title stringlengths 15 163 | paper_decision stringclasses 4
values | review_1 stringlengths 853 32.6k | rebuttals_1 stringlengths 0 15.1k | review_2 stringlengths 1.03k 35.6k | rebuttals_2 stringlengths 0 15.1k | review_3 stringlengths 807 27.4k ⌀ | rebuttals_3 stringlengths 0 15k ⌀ | review_4 stringlengths 780 22.2k ⌀ | rebuttals_4 stringlengths 0 15.1k ⌀ | review_5 stringclasses 171
values | rebuttals_5 stringclasses 166
values | review_6 stringclasses 25
values | rebuttals_6 stringclasses 24
values | review_7 stringclasses 4
values | rebuttals_7 stringclasses 4
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
One-Step Generalization Ratio Guided Optimization for Domain Generalization | Accept (oral) | Summary: The paper presents GENIE (Generalization-ENhancing Iterative Equalizer), an optimizer aimed at improving domain generalization (DG) by using the One-Step Generalization Ratio (OSGR). GENIE dynamically equalizes the contribution of each parameter to loss reduction, preventing overfitting to domain-specific feat... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful and constructive review. We greatly appreciate your insightful comments and the opportunity to clarify several important points you raised.
# Proof Clarity
You suggested additional clarity in the proof derivations, particularly regarding Theorem 3.1. Theore... | Summary: This paper proposes GENIE, a novel stochastic optimizer designed for Domain Generalization (DG) tasks. Unlike standard optimizers (SGD, Adam, etc.) that can over-emphasize certain “spurious” features, GENIE uses a metric called One-Step Generalization Ratio (OSGR) to guide parameter updates. The key idea is to... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and constructive review.
# A1
As you pointed out, our initial submission reported only the best single trial due to computational constraints. Following your suggestion, we have now conducted additional experiments with three independent trials per optimizer ... | Summary: This paper proposes a novel optimizer that leverages the one-step generalization ratio to assess each parameter’s contribution to loss reduction, aiming to promote domain-invariant feature learning.
Claims And Evidence: The paper’s claims are clearly stated, and the experiments presented provide convincing ev... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. Below, we address the points raised under "Experimental Designs or Analyses":
# A1
Because of limited time and computational resources, we were unable to include experiments on additional tasks such as object detection or segmentation within the submission pe... | null | null | null | null | null | null | null | null |
The Emperor's New Clothes in Benchmarking? A Rigorous Examination of Mitigation Strategies for LLM Benchmark Data Contamination | Accept (poster) | Summary: This paper discusses a way to evaluate Benchmark Data Contamination (BDC) mitigation strategies. The authors set up two key standards, Fidelity and Contamination Resistance, as criteria of assessing reliability of each method. By following a rigorous evaluation pipeline, experiments on 10 LLMs, 5 benchmarks, a... | Rebuttal 1:
Rebuttal: > Q1: Why high fidelity is necessary
High fidelity is necessary because a low fidelity score indicates that the updated benchmark has undergone **excessive** changes from the original benchmark, which can introduce two practical issues:
(1) **Answer invalidation**: The modifications may alter th... | Summary: Designed a systematic and controlled pipeline to provide fine-grained and comprehensive assessment of existing benchmark data contamination mitigation strategies. They focus on a question-level study experimenting with 10 LLMs, 5 benchmarks, 20 mitagation stagetries with 2 scenarios. From this, they find that ... | Rebuttal 1:
Rebuttal: > W1: More datasets and models
Our current study includes 10 LLMs, 5 benchmarks, 20 mitigation strategies, and 2 contamination scenarios, yielding 10×5×20×2 = 2000 evaluation results. While we believe this already provides a comprehensive analysis, we agree that including more models and benchmar... | Summary: This paper introduces a systematic pipeline and proposes two metrics—fidelity and contamination resistance—to provide a fine-grained and comprehensive assessment of existing benchmark data contamination (BDC) mitigation strategies. The authors evaluated 20 different BDC mitigation approaches across 10 LLMs, 5 ... | Rebuttal 1:
Rebuttal: > W1 & Q1: Misunderstanding of contradictory results
We clarify that our claim is not contradictory: while some **semantic-preserving** mitigation strategies (e.g., MPA and ITD) achieve significantly higher resistance scores than the vanilla case on **certain benchmarks** (e.g., MMLU, TruthfulQA,... | Summary: This paper investigates mitigation strategies for benchmark data contamination (BDC) in LLM evaluation. The authors argue that current approaches for assessing BDC mitigation strategies, which focus on aggregate accuracy metrics, have significant limitations. To address this, they propose two metrics---fidelit... | Rebuttal 1:
Rebuttal: > W1: More complex evaluation tasks
Thank you for the insightful suggestion. As the first work to rigorously assess BDC mitigation strategies for LLMs, we focus on commonly used evaluation tasks as adopted in prior BDC mitigation studies [1-3]. We agree that extending the analysis to more complex... | null | null | null | null | null | null |
Optimizing Test-Time Compute via Meta Reinforcement Finetuning | Accept (poster) | Summary: The paper formalizes the problem of optimizing test-time compute as a meta-reinforcement learning problem and proposes to use cumulative regret as an optimizing objective instead of barely the outcome reward. The cumulative regret can be calculated by estimating the information gain. The authors further develo... | Rebuttal 1:
Rebuttal: Thank you for your positive review of the paper! We will add more experimental details to the Appendix – in particular, regarding the experimental setting for both MRT (STaR), MRT (RL), and analysis on R1, and we will also cite the [o1](https://openai.com/o1/) and [o3](https://openai.com/index/ope... | Summary: This paper introduces Meta Reinforcement Finetuning (MRT), a framework to optimize how large language models (LLMs) utilize test-time computational resources. The authors frame test-time compute optimization as a meta reinforcement learning (RL) problem, where the LLM generates a stream of token episodes (e.g... | Rebuttal 1:
Rebuttal: Thank you for your feedback! To address your concerns, we clarify the notion of token efficiency and interpret the results of MRT in comparison with length-penalty and baseline GRPO, add new results running MRT on top of DeepSeek-R1 models to extend beyond three-episode setting, and add numerous v... | Summary: This paper suggests a novel perspective on test time compute through long generation through the formulation of meta-rl. It suggests that the correct way to trade exploration and exploitation, in this case, is through the notion of cumulative regret. Furthermore, it claims that we should judge if a partial res... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We've added new results measuring regret for MRT and baselines, showing MRT attains smaller regret and improved performance in more general settings. We will also update the paper with more clarifications and the definition of STaR. **If your concerns are addressed, we... | null | null | null | null | null | null | null | null |
LAST SToP for Modeling Asynchronous Time Series | Accept (poster) | Summary: The authors propose a method for modeling temporal event sequences by finetuning pretrained language models. The paper shows that by using a novel prompt tuning method they are able to outperform several baselines and ablations.
Claims And Evidence: On the whole, yes. I am generally skeptical of methods that ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive evaluation, thoughtful suggestions, and recognition of our contributions, including the LASTS representation and the Stochastic Soft Prompting (SToP) mechanism. We respond to each comment below:
**Random Initialization:** Thank you for highlighting this relev... | Summary: This paper presents LASTS (Language-modeled Asynchronous Time Series), a novel framework for modeling asynchronous time series data using Large Language Models (LLMs). The approach addresses the challenges of irregular timing and diverse event types by representing asynchronous time series as natural language ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful summary and generous assessment of our work. We are glad that the core contributions—LASTS and Stochastic Soft Prompting (SToP)—were found meaningful and well-supported. Below, we address the specific questions raised:
**Train/Validation/Test S... | Summary: This paper introduces a novel framework for modeling asynchronous time series data using Large Language Models (LLMs). Unlike regular time series with evenly spaced time points, asynchronous time series consist of timestamped events occurring at irregular intervals, each described in natural language. This wor... | Rebuttal 1:
Rebuttal: Thank you for recognizing the creative integration of concepts, practical applicability, innovation in training techniques, and comprehensive analysis in our work. Responses to key points raised:
**Chronos: Limited analysis; inclusion of TEMPO:** Chronos performs poorly as expected, given its rel... | Summary: This paper presents LASTS, a novel framework that uses large language models (LLMs) to model asynchronous time series—sequences of events that occur at irregular intervals and are described in natural language. Unlike traditional methods that rely on fixed time intervals and predefined event categories, LASTS ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed review. We appreciate the recognition of our method's novelty, the thoroughness of our experimental evaluation, and the relevance of our chosen tasks and benchmarks. We also thank the reviewer for highlighting how our work aligns with broader scie... | null | null | null | null | null | null |
HyperNear: Unnoticeable Node Injection Attacks on Hypergraph Neural Networks | Accept (poster) | Summary: This paper focuses on the vulnerability of hypergraph neural networks (HNNs) to node injection attacks. The authors introduce HyperNear, a novel node injection attack method specifically designed for HNNs, which exploits the homophily property to improve stealthiness. Through extensive experiments, the study d... | Rebuttal 1:
Rebuttal: We sincerely appreciate your **comprehensive and positive evaluation** of our work, particularly your recognition of our **theoretical contributions, experimental rigor, and positioning within the broader adversarial attack literature on hypergraphs**. Your feedback reinforces the significance of ... | Summary: This work introduces HyperNear, a homophily-preserving node injection attack for hypergraph neural networks (HNNs). It provides a theoretical analysis of hypergraph vulnerability and demonstrates that homophily can be leveraged to enhance attack stealth. Extensive experiments show that HyperNear is highly effe... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed review and for recognizing the novelty and contributions of our work, including the introduction of HyperNear, its theoretical grounding, and its strong empirical results. Below, we address your comments point by point.
Weakness1: Figure 4 illustrates the dif... | Summary: This paper proposes a black-box node injection attack on Hypergraph Neural Networks (HNNs), named HyperNear. Unlike previous gradient-based white-box attacks on HNNs, this method does not require access to model parameters or gradients. Instead, it strategically injects malicious nodes and optimizes their conn... | Rebuttal 1:
Rebuttal: Claims&W2&W3: Thank you for your valuable feedback and for recognizing the effectiveness of our attack methodology.
Our claim of stealthiness is based on the observation that homophily-aware attacks introduce perturbations aligned with existing structural patterns, making them less detectable tha... | Summary: The authors proposed a node injection attack algorithm for hypergraph neural networks in black-box setting.
Claims And Evidence: Problematic claims:
1. Un-noticability: I do not understand why the authors claim unnoticability where Figure 4(a) clearly distinguishes that "After attack" distribution is bimodal ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed feedback and address your concerns below.
Claims1: Our claim of unnoticeability is **relative**, meaning that our attack is designed to be less detectable compared to naive perturbations. **The degree of unnoticeability also varies across datasets due to diff... | null | null | null | null | null | null |
Discrepancy Minimization in Input-Sparsity Time | Accept (spotlight poster) | Summary: The paper gives a new algorithm for discrepancy minimization over real valued matrices that nearly runs in input-sparsity time. Specifically, building on Bansal's and Larsen's previous algorithms, the authors give a
1) A combinatorial algorithm that runs in time $\tilde{O}(nnz(A) + n^3)$ time.
2) If Fast M... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments and thorough evaluation of our paper.
### W1 and W2: Technical Density and Intuition
We appreciate the reviewer's concern regarding the technical density of our presentation. Given the complex nature of discrepancy minimization and the d... | Summary: The authors develop a new, faster algorithm for approximate discrepancy minimization with bounds on the computation time depending on the input-sparsity. Their algorithm is optimal for "tall" matrices, i.e. m x n matrices with m being a polynomial in n.
Additionally, the accuracy of the approximation matches a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
### Theoretical Claims: The proof of Theorem C.3 (page 19) claimed that a particular set of eigenvectors are orthogonal, which is not true in general.
Because any real symmetric matrix admits an orthogonal diagonalization, its eigenvectors can alw... | Summary: The paper is on discrepancy minimization for real matrices - goal is to develop constructive methods that exploit input sparsity. Algorithmic discrepancy is a well-studied topic in TCS and there have been many breakthroughs in the last 15 years, starting with Bansal. Recently there was a result for binary ma... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and for recognizing the novelty, significance, and technical contributions of our work.
### W1: Connection to existing works
While our work indeed builds upon Larsen's algorithm and Lovett–Meka random walk method, we would like to highlight the sign... | Summary: This paper proposes an improved randomized algorithm for discrepancy minimization problem for real-valued matrices m*nmatrices A with m = poly(n). The paper builds on top of work of Larsen and proposes an improvements to Larsen's algorithm that allow authors to achieve a combinatorial algorithm that runs in in... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's recognition of our contributions, specifically, including significant runtime improvements, novel algorithmic techniques, clear comparisons to existing literature, and rigorous theoretical justifications. We believe our contributions represent a significant ste... | null | null | null | null | null | null |
AutoAL: Automated Active Learning with Differentiable Query Strategy Search | Accept (poster) | Summary: This paper addresses the active learning problem. Given the existence of numerous active learning methods for selecting the most informative samples, this work proposes an end-to-end framework that integrates multiple approaches. The framework consists of SearchNet and FitNet:
SearchNet assigns a score to eac... | Rebuttal 1:
Rebuttal: We thank for your feedback and the confirmation of our proposed AutoAL! We agree that AutoAL is an end-to-end framework could effectively select the best strategy for a given dataset or task. We also thank for your valuable question, so we want to clarify the followings:
**Q1:** Is there any opti... | Summary: The paper proposes an algorithm selection strategy for active learning. The proposed method utilizes the existing labeled dataset and trains differentiable policies for data selection. Experiments are conducted on numerous datasets showing the effectiveness of their algorithm.
Claims And Evidence: I did find ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate that you confirm our contribution to the community on solving the strategy selection problem and our method: using differentiable bi-level framework.
For your concerns, we add more experiments to show the results as below:
**Q1:** The author sho... | Summary: This paper addresses the challenge of active learning (AL) by proposing AutoAL, a automated active learning framework. The authors highlight that optimal AL strategies vary across different datasets and problem settings. To address this, AutoAL first extracts scores from multiple acquisition functions and then... | Rebuttal 1:
Rebuttal: Thank you for your feedback. For your questions, we make the following comments to clarify our points:
**Q1:** **Sampling Bias in Actively Selected Data**
**A1:** For our initial seed dataset setting, it is i.i.d., ensuring an unbiased starting point. Traditional single-criterion AL strategies,... | Summary: The proposed AutoAL is an automatic query strategy search algorithm that utilizes bi-level optimization framework to select optimal AL strategies built upon existing uncertainty and diversity-based approaches.
Claims And Evidence: Yes, most of the claims are supported by clear and convincing evidence. The com... | Rebuttal 1:
Rebuttal: hank you for your valuable feedback. We appreciate that you confirm our method development, promising performance, and fluent paper writing.
For your concerns, we make the following comments to clarify our points:
**Essential References Not Discussed:** We thank for your efforts in finding these... | Summary: This paper proposes a new method for active selection that leverages existing AL algorithms as constituent agents. It consists of two neural networks, fitnet and searchnet, each trained using the pool of data that has already been labeled. searchnet is fit to select the best ament a set of pre-chosen active le... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and time. We appreciate that you confirm our contribution and appreciate our work.
For your questions, we add more experiment results for your reference:
**Q1:** Experiments are only done on image data and with resnets. I wonder how contingent the performance ... | null | null | null | null |
Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning | Accept (poster) | Summary: This paper addresses the over-refusal problem in aligned LLMs that unnecessarily reject benign user prompts. The authors identify specific layers whose latent representations best distinguish between benign and malicious prompts, then selectively adjust embeddings to move prompts "just enough" from rejection t... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have provided our responses below, and we hope they clarify the points you raised. If our responses have adequately addressed your initial concerns, we would be grateful if you would consider adjusting your evaluation accordingly.
## (A)
We define pseud... | Summary: The paper proposes a fine-tuning based method to solve the over-refusal problem encountered by many LLMs. The method first tries to extract an over-refusal vector from the models using different prompts and then it tries to steer the model towards the embedding as defined in equation 9. The overall performance... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have provided our responses below, and we hope they clarify the points you raised. If our responses have adequately addressed your initial concerns, we would be grateful if you would consider adjusting your evaluation accordingly.
## (A)
Our approach d... | Summary: Language Models (LMs) must balance refusing unsafe prompts while complying with benign ones. Despite safety training, LMs often refuse benign prompts that contain spurious correlations with harmful ones, a behavior known as over-refusal. This paper introduces ACTOR, a technique inspired by representation engin... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful remarks and the positive rating you assigned to our paper. Below, you'll find our responses, which we hope clarify the points you raised.
## (A)
While multi‐turn attacks indeed pose a more realistic challenge, there are currently no established benchmarks specifica... | Summary: This paper focuses on addressing the over-refusal issue in aligned LLMs. The proposed technique, ACTOR, leverages internal activation for fine-tuning a single layer of the model to reduce the over-refusal rate.
## update after rebuttal
Thanks for the authors' response, which addresses most of my concerns.
... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Below, you'll find our detailed responses, which we hope clarify the points you raised.
## (A)
We appreciate the opportunity to clarify the basis of our claims in Sec 2. While we had experimental results (Sec 4) that support these points, we realize they w... | null | null | null | null | null | null |
TSP: A Two-Sided Smoothed Primal-Dual Method for Nonconvex Bilevel Optimization | Accept (poster) | Summary: This paper investigates a bilevel optimization problem where both the upper and lower levels are nonconvex, making it a challenging problem. The author proposes a smoothed-type single-loop algorithm and provides a theoretical complexity guarantee for convergence to a KKT-type stationary point. Numerical experi... | Rebuttal 1:
Rebuttal: We thank reviewer fDFn for your helpful comments and questions.
**Theoretical Claims:**
*I find the key contribution of this paper to be its ability to handle nonconvexity in the lower-level problem, which, to my knowledge, has been a significant challenge in bilevel optimization. As claimed by t... | Summary: This paper proposed a single-loop method for solving the stochastic bilevel optimization problem with weakly convex lower-level problem. The proposed method is proved to achieve a convergence rate of $O(\epsilon^{-4})$ in terms of a smoothed reformulation. Some experimental results on data hyper-cleaning task ... | Rebuttal 1:
Rebuttal: We thank reviewer bXuu for your helpful comments and questions.
**Claims And Evidence:**
*My main concern is the gap between the penalty reformulation and the original bilevel problem. It seems that both of the proposed method and the convergence analysis are based on the reformulation. *
> Corr... | Summary: This paper presents a smoothed primal-dual algorithm for solving stochastic bilevel optimization problems where the lower level problem is possibly nonconvex.
The authors first use Moreau envelope reformulation for the lower level problem and then use the smoothed primal-dual method to solve the resulting cons... | Rebuttal 1:
Rebuttal: We thank Reviewer 85Pr for your positive feedback, thoughtful comments, and constructive questions.
Questions:
*Is $p$ changing for different? Could the authors specify the value of $p$ we should take in the algorithm and main theorem?*
> In theory, $p$ is a constant and should be chosen to be o... | Summary: This paper introduces SPD (Smoothed Primal-Dual), a first-order gradient-based primal-dual method for solving bilevel optimization problems, potentially with a nonconvex lower-level problem. SPD is based on a Moreau envelope-based reformulation of the bilevel problem and employs a proximal primal-dual Lagrangi... | Rebuttal 1:
Rebuttal: We thank reviewer Dp7u for your helpful comments and questions.
**Claims And Evidence:**
*This statement about equilibrium constraints and bilevel optimization*
> Will remove that statement.
*On lines 69-71*
> Will remove the statement.
*contributions section (lines 167-169)*
> Will add "ap... | null | null | null | null | null | null |
SADA: Stability-guided Adaptive Diffusion Acceleration | Accept (poster) | Summary: This paper proposes SADA, a novel paradigm that unifies step-wise and tokenwise sparsity decisions using a shared criterion based on the denoised latent x0. By aligning with modern numerical solvers that rely heavily on x0, SADA offers more stable pruning decisions and preserves important visual details throug... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful and constructive comments.
**Q1: Can its speedup be extended to $2 \times$ or beyond?**
**A1:** Yes, it can. To apply an faster configuration, we leverage the inherent stability of the per‐step data reconstruction $x_0^t$. When the $x_0^t$ traje... | Summary: The paper proposes SADA (Stability-guided Adaptive Diffusion Acceleration), a method to accelerate diffusion models by jointly optimizing step-wise and token-wise sparsity using a unified criterion based on the denoised latent \( x_0 \). Key contributions include: (1) alignment of pruning decisions with \( x_0... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful feedback and kind support for our work.
Based on the suggestions from Reviewer 4E6r and DBkM, we implement an aggressive version of SADA:
1. Implementing uniform step-wise pruning when the $x_0^t$ trajectory is stable, using Lagrange interpolatio... | Summary: The paper proposes SADA, a training-free acceleration method for diffusion models that unifies step-wise (temporal) and token-wise (spatial) sparsity using a stability criterion based on the denoised latent $ x_0 $. Specifically, the paper uses a unified $ x_0 $-guided sparsity criterion for step skipping and... | Rebuttal 1:
Rebuttal: We thank the reviewer for comprehensive comments.
**Q1: Aggressive configuration of SADA**
**A1:** To implement an aggressive version, we leverage the inherent robustness of the per‐step data reconstruction $x_0^t$. When the $x_0^t$ trajectory demonstrates high stability (e.g., the second half o... | null | null | null | null | null | null | null | null |
Adapting Precomputed Features for Efficient Graph Condensation | Accept (poster) | Summary: To address the efficiency issue in graph condensation (GC), this paper proposes GCPA, a two-stage framework comprising precomputation and diversity-aware adaptation. The precomputation stage aggregates structural and semantic information for competitive performance, while the adaptation stage refines features ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the examination of our work and the thoughtful comments provided. Kindly find our responses to the raised comments and questions below.
**Q1: Some recently proposed efficient GC are not discussed, including SimGC[1], EXGC[2], and CGC[3].**
We thank the reviewer for high... | Summary: This paper propose the GCPA method, which not only bring the unbelievable efficiency into the graph condensation process but also gains considerable results, for example, for the Ogbn-products dataset, the conventional trajectory method calls for 452 hours in collecting the trajectories, but the GCPA only cost... | Rebuttal 1:
Rebuttal: We thank the reviewer for the examination of our work and the thoughtful comments provided. Kindly find our responses to the raised comments and questions below.
**Q1: Can you ablate the adaptation stage to the other expensive matching processes? The precomputation stage seems like a normal trick... | Summary: This paper introduces Graph Condensation via a Precompute-then-Adapt Approach (GCPA), an efficient method for condensing large-scale graphs to accelerate Graph Neural Network (GNN) training. The proposed framework is more computationally efficient than trajectory matching methods and instead consists of two st... | Rebuttal 1:
Rebuttal: We thank the reviewer for the examination of our work and the thoughtful comments provided. Kindly find our responses to the raised comments and questions below.
**Q1: Code cannot be opened.**
We apologize for the inconvenience. It appears there was a temporary issue. We have refreshed the repos... | Summary: This paper proposes an efficient graph condensation method composed of aggregation and contrastive learning stages. Extensive experiments indicate that this approach achieves performance comparable to state-of-the-art condensation methods, while significantly improving computational efficiency.
Claims And Evi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the examination of our work and the thoughtful comments provided. Kindly find our responses to the raised comments and questions below.
**Q1: The performance on Citeseer is unexpectedly high, surpassing even the most advanced GNNs on the original Citeseer graph.**
Thank... | null | null | null | null | null | null |
Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting | Accept (poster) | Summary: The paper introduces Proxy-FDA, a novel feature-space regularization method designed to prevent concept forgetting during the fine-tuning of vision foundation models. The key idea is to align the local structures of pre-trained and fine-tuned feature distributions using nearest neighbor graphs, which is furthe... | Rebuttal 1:
Rebuttal: Thank you for the recognition of our work and constructive feedback. Below is our response to each concern, as well as new comparisons on compute cost.
**Q1: Sensitivity to hyperparameters like the neighborhood size K and the scalar s for proxy generation**
Given the held-out validation set of e... | Summary: The paper proposes Proxy-FDA, a regularization method for fine-tuning vision foundation models that mitigates concept forgetting by aligning local structural relationships in feature spaces. The core innovation lies in preserving neighborhood structures via nearest neighbor (kNN) graphs derived from pre-traine... | Rebuttal 1:
Rebuttal: Thanks for the detailed feedback! Below is our response to the main questions and "weaknesses".
**Q1: Proxy diversity and novelty lack quantitative metrics. Could FID evaluate synthetic feature quality?**
As suggested, many metrics are available to quantify our proxy feature diversity. Also, hig... | Summary: This paper presents a new approach to mitigate concept forgetting in model fine-tuning (robust fine-tuning) by building on existing feature-matching methods. Specifically, this work aims to align the feature structure by regularizing the feature space using k-nearest neighbors (KNN) within each batch. They als... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback on our work. Below is our point-by-point response to your questions.
**Q1: Motivation discussion: why Proxy-FDA improves performance.**
(Proxy-)FDA is essentially a feature-space regularization term added to the task loss during model fine-tuning. The goal... | Summary: This paper introduces a novel approach to mitigate concept forgetting during model fine-tuning by extending existing feature-matching methods. The authors propose to preserve feature structure by regularizing the feature space using k-nearest neighbors (KNN) within each batch. Additionally, they develop a meth... | Rebuttal 1:
Rebuttal: Thanks for the constructive feedback on our work. Below we include the results of requested experiments, and respond to your specific comments.
**Q1: Justify motivation of using kNN feature-based FDA, and differences from knowledge distillation.**
In response, our introduction section (paragraph... | null | null | null | null | null | null |
On the Robustness of Reward Models for Language Model Alignment | Accept (poster) | Summary: The paper provides a new theoretical analysis framework to understand the robustness of RM in LLMs.
Claims And Evidence: Yes, I think most of the claims made in the submission clear and convincing.
Methods And Evaluation Criteria: See Questions For Authors part.
Theoretical Claims: I didn't check all the pr... | Rebuttal 1:
Rebuttal: We appreciate your suggestions on the typo and the section title. We will make sure to address them in the final version of our paper.
**Q1 - Quantifying disjointness between datasets:** We appreciate the reviewer’s question for disjointness quantification as it would make the splitting ... | Summary: This paper investigates the issue of over-optimization in reward models (RMs) within RLHF, identifying excessive hidden state norm dispersion as a key factor. To address this, the authors introduce batch-wise sum-to-zero regularization (BSR), which constrains reward magnitudes by ensuring batch-level zero-cent... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and will add the suggested paper to the related works.
**Q1 - Normalization techniques to implicit rewards of DAAs**: DAAs use the language model as an implicit reward model, differing from our classifier-based reward models in that they u... | Summary: This paper explores the challenges of over-optimization in reward models used in RLHF of LLMs. It identifies the dispersion of hidden state norms as a primary cause of over-optimization and proposes batch-wise sum-to-zero regularization (BSR) to address this by penalizing outliers and controlling reward disper... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment and would like to further discuss the addressed points.
**W1 - Limited Experimental Results**: Our experimental design can be streamlined into threefold: (1) assessing alignment between proxy RMs and gold RMs with different learning objectives, (2) pr... | Summary: The paper investigates the cause of reward model over-optimization in RLHF and finds that it stems from the increasing variance of the final-layer outputs (hidden states) in the reward model (RM). The authors propose Batch-wise Sum-to-Zero Regularization (BSR) for RM training, which penalizes the second moment... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed comments. Below, we address each concern:
1. **Claims/Evidence #1 – Notations in Section 4.2**: We regret the confusion from the ambiguous explanations in lines 261–271 and acknowledge that lines 265–269 apply only to a specific case. In the final version, ... | null | null | null | null | null | null |
Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability | Accept (poster) | Summary: This paper shows that the fixed share algorithm is able to obtain optimal dynamic regret under mixable losses with improper online learning. They also obtain the first gradient variation based dynamic regret bounds under curved losses. The results are novel to the best of my knowledge.
### Post Rebuttal ####
... | Rebuttal 1:
Rebuttal: Thanks for your expert comments! We will address your main concerns regarding the literature comparisons. Without a doubt, Baby and Wang pioneered the line of dynamic regret for exp-concave/strongly convex functions. While we attempted to make a comparison, we unfortunately missed two relevant ref... | Summary: This paper studies non-stationary online convex optimization with mixable loss functions. The class of mixable function includes the exp-concave functions. This paper proposes a fixed-share algorithm for continuous space. In each round, the proposed algorithm requires to obtain a decision satisfying a certain ... | Rebuttal 1:
Rebuttal: Thanks for your very careful review and pointing out two technical problems. We have provided a detailed proof for equation (13), and clarify that our Theorem 1 indeed requires the mixability of loss functions over $\mathbb{R}^d$. These issues will not affect the key contributions of our paper, bu... | Summary: This work proposes an algorithm for non-stationary online learning under mixable losses. They provide better dynamic regret bounds in comparison to the existing results in terms of the dependence on the dimension and logarithmic redundancy.
## update after rebuttal
I keep my score which remains positive.
Cla... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the novelty of our methods and for the constructive suggestions. In the revision, we will include a more detailed comparison with Baby and Wang (2021) in the introduction, highlighting the issue of proper learning and the underlying assumptions. Below, we add... | Summary: This paper considers online convex optimization (OCO) with mixable stage cost functions. The paper proposesseveral algorithms based on exponential weights with fixed share updates to achieve an improved dynamic regret bound than the bound in (Baby & Wang 2021). The improvements are in two aspects: improvement ... | Rebuttal 1:
Rebuttal: We thank the reviewer for very helpful comments. The main concerns are about i) presentation and ii) technical questions on the construction of mixability prediction for general functions. We first provide a concise answer and will expand the details later.
- **[On Presentation]** Our paper indee... | null | null | null | null | null | null |
Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization | Accept (poster) | Summary: This paper introduces the Stabilized Proximal Decentralized Optimization (SPDO) method, which achieves state-of-the-art communication and computational complexities within the Proximal Decentralized Optimization (PDO) framework. The authors also propose an accelerated variant (Accelerated-SPDO) based on the Mo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback.
> The paper would benefit from providing a proof sketch for key theoretical results, particularly for the convergence analysis.
Thank you for the suggestion.
We promise to add the proof sketch in the camera-ready version.
> The Accelerated-SPDO a... | Summary: This paper studies decentralized optimization, and it proposes several decentralized methods and analyses their convergence. Specifically, they show that their methods achieve the state-of-the-art communication and computation complexity.
Claims And Evidence: The claims are fair.
Methods And Evaluation Crite... | Rebuttal 1:
Rebuttal: We thank the reviewer for examining our paper.
> The methods are closely related to two existing works (Scutari \& Sun, Li 2020) as discussed in the paper. However, the connection is not clear enough: [...]
Our paper and existing papers [1,2] use a slightly different notation, which might confus... | Summary: The paper studies decentralized optimization where multiple nodes, each holding a local function f_i, aim to minimize the average f(x) = \tfrac{1}{n}\sum_i f_i(x). Traditional decentralized methods are constrained by communication overhead and data heterogeneity. The authors propose a Proximal Decentralized Op... | Rebuttal 1:
Rebuttal: We thank the reviewer for your constructive comments.
We have addressed the concerns about missing ablation studies and included the results in our rebuttal below. We agree that these additional experiments significantly strengthen our paper. We kindly ask the reviewer to reconsider the evaluation... | Summary: This paper provides a decentralized optimization method for convex optimization under the second-order similarity. The main contribution is improving the term $\delta_{\max}$ or $L$ to $\delta$ in the complexity to $\delta$.
## update after rebuttal
The authors have addressed my questions, and I decided to ke... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and careful review.
> The main idea seems be to directly combine the inexact gradient sliding (Kovalev et al., 2022) with Multiple Gossip and gradient tracking. Can you highlight the main technical novelty in the algorithm and analysis?
We would li... | null | null | null | null | null | null |
CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models | Accept (poster) | Summary: This paper introduces the Clinical Large-scale Integrative Multi-modal Benchmark (CLIMB), a benchmark unifying diverse clinical data across imaging, language, temporal, and graph modalities. The dataset comprises 4.51 million patients distributed across multiple modalities. The authors conduct extensive empiri... | Rebuttal 1:
Rebuttal: W1-2, Q1-2, C1-2: In our previous experiments, both pretraining and multitask learning are performed in Exp. 1. We added experiments below comparing unsupervised pretraining vs supervised multitask learning.
First, we found time series models benefited substantially from unsupervised pretraining... | Summary: This paper introduces a large-scale clinical multimodal benchmark. The authors conduct multitask pretraining, few-shot transfer, and multimodal fusion. Based on the constructed data, they provide extensive experiment results to answer the proposed research questions.
Claims And Evidence: Yes
Methods And Eval... | Rebuttal 1:
Rebuttal: Thanks for your feedback regarding Figures 3.a and 3.b. Both figures illustrate two different experiments we conducted on CLIMB: multitask learning and transfer learning. The figures display example data from CLIMB (such as x-rays, CT scans, etc.) as inputs to the model and display evaluation on t... | Summary: This paper introduces the Clinical Large-scale Integrative Multimodal Benchmark (CLIMB), which integrates diverse clinical data across imaging, language, time-series, and graph modalities. CLIMB consists of 4.51 million patient samples (19.01 terabytes), covering 2D imaging, 3D video, and multimodal data. Empi... | Rebuttal 1:
Rebuttal: We appreciate reviewer SVNB's feedback. Besides our scale and focus on multimodal, our work distinguishes itself from related research in several ways:
- Our focus extends beyond confirming general pretraining benefits by specifically targeting underrepresented regions and modalities (ultrasound,... | Summary: The paper introduces CLIMB (Clinical Large-scale Integrative Multimodal Benchmark), a clinical benchmark that puts together a large number of existing datasets across different modalities with a strong focus on vision, including 1D, 2D, and 3D signals, as well as graph data. The authors conduct a thorough comp... | Rebuttal 1:
Rebuttal: We thank Reviewer pTYB for the positive reviews and constructive feedback. We have addressed all typographical errors in our manuscript.
Regarding the definition of OOD datasets (Claim 1), we selected OOD datasets primarily based on task differences and provided a comprehensive list with justific... | null | null | null | null | null | null |
Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization | Accept (poster) | Summary: The paper uses different mixed Lp norms to run SGD
Claims And Evidence: The main takeaway is that there are different Lp norms boost performance of optimization for different problems. For CNN's for example they find L2 to work best but for LLMs they find another L3 to work better for example. Unfortunately t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions.
>**Error Bar and Standard Deviation**
> No error bars or standard deviation in tables
We ran 3 random seeds to obtain the error bar.
CIFAR:
| **Optimizer** | **Train NLL @50** | **Train NLL @100** | **Train NLL @200** | **Test ACC @50**... | Summary: This paper introduces Stacey an optimisation algorithm targeted at training deep neural networks (DNNs). Stacey generalises SignSGD and conventional SGD, in a p-norm sense, where SGD uses the 2-norm to measure distance and SignSGD uses the inf-norm. On top of this Stacey include a acceleration scheme to aid th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and suggestions, and we respond to their questions below.
> **Missing Reference**
Thanks for the suggestion. We will provide a citation and include it as a baseline in the updated manuscript.
> **Hyperparameters**
Though we may tune $\tau$ an... | Summary: This paper introduces **STACEY**, a novel optimization algorithm designed to accelerate stochastic steepest descent via ℓp-smooth nonconvex optimization. The key contributions of this work include:
- The development of **STACEY**, which incorporates primal-dual iterate interpolation to improve convergence rate... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and suggestions, and we respond to their questions below.
> Detailed **computational complexity in terms of runtime and memory consumption** compared to SGD, Adam, and Lion
**Runtime**
Let $d$ be the number of parameters, and let each “basic o... | null | null | null | null | null | null | null | null |
A Physics-Augmented Deep Learning Framework for Classifying Single Molecule Force Spectroscopy Data | Accept (poster) | Summary: This paper presents a machine learning-based approach for classifying single-molecule force spectroscopy (SMFS) data from protein unfolding experiments. Specifically, the model distinguishes force measurements originating from valid single-molecule unfolding events versus artifacts. While ML-based classificati... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We address your concerns below and welcome further discussion. If there are no additional issues, we would appreciate your consideration in raising our score.
**A Challenging Problem:** Our lab studies novel molecules like dystrophin and utrophin to underst... | Summary: The authors propose a physics-inspired architecture to classify single molecule events from force spectroscopy data. They provide datasets, including simulations, to evaluate their method compared to previous baselines showing improved performance.
Claims And Evidence: - The proposed model outperforms baselin... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We have addressed your questions and concerns below. If there are any remaining issues, we would be happy to discuss them further. If there are no additional concerns, we would appreciate your consideration in raising our score.
1. *The main concern I have ... | Summary: The paper introduces Polymer Elastic Models Neural Networks (PemNN), a deep learning model designed to classify molecular force curves as originating from no molecule, single molecule or multiple molecules.
Claims And Evidence: Yes, the claims are supported by clear evidence.
Methods And Evaluation Criteria:... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We have addressed your questions and concerns below. If there are any remaining issues, we would be happy to discuss them further.
1. *Figure 4: what do the numbers represent?*
**Author response**:
We use classification accuracy as the metric to compare ... | Summary: this work proposes a deep learning model to classify SMFS curves.
the model consists of two branch, one is "based on physics" and another is called "force-trace", followed by fusion modules.
their experiments show improved accuracy.
Claims And Evidence: they claimed "superior performance compared to sota base... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We have addressed your questions and concerns below. If there are any remaining issues, we would be happy to discuss them further. If there are no additional concerns, we would appreciate your consideration in raising our score.
We would like to emphasize t... | null | null | null | null | null | null |
RepoAudit: An Autonomous LLM-Agent for Repository-Level Code Auditing | Accept (poster) | Summary: This paper presents REPOAUDIT, a system for auditing source code using large language models to identify and report software bugs. The system is designed to detect common vulnerabilities such as null pointer dereferencing, memory leak, and use after free. It utilizes a combination of parsing libraries, large l... | Rebuttal 1:
Rebuttal: **1.Bug Customization**
Please refer to the response to the first concern **Bug Customization** of [Reviewer yKxy](https://openreview.net/forum?id=TXcifVbFpG¬eId=ZFh3alkmPr).
**2.Case Studies of FPs/FNs of RepoAudit**
Thank you for your suggestions. We collected the following typical cases a... | Summary: This paper proposes RepoAudit, an autonomous LLM-powered code auditing framework that can compete with current academic and industry solution tools. It consists of an initiator, an explorer, and a validator, working together to efficiently analyze GitHub repositories for code quality, security vulnerabilities,... | Rebuttal 1:
Rebuttal: **1.Model Choice**
We evaluated RepoAudit using two additional LLMs, namely DeepSeek R1 and GPT-4 Turbo, which detected 44 and 14 true bugs with precisions of 75.86% and 35.90%, respectively. More detailed statistics of RepoAudit powered by DeepSeek R1 and GPT-4 Turbo were provided in Appendix C.... | Summary: This paper attempts to address a key concern in LLM-based code auditing systems where repositories are too complex and big to be effectively audited by LLMs. To address this, RepoAudit explores the repository on demand by analyzing data flow relations between different sections of the repository to build a mor... | Rebuttal 1:
Rebuttal: **1.Customization and Expert Knowledge**
What we meant in the introduction section was that in order to detect a new type of bug, a new tool often needs to be developed inside some compiler, implementing bug-specific code checking rules. This often requires substantial compiler and program analys... | Summary: The paper presents RepoAudit, an autonomous LLM-agent designed for repository-level code auditing. RepoAudit leverages large language models to find critical bugs such as null pointer dereference, memory leak, and use-after-free. The agent efficiently scans code repositories by utilizing an agent memory system... | Rebuttal 1:
Rebuttal: **1.Effectiveness in mitigating LLM's intrinsic limitations**
Apart from the case studies in Sections 2.2 and 2.3, we evaluated RepoAudit-NoAbs that excludes program abstraction and pointer handling. The column **RepoAudit-NoAbs** in Table 5 demonstrates that this ablation decreases the number of... | null | null | null | null | null | null |
Distributionally Robust Policy Learning under Concept Drifts | Accept (poster) | Summary: This paper propose the distributionally robust method for offline bandit under concept shift, where the P(Y|X) is shifting. They propose a doubly robust method and DRO under KL divergence for offline policy learning. And they show the Asymptotic normality of the ope and propose a policy learning and the corres... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for dedicating the time to review our paper and for providing the insightful comments. Due to the character limit, we cannot upload the revised manuscript, however we have edited according to the reviewer's helpful suggestions. Reference can be found in our repl... | Summary: This paper investigates distributionally robust policy learning with concept shift. While this problem has been previously studied in the literature, the current work extends to a more general setting where the context space is not necessarily finite. To address this generalized setup, the authors propose a do... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for dedicating the time to review our paper and for providing the insightful comments. Due to the character limit, we cannot upload the revised manuscript, however we have edited according to the reviewer's helpful suggestions. Reference can be found in our repl... | Summary: This paper develops a distributionally robust policy learning framework under concept drift by focusing on shifts in conditional reward distributions while assuming stable covariate distributions. It introduces a doubly robust estimator with root‑n convergence for policy evaluation and proposes an efficient po... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for dedicating the time to review our paper and for providing the insightful comments. Due to the character limit, we cannot upload the revised manuscript, but we have edited according to the reviewer's helpful suggestions. We use the following references.
[1] ... | null | null | null | null | null | null | null | null |
Mind the Gap: A Practical Attack on GGUF Quantization | Accept (poster) | Summary: The paper investigates the question of whether the quantization error in an LLM can be exploited towards practical attacks, that can lead the model to output maliciously on specific inputs, while not dropping significantly on standard benchmarks.
While this fact has been shown before by Egashira et al. (NeurI... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their efforts spent reviewing our paper, understanding the strength of our work, and providing many insightful comments. We address the reviewer’s questions and comments below.
**Q1: Is the claim in the abstract, *“Our key insight is that the quantiz... | Summary: This paper introduces a novel practical attack on GGUF quantization. It exploits the quantisation errors inherent in GGUF to hide malicious behavior into quantised models. The malicious behaviour of the model remains hidden in full precision but is revealed when the model is quantised.
Claims And Evidence: Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their acknowledgement of the strength of our work. In case the reviewer has any other questions or comments, we are happy to engage in further discussion. | Summary: This paper presents a adversarial attack on GGUF quantization, a popular post-training quantization (PTQ) method in LLM. The core contribution is an error-based interval estimation technique, which exploits quantization errors to enable adversarial attack on LLMs. The authors demonstrate the attack's effective... | Rebuttal 1:
Rebuttal: We thank the reviewer for the efforts spent reviewing our paper and the positive assessment. We address the reviewer’s questions and comments below.
**Q1: Can you elaborate on whether it is reasonable to assume that the adversary has access to the quantization algorithm?**
Certainly. We agree wi... | Summary: This paper introduces a backdoor attack targeting GGUF quantization, a widely used optimization-based post-training quantization method for LLMs. The paper proposes an error-based interval approach to construct malicious quantized models that behave normally in full precision but exhibit targeted malicious beh... | Rebuttal 1:
Rebuttal: First, we thank the reviewer for the time spent reviewing our paper and the positive assessment. We address the reviewer’s questions and comments below.
**Q1: Can you please extend and reframe the literature review about the backdoor attack?**
Certainly. We acknowledge the importance of covering... | null | null | null | null | null | null |
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality | Accept (poster) | Summary: This paper proposes the Fed-ICL framework to harness the benefits of ICL while ensuring privacy preservation in sensitive settings, which is the first framework of iterative optimization of federated learning (FL) with a parameter-free communication scheme to enable iterative refinement of responses. The autho... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable time and effort in providing detailed feedback on our work.
---
> **Q1:** The evidence is convincing in general, but I think it might be better to show their framework's robustness to privacy attacks since they mention that they combine the effici... | Summary: This paper introduces Fed-ICL, a framework that enhances in-context learning (ICL) for QA tasks. Specifically, Fed-ICL leverages iterative interactions between clients and a central server, progressively refining responses while maintaining low communication costs (by transmitting the context). The authors pro... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable time and effort in providing detailed feedback on our work.
---
> **Q1:** The paper focuses only on the QA dataset, and it is unclear whether it can generalize to more challenging tasks.
**A1:**
First, we would like to highlight that this work ... | Summary: The paper proposes **Federated In-Context Learning (Fed-ICL)**, a framework for QA tasks that combines in-context learning and federated learning without transmitting model parameters. Fed-ICL enables clients to iteratively refine responses by sharing answers—not models—preserving privacy and reducing communic... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable time and thoughtful feedback. We also appreciate your kind acknowledgment of our contributions to both the experimental and theoretical aspects of the work in Claims and Evidence. | Summary: The paper introduces Fed-ICL, a novel framework that blends federated learning with in-context learning to tackle question-answering tasks in a privacy-preserving manner. Fed-ICL operates in a round-based manner, iteratively refining answer quality through client-server communication. The authors support their... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable time and effort in providing detailed feedback on our work.
---
> **Q1:** The convergence guarantee is derived under a simplified linear self-attention model. While this is common for theoretical analysis, it remains an open question how these gua... | null | null | null | null | null | null |
Unlocking the Power of SAM 2 for Few-Shot Segmentation | Accept (poster) | Summary: This paper utilizes SAM 2 and DINO-v2 to solve the few-shot segmentation problem. The authors first point out that the class-agnostic matching ability of SAM 2 is useful for few-shot segmentation, but SAM 2 focuses too much on the identity of objects, which makes it unsuitable for FSS. To address this issue, t... | Rebuttal 1:
Rebuttal: > Evaluation on LVIS-92$^i$.
Thanks for this precious suggestion, conducting evaluation on LVIS-92$^i$ that includes 920 classes can indeed show the **excellent generalizability** of our method. We select both Matcher and SINE (A Simple Image Segmentation Framework via In-Context Examples, NeurIP... | Summary: This paper leverages SAM2’s strong matching ability to do the few-shot segmentation. Considering the matching of SMA2 is for sam2-object matching, the paper introduces Pseudo Prompt Generator (PPG) to generate pseudo query memories, and further design Iterative Memory Refinement (IMR) to supplement this memory... | Rebuttal 1:
Rebuttal: > Evaluation on LVIS-92$^i$.
Conducting evaluation on LVIS-92$^i$ (920 classes) can show the **excellent generalizability** of FSSAM. We select both Matcher and SINE for comparisons. Since SINE is trained with COCO (80 classes) and directly test on LVIS-92$^i$, we directly take our trained 1-shot... | Summary: This paper presents FSSAM, which leverages SAM2 for few-shot segmentation.
FSSAM designs a Pseudo Prompt Generator to generate pseudo query memories, an Iterative Memory Refinement to iteratively refine pseudo query memories, and a Support-Calibrated Memory Attention to suppress background noise.
Extensive e... | Rebuttal 1:
Rebuttal: > Larger number of parameters than other methods and result in additional computational cost.
Thanks for this comment. Our parameter number is actually much smaller than most of foundation-based FSS methods. We select some methods and summarize their parameter number, learnable parameter number, ... | Summary: The paper introduces the Few-Shot Segment Anything Model (FSSAM), a novel method that leverages the powerful matching capabilities of SAM 2 to enhance few-shot segmentation tasks. The authors address the challenge of adapting SAM 2's same-object matching ability to the different-object matching required in few... | Rebuttal 1:
Rebuttal: > Computational burden and learnable parameters.
Thanks for your valuable suggestion! We agree it would be better to include the **parameter number** for further comparisons. We select some methods and summarize their parameter number, learnable parameter number, as well as the 1-shot mIoU scores... | null | null | null | null | null | null |
Jacobian Sparse Autoencoders: Sparsify Computations, Not Just Activations | Accept (poster) | Summary: **Summary**
Sparse Autoencoders (SAEs) help interpret latent activations in LLMs but do not explicitly reveal how computations are performed. This paper extends SAEs to study the sparsity of the computational transformations within MLP layers of transformer-based LMs. The authors train two SAEs—one before and... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and useful recommendations.
### Qualitative results and insights
We have updated the manuscript with qualitative [results](https://anonymous.4open.science/r/jacobian-saes-icml-D7BF/jacobian-saes-icml-examples.pdf). Specifically, we plot max-activating exampl... | Summary: The authors introduce Jacobian SAEs, as a form of dictionary learning that also facilitates circuit analysis by creating sparse computational graphs. They jointly train SAEs on both input and output to an MLP layer, and make the Jacobian sparse by adding its L1 norm to the loss function.
Though computing and ... | Rebuttal 1:
Rebuttal: Thanks for your careful and considered review!
### 1. Transcoders vs JSAEs
A few days ago, Anthropic published a huge new paper on circuit tracing, and which cites our work [1].
The key idea in our work and [1] is:
* Take multiple layers in network.
* Decompose each layer into sparse latents (we... | Summary: This work introduces Jacobian Sparse Autoencoders (JSAEs), an extension of sparse autoencoders (SAEs) designed to sparsify not only latent activations but also the computational graph (approximated via the Jacobian) of an MLP layer in LLMs models.
Authors also show how to compute efficiently the Jacobian thank... | Rebuttal 1:
Rebuttal: Thanks for your positive review, which notes "The idea of sparsifying the computation (not just the representation) is new and compelling. It nicely bridges concepts from automated circuit discovery and dictionary learning.
Insightful findings for discovering sparse computation in LLMs.
The papers... | Summary: This paper addresses the problem of better understanding computations in deep models, particularly LLMs. Recently sparse autoencoders (SAEs) have become popular as a tool to mechanistically understand a model, by decomposing features learnt at any layer into a sparse set of disentangled concepts. This work pro... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review, noting "(1) The paper looks into an interesting and potentially useful problem, of understanding computations made by large models, as opposed to just examining what features were learnt as is typically done with SAEs. (2) The proposed idea of learning sparse ... | null | null | null | null | null | null |
Privacy Amplification Through Synthetic Data: Insights from Linear Regression | Accept (poster) | Summary: The paper offers a theoretical analysis of the privacy loss of releasing synthetic samples in linear regression. It demonstrates that, under a strong threat model where an adversary controls the seed of the generative model, releasing even a single synthetic sample can result in privacy leakage equivalent to t... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer pnmm for providing valuable feedback and pointing out several valid issues. Below, we address and discuss each point.
## Claims And Evidence
> Privacy leakage is not about recovering model parameters
We agree that our sentence might be confusing. What we mean her... | Summary: This paper investigates privacy amplification from synthetic data release within the specific setting of linear regression.
The authors first establish negative results, showing that an adversary controlling the seed of the generative model can induce the maximum possible privacy leakage from a single query. ... | Rebuttal 1:
Rebuttal: We thank Reviewer ukYX for their feedback. Below, we address each concern separately.
## Weaknesses
> The amplification only holds when the number of generated points is less than the dimension
This is correct. However, our results do not imply that privacy amplification does not happen when t... | Summary: This paper explored the privacy amplification properties of hiding the generative model in private synthetic data generative contexts. Differentially private generative models produce synthetic data that formally inherits the same privacy guarantees. In practice, it has been observed that when the synthetic da... | Rebuttal 1:
Rebuttal: We thank Reviewer c2xJ for their interesting and positive feedback. Below, we address each concern separately.
## Limitations
> Restricting the focus to linear regression provides a clean case study but limits the generalizability of the findings: it’s unclear how well these results could extend... | Summary: This paper investigates the privacy amplification effect that could be gained when hiding the model that has been used to generate differentially-private synthetic data. The objective is to be able to quantify the privacy gain obtained by releasing only a limited number of synthetic data and not the model itse... | Rebuttal 1:
Rebuttal: We thank Reviewer RSvt for their feedback. Below, we address each concern separately.
## Weaknesses
> The term "privacy amplification" used in the title is exaggerated
In our paper, the phrase "privacy amplification from synthetic data release" refers to potential privacy gains achieved by rele... | null | null | null | null | null | null |
Scaling Large Motion Models with Million-Level Human Motions | Accept (poster) | Summary: The paper introduces MotionLib, the first million-level dataset for motion generation, which is 15× larger than previous datasets and includes hierarchical text descriptions. Using MotionLib, the authors train Puppet, a large-scale motion model that demonstrates robust generalization across diverse human activ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thoughtful review and positive feedback. We have carefully considered your questions and suggestions and provide our responses below. Please let us know if you require further clarification.
---
## **Response to: Insufficient visualizations in supplementary mate... | Summary: This paper explores various design choices for building large motion models, inspired by the success of LLMs. In the absence of a large-scale motion dataset, it first introduces MotionLib, the first million-level motion dataset, obtained by automatically annotating 3D motion and text descriptions from publicly... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thoughtful review and positive feedback. Below are our responses to your questions and suggestions. Please let us know if you require further clarification.
## **Response to: the discussion of LMM [1] and Dataset Comparison**
We appreciate you highlighting the re... | Summary: This paper proposes a dataset, a VQVAE, and a motion generation model. The dataset MotionLib comprises over 1.2M motion sequences with hierarchical and detailed text annotations. The VQVAE uses a 2D-LFQ for a lookup-free tokenizer. The text-to-motion model is trained on the proposed dataset and VQVAE.
Claims ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We appreciate your valuable feedback. Due to space constraints, **we only provide concise responses below but would present more during discussion.** Please let us know if any clarification is needed.
## W1: WHAM accuracy & dataset validity
While no motion estimation algorithm is p... | Summary: The paper investigates scaling motion generation models based on million-level data and LLM-style architecture. The authors first contributes a million-level human motion dataset, named MotionLib. Training models on this data, they highlight the importance of scaling both data and model size for advancing moti... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thoughtful review and positive feedback. We have carefully considered your questions and suggestions and provide our responses below. Please let us know if you require further clarification.
---
## **Response to: Questions about UNSEEN-90K Dataset in OOD Experim... | null | null | null | null | null | null |
Locality Preserving Markovian Transition for Instance Retrieval | Accept (poster) | Summary: This paper tackles the problem of instance retrieval, or finding the image most similar in a dataset to a query image. Existing methods suffer from long-range propagation of similarity information, which the authors improve on with three components. BCD- They improve similarity propagation by combining multipl... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive assessment and recognition of our contribution to manifold reranking. In this work, we address the fundamental challenge of manifold ranking by introducing the proposed **Locality Preserving Markovian Transition (LPMT)**. Our approach establishes a structured,... | Summary: The paper introduces the Locality Preserving Markovian Transition (LPMT) framework to improve instance retrieval by overcoming the limitations of traditional diffusion-based re-ranking methods. Standard methods suffer from diminishing positive signals over long diffusion paths, which weakens their discriminati... | Rebuttal 1:
Rebuttal: **Q1: Will the incorporation of extra distractor images introduce an inductive bias?**
An additional one million distractor images are introduced to simulate a large-scale image database. Compared to the original ROxf and RPar datasets, the expanded database includes a larger number of hard nega... | Summary: Existing re-ranking methods tend to reduce discriminative power over several steps. This paper proposes the LPMT framework for accurate manifold distance measurement, thereby enhancing the retrieval process. The proposed method is supported by several theoretical analyses, and experiments demonstrate significa... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments on the advanced applications of retrieval in the NLP domain. However, we believe that the key focus of retrieval tasks differs between the fields of image and NLP. We hope the following response will be helpful to emphasize our contribution to manifol... | Summary: In this paper, the authors focused on the diffusion-based re-ranking for instance retrieval. Considering the issue of decaying positive signals and the impact of disconnections in the existing methods, the authors proposed the Locality Preserving Markovian Transition (LPMT) framework. The proposed method consi... | Rebuttal 1:
Rebuttal: **Q1: Concerns about the involvement of multiple components and hyperparameters.**
Regarding the concern about multiple components and hyperparameters. Although our method consists of multiple modules, each can be implemented in a relatively straightforward manner. For example, the BCD objective... | null | null | null | null | null | null |
Learning Latent Graph Structures and their Uncertainty | Accept (poster) | Summary: Exploiting the structure of the problem of interest is often key to achieve good generalization with the trained model. For real world applications, it might be known that underlying relational information is shaping the observed data but this latent structure remains often hidden. Some previous works have pro... | Rebuttal 1:
Rebuttal: **Claims and Evidence**
**a**.
First of all, we appreciate your constructive comments. We see your point, but the issue with the proposed approach is not that the graph structure provided in the real-world dataset is not a distribution - we agree that the ground-truth distribution can be a Dirac ... | Summary: In this paper, the authors investigate the calibration of latent graph structure distributions in the context of graph structure learning. They propose an optimization procedure for a predictive probability model that ensures not only learning the best predictive model but also calibrating the latent distribut... | Rebuttal 1:
Rebuttal: **Wa**.
There might be some misunderstanding here, please allow us to clarify that the assumption is indeed satisfiable.
Both Equations 1 and 2 can model continuous or discrete outputs, but their respective outputs $y$ and $\hat y$ take values from the *same* set $\mathcal Y$.
$\Delta$ is a dissi... | Summary: This paper carefully studies the problem of how the optimal latent graph can be learned given observational information from both theoretical and empirical perspectives. It proves that optimizing the usual point prediction does not guarantee calibration of the adjacency matrix distribution. It also provides a ... | Rebuttal 1:
Rebuttal: **W1**.
We appreciate your suggestion. If the paper is accepted, we will use part of the camera-ready additional page to improve the presentation.
**C1**.
Yes, this is a relevant point. This is why we run experiments in controlled settings to test our method beyond those assumptions. In particul... | Summary: This paper deals with the problem of learning on graph in a setting where the graph (Adj. matrix: A) is unobserved and is to be estimated from the training data (node features: x, node labels/targets: y). It theoretically shows that the optimal point estimate does not guarantee the calibration of the latent gr... | Rebuttal 1:
Rebuttal: **W1**.
Part of our results can indeed be applied to more general latent variable models, and we view this broader applicability as a strength rather than a weakness. As shown in the Experiments Section, our theoretical results can be successfully applied to Graph Structure Learning problems, maki... | null | null | null | null | null | null |
Homophily Enhanced Graph Domain Adaptation | Accept (poster) | Summary: This paper investigates graph domain adaptation through homophily alignment. The authors argued that the graph homophily has been overlooked by existing graph domain adaptation works. To address this issue, the authors first conduct some empirical analyses and find that the homophily discrepancies indeed exist... | Rebuttal 1:
Rebuttal: Thank you for your appreciated feedback. Below, we address the concerns and questions raised in the weaknesses section. Please feel free to reach out if further clarification is required.
# Q1
**Justification:** Our model is designed to separately process graph signals with different levels of h... | Summary: This paper proposes a novel Graph Domain Adaptation algorithm which solves graph homophily disparity for effective domain alignment. It shows that homophily distribution shifts exist wildly in GDA datasets and could damage GDA performance in both empirically and theoretically ways. Inspired by theoretical resu... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! Below, we address the concerns and questions raised in the weaknesses section. Please feel free to reach out if further clarification is required.
# Q1
Thanks for your constructive comment! Following your suggestion, we also conducted [additional experim... | Summary: This paper studies the problem of Graph Domain Adaptation problem through analysis homophily shift. This study reveals that homophily distribution shift negatively influences target domain accuracy in an empirical study. Empirical study reveals that homophily discrepancy exists in many benchmarks and provides ... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We would also appreciate your agreement on our method's novelty and effectiveness. Below, we address the concerns and questions raised in the weaknesses section. Please feel free to reach out if further clarification is required.
# Q1
$ KL(Z_L^S \| Z_L^T) $ aligns th... | Summary: This paper investigates the graph domain adaptation (GDA) problem highlighting the importance of handling the shift across graph homophily distribution between the source and target graphs. They motivate the issue from both the empirical aspect and from theoretical analysis. Empirically, it has been observed t... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We would also appreciate your agreement on our method's novelty and effectiveness. Below, we address the concerns and questions raised in the Claims And Evidence, Weaknesses, Theoretical Claims, and Questions For Authors section. Please feel free to reach out if furthe... | null | null | null | null | null | null |
TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization | Accept (poster) | Summary: Recent work in RLHF has revealed the benefits of utilizing fine-grained rewards. The combination of token-level guidance with DPO, however, remains to be explored.
To address this challenge, this paper decomposes the sequence-level RL formulation in the original DPO derivation as a sequence of token-level RL p... | Rebuttal 1:
Rebuttal: **Q1 Comparison with existing works**
All [1], [2] and ours are based on the assumption of dense token-level reward to derive respective DPO loss functions. Eq. (8) in our work seems similar to Eq. (12) in [1], but our Eq. (8) is not solvable since $s_t\sim\mathcal{D}\_t$ is dependent on the poli... | Summary: This paper proposes a method that integrates Direct Preference Optimization (DPO) with token-level rewards. The paper first provide an upper bound for rewards in sentence-level LLM generation by decomposing the problem into a series of token-level reward maximization tasks. Building on this foundation, the pap... | Rebuttal 1:
Rebuttal: **Q1 Clarification regarding Equ. 8**
By [1] (Thanks to Reviewer **aioX**), Equ. (8) has connections to an approximation approach common in prior RL works [2, 3, 4]. [1] pointed out this by providing 5 papers including [2, 3, 4], and followed this line to derive the loss function for their DPO,... | Summary: This paper introduces TGDPO, an enhanced version of Direct Preference Optimization (DPO) that incorporates token-level reward guidance to address the limitations of conventional sentence-level DPO. While prior methods like Proximal Policy Optimization (PPO) benefit from fine-grained token-level rewards, DPO, f... | Rebuttal 1:
Rebuttal: **Q1 TGDPO's dependence on the choice of $\hat{r}$**
Trained token-level rewards have shown effectiveness for PPO [1, 2]. For DPO, it is interesting to ask if there exists a framework that can incorporate a trained token-level reward explicitly for better performance. Our TGDPO fills this nontriv... | Summary: The paper presents TGDPO, a formulation of direct preference optimization (DPO) with an implicit token-level reward instead of an implicit sentence-level reward, and shows that this novel method outperforms standard DPO and provides interpretable training dynamics
More precisely, the paper makes the following... | Rebuttal 1:
Rebuttal: **Q1 Experiment on SFT model**
Thank you for the advice! Below we show the experiment results on UltraFeedback using the open-sourced SFT model OpenRLHF/Llama-3-8b-sft-mixture, which has not been trained by RLHF. Our TGDPO using SimPO's token-level reward achieves much better performance than bas... | null | null | null | null | null | null |
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster | Reject | Summary: This paper proposes TS-RAG, a retrieval-augmented forecasting framework that enhances zero-shot time series prediction by integrating retrieval-augmented generation (RAG) with a pretrained Time Series Foundation Model. The model consists of two key components: a retriever that selects relevant historical time ... | Rebuttal 1:
Rebuttal: Q1:
Thank you for your concern. The TSFM encoder used for retrieval is frozen during both pretraining and inference. It is directly adapted from a pretrained TSFM model on diverse datasets, requiring no further fine-tuning.
Q2:
Thank you for the observation. We agree our augmentation module dif... | Summary: The authors introduce TS-RAG, a method designed to enhance the performance of a Time-Series Foundation Model (TSFM) by augmenting time-series sequences using an external database. The approach leverages the TSFM’s encoder to embed the input query, retrieve relevant candidate sequences, and weight them using a ... | Rebuttal 1:
Rebuttal: **Reproducibility:** Thank you for your suggestion, we are glad to provide code, pretrained models, datasets and knowledge base via the anonymous link: *https://anonymous.4open.science/r/TS-RAG-F4DB*
**W1: TS-RAG enhances interpretability** Thank you for your comment. TS-RAG improves interpretab... | Summary: This paper proposes TS-RAG, a retrieval-augmented generation based method aimed at improving the generalization ability and interpretability of time series forecasting tasks. This framework does not require task-specific fine-tuning, enabling effective zero-shot forecasting while also providing interpretabilit... | Rebuttal 1:
Rebuttal: **W1: Training loss and Reproduce** Thank you for your question. TS-RAG uses the same loss as the backbone TSFM during training. Specifically, when using Chronos-bolt as the backbone, we adopt the quantile regression loss used in its original implementation. For full reproducibility, we are glad ... | Summary: This paper presents TS-RAG, a retrieval-augmented-generation-based time series forecasting framework. TS-RAG leverages pre-trained time series encoders to retrieve semantically relevant time series segments from a dedicated knowledge database. Next, it develops a learnable Mixture-of-Experts (MoE)-based augmen... | Rebuttal 1:
Rebuttal: **Q1: Claims**
We appreciate the reviewer’s concern. While TSFMs are pretrained on diverse datasets and perform well in zero-shot and few-shot settings, they can still struggle with non-stationary data and distribution shifts. Existing TSFMs lack mechanisms to deal with this problem, which motiva... | null | null | null | null | null | null |
ROS: A GNN-based Relax-Optimize-and-Sample Framework for Max-$k$-Cut Problems | Accept (poster) | Summary: This paper proposes ROS, a GNN-based L2O method, to obtain high-quality max-$k$-cut solutions. The one-hot encoding of each node is relaxed to continuous variables, a GNN is used to do node classification task, i.e., assigning nodes into $k$ partitions, and the continuous output from GNN is then used to constr... | Rebuttal 1:
Rebuttal: ## Claims And Evidence
**Response to C1:** Theorem 3.2 requires a global optimum, which is why we introduce Theorem 3.3. It theoretically establishes the expected equivalence between relaxed and integer solutions for all feasible points, not just the global optimum. Our sampling algorithm and expe... | Summary: The paper introduces, a GNN-based framework for solving the Max-k-Cut problem by relaxing the discrete optimization problem into a continuous optimization task. A Graph Neural Network (GNN) optimizes the relaxed problem, followed by a sampling-based algorithm to obtain a discrete solution. The authors integrat... | Rebuttal 1:
Rebuttal: ## Claims And Evidence
**Response to C1:** Please refer to "Response to W2" for Reviewer bfPu.
## Methods And Evaluation Criteria
**Response to M1:**
- NeuroCUT [1] is a reinforcement learning-based partitioning method, while DGCLUSTER [2] and DMoN [3] employ graph neural networks to optimize cl... | Summary: This paper introduces ROS, a GNN-based framework for Max-k-Cut. The authors propose a solution that relaxes the problem to a continuous space, optimizes it with a neural network, and samples a discrete solution. They compare with existing neural and non neural baselines and show they are better in terms of qua... | Rebuttal 1:
Rebuttal: ## Essential Reference Not Discussed
**Response to R1:**
- NeuroCUT [1] is a reinforcement learning-based partitioning method designed for graph clustering, which aims to minimize inter-cluster connections, whereas Max-$k$-Cut seeks to maximize inter-partition connections. Additionally, while Ne... | Summary: The paper proposes a GNN-based solver for the mak-k-cut problem.
Claims And Evidence: Yes. But I do have many questions.
Methods And Evaluation Criteria: Some points may not be clear enough. For example,
- Does other baselines use the same training data as the training+finetuning datasets of ROS? If not, ca... | Rebuttal 1:
Rebuttal: ## Methods And Evaluation Criteria
**Response to M1:** Please see "Response to W2" for Reviewer bfPu.
## Weakness
**Response to W1:** The Max-$k$-Cut problem is a fundamental NP-complete problem with applications in physics [1], power networks [2], and data clustering [3]. While ROS is tailored f... | null | null | null | null | null | null |
Retrieval-Augmented Language Model for Knowledge-aware Protein Encoding | Accept (poster) | Summary: The paper presents Kara, a knowledge-aware retrieval-augmented protein language model, designed to explicitly integrate knowledge from protein knowledge graphs (PKGs) into protein language models (PLMs). Unlike previous methods that implicitly embed knowledge, Kara directly injects structured knowledge through... | Rebuttal 1:
Rebuttal: __Thanks for your kind comments, we place all tables and figures in this anonymous link(https://anonymous.4open.science/r/Rebuttal-F1C0/README.md)__
``W1. Claims needing stronger justification: (1) The advantage of direct knowledge injection over implicit embedding is not clearly isolated. A cont... | Summary: This article proposes a knowledge-aware retrieval-augmented protein language model named Kara. During the pre-training phase, it extracts structural and knowledge information from protein KGs through contextualized virtual tokens, which are jointly embedded into the protein sequence encoding. The optimization ... | Rebuttal 1:
Rebuttal: __Thanks for your kind comments, we place all tables and figures in this anonymous link(https://anonymous.4open.science/r/Rebuttal-F1C0/README.md)__
``W1. The baselines used in the paper should include more recent papers.``
Thanks for your kind comment. As shown in Table 1, we compare the perfor... | Summary: This paper proposes Kara, a knowledge-aware retrieval-augmented language model for protein representation learning, explicitly integrating protein knowledge graphs (PKGs) with protein language models (PLMs). The key innovation lies in using contextualized virtual tokens and a knowledge retriever, allowing expl... | Rebuttal 1:
Rebuttal: __Thanks for your kind comments, we place all tables and figures in this anonymous link(https://anonymous.4open.science/r/Rebuttal-F1C0/README.md)__
``W1. The following claim requires further clarification: Kara effectively avoids catastrophic forgetting due to the unified integration of knowledg... | Summary: The paper presents Kara, a knowledge-aware retrieval-augmented protein language model that explicitly integrates protein knowledge graphs (PKGs) with protein language models (PLMs), enhancing protein representation learning with task-specific knowledge and graph structure information.
Kara predicts potential g... | Rebuttal 1:
Rebuttal: __Thanks for your kind comments, we place all tables in this anonymous link(https://anonymous.4open.science/r/Rebuttal-F1C0/README.md)__
``W1. Stronger baselines may be considered.``
As shown in Table 1, we compare the performance of Kara, SaProt, and ProSST on the ProteinGYM benchmark. Kara, w... | null | null | null | null | null | null |
Optimal Fair Learning Robust to Adversarial Distribution Shift | Accept (poster) | Summary: This paper demonstrates that randomized fairness-aware classifiers have the local Lipschitz property, which makes them (somewhat) robust to adversarial perturbations of their training data. The authors demonstrate that randomization is crucial for this property (confirming a previously known result), but also ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and for appreciating the novelty of our results and the clarity of presentation. We recognize the importance of making the intuition in Section 1.2 as clear as possible for better readability of the rest of our paper. Acting on your suggestion, we will update... | Summary: This paper analyzes/bounds the robustness of the optimal fair classifier $f$ (w.r.t. a distribution $\mathcal P$) under distribution shifts in terms of both accuracy and performance. The specific setting considered is binary class, binary group, in the attribute-aware setting, and the fairness criteria consid... | Rebuttal 1:
Rebuttal: Thank you for your time and insightful feedback. We will cite and discuss the novel results in the papers you pointed out. However, our results are incomporable to those of the papers mentioned. Fundamentally, the underlying settings are different, and these results complement each other.
**1. Co... | Summary: The paper studies the fairness-aware classification problem, where the goal is to maximize accuracy subject to the demographic parity or equal opportunity fairness constraint. The paper first discusses Claim 1 that a deterministic classification rule can have high sensitivity to perturbations to the data distr... | Rebuttal 1:
Rebuttal: Thank you for your time and insightful feedback. We respond to your comments below.
**1. Real-world datasets:** Our setup assumes a distribution over a discrete domain space $X$, which is a standard assumption for real-world tabular datasets (e.g., COMPAS) with features such as age, race, DoB, ... | Summary: The authors study the robustness of the Fair Fair Bayes Optimal Classifier (BOC) to adversarial noise in the data distribution. They show that the robustness guarantee for BOC breaks down when fairness constraints are aded and propose a randomized Fair BOC that is robust to malicious noise in the data distrib... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback, and for appreciating the importance of our contribution, and the clarity of presentation. We respond to your comments below.
**1. Experiments:** We emphasise that the focus of our paper is to advance the theoretical foundations of robust fair learning, bu... | null | null | null | null | null | null |
Tokenized Bandit for LLM Decoding and Alignment | Accept (poster) | Summary: The paper "Tokenized Bandit for LLM Decoding and Alignment" introduces a novel Tokenized Bandit (TB) framework to address LLM decoding and alignment challenges. It models LLM decoding as a sequential decision-making problem, using multi-armed bandit (MAB) and linear bandit (LB) techniques to optimize token sel... | Rebuttal 1:
Rebuttal: We truly appreciate your insightful comments.
We will first make a general remark on our main contribution, answer major concerns and then remaining questions.
# General remark on main contribution / assumption
We kindly refer to our **response (General remark on main contribution) to Reviewer ... | Summary: The paper introduces two new bandit variants, the tokenized linear bandit (TLB) and tokenized multi-armed bandit (TMAB), which involve sequentially constructing a sequence of tokens to optimize a (random) utility function of a user, given a query. They introduce the DDMC assumption on token sequences and const... | Rebuttal 1:
Rebuttal: We truly appreciate your detailed feedback and insightful comments.
We will first make a general remark on our main contribution, and then answer the reviewer’s comments/questions one by one.
# General remark on main contribution
We kindly ask the reviewer to see **our response (General remark o... | Summary: The paper introduces the Tokenized Linear Bandit (TLB) and Tokenized Multi-Armed Bandit (TMAB), which are variants of the classical linear and stochastic multi-armed bandit problems, inspired by the decoding and alignment processes in large language models (LLMs). In these problems, a user submits a query (con... | Rebuttal 1:
Rebuttal: We truly appreciate your detailed feedback and insightful comments, in particular we are glad that the reviewer enjoys our problem and approach.
As the reviewer suggested, we agree that numerical results demonstrating our algorithm’s performance would greatly improve our paper - we appreciate you... | Summary: This paper introduces Tokenized Linear Bandit (TLB) and Tokenized Multi-Armed Bandit (TMAB), two variants of bandit algorithms designed for LLM decoding and alignment. These frameworks model LLM decoding as a sequential decision-making process, where a decision-maker selects tokens iteratively to form a comple... | Rebuttal 1:
Rebuttal: We truly appreciate your insightful comments.
We will first make a general remark on our main contribution, and then answer each comment.
# General remark on main contribution
First, the main focus of our paper is to provide a **theoretical foundation** of tokenized versions of multi-armed bandi... | null | null | null | null | null | null |
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration | Accept (poster) | Summary: The paper proposes a state modelling framework to infer meaningful beliefs about the unobserved state while filtering redundant information. It reconstructs other agents’ observations using an encoder-decoder. To overcome the sparse reward challenge, this paper proposes a adversarial count-based intrinsic expl... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and input. We respond to your comments and questions below.
> 1. The motivation of the proposed method is not clearly stated.
We respectfully disagree with the reviewer's comment. In Lines 28-36 (right), we clearly state our main motivation for this paper: "we a... | Summary: The paper presents a novel approach to cooperative multi-agent reinforcement learning (MARL) under partial observability by introducing a state modelling framework combined with adversarial exploration. In this framework, each agent infers a latent belief from its local observation using a variational encoder–... | Rebuttal 1:
Rebuttal: > 1. Motivation and Discussion about Assumptions of related work
We respectfully disagree with the reviewer's comment. In the introduction (Lines 43–64), we provide a detailed discussion of significant drawbacks and problematic assumptions of existing agent modelling (AM) methods. Remarkably, man... | Summary: In most Multi-Agent Reinforcement Learning (MARL) problems, agents operate under partial observability, making decisions based on their observations and beliefs rather than the full state, and a naïve integration of the full state to each agent’s observation can introduce irrelevant information, hinder explora... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and input. Please see our responses below:
> 1. Scalability with more agents
Thanks for this question. Along with Spread-8 and LBF 7s-20x20-5p-3f (see Fig. 2, 3), we also add results on other large LBF tasks: namely 8s-25x25-8p-5f and 7s-30x30-7p-4f. We note tha... | Summary: This paper proposes State Modelling for Policy Enhancement through Exploration, a novel approach to cooperative multi-agent reinforcement learning in partially observable environments without communication. The method enables agents to infer meaningful belief representations about unobservable states through v... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and input, along with the positive evaluation. We respond to your comments and questions below.
> 1. A more detailed analysis in the style of rliable [42] could be more informative than some subfigures in Fig 3 and 4.
Thanks for this comment. We will consider u... | Summary: This paper proposes State Modelling for Policy Enhancement through Exploration, a novel approach to cooperative multi-agent reinforcement learning in partially observable environments without communication. The method enables agents to infer meaningful belief representations about unobservable states through v... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and input, along with the positive evaluation. We respond to your comments and questions below.
> 1. A more detailed analysis in the style of rliable [42] could be more informative than some subfigures in Fig 3 and 4.
Thanks for this comment. We will consider u... | null | null | null | null |
Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism | Accept (poster) | Summary: The paper proposes an adaptive intervention strategy aiming to use the shared automony to improve the robot execution process. Previous robot -gated designs rely on the entropy to judge whether to let the human intervent. Using this strategy, the robot would frequently ask humans for the help, whihch is constl... | Rebuttal 1:
Rebuttal: Thank you for your effort to thoroughly review our paper and for your feedback. In response to your feedback, we have included qualitative evaluations and ablation studies that have strengthened the study.
__Experimental Designs Or Analyses:__
>1.Qualitative evaluations are missing.
We include ... | Summary: This paper develops an approach to imitation learning called Adaptive Intervention Mechanism (AIM) that learns whether to ask an expert for an action label based upon whether AIM thinks the imitation learner already knows the correct action. An objective function is developed (Equation 3) that governs this AIM... | Rebuttal 1:
Rebuttal: Thank you for reading our paper in detail and providing valuable suggestions. We summarize and respond to each question as follows:
__Claims And Evidence:__
>However, the term "sufficient" in the third claim (Line 98) should be softened unless providing a proof.
We revise Line 98: “The expert d... | Summary: The authors proposed Adaptive Intervention Mechanism (AIM), a new robot-gated shared autonomy mechanism that better align agent with human expert thorugh a proxy Q-function. This algorithm requires less human monitoring comparing to human-gated interactive imitation learning methods, while more intelligently a... | Rebuttal 1:
Rebuttal: Thank you for taking the time to carefully read through and understand our paper, and provide constructive feedback. We summarize and respond to each question as follows:
__Other Strengths And Weaknesses:__
>1.Why L2 distance instead of other distance metrics? Is $a_h$ unique and deterministic i... | null | null | null | null | null | null | null | null |
A Rescaling-Invariant Lipschitz Bound Based on Path-Metrics for Modern ReLU Network Parameterizations | Accept (poster) | Summary: The paper proves a new reparameterization invariant Lipschitz bound in terms of the “path-metrics” of the parameters. The bound applies generally to network architectures with pooling and skip connections. Using the bound, the authors propose a rescaling-invariant pruning criterion.
Claims And Evidence: The a... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your points below.
1. > what if there is a normalization layer on the input
We assume you refer to batch normalization. As detailed in Gonon et al. 2024, batch normalization layers *as they behave at inference* are indeed covered in the path-lifting framew... | Summary: The paper derives a Lipschitz upper bound for neural networks with ReLU and k-max-pooling activations. For two parameters $\Theta$ and $\Theta'$, the paper shows that $||R_{\Theta}(x)-R_{\Theta'}(x)||_1\leq max(||x||_∞,1) ||\Phi(\Theta)-\Phi(\Theta')||_1$, with an assumption that $\mathrm{sign}(\Theta)=\mathrm... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your points below.
1. > assumption of sign consistency and extension to cases where only a few edges have different signs?
As shown by the example in Figure 5, page 13 (that we will move to the main text), the sign assumption cannot be simply removed in Theo... | Summary: This paper introduces a novel Lipschitz bound for modern ReLU neural networks that is invariant under neuron-wise rescaling transformations. The key idea is to leverage a "path-lifting" function which transforms the network parameters into a high-dimensional path space, where each coordinate corresponds to the... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your points below.
1. > The major limitation is the assumption of parameters/assumption of sign consistency
As shown by the example in Figure 5, page 13 (that we will move to the main text), the sign assumption cannot be simply removed in Theorem 3.1. This i... | null | null | null | null | null | null | null | null |
Graph Diffusion for Robust Multi-Agent Coordination | Accept (spotlight poster) | Summary: This paper introduces MCGD (Multi-agent Coordination based on Graph Diffusion), which is a novel framework for offline multi-agent reinforcement learning (MARL) that aims to improve coordination effectiveness and robustness of the policies in dynamic environments. Specifically, MCGD uses graph to model the rel... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback. We have addressed all comments and revised the manuscript accordingly. Responses are organized by reviewer Weaknesses (W) and Questions (Q), with relevant figures and tables provided in the [anonymized supplementary material](https://a... | Summary: This paper introduces Multi-agent Coordination based on Graph Diffusion (MCGD), a novel framework for offline multi-agent reinforcement learning (MARL) that uses graph diffusion models to enhance coordination in dynamic environments. MCGD constructs a coordination graph to capture multi-agent interactions and ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback. We have addressed all comments and revised the manuscript accordingly. Responses are organized by reviewer Weaknesses (W) and Questions (Q), with relevant figures and tables provided in the [anonymized supplementary material](https://a... | Summary: This paper introduces MCGD, the first offline MARL algorithm based on graph diffusion models. MCGD employs a discrete diffusion process on graphs to model cooperative relationships among agents, while using a continuous anisotropic diffusion process to model each agent’s action distribution. The authors claim ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback. We have addressed all comments and revised the manuscript accordingly. Responses are organized by reviewer Weaknesses (W) and Questions (Q), with relevant figures and tables provided in the [anonymized supplementary material](https://a... | Summary: This paper uses a graph diffusion approach to study MARL problems. This method incorporates graph diffusion in order to incorporate changes in multi-agent coordination dynamics (such as an agent dropping out). The goal of the approach is to be able to more seamlessly handle out-of-distribution states and actio... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback. We have addressed all comments and revised the manuscript accordingly. Responses are organized by reviewer Weaknesses (W) and Questions (Q), with relevant figures and tables provided in the [anonymized supplementary material](https://a... | null | null | null | null | null | null |
Hot PATE: Private Aggregation of Distributions for Diverse Tasks | Reject | Summary: Hot PATE extends the Private Aggregation of Teacher Ensembles (PATE) framework to diverse and open-ended tasks, addressing the fundamental tradeoff between privacy and diversity in generative AI. While the PATE framework works best in the classification settings with a small set of labels, Hot PATE can remedy ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and comments.
**Question 1**
*״Try Hot PATE on real-world datasets to substantiate the practical impact of Hot PATE and improve confidence in its general applicability״*
Hot PATE is a mathematically rigorous framework, which we consider to be our main contribut... | Summary: The PATE framework was designed for classification tasks where there is a single ground-truth label; however for tasks like sequential text generation, there might be multiple “good” responses. This paper proposes to extend the PATE framework to diverse tasks like this (where the responses are distributions ra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and excellent comments and suggestions. We will use them to improve the presentation.
**Question:** *“Besides sequential text generation, what other applications would hot PATE work well for?”*
**Response:**
Hot PATE is suitable for "soft" tas... | Summary: This paper introduces Hot PATE, a privacy-preserving method for auto-regressive models in open-ended tasks. It addresses the challenge of preserving diversity and privacy by coordinating teacher models through shared randomness and positive correlation voting. Key contributions include mathematically formalizi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and will do our best to improve the presentation.
**Question 1:**
-- *our demo “only counted the diversity”*.
Our demo reports on the diversity-privacy tradeoff. Diversity is measured by the number of returnable tokens for a **given privacy level** (me... | null | null | null | null | null | null | null | null |
Online Uniform Sampling: Randomized Learning-Augmented Approximation Algorithms with Application to Digital Health | Reject | Summary: The authors propose an algorithm for online uniform sampling (OUS) to distribute a constrained sampling budget across unknown decision times as uniformly as possible over risk times. They consider cases of whether the number of risk times is both known and unknown, and present algorithms for both scenarios tha... | Rebuttal 1:
Rebuttal: We are glad that the reviewer found our paper "well-written with notable clarity and presentation." We thank the reviewer for the valuable feedback and respond them in detail below.
- **Proof of Lemmas 3.1 and 4.1** Sorry for the sloppiness in the proofs. To ensure exact satisfaction of $\mathbb{E... | Summary: This paper studies the following online prediction problem: Let $\tau^*$ be an unknown number in $[b,T]$, where $b$ and $T$ are known. At every step $t\le \tau^*$, the learner needs to predict a number $p_t\in [0,1]$. The goal is to maximize
$
\sum_{t=1}^{\tau^*}p_t-\frac{1}{\tau^*}\ln\left(\frac{\max_t p_t}{\... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback. We clarify that our work is positioned as an applied paper motivated by digital health applications rather than a purely theoretical paper. Below, we illustrate the type of guarantees that competitive ratios provide, detail the computation of competitive rat... | Summary: This paper investigates the problem of online uniform sampling (OUS), where the goal is to allocate a budget uniformly across unknown decision times. The authors formulate the OUS problem as an online optimization problem and propose randomized algorithms to address it. To evaluate the performance, they consid... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback. Below we address each point in detail to further clarify and strengthen the paper. We have also included SeqRTS as a benchmark in the synthetic experiments. We would be happy to provide further clarification if needed.
- **The need for consistency** While pe... | Summary: The topic of this paper is online uniform sampling problem (OUS) - motivated by applications in digital health.
OUS problem is to distribute a sampling budget b uniformly across unknown decision times in horizon [1,T]. An adversary chooses a value tau* in interval [b,T], revealed only online. At each dec... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and are glad that the reviewer found our work as "a principled approach to the OUS problem and technically nice and non-trivial."
- **Weighted risk times:** Weighting risk times differently implies varying risk levels, prioritizing higher-risk times. O... | null | null | null | null | null | null |
PTTA: Purifying Malicious Samples for Test-Time Model Adaptation | Accept (poster) | Summary: The paper presents PTTA, a plug-and-play method for purifying malicious (unhelpful) samples for test-time adaptation.
PTTA selects benign samples by comparing the samplewise gradients.
Instead of simply filtering out malicious samples, PTTA transforms them into benign samples via Mixup with benign samples.
PTT... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort put into reviewing our paper and providing valuable feedback. We would like to address your questions below and provide a link to figures and tables.
[link] https://anonymous.4open.science/r/PTTA/tab_fig.pdf
> **R4Q1**: On the claim on entropy-accura... | Summary: The paper introduces a method called Purifying Malicious Samples for Test-Time Model Adaptation (PTTA), a plug-and-play solution. Instead of filtering out, the authors identify that malicious samples in test data, though reflecting the data distribution, can undermine the stability of TTA algorithms. To addres... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort put into reviewing our paper and providing valuable feedback. We would like to address your questions below and provide a link to figures and tables.
[link] https://anonymous.4open.science/r/PTTA/tab_fig.pdf
> **R3Q1**: The paper lacks quantification... | Summary: Existing TTA algorithms often focus on selecting benign samples for self-training, which leads to wasted test data. To address this, the authors propose PTTA, which uses a saliency indicator to identify benign samples with opposing effects on the objective function and combines them with malicious samples via ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort put into reviewing our paper and providing valuable feedback. We would like to address your questions below and provide a link to figures and tables.
[link] https://anonymous.4open.science/r/PTTA/tab_fig.pdf
> **R2Q1**: What's the results of purifyin... | Summary: This paper focuses on leveraging malicious samples during Test-Time Adaptation (TTA) to improve data utilization. The authors propose PTTA, a plug-and-play method that retrieves benign samples with maximal divergence from malicious samples and employs a Mixup strategy to purify malicious samples for TTA. PTTA ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort put into reviewing our paper and providing valuable feedback. We would like to address your questions below and provide a link to figures and tables.
[link] https://anonymous.4open.science/r/PTTA/tab_fig.pdf
> **R1Q1**: Lacks detailed computational ... | null | null | null | null | null | null |
LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models | Accept (oral) | Summary: The paper introduces a benchmark for scientific equation discovery, where the model are able to use both input/output values along with a problem description in human language, into constructing an equation that describes the data well. The model tested on this benchmark would be measured by the accuracy of th... | Rebuttal 1:
Rebuttal: Thank you for dedicating your time and expertise to review our submission. Please find our responses below.
> it would be interesting to see how traditional symbolic regression methods would fare on the dataset... which would make the need for LLM-based discovery stronger, and the dataset more use... | Summary: The paper introduces LLM-SRBench, a novel benchmark designed to evaluate the capabilities of LLMs in scientific equation discovery. The key motivation behind the benchmark is to prevent trivial memorization by LLMs, which has been a limitation in existing equation discovery benchmarks. The benchmark consists o... | Rebuttal 1:
Rebuttal: Thank you for dedicating your time and expertise to review our submission. Please find our responses below.
> A systematic classification of failure cases across different domains would provide valuable insights. Examining where and how models fail—whether due to misidentified variables, incorre... | Summary: This paper introduces LLM-SRBench, a benchmark designed to evaluate LLMs on scientific equation discovery tasks. The authors identify a key problem: existing benchmarks like Feynman equations can be solved by LLMs through memorization rather than actual discovery. To address this, they develop two benchmark ca... | Rebuttal 1:
Rebuttal: Thank you for dedicating your time and expertise to review our submission. Please find our responses below.
> * However, it would be good to include more detailed case studies or error analyses to better understand which types of equations or mathematical patterns pose the greatest challenges for... | Summary: This paper introduces LLM-SRBench, a benchmark designed to evaluate Large Language Models' capabilities in scientific equation discovery. The authors address a limitation in existing benchmarks: they primarily consist of well-known equations from textbooks that LLMs may have memorized during training, potentia... | Rebuttal 1:
Rebuttal: Thank you for dedicating your time and expertise to review our submission. Please find our responses below.
> * there's no systematic exploration of whether different methods have domain-specific advantages.
We agree this is an important consideration, but it falls outside our study's scope. O... | null | null | null | null | null | null |
Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive Forecasting | Accept (poster) | Summary: The paper addresses time series forecasting by aligning linear-attention Transformer with vector autoregressive (VAR). The authors reveal that while single-layer linear attention mechanisms naturally exhibit a dynamic VAR structure, but multi-layer Transformers can misalign with the autoregressive forecasting ... | Rebuttal 1:
Rebuttal: >Linear attention outperform ... may not be the consensus
>Linear Transformers are not the dominant ...
Thank you for the question. Our claim is based on [1] (https://arxiv.org/abs/2410.03159, cited at line 479), which suggests AR linear attention may outperform vanilla attention in TSF. We also ... | Summary: This paper addresses the misalignment between deep Transformer architectures and autoregressive objectives in time series forecasting (TSF), proposing Structural Aligned Mixture of VAR (SAMoVAR) to integrate interpretable dynamic VAR weights into multi-layer linear Transformers. By reorganizing the input-outpu... | Rebuttal 1:
Rebuttal: >No theoretical analysis for robust path pruning
Thank you for the insightful question. We provide a proof sketch below.
**Theorem 1**: RMSNorm applied to query and value vectors bounds the magnitudes of dot products in temporal influence paths, preventing numerical instability.
**Proof Sketch*... | Summary: This paper demonstrates that a single linear attention layer behaves like a dynamic VAR model and that deeper Transformers can be restructured to align with autoregressive objectives. Based on these insights, the authors introduce SAMoVAR, a Transformer variant that leverages dynamic VAR weights to enhance for... | Rebuttal 1:
Rebuttal: >Why does the validation loss in Figure 4 exhibit significant oscillations during the training process?
Thank you very much for taking the time to read our paper and for acknowledging its contributions.
We apologize for the misunderstanding. In Figure 4, we cropped the Y-axis of the validation l... | Summary: This paper demonstrates that autoregressive linear attention can be interpreted as a rank-1 vector autoregressive (VAR) model. Building upon this perspective, the authors introduce SAMoVAR, a novel model achieved by stacking multiple linear attention layers. SAMoVAR overcomes the inherent rank-1 limitation of ... | Rebuttal 1:
Rebuttal: >However, integrating more explicit discussions about linear attention...
Thank you for the valuable suggestion. Currently, related literature is only discussed in the Introduction and Background. We will add a Related Work section in the revision for a more thorough and structured discussion.
>... | Summary: This paper proposes structural modifications to linear Transformer architectures to better align them with the Vector Autoregressive (VAR) framework, which is widely used in time series forecasting. The authors show that while a single-layer linear attention module can naturally express a dynamic VAR structure... | Rebuttal 1:
Rebuttal: >Performance differ from those presented in original papers ...
Thank you for your valuable question. For each baseline and output length $ L_P $, we run experiments with input lengths $ L_I \in \{512, 1024, 2048, 4096\} $ and report the best result. This avoids biases from models' sensitivity to... | null | null | null | null |
Tree-Sliced Wasserstein Distance with Nonlinear Projection | Accept (poster) | Summary: The authors introduce the following:
1. Generalized Radon transforms in the system of lines.
- These transforms extend the concept of the Radon transform by incorporating systems of lines and allowing for nonlinear projections, which improve the flexibility and applicability of the SW distance.
2. Generali... | Rebuttal 1:
Rebuttal: We direct the Reviewer to Tables R1-2 and Figure R1, available at https://sites.google.com/view/nonlinear-tsw-4.
**Q1. It seems that any continuous injective function $h$ can be used to define the generalized Radon transform. However, the criteria for selecting a suitable $h$ for different datase... | Summary: This work proposes to extend the Tree Sliced-Wasserstein distances, defined using linear projections on system of lines, by using nonlinear projections instead. The authors study the use of two different non linear projections: circular projections and spatial projections. They also propose to use a spatial pr... | Rebuttal 1:
Rebuttal: We direct the Reviewer to Table R1-2 at https://sites.google.com/view/nonlinear-tsw.
**Q1. The paper feels a bit incremental, but there are lots of results, which compensate.**
**It is not really stated when one would prefer one type of non linear projection compared to another.**
**Answer Q1.*... | Summary: This paper extends the Tree-Sliced Wasserstein (TSW) distance, an alternative to the Sliced Wasserstein (SW) distance that leverages tree-based metric spaces, by allowing the use of nonlinear projections. More precisely, the authors explore generalized Radon transforms (previously used in existing SW variants,... | Rebuttal 1:
Rebuttal: We direct the Reviewer to Table R1-4 and Figure R1 at https://sites.google.com/view/nonlinear-tsw-2.
**Q1. Explicitly highlight ... to prior work.**
**Answer Q1.** The key technical challenge in developing this approach lies in proving the injectivity of the proposed Radon Transforms, which ensu... | Summary: The authors introduce several new variants of tree-sliced Wasserstein distance, which was introduced in [TPTLN '24]. This is done via two new proposed Radon transforms: (1) the generalized Radon transform on systems of lines and (2) the spatial Radon transform on systems of lines. Using their new Radon transfo... | Rebuttal 1:
Rebuttal: We direct the Reviewer to Table R1-2 at https://sites.google.com/view/nonlinear-tsw.
**W1. [...] (many variants of TSW) outperform SW or TSW with linear projection**
**Q1. [...] (intuition) one (TSW) over another**
**Answer.** Our motivation for proposing the non-linear projection framework is ... | null | null | null | null | null | null |
Tightening Causal Bounds via Covariate-Aware Optimal Transport | Accept (poster) | Summary: The manuscript introduces a novel method for bounding treatment effects using covariate information, reframing the problem as an optimal transport task. Specifically, the authors propose adding a penalty term to the standard optimization objective that encourages covariates to have similar distributions in bot... | Rebuttal 1:
Rebuttal: We highly appreciate the reviewers' summar and comments.
> Several other PI methods have been published in recent years that do not necessarily rely on optimal transport theory, ...
The other PI methods deal with the case where there is unobserved confounder/leaky IV, such that the observed marg... | Summary: The paper investigates the problem of tightening partial identification (PI) bounds in causal inference by incorporating covariate information through a conditional optimal transport (COT) framework. The authors propose a novel relaxation that reduces COT to standard optimal transport (OT), improving computati... | Rebuttal 1:
Rebuttal: We highly appreciate the positive comments of the reviewer and are happy to answer any question if needed. | Summary: This paper leverages the conditional optimal transport (COT) to derive or tighten the partial identification (PI) bounds for some causal estimands. Since the COT is not easy to compute in practice, the authors propose a relaxation based on mirror covariates, leading to a optimization problem whose objective fu... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's summary and questions. In the following, we address each question individually.
> In Assp 3.3, why is it important to assume that $\nabla_y h(y, \cdot)$ is injective for all $y \in \mathcal{Y}$ ?
This is because we define the $V_{ip}(\eta)$ by the expectatio... | Summary: This paper studies partial identification intervals for the Rubin causal model, which is an important problem in the causal inference literature. The problem arises from the fact that we can never observe the counterfactual. Indeed, while the treatment effect (obtained when choosing h(Y(1), Y(0)) = Y(1) - Y(0... | Rebuttal 1:
Rebuttal: We highly appreciate the positive comments of the reviewer and are happy to answer any questions if needed. | Summary: This paper tackles the challenge of partial identification (PI) in causal inference, where causal estimands depending on the joint distribution of potential outcomes are not fully identifiable. While incorporating covariate information can tighten PI bounds, solving the corresponding Conditional Optimal Transp... | Rebuttal 1:
Rebuttal: We address potential limitations.
> The penalty tuning.
We provide a data-driven selection method for Q1, which works well in Fig 3, 4.
> Efficiency loss relative to COT.
No direct estimator of COT has been established (see Q2). Although there is a gap of Vip and Vc, we maintain consistency a... | null | null | null | null |
ALS: Attentive Long-Short-Range Message Passing | Reject | Summary: The paper presents Attentive Long-Short-range Message Passing to handle long-range dependencies while avoiding excessive memory usage and the over-smoothing problem
Claims And Evidence: The authors conduct extensive experiments on 14 datasets, covering homophilic, heterophilic, and long-range graph benchmarks... | Rebuttal 1:
Rebuttal: ## Response to concerns regarding novelty
We have carefully examined the references you kindly pointed out and would like to clarify several aspects:
> computing only the non-zero gradients of PPR (Theorem 3.1) was already proposed in [1]
We respectfully note that the computation of non-zeros i... | Summary: Overall, the core contribution of ALS includes a differentiable personalized PageRank and a short-range message-passing module for effectiveness and efficiency consideration of graph deep learning. The experiments are extensive, and the results are competitive. The writing and the organization of the paper can... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and valuable feedback.
We hope our following clarifications help the reviewer better appreciate the significance of our contributions, and we would be happy to provide any additional information that might assist in their evaluation.
## Oversmoothing
>... | Summary: This study introduces Attentive Long-Short-range message passing (ALS), which combines personalized PageRank to address over-smoothing and utilizes GAT for capturing complex data dependencies, significantly reducing memory footprint and computation time. Extensive experiments show that ALS achieves competitive... | Rebuttal 1:
Rebuttal: ## Oversmoothing
> While the authors claim that the proposed method mitigates oversmoothing, the experimental results do not provide a detailed analysis.
We appreciate this observation regarding oversmoothing.
As established in prior work [1,2], the incorporation of Personalized PageRank (PPR) i... | Summary: This study introduces Attentive Long-Short-range (ALS) message passing, which incorporates personalized PageRank to address the over-smoothing issue in long-range message propagation. Additionally, it utilizes implicit differentiation to effectively improve the GAT computation overhead.
Claims And Evidence: W... | Rebuttal 1:
Rebuttal: ## Link prediction and graph classification
> This narrow focus raises concerns about the method’s generalizability to other graph-related tasks, such as link prediction or graph classification.
> The study lacks an analysis of the method’s generalizability to other graph-related tasks.
We sinc... | null | null | null | null | null | null |
Generative Audio Language Modeling with Continuous-valued Tokens and Masked Next-Token Prediction | Accept (poster) | Summary: The paper proposes a text-to-audio model that leverages diffusion-based designs and causal language models, named AudioMNTP. In detail, the model applies a transformer-based decoder for the next-token prediction of the feature in latent space before being forwarded into the diffusion-based structure, then foll... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback! Below, we address all comments and outline planned improvements.
---
# 1. Presentation Improvement
We agree the current version can be clearer and will revise structure, improve diagrams, and expand explanation in the final version.
> If you have specif... | Summary: This paper presents a novel approach for generative audio language modeling using continuous-valued tokens instead of discrete tokens. The key contributions include:
1. Following previous works, such as masked autoregressive (MAR), which introducing continuous-valued audio tokens to replace discrete ones, imp... | Rebuttal 1:
Rebuttal: # 1. Inference Latency Comparison with Diffusion Models
We appreciate the reviewer’s comment and will include a latency discussion in the final version. We present the latency comparison in Table A.
**Table A. Latency comparison of TTA models**. Measured with **batch size = 1** on a single NVIDI... | Summary: This paper investigates generative audio language modeling with continuous-valued tokens. It begins with a next-token prediction approach in which each latent embedding (i.e., token) from an autoencoder is iteratively produced via a token-wise diffusion process. Building on this, the authors propose Masked Nex... | Rebuttal 1:
Rebuttal: We are sincerely grateful for your kind words and thoughful comments. Regarding our weaknesses, we address them in the following:
# 1. Larger scale in both model size and dataset
We fully agree that exploring the scaling behavior of our proposed methods would provide valuable insights. In this c... | null | null | null | null | null | null | null | null |
ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals | Accept (poster) | Summary: This paper introduces a new deep learning based approach for approximating Koopman eigenvalues based on Residual DMD. By learning a dictionary representation that minimizes a spectral residual loss function, the authors demonstrate their approach is able to perform well on several data sets.
Claims And Eviden... | Rebuttal 1:
Rebuttal: 1. Thank you for your insightful suggestions. The updated figures and proof can be seen here: https://anonymous.4open.science/r/rebuttal_materials-14918/
2. Thank you for raising this point about Barron spaces and the network architecture. To clarify, Barron spaces (Appendix A.3) provide a theore... | Summary: This research presents a novel method for approximating the spectral components of the Koopman operator for discrete-time deterministic dynamical systems by minimizing spectral residuals. Unlike traditional methods that rely on predefined dictionaries, this approach utilizes a neural network to optimize dictio... | Rebuttal 1:
Rebuttal: 1. We would like to thank the reviewer for the helpful comments. The updated figures and proof can be seen here: https://anonymous.4open.science/r/rebuttal_materials-14918/
2. Regarding “Hankel-DMD works almost as well as the proposed method”: While Hankel-DMD performs well in Experiment 1's lowe... | Summary: The paper focuses on Koopman operator analysis and builds on the Residual Dynamic Mode Decomposition (ResDMD), which uses the spectral residual to evaluat the accuracy of a Koopman operator approximation and to perform filtering of a computed spectrum. Here, the proposal is to use the spectral residual iterati... | Rebuttal 1:
Rebuttal: 1. We would like to thank the reviewer for the helpful comments and suggestions. The updated figures and proof can be seen here: https://anonymous.4open.science/r/rebuttal_materials-14918/
2. Indeed, we didn’t notice the paper [R1]: "Another Look at Residual Dynamic Mode Decomposition in the Regi... | Summary: The paper introduces ResKoopNet, a neural network-based method for learning Koopman operator representations of high-dimensional nonlinear dynamical systems.
ResKoopNet aims to address limitations of previous methods of learning Koopman operators from data such as Extended Dynamic Mode Decomposition that disc... | Rebuttal 1:
Rebuttal: 1. We would like to thank the reviewer for the helpful comments. The updated figures and proof can be seen here: https://anonymous.4open.science/r/rebuttal_materials-14918/
2. Regarding the benchmarking question:
(1) In the 2nd experiment, the 300 basis functions we have chosen are indeed diffe... | null | null | null | null | null | null |
Dialogue Without Limits: Constant-Sized KV Caches for Extended Response in LLMs | Accept (poster) | Summary: The authors consider the problem of maintaining a fixed size KV cache during autoregressive generation with large language models. The key idea is to retain recent tokens and a limited number of old tokens according to a dynamic selection algorithm that uses the attention patterns of future tokens on past toke... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We have addressed the points raised in the review below:
**Q-1) Selection Bias in H$_2$O**
R-1) H$_2$O retains tokens based on aggregated attention scores. This introduces a selection bias because it retains early tokens even when they do not significant... | Summary: The authors introduce MorphKV, a KV cache compression method for large language models (LLMs). MorphKV is an inference-time technique that maintains a fixed-size KV cache in autoregressive Transformers, addressing the issue of memory expansion as sequence length increases. Unlike traditional approaches that re... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
**Q-1) Comparison with Prior Works**
R-1) MorphKV’s superior performance mainly stems from its more accurate token selection policy.
StreamingLLM retains the KVs of the first few initial tokens called *attention sinks* and a sliding window of tokens, ev... | Summary: This paper introduces **MorphKV**, a method that dynamically selects caching tokens in pre-trained language models during inference. Unlike prior approaches such as **streamingLLM** and **SnapKV**, MorphKV employs two metrics—*sum fusion* and *max fusion*—to identify and retain tokens most closely attended to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We have addressed the points raised in the review below:
**Q-1) Analysis on Larger Models**
R1) MorphKV remains effective for larger models, as demonstrated by our recent evaluations with larger models. We request the reviewer consult **Table-1** and **T... | Summary: The paper introduces MorphKV, a novel method for efficiently managing key-value (KV) caches in Large Language Models (LLMs) while maintaining memory efficiency and model accuracy. The method overcomes the problem of growing memory requirements for KV caches during inference by employing a dynamic, correlation-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work. We have addressed the point raised in the review below:
**Q-1) Paper Could Explore PyramidKV, Ada-KV, HeadKV Advancements**
R-1) Thank you for the excellent suggestion. MorphKV is orthogonal to methods like PyramidKV, which optimizes the ... | Summary: This work designed and developed an efficient KV cache management technique to keep constant KV size while achieving higher accuracy for long context and long response tasks. The author has compared with relevant works, e.g., SnapKV, H2O and full attention, etc, using different model and different benchmarks ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We have addressed the points raised in the review below:
**Q-1) Effect of Window Size on MorphKV**
R-1) The window size indeed affects the performance of MorphKV. In practice, **all** KV cache pruning methods rely on hyperparameters: examples include tot... | Summary: This paper presents MorphKV to reduce the KV cache in long LLM context. Its dynamic KV selection algorithm improves the accuracy by 18.6 and 13.6 compared to previous SnapKV and H2o, while reducing KV by 88.1 and 51.6.
## update after rebuttal
During the rebuttal, the authors add experiments on larger (24B, 3... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback on both the structure of our paper and the results. We have addressed the points raised in the review below:
**Q-1) Analysis on Larger Models**
R-1) MorphKV remains effective even for larger models. We evaluate MorphKV using long-response tasks from the Lon... | null | null |
Tree-Sliced Wasserstein Distance: A Geometric Perspective | Accept (poster) | Summary: The paper introduces a novel approach to **projected Optimal Transport (OT) computation**, termed **Tree-Sliced Wasserstein (TSW) Distance**. The key contributions include:
1. **Tree Systems**, a generalization of straight-line projections that incorporate hierarchical structures.
2. **Radon Transform on ... | Rebuttal 1:
Rebuttal: **Answer W2.** All the lines in a tree system are infinitely long. In practical applications, empirical measures have bounded support. As a result, when these measures are projected onto the lines of a tree system, the resulting measure on the tree system also has bounded support.
*It is worth no... | Summary: This paper presents a new variant of the sliced Wasserstein distance, called the tree-sliced Wasserstein distance on systems of lines, or TSW-SL. The main idea is that instead of iteratively projecting the distributions to a random line and computing the average of these 1D Wasserstein distances (as is done in... | Rebuttal 1:
Rebuttal: **Q1. Is it true that your algorithm generates a 1D Wasserstein problem instance? ... If I understood correctly, would it be more accurate to use a name other than tree-sliced Wasserstein?**
**Answer Q1.** In the Sliced Wasserstein (SW) framework, each line projection leads to a 1D Optimal Transp... | Summary: The authors study the sliced-Wasserstein distance and propose replace projecting measures onto one-dimensional lines with a more complex structure, which they call a tree system. They propose a novel variant of Radon transforms for tree systems which leads to an efficient metric which they call Tree-Sliced Was... | Rebuttal 1:
Rebuttal: **Answer Q1+Q2.** It appears that the Reviewer may have misunderstood certain key aspects of our paper, as several important points seem to have been overlooked.
Respectfully, we do not claim that TSW-SL is a simplified version of the sliced Wasserstein (SW) distance. On the contrary, TSW-SL is ... | Summary: The paper proposes a novel variant of Sliced Wasserstein (SW) distance, termed Tree-Sliced Wasserstein Distance on Systems of Lines (TSW-SL). The key innovation is replacing one-dimensional projection lines in SW with tree systems, which allow for better preservation of topological structures while maintaining... | Rebuttal 1:
Rebuttal: Based on the two sections discussed below in the review, it appears the Reviewer may have fundamentally misunderstood our framework. Let us clarify this step by step:
**Claims and Evidence.** The term $\alpha(a_i)_l$ represents the mass allocated to the projection of point $a_i$ onto line $l$, no... | null | null | null | null | null | null |
When Bad Data Leads to Good Models | Accept (poster) | Summary: The paper makes the claim that bad data is important to include during LLM pretraining. The authors include a variety of experimental results in support of this claim to show that by including a greater percentage of toxic data during pretraining, downstream alignment can be further improved.
Claims And Evide... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and insightful questions; please see our point-by-point responses below. Thanks for pointing out the typo and improved plotting, we've updated our draft.
## Difficult to draw strong conclusions from experiment on 1B level model
We acknowledge ... | Summary: This paper examines whether training on more toxic data in LLMs can reduce toxicity by enabling more disentangled features (which recognize toxicity) and then reducing the contribution of those features. They show in a toy setting how training on more data helps disentangle features. Afterwards, the authors th... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and insightful questions; please see our point-by-point responses below. Thanks for pointing out the typo and improved plotting, we've updated our draft.
## **Concern**: These results have been somewhat observed in prior papers, as mentioned b... | Summary: The paper challenges the conventional belief that filtering out toxic data from the pretraining corpus of large language models (LLMs) is always beneficial. The authors argue that including toxic data in pretraining can improve the model's ability to control and reduce toxicity during post-training, ultimately... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging comments! | Summary: This paper proposes a novel approach to improving the performance of LLMs by incorporating toxic data during pretraining. The authors suggest that including a controlled amount of toxic data in pretraining, when combined with post-training techniques, can lead to better overall performance. To investigate this... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and insightful questions; please see our point-by-point responses below.
## Non-Monotonic Toxicity Trend in Fig. 6
We will add a discussion in L318 (right column) to address this:
The initial decrease in toxicity is due to the model learni... | null | null | null | null | null | null |
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems | Accept (poster) | Summary: This work develops a physics-informed learning approach for constrained optimal control problem, where the performance objectives are represented using a cost function and safety conditions are formulated using state constraints. A conformal prediction-based safety verification approach is developed with proba... | Rebuttal 1:
Rebuttal: # Method application to non-convex OCP
Our method extends beyond convex OCPs. While a convex cost function leads to a convex epigraph formulation that standard optimizers can efficiently solve, our approach is not restricted to them. By leveraging dynamic programming to solve the epigraph formul... | Summary: In this work, the Authors propose a novel framework for the certified safety of autonomous agents based on the combination of the epigraph-based formulation of the optimal control problem, DeepReach, and conformal predictions. They test the proposed framework in three simulated environments and show the advant... | Rebuttal 1:
Rebuttal: # Clarification on Curriculum Learning
As discussed in Sec. 3.1, we first pre-train the DNN to learn the value function at the terminal time ($t=T$)—i.e., the boundary condition of the HJB-VI—using $\lambda = 0$. We then apply curriculum learning, gradually decreasing $t$ from $T$ to $0$, so the t... | Summary: This paper proposes a novel Physics-Informed framework to address the co-optimization problem of safety objectives and performance objectives for Constrained Reinforcement Learning (CRL). The paper reformulates the co-optimization problem as a state-constrained optimal control problem (SC-OCP) with epigraph fo... | Rebuttal 1:
Rebuttal: # How is the policy $\pi_{\theta}$ synthesized?
The final policy $\pi_{\theta}(t, x)$ is synthesized by first determining the optimal $z^*$ by solving the following optimization problem for any $(t,x)$:
\begin{equation}
\begin{aligned}
z^* = &\arg\min_{z \in \mathbb{R^+}} \; z \\
\... | Summary: The paper addresses the challenge of simultaneously optimizing performance and safety in autonomous systems by formulating it as a state-constrained optimal control problem. The key contribution is a physics-informed machine learning (PIML) framework that efficiently approximates the Hamilton-Jacobi-Bellman (H... | Rebuttal 1:
Rebuttal: # Clarification regarding the Pursuer Evader Problem
In the Pursuer-Evader case study (Section 4.2), the objective is for a pursuer robot to chase a moving target and reach as close to this target as possible within the time horizon. The human figure serves only as a placeholder for the evader an... | null | null | null | null | null | null |
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts | Accept (poster) | Summary: The authors propose an MoE extension to an existing Time series based foundation model MOIRAI. The authors extends the standard Moirai to MoE to reduce the dependency on human-imposed frequency decomposition as it is not a reliable grouping of pre-training data. They claim that different frequencies can displ... | Rebuttal 1:
Rebuttal: **[C1] A weakness of this study is around the evaluation of the probabilistic forecasting component of the paper - which I think is more valuable than the deterministic evaluations performed. The results are indeed impressive but a statistical analysis of the probabilistic forecasting performance ... | Summary: This paper proposes MOIRAI-MOE, a time series foundation model based on the Mixture of Experts (MoE). It replaces the traditional frequency-grouping strategy with data-driven token-level specialization, thus addressing the pre-training challenges posed by the high heterogeneity of time series data. The model a... | Rebuttal 1:
Rebuttal: **[C1] While pruning is mentioned as future work, no concrete solution is provided.**
Thanks for the comment. A concrete pruning solution is to first evaluate expert usage by tracking gating activations during pretraining. Identify experts with significantly fewer activations (e.g., activated les... | Summary: This paper introduces a novel foundational model for time series forecasting, building upon the architecture of Moirai. The primary motivation is to address a key limitation of existing time series foundational models, which rely on manually imposed clustering—such as specialized layers for different time seri... | Rebuttal 1:
Rebuttal: **IMPORTANT: Figures 1, 2, 3 and Tables 1, 2 are provided here: https://drive.google.com/file/d/1bwJ7dyji_OnSNkYXpA6IOpwnvF6nOZmS/view**
**[C1] Testing on synthetic datasets with clear clustering structures. Assess clustering uniqueness in homogeneous datasets or evaluate whether Moirai-MoE corre... | Summary: The paper focuses on the pretraining of time series foundation models using large time series corpora.
The paper argues that there are significant drawbacks to current approaches that address heterogeneity by grouping time-series based on human-identified features such a frequency. The paper proposes an alter... | Rebuttal 1:
Rebuttal: **[C1] Code repository are corrupted.**
The issue appears to be related to viewing the files directly through the web interface. We verify that downloading the repository locally resolves the issue.
**[C2] The paper seems to be a relatively straightforward application of existing sparse MoE tech... | null | null | null | null | null | null |
Towards Theoretical Understanding of Sequential Decision Making with Preference Feedback | Accept (poster) | Summary: This paper considers sequential decision making with preference feedback. The authors build a theoretical formulation linking preferences, utilities (i.e., non-Markovian rewards), and Markovian rewards, and then study the connections between them. First, the authors model preference feedback using a partial (p... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our paper. Below, our answers to the Reviewer's comments and concerns.
> The writing and readability of this paper should be improved. This paper is hard to follow. The abstract is a bit long.
We thank the Reviewer for raising this point. We wil... | Summary: This paper establishes a rigorous theoretical framework for sequential decision-making with preference feedback, where agents learn from comparative evaluations of trajectories rather than explicit reward signals. The authors make several key contributions:
1. They model preference feedback using partial preo... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our work and we appreciate the Reviewer's understanding of the relevance of the proposed framework. Below, our answers to the Reviewer's comments.
> Some theoretical results largely follow or implied by existing known results (e.g., Theorem 4.2).... | Summary: The authors consider the setting of sequential decision-making problems in which only preferences over trajectories are provided, specifically partial (pre)orders. This allows for situations where comparisons of pairs of trajectories are not available (incomparabilities). After several definitions to precisely... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our work, for understanding the relevance of the QP and of the error bound. Below, our answers to the Reviewer's questions.
> As for inverse RL, I was surprised that the authors did not cite work that directly connects preference elicitation and ... | Summary: This paper aims to build a theoretical basis linking the preference-based MDP, the utility-based MDP, and the reward-based MDP. Specifically, this paper formulates these three settings in Section 3, and discusses the connections between the preference-based MDP and the utility-based MDP in Section 4. In Sectio... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our work. Below, our answers to the Reviewer's questions and concerns.
> It seems that some parts of this paper are well-known results from the classical choice theory and existing work, such as Theorem 4.2. Might the authors clearly explain in t... | null | null | null | null | null | null |
Regret-Free Reinforcement Learning for Temporal Logic Specifications | Accept (poster) | Summary: The paper tackles reinforcement learning (RL) under linear temporal logic (LTL) specifications in unknown Markov decision processes (MDPs). The primary goal is to guarantee sublinear regret with respect to the (unknown) optimal probability of satisfying an LTL property. From classic reach-avoid setting, author... | Rebuttal 1:
Rebuttal: We thank you for the detailed and useful feedback! We plan to improve our paper taking your comments into account. Our response to your specific questions are summarized as follows.
**Discussion on the practicality of known lower bounds for transition probabilities:**
We agree with the reviewer... | Summary: This paper tackles the problem of reinforcement learning (RL) for satisfying linear temporal logic (LTL) specifications in unknown environments modeled as Markov Decision Processes (MDPs). The authors propose what they claim is the first regret-free online RL algorithm for LTL objectives. The approach centers... | Rebuttal 1:
Rebuttal: Thank you for appreciating the depth and technical strength of our results. We plan to improve our paper taking your comments into account. Our response to your specific questions are summarized as follows.
**Computational complexity:**
The primary computational steps in Algorithm 1 involve two ... | Summary: This paper proposes a regret-free online RL algorithm for learning policies that satisfy infinite-horizon LTL specifications in unknown MDPs. The core contribution is an algorithm that, for reach-avoid specifications (a subclass of LTL), builds a sequence of optimistic policies using *interval* MDPs and extend... | Rebuttal 1:
Rebuttal: We thank you for the detailed and useful feedback! We plan to improve our paper taking your comments into account. Our response to your specific questions are summarized as follows.
**Assumptions used in the paper:**
We do not assume the SSP-communicating property for MDPs. Instead, we **only** ... | null | null | null | null | null | null | null | null |
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces | Accept (poster) | Summary: The paper presents the Local Intersectional Visual Spaces (LIVS) dataset, a community-driven benchmark designed to align text-to-image (T2I) models with intersectional criteria for inclusive urban design. Through a two-year collaboration involving 30 community organizations, the authors iteratively refined 634... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s feedback. In response, we have clarified our methodological framing of pluralistic alignment, explicitly acknowledged the limitations regarding global metrics such as CLIP and FID, and revised Figure 1 for consistency and clarity.
---
### 1: The claimed plural... | Summary: This paper introduces a new dataset LIVS, which encodes community-generated plurastic preference data towards text-to-image for urban planning. This dataset is is built from data collected from 30 community organizations to develop a framework of 6 axes along which urban public space design can be evaluated. B... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We clarified how criteria were defined, explained neutral judgments, revised Figure 1, and added detail on axis-specific outcomes and image generation settings.
---
### Question 1: Axes Relevance and Plausibility
**Response:**
It was both, since parti... | Summary: The authors contribute LIVS, a benchmark for aligning text-to-image (T2I) models with respect to multiple criteria (Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity) in the context of urban public space design. The benchmark was developed via two-year participatory process with 30 commu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and constructive feedback. Below, we respond to each comment. Where revisions are needed, we provide the updated text.
---
### Comment 1: Multi-criteria preference learning but collapsing annotations, neutral annotations not leveraged; disagreements acros... | Summary: The authors of this paper collected a human preference dataset (called LIVS) of generated images about public spaces. The preference focuses on evaluating six metrics, including Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity. Then, they finetune a Stable Diffusion XL model using Direc... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. We clarified the rationale behind using community-informed prompts over universal keywords, explained the three-criteria annotation design, and updated key sections for clarity.
---
### Comment 1: Necessity of the LIVS Dataset vs. Enhanced Prompting
**Resp... | null | null | null | null | null | null |
Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models | Accept (poster) | Summary: The paper proposes Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models (SMD), a novel method for multi-robot motion planning (MRMP) that integrates constrained optimization into the sampling process of diffusion models. Although diffusion models have demonstrated promising capabilities in ... | Rebuttal 1:
Rebuttal: Thank you for the helpful review, in particular the acknowledgement of the **strong empirical performance** of our proposed SMD and the contribution of **a comprehensive benchmark** for MRMP. We provide below our answers to your insightful questions.
- **Q1: SMD’s inference time with more robots ... | Summary: The paper tackles constraint enforcement for trajectory generation with diffusion models in the context of multi-agent motion planning. Instead of encoding constraints as auxiliary energy terms, the paper proposes to project intermediate generations on to the collision-free manifold. The projection operator is... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback including acknowledging the **superior performance of our SMD in complex scenarios**. We provide below our answers to your valuable questions:
**Questions For Authors:**
- **Q1: How can the framework be used to handle general multi-agent planning constraints ... | Summary: This paper proposes a new method for tackling the constraint satisfaction challenge in Multi-Robot Motion Planning (MRMP). The paper highlights challenges in existing methods, such as learning-based approaches, that struggle with obeying hard constraints. The proposed method, SMD, addresses these issues by inc... | Rebuttal 1:
Rebuttal: We thank Reviewer 9rTn for the insightful feedback including acknowledging the **effectiveness** of our proposed SMD and **comprehensive benchmark** for evaluating MRMP. We provide below our answers to your valuable questions:
- **Differentiation from Christopher et al. (2024)**
Indeed, our work... | Summary: This paper introduces Simultaneous MRMP Diffusion (SMD), a novel method for enforcing critical constraints, such as collision avoidance and kinematic feasibility, in Multi-Robot Motion Planning (MRMP). SMD integrates constrained optimization into the diffusion process to generate collision-free, kinematically ... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments, in particular the acknowledgement of the **strong empirical performance** of our proposed SMD and **a novel benchmark** for MRMP. We provide below our answers to your constructive questions.
- **Q1: Does projection actually help guarantee collision avoidance an... | null | null | null | null | null | null |
Off-Policy Evaluation under Nonignorable Missing Data | Accept (poster) | Summary: The authors study and propose OPE for RL under monotone MNAR missing.
Specifically, they construct an IPW-based correction of value-based OPE and show that, unlike an uncorrected method, the proposed method is unbiased with MNAR missing process under the existence of a shadow variable.
They also conducted synt... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful questions and the time you spent reviewing our paper. We really appreciate your insights and are happy to discuss any further ideas or questions you may have.
**Regarding Assumptions (a), (b), and (e):**
Below is a more detailed explanation of what each assumption mean... | Summary: The paper analyzes the problem of policy evaluation in the presence of missing data. The authors distinguish between two types of missing data:
- **Missing at Random (MAR):** Data is missing independently of unobserved factors.
- **Missing Not at Random (MNAR):** Data is missing due to a hidden cause, intr... | Rebuttal 1:
Rebuttal: **Response to "Summary-Claims And Evidence”:**
The reviewer raised a concern that *the comparison between V-IPW and V-CC relies on the assumption that V-CC is negatively biased.* Actually, Our real-world experiment consists of two parts: the first (Table 2) is based on the original sepsis dataset... | Summary: This paper studies OPE when trajectories are truncated/missing and the missingness is non-ignorable. A new estimator based on inverse probability weighting is proposed, with theoretical justification for its unbiasedness and consistency properties. Experiments were conducted on a synthetic and a semi-synthetic... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful questions and the time you spent reviewing our paper. We really appreciate your insights and are
happy to discuss any further ideas or questions you may have.
**Clarification on Terminology:**
We sincerely appreciate the reviewers' feedback on clarifying terminology ac... | Summary: The paper proposes the challenge of non-ignorable missing data in policy evaluation in reinforcement learning, which is the type of missingness that has dependency with the reward and next state value. The intuition for the significance of the problem and its applications in practice is well explained. Moreove... | null | null | null | null | null | null | |
PokéChamp: an Expert-level Minimax Language Agent | Accept (spotlight poster) | Summary: This paper introduces PokeChamp, and LLM combined with a game-playing agent to perform minimax search for winning Pokemon battles. The authors replace several parts of minimax search with an LLM and introduce a Pokemon battling dataset to understand LLM agent's failures. PokeChamp is able to reach the top 90% ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the value of our exploration of LLM agents for partially observable games and appreciating our dataset contribution. These are indeed core strengths of our work.
**LLM utilization**: LLMs are key components in our system to achieve the claimed performance. Our work repre... | Summary: The paper introduces PokéChamp, an agent that leverages minimax-based search to play competitive Pokemon battles. Specifically, the LLM performs action sampling, opponent modeling and state value calculation, allowing it to navigate partially observable state spaces of the battles. The authors also present var... | Rebuttal 1:
Rebuttal: Thank you for highlighting our framework's novelty and effectiveness in integrating minimax search with LLMs, and for recognizing PokéChamp's strong performance across multiple benchmarks.
**generalizability beyond Pokémon battles**: Our framework naturally applies to any two-player zero-sum comp... | Summary: This paper introduces PokeChamp, an LLM-powered game-theoretic agent designed for competitive Pokémon battles. PokeChamp uses LLM-guided minimax search to model decision-making in partially observable environments. It outperforms all prior LLM-based and heuristic-based Pokemon bots.
## update after rebuttal
T... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate your recognition of PokéChamp's strong performance against prior bots and human players.
**Elo ratings and active players**: The Elo system on Pokémon Showdown (which we use for evaluation) only includes active players by design. Inactive accoun... | Summary: The authors introduce a novel RL agent that integrates and LLM into the tree-search process showing that their method can provide acceptable decisions in complex game states.
Claims And Evidence: The authors claim that their method is SOTA on pokemon which they test in multiple ways and against multiple other... | Rebuttal 1:
Rebuttal: **More detailed model behavior analysis beyond Section C**: In addition to Section C, our paper provides the following analyses on our model/method with respect to the mechanics and strategies present in this game. In Section 4.3 (page 5-6), we present benchmark puzzles specifically designed to te... | null | null | null | null | null | null |
The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret | Accept (poster) | Summary: This paper seeks to characterize the relationship between data distributions over the state-action space of a prescribed Markov decision process (MDP), reward learning from such data distributions, and the resulting regret (which the authors define as normalized suboptimality w.r.t. the true reward function) o... | Rebuttal 1:
Rebuttal: Thank you for your thorough review! We address your main concern below.
> It would be more satisfying and clearly useful to have existence results or at least worked examples showing that the sufficient conditions of Prop. 3.3 and Cor. 3.4 do hold under reasonable conditions. [...] 1. When might ... | Summary: This paper defines a notion called "error-regret" mismatch in the context of optimizing a learned reward function. Error-regret mismatch refers to when the learned reward is close to the true reward on a fixed distribution (low error), but when optimized the learned reward leads to a policy which performs poor... | Rebuttal 1:
Rebuttal: Thank you very much for your review! We are happy you found the paper clear, and that you highlighted the significance of this work.
We would also like to thank you for pointing out two further related works. We are integrating them into our revised related work section. In the following, we prov... | Summary: The paper considers the problem of reward learning where the environment is modeled as an MDP and an unknown reward is estimated with a learning algorithm whose solution is used as a proxy objective in a downstream policy optimization setting. This paper formalizes conditions under which learned reward functio... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback! Due to the 5000-character limit, we have to focus on your main concerns in this rebuttal. Please share any additional issues you'd like us to address.
# Addressing your remarks about our proofs
We appreciate your thorough technical review. We apologize for in... | Summary: The paper states an important issue in RLHF, that is the error-regret mismatch, which is fundamental due to the distribution shift of the induced data by the fine-tuned policy. The core contribution of the paper is to theoretically analyze the possibility of error-regret mismatch, assuming accurate estimation ... | null | null | null | null | null | null | |
Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis | Accept (poster) | Summary: The paper addresses the challenge that the theoretical underpinnings of graph prompting remain underexplored, particularly highlighting the lack of rigorous theoretical proof regarding why and to what extent it works. This has often been seen as a "dark cloud" over the field, hindering further progress. In res... | Rebuttal 1:
Rebuttal: > W1. The paper does not offer theoretical guidance for designing new graph prompting methods. While it rigorously analyzes existing methods, it does not extend the theoretical framework to propose novel techniques.
Thank you for pointing this out. In fact, our theoretical framework can provide f... | Summary: This study aims to provide solid theoretical analysis of graph prompts. The theoretical findings include the capabilities of graph prompts on GCN models with and without non-linear layers, the error bound of the data operations by graph prompts for both a single graph and batch of graphs, and the error distrib... | Rebuttal 1:
Rebuttal: > W1
- The motivation of this distance we used is that: we assume once we are given an anchor/target graph embedding, our theory proves that we can use graph prompt to approximate such embedding. The given embedding is not necessarily the only optimal graph embedding. It can be any one. The purpo... | Summary: The paper theoretically analyzes graph prompting. First, it shows that the main reason why graph prompting works is because it can simulate graph operations, and why this is important when encountering new tasks. Second, it presents upper bounds on the error of graph prompt when simulating graph operations. T... | Rebuttal 1:
Rebuttal: > C1: Why aren't we considering a completely different training task defined on a different dataset than the downstream task? I understand that this setting is significantly more challenging, but otherwise, I believe we are not really testing the generalization ability of graph prompting.
Thank ... | Summary: The paper presents a comprehensive theoretical analysis of graph prompting, a novel technique aimed at adapting pre-trained GNN models to downstream tasks without retraining. It introduces the concepts of "bridge sets" and "ϵ-extended bridge sets" to explain the capacity of graph prompts to simulate graph tran... | Rebuttal 1:
Rebuttal: > C1: Despite rigorous theoretical insights, practical application and generalization to various real-world graph tasks might still face challenges in prompt design and optimization.
Thank you for your insightful comment. This paper primarily focuses on providing theoretical insights and rigorous... | null | null | null | null | null | null |
Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble | Accept (poster) | Summary: The paper studies the propagation of chaos of two-layer neural network in the mean-field regime. The authors first obtain a uniform-in-time propagation of chaos (PoC) bounds that does not depend on the LSI constant, and maintain the "original" rate of convergence. Then, the authors apply the PoC bounds to mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper.
**1.2 Mismatch between the ensemble methods in Sections 4 and 5.2**
First, we would like to clarify that the ensemble method used in Section 5.2 is exactly the same as the one proposed in Section 4. Specifically, the ensemble in Section 4.2 is taken o... | Summary: The paper proposes an improved bound on the convergence of neurons (of a network) under mean field Langevin dynamics to an iiid distribution. This argument is known as the propagation of chaos. The convergence of the empirical finite-N distribution to the limiting iid distribution is controlled by time and num... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper and for the positive feedback. We will revise the manuscript accordingly, following your suggestion. | Summary: This paper improves the Propagation of Chaos (PoC) error bound for Mean-Field Langevin Dynamics (MFLD) by refining the defective Log-Sobolev Inequality (LSI) and introducing the Uniform Directional LSI (UD-LSI). Additionally, it proposes a PoC-based model ensemble method, which is supported by both theoretical... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper.
**Uniform directional LSI (UD-LSI)**
We can theoretically validate the UD-LSI in the setting of Example 3.5 by leveraging a known result (e.g., Lemma 6 in [1]): Let $\nu \propto \exp( -H-V)$, where $V,H: \mathbb{R}^d \rightarrow \mathbb{R}$, with $V$ ... | Summary: The paper establishes improved uniform-in-time propagation of chaos bounds for MFLD by removing the exponential dependence on entropy regularization, and applies this result to propose a model ensemble strategy.
Claims And Evidence: The central claim of the paper is that it establishes an improved PoC result ... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper.
__Assumption 2.1, 3.2–3.4, Example 3.5, and Q3__
Assumptions 2.1, 3.2–3.4 are all satisfied in several settings considered in the mean-field Langevin literature (e.g., [1–6]). Basically, these assumptions follow for two-layer NNs with smooth and bound... | null | null | null | null | null | null |
Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability | Accept (poster) | Summary: This paper studies individual perceptual variability by generating controversial stimuli—images perceived differently by various individuals. To do so the authors 1) sample images on the perceptual boundary of ANNs, 2) collect subject-specific labels through psychophysics experiments on the previously generate... | Rebuttal 1:
Rebuttal: # Response to reviewer Aam6
We sincerely thank the reviewer for the support of our work. Below we address the reviewer’s concerns and questions:
1. **Question:**
How exactly are the uncertainty or controversial guidance signals integrated into the diffusion process?
**Response:**
... | Summary: The functional alignment between artificial neural networks (ANNs) and the human visual system has been a major hot topic in recent years. In this study, the authors generated images that lie on the perceptual boundaries of various ANNs and examined their relationship with individual differences in human perce... | Rebuttal 1:
Rebuttal: # Response to reviewer YcpF
We sincerely thank the reviewer for the support of our work. Below we address the reviewer’s concerns and questions:
1. **Comment:**
The MNIST dataset is not suitable for examining human perceptual variability, as evident from Figure 4b, where participants' judg... | Summary: This paper studies the human perceptual judgements by generating controversial stimuli that lie close to the boundary between different classes. The experiments first show that finetuning vision networks on data collected from human judgements enables these models to better capture the human behavior. They the... | Rebuttal 1:
Rebuttal: # Response to reviewer 62LP
We sincerely thank the reviewer for the positive feedback. Below we address the reviewer’s concerns and questions:
1. **Comment:** The success rate in selective manipulation of human behavior is relatively low, showing limited success in using the proposed approach. T... | null | null | null | null | null | null | null | null |
Boosting Multi-Domain Fine-Tuning of Large Language Models through Evolving Interactions between Samples | Accept (poster) | Summary: The authors propose EVolving Interaction-guided Curriculum (EVIC), a training technique that aims to improve the performance of LLM multi-domain fine-tuning. EVIC iteratively finds the most “helpful” samples in the training set (those that are likely to have helpful influence on the model’s overall loss), then... | Rebuttal 1:
Rebuttal: Dear Reviewer zLAH,
Thank you for your valuable review. We respond to each comment as follows and sincerely hope that our response can properly address your concerns.
Figures and Tables can be found in **zLAH.md** in **https://anonymous.4open.science/r/ICML25-EVIC-D5E8**
# Methods And Evaluatio... | Summary: This paper leverages a way to estimate the training data samples' influence on each other by leveraging the gradients of Adam and projecting it into a lower dimensional space from Xia et al. 2024 [1] and iteratively using this computation in order to select samples to train in the multi-domain fine-tuning sett... | Rebuttal 1:
Rebuttal: Dear Reviewer JcLd,
Thank you for your valuable review. We respond to each comment as follows and sincerely hope that our response can properly address your concerns.
Figures and Tables can be found in **JcLd.md** in **https://anonymous.4open.science/r/ICML25-EVIC-D5E8**
# Other Strengths And W... | Summary: This work presents a curriculum learning method to improve the multi-domain fine-tuning of LLMs. Specifically, the idea is to model the Adam gradient interaction between examples and select the example with the best total benefit on learning other examples. This whole process starts with a warmup stage with ar... | Rebuttal 1:
Rebuttal: Dear Reviewer q9CU,
Thank you for your valuable review. We respond to each comment as follows and sincerely hope that our response can properly address your concerns.
Tables can be found in **q9CU.md** in **https://anonymous.4open.science/r/ICML25-EVIC-D5E8**
# Claims And Evidence
> C1: There ... | null | null | null | null | null | null | null | null |
Differential Privacy Under Class Imbalance: Methods and Empirical Insights | Accept (poster) | Summary: This work looks at training classifiers with differential privacy (DP) guarantees in the presence of data imbalance (in the binary classification case) while enforcing fairness guarantees. They look at data augmentation methods and in-processing methods where fairness is attempted to be imposed by changing the... | Rebuttal 1:
Rebuttal: > **…limited to binary classification…insights on generalizing to the multiclass setting?**
Thank you for this interesting direction for extending our work; many of our results do extend naturally to multi-class settings. We’ll update our revised paper with an expanded version of the following:
... | Summary: This paper studies the problem of privacy when the dataset is imbalanced such that there is one class that has significantly less data points than the other class. Specifically, the paper tackles the problem of training a binary classifier on imbalanced data. Known techniques for up-sampling the minority class... | Rebuttal 1:
Rebuttal: > **The paper is very well written and easy to read and follow.**
Thank you for your positive feedback regarding the clarity and readability of our paper. We spent a lot of time considering how best to present the nuances of this particular classification setting, and so appreciate this recogniti... | Summary: This paper deals with the (in)consistency of differential privacy and imbalanced class learning, especially binary classification problem where the minority class is very small. The non-private learning algorithms for the imbalanced classes usually increase the weights of minority classes through oversampling,... | Rebuttal 1:
Rebuttal: > **...related to invidualized differential privacy, not discussed...**
We thank the reviewer for raising this point. While our sensitivity analysis -- specifically, how certain samples in imbalanced datasets incur higher privacy loss -- echoes themes from individualized/personalized DP, our work... | Summary: The paper explores class imbalance in differentially private ML settings. The authors consider common pre-processing and in-processing methods for dealing with class imbalance, and look at extending them to the DP setting. They show that some commonly used non-private methods like SMOTE are not well suited to ... | Rebuttal 1:
Rebuttal: > **(Q1) Why XGBoost instead of using GEM+LogReg?**
Thank you for your comment, hopefully we can clarify our choices here. Our primary goal was to compare general approaches to handling imbalanced classification under differential privacy -- not necessarily to benchmark model families (e.g., logi... | null | null | null | null | null | null |
RepLoRA: Reparameterizing Low-rank Adaptation via the Perspective of Mixture of Experts | Accept (poster) | Summary: This work studies a new variant of LoRA. First, the authors show that under certain settings, LoRA requires exponential sample complexity. Then, they introduce a simple reparameterization strategy, which builds a single generator for Q, V layers. The generator can be a single layer with or without activations.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and would like to address the concerns as follows:
+ **Regarding the comparison with other variants of LoRA:** Following the reviewer’s suggestion, we conducted an additional experiment on the image classification task using the FGVC dataset to compare RepLo... | Summary: The authors proposed two reparametrizations of LoRA under which the convergence rate improves from $\mathcal{O}_P(\frac{1}{\log^{\tau}(n)})$ of vanilla LoRA to $\mathcal{O}_P(\sqrt{\frac{\log (n)}{n}})$. Empirical results demonstrate that both reparametrizations outperform vanilla LoRA on real datasets.
Claim... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and would like to address the concerns raised as follows:
+ **Regarding the discrepancy between the theoretical results and the experiment setup**: Thanks for this feedback. We would like to clarify that the assumption in Section 4.2 that $A_Q=A_V$ ... | Summary: This paper proposes RepLoRA, a method that reparameterizes the low-rank matrices of LoRA using a lightweight MLP. RepLoRA surpasses baseline LoRA by up to 40.0% and achieves similar results with baseline with only 30.0% of the training data. Additionally, this work provides a theoretical analysis of LoRA from ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback and would like to address your concerns as follows:
**Regarding the analysis of the theoretical results:** Our theoretical analysis demonstrates that LoRA with reparameterization offers superior *sample efficiency* compared to LoRA wit... | Summary: The apper combines LoRA into multi-head parts of MSA and treats different heads as experts to build a mixtural of experts. In addition, authors use lightweight MLP to conduct reparameter opertations, which improves sampling efficiency while reducing data requirements compared to the original LoRA.
Claims And ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful feedback. In response to their concern, we’ve expanded our analysis to include additional comparisons with three LoRA adapters: VeRA [1], DoRA [2], and MoR [3], as suggested by the reviewer. These experiments were carried out on the image classification task... | null | null | null | null | null | null |
SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding | Accept (poster) | Summary: The paper addresses the problem of visual hallucinations in LVLMs by introducing a training-free framework called SECOND, which adaptively selects visual patches on multiple scales and applies Contrastive Decoding (CD) between the intermediate stage logits and the logits from the fine-grained expert. The paper... | Rebuttal 1:
Rebuttal: Dear Reviewer i6dn,
We greatly appreciate your valuable feedback on our paper. We address the raised concerns and questions below.
**W1: Essential References Not Discussed**
Thank you for the suggestion. We have added the mentioned works to Sec. 2. In particular, we included OPERA in our comput... | Summary: This paper focuses on how to improve the hallucination of MLLMs. Firstly, the paper conducts some analysis of hallucinations in MLLMs, proposing two metrics, namely the Hallucination Probability and the Attention Dice Coefficient, and introducing the research motivation of needing to enhance the model's visual... | Rebuttal 1:
Rebuttal: Dear Reviewer Ekip,
We really appreciate your thorough review of our paper. We address the raised concerns and questions below.
**W1: The formula definition of Hallucination probability proposed in Sec. 3 seems to lack support from relevant theoretical papers (not mentioned in the paper)**
We s... | Summary: In this paper, a decoding method for LVLMs named SECOND is proposed. SECOND consists of selective multi-scale feature integration and multi-stage contrastive decoding. The first method, selective multi-scale feature integration leverages multi-scale feature map with patch selection scheme, where important patc... | Rebuttal 1:
Rebuttal: Dear Reviewer Lfoe,
We greatly appreciate your valuable feedback on our paper. We address the raised concerns and questions below.
**W1: L153 (left column): The implication of the attention Dice coefficient could be briefly introduced for better clarity.**
Thank you for the insightful comment. ... | Summary: This paper introduces SECOND, a training-free approach to mitigate perceptual hallucination in LVLM. More specifically, it progressively refines (by patch selection) multi-scale visual information in an object-centric manner, and uses multi-stage contrastive decoding to reduce perceptual hallucinations. Resu... | Rebuttal 1:
Rebuttal: Dear Reviewer KN5t,
Thanks for your valuable feedback! We provide point-by-point responses to address your concerns below.
**W1: More tasks could be introduced to verify SECOND’s effectiveness on common VLM tasks, such as captioning, document understanding, infographics reasoning etc.**
Thank y... | null | null | null | null | null | null |
Time Series Representations with Hard-Coded Invariances | Accept (poster) | Summary: This paper posits that invariances to the deformations are critical for time series tasks such as classification. The paper mathematically formulates the invariance in the language of group theory and further technically designs efficient and hard-coded invariant convolutions for specific deformations commonly... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and thoughtful feedback. In the following, we thoroughly address each identified weakness and question.
**[Experimental Design]**
- **Performance on UCR normalized data:**
We address your concern in Q1 below. To clarify, synthetic deformations are us... | Summary: This paper proposes a novel mathematical method to consider the deformation-invariance during representation learning, which is beneficial for downstream tasks such like classification. It designs a G-variant convolution model called TS-TCC to obtain deformation-invariant embeddings, which provides robustness ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their time and thoughtful evaluation of our work. In the following section, we address any concerns raised.
**[Claims and Evidence]**
> "However, the basic assumptions about the deformation phenomenon seem not strong enough[...] While in forecasting tasks, suc... | Summary: The paper proposes convolutional neural network operations explicitly designed with hard-coded invariances (e.g., scaling, offset shift, linear trends) for improved time series representation learning. By formulating invariances through group theory and embedding them directly into convolutional layers, the au... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and effort in evaluating our work. Below, we provide our responses to the main suggestions.
**[Claims And Evidence]**
> "authors claim computational efficiency benefits [...], additional explicit runtime comparisons [...]".
We have demonstrated the c... | Summary: The article introduces a mathematical framework for integrating invariant into convolution operators for time series. The scaling, offset shifting, and trend invariant are particularly studied. A large number of experiments are then conducted, demonstrating the advantages of these convolutions in terms of thei... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time spent to evaluate our manuscript. We next reply to their key suggestions and comments on our work.
**[Evaluation Criteria]**
>"The authors could have produced a synthetic diagram of the model's performance [...]."
We appreciate the reviewer's sug... | null | null | null | null | null | null |
Finding Wasserstein Ball Center: Efficient Algorithm and The Applications in Fairness | Accept (poster) | Summary: This paper considers fairness in representing a set of distributions and proposes to use the Wasserstein Ball Centers (WBC) as a representative of a distribution instead of the Wasserstein Barycenter (WB). Given a set of distributions $\mu_1, \ldots, \mu_N$, the Wasserstein Ball Center is defined as a distribu... | Rebuttal 1:
Rebuttal: **Q1** On the missing part of the vector $\boldsymbol{b}$.
We sincerely appreciate the reviewer’s meticulous reading. Yes, there is a missing $\boldsymbol{0}_N$ as the last $N$ terms of vector $\boldsymbol{b}$. We will correct this typo in the final version. Notebly, since the design of our algo... | Summary: This paper introduces the concept of "Wasserstein Ball Center" (WBC) as an alternative to the traditional Wasserstein Barycenter (WB) for finding a representative probability distribution from multiple input distributions. While WB minimizes the sum of Wasserstein distances from the barycenter to all input dis... | Rebuttal 1:
Rebuttal: **Q1:** Missing references
Thank you for your valuable suggestions on the references, and we will add them to the revised version.
**Q2:** Algorithm for free-support Wasserstein ball center (WBC)
For the free support Wasserstein barycenter, many previous researches [1-3] apply block coordin... | Summary: This paper considers the following problem: Given a set of N probability measures, find a probability distribution that, minimizes the maximum distance to any input distribution. Intuitively, we can think of this as the problem of finding the center and radius of the “smallest Wasserstein ball” that encloses a... | Rebuttal 1:
Rebuttal: **Q1:** Comparison between our work and 1-center problem for arbitrary metric space.
Thanks for the question regarding the connection between our WBC problem and the 1-center problem in arbitrary metric spaces. We will add more references and explanations in our paper. In Euclidean space, 1-cente... | null | null | null | null | null | null | null | null |
Average Certified Radius is a Poor Metric for Randomized Smoothing | Accept (poster) | Summary: This paper studies the shortcomings of Aversage Certified Radius (ACR) as a performance metric for randomized smoothing. It shows theoretically that this metric can be “hacked” by a trivial classifier with an arbitrarily large certified radius on a small number of “easy” input points, thereby achieving SOTA pe... | Rebuttal 1:
Rebuttal: We thank Reviewer $\Rg$ for the insightful review. We are happy that Reviewer $\Rg$ finds that our paper is easy to understand, and that our work provides both theoretical and empirical evidence to justify our conclusion. We address all concerns from Reviewer $\Rg$ below. We include new results, n... | Summary: This paper critiques the use of Average Certified Radius (ACR) as an evaluation metric for assessing the performance of certifiably robust classifiers, specifically focusing on randomized-smoothing-based approaches for robustness certification under the $\\ell_2$ perturbation threat model.
(To give backgroun... | Rebuttal 1:
Rebuttal: We thank Reviewer $\Rn$ for the insightful review. We are happy that Reviewer $\Rn$ finds our work is important and valuable, and points out imperfect expressions. We will address all the concerns raised by Reviewer $\Rn$ in the following. We include new results, named with Figure S1 etc., in the ... | Summary: The authors make a strong claim that the Average Certified Radius (ACR) - which is widely used through the Randomized Smoothing (RS) community - is not a good metric at all for a number reasons. They prove it, and provide the ways how it can be exploited for improving ACR.
## update after rebuttal
Authors pro... | Rebuttal 1:
Rebuttal: We thank Reviewer $\Rp$ for the insightful review and faithful interpretation of our work. We are happy that Reviewer $\Rp$ finds our work important, sound and solid. We will resolve all the concerns below. We include new results, named with Figure S1 etc., in the [anonymized link](https://mega.nz... | Summary: The authors investigate the validity of the Average Certified Radius (ACR) as a measure for robustness.
Claims And Evidence: Claims and Evidence:
C1. Authors theoretically show that with a large enough certification budget, ACR of a trivial classifier can be arbitrarily large, and that with the certificati... | Rebuttal 1:
Rebuttal: $\newcommand{\Ru}{\textcolor{green}{uKgY}}$
$\newcommand{\Rp}{\textcolor{blue}{Ptgp}}$
$\newcommand{\Rn}{\textcolor{fuchsia}{nJsf}}$
$\newcommand{\Rg}{\textcolor{purple}{gJ9M}}$
We thank Reviewer $\Ru$ for the insightful review. We are happy that Reviewer $\Ru$ finds our work important and experi... | null | null | null | null | null | null |
The Case for Learned Provenance-based System Behavior Baseline | Accept (poster) | Summary: This paper proposes a new ML method for anomaly detection in provenance graphs. The results demonstrate the effectiveness of the proposed method.
Claims And Evidence: The claims and assumptions as well as the evaluation metrics are reasonable to me.
Methods And Evaluation Criteria: The proposed method is wel... | Rebuttal 1:
Rebuttal: Thank you for your review efforts and insightful comments. We provide our responses to each specific issue in order.
Q1: I would suggest adding experiments on transformers if possible.
A1: **Intrusion detection and threat analysis is a computationally intensive task with stringent real-time req... | Summary: This paper proposes a learning-based anomaly detection method for provenance graphs, which are critical for cybersecurity. The approach decouples provenance graphs into system events, encodes them adaptively to handle out-of-vocabulary (OOV) elements and normality shifts, and trains lightweight regression mode... | Rebuttal 1:
Rebuttal: Thanks for your review efforts and insightful comments. We provide responses to each specific issue.
Q1: GNN-based anomaly detection & Temporal graph learning
A1: GraphSAGE samples k-hop neighbors instead of traversing the entire graph. **APT attacks exhibit complex and prolonged spatiotemporal ... | Summary: This paper proposes a novel learning-based anomaly detection method that effectively embeds and analyzes large-scale provenance graphs. The approach integrates dynamic graph processing with adaptive encoding mechanisms, which facilitates compact embeddings, effectively addresses out-of-vocabulary (OOV) element... | Rebuttal 1:
Rebuttal: Thank you for your review efforts and insightful comments. We provide our responses to each specific issue in order.
Q1: The selection of learning models does not include a discussion on Transformer or GRU. It would be beneficial to discuss and compare more recent embedding models.
A1: **Intrusi... | Summary: The paper proposes a learning-based anomaly detection workflow for large-scale provenance graphs, addressing challenges like out-of-vocabulary elements and normality shifts in dynamic environments. It integrates dynamic graph processing with adaptive encoding to create compact embeddings, improving anomaly det... | Rebuttal 1:
Rebuttal: Thank you for your review efforts and insightful comments. We provide our responses to each specific issue in order.
Q1: The paper feels more like a benchmark for a new application scenario, with limited methodological innovation.
A1: This manuscript work towards to address an important and open... | null | null | null | null | null | null |
DocKS-RAG: Optimizing Document-Level Relation Extraction through LLM-Enhanced Hybrid Prompt Tuning | Accept (poster) | Summary: The authors of the paper propose a novel approach for document-level relation extraction. During the training phase, they prepare two additional texts: one sourced from DocKG and the other from SetRAG, which are concatenated and utilized as a prefix in the final prompt. Subsequently, they fine-tune a small ope... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
C1: Although the paper provides a detailed methodology, some sections could benefit from greater clarity. For instance, the hybrid prompt generation process may require more concrete examples to fully illustrate the differences and advantages over conventiona... | Summary: In this paper, the authors propose a DocKS-RAG method to combine structural knowledge and semantic information for document-level relation extraction task. In DocKS-RAG, the authors first rely on GNNs to construct a document-Level knowledge graph and retrieve relevant information from this graph according to t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing and affirming our work. Regarding the questions raised during the review process, we have carefully considered them and provided detailed responses as follows:
Q1: In Introduction, the authors state that “graph-based methods … lack of sufficient con... | Summary: In this work, the authors introduce DocKS-RAG, a framework that enhances large language models for document-level relation extraction. By integrating structural knowledge from a Document-level Knowledge Graph (DocKG) with semantic insights from a Sentence-level Semantic Retrieval-Augmented Generation (SetRAG) ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our paper.
C1: When mentioning the sentence-level Semantic Retrieval-Augmented Generation, it might be useful to briefly explain how it differs from typical retrieval mechanisms.
Response to C1: Thank you for your valuable comment. Our proposed SetRAG modu... | Summary: The paper introduces DocKS-RAG, a novel framework aimed at enhancing document-level relation extraction (RE) by integrating large language models (LLMs) with structured knowledge graphs. The proposed method combines a Document-level Knowledge Graph (DocKG) with a Sentence-level Semantic Retrieval-Augmented Gen... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and insightful questions.
Q1: While DocKS-RAG achieves high performance, the ablation studies suggest that even without blending structural and semantic components, competitive results can be obtained. For example, simpler setups without DocKG or SetRAG achiev... | null | null | null | null | null | null |
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment | Accept (poster) | Summary: The paper proposes the GOAT, an SVD-derived LoRA-MoE finetuning framework. Through SVD decomposition, the authors found that existing LoRA finetuning schemes are insufficient due to restrictive training on only specific pre-selected SVD segments. Based on this finding, the authors proposed employing a LoRA-MoE... | Rebuttal 1:
Rebuttal: # Response to Reviewer `4qHz`
> Q1: Lemma 3.5 requires (non-intuitive) knowledge about Leaky-ReLU with negative slope of sqrt(5) resulting in Var(A)=1/(3n). Please cite the source or provide additional proof for this information.
>
Thanks for your suggestion. We follow the derivation of the comm... | Summary: This paper proposed a PEFT method with SVD-structured MoE and theoretical scaling. It initializes LoRA MoE experts with distinct singular value segments, and derives an optimal weight alignment strategy and scaling scheme to improve both convergence speed and performance. Extensive experiments on 25 tasks vali... | Rebuttal 1:
Rebuttal: # Response to Reviewer `5G79`
> Q1:In Theorem 3.1, the authors claim that ‘we can align LoRA with Full FT’, ‘addresses the performance gap in single LoRA architectures’. Actually, the proposed method is still worse than full finetuning in most of the tasks, as shown in the experiments. In my opin... | Summary: This paper presents GOAT (Great LoRA Mixture-of-Experts), a novel framework to enhance the LoRA MoE structure for fine-tuning LLMs. GOAT (1) adaptively initializes each expert using different SVD segments to integrate relevant priors from pre-trained models, and (2) derives a theoretical scaling factor that al... | Rebuttal 1:
Rebuttal: # Response to Reviewer `9dgB`
> Q1: Why does gradient alignment with Full FT improve performance theoretically, given that Full FT's updates aren't always optimal due to data/learning rate dependencies?
>
Thanks for your insightful question. First, Full FT outperforms LoRA in most cases, making ... | Summary: The paper proposes a novel fine-tuning framework for LoRA (Low-Rank Adaptation) MoE (Mixture-of-Experts). Two challenges identified in the paper: 1) how to design an effective initialization for the matrices A and B across different experts. 2) unaligned optimization leads to large gradient gap and slow co... | Rebuttal 1:
Rebuttal: # Response to Reviewer `ETXs`
> Q1: The performance evaluation metrics are unclear.
>
Sorry for the confusion. Here is a more detailed explanation of our performance metrics:
- NLU & CV: Accuracy, except for CoLA (Matthew’s correlation). See Appendix E.1 for details.
- CommonSense: Exact match... | null | null | null | null | null | null |
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes | Accept (poster) | Summary: The paper improves the stochastic Poisson surface reconstruction [25], which combines the interpolation and surface reconstruction into a single stage. The method avoids the complicated finite element method and makes use of Fourier transformation. It also proposes to use Monte Carlo samples from the posterior... | Rebuttal 1:
Rebuttal: Thank you very much for your review! Let us address your points below:
> "Fourier domain analysis … limit the practical applicability of the methods?”
> “Why torus? Is there any topology constraint?”
In short: **no, it does not limit applicability**. This is because the periodic boundary condi... | Summary: The paper uses techniques from geometry Gaussian process to speed up the stochastic Poisson surface reconstruction method.
## Update after rebuttal
I appreciate the authors' efforts in providing a more nuanced discussion and additional comprehensive results. Given this, I keep my score which is already posit... | Rebuttal 1:
Rebuttal: Thank you for your review! We appreciate that you mentioned our method **“has the potential for broad applicability”** and that we have a **“mathematically principled approach”** which was indeed part of our motivation for this work.
> “I'm not convinced by the claim that the proposed method qual... | Summary: Poisson surface reconstruction (Khazdan et al.) is the task of fitting a function $v(x)$ to point cloud data $(x_i, v_i)_i$ and solving $\Delta f = \nabla \cdot v$ for $f$ (subject to, e.g., Neumann boundary conditions). Then, the zero-level set of $f$ is the desired surface.
Stochastic poisson surface reconst... | Rebuttal 1:
Rebuttal: Thank you for your review! We are very happy that you recognized our approach as **“more efficient”** than prior ones and that our **“claims are supported relatively well by evidence.”** We address your questions below:
> “manipulation of Fourier coefficients … something like a linear solve.”
>... | Summary: In the paper, the authors reformulated the stochastic Poisson surface reconstruction by introducing geometric Gaussian processes and periodic kernels. Their proposed method achieves similar results while addressing a number of limitations to increase computational efficiency.
Claims And Evidence: The claims m... | Rebuttal 1:
Rebuttal: Thank you for your review! We appreciate your recognition that our approach **“increase[s] computational efficiency”** and that our paper has **“little weakness”** and are **delighted by these comments**! We address your key comments below:
> “no quantitative evaluations and comparisons of recons... | null | null | null | null | null | null |
Rectifying Conformity Scores for Better Conditional Coverage | Accept (poster) | Summary: The paper presents a novel method to achieve better conditional coverage in conformal prediction for single-output and multi-output regression. The central idea is to start from a classical nonconformity score, and adjust it to improve for conditional coverage. The adjustment is a factor that is obtained by es... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough and constructive feedback, which helps improve our manuscript. Below, we address your valuable points:
The **main limitations** of RCP can be summarized as follows:
- The quality of the prediction regions heavily depends on the basic conformity score $V$. Fo... | Summary: The paper considers the problem of producing conformal prediction sets with conditional guarantees. The idea is to rectify non-conformity scored by to use additional hold out data to fit a quantile regressor that is then applied to the non-conformity score. Marginal coverage guarantees are obtained using the r... | Rebuttal 1:
Rebuttal: We acknowledge the critical feedback and aim to address the raised points. Below we clarify why **the undiscussed references, while indeed valuable, mostly address aspects different from the specific problem we focus on**. The following discussion highlights the distinctive advantages of our RCP m... | Summary: This paper introduces Rectified Conformal Prediction (RCP), a novel method for improving conditional coverage in conformal prediction while maintaining exact marginal coverage. The core idea is to transform conformity scores in a way that aligns their conditional quantiles across different covariates. This tra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough and constructive feedback, which helps improve our manuscript. Below, we address your valuable points:
**Effectiveness of quantile estimation in high-dimensional settings:**
Indeed, quantile estimation accuracy critically affects RCP performance. In our expe... | Summary: This paper introduces Rectified Conformal Prediction (RCP), a novel framework for improving conditional coverage in conformal prediction while preserving exact marginal validity. The key idea is to learn a transformation of the conformity score such that the ($1-\alpha$)-quantile of the transformed score becom... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough and constructive feedback. Below, we address your questions:
Q1: **RCP's conditional coverage guarantee hinges on accurate estimation ...** Even if the quantile regressor is misspecified, RCP’s conformal calibration guarantees valid marginal coverage by cons... | null | null | null | null | null | null |
Robust Multimodal Large Language Models Against Modality Conflict | Accept (poster) | Summary: This paper investigates MLLM hallucination from a novel modality conflict perspective. Specifically, authors propose a setup where inputs from different modalities conflicts each other and put MLLMs in a dilemma. MLLMs are expected to address modality conflict first to answer correctly. A benchmark Multimodal ... | Rebuttal 1:
Rebuttal: We truly appreciate your positive assessment of our paper. We are also grateful for the time and effort you invested in reviewing it. | Summary: This paper is well-written and presents a timely investigation into modality conflicts as an understudied source of hallucinations in multimodal large language models (MLLMs). The authors demonstrate commendable effort in constructing a comprehensive conflict dataset
spanning three critical dimensions (object,... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and suggestions. We give a point-by-point response to each of your concerns below. Following the ICML 2025 Peer Review FAQ, we post all additional results to the anonymous link: [https://anonymous.4open.science/api/repo/11639-F609/file/Additional_Results.pdf?... | Summary: This paper examines hallucinations in multimodal large language models (MLLMs) by focusing on "modality conflict" - inherent conflicts between different input modalities that create dilemmas for models. The researchers created a dedicated dataset called Multimodal Modality Conflict (MMMC) and evaluated three m... | Rebuttal 1:
Rebuttal: We are delightful to see your positive remarks on our proposed research topic and experimental designs. We provide discussions about your suggestions as follows. All related results are available at the anonymous link [https://anonymous.4open.science/api/repo/11639-F609/file/Additional_Results.pdf... | Summary: The paper investigates modality conflicts, which are the hallucination issues that are presented when the text and visual information are inconsistent. The paper defines modality conflict in terms of objects, attributes, and relationships, and constructs a Multimodal Modality Conflict (MMMC) dataset to evaluat... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and suggestions. We give a point-by-point response to each of your concerns below. Following the ICML 2025 Peer Review FAQ, we post all additional results to the anonymous link: [https://anonymous.4open.science/api/repo/11639-F609/file/Additional_Results.pdf?... | null | null | null | null | null | null |
Learning Distances from Data with Normalizing Flows and Score Matching | Accept (poster) | Summary: The article proposes and compares several methods for estimating distances derived from Riemannian metrics that reflect the data distribution. In particular, the chosen metric should "compress" distances in regions of high mass concentration and "stretch" distances where the mass is lower. To achieve this, the... | Rebuttal 1:
Rebuttal: ## Rebuttal to Reviewer nn54
We thank the reviewer for their careful and detailed reading of our work, and for the helpful questions and clarifications.
---
### Novelty of the Algorithms
> The algorithms used by the authors are already present in the literature.
While the Fermat distance itse... | Summary: The paper presents a method to learn distances from data by integrating normalizing flows and score matching into the computation of density-based distances (DBDs), specifically Fermat distances. It addresses the shortcomings of existing methods by introducing a stable numerical approach to compute true geodes... | Rebuttal 1:
Rebuttal: ## Rebuttal to Reviewer XLew
Thank you for your positive assessment of our work and for your thoughtful suggestions.
---
### Preliminary Results on Real-World Dataset
Please see our response to reviewer AqTp for a preliminary experiment on MNIST. We find that the distances obtained with our me... | Summary: This paper addresses the problem of learning distance metrics from data, specifically focusing on density-based distances (DBDs). The authors highlight that existing methods for estimating Fermat distances suffer from poor convergence and scaling issues in high dimensions due to inaccurate density estimates an... | Rebuttal 1:
Rebuttal: ## Rebuttal to Reviewer EAyN
We thank the reviewer for their detailed and constructive comments.
---
### Simplicity of Experiments
Please see our response to reviewer AqTp for results on an experiment on MNIST.
---
### Missing Recent Work
Thank you for pointing out our oversight. We will ad... | Summary: In this paper, the authors propose to learn a Riemannian metric from data using a class of Fermat metrics, which are metrics that are equal to the Euclidean metric rescaled at each point by (a power of) the reciprocal of the probability density of the data. This way, geodesics tend to follow high density regio... | Rebuttal 1:
Rebuttal: ## Rebuttal to Reviewer AqTp
We thank the reviewer for their thoughtful and constructive feedback. Below, we address each of the main points and questions raised.
---
### How large can the dimension become for the relaxation method to stay efficient and tractable? The finite difference scheme m... | null | null | null | null | null | null |
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains | Accept (poster) | Summary: This paper investigates the vulnerabilities of retrieval systems to various poisoning attacks. The authors first analyze multiple corpora, retrievers, and datasets, highlighting the significant safety risks in retrieval. They then attribute retriever failures to the limitations of the existing document embeddi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for investing their time and effort in reviewing our manuscript and providing valuable feedback. We will address their comments point by point in the following and incorporate them into our revision.
> Q: additional retrieval cases ..., especially in the legal dom... | Summary: This paper focuses on the adversarial robustness of the retrieval system of RAG against data poisoning attacks.
Three major safety risks are discussed, including the leakage of PII, adversarial recommendations, and the vulnerability to jailbreaking attacks.
Extensive experiments on five Medical QA datasets de... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for investing their time and effort in reviewing our manuscript and providing valuable feedback. We will address their comments point by point in the following and incorporate them into our revision.
>Q: In Lines 105 (left) and 150 (right), the notation refers to ... | Summary: This paper explores a characteristic of poisoned documents in embedding spaces termed the orthogonal augmentation property. It suggests that appending target information to a poisoned document containing the target query shifts its embedding orthogonally to the query, preserving its retrievability by the query... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for investing their time and effort in reviewing our manuscript and providing valuable feedback. We will address their comments point by point in the following and incorporate them into our revision.
>Q: Universal poisoning attacks against RAG have already been sh... | Summary: The paper explores the vulnerability of Retrieval-Augmented Generation (RAG) systems, specifically in knowledge-intensive domains like medical and legal Q&A. The authors demonstrate that retrieval models used in RAG are susceptible to universal poisoning attacks, where adversaries inject manipulated documents ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for investing their time and effort in reviewing our manuscript and providing valuable feedback. We will address their comments point by point in the following and incorporate them into our revision.
>Q: Lack of Novelty. The method appears to be simply an integrat... | Summary: This paper demonstrates the vulnerability of retrieving systems in RAG to universal poisoning attacks. Through examples in medical Q&A, the paper reveals that due to the orthogonal augmentation property, the deviation from the query’s embedding to that of the poisoned document tends to only shift in the orthog... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for investing their time and effort in reviewing our manuscript and providing valuable feedback. We will address their comments point by point in the following and incorporate them into our revision.
>Q: One weakness is that the orthogonal augmentation property re... | null | null | null | null |
DANCE: Dual Unbiased Expansion with Group-acquired Alignment for Out-of-distribution Graph Fairness Learning | Accept (poster) | Summary: This paper propose DANCE to improve fairness performance of GNNs under distribution shifts. DANCE addresses two key challenges: sensitive group imbalance and the trade-off between fairness and model performance. DANCE uses unbiased mixup to balance sensitive attributes, fairness-aware adversarial learning to i... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
> Q1. High computation... | Summary: This paper proposes DANCE, a novel framework for enhancing graph neural network fairness learning under distribution shifts by generating unbiased virtual graph data through dual expansion (structural and feature-based) and aligning node representations. It specifically tackles sensitive group imbalance and fa... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
>Q1. The framework inv... | Summary: The paper proposes a method to improve fairness in graph neural networks (GNNs) under distribution shifts. It introduces dual graph expansion to generate unbiased virtual graph data, group-acquired alignment to prioritize negative pairs with identical sensitive labels, and representation disentanglement to sep... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
> Q1. The performance ... | Summary: This paper proposes the DANCE method, which aims to address the problem of fair learning of graph neural networks (GNNs) under distributional bias. Traditional methods assume that training and testing data are identically distributed, whereas distribution bias is prevalent in real-world scenarios, leading to d... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper and your insightful review. Here we address your comments in the following.
>Q1. Why Theorem 3.1 can show that the graph diffusion method can precisely control the propagation of information between different groups?
To clarif... | null | null | null | null | null | null |
Breaking the Barrier of Hard Samples: A Data-Centric Approach to Synthetic Data for Medical Tasks | Accept (poster) | Summary: This paper introduces a novel approach to synthetic data generation, leveraging a combination of statistical modeling and generative techniques to produce high-fidelity, diverse datasets for machine learning applications. The proposed methodology is designed to enhance the realism and utility of synthetic data... | Rebuttal 1:
Rebuttal: * **Ablation Studies and Components- Reviewers BQrC, VmCi**
We did not conduct an ablation study because the preprocessing step we adopted already serves as a form of comparison itself. Specifically, we chose to use our proposed preprocessing method as a comparison to traditional preprocessing tec... | Summary: The paper focuses on generating training data for regression models in the medical domain. The proposed approach is based on two existing methods, which the authors refer to as Traditional Generative Techniques and PreProcess methods. In the Traditional approach, the method does not consider the difficulty dis... | Rebuttal 1:
Rebuttal: * **Hyperparameter Sensitivity- Revwers sXia, BQrC (Q1), VmCi**
The choice of the hard sample threshold is based on the performance of the profiling framework. In Appendix F, we provide a detailed explanation of this selection process. Specifically, the threshold is determined by identifying the ... | Summary: The paper introduces Profile2Gen, a novel data-centric framework that generates and refines synthetic data specifically for regression tasks in medical applications. By profiling the original dataset into easy, ambiguous, and hard samples, the framework trains separate generative models and later refines the s... | Rebuttal 1:
Rebuttal: * **Generalization vs. Diversity Trade-off:** Profile2Gen, which incorporates post-processing, reduces Wasserstein's similarity between real and synthetic samples. This indicates that the generated samples are less similar to real data than other techniques. Here is the highlight: the similar samp... | Summary: This paper introduces Profile2Gen, a data-centric framework designed to enhance the generation and refinement of synthetic data for medical regression tasks. The key innovation lies in profiling and addressing hard-to-learn samples, which traditionally hinder model performance and generalization. The authors e... | Rebuttal 1:
Rebuttal: Dear reviewers:
We want to thank you all for your very careful and thoughtful reviews. We were very encouraged by the numerous and significant strengths that you all identified in our study. Namely:
* An innovative data-centric approach integrating data profiling and synthetic data generation targ... | null | null | null | null | null | null |
KIND: Knowledge Integration and Diversion for Training Decomposable Models | Accept (poster) | Summary: This paper tårgets on training a better pre-trained model for downstream tasks. Concretely, they propose KIND (Knowledge Integration and Diversion). It utilizes SVD to yield basic components, and then classify them into two categories, learngenes and tailors. The former captures class-agnostic features, while ... | Rebuttal 1:
Rebuttal: Dear Reviewer Dcjo,
We sincerely appreciate your insightful comments and your recognition of both the novelty and soundness of our methods, as well as the contribution of our benchmark to the community. Below, we provide a detailed response.
>**Q1: Discussions on the limitations of the proposed... | Summary: This manuscript proposes a novel pre-training method named KIND, aiming to address the adaptability issues of traditional pre-trained models in different tasks and deployment scenarios. KIND integrates and distributes knowledge by using SVD during the pre-training process, creating learngenes and tailors respe... | Rebuttal 1:
Rebuttal: Dear Reviewer Y7yt,
We sincerely appreciate your insightful feedback and your recognition of the innovation and practicality of our work. Below, we provide our detailed response.
>**Q1: Lack of comparison of similar methods (e.g., FacT and BOFT).**
FacT and BOFT leverage matrix decomposition t... | Summary: ## Summary
This work applies SVD on the weight matrices $W_q, W_k, W_v, W_o, W_{in}, W_{out}$ of pretrained diffusion transformers (DITs). Then finetunes the SVD decomposed matrices U, $\Sigma, $V$ with target label information. The SVD decomposed matrices are futher splited into two parts to store 1) general ... | Rebuttal 1:
Rebuttal: Dear Reviewer QbMr,
We sincerely appreciate your valuable comments and recognition of our work’s innovation and performance. Below is our detailed response.
>**Q1: Where does the KIND apply, pretraining or post-training? What do you want to compare with, pretraining approaches or post-training ... | Summary: This paper proposes a method to decompose a model’s learnable matrices into class-agnostic knowledge (learngenes) and class-specific knowledge (tailors) using Singular Value Decomposition (SVD). The learning process for tailors is regulated by a class gate, ensuring that only one class is activated per image. ... | Rebuttal 1:
Rebuttal: Dear Reviewer abHL,
We sincerely appreciate your recognition of our practicality and efficiency.
Due to length constraints, **experimental tables and figures, are provided via anonymous links (permitted by ICML25)**.
>**Q1:Class Gate and Sparse Gradients**
Parameter updates remain sufficient ... | null | null | null | null | null | null |
Fast Large Language Model Collaborative Decoding via Speculation | Accept (poster) | Summary: This paper introduces "Speculative Ensemble" (SE), a novel framework that accelerates Large Language Model (LLM) ensembles without sacrificing performance. While ensemble methods enhance LLMs by combining multiple models, they suffer from high computational costs. The authors build on speculative decoding—wher... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. Below, we address each concern in detail.
**Claims And Evidence 1: compare to PE when model sizes are close**
First, we evaluate the speedup of *parallel ensemble* (PE). However, PE is even slower than the sequential ensemble. For det... | Summary: The authors extend speculative decoding to ensemble models and demonstrate, through both theoretical analysis and empirical results, that their approach outperforms standard ensemble baselines.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: The theoretical analysis largely... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. Below, we address each concern in detail.
For clarity and brevity, we use the following abbreviations: Experimental Designs Or Analyses (EDOA), Weaknesses (W), and Questions (Q).
**W1: Speculative Ensemble (SE) offers two non-trivial ... | Summary: ## Update after Rebuttal
My concern regarding the insufficient analysis of the quality–speedup trade-off was addressed by the authors’ rebuttal, therefore I have reflected this by increasing my score from 2 to 3, i.e., leaning towards acceptance.
However, the results of comparing with non-ensemble baselines ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. Below, we address each concern in detail.
For brevity, we use the following abbreviations: Claims and Evidence (CAE), Methods and Evaluation Criteria (MAEC), Theoretical Claims (TC), Experimental Designs or Analyses (EDOA), Weaknesses ... | Summary: This paper proposes Speculative Ensemble, accelerating ensemble speed without sacrificing the ensemble quality, inspired by speculative decoding. They theoretically prove the speed improvement over standard ensemble approaches. Experimental results also support their arguments and better ensemble efficiency.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. Below, we address each concern in detail.
For clarity and brevity, we use the following abbreviations: Methods and Evaluation Criteria (MAEC), Theoretical Claims (TC), Experimental Designs or Analyses (EDOA).
**MAEC1: contrastive deco... | Summary: This paper proposes "Speculative Ensemble", a method for speeding up auto-regressive generation from LLM ensembles using ideas from speculative decoding. For example, in the case of a two model ensemble, one can treat one of the models as the draft model, generate tokens with that model, and then process those... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the time and effort spent reviewing our submission and greatly appreciate your insightful comments and constructive suggestions. Below, we have done our best to address each of your concerns in detail.
**Methods And Evaluation Criteria: report the "raw speeds"*... | null | null | null | null |
STD-FD: Spatio-Temporal Distribution Fitting Deviation for AIGC Forgery Identification | Accept (poster) | Summary: In this paper, the authors propose a deepfake detection method based on “temporal distribution fitting deviations.” Specifically, they argue that existing reconstruction-based approaches treat the diffusion model as a black box, which limits their generalizability. In contrast, the authors decouple the samplin... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work. Below are our responses to your questions (Q).
**Q1: Methodological Coupling & DFactor Universality**
Sorry for the misunderstanding. Reconstruction-based approaches rely on the magnitude of reconstruction error to distinguish real from fake images. When the r... | Summary: This paper proposes an AIGC forged image detection method based on Spatio-Temporal Distribution Fitting Deviation (STD-FD). For forged images, the authors decompose the spatio-temporal features of the generation process, employ superpixel segmentation to divide semantic units, and extract the DFactor in the sp... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our method's novelty and effectiveness. Below are our responses to your Questions (Q) and Weaknesses (W):
**W: Addressing targeted attacks against diffusion sampling mechanics would strengthen real-world applicability claims.**
Thank you for your insightful feed... | Summary: This paper proposes STD-FD, a detection framework for AI-generated image forgeries that analyze spatio-temporal distribution deviations inherent in diffusion models' generative processes. By modeling how noise residuals evolve across temporal sampling steps and decomposing spatial patterns through superpixel s... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review and constructive comments. We sincerely appreciate your recognition of the innovation and thoroughness of our work. Below are our responses to your Questions (Q) and Weaknesses (W):
**Q1|W1: Is the essence of the distribution fitting bias the temporal ... | Summary: This work presents the Spatio-Temporal Distribution Fitting Deviation (STD-FD) method for detecting image forgery in AI-Generated Content (AIGC), specifically leveraging generative diffusion models. The authors designed DFactors, which capture deviations in temporal distribution during the diffusion process. E... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our work. **However, there is a significant misunderstanding: we do not use intermediate steps from the forgery generation architecture (Model A). Instead, we employ a general diffusion model (Model B) to obtain its intermediate process. Without knowing the specific arc... | null | null | null | null | null | null |
Simple Policy Optimization | Accept (poster) | Summary: This paper theoretically identifies a flaw of the clipping technique in PPO's objective and proposes a solution to it. Empirical results show that the proposed solution achieves comparable or better performance on MuJoCo and Atari, and the performance improves as the policy network scales.
Claims And Evidence... | Rebuttal 1:
Rebuttal: Dear Reviewer uY8N,
Thank you for your positive feedback. Below, we will address your concerns.
>Figure 10: The results on Atari are averaged over only 3 random seeds, which I think is not sufficient.
Thank you for your suggestion. Given the high computational costs associated with Atari enviro... | Summary: This paper introduces Simple Policy Optimization (SPO), a first-order algorithm that modifies PPO's policy loss to achieve stronger theoretical properties, particularly in bounding the probability ratios between successive policies. The authors argue that by optimizing a lower bound under TV divergence constra... | Rebuttal 1:
Rebuttal: Dear Reviewer eeAe,
Thank you for your constructive feedback. Below, we will address your concerns.
>Partially. The claim regarding improved theoretical properties is plausible, but empirical support for SPO’s superiority over PPO and TRPO is limited.
>The paper lacks a clear demonstration of s... | Summary: The paper introduces Simple Policy Optimization (SPO), a new unconstrained first-order reinforcement learning algorithm designed to effectively combine strengths from Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). SPO modifies PPO's objective by proposing a novel surrogate loss... | Rebuttal 1:
Rebuttal: Dear Reviewer 8JyT,
Thank you for your comment. Below, we will address your concerns.
>The authors claim that "TV divergence offers a larger solution space compared to methods incorporating a looser KL divergence constraint." However, the fundamental issue is that when optimizing the surrogate o... | Summary: This paper studies an alternative of PPO, named Simple Policy Optimization (SPO), by optimizing a tighter performance lower bound using Total Variation (TV) divergence. The authors are concerned with PPO’s limitation in constraining probability ratios, which is an important problem to study.
Claims And Eviden... | Rebuttal 1:
Rebuttal: Dear Reviewer SqU8,
Thank you for your constructive feedback. Below, we will address your concerns.
>PPO is widely used in large-scale RL problems, and KL regularization will be incorporated when the policy NN is complex, such as LLM...
We appreciate your suggestion. However, the purpose of KL ... | null | null | null | null | null | null |
Selective Response Strategies for GenAI | Accept (poster) | Summary: This paper introduces the concept of "selective response" for GenAI systems.
Based on this notion, the contribution of the paper is two-fold.
First, on the conceptual side, it presents a stylized model with two platforms (a GenAI system and a human-driven forum) where users choose between them sequentiall... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful evaluation and for finding our work novel.
Addressing the reviewer's questions:
1. Regarding "*how users might react if they become aware of selective response strategies from GenAI?*"
While we model users as non-strategic and indifferent to the actions ... | Summary: This paper contributes a framework for optimizing model generation for data generation (e.g., encouraging engagement on a forum) and long-term revenue rather than for quality and completeness of response. The authors adopt a two party model where a generative model and a forum share a user set. Over a series ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful feedback and for finding our work novel.
We find the works you mentioned, namely Bergemann & Bonatti (2024), McIntyre & Srinivasan (2017), and Shumailov et al. (2023), highly relevant and will incorporate them into our literature review. Thank you for pointing... | Summary: In this paper, the authors introduce a selective response model where a "GenAI system" behaves strategically -- e.g, giving lower quality response, does not respond, etc. They provide a game theoretic based proof that shows that selective response can be beneficial in practice, when the other option is to util... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive and helpful feedback, we hope to leverage it to improve our paper.
Indeed, our work "*is also related to algorithmic deferral.*" We thank the reviewer for making this connection, which we weren't aware of. We shall address the two papers in our related work.... | Summary: The paper introduces a novel strategy called "selective response" for Generative AI (GenAI) systems, particularly in the context of human-based forums like Stack Overflow. The main idea is that GenAI could strategically provide inaccurate or conservative responses to queries involving emerging topics and novel... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful and positive evaluation of our work.
Regarding "*The paper does a good job of citing relevant literature, but it could benefit from discussing more recent work on the interaction between AI systems and human content creation platforms*". Given your advice, ... | null | null | null | null | null | null |
Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer | Accept (poster) | Summary: This work provides a theoretical analysis of gradient descent dynamics in deep linear networks trained at large widths from random initialisation. Specifically, gradient descent dynamics, hyper-parameter transfer effects and asymptotic descriptions for deep networks were analysed and discussed.
Claims And Evi... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our theoretical contributions and the novelty of attempting to capture the hyperparameter transfer effect.
### Methods and Evaluation Criteria
*The evaluation is purely theoretical and does not include standard deep learning benchmarks.*
The purpose of th... | Summary: This work theoritically characterizes the gradient descent dynamics in deep linear networks in the asymptotic limit of infinite data and width of the network. They study the limiting behaviour of both deep linear network and residual deep network for both isotropic data and data with power-law covariance and s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and detailed comments and questions. We address the main concerns below.
### Theoretical Claims
*$\gamma_0$ is defined nowhere in the text and it is hard to grasp the meaning of this quantity alhtough i see several results depend on it.*
We will... | Summary: This paper develops a DMFT based theory for deep linear networks (with and without residual connections) in GD and SGD settings. The authors show that the theory captures the effect of initialization, dataset correlations, width and depth. Moreover, they show hyperparameter transfer with width and depth.
Clai... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and their support. Below we address the weaknesses.
### Weaknesses
**hyperparameter transfer results are known in prior literature in much more complex settings**
While there are already several cases where hyperparameter transfer are documented... | Summary: The authors analyze several models of deep linear networks (FCNs, ResNets) trained on Gaussian (iid, and power-law covariance) data with noisy Gradient Descent, focusing on hyperparameter transfer between small and large models. They develop a DMFT formulation of the problem which can accommodate finite width,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and thoughtful questions. Below we address the key questions and concerns and hope that the reviewer will be satisfied with our answers and consider an increase in their score.
**Result Delivered as Complex Nonlinear Equations**
The theoretical r... | null | null | null | null | null | null |
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models | Accept (poster) | Summary: This paper studies sparse autoencoders (SAEs) trained on different layers and modules (residual stream, MLP and attention), proposes using cosine similarity to locate predecessor features in the previous layer given any target feature represented by SAE decoder weights. Through this approach, this paper traces... | Rebuttal 1:
Rebuttal: Thank you for valuable questions.
> To further validate the goal in Section 3.3 of “tracking the evolution of feature”, it would be nice to improve this step by ensuring the features considered are rarely activated on other datasets so they are tailored to the examined distribution.
>
We apprec... | Summary: The paper introduces a new approach to systematically map features discovered by SAEs across consecutive layers of LLMs. By using a data-free cosine similarity technique, the authors trace how specific features persist, transform, or first appear at each stage. This method yields granular flow graphs of featur... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions.
> 1. Some layers share less cosine similarity with others, e.g., the first layer; does this affect the mapping/steering?
We observe that layers indeed vary in feature-matching quality, though we have not yet quantified this systematically (e.... | Summary: The authors introduce a method to allow for cross layer and mulit-module (mlp, residual stream, attention block) level mapping of SAE based features, creating a flow graph using a data-free cosine similarity technique (between feature embeddings and encoder blocks), that allows a person to interpret and trace ... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and insightful questions.
---
**Q1.** The purpose of Figure 4 is indeed to show that these groups differ significantly. Below are the raw counts of elements in each group:
| Nowhere | RES | MLP | ATT | RES & MLP | RES & ATT | MLP & ATT | RES&MLP&ATT |
| --- |... | Summary: The paper investigates whether features in LLM activations identified by SAEs trained on individual activation locations - namely residual streams, attention outputs and MLP outputs - can be linked to one another, so that we can, broadly speaking, try to answer questions like "where did a feature come from?" a... | Rebuttal 1:
Rebuttal: Thank you for your valuable and extensive review. We aim to address your concerns below.
**Claims And Evidence**
Our work examines SAEs at various points in the model and linking their features allows us explore its computational structure—such as the technique described in Appendix F, which mim... | null | null | null | null | null | null |
LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models | Accept (poster) | Summary: the paper presented a training-free KV cache optimization, named LaCache, for long-text generation tasks. the proposed framework employs ladder-shaped KV cache storage pattern and an iterative compaction mechanism to enable LLMs to better capture long-range dependencies, optimize memory usage, and sustain con... | Rebuttal 1:
Rebuttal: Thank you for your time and constructive suggestions! We have addressed all your comments and suggestions as follows.
---
**Q1. Evaluation on more datasets: Needle-in-a-Haystack (NIAH) & RULER**
Following your suggestions, we have added experiments on the NIAH and RULER datasets. Please check o... | Summary: This paper proposes a method to compress KV cache with the goal of storing different sets of tokens in different layers, termed Ladder-Shaped KV Cache (LaCache). The idea is to keep earlier tokens in the sequence in the lower layer and the later tokens in the deeper layer, which intuitively makes sense.
Claim... | Rebuttal 1:
Rebuttal: Thank you for your time and constructive suggestions! We have addressed all your comments and suggestions as follows.
---
**Q1. Experiments on longer-context models trained on longer inputs**
Thank you for the suggestion! We have added experiments using both Llama3.2-3B-Instruct-128k and LongCh... | Summary: * Paper proposed a training-free KV Cache compression which stores KV pairs not only sequentially (left-to-right within
each layer) but also across layers (from shallow to deep), giving deeper capabilities to capture long-range dependencies.
* Proposes iterative compaction mechanism that progressively compres... | Rebuttal 1:
Rebuttal: Thank you for recognizing the insightfulness and promising results of our work, as well as for the constructive suggestions! We have addressed all your comments and suggestions as follows.
---
**Q1. More ablation studies and analyses on hyperparameters**
Thank you for the suggestion! We highlig... | Summary: The paper introduces LaCache, a scheme for progressive cache eviction for more efficient long context processing. Rather than evicting the same tokens at each layer, LaCache evicts tokens using a ladder-like scheme so that earlier layers maintain tokens from earlier in the context and later layers retain token... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions and analysis offered by our work, as well as for the constructive suggestions! We have addressed all your comments and suggestions as follows.
---
**Q1. Experiments on non-perplexity tasks with a long context regime (>>16k)**
Following your suggestion... | null | null | null | null | null | null |
A Theoretical Framework For Overfitting In Energy-based Modeling | Accept (poster) | Summary: This paper analyses the training dynamics of learning a multi-dimensional Gaussian distribution from data. The training dynamics considered here is a continuous time gradient ascent optimizing the maximum likelihood objective. Under some assumptions on the starting point of the learning dynamic, the authors us... | Rebuttal 1:
Rebuttal: **Methods and evaluation criteria**
We answer this comment about the choice of the spectrum in the answer to Rev. MN9R.
**Relation To Broader Scientific Literature:** Our work is focusing on the full-batch case since it is the typical case for the BM since you only need to compute the covariance... | Summary: The given paper provides a theoretical analyses of overfitting in Energy-based models. Particularly, the scope of the paper is restricted to the analyses of Gaussian Energy-based Model (GEBM), wherein the authors show that the maximum likelihood (ML) training dynamics of GEBM is decomposed into different times... | Rebuttal 1:
Rebuttal: **Claims and evidence:**
We thank the Reviewer for the suggestion, we will put in the appendix additional plots highlighting the behavior in $m$ of the down-sampled eigenvalues for both the GEBM and the BM.
**Methods and evaluation criteria:**
The eigenvalue spectra of the covariance matrices of ... | Summary: This paper proposes a theoretical framework for analyzing overfitting in energy-based models (EBMs). The framework is built upon two special cases of EBMs (namely, Gaussian EBM and Boltzmann Machine for inverse Ising model), which admit analytical (or partially/asymptotically analytical) solutions for the lear... | Rebuttal 1:
Rebuttal: **Methods and evaluation**
1. It is true that the choice of the generation quality measure with the Frobenius norm does not reflect a measure of distance between two probability distributions in general, but it is inspired by real experiments where actually such a metric is widely used, for ins... | Summary: The authors present an analysis of training dynamics and overfitting in different settings (infinite data, limited data, continuous domain, binary domain) for a specific class of EBMs. The basic idea is to project the training dynamics to the principal components of the coupling matrix, which allows (in the cl... | Rebuttal 1:
Rebuttal: ## **1. Common reply about model relevance and applicability to more complex EBMs**
We thank all the reviewers for their comments.
All Reviewers have raised important concerns regarding the generality of the results presented in the paper, particularly their applicability to more complex or real... | null | null | null | null | null | null |
DriveGPT: Scaling Autoregressive Behavior Models for Driving | Accept (poster) | Summary: This paper explores behavior modeling for autonomous driving and investigates the scaling properties from data to model parameters. The proposed method, DriveGPT, validates the benefits of scaling up both training data and compute, demonstrating improved model scalability as data increases—consistent with find... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your review and positive comments about our work. We are encouraged by your recognition of the value of our work for the future development of autonomous driving.
We address your comments and questions below.
---
**Larger models beyond 94M**
We agree with the... | Summary: This paper presents DriveGPT, a scalable behavior model for autonomous driving. The model has 1.4B parameters and 120M data are trained. DriveGPT is ∼3x larger and is trained on ∼50x more data sequences than existing published behavior models.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your time and feedback. We appreciate the positive assessment of our well-written paper, good experiment results, and contribution to exploring the scaling law of the motion prediction large models.
We address your comments and questions below.
---
**WOMD top-... | Summary: The paper presents a large transformer model predicting future ego agent states in a Birds Eye View for autonomous driving. The focus lies on an investigation of the scaling properties of transformers for behavior modeling by increasing the model and dataset size significantly. The method beats some baselines ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your detailed and thoughtful feedback. We appreciate the positive assessment of our work as a valuable contribution to the autonomous driving literature through our large-scale scaling experiments, with simple straightforward experimental design and good perform... | null | null | null | null | null | null | null | null |
Sparsing Law: Towards Large Language Models with Greater Activation Sparsity | Accept (poster) | Summary: This paper tackles activation sparsity in LLMs to boost efficiency. They introduce CETT-PPL-1%, a sparsity metric that keeps perplexity within 1% of dense models, cutting activations. They explore four factors (pre-training data, activation function, width-depth ratio, model scale) across 0.1B to 1.2B models, ... | Rebuttal 1:
Rebuttal: Thank you for your excellent review. These will encourage us to further improve the quality of our work and continuously forge ahead on the research path.
## Works in "Essential References Not Discussed"
Thank you for pointing out these two works! They are both related to our paper. We discuss t... | Summary: This paper investigates activation sparsity in LLMs through extensive experiments. The main findings include:
1) A quantitative analysis of sparsity patterns across model scales and width-depth ratios;
2) The relationship between activation sparsity ratio and data scale;
3) Achievement of a 93.52% sparsity rat... | Rebuttal 1:
Rebuttal: Thank you for your excellent review. These will encourage us to further improve the quality of our work and continuously forge ahead on the research path.
## Practical Acceleration using Activation Sparsity
In Section 6, we present an acceleration experiment based on our 2.4B sparsely-activated ... | Summary: This paper addresses three main directions related to activation sparsity. First, they introduce a new metric which they show to be better than existing activation sparsity metrics. Then, they explore the relationship between various details of the training process with the ability for a model to achieve high ... | Rebuttal 1:
Rebuttal: Thank you for your excellent review. These will encourage us to further improve the quality of our work and continuously forge ahead on the research path.
## Ablation Studies in "Claims And Evidence"
Thank you for reminding us of the ablation studies on the 2.4B model. As it is really expensive ... | null | null | null | null | null | null | null | null |
Provable Efficiency of Guidance in Diffusion Models for General Data Distribution | Accept (poster) | Summary: This paper presents a theoretical analysis of classifier-free guidance (CFG) in diffusion models, demonstrating that guidance enhances sample quality by reducing the expected ratio of poor samples, as measured by classifier probability. The authors establish a connection between their proposed metric and the I... | Rebuttal 1:
Rebuttal: Thanks for your valuable questions. Below we provide a detailed point-by-point response.
**Experiments on real dataset.** We have added experiments on the ImageNet dataset to validate our theory. Please refer to our response to Reviewer EKNn of **Experiments on real dataset**.
**Further explanat... | Summary: This paper gives a novel theoretical analysis of classifier guidance. Whereas prior work focused on special cases, e.g. mixtures of Gaussians and compactly supported distributions, this paper establishes a guarantee under minimal distributional assumptions. Specifically, they consider the functional given by t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback! Below, we provide a detailed point-by-point response.
**Explanation of main analysis idea.** In the revised version, we will add the following explanations to better communicate the high-level ideas behind the analysis:
''**A glimpse of the ma... | Summary: In this paper, the authors analyze the effect of diffusion guidance under general data distributions. Their study reveals that guidance does not necessarily improve sample quality in all cases, but it enhances overall sample quality. Specifically, they prove that under the influence of guidance, the proportion... | Rebuttal 1:
Rebuttal: Thanks a lot for the reviewer's helpful comments and valuable feedback.
Below, we provide a point-by-point response, which has also been incorporated into the revised version of our manuscript.
**Experiments on real dataset.**
Notice that classifier-free guidance (CFG) was originally validated o... | null | null | null | null | null | null | null | null |
Directly Forecasting Belief for Reinforcement Learning with Delays | Accept (poster) | Summary: This paper addresses reinforcement learning with delayed observations by proposing a Directly Forecasting Belief Transformer (DFBT). DFBT treats state estimation as a sequence modeling problem—predicting the current (and intermediate) states directly from past delayed observations instead of forecasting them i... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer 4D3p's thoughtful comments. Our detailed responses to your questions and concerns are as follows:
>### Q1: One potential weakness is that DFBT requires an offline dataset to pre-train the belief model. In scenarios where such data is not available, one would have ... | Summary: This paper introduces a method for directly predicting the current belief state in reinforcement learning with delays using a Transformer-based model. The main idea is to use Transformers for state forecasting to help mitigate the effects of observation delays in RL environments. The approach is simple, modula... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer dQtK's thoughtful comments. Below, we give responses to your questions and concerns.
>### Q1: Contribution and Novelty Clarification
First, we would like to express our gratitude for the reviewer's comments. We recognize that our current statement may have led to... | Summary: The authors focus on reinforcement learning with delayed observations. To mitigate this issue, most prior work learns a dynamics model which, given a known delay time $\Delta t$, rolls out a dynamics model from $t$ to $t + \Delta t$. The policy then makes decisions based on $s_{t + \Delta t}.
While prior work... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer KEME's thoughtful comments. Below, we give responses to your questions and concerns.
>### Q1: Evaluation of other MuJoCo tasks.
In this work, we consider the MuJoCo benchmark. To ensure transparency and reproducibility in belief training, we utilize the open offli... | null | null | null | null | null | null | null | null |
The Role of Sparsity for Length Generalization in LLMs | Accept (poster) | Summary: This work suggests that length generalization occurs as long as each predicted token depends on a small (fixed) number of previous tokens. This work also conducts experiments on synthetic tasks and natural language.
Claims And Evidence: Yes, the experiment results support the claims and evidence
Methods And ... | Rebuttal 1:
Rebuttal: Thank you for your comments. Below we contrast our work to each of the papers you mentioned. We want to emphasize that with the exception of the 4th paper below (whose theory is different from ours), none of them provide any theoretical analysis, unlike our paper. Moreover, we wish to emphasize th... | Summary: This paper studies why LLMs sometimes can or cannot generalize to input sequences longer than those they were trained on. The authors propose a theoretical framework centered on the concept of "sparsity": the idea that good length generalization occurs when each predicted token depends on only a small, fixed n... | Rebuttal 1:
Rebuttal: Thank you for your positive comments!
Regarding the how the $k$ influential tokens are chosen: please see the discussion around Eq.~(20) in the appendix.
---
Rebuttal Comment 1.1:
Comment: I thank the authors and after reading their response i decided to keep my overall score to 4. | Summary: This work mathematically studies the context length generalization for a single next-token prediction task. It obtains a length generalization error upper bound when the task satisfies certain sparsity (i.e., only a subset of tokens in a context is important to solve the task), locality (i.e., the important to... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback!
**Regarding the various $L$ factors.** Thank you for these great insights that allow us to improve our bounds!
Though we did not attempt to optimize these factors in our submission, as you point out, one can indeed improve the dependence on $L$ and $\bar L$,... | null | null | null | null | null | null | null | null |
AutoEval Done Right: Using Synthetic Data for Model Evaluation | Accept (poster) | Summary: - Paper addresses the problem of evaluating ML models with limited human validation data.
- Proposes Autoeval, an approach pairing limited human data with large amounts of AI synthetically labeled data to get model eval scores
- The primary contribution is the framework based on the existing PPI work
- Evalua... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful comments and positive feedback on the relevance of our work, the strength of our benchmark, and of our theoretical analyses. Below are detailed answers to your comments.
**Understanding when AutoEval > classic.**
Our approach adjusts to the quality of synthetic l... | Summary: This paper proposes an approach called "autoevaluation". Given a small set of human-labelled examples, and a larger set of (iid) unlabelled examples, the proposed algorithm can synthetically assign labels in a comparatively efficient and unbiased manner. The authors validate their approach using experiments on... | Rebuttal 1:
Rebuttal: Thank you for your kind and insightful comments. We are grateful for your appreciation of our work, particularly our methodologically sound approach and practical demonstration of efficient model evaluation across multiple domains.
Regarding your comment on the relevance of AutoEval to address be... | Summary: This paper studies an important question in auto-evaluation --- how to efficiently combine model prediction (imputed output) for abundant unlabeled data with limited gold-standard data to obtain efficient estimation for expected metrics for underlying distributions. The paper's method is a direct result from a... | Rebuttal 1:
Rebuttal: We thank you for your detailed and thorough feedback. We are grateful for your acknowledgement of the importance of the problem we are tackling, on the clarity of our claims and the methodological soundness of our approach.
Your review raises a number of points relating to the theoretical contribu... | Summary: The goal of this work is to reduce the cost and time of evaluating machine learning models using AI-labeled synthetic data. Introduces algorithms for auto evaluation that improve sample efficiency while remaining unbiased.
Claims And Evidence: • This problem has been tackled in the literature with different n... | Rebuttal 1:
Rebuttal: We thank you for your comments. We appreciate your recognition of our work's practical relevance in reducing ML evaluation costs and the strength of our benchmark.
Please find below point-by-point answers to your comments.
**Positioning of our approach relative to semi-supervised training strate... | null | null | null | null | null | null |
Regression Trees Know Calculus | Reject | Summary: The paper proposes a method to obtain gradients from regression trees. The gradient estimate is similar to a finite difference using mean responses across splits divided by size of node along dimension. Paper presents a Monte Carlo estimator and a partition-based estimator of integrated gradient quantities. Pa... | Rebuttal 1:
Rebuttal: Thanks much for your helpful comments. In addition to our responses to your helpful feedback and questions, we have conducted a new simulation study investigating the empirical performance of our method in estimating gradients (see below and Figure 1 [HERE](https://imgur.com/a/icml-11817-rebuttal-... | Summary: The paper develops a simple and computationally efficient approach for estimating gradients from a decision tree, essentially by computing a finite difference across all of the nodes on the way to the leaf that contains the point at which a gradient is required. These gradients are then used for active subspac... | Rebuttal 1:
Rebuttal: Thanks much for your helpful comments. In addition to our responses to your feedback and questions, we have conducted two new simulation studies, 1) investigating the empirical performance of our method relative to Gaussian Processes in estimating gradients (see below and Figure 2 [HERE](https://i... | Summary: The paper proposes an estimator of gradients and integrated gradients based on regression trees. The proposed method estimates function gradients by finite differences between adjacent regions split by a regression tree node. Building upon this estimator, Monte Carlo based and partition-based estimators are de... | Rebuttal 1:
Rebuttal: Thanks much for your helpful comments! In addition to our responses to your feedback and questions, we have conducted three new simulation studies 1) investigating the effect of correlation on our gradient estimates (see below and Figure 6 [HERE](https://imgur.com/a/icml-11817-rebuttal-yyVmZpe)), ... | Summary: In this paper, the authors propose an efficient method to estimate the gradients of the underlying function learned by regression trees. In a nutshell, by computing a quantity resembling finite differences at a tree’s nodes—based on the extent of a given node and the function values in its subtrees—one can est... | Rebuttal 1:
Rebuttal: Thanks much for your helpful comments! We have incorporated more datasets as suggested. Below, we discuss these new results and subsequently respond to your other helpful feedback.
**Regarding your suggestions for more datasets:**
Thanks for suggesting these datasets. We have incorporated the Ta... | null | null | null | null | null | null |
TopoTune: A Framework for Generalized Combinatorial Complex Neural Networks | Accept (poster) | Summary: The paper suggests a general way to lift GNN architectures to work with simplicial and cell complexes. They implemented their project within a well-known benchmarking suite. The projected is completed with a benchmarking effort covering training on multiple classical graph datasets.
Claims And Evidence: The e... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We believe they strengthened our work. We are happy to read you found the work accessible for someone not coming from TDL.
**Note on Reviewer's Summary:** GCCNs and TopoTune extend not only GNNs but also non-GNN neural networks. Our framework is also not li... | Summary: This paper aims to further topological deep learning by allowing for the easy adaption of any GNN into network for cell complexes. The basis for their method is representing the cell complexes with augmented hesse graphs, running GNNs on these graphs separately and then combining features from each of the grap... | Rebuttal 1:
Rebuttal: Thank you for your review. We are happy to read that you believe there is potential for significant impact and acceleration of TDL.
We answer your main question here. Yes, any neural network (not even necessarily a GNN) can be easily incorporated into TopoTune. Practically speaking, as long as th... | Summary: The paper introduces Generalized Combinatorial Complex Neural Networks (GCCNs) extending Topological Deep Learning (TDL) models to the combinatorial domain. It generalizes Combinatorial Complex Neural Networks (CCNNs), offering improved expressivity and performance, often with reduced model complexity. To faci... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback. We address comments and questions below.
**(Weakness 1) Hodge Theory and Spectral Filtering:** Thank you for your thoughtful comment. While the cited works integrate information across simplices, they are restricted to simplicial complexes due to their relianc... | Summary: This paper introduces generalized combinatorial complex neural networks (GCCNs), which provide a general technique for turning any existing (graph) neural network architecture into a topological network, which operates on combinatorial complexes. Their method operates by turning a combinatorial complex into a ... | Rebuttal 1:
Rebuttal: Thank you for your time as well as your positive and thoughtful review. We are happy to read that the method made sense and the contribution was well justified. We address the raised points about weaknesses and questions below.
**(Weaknesses) Comparing to standard architectures:** We initially on... | null | null | null | null | null | null |
Softmax is not Enough (for Sharp Size Generalisation) | Accept (poster) | Summary: This paper can be divided into 3 parts:
1) The observation and proof that softmax-based architectures (such as Transformers) will have a "dispersion" phenomenon when tested on longer inputs than they are trained on.
2) The observation that this dispersion phenomenon can degrade the length-generalization perfor... | Rebuttal 1:
Rebuttal: Dear Reviewer n5x1,
We are highly pleased to read your review, and really appreciate your positive view on our results and their significance!
In what follows, we reply to all of the points you raised:
> On the other hand, the adaptive temperature sampling scheme is less convincing, more ad ho... | Summary: The function softargmax (commonly referred to as softmax), which is used to create probability output vectors and attention heads within neural networks, becomes less like argmax (less sharp) as the number of elements over which the softmax is applied increases. This is detrimental for learnt circuits within t... | Rebuttal 1:
Rebuttal: Dear Reviewer jdu5,
Thank you so much for the very careful review and the vast amount of useful suggestions for improving our work! We are very happy you appreciated our efforts to raise awareness of the dispersion issues of `softmax` in the ICML community!
We provide detailed answers to your co... | Summary: The authors argue that modern deep learning architectures are fundamentally incapable of learning sharp functions(for example, max) due to the disperse nature of the softmax function in out-of-distribution settings. In addition, the authors propose an adaptive temperature mechanism as a plug-in technique at in... | Rebuttal 1:
Rebuttal: Dear Reviewer 5ZBY,
Thank you for your careful review and the positive assessment of our contribution! We are very grateful for your comments, and provide our responses below – hoping that they are to your liking!
> The authors clearly discuss the disperse problem of softmax function. I wonder h... | Summary: This paper studies the *sharpness* of the softmax function from a *size generalization* perspective. The authors regard a function as being **sharp** if its output can be expressed using a constant number of inputs. The authors refer as **size generalization** the study of what happens when the function is sub... | Rebuttal 1:
Rebuttal: Dear Reviewer WiLd,
We would like to thank you for carefully considering our paper. While we regret that your initial rating of our paper was negative, we believe you raised important points and that there is a clear discussion to be had, and that we may be able to provide relevant arguments for ... | null | null | null | null | null | null |
SAE-V: Interpreting Multimodal Models for Enhanced Alignment | Accept (poster) | Summary: The paper proposes SAE-V, a framework that utilizes SAEs trained on top of multimodal large language models (MLLMs) to measure image-text alignment. Specifically, for a given SAE feature, it retrieves the top activating tokens and image patches and computes their cosine similarity score, which produces an alig... | Rebuttal 1:
Rebuttal: # Your suggestions are insightful and will enhance the completeness of our paper!
We used all available resource and devoted efforts to conduct additional experiments. We addressed all your negative comments below and will add them into the revision. **If this rebuttal addresses your concerns, we... | Summary: - This paper straightforwardly extends the SAE framework to MLLMs, calling it the SAE-V framework.
- The authors introduce he cosine-sim scores as the cosine-sim b/w the TopK activated image and text features for a given input based on SAE activations.
- Based on the cosine-sim scores, the authors filter train... | Rebuttal 1:
Rebuttal: # Thanks for your valuable suggestion!
During rebuttal period, we used all available resource and devoted efforts to conduct additional experiments. We addressed all your negative comments below and will add them into the revision. **If this rebuttal addresses your concerns, we earnestly and kind... | Summary: This work aims to improve the vision language alignment performance of multimodal foundation models by finetuning data selection and filtering using interpretable tools, i.e., improved SAE. Specifically, it uses the alignment scores between selected topK vision-language tokens determined by SAE to select the f... | Rebuttal 1:
Rebuttal: # Despite some misunderstandings, we conducted more experiments to clarify your valuable concerns.
We conducted additional experiments, addressed your comments, and would add them into revision. **If this rebuttal addresses your concerns, we kindly ask you to consider raising the score.**
## Met... | Summary: This paper introduces SAE-V, a framework that extends Sparse Autoencoders (SAEs) to multimodal large language models (MLLMs). The authors argue that MLLMs present unique interpretability challenges due to the complex semantic space created by integrating visual modalities with text. SAE-V aims to address these... | Rebuttal 1:
Rebuttal: # We deeply appreciate your thoughtful insights that will significantly strengthen our paper's overall presentation.
In the rebuttal period, we used all available resources and devoted efforts to conduct additional experiments. We addressed all your negative comments below and will add them into ... | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.