complete transition into one token allowing them to expand to larger contexts for I... | Rebuttal 1:
Rebuttal: Thank you for the review. Based on your feedback, we believe the main points of discussion can be distilled into the following:
- **Does Adaptability (or inference-time learning) happen, or is it just Task Identification (context conditioning)?**
- **How to scale to non-vector-based/proprioceptiv... | null | null | null | null | null | null |
BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms | Accept (poster) | Summary: This paper introduces BANDITSPEC, which adaptively selects configurations for speculative decoding to improve inference speed. Unlike previous approaches that use fixed speculative decoding configurations regardless of context, BANDITSPEC formulates hyperparameter selection as a Multi-Armed Bandit problem, ena... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reply and acknowledge the soundness of our theoretical results. We answer the questions regarding the experiments as follows:
>**Q1**: Incomplete competitor comparison with adaptive speculative decoding algorithms like SpecDec++
Thank the reviewer for the p... | Summary: The paper introduces BanditSpec, a training-free online learning framework to route prompts to suitable off-the-shelf specualtive decoding methods. The authors formulate the problem as a Multi-Armed Bandit (MAB) problem and propose two bandit-based algorithms, UCBSpec and EXP3Spec, to adaptively choose differe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading and feedback.
>**Q1**: Computational overheads introduced by the proposed method, such as memory usage and latency.
Thank the reviewer for the advice! For the memory and memory bandwidth usage, please kindly refer to this anonymous link [Table_Memo... | Summary: This paper proposes a training-free online learning framework to adaptively choose the configuration of the hyperparameters for speculative decoding as text is being generated. Specifically, this paper first formulates this hyperparameter selection problem as a Multi-Armed Bandit problem, and proposes two band... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and helpful suggestions.
>**Q1**: It would be more convincing if the authors could provide more supports for the reasonableness of formulating draft model selection across different decoding steps as a over-simplified multi-armed bandit problem.
We ... | null | null | null | null | null | null | null | null |
Variational Counterfactual Intervention Planning to Achieve Target Outcomes | Accept (poster) | Summary: The paper introduces Variational Counterfactual Intervention Planning (VCIP), a framework for determining optimal intervention sequences in personalized healthcare and other temporal decision-making systems.
Claims And Evidence: yes
Methods And Evaluation Criteria: they do
Theoretical Claims: checked but n... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and recognition of this work. Below we address your main concerns:
**Regarding Concerns about Data:**
"Dependence on observational data quality" is a common challenge in causal inference, especially in medical data analysis where EHR data usage can introduce qu... | Summary: This paper addresses the problem of time varying treatment effect, aiming at finding the sequence of treatments that optimize a target outcome, instead of the typical problem of predicting potential outcomes. It uses the g-formula and a variational approach to estimate the conditional likelihood of achieving t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. Below we address your main concerns:
**Regarding Concerns about Evaluation Criteria:**
Our primary contribution is a novel "counterfactual target achievement" problem formulation, which differs fundamentally from counterfactual estimation addressed by models... | Summary: The paper introduces an approach named "variational counterfactual intervention planning (VCIP)" to address the problem of optimal sequences of interventions selection towards a target outcome. The method is useful particularly in healthcare scenarios. Traditional counterfactual estimation methods suffer from ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and for acknowledging our work. We will address your main concerns below.
**Regarding Concerns about Claims and Evidence:**
The **Consistency** assumption is typically satisfied in clinical settings where treatments are well-defined and outcomes can be stably ... | Summary: This paper presents a new method for finding desirable intervention sequences for individual instances.
First, the authors formulate the task of finding effective intervention sequences as an optimization problem that maximizes the likelihood of the target outcome after the intervention.
Then, the authors pr... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and appreciation of our work. Below, we address your main concerns:
**Explanation of GRP and RCS Metrics:**
GRP focuses on how the model ranks a sequence $\bar{a}\_{t,\tau}$ that can definitely achieve $Y\_{target} = Y[\bar{a}\_{t,\tau}]$. Ideally, GRP should b... | null | null | null | null | null | null |
Matrix Completion with Incomplete Side Information via Orthogonal Complement Projection | Accept (poster) | Summary: This paper studies the problem of matrix completion with side information. When the side information is not complete, the authors propose to use orthogonal complement projection to minimize the signals outside the side information, instead of constraining the recovered matrix in the space spanned by side infor... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. We address all questions point by point below.
**Q1: How are representative algorithms selected? Is there a SOTA method for each dataset?**
A1: For each dataset, we emphasize the comparison within the scope of matrix completion methods. To ensure a compre... | Summary: In this work, the authors propose a new matrix completion method with incomplete side information. The incompleteness of the side information is defined and the solution called Orthogonal Complement Matrix Completion (OCMC) is developed. Theoretical analysis is given to show the upper bound of the errors. Expe... | Rebuttal 1:
Rebuttal: Thank you for the constructive questions. We address them point by point below.
**Q1: The effect of $P_{A^\perp B^\perp}(X)$ in (7) when the side information is complete.**
A1: When the side information is complete, the column and row spaces of $R$ are fully contained in the column space of $A$... | Summary: This paper addresses matrix completion with incomplete side information. The authors propose an Orthogonal Complement Matrix Completion (OCMC) model that leverages orthogonal complement projection derived from available side information. The key insight is that when side information is incomplete, focusing on ... | Rebuttal 1:
Rebuttal: Thanks for the reviewer about the constructive questions. We have addressed all the questions point by point in the following response.
**Q1: Setting of the parameter $\lambda$ when completeness level of side information is unknown.**
A1:The relation between $\lambda$ and completeness level is... | null | null | null | null | null | null | null | null |
QuanONet: Quantum Neural Operator with Application to Differential Equation | Accept (poster) | Summary: This paper proposes QuanONet, A quantum analogy of neural operators, which can be executed in quantum computers. The paper extends the classical universal approximation theorem. The proposed architecture QunONet retains powerful generalization of classical neural operators. The work also highlights a version c... | Rebuttal 1:
Rebuttal: > Judging by the brevity of Appendix E, I am not fully convinced whether these theorems show how the architectural advancements presented in this works is justified.
We apologize for the confusion caused by the over-brevity of the theory and add a detailed proof of a stronger version of quantum ... | Summary: This paper proposes a new model namely QuanONet (and TF-QuanONet) which is the first model purely based on quantum circuit. The paper further generalizes the approximation theorem of DeepONet to quantum setting. It has experiments on ODEs and PDEs and it shows advantages of QuanONet compared to previous quantu... | Rebuttal 1:
Rebuttal: > How many Qbits we would need to run this model?
Based on the experience of DeepONet, operator problems can be solved well by taking vector dimensions as 10-1000, so only less than 10 qubits are needed to build QuanONet. More discussion is visible in our first response to reviewer LZaG. Notably,... | Summary: The paper introduces QuanONet, a quantum neural operator framework designed to solve differential equations using pure quantum circuits. The authors extend classical universal approximation theorems to quantum settings, proving that quantum neural networks (QNNs) can approximate operators for differential equa... | Rebuttal 1:
Rebuttal: > The choice of 5 qubits and fixed Hamiltonian may limit exploration of quantum advantages.
We choose the number of qubits to be 5 mainly based on the experience of DeepONet that operator problems can be solved well by taking vector dimensions as 10-1000, so the quantum state dimension $2^5=32$ ... | Summary: The paper introduces QuanONet, a quantum neural network framework for learning nonlinear operators in differential equations. The primary contribution is extending classical universal approximation theorems for operators to quantum state versions. Two variants are proposed: a standard QuanONet with hardware-ef... | Rebuttal 1:
Rebuttal: > The performance gain over classical methods isn't consistently demonstrated across all cases, which weakens the overall impact.
We add more experiments with larger scale with batch size 100, 100K max iterations and for five runs.
1) ODE: 10K train instances and 10K test instances.
2) PDE: 100... | null | null | null | null | null | null |
Adaptive Self-improvement LLM Agentic System for ML Library Development | Accept (poster) | Summary: The authors propose an agentic system with adaptive self-improvement capabilities, specifically designed for synthesizing high-performance ML libraries. The proposed synthesis algorithm targets architecture-specific programming languages (ASPLs), with experiments conducted on Streaming Tensor Programs. The pri... | Rebuttal 1:
Rebuttal: We thank Reviewer 4wDa for the positive comments and helpful feedback. We were encouraged that the reviewer enjoyed reading the paper, found our ideas novel, and took the time to review the appendix. We will include all the discussions and results below in the revised version.
## Run-time perfor... | Summary: The paper suggests an (agentic) system based on LLMs that self-improves using sampling to learn programming for (architecture) specific languages. It claims that this is a complex task for which little data is available therefore necessitating the need for a reasoning system.
## update after rebuttal
I ackno... | Rebuttal 1:
Rebuttal: We thank Reviewer 6GnU for the constructive comments and helpful feedback. We are encouraged that the reviewer found our improvement non-trivial. Below, we respond to the raised concerns.
## Challenge of this programming task
> *”It is not clear why this programming task should be so challenging.... | Summary: This paper proposes an adaptive self-improving agent system to unleash the ability of LLM to perform complex reasoning using limited data. It aims to automate the ML library development process using ASPL. To evaluate this, this paper builds a benchmark to conduct experiments to demonstrate the effectiveness o... | Rebuttal 1:
Rebuttal: We thank Reviewer hNb4 for the positive comments and helpful feedback. We are encouraged to hear the reviewer found our experiments and demonstrations clear and convincing. We also appreciate the reviewer’s careful review of the entire appendix content for prompt details.
## Other agentic methods... | Summary: This paper introduces a novel task: utilizing LLM Agents that adaptively evolve to develop architecture-specific programming languages, addressing the challenges faced by human engineers in developing corresponding languages for rapidly evolving hardware. The experimental results appear very promising, and the... | Rebuttal 1:
Rebuttal: We thank Reviewer qrAB for positive comments and helpful feedback on our work. We are encouraged to hear the reviewer found the task and method to be innovative and the experimental results to be promising.
## Larger scale evaluation potential
> *”The benchmark dataset, consisting of only 26 ta... | null | null | null | null | null | null |
GS-Bias: Global-Spatial Bias Learner for Single-Image Test-Time Adaptation of Vision-Language Models | Accept (poster) | Summary: This paper introduces Global-Spatial Bias Learner (GS-Bias), a test-time adaptation (TTA) method designed to improve the zero-shot generalization of vision-language models (VLMs) like CLIP, while keeping computational costs low. The core innovation is the addition of two learnable biases—global bias and spatia... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s encouraging and valuable comments on our paper. Below, we address the raised concerns.
**Q1: The proposed global bias mechanism, which relies on global prediction consistency, is not a novel idea.**
**A1:** We acknowledge that relying on global prediction con... | Summary: This paper introduces Global-Spatial Bias Learner (GS-Bias), a test-time adaptation method for vision-language models (VLMs). GS-Bias's main idea is to learn two biases at the output logits of CLIP:
- Global bias that captures semantic consistency across augmented views of a test image
- Spatial bias that lear... | Rebuttal 1:
Rebuttal: **Q1: A typo in the title of the paper.**
**A1:** We sincerely appreciate your thorough and responsible review of our manuscript. We apologize for the typo caused by our oversight, and we will correct "GB-Bias" to "GS-Bias" in the final version.
**Q2: Provide concrete inference examples to illu... | Summary: This paper introduces GS-Bias, a novel test-time adaptation (TTA) method for Vision-Language Models (VLMs). The approach aims to improve zero-shot generalization by learning two biases: a global bias that captures the global semantic features of a test image through consistency across augmented views, and a sp... | Rebuttal 1:
Rebuttal: **Q1: Provide a more comprehensive analysis of the number of spatial regions and the reasons for setting of the number of regions.**
**A1:** Thank you for your insightful comments. In response, we have expanded our analysis by incorporating two new experiments:
- **Exp1:** Computing the number of... | null | null | null | null | null | null | null | null |
Stronger Neyman Regret Guarantees for Adaptive Experimental Design | Accept (spotlight poster) | Summary: Based on a stronger assumption, this paper improves the vanilla Neyman regret upper bound from Dai et al., 2023 by modifying the parameters of an existing algorithm. Additionally, the paper considers a contextual multi-group Neyman regret upper bound, for which the authors propose a corresponding algorithm and... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our paper!
We would like to comment/expand on the point about the lower bounds for the contextual multigroup design: The primary goal of our manuscript on that front is to introduce the multigroup approach to the sequential ATE estimation liter... | Summary: The authors make two main contributions in this paper: Firstly, in the non-contextual adaptive experimental design case, using stronger assumptions on potential outcome bounds, they change the learning rate and clipping schedules in Dai's ClipOGD algorithm to obtain stronger $O(log T)$ Neyman regret guarantees... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our paper and for the interesting questions.
The classical work by Wald is indeed the historical underpinning of much of the research in sequential experimental design, particularly on the hypothesis testing side. Our work shares the broad mot... | Summary: This work studies the design of adaptive, sequential experiments for ATE estimation in the design-based potential outcomes setting. The authors develop adaptive designs without/with covariates to achieve sublinear Neyman regret.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claim... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our paper, in terms of both its contributions and writing, and for the interesting questions!
To address your points in the Strengths and Weaknesses section:
1. Adapting our theory to the Hajek estimator could indeed be a potential follow-up di... | Summary: This paper explores efficient ATE estimation in adaptive experimental designs. The authors focus on Neyman regret, which quantifies the variance difference between the inverse-propensity-weighted (IPW) estimator under the proposed adaptive design and the best fixed design in hindsight. Prior work (e.g., Dai et... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and positive assessment of our paper! We will implement the bullet points in Other Comments and Suggestions.
The suggested paper of Neopane et al (2025) is an independent and concurrent work to ours. We are happy to cite and briefly discuss it. Its s... | null | null | null | null | null | null |
Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling | Accept (poster) | Summary: This paper proposed a new framework called over-tokenized transformer for language modeling, which decouples input and output vocabularies for performance improvement while leveraging the $n$-gram tokens. They have experimentally shown a log-linear relationship between the input vocabulary size and the model t... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have taken to review our manuscript, and are truly thankful to the reviewers for the insightful comments. As a response, we address each point individually.
## Analysis of the training & inference speed
To illustrate training efficiency, we show training throu... | Summary: This paper proposed methods to create much larger input/output vocabularies for transformers. For the input, (causal) n-grams embeddings are used. These are hierarchical in that they are the sum of n-grams for multiple values of n, including the original single valued token. Similarly they suggest Over Decodin... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have taken to review our manuscript, and are truly thankful to the reviewers for the insightful comments. As a response, we address each point individually.
## About Questions on Claims And Evidence
As far as the over-encoding technique itself, indeed, it is ... | Summary: This paper introduces a novel method of scaling vocabulary size for LLMs, where given an existing tokenizer, the model constructs n-gram representations on-the-fly. Several algorithmic optimizations (matrix decompositions) are made to limit the size of the embedding table while handing the exponential growth o... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have taken to review our manuscript, and are truly thankful to the reviewers for the insightful comments. As a response, we address each point individually.
### Performance on Few-shot Tasks
We have conducted few-shot evaluations for in-house experiments. Our... | Summary: This paper reveals the scaling law of vocabulary size. They decouple the encoding and decoding vocabulary and introduce Over-Tokenized Transformers. Using CFG, they demonstrate the advantages of larger vocabulary size in synthetic settings. With this intuition, they design and train language models with larger... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have taken to review our manuscript, and are truly thankful to the reviewers for the insightful comments. As a response, we address each point individually.
### The choice of using n-gram tokens.
Continuing to train BPE to obtain a larger vocabulary is indeed... | null | null | null | null | null | null |
Robust Secure Swap: Responsible Face Swap With Persons of Interest Redaction and Provenance Traceability | Accept (poster) | Summary: The work proposes a novel method to transfer a general face swap method to a secure face swap method, where POI is rejected and non-POI is passed to generate swapped face image with a tracable, unique, invisible watermark. Specifically, an ID Passport layer is proposed to recognize if the input face image is P... | Rebuttal 1:
Rebuttal: ### **(A) Other Weaknesses**
- **A-Q1: ID passport layer.**
**(I) Motivation**. Our design of the ID passport layer is driven by the goal of enabling ID-sensitive processing within the faceswap model. It is motivated by the need to differentiate outputs between POI and nonPOI based on ID informa... | Summary: This paper introduces a method to prevent unauthorized face swaps involving persons of interest (POIs), while embedding an invisible watermark in non-POI results. Experiments demonstrate that the method maintains the performance of the original face swap model, effectively prevents unauthorized swapping, and e... | Rebuttal 1:
Rebuttal: ### **(A) Other Weaknesses**
- **A-Q1(I): Comparisons with other methods addressing unauthorized face swaps and watermark.**
**Compare with anti-faceswap methods.** Existing methods to address unauthorized face swaps fall into two categories: proactive protection and post-hoc detection. Post-ho... | Summary: The paper presents a method that incorporates a trainable adapter into an existing GAN-based face-swapping pipeline to safeguard the privacy of Persons of Interest (POIs) by redacting their appearance in the output. Additionally, it embeds a watermark for traceability while preserving identity transferability ... | Rebuttal 1:
Rebuttal: ### **(A) Method & Evaluation Criteria**
- **A-Q1: Performance on FFHQ.**
We did not consider FFHQ as it lacks ID labels to support direct POI reduction evaluation. Still, we performed evaluation via data augmentation. To evaluate FFHQ as nonPOI, we trained models with FFHQ and calculated the qual... | null | null | null | null | null | null | null | null |
Time-Aware World Model for Adaptive Prediction and Control | Accept (poster) | Summary: The work presents a world model conditioned on time step size, showing that training with a sampling of different time step sizes can improve long horizon prediction stability. The algorithm conditions the world model on the time step size and uses 4th order Runge-Kutta to integrate the dynamical model. Experi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comprehensive reviews and suggestions!
**Weakness 1.** The Nyquist sampling motivation is weak given the lack of a theoretical or empirical method to establish the connection to the theoretical signal optimization needs.
A1. We have added the theoretical analysis of ... | Summary: This paper proposes Time-Aware World Models (TAWM) to enhance the robustness of world models in various frequency control tasks. During training, TAWM takes the randomly sampled transition time interval ($\Delta t$) as an additional condition, enabling it to adapt to the control frequency of the test environme... | Rebuttal 1:
Rebuttal: Please see additional experimental results [here](https://sites.google.com/view/anonymous-site-rebuttal-6714).
**Q1. Gap between experimental design and motivation (data collection frequency matches the testing frequency in most manipulation tasks).**
A1. We appreciate the reviewer’s concerns bu... | Summary: This work introduces Time-Aware World Model (TAWM), a model-based approach designed to explicitly incorporate the temporal dynamics of environments. By conditioning on the time step size, ∆t, and training over a diverse range of values ∆t – rather than relying on a fixed time step size – TAWM enables learning ... | Rebuttal 1:
Rebuttal: Please see additional experimental results [here](https://sites.google.com/view/anonymous-site-rebuttal-6714).
**Q1. Lack of theoretical claims**
A1. To corroborate our empirical results, we offer additional theoretical analysis on the sample efficiency of our proposed time-aware world model. He... | null | null | null | null | null | null | null | null |
How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction | Accept (poster) | Summary: In this paper, the authors provide a detail theoretical analysis on the effect of data augmentation on the downstream classification performance in contrastive learning, where the intra-class and inter-class augmentation overlap are considered. Based on these results, the authors propose to apply SVD on the in... | Rebuttal 1:
Rebuttal: We are grateful to you for your valuable comments and constructive suggestions.
**Q1:** A constant 2 appears suddenly. Inequality (4) holds only when inner product is greater than 0.
**A1:** Thanks. The constant 2 is not necessary. We have deleted this constant factor. Therefore, inequality (4)... | Summary: This paper investigated theoretically the impact of labeling error on the downstream classification performance of contrastive learning. The authors demonstrate—both theoretically and empirically—that employing a moderate embedding dimension, data inflation, weak augmentation, and SVD fosters greater graph con... | Rebuttal 1:
Rebuttal: We are grateful to you for your valuable comments and constructive suggestions.
**Q1:** Still no evidence showing whether the labeling error is sufficiently small in this paper.
**A1:** Thanks for your constructive comment. Our work didn’t guarantee that the label errors are sufficiently small,... | Summary: This paper theoretically analyzes the effect of labeling errors, particularly cases where an augmented example may belong to a different class than the original example. Based on this, the authors further study how performing dimensionality reduction on representations can mitigate the negative impact of label... | Rebuttal 1:
Rebuttal: We are grateful to you for your valuable comments and constructive suggestions.
**Q1:** Since the labeling issue ultimately stems from imperfections in the augmentation process, could it be addressed simply by making more careful choices in augmentation? For instance, setting the cropping ratio ... | Summary: This paper investigates the impact of labelling errors introduced by augmentation in contrastive learning. The authors first demonstrate labelling errors in terms of positive and negative pairs and theoretically prove that these errors affect the upper and lower bounds of the downstream classification risk. Fu... | Rebuttal 1:
Rebuttal: We are grateful to you for your valuable comments and constructive suggestions.
**Q1:** ImageNet?
**A1:** Thanks. Considering the size of the original ImageNet is too large, it is hard to complete the experiment on it before the deadline (March 31 (AoE)). We have added experiments on TinyImageNe... | null | null | null | null | null | null |
Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance | Accept (poster) | Summary: This paper takes a renewed look at analysing convergence of score-based generative models, in the Wasserstein-2 metric. The explore guarantees under a weaker assumption than log-concavity, namely weak log-concavity (introduced in Conforti, 2023). This enables the authors to obtain natural non-asymptotic c... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful comments and supportive review.
**On Gaussian Mixtures.** As highlighted in Remark 4.2, the generalization of Proposition 4.1 to Gaussian mixtures with non-isotropic components is indeed straightforward. It involves considering bounds based on the minimum o... | Summary: The paper studies the properties of Score-based Generative Models (SGMs) beyond the conventional setting where the data distribution $\pi_{\mathrm{data}}$ is log-concave and satisfies certain regularity conditions. The analysis is based on the three approximations that must be made to implement SGMs. First, o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and positive feedback.
**Assumption H2.** We emphasize that Assumption H2 is fully comparable to the standard estimation error assumptions widely used in the literature (see, e.g., Conforti et al., 2025; Benton et al., 2024; Chen et al., 2022a). In particu... | Summary: This paper looks at showing that diffusion models can be quickly
sampled from with bounded W2 error. There is a long line of work
showing this for TV or KL error, but converting those to W2 incurs a
significant penalty. This paper shows a W2 bound directly, assuming
one-sided lipschitzness and weak log conca... | Rebuttal 1:
Rebuttal: We appreciate the detailed comments and constructive feedback. We highlight that, as stated in Theorem 3.4 and its accompanying discussion, this result indeed provides a bound up to a multiplicative constant depending on $\alpha, M, L_U$, and the second-order moment $m_2$ of $\pi_{\rm data}$, via ... | Summary: This paper considers the sampling efficiency of diffusion models driven by the OU process in terms of Wasserstein distance under a new kind of "tilted" score-estimation assumption. The theorem only needs a weaker curvature condition for the potential function rather than the standard log-concavity. These condi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort taken to provide such detailed and insightful feedback. These comments have been valuable in the clarification of key aspects of our work and strengthen its presentation.
**Assumption H2.** We have clarified in the main text the comparability of Assum... | null | null | null | null | null | null |
Distinguishing Cause from Effect with Causal Velocity Models | Accept (poster) | Summary: The authors proposed a novel solution to the bivariate causal discovery problem. The key idea is to view the SCM as a flow. The flow model is learned by posing the continuity constraints (minimizing an objective that forces the continuity equation). The value of this objective is further used to decide the cau... | Rebuttal 1:
Rebuttal: We thank the reviewer for their critical reading of our paper. The reviewer raises a few concerns and points of confusion that we believe can be addressed.
**Reliance on score/density estimate**: Please see [response to reviewer 9rbC](https://openreview.net/forum?id=gV01DWTFTc¬eId=yMKZKiiUzH).... | Summary: The paper proposes a bivariate causal discovery algorithm utilizing velocity models, viewing structural causal models as dynamical systems where the cause variable acts like time. The approach establishes a relationship between causal velocity and score functions of data distributions, which is exploited for d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and analysis of our paper. The reviewer lists a few weaknesses and questions that we would like to address.
**Extension to multivariate settings**: Please see our [response to reviewer xsfW](https://openreview.net/forum?id=gV01DWTFTc¬eId=pI8MqzA... | Summary: This paper delves into how to distinguish between causes and effects in causal relationships through Causal Velocity Models. It proposes a novel framework that treats bivariate Structural Causal Models (SCMs) as dynamical systems and parameterizes these models using causal velocity. The core idea of this metho... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and analysis of our paper. The reviewer seems to have appreciated the main benefits of the proposed method (bivariate causal discovery with minimal model assumptions), with some reservations about its reliance on nonparametric score estimation. Our r... | Summary: This work studied a class of bijective structural causal models from the perspective of dynamical systems. The identifiablity of the causal model was shown through velocity functions. A loss function is proposed to solve for the velocity function, as well as being used to quantify how well a bijective causal m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
**Extension to multivariate settings**: The multivariate case is not a straightforward extension of the bivariate setting. To extend the velocity interpretation we would need to carefully define a multivariate time. We agree that this extension is intere... | null | null | null | null | null | null |
Auditing Prompt Caching in Language Model APIs | Accept (poster) | Summary: This paper ascertains that hint caching in the LLM API can precipitate privacy breaches and divulge model architecture information. To prevent such issues, API providers are recommended to permit only user-level caching and make caching policies public for enhanced transparency. Through these examinations, the... | Rebuttal 1:
Rebuttal: Thank you for the detailed and thoughtful review! We address the main points below.
## 1. Request routing strategies
We can show that our experimental results are valid regardless of routing strategies. Under the null hypothesis $H_0$ of no caching, request-routing will be independent of whether... | Summary: This paper investigates the privacy leakage caused by the prompt caching in LLMs. Basically, prompt caching improves the efficiency of inference by caching and reusing the internal results of previous prompts. The attack could infer whether a given prompt has been used (cached) by simply checking the time to t... | Rebuttal 1:
Rebuttal: Thank you for the detailed and thoughtful review! We address the main points below.
## Practical exploitability
We agree that practical exploitations of prompt cache sharing are challenging. The attacker needs to guess a long prompt prefix to check if it is cached, as you described. As we discus... | Summary: The paper explores a novel privacy risk for hosted language models: timing attacks on the host's prefix cache. They use hypothesis testing to demonstrate that popular LM providers are indeed using prefix caching.
Claims And Evidence: I believe all of the claims made are well supported with statistical evidenc... | Rebuttal 1:
Rebuttal: Thank you for the detailed and thoughtful review! We address the questions below.
## Speculative decoding
This is an interesting point. We have thought about this, and we believe that speculative decoding should not impact the TTFT when we set the max response tokens to 1. First, we note that sp... | Summary: This paper presents an empirical audit of prompt caching mechanisms in language model APIs. It demonstrates that timing differences, arising from cache hits and cache misses, can potentially leak private information and even reveal details about a model’s architecture. The study employs statistical hypothesis ... | Rebuttal 1:
Rebuttal: Thank you for the detailed and thoughtful review! We address the main points below.
## Random prompts produce cache misses
We assume that random prompts produce cache misses because it is exceedingly unlikely that a random prompt shares a prefix of noticeable length with any cached prompts. In t... | null | null | null | null | null | null |
BECAME: Bayesian Continual Learning with Adaptive Model Merging | Accept (poster) | Summary: The paper proposes BECAME, a Bayesian continual learning framework that adaptively merges task-specific models to balance stability and plasticity. Key contributions include:
* A closed-form solution for merging coefficients derived via Bayesian principles, proving that merging models along a linear path can ... | Rebuttal 1:
Rebuttal: We appreciate your time and highlighting these points. Below, we address each concern in detail.
## Reference
We acknowledge the significance of AdaMerging[1] in model merging and promise to cite it to recognize its contribution. Additionally, we will expand the appendix to **provide an comprehens... | Summary: The paper presents BECAME, a Bayesian Continual Learning framework designed to address the stability-plasticity dilemma in continual learning. The method combines gradient projection methods with model merging to balance retaining prior knowledge (stability) and learning new tasks (plasticity). The key contrib... | Rebuttal 1:
Rebuttal: We appreciate your careful review and the constructive suggestions. Your recognition of this work is truly encouraging. Here we present our detailed clarifications and updates, which we believe further enhance our paper.
## Evaluation&C1
In response to your suggestion, we compute Averaged Anytime ... | Summary: This paper introduces a novel framework called BECAME to address a crucial problem in continual learning, i.e., retaining prior knowledge while learning new tasks to achieve stability and plasticity. From the perspective of Bayes continual learning, BECAME develops a novel merging mechanism to bridge the gap b... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and the insightful comments on our theoretical framework and experimental validations. Below, we detail our clarifications and additional proof.
# Theory
1. We agree that the assumption of independence across tasks is necessary for Eq. 6 and will fix it... | Summary: The paper proposes a method for continual learning based on updating the parameters for old tasks under an approach with limited plasticity, e.g. gradient projection methods, and then merging with parameters trained more freely for the new tasks. The paper proposes an approach for determining the merging coeff... | Rebuttal 1:
Rebuttal: We appreciate your time for the review and respect your concern. However, we respectively argue our current experiments have sufficiently validated our contributions.
## Experiment, W2
We believe our experimental design and results sufficiently support our claims for the following reasons:
1. We *... | null | null | null | null | null | null |
PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design | Accept (poster) | Summary: The paper focuses on the task of generative binder design, modeling protein-protein complexes. This is a critical and impactful task in protein design. The authors introduce *PPDiff*, which uses a protein sequence and backbone co-design strategy during generation leveraging a joint diffusion framework. An impo... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's dedication in providing detailed feedback. We have clarified all the reviewer’s concerns and conducted additional experiments accordingly. All ablation studies were conducted by generating one candidate and we evaluated the resulting complexes using AF3 with the same s... | Summary: This paper presents PPDiff, a novel diffusion-based model for protein-protein complex design. The model aims to generate protein binders with high affinity for arbitrary target proteins by simultaneously designing both the sequence and structure of the binder. PPDiff builds upon the Sequence Structure Interlea... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer for the insightful and constructive feedback. We have conducted additional experiments as suggested. Detailed responses to specific questions are provided below:
**Q1: To strengthen the in silico validation, could the authors provide docking scores for the desig... | Summary: The authors propsoe a new diffusion method to tackle the protein complex problem. They define a co-design diffusion method that generates both the structure and the sequence of a protein complex. Then, they introduce a new architecture based on causal attention mechanism as well as knn equivariant layers. They... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments and insightful suggestions. We have addressed all raised concerns and conducted additional experiments as recommended. Please find detailed responses to the specific points below:
**W1:Essential References Not Discussed:I think [1] should ... | Summary: The paper presents a diffusion based generative model to create binding molecules for given protein targets. To do so the paper proposes a joint model which combines both the coordinates and types of residue sites. The major novelty resides in the score function network which alternates between self-attention ... | null | null | null | null | null | null | |
Learning to Steer Learners in Games | Accept (poster) | Summary: The work extends the work by Deng et al, 2019, in showing that one of the key elements of steering is the knowledge of $B$ (Section 4). The authors give some results for steering under (pessimistic) approximations of the 'best response regions' (Def. 5.1) (Section 5) and show upper and lower bounds (Section 5.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful assessment and constructive critiques, which is essential for us to further improve our work. We give section-wise responses and clarifications to the mentioned issues.
### Essential References Not Discussed:
> *"The authors might also consider referring to ... | Summary: This paper studies repeated two-player Stackelberg games where the follower (learner) uses a no-regret algorithm to choose responding actions and the leader (optimizer) aims to play against the learner. Unlike most previous works (e.g., [Deng et al, 2019]) that assume that the optimizer knows the learner's ut... | Rebuttal 1:
Rebuttal: We sincerely appreciate the great amount of time and thought the reviewer has invested in evaluating our manuscript. We are grateful for the precise recognition of our contribution and novelty, and the insightful feedbacks allowing us to refine and improve our work. We address each concern section... | Summary: This paper studies the problem of learning the Stackelberg equilibrium against an unknown follower who plays against the leader with some no-regret learning algorithm. They first show a negative result, which shows that this is impossible if no information about the follower is known by the leader. Then they p... | Rebuttal 1:
Rebuttal: We would first like to thank the reviewer for thoughtful review and recognition of the novelty in the research topic we proposed. Please find our response listed section-wise below:
### Other Strengths And Weaknesses:
> *"...I find the contribution of Section 6 unclear, where learning to steer t... | Summary: The authors propose a new method to steer no-regret learners to a stackelberg equilibrium in repeated two player bimatrix games. There are two main contributions, The first is an impossibility result that there exists a no-regret learner that prevents an optimizer from achieving the stackelberg equilibrium whe... | Rebuttal 1:
Rebuttal: Before addressing the concerns, we would like to thank the reviewer for carefully reading the paper, providing insightful feedbacks and pointing out related empirical fields, all being really helpful for improving our work. Pleas find our response listed section-wise below:
### Methods and Evalua... | null | null | null | null | null | null |
Structure-Guided Large Language Models for Text-to-SQL Generation | Accept (poster) | Summary: This paper introduces SGU-SQL, a structure-guided framework for text-to-SQL generation using large language models (LLMs). By leveraging syntax trees and database schema graphs, SGU-SQL recursively decomposes queries into subtasks guided by SQL syntax, enabling incremental and accurate SQL generation. Experime... | Rebuttal 1:
Rebuttal: Dear Reviewer jn4B,
Thank you for your recognition of our work and for providing such thorough and insightful feedback. Your comments and suggestions are invaluable in helping us improve the quality and clarity of our work.
---
1. **Insufficient details for error analysis.** Thank you for your t... | Summary: The author has proposed Structure Guided text-to-SQL framework. At a high level, it i) represent user query as a graph, vertex is key word and edge is relationship, ii) use schema graph to represent database schema, iii) linking with dual graph encoding (with Relational Graph Attention Network), and iv) apply ... | Rebuttal 1:
Rebuttal: Dear Reviewer 5QmZ,
Thank you for your expertise and insightful comments. Below are detailed responses to your comments and suggestions:
---
1. **Performance on BIRD**. Thanks for your insightful comments. For a thorough evaluation of SGU-SQL's performance, we added top-performing models from t... | Summary: This paper addresses the challenge of generating precise SQL queries from natural language, particularly when handling ambiguous user intents, complex database schemas, and SQL’s rigid syntax. The authors propose SGU-SQL, a framework that enhances Text-to-SQL generation by modeling structural relationships bet... | Rebuttal 1:
Rebuttal: Dear Reviewer wUD6,
We are deeply grateful for your recognition of our work and also appreciate your time and effort in providing insightful suggestions that can help further polish our paper. Below are detailed responses to your comments and suggestions:
---
1. **The effect of the base LLMs**.... | Summary: This paper proposes a novel methodology to enhance the schema linking and complex SQL generation of LLMs for the text-to-SQL domain. Current LLM-based text-to-SQL methods face several challenges like ambiguous user intent, sophisticated database schema which often lacks proper documentations, and complex synta... | Rebuttal 1:
Rebuttal: Dear Reviewer hLFK,
Thanks a lot for your detailed feedback. We really appreciate your time and effort in pointing out the potential concerns related to our paper, and also, thanks a lot for the opportunity to clarify the technical details and contribution of our framework.
To avoid any potentia... | null | null | null | null | null | null |
Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions | Accept (poster) | Summary: The paper introduces a new perspective about jailbreak research -- does LLM generate harmful response that is actually actionable and informative? This is a critical perspective, as existing works didn't explore this direction well. To measure this, the paper suggests a new metric called HarmScore, providing a... | Rebuttal 1:
Rebuttal: Thank you for your encouraging feedback and for recognizing the significance of our contributions. We are grateful that you found our work clearly motivated, supported by extensive experimental results, and offers a critical new perspective on jailbreak attacks. This aligns with our primary goal o... | Summary: This proposes HarmScore and a HarmScore-based workflow to elicit harm based from LLMs that are trained in more than one languages. There are several models in the loop to 1. decompose the harmful user instruction to sub questions; 2. models to search which language is most vulnerable for the target LLM. 3. com... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the engineering strength of our work and for your thoughtful suggestions! We appreciate your willingness to reconsider your score based on a clearer explanation of our contributions.
---
**1. Framing of contributions**
> The paper has more engineering contribution than... | Summary: The paper studies jailbreaking LLMs and makes two key contributions: 1) the paper presents a new metric (HarmScore) for jailbreak effectiveness as an alternative to the commonly adopted attack success rate (ASR). The paper first considers four attributes of LLM responses (Informativeness, Conciseness, Actionab... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable suggestions! We would like to clarify the experimental details and will ensure these are clearly presented in the revised paper.
---
**1. Refusal string in HarmScore**
We use the list of refusal strings from the GCG paper [1] to check whether a res... | Summary: This paper investigates vulnerabilities in large language models (LLMs) by demonstrating that harmful jailbreaks can be elicited through simple multi-step and multilingual interactions.
- First, the authors identify actionability and informativeness as key attributes that constitute a harmful jailbreak respo... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and for acknowledging the significance of our work and its robust experimental results! We appreciate the opportunity to expand on our language evaluation and jailbreak experiments.
---
**1. Reporting ASR and HarmScore for individual languages**
> Report ASR a... | null | null | null | null | null | null |
Visual Generation Without Guidance | Accept (poster) | Summary: 1. They proposed guidance-free training, which reparameterizes the conditional model as a combination of trainable sampling model and frozen unconditional model.
2. They introduced pseudo-temperature input (β) to control the fidelity-diversity trade-off.
3. They reached similar performance compared to CFG ac... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer 2PrL (Part 1/1)
We are glad the reviewer finds our method to be versatile and generally applicable. This is exactly what we are trying to convey in this paper.
**Q1: More comprehensive evaluation metrics like sFID, precision, Recall**
**A1:**
We have conducted a... | Summary: This paper introduces Guidance-Free Training (GFT), a novel method for training visual generative models that eliminates the need for Classifier-Free Guidance (CFG) during inference while maintaining comparable generation quality. The key insight is to do direct optimization of the desired sampling distributio... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer DEzb
**Q1: GFT introduces an additional hyperparameter $\beta$ that requires tuning in training and still in inference.**
**A1:**
We believe there is some misunderstanding over how we tune and inject $\beta$.
1. $\beta$ is not a training hyperparameter and **it ... | Summary: * “Visual Generation Without Guidance” presents Guidance-Free Training (GFT), a novel approach for visual generative models that aims to eliminate the need for guided sampling and reduce computational costs.
* The core of GFT design is to transform the target sampling model into an easily learnable form. Inst... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer oBo4 (Part 1/1)
We thank the reviewer for the insightful review. We are really glad to see the reviewer shares the same belief as us that Guided sampling should eventually be removed from visual modeling. We are also greatly motivated by the high praise given to our... | Summary: In this work the authors have proposed a technical to improve the standard classifier-free guidance. The key idea is to directly optimize the target guided noise regressor (as described in Eq. 6) after converting Eq. 4 into another form amenable for this purpose. The standard CFG demands running the diffusion ... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer 5kGx (Part 1/2)
We thank the reviewer for the very detailed comments! We summarize concerns into four categories:
## Computational/Memory efficiency
**Q1: The reformulation results in two different models active and up to optimization during training, ... Are they s... | null | null | null | null | null | null |
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry | Accept (spotlight poster) | Summary: This paper introduces a framework to study subtypes of the rich regime during training of natural or biological neural networks.
Specifically, the authors use the known concept of manifold capacity to distinguish different phases of training and extract insights for ML and neuroscience.
## update after rebut... | Rebuttal 1:
Rebuttal: We thank reviewer EQUL for their thorough evaluation and insightful questions. Below we address the reviewer questions on (1) our method, (2) experimental results. About the theoretical result, please refer to our response to reviewer NZt7, section 2, which address similar question.
1. Method and... | Summary: The authors revisit the dichotomy of *lazy learning*, where neural networks do not learn data-dependent features and instead act essentially as a kernel machine, and *feature learning*, where they do. They argue that there are in fact several distinct learning regimes in the rich regime, which they untangle by... | Rebuttal 1:
Rebuttal: We thank reviewer NZt7 for their thoughtful reviews and suggestions. We appreciate the reviewer recognized that our works “presents an interesting and really well-written exploration of how various quantities describing the geometry of the task manifold evolve during the training dynamics of neura... | Summary: Numerous studies in representation learning have been conducted to evaluate the quality of features learned by DNNs, particularly in determining whether a neural network functions within the lazy or rich regime. In this paper, the authors presented theoretical foundations grounded in manifold capacity theory t... | Rebuttal 1:
Rebuttal: We thank reviewer **sxs9** for their thorough evaluation of our paper’s motivation, methods, and results, as well as the valuable feedback and insightful questions! We greatly appreciate the reviewer for recognizing that our work “offers substantial potential as an interpretable tool for elucidati... | Summary: This paper uses manifold capacity measures to assess neural representations and learning in the rich and lazy learning regimes. The authors show, both numerically and, in some cases, analytically, that these manifold capacity measures provide a deeper understanding of learning dynamics and neural representatio... | Rebuttal 1:
Rebuttal: We thank reviewer **guAP** for spending time and effort to thoroughly read, evaluate, and provide detailed comments and suggestions for our manuscript!
We greatly appreciate the reviewer for recognizing that our works provide `a deeper understanding of learning dynamics and neural representations... | null | null | null | null | null | null |
Implicit degree bias in the link prediction task | Accept (poster) | Summary: This paper studies the degree bias when benchmarking link prediction methods. Since most graphs follow a power-law distribution, the connected edges are very likely to be formed by two nodes with high degrees. However, the negative edges are often sampled by node, which results in a set of edges formed by node... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive feedback! We also appreciate your remarks on the clarity of our analysis and writing. We have addressed each point raised as follow.
## Dataset statistics
We agree that including all graph dataset statistics improves clarity. We have added a s... | Summary: Observing existing link prediction models often sample partial negative edges for evaluation; this paper hypothesized that this sampled evaluation includes degree bias and would cause the link predictor model to over-fit to capture node degree signal in making a prediction. After empirical and theoretical anal... | Rebuttal 1:
Rebuttal: First of all, thank you for your time and for providing constructive comments on our manuscript!
### > many recent works have come up with more advanced negative sampling strategy, and therefore, I am not sure whether the discovered bias would appear in other places, such as [1] Li, Juanhui, et a... | Summary: This paper argues that the existing benchmark for link prediction inevitably involves implicit degree bias during the positive and negative edges sampling since both sampling distributions are theoretically proven to be inconsistent. Consequentially, existing link prediction methods implicitly overvalue the ch... | Rebuttal 1:
Rebuttal: Thank you for your thorough and meticulous review and many constructive comments!
> ### Alignment is only evaluated between AUC-ROC and VCMPR, ...while neglecting the ranking ability (i.e., NDCG)
We agree with the point and now use NDCG@k as our evaluation metric and report VCMPR@C results in Ap... | Summary: This paper is focused on the link prediction task and it shows how the sampling procedure applied in the evaluation of link prediction methods is biased towards high degree nodes. More specifically, the selection of random negative pairs to be distinguished against positive pairs leads to negative pairs connec... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and for pointing out the important distinction between retrieval and ranking in recommendation systems!
> ### Response to "Claim and Evidence" section
In response to the reviewer's comment along with the comment from reviewer 9kyj, we have now used NDCG@K as... | null | null | null | null | null | null |
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG | Accept (poster) | Summary: The paper introduces EEG-DisGCMAE, a pre-training framework for EEG-based classification using graph neural networks (GNNs). The method integrates graph contrastive learning and masked autoencoders for self-supervised pre-training, followed by graph topology distillation to transfer knowledge from high-density... | Rebuttal 1:
Rebuttal: **Thank you for all your comments. We respond to your comments one by one as below.**
---
### 🟩 **Q1: Claims and Evidence**
**(1)**
We understand your concerns; however, we believe they stem from a misunderstanding of our approach. Our method is purely based on functional connectivity EEG gr... | Summary: This paper introduces a knowledge transfer model based on graph networks and distillation methods, which enables low-density EEG to learn the representation of high-density EEG to better handle downstream tasks. The authors conduct a large number of experiments to demonstrate its effectiveness.
Claims And Evi... | Rebuttal 1:
Rebuttal: **Thank you for all your comments. We respond to your comments one by one as below.**
---
### 🟩 **Q1: Methods and Evaluation Criteria**
**(1)**
Our pre-training framework and distillation loss are designed to be general and compatible with both major types of GNNs: local message-passing mode... | Summary: The study presents EEG-DisGCMAE as a novel and effective approach for EEG-based classification tasks, demonstrating that self-supervised graph pre-training combined with topology-aware knowledge distillation significantly improves LD EEG model performance. The findings suggest that LD EEG devices, which are mo... | Rebuttal 1:
Rebuttal: **Thank you for all your comments. We address each comment individually below.**
---
### 🟩 **Q1: Claims And Evidence: (Generalization to Emotion Recognition)**
To further evaluate the generalization ability of our model, we conducted additional experiments on the **SEED** dataset, a widely use... | null | null | null | null | null | null | null | null |
Open Materials Generation with Stochastic Interpolants | Accept (poster) | Summary: The paper presents an extension of stochastic interpolants for the modelling of crystalline materials. Stochastic interpolants are a general framework that encompasses diffusion models and flow matching as specific instances. As the fractional coordinates live on a torus, they adapt the interpolants to respect... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thorough engagement with our manuscript and thank them for their insightful feedback.
## Ablation studies, hyperparameters, and model performance
Regarding the performance of different stochastic interpolants, we direct the reviewer to the CSP ablation study tables ... | Summary: The paper introduced Open Materials Generation (OMG), a framework that leverages stochastic interpolants in generative models for inorganic crystalline materials. The method is built on existing architecture in the literature (CSPNet), which is based on an equivariant graph neural network (EGNN). The authors a... | Rebuttal 1:
Rebuttal: We thank the reviewer for raising questions about our paper, and address them topically below.
## Comparison with FlowLLM
We agree that FlowLLM is an important material generation method representing the most recent trends. It uses an LLM to sample structures and FlowMM to refine them. We note th... | Summary: This paper introduces a framework called OMG that applies stochastic interpolants to generate inorganic crystalline materials. The authors adapt the stochastic interpolants framework to handle periodic boundary conditions for crystal structures and integrate discrete flow matching for atomic species. Their app... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and address their concerns below.
## DFT relaxation
We agree that DFT relaxations offer a more rigorous evaluation of structure stability. As such, we are currently running DFT calculations for a large batch of generated structures. To assess consistency b... | Summary: This paper extends flow-based inorganic crystalline structure prediction (CSP) to the stochastic interpolants (SI) framework. The authors use an equivariant graph representation (CSPNet) and wrapped interpolants to account for periodic boundary conditions of atomic coordinates and discrete flow-matching (DFM) ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive comments. Below we address the main concerns and the three questions raised.
## Novelty and contribution
We acknowledge that the novelty of our work could have been more clearly emphasized. To clarify:
- While DiffCSP and MatterGen specific... | null | null | null | null | null | null |
Contextual Bandits for Unbounded Context Distributions | Accept (poster) | Summary: Stochastic contextual bandits, where there is a set of K actions, and at each round $t$ the learner observes the current context $X_t$ generated from the fixed distribution. This paper considers the nonparametric setting with the standard assumption of zero-mean noise and Lipsthiz reward function. The authors ... | Rebuttal 1:
Rebuttal: Thanks the reviewer for your careful reading of this paper!
We respond to your comments as follows.
1. We have read the paper you have mentioned: Self-Tuning Bandits over Unknown Covariate-Shifts. In ICALT. We think that this paper is indeed highly relevant, so we will compare it with our paper ... | Summary: Contextual bandit is important in recommendation systems, healthcare, etc. Existing works focus primarily on linear bandits (or other parametric bandits). While some papers study nonparametric bandits, they assume that the support is bounded. In this work, the authors study nonparametric contextual bandit with... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback on the importance and novelty of this paper. We reply to questions as follows.
1. $C_\alpha$ is a constant. In eq.(38), $K$ has $h^{d-\alpha}$ dependence. Throughout the paper, $C_\alpha$ remains a constant.
2. Thanks for the suggestion. We think th... | Summary: In this paper, the authors study contextual bandit problems under the Tsybakov margin condition. They consider settings where the context distribution is either bounded or unbounded but heavy-tailed. Compared to the literature, they work under a weaker version of the Tsybakov margin condition, allowing them to... | Rebuttal 1:
Rebuttal: Thanks for your review. We are encouraged that you agree that our claims are all clear and proved.
Regarding the novelty of algorithm. **We disagree that "the authors merely restate how the parameter $k$ is chosen"**. For our adaptive method, we select $k$ according to eq.(16). For Reeve et al. (... | Summary: The paper studies the setting of contextual bandits for unbounded context set. The paper then proposed an idea of algorithm design based on k-nearest neighbor, with a special design of the optimism term. With an adaptive choice of $k$, the algorithm achieves the minimal-optimal regret.
Claims And Evidence: Al... | Rebuttal 1:
Rebuttal: Thank you very much for acknowledging the writing of this paper. We respond to questions and weaknesses as follows.
**1. Importance of unbounded context distribution**
The bounded action space can be easily generalized to unbounded action space (assuming continuous action space). Unbounded actio... | null | null | null | null | null | null |
Lightweight Online Adaption for Time Series Foundation Model Forecasts | Accept (poster) | Summary: This paper identifies that existing foundation models (FMs) fail to fully utilize the large amount of online feedback obtained during the deployment phase. This is due to the high computational cost associated with regular retraining or fine-tuning, which often leads to the neglect of this valuable feedback. T... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your detailed review and constructive comments. We are happy that you thoroughly assessed our claims and found that they held up. We provide answers to your question below.
**1. Adding details to ablations section**
Thank you for raising this issue. We have updated t... | Summary: This paper introduces AdapTS, a lightweight mechanism designed to enhance the adaptability of Foundation Models (FMs) for time series forecasting by incorporating online feedback. Traditional FMs remain fixed after deployment due to the high computational cost of online updates, preventing them from adapting t... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and questions! We provide answers to your questions below and hope we have satisfactorily answered them, in particular by performing several additional experiments.
**1. Why fixed FMs?**
The main reason we keep the FM fixed is due to the computational expense ... | Summary: This paper proposes AdapTS, a lightweight method for the online adaptation of time series foundation model forecasts. It consists of an AdapTS-Forecaster and an AdapTS-Weighter. Experiments clearly show that AdapTS can significantly improve prediction performance across multiple models and datasets.
Claims An... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your kind words about our work and constructive comments/questions. We are particularly happy that you found that AdapTS gives "significant performance improvement with low computational cost" and is "applicable to any FM". We have included additional baseline comparis... | Summary: The paper proposes a method to combine foundation model forecasts with forecasts from an online learner. They innovate on 2 components, the online learner, and the algorithm to combine the forecasts. The online learner is a linear model in the frequency domain, learned via efficient closed form updates. The al... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for your comments and constructive feedback. We are especially happy you found that "the contributions of the paper are novel and present ideas on how to ensemble predictions in an online fashion". We answer the comments you had below and are thankful that you pointed out ... | null | null | null | null | null | null |
Maximum Noise Level as Third Optimality Criterion in Black-box Optimization Problem | Reject | Summary: The paper studies zero-order optimization of highly-smooth strongly-convex functions. The main result is an algorithm for this setting which works even if there are sufficiently small biases in the oracle, by generalizing the analysis of an algorithm of Vaswani et al. (2019) to account for such biased oracles.... | Rebuttal 1:
Rebuttal: Dear **Reviewer ubQW**,
Thank you for your feedback. The specific comments are addressed below:
>**Throughout the paper, the authors claim to be the first paper taking into account biases of oracles. This is simply incorrect…**
With all due respect, we disagree with your comment that we claim t... | Summary: This paper proposes a zero-order method.
## update after rebuttal:
This type of writing issue cannot be resolved in the reviewing process of conferences.
Claims And Evidence: A paper with this level of writing should not be considered at all regardless of its contribution.
Methods And Evaluation Criteria: A... | Rebuttal 1:
Rebuttal: Dear **Area Chair** and **Senior Area Chair**,
Please check the Official Review by **Reviewer NqNV** for quality, constructiveness, correctness and satisfaction with the ICML Code of Conduct.
With Respect,
Authors | Summary: This paper theoretically analyzes the black-box optimization with noisy feedback under the accelerated stochastic gradient descent framework. In particular, this paper generalizes existing convergence results for accelerated stochastic gradient descent to the case where the gradient oracle is biased. In addit... | Rebuttal 1:
Rebuttal: Dear **Reviewer Hj39**,
Thanks for the insightful review. We are happy to see that the reviewer emphasized that the article provides a good theoretical analysis. The specific comments are addressed below:
>**This paper claimed achieving an improved iteration complexity. However, it seems that ... | null | null | null | null | null | null | null | null |
Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks | Reject | Summary: This paper proposes a series of elements for boosting the unsupervised learning of encoding-decoding direction pairs. Specifically, the paper proposes a signal-distractor model to encode various concepts and distractor components, several regularization losses to optimize the upper bounds and enforce the spars... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and thoughtful suggestions, which can be instrumental in improving our work. We greatly appreciate the time and effort the reviewer invested in providing constructive feedback. Below you may find some clarifications regarding your specific comments.
*... | Summary: The paper is related to the work that tries to extract concepts to understand deep vision networks better, which are called concept-based explanations. The paper's contribution is an approach to learning the concept encoding-decoding direction jointly, in an unsupervised manner. The paper is strongly built on ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the careful review and insightful comments provided by the reviewer, which can greatly contribute to enhance our manuscript. The time and effort of the reviewer in providing constructive feedback are truly valued.
- **Mechanistic Interpretability**: This work is related to ... | Summary: The paper introduces a method for jointly learning concept “encoding” and “decoding” directions in an unsupervised manner. It uses a combination of interpretability-driven loss terms (e.g., sparsity, margin constraints), and alignment with the network’s uncertainty region. Experiments on synthetic data and Res... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for her/his thoughtful review and valuable suggestions, which can help improve the clarity and quality of our manuscript. We truly appreciate the time and effort she/he dedicated to providing constructive feedback. Below you may find specific answers to some of the po... | null | null | null | null | null | null | null | null |
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