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K$^2$IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes | Accept (poster) | Summary: The paper proposes K²IE, a kernel method-based intensity estimator for inhomogeneous Poisson processes, combining the computational efficiency of classical kernel intensity estimators (KIEs) with edge-correction capabilities from reproducing kernel Hilbert spaces (RKHS). By reformulating the problem using a pe... | Rebuttal 1:
Rebuttal: We thank the reviewer for giving valuable comments. Below, we provide a detailed response to each question. We will include all discussions in the revised manuscript.
**The equivalence between ( q(\cdot,\cdot) ) and ( h(\cdot,\cdot) ) in Equation 8 is asserted but lacks a proof ... Can this be... | Summary: **Summary:**
The authors consider modelling the intensity function of a Poisson point process as belonging to an RKHS, and then fitting this based on a regularised squared error objective. This yields a method which is similar to other kernel intensity estimators (which were not motivated via RKHS), in that no... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the highly positive comments. Below, we provide a detailed response to each comment.
**I list some possible related works for the authors' consideration, however these are definitely not essential to cite.....**
We appreciate the suggestion of including th... | Summary: This paper introduces K2IE, a kernel method-based kernel intensity estimator for inhomogeneous Poisson processes, which formulates the intensity estimation as a penalized least squares loss minimization in RKHS. A key theoretical contribution is the establishment of a specialized representer theorem leading to... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the highly positive and constructive comments, by which we are strongly encouraged. Below, we provide a detailed response to each of the comments.
**Why are real-world datasets not included in the evaluation? This would be critical to support claims of prac... | Summary: The paper develops a new kernel based estimator for intensity in inhomogeneous Poisson processes. The estimator is shown to be a associated with a unique reproducing kernel Hilbert space and is compared to some previous estimation methods in a simulation study. The simulation study shows that the new methods a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the deep understanding of our model and the constructive comments. We provide a detailed response to each comment.
**Why not include a lambda>=0 constraint?**, **My feeling is that the paper presents a theoretically sound solution to the wrong problem.**
As pointed out,... | null | null | null | null | null | null |
Fast Tensor Completion via Approximate Richardson Iteration | Accept (poster) | Summary: This paper addresses the computational challenges of Tensor Completion (TC) by proposing a novel method that integrates Tensor Decompositions (TDs)—including CANDECOMP/PARAFAC (CP), Tucker, and Tensor Train (TT)—with a lifting framework.
The authors reformulate TC as a structured tensor decomposition problem... | Rebuttal 1:
Rebuttal: Thank you for your review and careful read. Please see our responses to your questions and weaknesses below.
> The general TC problem of eq. (1) is not alligned with the approach of the current paper where a TD approach is used meaning that the approximation of the data tensor is a low-rank tenso... | Summary: This paper addresses the problem of tensor completion (TC) by proposing a novel approach that leverages approximate Richardson iteration and structured tensor decomposition (TD) algorithms. The authors introduce a lifting technique to transform the TC problem into a structured linear regression problem, enabli... | Rebuttal 1:
Rebuttal: Thank you for your review, commenting on its novelty, and carefully checking the proofs of the main lemmas/theorems. Please see our responses to your questions and weaknesses below.
> I’m curious—when the number of observed values is small, does the author’s method have an advantage over traditio... | Summary: In this paper the well-known tensor completion problem for several popular low-rank tensor formats is considered. This problem is solved through a modified (randomized) ALS method, where it is assumed that we have access to all elements of the tensor (the so-called “lifting approach”). In those points whose va... | Rebuttal 1:
Rebuttal: Thank you for your review and for going over the theoretical results and experiments carefully. Please see our responses to your questions and weaknesses below.
> The experiments presented are rather synthetic and small, it is not clear where in real-world applications these techniques can be app... | Summary: This paper introduces an efficient tensor completion algorithm, approximate-mini-ALS, which transforms unstructured tensor completion into a structured tensor decomposition problem using a lifting strategy and approximate Richardson iteration. By leveraging leverage score sampling, the method achieves sublinea... | Rebuttal 1:
Rebuttal: Thank you for your review and for recognizing that our approach is an innovative combination. Please see our responses to your questions and weaknesses below.
> In the experimental section, the author presents too few experimental results, which do not provide sufficient evidence to convincingly ... | null | null | null | null | null | null |
KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies | Accept (poster) | Summary: This paper proposes KEA, an exploration strategy for off-policy RL algorithms such as SAC, DQN, and SQL. Since these algorithms have built-in exploration strategies that interact with novelty-based methods like RND and NovelD, the authors introduce a switching mechanism to decouple them. This mechanism allows ... | Rebuttal 1:
Rebuttal: We are grateful to Reviewer Numr for the valuable feedback and insightful questions.
- **Theoretical Explanation**
We have provided the theoretical explanation in our response to **Reviewer hH71**. Please refer to that section for a detailed discussion.
- **Why the Agent Cannot Identify Unvis... | Summary: This paper proposes KEA, which aims to balance novelty-based exploration and SAC’s inherently stochastic-based exploration. The authors argue that naively combining novelty-based exploration with SAC results in suboptimal performance. To address this issue, KEA introduces a co-behavior agent that works alongsi... | Rebuttal 1:
Rebuttal: We greatly appreciate Reviewer CNJg for the helpful comments and thoughtful suggestions.
- **Theoretical Explanation**
We have provided the theoretical explanation in our response to **Reviewer hH71**. Please refer to that section for a detailed discussion.
- **Broader Contribution Beyond the ... | Summary: This paper presents KEA, a RL-based to enhance exploration efficiency in sparse reward environments. The authors propose a proactive coordination mechanism between novelty-based exploration methods and the stochastic policy of Soft Actor-Critic . KEA introduces a co-behavior agent and a dynamic switching mecha... | Rebuttal 1:
Rebuttal: We are grateful to Reviewer hH71 for the constructive and valuable feedback.
- **Theoretical Explanation of Exploration Strategy Interaction Problem**
> **Problem Setup and Assumptions**
>
> Consider an MDP with states $S=$ { $s_0, s_1, s_2$ }, actions $A=$ { $a_1, a_2$ }, and deterministic tran... | Summary: This paper proposes an exploration technique for sparse reward problem. They propose to use a co-behavior agent to reduce the interference when combining two exploration mechanism. In particular, they have the co-behavior agent to perform novelty-based exploration while the standard agent explores through trad... | Rebuttal 1:
Rebuttal: We thank Reviewer Acaj for the valuable suggestions and helpful comments.
- **Theoretical Explanation**
We have provided the theoretical explanation in our response to **Reviewer hH71**. Please refer to that section for a detailed discussion.
- **Computational Overhead of the Additional Agent... | Summary: This paper introduces KEA (Keeping Exploration Alive), a method designed to address coordination issues that arise when combining Soft Actor-Critic (SAC) with novelty-based exploration methods. The research identifies that when SAC's stochastic policy exploration coexists with novelty-based exploration, the co... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer nUkT for the thoughtful and detail feedback.
- **Theoretical Explanation**
We have provided the theoretical explanation in our response to **Reviewer hH71**. Please refer to that section for a detailed discussion.
- **Rationale Behind the Threshold-Based Switching Me... | null | null | null | null |
Habitizing Diffusion Planning for Efficient and Effective Decision Making | Accept (poster) | Summary: This paper introduces a novel and general framework that can accelerate the existing diffusion-based planning models. The motivation is that, most of the diffusion-based planning methods are very slow due to the iterative denoising steps during deployment. In this work, a VAE-like learning framework is propose... | Rebuttal 1:
Rebuttal: Thank for reviewing our paper and acknowledging "***idea is very straightforward and novel / easy to read / extensive experiments / reasonable ablation studies.***" We believe the following response can address your concerns.
> Q1: Comparison with other generative decision-making baselines
Than... | Summary: This paper presents Habi, a framework that habitizes diffusion planning into faster decision-making models by using a VAE-based approach inspired by biological habit formation. While the method demonstrates impressive speedups and maintains comparable performance to diffusion planners, the technical innovation... | Rebuttal 1:
Rebuttal: We greatly appreciate your detailed and comprehensive comments, the following responses are to address your concerns.
>Q1: Position Habi as an "acceleration technique" rather than a standalone algorithm.
Thank you for the clarification. Actually, we indeed position Habi as a general acceleration... | Summary: This paper has introduced a simple yet effective framework to speed up Diffusion-based planners (Habi). During training Habi learns:
- A prior encoder for context (state)
- A Posterior encoder and decoder for distilling learned planning in diffusion.
- A critic for evaluating actions.
I like the elegant idea ... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful comments and acknowledging "***Simple yet effective approach / Well-motivated idea / Well written paper / elegant idea and strong performance***". We will address all of your concerns as follows.
> Q1: Does the expert/teacher planner have to be a diffusion-based mode... | Summary: This paper proposes the Habi algorithm (a Diffusion Planner), which combines excellent performance with high-frequency inference speed. It utilizes a VAE-like inference framework to distill information from the diffusion planning process. Extensive experiments were conducted across various environments, achiev... | Rebuttal 1:
Rebuttal: We are grateful to your thoughtful comments and acknowledging our work "***written very clearly / research question is very important / method makes sense / supported by extensive experimental validation / simple and elegant***" as well as bringing the questions. We address your questions as follo... | null | null | null | null | null | null |
LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning | Accept (poster) | Summary: This paper presents LIFT (Low-rank Informed Sparse Fine-Tuning), which introduces the idea of Principal Weights—parameters with the largest magnitude after low-rank approximation—as the most critical ones for LLM fine-tuning. This research in very important to the community, as sparse fine-tuning methods have ... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for the insightful and constructive feedback. We'd like to address your concerns as follows. **The supplementary figures/tables are in the [rebuttal link here](https://github.com/icml12437/ICML2025_12437).**
# Q1: Layer-wise analysis on LIFT
In Appendix G.4 of our paper, ... | Summary: The authors propose a novel sparse fine-tuning approach, LIFT, which identifies so-called Principal Weights. By only training these Principal Weights, LIFT outperforms full-parameter fine-tuning in multiple benchmarks including commonsense reasoning, math reasoning, and GLUE tasks. Specifically, Principal Weig... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for the insightful and constructive feedback. We'd like to address your concerns as follows.
# Q1: Overlap between Principal Weights and the original largest-magnitude weights
In **Appendix G.6** of our paper, we discussed the overlap between parameters selected by LIFT ... | Summary: This paper proposes a novel sparse fine-tuning method, LIFT, that identifies and fine-tunes the critical parameters (Principal Weights) through SVD-based rank reduction. Extensive experiments demonstrate that LIFT significantly outperforms existing parameter-efficient fine-tuning (PEFT) approaches, including L... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for the constructive feedback. We'd like to address your concerns as follows. **The supplementary figures/tables are in the [rebuttal link here](https://github.com/icml12437/ICML2025_12437).**
# Q1: Comparison with recent sparse fine-tuning methods
We compare LIFT with spa... | Summary: This paper introduces a sparse fine-tuning approach, LIFT, which identifies and updates what the authors call “Principal Weights” in LLMs. The central claim is that the most critical parameters for downstream fine-tuning can be found by first applying low-rank approximation (e.g., SVD) to each weight matrix, t... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for the constructive feedback. We'd like to address your concerns as follows. **Please find the supplementary figures/tables in the [rebuttal link here](https://github.com/icml12437/ICML2025_12437).**
# Q1: Higher-level analysis on LIFT
In our paper, we conduct higher-leve... | null | null | null | null | null | null |
A Non-Asymptotic Convergent Analysis for Scored-Based Graph Generative Model via a System of Stochastic Differential Equations | Accept (poster) | Summary: This paper provides a non-asymptotic convergence analysis for score-based graph generative models (SGGMs), which involve coupled stochastic differential equations for graph structure and node features. The authors explore convergence bounds across three graph generation paradigms and identify factors like grap... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for positive comments and insightful questions. These greatly help us in making our paper better, and we appreciate for the opportunity to address your questions here.
# Independency in the forward equation
* We would like to clarify that the independence assumption in ... | Summary: The paper presents an analysis of graph diffusion processes building heavily on results from https://arxiv.org/abs/2211.01916 (at least based on my reading?) . They find that graph size is much more determinative for convergence rate than feature complexity (mirroring results from https://proceedings.neurips.c... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for the positive comments and thoughtful suggestions. These greatly help us in making our paper better. Below, we outline the efforts we have undertaken and plan to take in response to your suggestions.
## Suggested Reference
Thank you for pointing out the relevant refer... | Summary: The authors present convergence analysis for score-based graph diffusion generative models where both the generation of the feature vectors at each node of the graph as well as the graph structure is generated based on a diffusion model.
Claims And Evidence: The claims made are clear
Methods And Evaluation C... | Rebuttal 1:
Rebuttal: Dear Reviewer, Thank you for your insightful comments! Your questions have helped us sharpen the presentation of our technical contributions, and we sincerely appreciate the opportunity to clarify them here.
## Why Not Formulate as a Single SDE on $\mathbf{G}_t$
You are absolutely right that, i... | null | null | null | null | null | null | null | null |
From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control | Accept (poster) | Summary: This paper proposes a safe PDE control method based on the diffusion model inspired by conformal prediction. Specifically, the authors propose two new phases of post-training and inference-time fine-tuning to accommodate the quantified uncertainty score from the calibration set for the safety constraint and ob... | Rebuttal 1:
Rebuttal: Thanks for comments. Below are our responses.
>Q1. Problem formulation (Eq. 1): like offline safe RL, no boundary and time conditions.
- \(C(u,w)=0\) in Eq. 1 is the PDE constraint. Following your advice, we will add boundary and time conditions along with explanations.
>Q2. PDE-safe control sett... | Summary: This paper introduces an approach that maintains safety constraints in PDE-constrained control problems. It employs uncertainty estimation with conformal prediction to optimize control while preserving safety. It fine-tunes a diffusion model using conformal prediction to produce safe control sequences. The exp... | Rebuttal 1:
Rebuttal: Thanks for the constructive review. Below are the responses.
>Q1. Lack of evaluation of its performance in real-world scenarios.
- In fact, the tokamak control in the paper is a near real-world experiment for controlled nuclear fusion which is a highly-nonlinear and highly coupled system. This env... | Summary: This paper introduces SafeDiffCon, a method that integrates safety constraints into diffusion models for PDE control tasks. By leveraging conformal prediction to quantify model uncertainty, the approach employs post-training with a reweighted loss and inference-time fine-tuning to align generated control seque... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments. Below are our responses.
>Q1. Inference time cost comparison with baselines. The proposed model's inference finetune might induce significant extra time cost.
- Great suggestion. We would like to point out that we **have accelerated the efficiency of inference*... | Summary: The paper introduces SafeDiffCon, a method integrating safety constraints into deep learning-based control of PDE systems through diffusion models. Addressing the gap in existing methods that neglect safety, SafeDiffCon employs conformal prediction to estimate uncertainty quantiles, which guide both post-train... | Rebuttal 1:
Rebuttal: We greatly appreciate your recognition. Below are our responses.
>Q1. Ablation studies on other two tasks, 1D Burgers' Equation and tokamak control.
- Thanks for the suggestion. We conduct ablation studies on these two tasks. Results still show that the absence of any module affects the model, cau... | null | null | null | null | null | null |
PIPA: Preference Alignment as Prior-Informed Statistical Estimation | Accept (poster) | Summary: The submission presents an approach called Prior-Informed Preference Alignment (PIPA) that accommodates paired and unpaired preference data, as well as answer and step-level annotations, for the purpose of doing offline preference alignment in language models. The approach is claimed to unify recent approaches... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the detailed summary of the PIPA-M and PIPA-N derivations, including their extension to the step-level setting, as well as for recognizing our comprehensive experiments on math tasks.
## 1. Connectio between PIPA and DPO
### 1.1 $\beta$
The reviewer mentions th... | Summary: The paper proposes a prior-oriented perspective on effectively leveraging negative samples in preference learning, given that MLE (SFT) is the optimal solution with positive samples. Within this perspective, DPO and KTO are unified by their use of different priors and loss functions for negative samples.
Buil... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for acknowledging our novel well-supported framework and comprehensive experiments in math. We will improve writing to make presentation more clear.
## 1. Why learnable $p^\theta(c=1|x)$ is better?
Unlike DPO (which uses a simple 0.5 prior) and KTO (which uses a complex ... | Summary: **Post-rebuttal Update**: I thank the authors for their detailed explanation on the unified framework. I have adjusted my score to 3.
---
The paper presents a novel framework called Prior-Informed Preference Alignment (PIPA), which unifies various preference optimization objectives for language model alignme... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for acknowledging our innovative framework and thorough experiments for mathematical tasks.
## 1. Unified derivation of PIPA-M and PIPA-N
Both PIPA-M and PIPA-N derive from the same MLE target, differing only in their prior assumptions. For data (x, y, c) where:
- x: qu... | Summary: The paper presents Prior-Informed Preference Alignment (PIPA), a unifying probabilistic framework for offline preference tuning of language models. It views alignment as a maximum likelihood estimation with constraints that tie the “correct” and “incorrect” output distributions to a reference prior. Within thi... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the recognition of our theoretical contribution in unifying existing preference alignment methods under a single probabilistic framework, and our experiments demonstrating PIPA's effectiveness on math reasoning tasks with stepwise feedback.
## 1. Hyperparameter vali... | null | null | null | null | null | null |
A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO | Accept (spotlight poster) | Summary: This submission studies the interplay between privacy and robustness in both RLHF and DPO, two main alignment methods for language models. It shows that, when considering a linear reward model for RLHF and a log-linear policy class for DPO, the problem of offline alignment reduces to parameter estimation for l... | Rebuttal 1:
Rebuttal: Thanks for your time and comments. We will recap your valuable comments and present our detailed response. We hope our answers will resolve your concern.
**1. About experiments.** Thank you for your valuable suggestion. In the next revision, we will aim to include our experiments within the main ... | Summary: The paper considers alignment problems such as RLHF or DPO, in a robust private setting. In this setting, we are given a preference dataset where each example contains an input text $s$, two actions $a_0, a_1$, and a label $y \in \{0, 1\}$ denoting which action is preferred in the example. Commonly, we assume ... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback. We will fix the typos in the next version. We now recap your comment and present our detailed response.
**Minor presentation weakness about LTC and CTL.** Thanks for your sharp question. You are absolutely right and we will make this point more clear in the pap... | Summary: The authors provide a theoretical framework to analyze the suboptimality gap of the learned policy in offline alignment, in the presence of privatized and corrupted labels. Specifically, they reduce two main paradigms, the Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DP... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation of our paper. We are delighted to hear that you find our methods promising and well-founded. | Summary: The paper develops a unified theoretical framework analyzing the impact of label corruption and privacy on two primary offline alignment methods: RLHF and DPO. The authors focus on the interplay between LDP and adversarial label corruption, formalizing three noise models: CTL (corruption then LDP), LTC (LDP th... | Rebuttal 1:
Rebuttal: Thank you for your time and positive evaluation of our paper. We're glad to hear that you found our methods well-motivated, our derivations clear, and our experimental results useful. | null | null | null | null | null | null |
Convergence Analysis of Policy Gradient Methods with Dynamic Stochasticity | Accept (poster) | Summary: This paper introduces PES, a phase-based policy gradient algorithm for optimizing (hyper-)policies. Throughout the phases, the stochasticity required for exploration is gradually reduced. However, within a single phase, the stochasticity level remains constant. This allows existing convergence analyses for con... | Rebuttal 1:
Rebuttal: We thank the Reviewer for reviewing our work and for recognizing that the proposed algorithms are practically relevant, have not been analyzed before, and appear theoretically well-founded. Below, we address the reviewer’s concerns.
### 1. On the dependence on $T$
For the AB results, the correct ... | Summary: The studies effects of exploration on the convergence of the policy gradient in RL. It proposes PES method that reduces the stochasticity with iteration, allowing sufficient exploration in the beginning and the convergence to the the optimal policy in the end. Further, it proposes another SL-PG method, and sho... | Rebuttal 1:
Rebuttal: We thank the Reviewer for reviewing our work and for recognizing its practical relevance. Next, we address the reviewer’s questions.
### 1. Clarification on the paper contribution
Our paper focuses on **actor-only PG methods** in **continuous state and action spaces**, using (hyper)policies with ... | Summary: This paper provides a global last-iterate convergence analysis for a widely used class of reinforcement learning algorithms, specifically deterministic policy gradient methods with dynamic stochasticity. It considers two common types of dynamic stochasticity: phased exploration scheduling (PES) and stochastici... | Rebuttal 1:
Rebuttal: We thank the Reviewer for reviewing our work, and for recognizing that we address a highly relevant scenario in RL, providing valuable insights for both theorists and practitioners, and offering a strong theoretical analysis. Below, we address the Reviewer’s concerns.
### 1. Relation between theo... | Summary: The paper focuses on the convergence analysis of policy gradient methods in RL with dynamic stochasticity. It introduces PES, a phase-based algorithm that reduces stochasticity through a deterministic schedule while running policy gradient subroutines with fixed stochasticity in each phase. The paper demonstra... | Rebuttal 1:
Rebuttal: We thank the Reviewer for reviewing our work and for recognizing the strengths of our theoretical analysis, and the relevance of addressing an open problem in the convergence of PG methods with dynamic stochasticity. Below, we address the Reviewer’s concerns.
### 1. Comparison against SOTA Deep R... | null | null | null | null | null | null |
FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models | Accept (poster) | Summary: This work focuses on enhancing Out-of-distribution (OOD) robustness for federated prompt learning on pretrained vision-language models. The authors propose a federated OOD-aware Context Optimization framework, i.e., FOCoOp, which contains two main modules, i.e., BOS and GOC. BOS not only enhances class-level m... | Rebuttal 1:
Rebuttal: **1 We are willing to release our code and have made the project available at [https://anonymous.4open.science/r/FOCoOp/](https://anonymous.4open.science/r/FOCoOp/). It will be included in the final version of the paper.**
**2 $\mathfrak{c}()$ in Eq.(7) is the cost function, which can be compute... | Summary: In this paper, the authors provide a Federated OOD-aware Context optimization framework named as FOCoOp. FOCoOp optimizes three sets of prompts for generalization, personalization, and detection, respectively. In the client local training, the authors devise a BOS module to maintain the class-level and distrib... | Rebuttal 1:
Rebuttal: **1 We will carefully refine current version and correct typos.**
**2 The supplemental materials consist of five sections: (A) related work details, (B) algorithms, (C) theoretical analysis of optimization, (D) datasets and implementation, and (E) additional experimental results.**
**In Section E... | Summary: Federated Prompt Learning (FPL) allows models to adapt across clients while maintaining data privacy. However, current methods face challenges balancing performance and robustness, especially when encountering out-of-distribution (OOD) data shifts, limiting their real-world reliability. This is mainly due to d... | Rebuttal 1:
Rebuttal: **1 Inconsistency in maintaining local OOD robustness in FPL.**
**FOCoOp aims to improve the OOD robustness of FPL concerning the global distribution, which covers all clients' training data.** The consistency we focus on lies in detecting semantic shifts beyond all client data and ensuring gener... | Summary: The paper focuses to devise an out-of-distribution (OOD) enabled federated prompt learning method based on CLIP model. In addition to global and local prompts, the papers proposes to learn OOD prompts to enable OOD detection at each client in the federated learning framework. Similarity scores in the CLIP embe... | Rebuttal 1:
Rebuttal: **1Reasons for using optimal transport (OT).**
**OT is a powerful tool for comparing distributions, and is used for different goals in BOS and GOC.**
**In BOS, OT is used to constrain uncertainty set in distributionally robust optimization (DRO).** Unlike KL-divergence, capturing categorical dis... | null | null | null | null | null | null |
Speculate, then Collaborate: Fusing Knowledge of Language Models during Decoding | Accept (poster) | Summary: This paper tackles the problem of knowledge fusion across language models while aiming to balance effectiveness and efficiency. The paper proposes CoSD that integrates speculative decoding to accelerate generation and employs probability-based classification for token selection. CoSD provides a flexible and ad... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s recognition of the strengths of our work. Regarding the weaknesses and questions raised, we address all concerns in detail below.
> However, some questions remain in the hyperparameter part. What is the hyperparameter for the decision tree? Do we need to adjus... | Summary: This paper introduces Collaborative Speculative Decoding (CoSD), a new inference-time algorithm designed to fuse complementary knowledge from multiple LLMs without additional model training or fine-tuning. CoSD leverages a draft model to autoregressively generate initial tokens, which an assistant model then v... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s recognition of the strengths of our work. Regarding the weaknesses and questions raised, we address all concerns in detail below.
> The number of samples for the decision tree. The hyperparameters of the decision tree.
We would like to clarify that using 3 da... | Summary: This paper addresses the challenge of language model knowledge fusion, aiming to effectively integrate complementary knowledge from multiple LLMs while maintaining efficiency. The authors propose CoSD, a method that classifies output tokens based on their probabilities to achieve fusion and leverages a specula... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's recognition of the strengths of our work. Regarding the weaknesses and questions raised, we address all concerns in detail below.
>What will happen if we swap the draft model and the assistant model? Since the authors claim that users don’t need to choose betw... | Summary: The paper introduces an algorithm called Collaborative Speculative Decoding (CoSD) that is designed to efficiently fuse the knowledge of multiple Large Language Models (LLMs) together at inference time, without requiring any additional model training. The key idea is to leverage the same inference paradigm fol... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's recognition of the strengths of our work. Regarding the weaknesses and questions raised, we address all concerns in detail below.
> However, I was surprised that no evaluation of speed/efficiency was done, as this seems to be a critical claimed strength over o... | null | null | null | null | null | null |
DeepLayout: Learning Neural Representations of Circuit Placement Layout | Accept (poster) | Summary: This paper proposes a framework to implement pre-training on circuit netlist and physical layout. Two loss functions are designed to realize the pre-training task. And the pre-training network can be applied to downstream tasks, such as wirelength and congestion prediction.
## update after rebuttal
After care... | Rebuttal 1:
Rebuttal: Q1
1.Ablation studies of ... is encouraged.
2.Ablation studies ... taken.
A1
Ablation study of encoder modules
Congestion prediction
| Method | 5 | 5 | 5 | 5 | 10 | 10 | 10 | 10 | 20 | 20 | 20 | 20 |
|---|---|---|---|--|--|--|--|--|---|--|--|--|
| | Pearsonr | MAE | RMSE | SSIM | Pe... | Summary: This paper presents a method to learn the layout representation of circuit for downstream tasks. The method is based on an graph neural network (GNN) and a mask training strategy.
## update after rebuttal
I keep my rating since the authors have answered my question, and I still slightly lean toward accept.
C... | Rebuttal 1:
Rebuttal: Q1
“most of the design in the proposed network seems not novel and exists in network/method for other domains. I personally think this is not critical as long as the method is working well for this specific domain..”
A1
We appreciate your thoughtful response and the opportunity to address our co... | Summary: This paper proposes a representation learning framework for backend circuit design by integrating GNNs and spatial transformers to capture both topological connectivity and geometric distribution of circuits. Also the authors propose a self-supervised learning method based on mask-based autoencoder for layout ... | Rebuttal 1:
Rebuttal: Q1
It could mention other graph-based learning techniques in EDA beyond CircuitGNN and discuss RL-based methods for backend placement to strengthen the background. if we miss any related papers, please feel free to point out. We will incorporate them in the final version.
A1
We sincerely appreci... | Summary: This paper proposed a mask-based approach for circuit layout representation learning. Specifically, a grid-based partition is dedicated to dealing with the mask operation on layout. Two tasks are utilized to illustrate the potential, including wirelength estimation and congestion prediction. The results outper... | Rebuttal 1:
Rebuttal: R1
Q1:
"Congestion and wirelength ... strength the paper."
A1
We have additionally designed post-routing timing prediction task that is directly related to evaluating post-routing layout quality.
Timing is directly correlated with the chip's performance. The results are presented below.
| Model ... | null | null | null | null | null | null |
Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency | Accept (poster) | Summary: The paper addresses the issues of limited diversity and replication of training images in text-to-image diffusion models by introducing a method to ensure that generated images are novel and diverse using sparse repellency.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: No... | Rebuttal 1:
Rebuttal: Thank you for your review and for your assessment that our experimental evaluation is extensive. To add to this, we add the additional experiments you have requested below, namely a runtime analysis and SPELL’s performance under text prompts of varying length. We also provide further results, incl... | Summary: This paper introduces an application of negative guidance on reference datasets (e.g., {training, validation} datasets) to enhance diversity and generate novel samples that differ from reference images. The paper leverages geometric steering to guide samples away from the reference dataset. To minimize the per... | Rebuttal 1:
Rebuttal: Thank you for your review and for your interest in the theory behind SPELL that enables its outperformance. We delve into the theoretical connections between SPELL and other methods below, as well as the experimental results for pixel-space diffusion. We also provide further results in the respons... | Summary: This paper introduces Shielded Diffusion, which aims to generate images outside of protected sets. These protected sets may include protected images, other data in the current batch, or data used during training. The authors determine whether the diffusion trajectory is expected to fall into protected sets dur... | Rebuttal 1:
Rebuttal: Thank you for your review and for acknowledging that SPELL tackles meaningful practical applications while remaining training-free. We are happy to provide the experimental results for high-resolution images, complex prompts, and compute overhead that you have requested below.
### Examples and ana... | Summary: This paper proposes sparse repellency (SPELL) to prevent diffusion models from generating images in a set of L2 balls. SPELL can be used to (1) protect diffusion models from generating training images; (2) encourage diversity between multiple generations. Extensive experiments shows the superiority of SPELL.
... | Rebuttal 1:
Rebuttal: Thank you for your review and for recognizing that SPELL is a simple way to tackle two current problems in diffusion models at once. We are happy to share explanations and your requested FLUX experiments below.
### Can image protection with SPELL be applied to text-to-image models?
Yes, there is n... | null | null | null | null | null | null |
BAME: Block-Aware Mask Evolution for Efficient N:M Sparse Training | Accept (poster) | Summary: This paper introduces a novel method, BAME, which maintains consistent sparsity throughout the N:M sparse training process. Specifically, BAME ensures sparse forward and backward propagation while iteratively performing Loss-aware Mask Adaptation (LMA) and Oscillation-aware Block Freezing (OBF) to adapt the ma... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful review and constructive comments. Please kindly see our responses to your questions below.
**Q1**: The proposed metric in Loss-aware Mask Adaptation is somewhat common in traditional sparse methods, which limits the novelty of the paper to some extent.
**A1*... | Summary: This paper presents a novel approach for preserving sparsity in DNNs during training, with a focus on N:M sparsity. The authors introduce BAME (Block-Aware Mask Evolution), a technique that ensures both forward and backward propagation remain sparse while iteratively pruning and regrowing weights within predef... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive and motivating comments. Please kindly see our response to your comment below.
**Q1**: While BAME demonstrates lower training overhead compared to MaxQ, its performance appears to be slightly inferior to MaxQ in certain cases.
**A1**: We appreciate this ins... | Summary: The paper introduces a novel approach called BAME (Block-Aware Mask Evolution) for training N:M sparse networks in an efficient manner. The authors argue that prior works often rely on dense gradient updates, which leads to considerable overhead. Instead, BAME keeps the network consistently N:M sparse througho... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive and motivating comments. Please kindly see our response to your concerns below.
**Q1**: Actual training or inference speed measurement of BAME.
**A1**: We appreciate this constructive feedback. We first clarify that **reporting N:M sparsity patterns and th... | null | null | null | null | null | null | null | null |
Non-Asymptotic and Non-Lipschitzian Bounds on Optimal Values in Stochastic Optimization Under Heavy Tails | Accept (poster) | Summary: This paper aims at proving confidence bounds over the minimum of a stochastic optimisation problem, ie, find a high probability confidence interval for the value of $F(x^\star) = \min E[f(x,\xi)]$, given sampled datapoints $(\xi_1,\dots,\xi_N)$. Thus, this task typically boils down to proving a lower bound and... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's very careful and insightful evaluation.
1. Re: "Other Comments": We will make sure to make full use of the 8 page limit. To that end, we will present part of our additional numerical results and add more discussions in response to the comments of this reviewer. We wil... | Summary: The paper studies confidence bounds for the optimal value of a stochastic optimization problem. For the convex case, the paper derives bounds for a set of heavy-tailed assumptions, and the bounds do not depend on the Lipschitz constant of the function. For the non-convex case, the paper provides bounds for bot... | Rebuttal 1:
Rebuttal: We thank the reviewer for in-depth evaluation and insightful comments. Below citations follow the same reference list as in our paper.
1. To answer the comment re: "[...]I don't see the challenge to derive bounds[...]", we would like to point out that we made conscious and non-trivial effort to s... | Summary: The paper presents non-asymptotic bounds on the minimal value of a stochastic optimization problem. The novel contributions are that the resulting bounds do not depend on global Lipschitz constants of the integrand function or the objective and operate for heavy-tailed function value and gradient distributions... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful comments.
1. Re: Relation To Literature: Average/expected norm of gradient is essentially the (non-central) moment, which typically has two components: **(A)**. the norm of the gradient of the expected cost function, and **(B)**. the central moment of the gra... | Summary: The paper provides non-asymptotic confidence intervals for the solutions of stochastic optimization problems. Unlike previous work, their approach simultaneously covers non-Lipschitz and heavy-tailed problems. They also include analysis of non-convex and overparametrized cases.
Claims And Evidence: Claims are... | Rebuttal 1:
Rebuttal: We appreciate the great effort by the review in evaluating our manuscript. All papers cited in this response are the same as those included in the original submission.
1. Following the comments, we conducted two experiments on a convex and a nonconvex problem. For the former, we considered a stoc... | null | null | null | null | null | null |
Retrieval-Augmented Perception: High-resolution Image Perception Meets Visual RAG | Accept (oral) | Summary: - The paper introduces a RAG-based approach to handle High-Resolution visual reasoning with MLLMs.
- The authors develop a Retrieval-Augmented Perception (RAP) framework to find the important image crops required for answering a given query based on the query-crop similarity and using those for inference.
RAP ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive comments and suggestions.
> **Q1:** Experiments on widely used benchmarks.
**R:** We appreciate the reviewer's suggestion. As suggested, we conduct additional experiments on five widely used benchmarks: **DocVQA, ChartQA, TextVQA, AI2D, an... | Summary: This paper proposes Retrieval-Augmented Perception (RAP), a training-free framework that enhances high-resolution image perception in multimodal large language models by leveraging RAG. RAP retrieves and fuses relevant image crops while maintaining their spatial relationships using a Spatial-Awareness Layout a... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the thoughtful comments and suggestions, as well as the positive support.
> **Q1:** The concern about the extra retriever increasing computation.
**R:** We appreciate the reviewer's comments. Although our *RAP* includes an additional retriever module, it stil... | Summary: This paper introduces a retrieval-augmented perception method for MLLMs, which retrieve and fuses relevant image crops from the full high-resolution image.
Specifically, a apatial-awareness layout is proposed, which is to maintain the relative positional relationships of the image crops.
In addition, a retriev... | Rebuttal 1:
Rebuttal: We truly appreciate Reviewer tave's constructive comments and positive support.
> **Q1:** The concern of the comparison methods are comprehensive enough.
**R**: We'd like to thank the reviewer for the advice. In fact, building on established works such as $DC^2$ [R1] and ZoomEye [R2], we conduct... | Summary: The paper works on a key challenge in the area of MLLMs -- the perception of high-resolution (HR) images. Centering on this significant problem, this paper leverages RAG to enhance MLLM’s ability to perceive HR images. The paper first explores the impact of the layout and the number of retrieved image crops on... | Rebuttal 1:
Rebuttal: We truly appreciate Reviewer h1HT's insightful comments and suggestions.
> **Q1:** Replace confidence score calculation in RE-Search.
**R:** The reviewer's point is well taken. We clarify that currently, APIs like OpenAI's [R1] provide the `logprobs` parameter, which returns the log probabilitie... | null | null | null | null | null | null |
UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation | Accept (poster) | Summary: In this work, the authors propose a method that jointly solves topology generation, condition confirmation, and property prediction. Their approach consists of two stages: first, using three sets of encoders and decoders to embed different modality conditions into discrete latents; second, employing a diffusio... | Rebuttal 1:
Rebuttal: Thank you for the reviewer’s time and constructive comments. We sincerely appreciate the opportunity to address the concerns and clarify our work.
**Q1:** Similar concepts have been explored in an image-text work (i.e., Janus, which is mentioned by the reviewer).
**A1:** We appreciate the review... | Summary: In this paper, the author proposed UNIMAE, a unified model that can tackle three tasks simultaneously, namely, the topology generation task, property prediction task, and condition confirmation task, by training a shared, aligned latent space using a novel TOT and frozen diffusion. In the three tasks, the UNIM... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their time and constructive feedback. Please find our detailed responses and corresponding revisions below:
**Q1:** One point in the work that "it will be simpler...the generation process" is not proven in later experiments.
**A1:** Thank you for your detailed... | Summary: The paper introduces UNIMATE, a unified model for mechanical metamaterial design that simultaneously addresses three key aspects: 3D topology, density condition, and mechanical property. Unlike previous approaches that typically consider only two modalities, UNIMATE integrates all three through a modality alig... | Rebuttal 1:
Rebuttal: Thanks for the reviewer's time and inspiring comments. Here, we summarize the major points from the reviewer and our rebuttal as follows:
**Q1:** The proposed model is evaluated on a new dataset and custom metrics.
**A1:** Our work focuses on developing a unified model capable of handling divers... | null | null | null | null | null | null | null | null |
When to retrain a machine learning model | Accept (poster) | Summary: This paper presents a novel approach to maintaining the performance of open-world machine learning models operating in continuously changing environments. The primary focus is on addressing a critical challenge in such settings: determining when the model should be retrained. The objective is to balance the tr... | Rebuttal 1:
Rebuttal: # Weaknesses
## 1. Although the paper provides a mathematical analysis for the proposed online objective, the objective itself appears relatively straightforward, as it primarily combines a standard loss function with retraining costs.
We disagree with the statement that the objective is straigh... | Summary: One underexplored problem is that of when to retrain a model, assuming a sequence of datasets over time that experience distribution shift. This problem can be formulated as an off-policy reinforcement learning problem, where the goal is to find a policy with minimum cost. Authors define cost as the sum of cos... | Rebuttal 1:
Rebuttal: # Methods
## 1. Bayesian model: ablations with/without feature $z$
Experimentally, we saw that without the shift feature $z$, the results were on average $5-10\%$ worse, with some datasets being more affected than others (the electricity and the synthetic datasets were mostly unaffected). We can ... | Summary: This paper faces the complex task of understanding when a machine learning model needs to be retrained in the presence of drift. In doing so, it takes into account the problem associated with the trade-off between retraining cost and poor model performance. Their approach is based on forecasting the evolution ... | Rebuttal 1:
Rebuttal: # Methods
## 1. Types of non-stationarities: approach is more suited for slowly changing environments rather than abruptly changing ones
That is a good point. It is true that the best scenario for our approach is a slowly changing environment, and that abrupt changes would be harder for our mod... | Summary: In this paper, the authors proposed a novel strategy to determine when to retrain deployed machine learning models. Specifically, the authors first developed a future performance forecaster for predicting the performance of models built in future time steps. Building on top of it, the authors model the problem... | Rebuttal 1:
Rebuttal: # Claims and Evidence
## 1. Beta r.v. motivation:
We agree that there could be a better motivation for the Beta distribution and will add sentences to improve the flow. Our choice to use a Beta distribution was motivated by the facts that 1) Beta distributions are appropriate (and a common choice)... | null | null | null | null | null | null |
Learning Safe Control via On-the-Fly Bandit Exploration | Accept (poster) | Summary: This paper proposes a safe control method to address the problem of infeasible safety filter. The authors use Gaussian processes to learn the system dynamics. They use the lower confidence bound of the control barrier function (CBF) to identify the feasibility of the safety filter, and sample exploratory contr... | Rebuttal 1:
Rebuttal: Thank you kindly for your review. We have corrected all typos and addressed all your major comments below. We want to stress at this point that our method does, in fact, guarantee safety, even throughout exploration.
**Safety guarantee during exploration:** Our method does guarantee safety during... | Summary: ## update after rebuttal
My evaluation of the paper has not changed after the rebuttal. The technical part of the paper is correct but does not bring much new insight on the topic. The assumption of known CBF for a system is stronger than existence of partial models and backup controllers. The experiments do n... | Rebuttal 1:
Rebuttal: Thank you kindly for your review. We have addressed your comments and questions below.
**Knowledge of CBF and difference to nominal understanding of the system dynamics/backup controller:** Although the CBF assumption cannot be ensured in a general setting, it still offers flexibility, and ther... | Summary: The paper proposed an online safe control algorithm with Gaussian process models of the dynamics and bandit-type exploration to learn the dynamics. Then, the learned dynamics are combined with a control barrier function to ensure online safety. The control signal is solved with a safety filter with a lower con... | Rebuttal 1:
Rebuttal: Thank you kindly for reviewing our paper. Please find our answers to your questions and comments below.
**Comparison with other references and baselines:** Thank you for suggesting the additional references. Although the methods of Sui et al. (2015), Wachi et al. (2018) and Prajapat et al. (2022)... | Summary: The work presents an approach to design certifiably safe feedback controllers for a priori unknown systems. The authors propose using gaussian processes to approximate the learned model (or its error with respect to the true model). Then, they leverage bandit theory to propose upper and lower bounds to the mod... | Rebuttal 1:
Rebuttal: Thank you kindly for your review. Below, you will find our answers to your comments.
**Regarding the claim of solvability of the benchmarks:** This claim is tied to the controller having a prior mean model of zero, no measurement data, and only a CBF to guide it. Although existing methods can eff... | null | null | null | null | null | null |
Feature Shift Localization Network | Accept (poster) | Summary: This paper proposes a feature shift localization network that can localize feature shifts between newly given two datasets by learning how to localize feature shifts using multiple datasets with various synthetic feature shifts. The experiments show that the proposed method can accurately and efficiently detec... | Rebuttal 1:
Rebuttal: *1. Including a more detailed explanation of the comparison methods would make the paper easier to understand.*
**We agree that the paper will benefit from a more detailed description of the competing methods. Therefore, we are adding a new section titled “Benchmarking Methods” in the Appendix th... | Summary: This paper presents FSL-Net, a neural network designed to effectively localize feature shifts through its architectural design. More specifically, FSL-Net is built in two stages, a statistical descriptor network which is proposed to extract underlying distributional information from the inputs, and a predictio... | Rebuttal 1:
Rebuttal: *1. It is not clear to the reviewer how applicable feature shift localization would be in the real-world, given that there could be cases where all or none of the feature presents any discernable feature shift.*
**We consider cases where either no features or only a subset exhibit a shift. If eve... | Summary: The authors propose a feature shift detector: the goal is to identify a maximum-size set of features with zero distance for the corresponding marginal distributions. The overall architecture uses three parts for different types of inputs: basic aggregated statistics, MMD-like features generated in linear (mome... | Rebuttal 1:
Rebuttal: *1. Why would one solve this problem instead of feature selection? Can you expand the experiments to include more traditional problem statements?*
**Feature shift localization appears in many industries. In healthcare, integrating multiple data sources leads to “batch effects” caused by differing... | Summary: This work presents the FSL-Net, a neural network designed to quickly and accurately identify feature shifts in large, high-dimensional datasets, overcoming challenges faced by existing methods. Trained on diverse datasets, FSL-Net can localize shifts in unseen data without requiring retraining. The method look... | Rebuttal 1:
Rebuttal: *[1] This work presents the FSL-Net, a neural network designed to quickly and accurately identify feature shifts in large, high-dimensional datasets, overcoming challenges faced by existing methods. Trained on diverse datasets, FSL-Net can localize shifts in unseen data without requiring retrainin... | null | null | null | null | null | null |
Diving into Self-Evolving Training for Multimodal Reasoning | Accept (poster) | Summary: This paper investigates self-evolving training for multimodal reasoning through the lens of reinforcement learning, identifying three key factors: Training Method, Reward Model, and Prompt Variation. The authors propose a continuous self-evolving training scheme that inherits optimizer states between iteration... | Rebuttal 1:
Rebuttal: Thanks for your reivew.
# Q1
> 1. ...shows that your PRM is…
> 2. How... relate to recent findings in papers like DS-R1…
## Q1.1 PRM Analysis
First, we want to clarify an important distinction between BoN selection and reranking using PRM. But due to the space limit, please refer to **Q1 in ... | Summary: This paper identifies three key components in multimodal reasoning models that require further exploration. It systematically analyzes and unveils the critical aspects of training methods, reward models, and prompt design. Additionally, it proposes the use of appropriate temperature adjustment to balance explo... | Rebuttal 1:
Rebuttal: Thanks for your review. And we appreciate your recognition of your work. We would address your concerns one by one:
# Q1
> Line 175-178 “...switching over the Improve and Generate steps too frequently makes the learning process unstable, leading to a lower score, especially on the in-domain test... | Summary: The paper introduces M-STAR—a framework that reframes self-evolving training for multimodal reasoning as a reinforcement learning (RL) problem. It identifies three critical factors (training method, reward model, and prompt variation) and proposes a continuous self-evolving training variant. A novel Process Re... | Rebuttal 1:
Rebuttal: # Q1
> However, the effectiveness of PRM as a reranker—despite underperformance on standard verification metrics—needs further clarification
Thank you for your question.
First, we would like to clarify an important distinction between Best-of-N (BoN) selection and reranking using PRM. When using... | Summary: The authors reframe self-evolving training for multimodal reasoning through the lens of RL and indentify three factors: training method, the use of reward model, and prompt variation. They train the first multimodal, process-based reward model for multimodal reasoning and
demonstrate its usefulness in further ... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our paper.
# Q1
> limited technical novelty
Overall we have contributed:
- A pilot study to enhance multimodal (MM) reasoning validated by comprehensive studies,
- The first self-evolving training recipe that blended online training and PRMs in M... | null | null | null | null | null | null |
Measuring In-Context Computation Complexity via Hidden State Prediction | Accept (poster) | Summary: The paper introduces the Prediction of Hidden States (PHi) loss to measure the complexity of computation in neural sequence models. The authors argue that traditional next-token prediction loss does not adequately capture the task complexity. To fix this, they propose evaluating the model’s ability to predict ... | Rebuttal 1:
Rebuttal: Document with additional figures: https://tinyurl.com/yp4ucedn
## 1. Location of the PHi Layer in the Model
> The PHi layer's effectiveness depends on where it is inserted in the model.
We want to emphasize that in a fully trained model—such as the transformer in Section 3.1—the results are very ... | Summary: This paper proposes a novel method for probing the hidden representations of neural sequence models: the prediction of the hidden states (PHi) layer. This layer combines an encoder that generates latent variables from the hidden states and an autoregressive LMs that generates latent variables from previous lat... | Rebuttal 1:
Rebuttal: Document with additional figures: https://tinyurl.com/yp4ucedn
## 1. Questions about the PHi Layer
> 1: is the autoregressive part of the PHi layer used to compute the output, or does it just enter to train the encoder?
The purpose of the autoregressive part (i.e., the causal self-attention laye... | Summary: I think this paper introduces the PHi (Prediction of Hidden States) layer as a novel way to measure the complexity of computation performed by neural sequence models by examining how predictable their hidden states are. The authors show that this metric correlates better with intuitively "interesting" computat... | Rebuttal 1:
Rebuttal: Document with additional figures: https://tinyurl.com/yp4ucedn
## 1. Robustness towards Hyperparameters and PHi Layer Placement
>...the paper lacks rigorous justification for where to place the PHi layer in pre-trained models
While it is true that the properties of the PHi layer vary depending o... | Summary: The paper proposes a "prediction of hidden states" (PHi) layer, which can be used to quantify the complexity of the computation being performed in a neural model. The layer exists between the activations of a sequence model such as a Transformer. It maps the activations to latent variables. It then computes th... | Rebuttal 1:
Rebuttal: Document with additional figures: https://tinyurl.com/yp4ucedn
## 1. Rationales for Mathematical Reasoning
> Perhaps most intriguingly, the proposed measure when applied to rationales for mathematical reasoning appears to correlate with answer accuracy.
We agree that this is an intriguing findin... | null | null | null | null | null | null |
Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation | Accept (poster) | Summary: This paper innovatively combines parallel spiking calculation with ANN-SNN Conversion to propose a high-performance and ultra-low-latency parallel conversion framework, which can also be applied in more general conversion scenarios (e.g. ReLU, QCFS with different quantization level). Experimental results have ... | Rebuttal 1:
Rebuttal: ## To Reviewer mMJc
We are pleased that you recognize the relevant content of this work in terms of theoretical claims and experimental validation, as well as pointing out that our method provides a new perspective for SNN supervised learning. We will elaborate on your questions and comments.
> ... | Summary: This work introduces a novel parallel conversion learning framework that establishes a mathematical mapping between each time step of parallel spiking neurons and the cumulative spike firing rate. The lossless and sorting properties of the conversion process are theoretically validated, and the optimal shiftin... | Rebuttal 1:
Rebuttal: ## To Reviewer nBwe
We are delighted that you think that our method is well written and organized, as well as being validated in both theoretical and experimental dimensions. We will discuss your questions in detail in the following content.
> The difference between the conversion method in this... | Summary: This paper propose a parallel ANN-SNN conversion framework. The author firstly categorizes and summarizes various conversion paradigms in the field of ANN-SNN conversion learning, then proposes an efficient conversion method based on parallel spiking computing, which relate each time-step to the cumulative spi... | Rebuttal 1:
Rebuttal: ## To Reviewer HMDR
We would like to thank for your acknowledgement about our approach in terms of theoretical analysis and performance advantages, we will provide further answers and clarifications for your questions and concerns.
> Can this method be generalized to other tasks?
Thanks for thi... | Summary: This work presents a novel route for SNN supervised learning by jointly adopting ANN-SNN Conversion and parallel calculation. The main contributions of the paper include the proof of optimal shifting distance, further promotion of parallel conversion framework based on QCFS, and experimental demonstration on v... | Rebuttal 1:
Rebuttal: ## To Reviewer 72NY
We sincerely appreciate your recognition for the novelty and experimental effectiveness of this work. We will strive to address your concerns in detail in the following section:
> The discussion on threshold recording and error calibration techniques is not sufficient.
**A1:... | null | null | null | null | null | null |
Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream | Accept (spotlight poster) | Summary: This paper explores how scaling model size, dataset size affects the alignment of artificial neural networks with primate visual ventral stream behaviors and neural responses. The scaling law is investigated over diverse models on benchmarks including v1, v2, v4, IT and behavior data.
The authors offer interes... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review — we’re glad you found the paper well-written and organized, appreciated the breadth of our experiments and visualizations, and found the findings insightful. We respond to each of your comments below point-by-point.
> The practical implications of the author... | Summary: This paper seeks to measure scaling laws for task-optimized models of the primate visual ventral stream. Several models from multiple families were trained with different amounts of compute and training data. They were then compared on their alignment to different areas of the visual cortex (V1, V2, V4 and IT)... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and thoughtful comments. We are particularly encouraged that the reviewer highlighted the key strengths that we were indeed most excited about in our paper:
- Our systematic, extensive evaluation of scaling laws across hundreds of models and diver... | Summary: In the paper "Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream", the authors investigate scaling laws for alignment of machine learning models to the primate visual processing. They assess neural alignment as well as behavioral alignment, and find that there is a saturation point in ... | Rebuttal 1:
Rebuttal: We appreciate your positive review and are glad you found the paper well-written, the results clear, and the evidence supporting our claims solid. Below, we address your questions point by point.
> In light of these previous works, it might be expected that scaling up models and data would be in... | Summary: The paper introduces scaling laws for task-optimized models in fitting neural recordings. These laws stem from the observation that neural networks trained on classification tasks have emerged as the most effective models for decoding neural activity in the brain. The study then evaluates a measure of alignmen... | Rebuttal 1:
Rebuttal: Thank you for your review - we’re glad you found the paper informative and comprehensive in its overview of models and progress on neural and behavioral benchmarks. We respond to each of your comments below.
> Claims And Evidence - limited model set:
The reviewer might have missed this in the p... | null | null | null | null | null | null |
A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation | Accept (poster) | Summary: In this work the authors present a unification of noise contrastive estimation (NCE) losses. In particular they consider a class of risks based on optimizing the density ratio of the model density compared to a known noise density (canonically a uniform distribution on a set known to contain the support of the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort in reviewing our paper. We will incorporate all the comments from the reviews and revise our manuscript accordingly. | Summary: This paper provides a unified perspective on various estimators for learning unnormalized distributions (also known as energy-based models) using Noise-Contrastive Estimation (NCE). Specifically, they introduces $\alpha$-Centered NCE ($\alpha$-CentNCE) and f-Conditional NCE (f-CondNCE) as generalized versions ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s careful assessment of our manuscript and their constructive feedback. Below, we respond to the points raised under *Weaknesses* to clarify our contributions. In summary, we believe that all the issues raised are important and warrant separate, dedicated future investig... | Summary: This paper presents a unified framework for learning unnormalized distributions through noise-contrastive estimation (NCE), introducing two variants: alpha-CentNCE and f-CondNCE. It demonstrates that alpha-CentNCE generalizes existing methods like MLE, MC-MLE, and GlobalGISO, while f-CondNCE reveals limitation... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort in reviewing our manuscript and for the valuable feedback. Below, we provide responses to the questions.
* We highlight that the discussion in Section 3 does not assume exponential family distributions, except in the comparison with GlobalGISO, which is spec... | Summary: The paper provides a unified perspective on noise-contrastive estimation (NCE) methods for learning unnormalized distributions, integrating several previously separate approaches under a common framework. It introduces two new variants: $\alpha$-centered NCE ($\alpha$-CentNCE) and $f$-conditional NCE ($f$-Cond... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s effort in reviewing our manuscript. We will incorporate the constructive feedback in our revision.
### Comments on Weaknesses
We acknowledge that our work primarily focuses on the theoretical unification of different estimators. As suggested by the reviewer, however... | null | null | null | null | null | null |
Generalization and Robustness of the Tilted Empirical Risk | Accept (poster) | Summary: Building on the notion of tilted empirical risk, this paper develops upper bounds of generalization error for tilted empirical risk (defined as tilted empirical risk minus the regular population risk) under negative tilt and moment-bounded loss. The first set of results (Theorems 3.5 and 3.11) are for the cas... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and generally positive assessment of the paper. We will address their concerns as detailed below.
> Distribution shift and domain adaptation literature
**R1:** Thank you for introducing the works on generalization bounds under distribution shift and do... | Summary: This paper gives a detailed and extensive study of the tilted empirical risk, focusing on particular on generalization bounds. Both uniform convergence bounds and algorithm-dependent information-theoretic bounds are provided, and robustness guarantees under distribution shifts are analyzed. On the basis of the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and generally positive assessment of the paper. We will address their concerns as detailed below.
> Also, the experiments on linear regression indicate a sizeable gap between the data-driven tilt parameter and the optimal one. Some further discussion of ... | Summary: This paper investigates the generalization error of the tilted empirical risk (TER), a non-linear risk metric for supervised learning introduced by Li et al. (2020). The study focuses on the robustness regime under negative tilt, where TER is used to mitigate the impact of noisy outliers.
The paper provides un... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and generally positive assessment of the paper. We will address their concerns as detailed below.
> finite hypothesis class
**R1:** We thank the reviewer for this valuable comment. Indeed, while Section 3.1 focuses on finite hypothesis class bounds, the... | Summary: This paper studies the generalization error of tilted empirical risk (TERM), a method for fair and robust learning in empirical studies. The paper first studies in-distribution generalization, considering unbounded loss, negative tilt parameters, and finite hypothesis spaces, and gives a convergence rate of $O... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and generally positive assessment of the paper. We will address their concerns as detailed below.
>Comparison between TERM and ERM
**R1:** We should clarify that we cannot derive an upper bound for ERM in terms of total variation distance if the loss f... | null | null | null | null | null | null |
Enhancing Statistical Validity and Power in Hybrid Controlled Trials: A Randomization Inference Approach with Conformal Selective Borrowing | Accept (poster) | Summary: This paper proposes to use Fisher Randomization Test (FRT) in RCTs when leveraging external controls (EC). Since FRT only uses the randomization distribution of a test statistic under the sharp null, it always provides valid type-I error control regardless of how the potentially biased ECs are incorporated. In... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful and constructive comments. Below, we have provided our detailed, point-by-point responses.
**1. Connection between MSE of $\hat{\tau}_\gamma$ and power of FRT**
(i) Variance and power: Theorem 2.4 (power analysis for consistent test statistics) shows that ... | Summary: This paper proposes a method for combining (potentially biased) external controls (ECs) with data from a randomized control trial, in an effort to improve power to detect causal effects, without sacrificing Type 1 error (false positive) control. For controlling Type 1 errors, the key insight is to use a Fishe... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful and constructive comments. Below, we have provided our detailed, point-by-point responses.
**1. Clarification on sharp null hypothesis**
The sharp null hypothesis $Y_i(1) = Y_i(0)$ for all $i \in$ RCT states that there is no individual treatment effect for... | Summary: This paper proposes a randomization inference framework that can combine the data from randomized controlled trials with external controls. The proposed method controls the Type-I error in finite samples by leveraging conformal inference to select appropriate samples from external controls. In particular, the ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful and constructive comments. Below, we have provided our detailed, point-by-point responses.
**1. Bound in Theorems 3.7 and 3.8**
We explain the key terms in Theorems 3.7 and 3.8 and their practical implications as follows:
(i) The term $c\Delta|\delta_1|... | Summary: Authors study integration of external (historical) controls into randomized controlled trials in a principled way using conformal p-values, to guarantee type-1 error rates with finite samples under potential violation of the exchangeability assumption between trial and external controls.
### update after rebu... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful and constructive comments. Below, we have provided our detailed, point-by-point responses.
**1. Clarifying the objective and the role of MSE minimization in improving FRT power**
Our primary objective is to improve the power of the Fisher Randomization Tes... | null | null | null | null | null | null |
Right Time to Learn: Promoting Generalization via Bio-inspired Spacing Effect in Knowledge Distillation | Accept (poster) | Summary: This paper, inspired by the spacing effect, proposes Spaced KD, which distills the student model by a teacher who pretrains s-steps ahead of the student. This paper demonstrates theoretically that Spaced KD produces flatter loss landscapes, and proves experimentally the superior performance of Spaced KD in bot... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We provide a point-to-point response as follows. We hope you may consider this a sufficient reason to raise the score. If you have any further questions, please let us know.
**Q1: The additional overhead that Spaced KD incurs compared to vanilla KD.**
The co... | Summary: The paper introduces Spaced Knowledge Distillation (Spaced KD), a novel method drawing on the biological spacing effect to enhance generalization in online and self-knowledge distillation by inserting intervals between teacher and student training steps. It makes notable contributions: firstly, the bio-inspire... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We provide a point-to-point response as follows. We hope you may consider this a sufficient reason to raise the score. If you have any further questions, please let us know.
**Q1: ImageNet-1K results are less comprehensive in Table 7.**
We would respectfully... | Summary: This paper proposes a space KD strategy, which is inspired by spacing effect in biological learning and memory. Overall, the experiments verified the effectiveness when comparing the proposed space KD with self KD(Zhang et al., 2019). However, there are many KD methods been proposed in recent years, it missing... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We provide a point-to-point response as follows. We hope you may consider this a sufficient reason to raise the score. If you have any further questions, please let us know.
**Q1: Review, comparison, and compatibility experiment on latest KD methods.**
Follo... | null | null | null | null | null | null | null | null |
Learning Initial Basis Selection for Linear Programming via Duality-Inspired Tripartite Graph Representation and Comprehensive Supervision | Accept (poster) | Summary: Linear Programming (LP) is fundamental to numerous real-world applications, driving significant investment and research into improving the Simplex method, a widely used algorithm for solving LPs. Over decades, various heuristics have been developed to enhance solver efficiency, one of which is choosing an opti... | Rebuttal 1:
Rebuttal: ### Fan et al. uses LIBSVM and STOCH datasets. Can the authors display their results on these two datasets as well?
> It would certainly be helpful to compare results across all datasets from the original work. However, since these two datasets were generated by the authors and are not publicly av... | Summary: The paper proposes a novel approach for selecting an initial basis for linear programming (LP) solvers using a duality-inspired tripartite graph neural network (GNN).
The following are the three main contributions:
- A tripartite graph representation for LP problems inspired by duality theory, which enhances ... | Rebuttal 1:
Rebuttal: ### limited discussion of computational overhead introduced by the more complex GNN architecture
### How does the computational cost of the tripartite GNN compare to the bipartite model, and how does this balance with the solver time savings? It would be valuable to include runtime comparison of t... | Summary: This paper proposes a GNN-based approach for learning initial basis selection in LP, aiming to accelerate the simplex method. Inspired by LP duality, the authors introduce a tripartite graph representation to better capture problem structure. Additionally, they design new loss functions to improve basic variab... | Rebuttal 1:
Rebuttal: ### The abstract states, "a closer initial basis does not always result in greater acceleration," but later mentions "achieving high prediction accuracy." It is unclear what accuracy refers to in this context—closeness to the optimal basis or actual solver acceleration.
### Similarly, the introduc... | Summary: The paper proposes a new Graph Neural Network model for predicting the initial basis in the simplex method for solving linear programming (LP) problems. They use a tripartite graph that includes a global node and also nodes for dual variables, in addition to nodes for constraints and primal variables in a bip... | Rebuttal 1:
Rebuttal: ### Comparison with other warm-start heuristics. The paper mentions a few non-ML methods for basis selection. It would be important to include them as a baseline, though previously probably has shown they are no better than the bipartite GNN.
> Thanks for your suggestion! We will incorporate thes... | null | null | null | null | null | null |
Towards Escaping from Class Dependency Modeling for Multi-Dimensional Classification | Accept (poster) | Summary: The paper proposes an approach to multi-dimensional classification (MDC), named DeCOupling Multi-dimensional classification (DCOM). Different from most MDC methods which explicitly model class dependencies through classifier chains or probabilistic graphical models (PGMs), DCOM captures partial class dependenc... | Rebuttal 1:
Rebuttal: We want to express our sincere gratitude for your invaluable comments and suggestions. According to your comments in *Claims And Evidence*, *Experimental Designs Or Analyses* and *Other Strengths And Weaknesses*, we summarize the following questions.
The point-to-point responses are given as follo... | Summary: This submission proposes a feature augmentation approach for multi-dimensional classification (MDC). I think the key motivation is to seek a set of augmented features $\mathbf{Z}$ to fulfill the partial conditional independence (6). In theory, it might work thanks to the notion of conditional independence of r... | Rebuttal 1:
Rebuttal: We want to express our sincere gratitude for your invaluable comments and suggestions. The point-to-point responses are given as follows:
-**Q1: I couldn't find details of the encoding network. If I missed some part of the paper, could you give me a pointer? Otherwise, could you add it in the nex... | Summary: In this paper, the authors propose a new method called DCOM to avoid class dependence modeling in multi-dimensional classification tasks. DCOM introduce an additional estimation of the gap between the joint probability and the product of marginal probabilities. Empirically, the authors verify the effectiveness... | Rebuttal 1:
Rebuttal: We want to express our sincere gratitude for your invaluable comments and suggestions. The point-to-point responses are given as follows:
- **Q1: Is it possible to design a strategy and dynamically adjust the coefficient of the three loss terms?**
A1: Thanks to the comments. It is indeed possibl... | Summary: This paper mainly focuses on multi-dimensional classification tasks and points out that existing works mainly focus on designing effective class dependency modeeling strategies but fail to solve the intercoupling of multiple classes. To solve this problem, this paper proposes a method, Dcom, to identify a late... | Rebuttal 1:
Rebuttal: We want to express our sincere gratitude for your invaluable comments and suggestions. The point-to-point responses are given as follows:
- **Question 1: Could you please provide more explanations for the assumptions mentioned in Eq. (3)?**
Answer for Q1: Eq.(3) serves as a sufficient yet non-ne... | null | null | null | null | null | null |
Stochastic Forward–Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets | Accept (poster) | Summary: This paper tackles an important practical issue: how to train diffusion models without directly accessing large volumes of clean (and potentially copyrighted) data. The authors propose a novel method called Stochastic Forward–Backward Deconvolution (SFBD). The approach begins with pretraining on a small set of... | Rebuttal 1:
Rebuttal: Thank you very much for the review. Below are our responses to your comments:
**Q1.** Regarding the non-trivial gap when $|\mathbf{u}|$ is large in Prop 2.
**A1.** Thank you for this insightful comment. We agree that the bound appears to grow with $\|\mathbf{u}\|$, and we would like to clarify ... | Summary: This work put forward a new training framework, dubbed Stochastic Forward–Backward Deconvolution (SFBD), a method for training diffusion models on noisy datasets while mitigating data memorization and copyright concerns.
Moreover, the theoretical analysis demonstrated that training solely on corrupted data is ... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our paper. Below are our responses to your comments:
**Q1.** How many training iterations are required?
**A1.** In most of our experimental settings, SFBD converges within four iterations. (see [[link](https://shorturl.at/I71Vq)] for results from additional iter... | Summary: This paper addresses the challenge of training diffusion-based generative models using datasets that are intentionally corrupted with noise to mitigate concerns around memorization and copyright infringement.
However, the authors show that in practice, the convergence rate for learning from noisy samples is ... | Rebuttal 1:
Rebuttal: Thank you very much for your comments. We will first clarify the distinction between our framework and standard denoising methods, and then address your comments point by point.
**How our method differs from a standard denoising algorithm.** SFBD alternates between denoising samples and fine-tun... | Summary: The authors consider the problem of training diffusion models with a small set of clean data and a large set of noisy data. This follows a line of recent works on developing techniques for training diffusion models under corruption in the training set. The main finding of this work is that without clean data p... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for the detailed feedback.
Before addressing specific points, we would like to clarify that the remark stating "the benchmarking of this work is unfair/incomplete" is, in our view, **inaccurate**. Specifically,
1. We were not aware of the missing baseline [... | null | null | null | null | null | null |
Diffusion Counterfactual Generation with Semantic Abduction | Accept (poster) | Summary: The authors proposed a method that incorporates semantic abduction in diffusion models for the preservation of exogenous noise in counterfactual generations. The inference is conducted with a CFG-style amortised, anti-causally guided DDIM sampling. Results are shown on three datasets: Morpho-MNIST, CelebA-HQ a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and are encouraged by their comments that semantic abduction is “interesting and intuitive”. Below, we address the major points raised:
> **“Semantic Abduction” and Novelty**
Refer to our response to reviewer vbtG16 for details regarding the ... | Summary: This paper explores diffusion models for counterfactual image generation by incorporating semantic abduction to enhance high-level semantic identity preservation causal consistency. The authors propose a structural causal model (SCM)-based framework that integrates diffusion models for counterfactual reasoning... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments regarding our framework’s “promising applications in medical imaging and causal reasoning tasks” with “well-designed” experiments and our integration of existing concepts in a “novel way”.
> **Scalability and Computational Costs**
Like most diffus... | Summary: This paper studies the image counterfactual generation problem using diffusion models. Specifically, the authors propose a suite of deep causal mechanisms, spatial mechanism, semantic mechanism, and anti-causal mechanism, for tractable counterfactual generation with respect to composition, reversibility, and e... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and are pleased that the paper was found to be “written exceptionally well with clear intuitions” and that the experiments “are extensive and show that the proposed abduction mechanisms are quite effective”. Below we address weaknesses and questions:... | Summary: The paper “Diffusion Counterfactual Generation with Semantic Abduction” explores the use of diffusion models for counterfactual image generation, a task that requires maintaining identity, visual fidelity, and causal consistency. The authors argue that while diffusion models have achieved state-of-the-art synt... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and for recognising the significance of our work. We're encouraged by reviewers acknowledging our framework as "an important step towards using generative models for more controlled, interpretable, and robust counterfactual reasoning". We also ap... | null | null | null | null | null | null |
Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs | Accept (poster) | Summary: The paper "Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs" introduces POST (Privacy Of Soft-prompt Transfer), a framework that enables efficient and privacy-preserving soft prompt tuning and transfer for Large Language Models (LLMs).
Key Contributions:
Privacy-Preserving Soft Prompt Tuning: PO... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the constructive feedback! We address the main points one by one below:
>**A more detailed discussion on public dataset selection risks**
We investigate the effect of the choice of public data in Table 15 in Appendix (section D.1), where we consider both public datasets... | Summary: This paper addresses the problem of inefficiency and privacy risks in soft prompt tuning on LLM provider-hosted models. Therefore, the authors propose a prompt transfer framework called POST, that first distill a smaller model to better match the teacher behavior, then prompt tuning on the small model using pr... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive feedback! We address the main points one by one below:
>**The quality of the transferred prompt heavily depends on the distilled model.**
We fully agree with the Reviewer. The influence of compressed model size (number of layers) on performance and compute ... | Summary: This paper proposes POST (Privacy Of Soft-prompt Transfer), a framework designed to efficiently and privately transfer soft prompts for adapting large language models (LLMs) to private downstream tasks. The core innovation involves locally tuning soft prompts on a small, distilled model derived from a larger L... | Rebuttal 1:
Rebuttal: We appreciate the Reviewer's insightful comments! Below, we address each comment in detail:
>**More rigorous exploration into why certain public datasets work better for transfer than others; Additional clarification or justification for the choice of public datasets.**
We thoroughly analyzed th... | Summary: This paper proposes POST (Privacy Of Soft-prompt Transfer), a framework designed to enable efficient and privacy-preserving transfer of soft prompts between LLMs. It has three major steps:
* Knowledge Distillation: The LLM provider first distills a smaller local model from the original large LLM.
* Local Promp... | Rebuttal 1:
Rebuttal: We appreciate the Reviewer's constructive review and address each point as follows:
>**The problem setting itself is unrealistic to me.**
Soft prompts are exposed via public APIs, such as NVIDIA NeMo, as we highlight in the abstract and introduction of our paper. Could the reviewer kindly clari... | null | null | null | null | null | null |
Towards Understanding Catastrophic Forgetting in Two-layer Convolutional Neural Networks | Accept (poster) | Summary: This paper presents a theoretical analysis of catastrophic forgetting in continual learning using a two-layer neural network model. It constructs a multi-view data structure consisting of task-specific, general, and random features and examines the learning dynamics of these features. The study identifies two ... | Rebuttal 1:
Rebuttal: We thank reviewer ZF1H for the insightful comments. We find the reviewer misunderstanding our framework, and we show a illustration for our framework in **Q3** in our response to reviewer iCej. We the answer the questions as follows.
**Q1**. Ambiguity in Feature Representation
**A1**. Yes, both ... | Summary: Continual learning is an important area in machine learning, and catastrophic forgetting is the most important problem in continual learning. Despite the empirical efforts on suppressing forgetting in continual learning, the theoretical understanding of catastrophic forgetting remains less studied, especially ... | Rebuttal 1:
Rebuttal: Thanks for your positive comments. We answer the questions as follows.
**Q1**. The authors only study the case of two-task continual learning.
**A1**. Our framework can be naturally extended to $M$-class CL. Our framework can be extended to $M$-task scenarios. A key modification is to assume $m+... | Summary: This paper investigates the catastrophic forgetting (CF) phenomenon in continual learning (CL) for convolutional neural networks (CNNs) from the perspective of training dynamics. The paper considers a multi-view data model with four components: task-specific features, general features, random features, and bac... | Rebuttal 1:
Rebuttal: We thank reviewer g9HH for the careful reading and insightful suggestions. We will fix typos and improve our paper based on these suggestions in our revised version. We answer the major concerns as follows:
**Q1**. The main implication of the theoretical results remains unclear.
**A1**. Intuitiv... | Summary: The paper aims to provide a theoretical understanding of catastrophic forgetting in a two layered CNN using a multiview data model to understand the learning dynamics of different features. The key theoretical insights are that task specific features have a larger signal than the general features and task spec... | Rebuttal 1:
Rebuttal: We thank the thoughtful and insightful review, and we answer the concerns as follows:
**Q1**. Generalizing the simple theoretical model to complex settings:
**A1**. Studying the learning dynamics in a two-layer CNN and multi-view model is a widely used approach for understanding deep learning. D... | null | null | null | null | null | null |
Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks | Accept (poster) | Summary: This paper tackles meta RL, where the goal is find optimal policies for MDPs that have the same state-action space but different transition and reward functions. This paper is similar to prior works like PEARL but additionally uses extra loss terms/objectives to improve performance. Their main ideas are:
- Vir... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive comments. We would like to address the main concerns raised, particularly regarding (1) computational cost, (2) the validity of state regularizaion method, and (3) t-SNE -based visualization.
**1. Computational Cost:** (common ... | Summary: In context-based meta RL, an agent is trained to assign a context vector to an MDP. It learns to identify the context, and to take actions (policy) based on it. This paper investigates the setting where an agent is tested in an MDP not seen during training.
Several ideas are introduced to improve generalizatio... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive comments. We would like to address the main concerns raised, particularly regarding (1) comparison with baselines handling distributional shift, (2) the scope and practicality of our method, (3) clarification on reward functions... | Summary: This paper introduces Task-Aware Virtual Training (TAVT), a novel meta-reinforcement learning algorithm designed to enhance generalization to out-of-distribution (OOD) tasks. The numerical results demonstrate that TAVT significantly enhances generalization to OOD tasks across various MuJoCo and Meta-World env... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive comments. We would like to address the main concerns raised, particularly regarding (1) the novelty of our method, (2) benchmark coverage, and (3) computational cost.
**1. Novelty of Our Approach:**
We appreciate the reviewer’... | Summary: This paper provides a new method for meta Reinforcement Learning that focuses on generalizing to novel tasks. The paper provides a method for generating virtual tasks that takes in to account task characteristics. A metric-based task representation is learned using the Bisimulation metric. Furthermore task pre... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive comments. We would like to address the main concerns raised, particularly regarding (1) issues of clarity, and (2) technical questions.
**1. Clarity Improvements in Weaknesses Part:**
We sincerely thank the reviewer for pointi... | null | null | null | null | null | null |
Tracking The Best Expert Privately | Accept (poster) | Summary: The authors develop differentially private algorithms for prediction with expert advice under dynamic regret (tracking the best expert) across three adversary types: stochastic with shifting distributions, oblivious, and adaptive. They achieve sub-linear regret bounds for all cases, notably providing explicit ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We address the reviewer's concerns and questions below and hope that they will reevaluate their score accordingly.
> No experimental results
We acknowledge the reviewer’s concern regarding the lack of experiments. However, our work is intentionally theor... | Summary: This work studies the online private learning problem, in the setting of online prediction with experts. The main focus is the relaxed notion of dynamic regret, where the best expert of the baseline can change at most S times. The main result considers three different models of the adversary: shifting stochast... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and finding our techniques to be broad and interesting. We address the reviewer's concerns and questions below and hope that they will reevaluate their score accordingly.
> Computational inefficiency
This is a good question and an important dire... | Summary: The paper studies the dynamic regret in online learning with differential privacy. The paper considers three adversaries: shifting distributions, oblivious, and adaptive. This paper provides both lower bound and upper bound for three different adversaries. Finally, similar to static regret, This paper establis... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We address the reviewer's concerns and questions below and hope that they will reevaluate their score accordingly.
> Authors may give details of the proof for differential privacy of algorithms...
We will make sure to include theorems about basic privac... | Summary: This paper studies differentially private online learning in the context of tracking the best expert, a problem where an algorithm dynamically selects from a set of experts to minimize cumulative loss over time. The authors develop differentially private algorithms for this problem under three types of adversa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We address their concerns below and hope they will reevaluate their score accordingly.
> Is it possible to design an efficient algorithm that attains same-order regret bounds in this setting?
This is a good question and an important direction of future ... | null | null | null | null | null | null |
Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation | Reject | Summary: The paper provides an analysis in different convex settings of the stochastic Polyak step size SPS* and a version with momentum/iterate averaging they call Iterate Averaging Adaptive Method (IAM). The paper analyzes convergence rates in both non-smooth and smooth settings, and shows that both SPS* and IAM are ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
thank you for your thoughtful review, and doing the expert work of checking our proofs and appendix, and catching the type-O on Lines 307-308. Regarding the weakness you highlighted
>It is not clear that the theory is relevant for the experiments in Figure 1 as the setting is ... | Summary: This paper analyzes the stochastic gradient method with Polyak stepsizes, termed
SPS*, for minimizing the expectation of a family of convex functions $f_{\xi}$.
The method adaptively selects stepsizes based on knowledge of $f_{\xi}(x^*)$,
the function value at the optimal point. The authors demonstrate that SP... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. It is a pleasure to have an expert reviewer. Below we address each of your questions.
>**Q1** mistakes …in Table 1
The rates we report throughout are the anytime rates of each method. We do not report the *fixed horizon* rate. We will now include the followin... | Summary: This paper presents a new anytime convergence result for stochastic polyak step-size which requires access to the loss for every training batch evaluated at a solution. They also provide a momentum variant of this method and prove convergence rates with respect to the final iterate. Lastly, they provide a smal... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comprehensive review. We have divided our answers in **M**ain **Q**uestions, and **Q**uestions
> **MQ1** anytime convergence formally?
We say $x_t$ has an anytime convergence rate if, once parameters are fixed, there exists a real function $r:[0,+\infty) \to [0,+... | Summary: This paper presents some optimization methods with idealized stochastic Polyak step (SPS) sizes and their convergence analyses under the convexity conditions of objective functions. The analyses are based on the following two methods:
[Section 2] Stochastic gradient descent (SGD) with SPS (Theorem 2.1) under ... | Rebuttal 1:
Rebuttal: Dear reviewer, the main issue you have raised is regarding our assumption in our theorems that the loss is convex. We will divide our answer into two parts. In our first part, we will show how to relax global convexity to a form of local convexity. Indeed, this extension follows straightforwardly ... | Summary: This paper studies an idealized method of stochastic Polyak step size called SPS*, assuming access to the optimal loss value that can be achieved by a solution.
It is shown that SPS* achieves the optimal convergence bound for globally Lipschitz function, and also enjoys anytime convergence with rate $O(1/\sqrt... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review, and appreciating our work.
**Q1**. It would be helpful if further discussions are provided on how to approximate the optimal loss value in general settings, and how the convergence analysis would be affected by the approximation error.
**A1**. T... | null | null | null | null |
Open-Det: An Efficient Learning Framework for Open-Ended Detection | Accept (poster) | Summary: In this paper, the authors present a novel learning framework for open-ended detection. Open-ended detection consists in detecting objects that are not known a priori. The proposed framework is based on four main components: 1) an open object detector, 2) a prompt distiller, 3) an object name generator and 4) ... | Rebuttal 1:
Rebuttal: Dear reviewer JNJc,
We sincerely appreciate your valuable feedback and have incorporated all suggestions into the manuscript for the next version.
**Q1: OED is close to OWD. It could ... section.**
We have carefully checked the OWD [1], which enables the detector to label unknown objects as ''... | Summary: This paper introduces **Open-Det**, which addresses the inefficiencies of previous open-ended methods, including slow training and high memory consumption. Open-Det achieves improved efficiency and performance through:
1. Enhancing the object detector,
2. A vision-language (VL) alignment module,
3. A VL... | Rebuttal 1:
Rebuttal: Dear reviewer AMke,
We thank your time and feedback. However, we respectfully disagree with comments (Q1 to Q7) and the assertions（the omitted citations and misleading novelty) for the following reasons:
**Q1: The ..., yet ... cite ... novelty.**
(1) **Reviewer's Factual Error:** The reviewer c... | Summary: This paper presents Open-Det, a novel and efficient framework for Open-Ended Object Detection (OED), addressing the issues of slow convergence, low efficiency, and reliance on large-scale datasets found in existing models like GenerateU. Open-Det consists of four core components: the Object Detector (ODR), the... | Rebuttal 1:
Rebuttal: Dear reviewer E2VT,
We deeply appreciate your suggestions and will include them into our revisions.
**Q1: Training free VL-SAM .. better.**
A direct performance comparison requires careful consideration of **model scales**:
* **VL-SAM**: VLM: 17B, LLM: 7B, SAM: 0.632B, totaling ``24.632B``
* ... | Summary: This paper proposes a new framework for open-ended detection. To address the problem of alignment between vision queries and text embeddings, the authors adopts a Bidirectional Vision-L;anguage Alignment module to obtain alignment score and uses a masked alignment loss to supervise the alignment beween queires... | Rebuttal 1:
Rebuttal: Dear reviewer EV8Z,
We sincerely appreciate your time and constructive suggestions. We have carefully integrated your suggestions into the manuscript and will include these updates in the next version.
**Q1: (1) Can the proposed ... more training data? (2) Comparing to ... improvement. It would... | null | null | null | null | null | null |
Reducing Tool Hallucination via Reliability Alignment | Accept (poster) | Summary: This paper discusses how LLMs suffer tool hallucinations, which can cause task failures and higher costs. It defines these errors as two main types: picking the wrong tool or using a tool incorrectly. The paper introduces RelyToolBench, a set of specialized tests and new metrics to measure and reduce such issu... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and valuable feedback. We provide our responses below:
# Response about Weaknesses
Thank you for recognizing the novelty of our framework. We clarify our key contributions as follows:
### (1) A systematic evaluation framework for tool hallucinations.
Our framewor... | Summary: LLMs can solve diverse tasks by using external tools. Investigating tool hallucination is crucial because hallucination can cause problems that are hardly recognized. This paper introduces RelyToolBench to evaluate tool hallucinations, including four types of hallucinations. Authors also propose Relign to redu... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and valuable feedback. We provide our responses below:
# Re. to Experimental Designs or Analyses
- (1) All experiments share the same hyperparameters unless stated otherwise. In all methods, the learning rate is set to 1e-5 for SFT and 1e-5 for DPO to ensure consi... | Summary: This paper addresses tool hallucination in LLMs, where models incorrectly select or misuse tools. It introduces RelyToolBench for evaluation and Relign, a reliability alignment framework that enables LLMs to defer, clarify, or adjust tool use. Using SFT and DPO, Relign reduces hallucinations, improves task rel... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and valuable feedback. We provide our responses below:
# Re. to Evaluation Criteria & Q4
### (1) Why did we introduce LLM-based evaluation?
Our evaluation process for tool hallucinations consists of both rule-based evaluation and LLM-based evaluation. Rule-based ... | Summary: The paper focuses on the problem of tool hallucinations. The authors categorize tool hallucinations into selection and usage hallucinations, providing a framework for targeted improvements. A new benchmark is introduced to capture these categories (RelyToolBench) and model fine-tuning methods (Relign) are expl... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and valuable feedback. We provide our responses below:
# Response about RePR
We apologize for any confusion caused by the description of the metric. In fact, **the model is not penalized multiple times**. RePR represents the proportion of tasks that were originally... | null | null | null | null | null | null |
Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning | Accept (poster) | Summary: This paper studies how to optimize a single static Spectral Risk Measure (SRM) for an RL agent, leveraging a distributional RL framework. By extending the state to track discounted returns and employing quantile-based updates, the authors propose a two-tier algorithmic approach:
Inner Optimization: Fix an app... | Rebuttal 1:
Rebuttal: Thank you for recognizing the strengths of our work. We address your questions below.
**Q1:**
We thank the reviewer for this insightful comment. We agree that an explicit comparison with [1] would improve the clarity of our contribution. While both works share methodological components, such as ... | Summary: This paper proposes a distributional RL algorithm for optimizing the Spectral Risk Measures (SRM, with CVar as a special case) of return in discounted MDPs.
Specifically, this paper makes use of a variational representation of SRM proposed by Kusuoka, thus transforming the optimization of SRM into a two-layer... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and for recognizing the strengths of our work. We are glad that you found our paper well-written. We address your questions below.
**Q1:**
Thank you for pointing this out. Theorem 2 of [1] and Theorem 3.1 of [2] provide a detailed discussion on the equivalenc... | Summary: This work focuses on the risk-sensitive RL, i.e., maximizing the return and managing worst-case scenarios. As distribution RL considers the return distribution, there are several works applying it to the risk-sensitive RL. Extending the widely used risk metric CVaR to Spectral Risk Measures (SRM), this work pr... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and for recognizing the strengths of our work. We are glad that you found our proposed method novel. We address your questions below.
**Missing References:**
Thank you for highlighting the missing references. The first reference is already discussed in the pa... | null | null | null | null | null | null | null | null |
Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition | Accept (poster) | Summary: This paper proposes a High-fidelity Earthquake Groundmotion Generation System (HEGGS), which is a new solution for generating realistic earthquake-induced ground motion waveforms. The major contributions contain: 1) a new benchmark using openly available seismic datasets, with paired observed waveforms of the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive feedback. Below, we address the key concerns raised.
> Motivation for using a diffusion model
In seismic synthesis, high fidelity is most important and desirable. However, waveform in high frequency (>1Hz) is very hard to generate especiall... | Summary: This paper presents a diffusion model for synthesizing earthquake ground motion data at a specific location with minimal conditioning information. Unlike previous approaches that rely on extensive geological and seismological data, the proposed method leverages the inherent information embedded in ground motio... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful feedback. Below, we address the main concerns raised.
> the scale of the metrics can vary significantly depending on the magnitude
In implementation, each metrics includes the waveform normalization process in default. Here are the details:
- P/... | Summary: They tackle the task of generating earthquake-caused ground motion waveform (something I know nothing about). The propose a end-to-end generation method
Claims And Evidence: - novel method: HEGGS (I don't have the expertise to say)
- HEGGS demonstrate its superior performance using earthquakes from North Amer... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s time and effort in evaluating our work. We acknowledge that the reviewer may not have direct expertise in seismology, and we appreciate their openness about this. Nevertheless, we are grateful that they found our use of diffusion models in this context intere... | null | null | null | null | null | null | null | null |
Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling | Accept (poster) | Summary: The paper introduces a novel framework called *SpaceFormer*, which models molecular as three-dimensional “images” by discretizing 3D space into grid units. This approach captures the spatial context of molecular more comprehensively than traditional methods that treat them as discrete collections of atoms. The... | Rebuttal 1:
Rebuttal: Due to word limit, we will not quote the original text but refer to the title of the reviewer's feedback content.
**1. Regarding Claims And Evidence**
Thank you for your insightful feedback. In our paper, we have provided substantial experimental evidence to validate our claims. For instance, ou... | Summary: This paper introduces SpaceFormer, a Transformer-based molecular pretrained representation (MPR) model that explicitly encodes the 3D space surrounding molecules, going beyond traditional atom-only representations. The method discretizes the 3D space into grid cells, applying adaptive grid merging for efficien... | Rebuttal 1:
Rebuttal: **1. Regarding "Minor weaknesses include no detailed runtime analysis and limited discussion on tasks where improvements were small (e.g., BBBP)." and "Why does SpaceFormer slightly underperform on BBBP compared to simpler baselines? Could it indicate an overfitting or a limitation in your method?... | Summary: This paper tackles molecular pretrained representation (MPR) by arguing that previous methods which focused solely on atom positions and types miss crucial physical context in the surrounding 3D space. Motivated by physical principles (e.g., the presence of electron densities and electromagnetic fields), the a... | Rebuttal 1:
Rebuttal: **1. Regarding "The experimental downstream tasks should also include widely used benchmarks such as QM9 and MD17, which are standard in molecular property prediction. Moreover, since grid/mesh-based methods are particularly adept at capturing long-range interactions [1, 2], the inclusion of the M... | null | null | null | null | null | null | null | null |
Optimal and Practical Batched Linear Bandit Algorithm | Accept (poster) | Summary: The paper introduces BLAE, a new algorithm for batched linear bandit problems. The central challenge in batched learning is balancing exploration and exploitation within discrete batches, as frequent updates may not be feasible in real-world applications. BLAE combines arm elimination and regularized G-optimal... | Rebuttal 1:
Rebuttal: Thank you for your time. However, with all due respect, we are very concerned by the current assessment. We sincerely hope to establish some common grounds with our response.
---
### **On Computational Cost**
To our best knowledge, all prior works on batched linear bandits have not provided theor... | Summary: This paper studies the batched linear bandit problem. The authors propose a new algorithm for this problem and show that this achieves near optimal regret. Compared to other algorithms which also achieve near optimal regret in this problem, the authors claim that their proposed algorithm is more computationall... | Rebuttal 1:
Rebuttal: We appreciate your time and review. However, with all due respect, there appear to be some misunderstandings, which we sincerely hope to clarify in our response.
---
### **Batched Learning Setting**
In the batched linear bandit setting, the learner chooses both the number of batches and when to ... | Summary: In this paper, the authors study the linear bandit problem under limited adaptivity, known as the batched linear bandit. They propose a novel batched algorithm that integrates arm elimination with regularized G-optimal design, achieving the minimax optimal regret in both large-K and small-K regimes for the fir... | Rebuttal 1:
Rebuttal: We sincerely thank you for reviewing our paper and providing valuable feedback. We appreciate your recognition of our work and your constructive comments. Below, we address each of your comments and questions in detail:
---
### **Performance in the Large $K$ Regime**
We thank the reviewer for ra... | Summary: The paper tackles the well-studied batched linear bandits problem and provided tight regret bounds in the small and large arms (K) closing the gap in the upper and lower bounds. Experiments show that they perform well on simulated datasets over the existing algorithms.
Overall: The paper is clearly laid out w... | Rebuttal 1:
Rebuttal: We sincerely thank you for taking the time to review our paper and for providing thoughtful and valuable feedback. We greatly appreciate your recognition of our work and your constructive comments. Below, we address each of your comments and questions in detail:
---
### **Experimental Results in ... | null | null | null | null | null | null |
TabNAT: A Continuous-Discrete Joint Generative Framework for Tabular Data | Accept (poster) | Summary: This paper proposes a model named TabNAT for tabular data synthesis (and imputation). The core idea is to use a diffusion model to generate all columns (of a row) with continuous values together, and a transformer to generate the categorical columns. Experimental results on multiple benchmark datasets show the... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback on our manuscript. We appreciate the time and effort invested in providing these valuable comments, which will help improve the quality of our paper. Below, we address the points raised:
### Choice of Experimental Datase... | Summary: The paper proposes a Non-Autoregressive transformer-based generative model for tabular data (TabNAT). It can handle both continuous and discrete columns. It uses a (non-causal) transformer to encode the input and masks to perform any-order training.
The overall modeling paradigm is quite similar to TabDDPM (K... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful assessment of our manuscript. We appreciate the recognition of our paper's strengths, including its clear writing, promising results, and well-supported claims regarding statistical fidelity, utility, and privacy preservation. Below, we address ... | Summary: The paper focuses on unconditional tabular data generation and conditional missing data imputation tasks. Considering the heterogeneous nature of tabular data, which includes both discrete and continuous variables, the proposed method utilizes a diffusion model to parameterize the conditional distributions for... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough review and constructive feedback on our manuscript. We have addressed the raised concerns as follows:
### The suitability of diffusion modeling for continuous columns in tabular data
We agree that this is an important consideration that deserves c... | Summary: The paper tackles the task of generating tabular data. It claims that the application of current autoregressive models for generating tabular data is limited due to two challenges:
1) tabular data contains heterogeneous types, whereas autoregressive next-token (distribution) prediction is designed for discrete... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive feedback on our manuscript. We appreciate the time and effort spent reviewing our work, and we believe the suggestions will significantly improve the quality of our paper. Below, we address each point raised by the reviewer.
##... | null | null | null | null | null | null |
HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation | Accept (spotlight poster) | Summary: The paper introduces HealthGPT, a medical vision-language model that unifies visual comprehension and generation through heterogeneous knowledge adaptation. Key contributions include H-LoRA, a parameter-efficient fine-tuning method that decouples task-specific knowledge via independent low-rank plugins, a hier... | Rebuttal 1:
Rebuttal: Thank you for your positive and insightful review, which greatly helps to refine the manuscript's quality and clarity. Below are our point-by-point responses.
## 1. Additional Details
We appreciate the reviewer’s insightful questions and welcome the opportunity to further elaborate on the releva... | Summary: This paper introduces a medical vision and generation framework based on LVLM. This pipeline consists of autoregressive generation, hierarchical visual perception, and heterogenous knowledge adaption. The authors provide experiment results on different datasets and compare with other baselines.
Claims And Evi... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. Your insights have helped us clarify HealthGPT's contributions in unified architecture design, task modeling efficiency, and real-world application potential. Below are our point-by-point responses.
# 1. Contribution Statement
**1.1 Core Contribution**
We a... | Summary: The paper introduces HealthGPT, a unified Medical Large Vision-Language Model (Med-LVLM) designed to integrate medical visual comprehension and generation. The proposed model employs innovative methods including Heterogeneous Low-Rank Adaptation (H-LoRA), hierarchical visual perception (HVP), and a three-stage... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s recognition of our work. Your recognition of the model’s originality, innovation, and effectiveness greatly encourages us and has helped improve HealthGPT. Below are our point-by-point responses.
## 1. Unified LVLM Comparison
Thank you for the suggestion. Be... | Summary: This paper presents HealthGPT, a medical large vision-language model (Med-LVLM) that unifies both comprehension and generation of medical images. The key contributions include:
1. Heterogeneous Low-Rank Adaptation (H-LoRA);
2. Hierarchical Visual Perception ;
3. Three-Stage Learning Strategy;
4. A curated... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed and thoughtful feedback, which greatly helped us refine our experiments and methodology, and better highlight HealthGPT’s innovation and practical value. Below are our point-by-point responses.
## 1. VQA Analysis
**1.1 Comparison with LLaVA-Med++**... | null | null | null | null | null | null |
Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing | Accept (poster) | Summary: This paper proposes an improved non-autoregressive peptide sequencing model incorporating a structured protein sequence curriculum learning strategy.
Claims And Evidence: The claims made in the submission are supported by clear and convincing evidence.
Methods And Evaluation Criteria: This field has an open ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments. We have addressed all of your concerns with point-by-point responses below:
> This field has an open benchmark dataset, and the method should be tested on it.
Thank you for pointing this out. We apologize for missing this benchmark da... | Summary: The work proposes a non-autoregressive transformer (NAT)-based curriculum learning framework to deduce the animo acid sequence from tandem mass spectrometry signals. The input peak signals are encoded with an transformer encoder, which is then used to predict the probability of tokens on each position. The dec... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their time and effort in providing thoughtful comments and feedback. Below, we provide a point-by-point response to your questions and concerns.
> Is the masking performed on $y'$ or $A$. Also, how is the CTC objective defined with masking?
The masking is pe... | Summary: This paper proposed a new method, named RefineNovo, that improves non-autoregressive transformers (NATs) for peptide sequencing by introducing a curriculum learning strategy and a self-refinement module (including a "difficulty annealing" strategy). It reduces training failures by over 90% and enhances sequenc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and for recognizing the novelty and effectiveness of our proposed method. We sincerely appreciate your effort and provide below point-by-point responses to your comments.
> The self-refining module has been explored for peptide generation to some ex... | null | null | null | null | null | null | null | null |
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series | Accept (poster) | Summary: The authors propose a factor model-based method to generate possible Granger causal hypotheses about time series data given by a time-dependent (i.e., non-static) data-generating process. The generated hypotheses are adjacency matrices and summarize possible causal effects among the observed system variables a... | Rebuttal 1:
Rebuttal: We are very grateful for the reviewer's thoughtful feedback and suggestions on how we can further improve our paper. We respond to their concerns below.
Question 1: "with how many seeds were the experiments conducted?"
Each dataset was curated using a fixed random seed ('9999' for Synthetic Syst... | Summary: This work introduces a deep generative factor model that uses weighted superposition of static graphs to achieve dynamic causal graph modeling and capture nonlinear relationships. For complex neural activity in the brain, the method does well in detecting time-varying interactions between neural variables. Thr... | Rebuttal 1:
Rebuttal: Question 1 (Parameter selection and training cost):
Most of our hyperparameters were chosen via grid-search, though we did identify some simple equations relating the $\rho$ and $\eta$ parameters in Eq. 5 to other parameters in the model and/or dataset that we used to adapt those specific parame... | Summary: This paper proposes a novel hypothesis generation framework for dynamic causal graphs in neuroscience, aiming to improve the efficiency of causal discovery in time-dependent systems. The authors introduce REDCLIFF-S, a model designed to estimate state-dependent causal interactions by combining:
1. Factor-base... | Rebuttal 1:
Rebuttal: We are very grateful for the reviewer's thoughtful feedback. We respond to their concerns below.
Question 1: "Are there any theoretical guarantees on when REDCLIFF-S produces unique causal structures? Does the factorization step introduce ambiguities in the inferred causal graphs?"
As we allude ... | Summary: The submission propose a method for causal discovery (at least this is what I understand).
Summarizing it seems that the proposed approach is another implementation of a non-linear Granger causality model with some particular choice of implementing architecture and loss function.
There are no theoretical res... | Rebuttal 1:
Rebuttal: Question 1: Our Goal
Our goal is to reduce the space of hypotheses that must be tested before a causal relationship is successfully identified. We allude to this in our abstract, but will make this more explicit in our final draft.
Question 2: Our Assumptions
We only assume our data consists of... | null | null | null | null | null | null |
Non-Asymptotic Length Generalization | Accept (poster) | Summary: Summary:
The paper analyzes the minimum input-length required for learning length generalizable DFA, CFG and C-RASP programs and provides non-asymptotic bounds for the minimal length inputs to differentiate different DFA and C-RASP programs.
Strengths:
The proof idea is quite interesting: the authors design... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer YXKG for their time, detailed comments, and constructive criticism.
**Author statement:** The submitted paper contained an error in the proofs, which we discovered only after submission. We’ve revised the proofs and sent the revised draft to the AC. Here are how the re... | Summary: The paper provides new theoretical results related to criteria on a training set such that an idealized learner can learn a function that exhibits length generalization.
The paper considers an idealized learner, termed the Minimum-Complexity Interpolator. The learner is defined with respect to a hypothesis cl... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer CEK9 for their time, detailed comments, and constructive criticism.
**Author statement:** The submitted paper contained an error in the proofs, which we discovered only after submission. We’ve revised the proofs and sent the revised draft to the AC. Here are how the re... | Summary: The paper considers the question of the theory of length generalization for abstract classes of models, with in mind applications to extending the theory underlying important questions about prevalent models such as transformers.
Here length generalization denotes the property that if the model is perfectly a... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer 5Dit for their time, detailed comments, and constructive criticism.
**Author statement:** The submitted paper contained an error in the proofs, which we discovered only after submission. We’ve revised the proofs and sent the revised draft to the AC. Here are how the re... | Summary: Taking up recent interest in length generalization of transformers and similar models, this paper studies length generalization for three formal models: DFAs, CFGs, and two-layer C-RASP, with the latter being the main technical contribution. C-RASP has recently been proposed as a formal model of some computati... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer qTut for their time, detailed comments, and constructive criticism.
**Author statement:**
The submitted paper contained an error in the proofs, which we discovered only after submission. We’ve revised the proofs and sent the revised draft to the AC. Here are how the re... | null | null | null | null | null | null |
Hybrid Spiking Vision Transformer for Object Detection with Event Cameras | Accept (poster) | Summary: This paper proposes a novel hybrid spiking vision Transformer model (HsVT) for event-driven object detection. Combining the advantages of ANN and SNN, the multi-stage spatial-temporal feature extraction module is designed, and LSTM and STFE are used to process temporal information respectively, reducing the nu... | Rebuttal 1:
Rebuttal: # Q1: Why choose 4 blocks? Have you tried other hierarchies (such as 3 or 5 blocks)?How does the model performance change if the number of blocks is increased?
Re: Thank you for your insightful question. In our experimental design, we chose to use 4 blocks based on the following considerations:... | Summary: This paper introduces Hybrid Spiking Vision Transformer (HsVT), a novel architecture combining Artificial Neural Networks (ANNs) and Spiking Neural Networks (SNNs) for event-based object detection. The key contributions include: A hybrid spatial-temporal framework integrating ANN-based self-attention modules (... | Rebuttal 1:
Rebuttal: # Q1: provide the new collected event dataset public links
Re: Thank you for your suggestion. To support the SNN community, we have made our event dataset publicly available: [Dropbox Link (Anonymous)]( https://www.dropbox.com/scl/fo/1bnsydo3yj5922tlsquo9/AE77sOynJY0-GAeSNBSqFJk?rlkey=1mqpsm658ou... | Summary: This paper introduces a Hybrid Spiking Vision System, combining Spiking Neural Networks (SNNs) with deep learning architectures to enhance computational efficiency and reduce power consumption. The study explores the effectiveness of this hybrid approach in visual tasks and presents experimental results demons... | Rebuttal 1:
Rebuttal: # Q1: Some theoretical justifications
Re: We have implemented a hybrid SNN + LSTM structure, where Leaky Integrate-and-Fire (LIF) neurons process temporal information, while convolutional layers and Batch Normalization are used for spatial feature extraction.
The input x is concatenated with the ... | null | null | null | null | null | null | null | null |
Approximating Latent Manifolds in Neural Networks via Vanishing Ideals | Accept (poster) | Summary: The authors propose neural networks motivated by the vanishing ideals, called VI-nets. They also investigate to obtain parameter-efficient representations of the vanishing ideal generators. They investigate the performance of the VI-nets theoretically and empirically.
Claims And Evidence: The prposed method i... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate your feedback and will address your comments in detail.
> In pratical situations, how to set the hyperparameters such as L' and S is not clear for me. Do you have any ideas about this?
Thank you for your question. Our paper addresses this from two perspec... | Summary: The paper aims to improve parameter efficiency of neural networks by truncating a pretrained neural network, finding the polynomial generators of the vanishing ideal of (a finite sample of) the latent manifold, and transforming the latent space into a linearly separable feature space. The proposed method impro... | Rebuttal 1:
Rebuttal: Thank you for your review, we appreciate the thorough feedback. Let us address your comments in detail.
> However, the evaluation is performed only on the CIFAR dataset. It would be better if the authors could also include other commonly used benchmarks for image classification.
In general, we a... | Summary: This paper introduces efficient methods for approximating vanishing ideals of class "manifolds" in a latent space of a neural network classifier, and uses the resulting approximate vanishing ideals to replace later layers of a truncated classifier neural network with a linear combination of polynomial features... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful comments. We have grouped related concerns and clarifications below and revised our paper accordingly.
**Overall Clarifications and Algebraic Geometry Context**
We used the manifold hypothesis to motivate our work, but we agree that currently, some of t... | Summary: Present a method to analyse latent manifolds - object representations at intermediate layers of deep networks - by finding a small set of polynomials which zero out at one object but not at other objects. This effectively trunfaces the deep network and replaces the head with a single, simple, non-linear layer ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for acknowledging the contributions of our work.
> Those "learning guarantees" are usually vavous bounds, and thus, an improved upper bound on something may or may not lead to improved performance.
We agree that these bounds are often vacuous. In that sen... | null | null | null | null | null | null |
Interpreting the Repeated Token Phenomenon in Large Language Models | Accept (poster) | Summary: This paper attempts the explain the phenomenon of "repeated tokens" with the behaviours of "attention-sink" in LLMs. Firstly, the authors find that besides the first token, the repeated tokens also have high attention scores and large hidden states norms. Then, the authors identify the neurons that contribute ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Next, we address the reviewer's questions and concerns:
__“I feel there is a gap between "repeated tokens could diminish the influence of the preceding prefix" and "repeated tokens could lead to training data leakage". I am looking forward to the... | Summary: This paper studies the "repeated token divergence" phenomenon in LLMs, where models fail to accurately repeat a single token when instructed to do so. The authors provide a mechanistic explanation linking this behavior to attention-sinks (where the initial token in a sequence receives disproportionately high a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Next, we address the reviewer's questions and concerns:
__“The paper shows that not all tokens can effectively induce attention-sinks when repeated (Figure 5). This unexplained variability suggests additional factors at play that aren't fully cap... | Summary: - This paper discusses the repeated token divergence issue, links it to a possible LLM phenomenon named attention sink and proposes a solution for it.
- This paper takes a Mechanistic Interpretability approach, analyzing the underlying mechanism of attention sinks in LLMs, and shows empirical evidence of this... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Next, we address the reviewer's questions and concerns:
__“Rope affects the queries and keys, not keys and values, this claim: "thus symmetry between all tokens is preserved" shall be further clarified. Rope will affect attention output according... | Summary: This paper focuses on the repeat phenomenon in LLMs. The authors view this problem from the view of attention sink. They show that the first attention layer marks the initial token, and the later MLP neuron amplifies its hidden state, creating an attention sink. A patching method is proposed to mitigate this e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Next, we address the reviewer's questions and concerns:
__“The main weakness of this work is that the task considered is very rare in the realistic application. Most users of LLMs will not let LLMs repeat some words. The lack of motivation discou... | null | null | null | null | null | null |
D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples | Accept (poster) | Summary: This paper addresses the issue the effectiveness of preference learning (e.g. Direct Preference Optimization (DPO)) for text-to-image diffusion model is limited by the visual inconsistency between the sample that aligns better with the text and the sample that aligns less with the prompt, making it difficult t... | Rebuttal 1:
Rebuttal: Thanks a lot for your time and efforts in reviewing our paper. We are happy that you think the intuition makes sense. In addition, we feel pleased that you approve our experiment design.
Below are responses to your concerns and suggestions. Please let us know if you require any further informati... | Summary: This paper introduces **D-Fusion**, a method for improving text-image alignment in diffusion models via **Direct Preference Optimization (DPO)**. It identifies a key challenge in prior DPO approaches—**visual inconsistency** between well-aligned and poorly-aligned images—which hinders the model's ability to le... | Rebuttal 1:
Rebuttal: Thanks for providing these detailed and helpful comments! We appreciate that you think our method well-motivated and approve of our experiments.
Responses to your concerns are as follows. We provide additional experimental results **at this link** (https://anonymous.4open.science/r/ICML-25-Rebutt... | Summary: This paper introduces D-Fusion, a novel approach to addressing the misalignment between generated images and their corresponding text prompts. D-Fusion constructs DPO-trainable visually consistent samples to mitigate the visual inconsistency present in previous DPO methods. First, the mask-guided self-attentio... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our paper and providing well-structured comments. We are delighted that you think our paper well-written and approve of our motivation and experiments.
Below are our responses to your concerns. Additional experimental results can be found... | Summary: This paper focuses on improving DPO for fine-tuning diffusion models. The key problem identified is visual inconsistency in training samples, which hampers the effectiveness of reinforcement learning-based fine-tuning methods like DPO.
Claims And Evidence: The claim that "D-Fusion works across different RL al... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and providing a lot of suggestions! We appreciate your recognition of our paper's clarity and your time reviewing the paper and supplementary materials in detail.
Responses to your suggestions are as follows. We provide additional experimental results **at this l... | null | null | null | null | null | null |
SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior | Accept (poster) | Summary: This paper presents an interpretable and steerable prompt moderation method called SafetyAnalyst. SafetyAnalyst categorizes the content being reviewed in a hierarchical manner using a harm tree and a benefit tree, based on attributes such as stakeholder, action, and effect. The classification results from thes... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful feedback. Below, we address each of the concerns raised.
## Response to the Reviewer's Questions
1. **Inference Time of WildGuard**
In Section 3.2 (line 307), we report that WildGuard’s inference time is 0.22 seconds per prompt, not 5.9 seconds. W... | Summary: This paper introduces a framework called SAFEANALYST for the safety moderation of AI behavior. Specifically, SAFEANALYST constructs a “harm-benefit tree” using chain-of-thought (CoT) reasoning and then aggregates the leaf nodes through a transparent model with interpretable weight parameters based on harm-bene... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful feedback. Below, we address each of the concerns raised and outline proposed updates to our manuscript.
1. **Moderation Target Limited to Prompts**
We chose prompt safety moderation for our primary evaluation because prior work has established many relev... | Summary: This paper introduces SafetyAnalyst, a novel AI safety moderation framework. Unlike existing AI safety systems that rely on opaque deep learning classifiers, SafetyAnalyst employs chain-of-thought (CoT) reasoning to construct a structured harm-benefit tree, which systematically evaluates the potential harms an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback.
1. **Knowledge Distillation Methods**
We acknowledge the importance of discussing knowledge distillation methods and justifying our approach. We propose adding the following to the manuscript:
- Related Work: Review curren... | Summary: This paper presents an interpretable guardrail model that is calibrated on safety preferences from human annotations and also allows steer-ability to align with various human safety values and preferences. The work uses an extended version of the taxonomy of Harmful actions that are generated in accordance wit... | Rebuttal 1:
Rebuttal: 1. **Bias Towards AIR-Bench and SORRY-Bench in Evaluation**
See response to Rev. PiLF on Evaluation in Table 2, where we highlight that AIR-Bench and SORRY-Bench include diverse, underrepresented risk categories, making their strong weighting in the overall performance score a benefit rather than ... | null | null | null | null | null | null |
MVA: Linear Attention with High-order Query-Keys Integration and Multi-level Vocabulary Decomposition | Accept (poster) | Summary: This paper introduces MVA (Multi-level Vocabulary decomposition Attention), a linear attention mechanism built upon high-order query-keys integration theory and multi-level vocabulary decomposition. The authors unify popular linear attention methods under their theoretical framework and try to address the perf... | Rebuttal 1:
Rebuttal: We sincerely appreciate the insightful feedback and guidance provided. Based on your suggestions, we have made substantial improvements to our work.
1. **Additional Experiments with MVA-SW on Qwen2.5-14B-1M and Qwen2.5-32B**
We have conducted further experiments with MVA-SW on Qwen2.5 models... | Summary: The paper introduces MVA, a novel linear attention mechanism to bridge the gap with softmax attention. The proposed approach uses:
1. High order QK integration theory - to integrate high and low frequency information to improve the approximation of softmax attention.
2. Multi-level Vocabulary Decomposition - t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful feedback and valuable support, which have greatly benefited us. Below, we provide further clarifications based on your suggestions. Thank you again for your guidance.
1. **Scaling to Larger Models**
Due to resource and time constraints, we are unable t... | Summary: The paper introduces MVA (Multi-level Vocabulary Attention), that uses high-order Query-Key (QK) integration with a recursive multi-level vocabulary decomposition to approximate Softmax attention. The paper combines sparse and linear attention, MVA achieves good performance upon finetuning on Mistral-7B.
Clai... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We sincerely appreciate the time and effort you have taken to review our work. Your insightful suggestions have significantly helped us refine the clarity and presentation of our paper. Below, we address each of your concerns in detail.
### 1. Clarification o... | Summary: This work proposes an efficient Transformer alternative, which unifies existing architectures in the field GSA/GLA/MetaLA/Based with theory and combines their strengths to achieve performance improvements over strong baseline approaches at model scales of up to 7B parameters. The theory examines whether existi... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. Your suggestions have helped us refine our methodology and validation, especially regarding dataset selection. Below, we provide responses to each of your concerns.
## 1. Essential References Not Discussed:
1. **Conclusion:** We emphasise the importance of ... | null | null | null | null | null | null |
Fast Incomplete Multi-view Clustering by Flexible Anchor Learning | Accept (poster) | Summary: This paper proposes FIML in this work for fast incomplete multi-view clustering. It simultaneously considers graph construction, anchor learning and graph partition in a unified framework, in which these parts boost each other for improving the effectiveness and efficiency for datasets with large scales. To be... | Rebuttal 1:
Rebuttal: Q1: The summarization of the objective function of the proposed FIML after Eq. (3) should be given.
A1: Thanks for the comment! The related summarization of the objective function for the proposed FIML after Eq. (3) is shown in the following. Then graph construction, anchor learning and graph par... | Summary: This paper proposes a novel fast incomplete multi-view clustering method for the data with large scales, termed Fast Incomplete Multi-view clustering via flexible anchor Learning (FIML), where graph construction, anchor learning and graph partition are simultaneously integrated into a unified framework for eff... | Rebuttal 1:
Rebuttal: Q1: The reason why each entry in the anchor graph describes the similarity between data sample and anchor.
A1: Thanks for the comment! The reason why each entry in the anchor graph Z describes the similarity between data sample and anchor can be explained by the fact that dimension of the anchor ... | Summary: This paper proposes a novel fast incomplete multi-view clustering method for the data with large scales, termed Fast Incomplete Multi-view clustering by flexible anchor Learning (FIML), where graph construction, anchor learning and graph partition are simultaneously considered in a unified framework for fast i... | Rebuttal 1:
Rebuttal: Q1: Compare the proposed FIML with the most related works in Introduction.
A1: Thanks for the comment! The existing works most related to FIML are FPMVS-CAG and SMVSC. FPMVS-CAG jointly performs anchor selection and subspace graph construction into a framework. Then the two processes can be negot... | Summary: This paper proposes a novel fast incomplete multi-view clustering method for large scale data termed as FIML, where graph construction, anchor learning and graph partition are simultaneously considered in a unified framework for fast incomplete multi-view clustering. The relation between anchor graph and simil... | Rebuttal 1:
Rebuttal: Q1: The authors should add more recent works for comparison in the experiment.
A1: Thanks for the comment! We have added a recent method for comparison in the experimental section, i.e., OMVCDR [a].
[a] One-Step Multi-View Clustering With Diverse Representation, 2024
The clustering results of O... | null | null | null | null | null | null |
Improving Zero-Shot Adversarial Robustness in Vision-Language Models by Closed-form Alignment of Adversarial Path Simplices | Accept (spotlight poster) | Summary: In this work, the authors focus on a topic, robustness of VLM (CLIP). The authors propose to solve a challenge in adversarial fine-tuning, and propose to use Taylor expansion to enlarge the dicision space, which can make the model more robust.
Claims And Evidence: The claims are supported by some math derivat... | Rebuttal 1:
Rebuttal: # Response to Rev. srgi
We thank Rev. for the constructive feedback. **Kindly also note additional Theory/Results in Resp. to Rev. EyNB**.
### **1. Abstract.**
Thank you. We have removed now math notations:
```
Vision-Language Models (VLMs), e.g., CLIP, excel at zero-shot classification due to... | Summary: This paper aims to enhance the robustness of CLIP for zero-shot image classification.
It emphasizes that existing defense methods often disregard intermediate adversarial samples along the trajectory, which are found to be beneficial in this study.
The proposed method, AdvSimplex, uses an efficent method to s... | Rebuttal 1:
Rebuttal: # Response to Rev. XcU1
Thank you for the constructive feedback. **Kindly also note additional Theory/Results in Resp. to Rev. EyNB**.
### **1. Compare Cost. 10x speedup.**
- Below are **training times for FARE, TeCoA, PMG-FT**.
- The *10x speed-up* is for our closed-form "infinite" sampling from ... | Summary: This paper proposes a new adversarial fine-tuning method for VLMs that enhances zero-shot adversarial robustness by leveraging adversarial simplices formed from a clean image and consecutive intermediate adversaries along the gradient ascent path. The proposed method employs a Taylor expansion of the model's p... | Rebuttal 1:
Rebuttal: # Response to Rev. td6r
Thank you for the constructive feedback. **Kindly also note additional Theory/Results in Resp. to Rev. EyNB**.
### **1. Theoretical derivations well-grounded/experiments support claims.**
Thank you. We have also a generalization of our Theorem 3.1 to simplices with more ver... | Summary: This paper tackles the challenge of making vision-language models (VLMs) more robust to adversarial attacks in zero-shot classification. Existing methods try to improve robustness by sampling adversarial examples along the decision boundary, but they come with high computational costs. To address this, the aut... | Rebuttal 1:
Rebuttal: # Response to Rev. EyNB
We thank Rev. for constructive feedback.
### **1. I checked key proofs...they appear to be correct.**
Thank you.
### **1b.** Why $\gamma$ disappears in Eq. 7? (for Rev. XcU1)
- In Eq. 5 \& 6, elements after the sum can be written as $(\sqrt{\alpha}+\frac{1}{2}\sqrt{\beta... | null | null | null | null | null | null |
Discovering Latent Causal Graphs from Spatiotemporal Data | Accept (poster) | Summary: This manuscript analyzes spatiotemporal data with the goal of finding a temporal structural causal model of underlying latents. This framework could be used for causal discovery of earth systems. The manuscript includes proof of identifiability of reasonable assumptions, and provides empirical results on rea... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thorough and insightful review.
**Predictive Performance Evaluation**: Our evaluation is consistent with the literature of causal discovery. As noted or observed in prior works [1, 2, 3] causal discovery/representation learning differs from prediction/forecasting. Caus... | Summary: The paper presents a causal discovery method called SPACY for structural causal models over spatiotemporal data embedded in grids. An identifiability theory is developed, and experiments compare to both structure-learning and causal representation learning models on both synthetic data and climate data.
Claim... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and for pointing us to the relevant prior work [1]. We will revise the manuscript to include this citation in the discussion of RBF kernels.
We envision SPACY being applicable across various fields—for instance, in neuroscience for analyzing brain i... | Summary: This paper proposes a spatiotemporal causal discovery framework named SPACY based on the method of variational inference. SPACY introduces spatial kernel functions, aggregates spatially adjacent points by utilizing these kernel functions, maps the observed time series to latent representations, and discovers t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and thoughtful feedback.
**Noise at latent vs. observation space**: We clarify that there are **two separate additive noises** at latent and observation space respectively. The Gaussian noise assumption applies only to the **observation space**, not... | Summary: The submission present SPACEY a spatio-temporal causal model which is specified over a reduced latent representation.
The authors, show that (in the continuous grid case) the model is partially identifiable, moreover the same insights should apply to the finite grid case, when the number of locations is large... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback.
We agree that both DCM and SPACY formulate the problem of inferring causal relationships between latent variables as a multi-level Bayesian model. However, they differ significantly in their assumptions and approaches.
DCMs aim to infer the c... | null | null | null | null | null | null |
Compute or Load KV Cache? Why Not Both? | Accept (poster) | Summary: The authors proposed a simple but effective method dealing with the kv cache prefilling: utilizing both the GPU computation and IO loading to get the free lunch of both, maximizing the loading speed with zero cost.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Evaluation is mainly based on the l... | Rebuttal 1:
Rebuttal: Thank you for your interest in Cake and for providing insightful feedback. We value your constructive comments and are pleased to address your concerns.
In our original experiments, we adopted the inter-token-latency-optimized configuration in vLLM v0.6.2, where the max number of batched tokens i... | Summary: The paper introduces a KV cache loading system called Cake, that optimizes computation and I/O in parallel to speed up LLM inference. It uses bidirectional scheduling for efficient resource use and adaptive scheduling to handle varying workloads. Evaluations show Cake cuts TTFT by 2.6×, making it a practical s... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and kind words about our paper. Below, we address your suggestions regarding the interaction of Cake with Prefill-Decode (PD) disaggregation. We will add the following discussion to the revised version.
* Cake is compatible with Prefill-Decode Disaggregation ... | Summary: This paper introduces Cake, a hybrid KV cache loading system that leverages both I/O resources (for loading) and computational resources (for re-computation). The authors observe that both I/O-only approaches (e.g. LMCache) and compute-only approaches (e.g. vLLM) fall short in practice in terms of minimizing T... | Rebuttal 1:
Rebuttal: Thanks for your insightful feedback, and we are glad to address your concerns.
1. **Dynamic I/O, workloads, etc.** Cake’s parameter-free design allows it to automatically adapt to dynamic conditions, such as fluctuating network bandwidth or GPU performance, as discussed in Part 3 of Section 4. Thi... | Summary: This paper introduces Cake, a novel KV cache loading system that optimally utilizes both computational and I/O resources in parallel, with a bidirectional scheduling strategy, and an adaptive scheduling mechanism. The proposed method can be seamlessly integrated in existing methods, with better TTFT.
Claims A... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and feedback on our paper. We are pleased to address your concerns regarding the compatibility and performance of our proposed method, Cake, with other acceleration techniques and system-level optimizations. We will add the following discussion to the revised... | null | null | null | null | null | null |
Losses for Deep Probabilistic Regression | Reject | Summary: The paper claims to be guided by the question: "What is the best probabilistic regression method?". In particular, it focuses on "direct" methods which turn supervised learning into probabilistic regression by using a different loss function. The authors summarize their contributions as introducing a taxonomy ... | Summary: The paper discusses losses for deep probabilistic regression. The authors identify the gap in the literature of scattered knowledge on deep probabilistic regression across various domains and propose a taxonomy of the method to unify the knowledge in that area. Based on that, they identify easy-to-achieve new ... | Summary: The authors began their research by observing that despite using probabilistic regression across various fields, there was no unified overview of these methods. First, they analyze various Probabilistic regression approaches and organize them from the perspective of "closed-form expressions of the CRPS of piec... | Summary: The paper offers a summary of probabilistic regression methods, primarily focusing on so-called "direct methods" in the supervised regime. The review discusses at length the strictly proper scoring rules: Continuous Ranked Probability Score and the Negative Loglikelihood. Various probabilistic methods are disc... | null | null | null | null | null | null | ||||
Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows | Accept (poster) | Summary: An alternative formulation of the p-Wasserstein distance is introduced and it consists in a constrained L^p minimisation problem for a given latent distribution. This alternative formulation allows the authors to both i) directly solve the Wasserstein primal minimization problem via stochastic gradient decent ... | Rebuttal 1:
Rebuttal: Dear reviewer,
thanks for taking the time to review our submission and for the kind appreciation of our contribution. We have carefully addressed all your comments below. According to the ICML review guidelines, we are not allowed to submit a revised manuscript at this point of the review proces... | Summary: This paper introduces a new way to compute optimal transport maps and Wasserstein barycenters using conditional normalizing flows.
Claims And Evidence: By order of appearance:
1. Correctness of OT and barycenter problems: I have doubts on the barycenter derivation, as well as doubts on the construction of th... | Rebuttal 1:
Rebuttal: Dear reviewer,
thanks for the very constructive feedback! Apologies for our terse replies (due to character limit).
**1. Can you make a plot between true and estimated $W_2$ distance?**
See here for Gaussian OT ($d=64$): https://pasteboard.co/jk8v0aZgFTom.png. We will add such pictures.
**2. ... | Summary: The authors propose a new method for finding Wasserstein-2 barycenter via Conditional Normalizing Flows (CNF) as well as computation of Optimal Transport (OT) maps from input distributions to the barycenter. The key advantage of the method is minimization of the primal OT problem by invertible pushforward bije... | Rebuttal 1:
Rebuttal: Dear reviewer,
please find our replies below.
**1. The authors do not discuss limitations of the proposed method.**
We agree that discussing the limitations is very important. We propose to add discussions of the following limitations:
1. Alg. 2 is suitable only for Wasserstein-2 barycenters.... | Summary: The paper proposes a new algorithm to compute the Wasserstein barycenter of a set of distributions and, by extension, the optimal transport between any pair of the given distributions. The method rests on a new representation of the barycenter objective in terms of conditional normalizing flows. Since this obj... | Rebuttal 1:
Rebuttal: Dear reviewer,
we have carefully addressed all your comments below. According to the ICML review guidelines, we are not allowed to submit a revised manuscript at this point of the review process, but below we discuss in detail the edits we will make based on your suggestions in the camera-ready ... | null | null | null | null | null | null |
Dueling Convex Optimization with General Preferences | Accept (poster) | Summary: This paper considers the problem of dueling bandits with general preferences, where the preference model between two decisions (called the transfer function) is not specified to some specific choices. The most technical challenge in this problem is the estimation of gradients since only the preference feedback... | Rebuttal 1:
Rebuttal: > “The guarantee of Theorem 2 only holds for some unknown decision generated in the optimization process. To find it out, additional operations are needed, which will lead to extra query complexity.”
You are right to notice that, due to the challenging feedback model, the nature of our convergenc... | Summary: This work studies convex optimization with dueling feedback and general transfer functions. The main contribution is an algorithm with $\epsilon^{-4p}$ convergence rate for smooth and convex functions and $\epsilon^{-2p}$ convergence rate for smooth and strongly-convex functions, where $p$ is the minimal degr... | Rebuttal 1:
Rebuttal: > “Are there any example of concrete problems in practice where p>1?”
First, we should note that even for $p=1$ our results constitute the first convergence bounds for convex optimization with (approximately) linear transfer functions.
Next, to your question, it is worth recalling what the trans... | Summary: This paper proposes an algorithm for the setting of dueling convex optimization, with a broad class of transfer functions.
Claims And Evidence: The claims are well supported.
Methods And Evaluation Criteria: The proposed methods make sense.
Theoretical Claims: I checked the proof for the non-strongly-convex... | Rebuttal 1:
Rebuttal: > “This work could be improved by justifying the algorithm design with experiments”
This is primarily a theoretical work in optimization with partial feedback, and our main goal is to understand the fundamental achievable convergence bounds in this setting. This is also the focus of much of the p... | null | null | null | null | null | null | null | null |
PhySpec: Physically Consistent Spectral Reconstruction via Orthogonal Subspace Decomposition and Self-Supervised Meta-Auxiliary Learning | Accept (spotlight poster) | Summary: The paper presents PhySpec, a novel method for hyperspectral image (HSI) reconstruction from RGB images. It addresses the "colorimetric dilemma" where existing methods fail to consistently reproduce ground-truth RGB from predicted HSI, compromising physical integrity. PhySpec uses orthogonal subspace decomposi... | Rebuttal 1:
Rebuttal: # Response to Reviewer yejW:
Thank you very much for your constructive comments and suggestions. We will try our best to address your concerns here.
### _Q1: Computational complexity and inference time of the meta-auxiliary testing phase._
**A1:** Thanks for your suggestion. We provide the infer... | Summary: PhySpec is presented in this paper. This is a method that attempts to reconstruct hyperspectral images from RGB images, ensuring the HSI reproduces the original RGB colours. It does it by learning its colour response, handling varying illumination, and uses orthogonal subspace decomposition achieve physical co... | Rebuttal 1:
Rebuttal: # Response to Reviewer Rrdb:
Thank you very much for your constructive comments and suggestions. We will try our best to address your concerns here.
### _Q1: Direct comparison with a plain U-Net baseline._
**A1:** Thanks for your suggestion. In fact, we have provided such a comparison in Table 2... | Summary: The authors propose a method of generating hyperspectral image data from RGB data. In addition to estimating the hyperspectral data, the authors also explicitly estimate the camera sensitivity curves and the illumination spectrum as latent variables in the network. As an auxiliary task, the camera sensor sensi... | Rebuttal 1:
Rebuttal: # Response to Reviewer Dmx9:
Thank you very much for your constructive comments and suggestions. We will try our best to address your concerns here.
### _Q1: Description of training and testing datasets._
**A1:** Thanks a lot for your concerns. Just as you envisioned, we use the training set of ... | null | null | null | null | null | null | null | null |
DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization | Accept (poster) | Summary: This paper introduces DreamDPO, an optimization-based framework for text-to-3D generation that aligns the generated 3D content with human preferences. Instead of relying on absolute quality scores from reward models, DreamDPO employs Direct Preference Optimization (DPO). The method operates in three iterative ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive reviews. We provide our responses below.
> **Q1:** Quantitative comparisons with DreamReward.
**A1:** Thanks for your suggestion. We conduct a quantitative comparison on GPTEval3D to evaluate human preference, including the correction of numbers. In specifi... | Summary: This paper proposes DreamDPO, an optimization-based method to better align 3D generation with human preferences for text-to-3D generation. In detail, it constructs pairwise examples to formulate a reward loss function for preferred images with lower loss and less preferred images with higher loss. It conducts ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive reviews. We provide our responses below.
> **Q1:** The computational efficiency comparison to other baselines.
**A1:** Thank you for your question. We summarize the computational cost of our method compared with other text-to-3D generation baselines as foll... | Summary: This paper introduces DreamDPO, an optimization-based framework for text-to-3D generation. The authors propose to integrate human preferences into the generation process through direct preference optimization. The authors claim that the key innovation is leveraging pairwise comparisons to guide optimization in... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive reviews. We provide our responses below.
> **Q1:** Ablation studies on the score gap threshold in the 3D setting.
**A1:** We present the ablation study of the score gap threshold $\tau$ in the 3D setting (see [Figure 2-1](https://imgur.com/a/XdVx2sn)). The ... | Summary: The paper introduces the DreamDPO framework, designed to enhance text-to-3D generation by aligning generated content more closely with human preferences. Traditional methods often fall short due to their heavy reliance on precise evaluations, restricting flexibility and applicability. DreamDPO employs an optim... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive reviews. We provide our responses below.
> **Q1:** Dependence on External Models.
**A1:** DreamDPO is compatible with external models and rule-based reward metrics, such as image quality metrics (e.g., BRISQUE [1]) and 3D-consistency evaluation. By leveragi... | null | null | null | null | null | null |
Online Learning in the Random-Order Model | Accept (poster) | Summary: This paper studies online learning in the random order model, where the loss functions are fixed in advance but are presented in a random order. It has been known that the random order model lies between the stochastic model, where the loss functions are drawn from a fixed distribution, and the adversarial mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback about the paper.
**Importance of the BIRTHDAY-TEST.** In Section 3, we prove that there is no black-box reduction from random-order to i.i.d. That is, we cannot hope to prove a statement of the form: “Any algorithm with sublinear regret in the stoch... | Summary: - This paper studies online learning in the random order model. Here, the adversary can pick an arbitrary sequence of loss functions, but must randomly permute them before presenting them one at a time to the learning algorithm.
- They show that, in full generality, online algorithms with low-regret under sto... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and positive feedback about the paper.
**Paper organization.** Unfortunately, we had to move part of our results to the appendix in the interest of space. We thank the reviewer for the suggestion, and we will exploit the extra page in the camera ready (and... | Summary: This paper introduces a general framework, referred to as SIMULATION, that adapts stochastic (i.i.d.) learning algorithms to the random order model without significantly altering their finite-time performance guarantees. The core idea is straightforward: partition the time horizon into blocks of geometrically ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback about the paper.
**Adversarial algorithms in RO** As we mention in Section 1.1., and detail in Appendix B, there is a natural hierarchy between the i.i.d., random order, and adversarial input models. In particular, any learning algorithm for the adversaria... | Summary: The paper studies a general online learning setting where in every round $t \in [T]$ there is an unknown loss vector $\ell_t$, the learner needs to make a decision $x_t \in \mathcal{X}_t$, and incurs loss $\langle x_t, \ell_t\rangle$ (there might also be constraints that need to hold across the whole time hori... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and positive feedback about the paper.
Regarding the specific comments and suggestions proposed:
1. Yes, that is correct. We will rephrase the sentence to make it clearer.
2. As correctly stated by the reviewer, Follow-the-leader is no-regret o... | null | null | null | null | null | null |
Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations | Accept (poster) | Summary: This paper studies the problem of learning discrete latent variable causal structures from mixed-type observational data using the graphical criteria of tensor rank conditions. To handle continuous observed variables, the author proposes a discretization method that ensures the discretized data satisfy the ful... | Rebuttal 1:
Rebuttal: Thanks for your careful and valuable comments. We will respond to these issues point by point.
>[W] The Mixture Oracle method, a relevant baseline, is not included in the experimental comparison, which limits the evaluation.
**WQ1**: We have added experimental results using the Mixture Oracle as ... | Summary: When observational data contain both continuous and discrete variables, learning the causal structure among latent variables becomes a critical problem. Existing methods are often sensitive to parameter estimation. In this paper, the authors propose a statistically testable approach, the tensor rank condition,... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and suggestions and thank you for your positive assessment of our work.
>[W1] Estimating tensor rank in practice can be challenging, yet the paper does not provide sufficient discussion on this issue.
**W1A1**: As discussed in the Sec. 5 (Lines 402–404), we e... | Summary: This work proposes a causal discovery algorithm for some class of causal graph involving discrete latent variables and both discrete and continuous observed variables. The algorithm is essentially an extension of Chen et al. (2024) which uses rank tests on the probability tensors of observed variables to infer... | Rebuttal 1:
Rebuttal: Thank you for your careful review. We address each point below and have corrected the typos. Please feel free to reach out with any further questions (due to limited space).
>W1W2: ... realistic dataset to test their approach.
A: Please see Appendix H.1.
>W3: Novelty is between low and moderate... | null | null | null | null | null | null | null | null |
Learning the Electronic Hamiltonian of Large Atomic Structures | Accept (poster) | Summary: This work focuses on scaling Hamiltonian prediction to large periodic structures. While a common ML problem, Hamiltonian prediction has been challenging in large structures due to the quadratic scaling of the Hamiltonian. The authors propose a partitioning scheme to enable distributed computing and batching of... | Rebuttal 1:
Rebuttal: **Isolation of Contributions:** Our two main contributions not found in other works are:
1. An augmented partitioning approach that allows arbitrarily large graphs to be broken down into independent partitions that (1) maintain the connectivity of the full structure through virtual nodes/edges, (... | Summary: The author propose a graph partitioning strategies to localize message passing and new networks which leverages SO(2) convolution for predicting the Hamiltonian for large atomic structures. The author demonstrates its capabilities by predicting the electronic Hamiltonian of various systems with up to 3,000 nod... | Rebuttal 1:
Rebuttal: **Non-diagonal part of Hamiltonian beyond cutoffs:**
**The use of a cut-off radius is standard practice in Hamiltonian prediction literature of bulk structures [3-4], which exploits the nearsightedness of the Hamiltonian matrix.** **Appendix F** include two studies on the nearsightedness of Hami... | Summary: The paper proposes a new method for applying GNNs to learn the electronic Hamiltonian for atomic structures beyond the unit cell. The paper starts by introducing the use of GNNs for property prediction on fairly small size unit cell materials and molecules and motivates the need to capture more complex materia... | Rebuttal 1:
Rebuttal: **1. One model for each material system**: Yes, the models are trained for a specific material system, similar to most other literature ([4-8]). The current work does not demonstrate a universal Hamiltonian prediction model, though this can be achieved with a larger library of datasets, as shown b... | Summary: The authors propose a method based on SO(2) graph neural networks to learn the electronic Hamiltonian matrix in the structural relaxation process of inorganic materials. To address computational challenges associated with defective or large-scale crystalline structures, they introduce an efficient partitioning... | Rebuttal 1:
Rebuttal: **1. Application performance**
Using the predicted H as an initial guess for SCF iterations is not our main focus, and the reduction in iterations also depends on the convergence threshold. We instead focus on key downstream applications where the Hamiltonian matrix H is the final product needed.... | null | null | null | null | null | null |
Layer by Layer: Uncovering Hidden Representations in Language Models | Accept (oral) | Summary: The paper proposes a framework for analyzing representations throughout model layers. From the perspective of matrix-based entropy, they primarily measure properties like compression (e.g., prompt entropy), geometric smoothness (e.g., curvature), and augmentation invariance (e.g., LiDAR). They argue that for a... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer U8pM for the detailed feedback and for indicating openness to reconsidering the evaluation. We appreciate the positive comments on our work. We address your specific points below. Note, that You may also be interested in the new experimental results provided for Reviewe... | Summary: This paper introduces a unified framework for evaluating representation quality in language models. The framework is based on information theory, geometry, and invariance to input perturbations. The authors analyze how each layer balances information compression and signal preservation, challenging conventiona... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and feedback. We appreciate you recognizing the novelty in our unified interpretation and extensive evaluation. You may also find our new results relevant (details in responses to Reviewers 9n2s regarding generative tasks and ctdG regarding unsupervised layer... | Summary: This study investigates whether intermediate layers of language models offer more informative representations compared to the final layers. The authors propose a unified framework for assessing representation quality, employing seven evaluation metrics categorized into three groups: information-theoretic, geo... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 9n2S for the detailed review, positive feedback on our claims, methods, and writing, and constructive suggestions. We are encouraged by the reviewer's assessment of our work as engaging and scientifically significant.
We address the reviewer's comments and questions bel... | Summary: This paper investigates representation quality across different layers of large language models (LLMs), challenging the conventional wisdom that final-layer embeddings are optimal for downstream tasks. Through systematic evaluation on 32 tasks from the Massive Text Embedding Benchmark, the authors demonstrate ... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer ctdG for the detailed, insightful review and constructive feedback. We appreciate the positive assessment of our claims, methods, theory, and experiments. Below, we address the reviewer's comments and questions, incorporating clarifications and new results. You may also... | null | null | null | null | null | null |
LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos | Accept (poster) | Summary: This paper introduces LIGHTNINGDRAG, a high-speed, high-quality drag-based image editing framework that significantly outperforms existing methods in both efficiency and success rate. Unlike prior approaches that rely on Generative Adversarial Networks or large-scale diffusion models—often requiring over a min... | Rebuttal 1:
Rebuttal: Thanks for the insightful feedback and appreciations in our work. Please find our response to your questions below.
**Q1**: Architectural design similar to Wear-Any-Way.
**A1**: Thank you for pointing this out. While our architectural design indeed draws inspiration from prior literature, includ... | Summary: The paper presents a new approach, LightningDrag, for drag-style editing problem. LightningDrag features in significantly faster inference speed and good generalization. LightningDrag is trained by watching paired video frames. Finally, the authors showcase the performance of LightningDrag by comparing with ot... | Rebuttal 1:
Rebuttal: Thanks for the insightful feedback and appreciations in our work. Please find our response to your questions below.
**Q1**: Need discussion on differences with Magic Fixup
**A1**: We acknowledge that our approach shares with Magic Fixup the general idea of leveraging video data; however, we resp... | Summary: This paper presents Lightningdrag, a diffusion model trained on video data, enabling accurate and consistent drag-based edits within seconds, leveraging source noise prior and point-following classifier-free guidance for improved accuracy and consistency.
Claims And Evidence: 1. Lightningdrag formulates drag-... | Rebuttal 1:
Rebuttal: Thanks for the insightful feedback and appreciations in our work. Please find our response to your questions below.
**Q1**: Lacking in quantitative ablation studies.
**A1**: Thanks for the advice. We conduct the detailed quantitative ablation study as you suggested. Results are given below.
To ... | Summary: This paper proposes LightningDrag, a feed-forward diffusion model designed to achieve drag-based image editing. LightningDrag employs a point embedding network to encode drag instructions from users and preserves the image identity using an ID adapter and appearance encoder. Experiments demonstrate that Lightn... | Rebuttal 1:
Rebuttal: Thanks for the insightful feedback and appreciations in our work. Please find our response to your questions below.
**Q1**: Limited novelties in architectural design.
**A1**: As discussed in the related work section, although we take inspiration from some previous literature for architecture des... | null | null | null | null | null | null |
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks | Accept (poster) | Summary: The paper proposes an approach for the generation of verifiable input spaces for a neural network:
Instead of providing an answer on whether a given input space region is safe, the paper instead proposes to generate an input region for which the NN is verified. The paper claims that this technique can be used ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful feedback. We address your concerns below.
---
### Theoretical Claims
**#1:** The paper inconsistently uses $f^* > 0$ for satisfaction and $f < 0$ for violation.
We leverage $f > 0$ to ensure satisfaction and $f < 0$ to ensure violation—these ar... | Summary: In Neural Network Verification, the goal is to verify that a certain input-output relation holds. E.g. all inputs in some local neighborhood should have the same classification. This is a challenging (NP-hard) task. The authors propose a new technique that can be used to compute a *underapproximation* of the p... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. We address your comments and suggestions below.
---
### Clarifications on Claims
> **Claim Concern:** The statement that "LEVIS-beta captures the entirety of the verifiable space" is too strong, especially since the method depends on random ... | Summary: The paper aims to find verifiable input space for a NN, i.e., input region where no adversarial example exists.
Claims And Evidence: C1. A MIP based verification framework that provides maximum verifiable input ball around a center c.
E1. The paper provides a clear formalization of this claim in eq. 1a-1f.... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful and encouraging review. We are glad to hear that you found our contributions clear and innovative. Below, we respond to your comments and suggestions.
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### Comments on Claims and Evidence
> **Comment C2 / Evidence E2:** The conversion of the ReLU funct... | Summary: This paper presents an algorithm for computing inner-approximations of neural network preimages as a union of balls. The method is split into two sub-methods, one for maximizing an inner-approximating ball, and another for generating new balls to append to the overall approximation. Experiments are conducted o... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive review. Below, we address the key concerns and questions you raised.
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### Major Weaknesses
> **Weakness #1:** Theorem 4.1 critically assumes that the input space is convex...
**Response:**
Thank you for identifying this. Empirically, most probl... | null | null | null | null | null | null |
Causal Abstraction Inference under Lossy Representations | Accept (poster) | Summary: This paper introduces projected abstractions and an algorithm to compute them from a low-level SCM.
### Update after rebuttal
First of all apologies for an initial review that may have read harsh.
So I carefully read the other reviewers' opinions and the rebuttals. It didn't really bring much more clarity. M... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback and believe some misunderstandings may have led to a harsh evaluation. We respectfully ask for reconsideration based on the clarifications below.
> In general the paper is very difficult to read [...]
**Response:** We recognize that the reviewer’s primary co... | Summary: This paper introduces a new notion of causal abstraction called "projected abstraction" that extends causal abstraction theory to handle lossy representations—situations where multiple low-level interventions with different effects map to the same high-level intervention. The authors show how to construct proj... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and review. We are happy that the work was understood. To answer your concerns:
>The experimental section seems to be good to me, but it will be difficult for readers not familiar with NCMs. I would recommend trying to give the experimental section ... | Summary: This paper presents a theory of causal abstractions that generalizes them beyond the usual abstract invariance condition. It formalizes the idea of general causal abstractions through the concepts of partial SCM projections, soft interventions and generalized queries. An algorithm to construct these is general... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and valuable insight in our experimental analysis. We address your comments below.
> I would like to see a discussion of the limitations of the proposed theoretical framework, as well as well as future perspectives.
**Response:** Indeed, one of the ... | Summary: This work addresses the problem of causal abstractions, providing an intriguing approach that could extend traditional fine-grained causal applications to more general scenarios. It emphasizes higher-level causal relationships and inferences. A key contribution of this work is the relaxation of the function cl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and detailed suggestions. Addressing the reviewers points:
> The Abstraction Function, as defined in Definition 4, is required to satisfy certain assumptions [...]
**Response:** Yes, broadly, the paper is focused on a specific family of abstractions ... | Summary: Existing causal abstraction frameworks often struggle with lossy abstraction functions, where different low-level interventions produce distinct effects but map to the same high-level intervention. To address this, the authors propose projected abstractions, a new framework that extends previous definitions to... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful questions and positive review. We appreciate the multiple suggestions and will address them as follows:
> L110-118 could be put into Def 2, as I got confused by undefined notations when reading Def 2.
**Response:** We will adjust Def. 2 to accommodate thi... | null | null | null | null |
When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets | Accept (poster) | Summary: The paper rigorously proves that, when the training points are orthogonal and we use the MMSE denoiser, score flow converges to the vertices of a hyperbox, wherein the vertices of the hyperbox are the partial sums of training points. Similarly, they prove that probability flow converges to a vertex on this hyp... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive feedback and are glad that they find this a fascinating contribution. The interplay between memorization and generalization is indeed intriguing.
# Low-Noise Regime Details
Theoretically, the boundary of the low-noise regime is determined by the diff... | Summary: Commonly, diffusion models use the probability flow ODE to generate high-quality images. Obviously, the score (or reverse) SDE can be used to generate images as well. But the process for the score SDE is understandable, since it is derived from the forward SDE. However, it is unclear on the distinction between... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s kind words. We are glad to hear that the paper is presented in an accessible and engaging manner and that it was enjoyable to read.
# Application to Higher Dimensional Data
Indeed, we make simplifications to allow tractability. Please note, however, that while... | Summary: The authors analyze the behaviour of score and probability flow ODEs when the score is estimated using min-cost shallow neural networks and restricted datasets. Under these assumptions, they derive theoretical results which find that the stationary points of PF-ODE and score flow ODE consist of summation of tr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback.
# Connection to Semantic Sums
We are sorry for the lack of clarity. We did not aim to claim that the virtual points in orthogonal data must be equivalent to combinations of semantic components of images as observed in Stable Diffusion. This is o... | Summary: The authors analyze memoization of diffusion models in shallow relu networks. To analyze this, they consider the probability flow ode, and obtain additional results by introducing a simpler score flow. Under some assumptions, they show that both ODE's have stationary points corresponding to the training points... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive feedback.
# Orthogonal Dataset Choice
First, when $N>D$, it is indeed impossible for the dataset to be exactly orthogonal. However, for standard i.i.d. Gaussian data $x_n$, it is easy to show (e.g., using the analysis in Section 3.2.3 of [R1] and ... | null | null | null | null | null | null |
The Sparse-Plus-Low-Rank Quasi-Newton Method for Entropic-Regularized Optimal Transport | Accept (poster) | Summary: This paper is about solving entropic-regularized optimal transport problem via a quasi-Newton method. Entropic-regularized OT problem has applications in machine learning, but its solution is difficult to find. The paper first presents some theoretical analysis Hessian sparsification, which can be used to solv... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments. Please see our point-by-point responpses below.
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> Not always, as ... on machine learning.
> Biggest weakness is ... of this conference.
We sincerely appreciate your feedback on our manuscript. Your observation regarding the limite... | Summary: This paper proposes a Sparse-Plus-Low-Rank Quasi-Newton （SPLR）method for entropic regularized Optimal Transport (OT). The proposed algorithm improves the approximation of the Hessian matrix by adding a low-rank term, thus better solving the dense situation, effectively solving the entropic-regularized OT probl... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments. Please see our point-by-point responpses below.
---
### Weakness 1/Comment 1
We thank you for raising this important point about our complexity analysis. Let us clarify the theoretical aspects of our algorithm's computational efficienc... | Summary: The paper concerns faster solver for the optimal transport (OT) problem, by proposing a new type of Hessian approximation within the quasi-Newton iterative solvers. Building on previous work that used sparse Hessian approximations, the authors introduce low-rank approximation added to the sparse format (hence ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments. We give our point-by-point responses below.
---
### Weakness 1
Thanks for raising this question on motivation. Our approach is motivated by two key insights:
1. **Problem-specific Sparsity**: The entropic-regularized optimal transport... | Summary: The authors propose a quasi-Newton algorithm for solving entropic optimal transport (EOT) problems. The classical Sinkhorn algorithm enjoys linear convergence with a rate independent of the problem dimension but depends exponentially on the supremum norm of the cost function. There have been some recent works ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments. We give our point-by-point responses below.
---
### Thm.3.3 and Cor.3.4
1. We respectfully clarify that positive definiteness cannot be directly guaranteed via the Gershgorin circle theorem in this case. Specifically, for the sparsifie... | null | null | null | null | null | null |
KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors | Accept (poster) | Summary: The paper introduces a novel dataset containing DEL screen data for two different proteins, and benchmarks the performance of several supervised ML methods in the context of learning from the provided data.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: N/A.
Experimenta... | Rebuttal 1:
Rebuttal: Thank you for your careful review of our work and your valuable feedback.
**Dataset description**:
We appreciate your specific comments on making our paper more accessible to machine learning researchers. We will revise Figure 2 by removing unnecessary technical details that do not contribute to... | Summary: The KinDEL paper provides a new dataset of laboratory measurements related to DNA-encoded libraries (DEL) together with baseline models that analyze these data. The experiments used a library of 81 million small molecules interacting with two kinases: MAPK14 and DDR1. The authors further extended the dataset... | Rebuttal 1:
Rebuttal: Thank you for dedicating your time to review our paper and for your positive feedback and valuable suggestions. We will include the number of data points for the testing sets in the main text as recommended. Furthermore, we agree that evaluating the generalization of the models on external dataset... | Summary: This paper introduces a dataset of DNA encoded libraries for two kinases, MAPK14 and DDR1, with 81 million compounds. This dataset will be of use in drug discovery applications and modeling of related biological processes.
Claims And Evidence: The claims of the paper are well supported by the tables shown in ... | Rebuttal 1:
Rebuttal: We appreciate the Reviewer's time and effort in evaluating our paper and are pleased with the overall positive feedback. The only concern raised pertains to our choice of venue for publication. We would like to address this by outlining our reasons for selecting this machine-learning conference ov... | null | null | null | null | null | null | null | null |
Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale | Accept (poster) | Summary: This paper introduces a new agent environment, WINDOWS AGENT ARENA, for testing agents' capabilities within the operating system (exclusively focusing on Windows OS). The authors claim that WINDOWS AGENT ARENA is fully scalable and parallelizable, which enables rapid evaluations compared to other similar bench... | Rebuttal 1:
Rebuttal: Thank you for your comments/feedback. Please see our answers below.
**RE: Reasons for poor agentic performance and further insights for future work**
* Due to character limits in our reply, we refer to our responses to Reviewers 88jp and sjR1 where we detail reasons for agents’ general/common fa... | Summary: This paper introduces Windows Agent Arena, a benchmark for evaluating multimodal operating system agents. The benchmark contains over 150 diverse tasks on the Windows OS platform and leverages the Azure cloud environment for parallel evaluation.
Claims And Evidence: The benchmark supports multiple modalities ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback; we address each point below.
**RE: Agent systems like Camel/smolagent & evaluation for these model systems**
* Our focus was on single agent systems, particularly state-of-the-art/popular multi-modal LLMs commonly used as agent/reasoning backbone... | Summary: The paper introduces **Windows Agent Arena**, a benchmark for testing multi-modal AI agents on real Windows OS tasks. It provides 154 tasks across everyday applications (office, web browsing, coding, etc.) and uses a scalable, parallel evaluation setup (e.g., using Azure) so that tests finish in about 20 minut... | Rebuttal 1:
Rebuttal: Thank you for your questions/feedback! We address your points in turn below.
**RE: Low agent performance**
* Yes, performance is low in absolute terms; however, in relative terms, the performance we've seen has largely been in line with trends from agent performance results on other comparable be... | Summary: This paper introduces Windows Agent Arena, a benchmark environment specifically designed for evaluating multi-modal AI agents within the Windows operating system. The authors develop a suite of 154 diverse tasks across various applications (Office, Web Browsing, Windows System, Coding, Media & Video, and Windo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments/questions. We address each point in turn below.
**RE: deeper analysis of performance disparities and failures across different task domains**
* Higher-quality accessibility trees on chromium web-browsers, less UI/element “clutter” and cleaner UI ... | null | null | null | null | null | null |
Latent Variable Causal Discovery under Selection Bias | Accept (poster) | Summary: This paper investigates the problem of Latent Variable Causal Discovery in the presence of Selection Bias and proposes a new statistical tool, Generalized Rank Constraints, to simultaneously handle latent variables and selection bias within linear Gaussian models.
## update after rebuttal
I have decided to ra... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's insightful comments and suggestions. Please see below for our response.
---
**(Q1)** The reviewer wonders how one might distinguish between selection bias and latent confounding in practice.
**A:** Thank you for this insightful question. We try to address it ... | Summary: The authors address the problem of causal discovery with latent confounders when the data has a selection bias. Here, the authors address this via generalized rank constraints that extend beyond conditional independencies. The ranks of covariance submatrices in biased data can reveal information about the late... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments. Below, we provide detailed response.
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**(Q1)** The reviewer suggests several additional experiments.
**A:** Thank you for the constructive suggestions. In light of them, we conducted new experiments to evaluate our method under vio... | Summary: In this paper, the authors propose the use of rank constraints to infer causal structure of the latent variables underlying a set of measurements, when these latent variables are themselves subject to selection bias (e.g. more conscientious individuals are more likely to fill out a full Big 5 questionnaire).
... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive comments. Please see below for our response.
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**(Q1)** The reviewer wonders how to identify latent structure and selection bias when the structural assumption of one-factor models are violated–such as when the number of latent variables is unknown, or ... | Summary: This paper extends the rank constraint and t-separation criteria in latent variable causal discovery to scenarios involving selection bias. The authors demonstrate that the rank constraint retains its informativeness even under selection bias, despite the potential invalidation of the linear Gaussian assumptio... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's constructive comments and helpful feedback. Please see below for our response.
---
**(Q1)** The reviewer asks about intuitions for the rank equivalence class.
**A:** Thank you for this constructive question. We fully agree that the following three problems ... | null | null | null | null | null | null |
Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge | Accept (poster) | Summary: This paper addresses a major challenge in personalized learning, where many existing methods assume high-quality, well-annotated benchmarks. In real-world settings, such benchmarks often exhibit long-tail distributions that negatively affect model performance. The authors propose a novel approach called Neural... | Rebuttal 1:
Rebuttal: > **Q1: However, the text lacks detailed information on the underlying mathematical principles of the Textmodality Collapse regularization, and a deeper explanation would be beneficial.**
**A1:** Thank you for the reviewer's insightful comment. In our paper, the goal of TC regularization is to pr... | Summary: Personalized learning, particularly data-driven approaches, faces challenges due to long-tail distributions in real-world benchmarks, which affect model performance. To address this, the authors propose NCAL (Neural-Collapse-Advanced Personalized Learning), which leverages Textmodality Collapse (TC) regulariza... | Rebuttal 1:
Rebuttal: > **Q1: However, the claim about mitigating class imbalance would benefit from further discussion or visualizations to highlight the improvement in class distribution. More visual evidence could strengthen the claim of enhanced category representation.**
**A1:** We appreciate the reviewer’s comme... | Summary: Personalized learning has gained significant attention due to its ability to address individual student needs. Still, many methods rely on the assumption of high-quality benchmarks, which are often unrealistic. To overcome this, the authors proposed NCAL, which utilizes a TC regularization to adjust the distri... | Rebuttal 1:
Rebuttal: > **Q1: While the paper provides evidence for the performance improvements and generalization ability of NCAL, the claim of it being “model-agnostic” remains somewhat unclear.**
**A1:** NCAL enhances feature learning by aligning with the simplex equiangular tight frame (ETF) structure through Tex... | Summary: The paper proposes a new method called Neural-Collapse-Advanced Personalized Learning (NCAL) to address the limitations of data-based personalized learning approaches that assume well-annotated benchmarks. In reality, these benchmarks often have long-tail distributions that affect model accuracy. NCAL introduc... | Rebuttal 1:
Rebuttal: > **Q1: However, the assertion that NCAL is model-agnostic would benefit from further clarification.**
**A1:** NCAL is designed to learn features that conform to the same simplex equiangular tight frame (ETF) structure by introducing TC regularization to optimize text distribution. As a result, N... | null | null | null | null | null | null |
Consensus Based Stochastic Optimal Control | Accept (poster) | Summary: This paper proposes the Momentum Consensus-Based Optimization (M-CBO) and Adaptive Momentum Consensus-Based Optimization (Adam-CBO) method to solve the stochastic optimal control problem. While the numerical results are nice, I do not think the theoretical analysis is significant. I will put details in “Theore... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback.
1. **On “ Assumption 4.1.4”**
Indeed, the assumption can be generalized as
$$\|\theta - \tilde\theta\| \leq \frac{(\mathcal{J} -\underline{ J})^\mu}{\eta}$$
for some $\mu,\eta>0$ .
When $\mu=\frac{1}{2}$, the condition... | Summary: The paper introduces Consensus-Based SOC using the Adam-CBO framework ( gradient-free, model-free, and mesh-free approach) for solving high-dimensional SOC problems. It claims to overcome limitations of existing model-based and model-free methods by improving convergence and stability. The authors then present... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and thoughtful suggestions for improving our work.
1. **On "Why comparison with existing SOC methods limited?"**
We would like to clarify that the goal of our work is to demonstrate that **our approach is applicable in a more general setting**—specifica... | Summary: The paper presents a scalable, gradient-free alternative for solving stochastic optimal control problems. By leveraging consensus-based updates and adaptive momentum, the proposed methods achieve efficient policy optimization without requiring explicit transition models or gradient computations. Theoretical gu... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and thoughtful suggestions for improving our work.
1. **On the lack of comparison with existing methods and experiments on high-dimensional problems without analytical solutions**:
We would like to emphasize that our primary goal is to address a more ge... | Summary: This paper considers a high-dimensional stochastic control problem and proposes two consensus based optimization algorithms to solve this problem. The proposed algorithms rely on Monte Carlo estimation to estimate the value function, which is used for choosing the optimal policy. Extensive simulation results a... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review and valuable feedback.
1. Regarding **the lack of comparison with gradient-based methods**, we would like to clarify that traditional gradient-based approaches typically fall into two specific categories:
+ Model-based methods: These methods ass... | null | null | null | null | null | null |
Communicating Activations Between Language Model Agents | Accept (poster) | Summary: This paper studies the multi-agent communication problem in the LLM scenario. Specifically, it proposes using hidden representations instead of natural language. Experiments on several multi-agent collaboration datasets demonstrate the effectiveness of the proposed method.
Claims And Evidence: Yes
Methods An... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and address their comments below:
1. > The theoretical depth of this paper can be further enhanced. For example, the result shows that the direct replacement strategy is the most efficient way. Basically, it means that we should discard all previous repre... | Summary: The paper proposes a novel method for inter-language model (LM) communication by directly exchanging model activations instead of using natural language.
Tested on two synthetic datasets (a coordination game and an investment decision task) and several reasoning benchmarks (GSM8k, MMLU subsets, Biographies), t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and address their comments below:
1. > All tested models come from the LLaMA family...
**Please see point #1 in the reply to Reviewer VKxi above.** In summary, we test AC using models **across the LLaMA, Qwen, and Gemma families**, and find that **AC bea... | Summary: The paper proposes an alternative method for communication between language models (LMs) that does not rely on natural language. Instead, the authors introduce a technique for LMs to communicate with activations. More specifically, intermediate activations from one model are injected into another's computation... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and address their comments below:
1. > Lack of comparison with other similar approaches such as model merging
To adequately scope our paper, we chose to limit our focus to task-agnostic methods. Model composition/merging methods are extensively discussed... | Summary: The paper considers this fundamental question "as LLMs are increasingly capable of handling larger, more complex tasks (sometimes with “super-human” ability), might they communicate more effectively in representations of higher dimension than natural language?". It proposed a simple technique where LMs communi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and address their comments below:
1. > The paper considers rather simple datasets (2 multiplayer and 7 reasoning tasks) and two models (llama 3b and 8b).
In Appendix B.6, we display results on **the entire MMLU benchmark (57 datasets)**, spanning various... | null | null | null | null | null | null |
UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules | Accept (poster) | Summary: This paper introduces UniSim, a model for simulating time-coarsened molecular dynamics of small molecules, peptides, and proteins. UniSim employs a multi-stage approach: First, a unified atomic representation model is pretrained on a diverse collection of molecular datasets (both equilibrium and off-equilibriu... | Rebuttal 1:
Rebuttal: - **Q1:** The first claim & its transferability
Firstly, we strongly agree that the term "reusable backbone" is more suitable to describe UniSim, which will be used in our revised manuscript.
Secondly, we argue that the force kernel is not strictly necessary for cross-domain generalization. Figu... | Summary: The paper introduces UniSim, a deep learning framework designed to simulate biomolecular dynamics over coarse-grained time steps. The method unifies the treatment of small molecules, peptides, and proteins by first learning a unified atomic representation through multi-task pretraining on a diverse set of 3D m... | Rebuttal 1:
Rebuttal: - **Q1**: Potential Issues: Although the experiments are comprehensive, the generated trajectories are relatively short, which may limit insights into long-term dynamics. So adding experiments about the exploration of with conformational space, such as the experiments in F3low, can further illustr... | Summary: The paper introduces UniSim, a deep learning-based unified simulator for time-coarsened MD simulation. The framework aims to improve the transferability and efficiency of long-timescale molecular simulations across different biomolecular domains (small molecules, peptides, and proteins), and consistes of a pre... | Rebuttal 1:
Rebuttal: - **Q1:** Writing clarity in the methods needs improvement. For example, the gradient-environment subgraph part was a bit confusing and needs some more intuition on the design.
Thanks for the question. We will elaborate on the design rationale of the gradient-environment subgraph as clearly as po... | Summary: The paper presents UniSim, a unified simulator for time‐coarsened dynamics of biomolecules. It proposes a multi-task pretraining method to learn a unified atomic representation across small molecules, peptides, and proteins. The simulator uses a stochastic interpolant framework to learn long-timestep state tra... | Rebuttal 1:
Rebuttal: - **Q1**: The validity metrics for protein simulations remain unsatisfactory.
We claim that the unsatisfactory validity metric of ATLAS stems from two primary factors:
1. We pursue a unified modeling framework across small molecules and proteins, which necessitates certain compromises in protei... | null | null | null | null | null | null |
Large Language Diffusion Models | Reject | Summary: This work presents the large language diffusion model (LLaDA), a masked diffusion that shows strong scalability outperforming self-constructed autoregressive large language models. In particular, LLaDA achieves comparable performance for in-context learning and instruction-following compared to SOTA LLMs, and ... | Rebuttal 1:
Rebuttal: # Response to Reviewer 8p3t
We thank Reviewer 8p3t for the recognition of our contributions and the thoughtful comments. Below is our point-by-point response.
## Q1: Contribution
Like most research, our work builds upon prior studies. We sincerely appreciate your recognition of our unique contribu... | Summary: This paper focuses on the architecture of large language models (LLMs) and discusses the effectiveness of non-autoregressive training for large models. Inspired by the approach of masked language models, it designs a Masked Diffusion Language Model and proposes a diffusion model-based generative architecture w... | Rebuttal 1:
Rebuttal: # Responses to reviewer wFKs
We thank Reviewer wFKs for the recognition of our contributions and the thoughtful comments. Below is our point-by-point response.
## Q1: AR baselines
We agree that the current results may still be insufficient to claim an "excellent" architecture for building LLMs, an... | Summary: The paper introduces LLaDA, a large language model based on diffusion models instead of autoregressive models (ARMs), which dominate in the large language modeling area currently. The model is trained from scratch using a pre-training and supervised fine-tuning (SFT) paradigm with a mask diffusion loss, achiev... | Rebuttal 1:
Rebuttal: # Responses to Reviewer OmMG
We thank Reviewer OmMG for the recognition of our contributions and the thoughtful comments. Below is our point-by-point response.
## Q1: Contributions
Thank you for recognizing our efforts in scaling masked diffusion models to an unprecedented 8B scale. Like most pri... | Summary: This paper scales up discrete diffusion models to a larger regime than has been seen in prior work (8B params, 2.3T tokens) and additionally perform supervised instruction tuning. They present discrete diffusion models as an alternative to autoregressive language modeling. They evaluate their discrete diffusio... | Rebuttal 1:
Rebuttal: # Responses to Reviewer TqxC
We thank Reviewer TqxC for the thoughtful comments. Below is our point-by-point response.
## Q1: Efficiency
We include an inference time analysis showing that LLaDA enables a trade-off between generation quality and inference efficiency.
We evaluate three representati... | null | null | null | null | null | null |
The Role of Randomness in Stability | Accept (spotlight poster) | Summary: This paper studies the notion of stability, which is defined (through various definition) as the probability of an algorithm to give the same result on two datasets sampled from the data distribution. More precisely, the authors aim at quantifying the amount of randomness needed to achieve stability, as random... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our work, comments, and questions. Below we clarify a few possible misunderstandings about our definitions, explain our writing choices, and discuss several minor modifications we will make to address the reviewers concerns:
**Q1: Missing Defini... | Summary: This paper addresses the randomness complexity of an algorithm: how many random bits an algorithm requires to satisfy certain property. Two properties are studied: replicability and differential privacy. It is shown that the randomness complexities of these properties are closely connected to the global stabil... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our work and helpful comments. We will make the suggested minor fixes.
*Regarding the end of the proof of Thm B.2:*
The $L$ in line 771 is a typo and should be $L_D$, our apologies. The point here is to bound the measure of the set of samples $S... | Summary: The authors study the randomness complexity of private and replicable algorithms, showing that this complexity is tightly related to the best global stability parameters of an algorithm for the same task. Letting $\eta_M$ be the best achievable replication probability for a deterministic algorithm solving prob... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our work and helpful comments (we will fix the minor issues noted).
The trade-off between sample and randomness efficiency is a very interesting question, and is not well understood even for the most basic examples (see e.g. “Geometry of Rounding... | Summary: This paper studies a type of stability for algorithms and its connections to differential privacy (DP). It is argued that such stability requires randomization of the algorithm, and results are given for translating between different measures of complexity. The theoretical results are explained with intuition,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and questions and will make all suggested fixes
We hope the below clarifications (and positive impressions of the area expert reviewers) raises the reviewer’s confidence in our work, and respectfully ask they consider using the confidence score rath... | null | null | null | null | null | null |
Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts | Accept (poster) | Summary: This work provides an in-depth analysis on the ability of pretrained language models to generalize from specific facts to broader implications.
The authors focus on understanding the underlying mechanisms that allow pre-trained language models to make such generalizations after being finetuned on particular fa... | Rebuttal 1:
Rebuttal: Thank you for the review! We're happy that you believe our extractive structures framework makes sense and that our experiments are sound! | Summary: This paper introduces extractive structures—model components that store, retrieve, and process facts—to explain how LMs generalize to implications of fine-tuned facts. The authors show these structures emerge during pretraining when models encounter implications of known facts. Experiments on multiple LMs conf... | Rebuttal 1:
Rebuttal: Thank you for your review! We are happy that you found our claims "well supported", our evaluations "well-designed and effective", and our experimental designs and analyses "both sound and compelling". We're also grateful for the writing suggestions!
> 1. Evaluation Details: In Appendix B.1, you ... | Summary: This paper studies the mechanisms of how language models perform two-hop out-of-context reasoning (OCR), where the model generalizes to implications of new facts acquired during fine-tuning that involve composing the new facts as the first or second hop with another known fact. A series of experiments is condu... | Rebuttal 1:
Rebuttal: Thank you for your review. We're happy you find that our claims are "supported by clear evidence from various angles", and that our "experimental designs and analyses are sound". We're also grateful for your constructive feedback! We'll now discuss your concerns.
> One metric that is somewhat unc... | Summary: This paper focused on two-step implication process in LLMs, and proposes "extractive structures" to analyze which part of the LLM syblayers are the dominants of implication at different types of problems (2 types in the paper) with a method to highlight them based on the output probability. The paper also disc... | Rebuttal 1:
Rebuttal: Thank you for the review! We're happy that you find that our claims are "well supported" for the two-hop setting. We're also grateful that you've pointed out areas to work on in terms of writing. We'll now address your questions and concerns.
> But it is somewhat questionable whether insights obt... | null | null | null | null | null | null |
Contradiction Retrieval via Contrastive Learning with Sparsity | Accept (poster) | Summary: The paper proposes a new method named SparseCL to leverage specially trained sentence embeddings designed to preserve subtle, contradictory nuances between sentences. SparseCL utilizes a combined metric of cosine similarity and a sparsity function to efficiently identify and retrieve documents that contradict ... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful comments provided for our work. Here are our responses to the proposed weaknesses and questions.
**W1: The overall design is rather simple with a well-known sparsity metric.**
As shown in Table 4, the idea of SparseCL is not limited to Hoyer sparsity, but al... | Summary: This paper addresses the task of contradiction retrieval—retrieving documents or passages that explicitly refute a given query. The authors introduce SPARSECL, a method that augments standard contrastive learning for sentence embeddings with a sparsity measure (specifically, the Hoyer measure) to capture subtl... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful comments on our work. Here are our responses to the proposed weaknesses and questions.
W1 & W2 see below Q2 and Q1
**W3: Some parts of the theoretical analysis could be more tightly integrated with empirical findings.**
We are not claiming that we have theore... | Summary: This paper introduces SparseCL, a novel approach for contradiction retrieval using sparse-aware sentence embeddings. The method addresses limitations of traditional similarity search and cross-encoder methods by training sentence embeddings to preserve sparsity of differences between embeddings of contradicti... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful comments provided for our work. Here are our responses to the proposed weaknesses and questions.
**L271 statement:**
This means that our SparseCL method can be combined with either a zero-shot embedding model or a fine-tuned embedding model, and we demonstr... | Summary: * Introduces a contradiction retrieval method using cosine similarity and Hoyer measure of sparsity. The novel idea being using sparsity of embedding differences as a function to model contradiction.
* Discusses other approaches namely bi-encoders and cross-encoder models along with their shortcomings and addr... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful comments on our work!
Thank you for pointing out the typos.
**"Some arguments lack evidence/clarity e.g. “embeddings designed to preserve subtle, contradictory nuances between sentences”, meaning of the scores from Zero Shot (Cosine) vs CL (Cosine) methods.... | Summary: The paper is about non-similarity based information retrieval which is currently under explored. In the paper they introduce SparseCL, a novel approach to address shortcomings in existing similarity search and cross-encoder models when retrieving arguments contradictory to the query from large document corpora... | null | null | null | null | |
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning | Accept (poster) | Summary: The paper proposes a simple technique to avoid the issue of deep RL agents collecting familiar low-rewarding data to avoid the issue of re-sampling this data during training as a way to improve sample efficiency of deep RL agents. The paper integrates the algorithm in a variety of algorithms, applies it on dif... | Rebuttal 1:
Rebuttal: Thanks for the insightful suggestions and helpful feedback of our work, and for the recognition of our work's motivation, performance, and potential academic impact.
**Q1: (1) Given that the training loss is so critical to filter data, what do the authors think about the unreliability of the los... | Summary: The paper introduces the learn to stop (LEAST) heuristic for online off-policy reinforcement learning (RL).
The core idea is the proposition of an adaptive stopping mechanism to prohibit including unhelpful-to-the-learning-task transitions to the replay buffer. The heuristics is then experimentally evaluated ... | Rebuttal 1:
Rebuttal: Thanks for the insightful suggestions and constructive feedback on our work. Please find our responses to each of the concerns below.
**Q1: What do you mean in Background by "In this paper, we investigate whether deep RL agents deployed on cumulative reward maximization tasks also suffer from sun... | Summary: The paper proposes a novel technique for improving RL training that allows an agent to terminate an episode early if the expected return drops below a heuristic threshold. The paper aims to show that this can speed up training as it avoids filling the replay buffer with uninformative trajectories. The authors ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful suggestions and useful feedback of our work. Please find our responses to each of the concerns below.
## Question
**Q1.1: Please verify the findings with more up-to-date algorithms such as SR-SAC, CrossQ,...,e.g., repeating the experiments on Mujoco tasks... | Summary: This paper introduces "Learn to Stop" (LEAST) to address the "sunk cost fallacy" in deep reinforcement learning (RL). The sunk cost fallacy refers to how RL agents must complete episodes even if the trajectory collected thus far is already poor, which ultimately provides low-quality data to the agent. LEAST al... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful suggestions and useful feedback of our work. Please find our responses to each of the concerns below.
**Q1: Can you demonstrate experimentally that LEAST truncates low-quality trajectories?**
We conduct additional experiments on Ant to analyze trajectory ... | null | null | null | null | null | null |
Batch List-Decodable Linear Regression via Higher Moments | Accept (poster) | Summary: In modern machine learning, collecting large datasets from a single source is often impractical. Instead, data is typically gathered in batches from multiple sources. This paper investigates the $\textbf{batch list-decodable linear regression}$ problem: given pairs $(X, y) \in \mathbb{R}^{d+1}$ drawn from the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and positive assessment of our work. We reply to the points mentioned individually below:
(**Applicability of the SoS-certifiability Assumption**) SoS certifiability of moments is by now a well-studied condition that is known to hold for broad families of in... | Summary: First, in the interest of full disclosure, this review is very lightly modified from a review I wrote for this paper for NeurIPS 2024.
This paper studies a problem in algorithmic robust statistics: batch list-decodable linear regression. The setting is as follows. We get $m$ batches of $n$ samples $(X_i,Y_i)$... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and the positive evaluation of our paper. We respond to the points raised individually below:
(**Assumptions on $X$**) The SoS certifiability of moments is a well-established condition, known to hold for various important distribution families, including sub-G... | Summary: This paper proposes an efficient polynomial-time algorithm for batch list-decodable linear regression, using higher-order moment information within a Sum-of-Squares (SoS) framework. Compared to previous methods, this approach notably reduces both the required minimum batch size and the final regression error. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and positive assessment of our work. We respond to their points below:
(**Distributional assumptions**) The condition of having SoS certifiably bounded moments is standard (in the algorithmic robust statistics literature) for leveraging higher-order moment inf... | Summary: This paper studies the list-decodable linear regression, under the setting that the algorithm can collect batches of samples. This paper can be seen as a follow-up of Das et al. 2023, which studies the same problem under same batch setting, however uses a batch size $n \geq \tilde{\Omega}(\alpha^{-1})$, number... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reading our paper. We respond to the points raised individually below:
(**Tradeoff between batch size and algorithm complexity is not discussed**) There indeed is a tradeoff between batch size and algorithm complexity. However, this is not a hidd... | null | null | null | null | null | null |
Gravity-Bench-v1: A Benchmark on Gravitational Physics Discovery for Agents | Accept (poster) | Summary: This paper introduces Gravity-Bench-v1, a novel benchmark for evaluating LLM agents on physics discovery tasks. The authors design an environment that simulates gravitational dynamics with high precision, where agents must strategically plan observations, analyze data, and reason to solve various tasks. The be... | Rebuttal 1:
Rebuttal: **(Response to 1\)** See our response to “points 2,4,5” of Reviewer f7pg.
**(Response to 2\)** See our response to “point 1” of Reviewer f7pg.
(**Response to 3\)** Regarding the prompting process and the manner in which the agents were informed of the budget, we adopted a minimalistic approach: ... | Summary: This paper proposes a new benchmark, “Gravity-Bench-v1”, for evaluating the scientific discovery capabilities of AI agents. This benchmark is based on gravitational physics (in particular, the two-body problem), and measures the ability of agents to discover hidden physical laws in a dynamic environment. The d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and valuable suggestions. Below, we address the main points:
**(Response to point 1)** We appreciate the feedback with respect to adding additional details regarding the human solutions. They were developed by one 2nd year PhD student, one professor... | Summary: The paper introduces a new benchmark called Gravity-Bench-v1 to test the discovery potential of LLMs. The which the benchmark different 2-body star systems are simulated. These simulations include out of distribution parts where the gravitationally force have a different proportion to r by adding an alpha to r... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed consideration. They raised a concern about whether our simulations overall, and the modified law of gravity in particular, are truly out-of-distribution (OOD) tests for the AI models with solutions that cannot be found online (thus incorporated into the pretra... | null | null | null | null | null | null | null | null |
On Path to Multimodal Generalist: General-Level and General-Bench | Accept (oral) | Summary: This paper presents a comprehensive benchmark that includes over 700 existing tasks, providing a foundation for evaluating multimodal large language models (MLLMs). It introduces a five-level classification framework designed to systematically categorize MLLMs based on their capabilities and functionalities. F... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your time, meaningful questions, and constructive suggestions. Also, your recognition of our paper means a lot to us, which is the source of power to push us forward and enhance this work/project for a greater meaning to the community. We address each concern in... | Summary: This paper pioneers the idea of a General-Level framework to evaluate MLLMs, allowing for an accurate assessment of MLLM capabilities.
The authors provide significant observations and principles on benchmark setting and design a sophisticated evaluation metric at different levels to maintain the rationality of... | Rebuttal 1:
Rebuttal: We are more than excited to receive your very strong recognition of our work, which means a lot! Also thanks for your detailed review and valuable suggestions. We have done our best to address all concerns and are happy to engage in further discussion to improve the clarity and quality of our work... | Summary: The paper introduces General-Level, a framework inspired by the autonomous driving industry's capability grading system, to classify Multimodal Language Models (MLLMs) across five levels based on their synergy in comprehension, generation, and multimodal interactions. To support this classification, the author... | Rebuttal 1:
Rebuttal: We appreciate you carefully reviewing our paper and raising meaningful questions and constructive suggestions. Below we address your concerns one by one and are open to further discussion. Hope you can raise your evaluation.
---
**Q1. Discussion of related evaluation benchmarks (HELM, VHELM, etc... | Summary: This paper introduces a five-tier General-Level framework that assesses multimodal generalists based on their synergy across comprehension, generation, and cross-modal interactions. Also, inspired by autonomous driving grading, it proposes a new benchmark, General-Bench, covering over 700 tasks and 325K instan... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s recognition of our work and the thoughtful, constructive feedback. Below, we provide point-by-point responses to each comment. Hope you can reevaluate our work if you feel the response is effective and useful.
---
**Q1. The “Limitations and Future Investigati... | Summary: The authors introduce General-Level, a comprehensive evaluation framework for multimodal generalist models that emphasizes synergy across tasks and modalities. It further presents General-Bench, an extensive benchmark covering over 700 tasks spanning various modalities to assess both comprehension and generati... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers' recognition of our work and the valuable feedback provided. Below, we provide detailed responses.
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**Q1. But I think the normalization and metric mapping methods are mentioned without thorough empirical validation, leaving their effectiveness in accura... | null | null | null | null |
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective | Accept (spotlight poster) | Summary: The work identifies a problem with PEFT for SAM - the breaking down of domain-invariant relations encoded during pre-training. It proposes InfoSAM, a model that minimizes a lower bound on the mutual information between the encoder and the decoder during PEFT. It does it in a Rényi entropy sense, without having... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback. In the following sections, we will provide a detailed response to each of your comments.
---
**Q1:Literature reviews for domain-invariant information.**
**The concept of domain-invariant information was first introduced in prior works on domain... | Summary: In this paper, the authors focus on parameter-efficient fine-tuning for segment anything (SAM) network from information theory aspect, and propose InfoSAM. Specifically, InfoSAM aims to mine the domain-invariant relations encoded in the pretrained model, and design a new knowledge distillation framework with t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review of our paper and the valuable insights you provided. In the following sections, we will provide a detailed response to each of your comments.
--------
**Q1:Risk of trivial solutions in relation module (RM).**
We clarify that the **teacher's RM and s... | Summary: This paper proposes InfoSAM, a new SAM fine-tuning framework that (1) compresses the domain pseudo-invariant information and (2) maximizes mutual information between a pre-trained teacher and a fine-tuned student model. Experiments across diverse datasets demonstrate that InfoSAM significantly enhances segment... | Rebuttal 1:
Rebuttal: We appreciate your detailed and valuable review on our paper. We will address each of your comments thoroughly in the following sections.
---
**Q1:Insufficient justification for $\alpha=2$.**
The core reasons for choosing $\alpha=2$ in matrix-based Rényi's $\alpha$-entropy are as follows:
(1)... | null | null | null | null | null | null | null | null |
AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism | Accept (poster) | Summary: This paper introduces AdaDecode, a self-speculative decoding method with an early exiting mechanism. Based on empirical findings that many simple and predictable tokens can be accurately generated at intermediate transformer layers, the authors propose three key contributions. First, they introduce a lightweig... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's insightful feedback.
**[Q1]: Could you present additional experiments demonstrating that AdaDecode's performance is robust to different choices of early exiting layers?**
**[A1]**: Please refer to Table 1 in this [PDF](https://anonymous.4open.science/r/Ada... | Summary: The authors present AdaDecode, a methodology to accelerate decoding without auxiliary models or modification of the model. The proposed approach adaptively predicts tokens from an intermediate layer based on confidence, using a set of additional lightweight LM heads whose predictions are verified using a rejec... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments and appreciation of our work.
**[Q1]: While approaches like SpecInfer (Miao et al., 2023b), Medusa (Cai et al., 2024), and its extension HYDRA (Ankner et al., 2024) are orthogonal to AdaDecode, it could be interesting to discuss further ... | Summary: The authors propose to use early exiting to accelerate autoregressive decoding in LLMs while leveraging adaptive layer parallelism for efficient hardware deployment. The early exiting framework uses a lightweight head at intermediate layers to enable high-confidence early token predictions. An additional verif... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments.
**[Q1]: The method offers no novelty compared to prior work and it is unclear what the authors are contributing.**
**[A1]**: We would like to highlight that our work provides an efficient solution to tackle the key limitations of speculative de... | Summary: The paper proposes to improve autoregressive decoding process in LLMs by decoding by using intermediate layer outputs when the confidence is high. A lightweight LM head is trained to decode the next token from such intermediate layer output enabling this method to be applied on pretrained models without requir... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful feedback.
**[Q1]: I would suggest saying that "$E^\star$ is likely to be full rank", as opposed to "must be full rank" in Appendix A and modifying the corresponding claim in Section 2.1 accordingly.**
**[A1]**: We provide an empirical validation confirmin... | null | null | null | null | null | null |
Counterfactual Contrastive Learning with Normalizing Flows for Robust Treatment Effect Estimation | Accept (poster) | Summary: The paper points out that the prediction of the individual treatment effect (ITE) is crucial for personalized therapy planning and proposes a contrastive learning approach (along the lines of SIMCLR) to estimate it. The accuracy of an estimator is measured in terms of the expected squared error of the ITE esti... | Rebuttal 1:
Rebuttal: We greatly appreciate your high recognition of our work, and are eager to share our thoughts with you.
$\textbf{Q1:}$ Relation to debiased ML.
$\textbf{R1:}$ Thanks for your insightful idea. We would like to clarify that our work primarily focuses on the challenges of deep learning-based causal ... | Summary: This paper presents FCCL, an ITE estimation method. FCCL integrates diffeomorphic counterfactual generation and contrastive learning to align treatment and control groups at a fine-grained, sample level, mitigating distribution shifts and approximating RCT randomization. By ensuring realistic counterfactuals a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and for recognizing the importance of heterogeneous treatment effect estimation. Please see below answers to the questions.
$\textbf{Q1:}$ Provide a proper definition of boundary samples and discuss why handling them is challenging.
$\textbf{R1:}$ We thank th... | Summary: The paper introduces Flow-based Counterfactual Contrastive Learning (FCCL), a novel approach for Individual Treatment Effect (ITE) estimation that integrates normalizing flows for realistic counterfactual generation and contrastive learning for fine-grained sample alignment. It derives a theoretical generaliza... | Rebuttal 1:
Rebuttal: Thank you for your insightful suggestions. We have addressed the comments related to the counterfactual generation and model robustness evaluation. Please see our responses below.
$\textbf{Q1:}$ How does FCCL compare to diffusion-based counterfactual generators?
$\textbf{R1:}$ We thank the revie... | Summary: The paper proposes FCCL framework for the ITE estimation. The proposed method can generate realistic counterfactuals by leveraging normalizing flows to ensure adherence to the data manifold, preserving semantic similarity to factual samples. The authors also derive a new generalization bound connecting ITE e... | Rebuttal 1:
Rebuttal: We appreciate your time and your thoughtful and encouraging comments. We hope our responses can resolve your concern.
$\textbf{Q1:}$ I suggest authors give some theoretical or empirical evidence to show that the bound is tight (at least tighter than the bound proposed in CFR).
$\textbf{R1:}$ Fol... | null | null | null | null | null | null |
Discrete Spatial Diffusion: Intensity-Preserving Diffusion Modeling | Reject | Summary: The authors proposed a discrete diffusion model that respects mass conservation. They define the forward process as a random walk of particles and train an NCSN to reorganize these particles during the reverse process. The total mass of the particles is preserved since these operations do not create or destroy... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful assessment of our results.
**4.1** [*Theoretical results*] We agree that the core derivation of DSD builds on the framework of Santos et al. (2023). That said, we submit that the paper includes theoretical contributions, albeit specific to DSD. These incl... | Summary: This paper introduces Discrete Spatial Diffusion (DSD), a new diffusion-based generative modeling approach that operates on discrete intensity units and enforces strict global conservation of these intensities. Traditional image-based diffusion models typically treat pixel intensities as continuous quantities,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments; we feel that the reviewer has understood the purpose of the work and offers concrete avenues for improvement, all of which we are able to address in our revision.
**3.1** [*computational complexity*] First, let us remark on the computational... | Summary: This paper presents Discrete Spatial Diffusion (DSD), a framework that ensures intensity preservation in diffusion models by using a continuous-time, discrete-state jump stochastic process. Unlike standard diffusion models that operate in continuous intensity spaces, DSD naturally incorporates stochasticity wh... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing a clear review that shows understanding of the work, constructive criticism for improvement, and good questions for clarification.
**2.1** [*incorporate quantitative metrics such as FID*] We emphasize that DSD primary advantage is for scientific applications, ... | Summary: Discrete Spatial Diffusion (DSD) is a novel generative diffusion modeling framework specifically designed for discrete spatial domains, explicitly preserving mass throughout the diffusion processes. Traditional diffusion models typically assume continuous pixel intensities, thereby limiting their applicability... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and questions.
**1.1** [*Difficult to understand*] We have tried our best to make it accessible and attempted to follow the conventions of [the theoretical paper](https://rb.gy/d7vqid). We will be happy to provide clarifications. It would be... | null | null | null | null | null | null |
Learning Dynamics in Continual Pre-Training for Large Language Models | Accept (oral) | Summary: This paper studies the training dynamics during continual pre-training. Specifically, they study the scaling law of the loss curve during continual pre-training (CPT), which considers the scaling law due to learning rate annealing and the scaling law due to the distribution shift. Based on this formulation, th... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful suggestions and valuable feedback.
**Response to "Claims and Evidence about sentences that are hard to understand":**
We apologize for misunderstanding caused by our use of the term "overlap." Our intended meaning was that the curves from different trans... | Summary: This paper explores the learning dynamics of continual pre-training and proposes scaling laws for the same. Based on the proposed scaling laws, the authors discuss several critical factors in continual pre-training. Overall, this is an educational paper to understand how continual pre-training works.
Claims A... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful suggestions and valuable feedback.
**Response to "Comments Or Suggestions 1 about loss potential and turning length":**
We apologize for any poor reading experience caused by organizational issues about these terms in our paper and will address these co... | Summary: This paper performs an empirical study of loss curves during continual pre-training. The paper seeks to characterize how the training loss on a new dataset will evolve when a pretrained model is subjected to new data in a CPT setup. To that end, the authors present a series of experimental setups showing how l... | Rebuttal 1:
Rebuttal: We sincerely appreciate your suggestions. We would also like to acknowledge the thorough and constructive feedback provided which help strengthen our work, which we summarize and respond as follows.
**Response to "Our work is empirical":**
Our work is indeed empirical. However, most scaling... | Summary: The paper explores learning dynamics in Continual Pre-Training (CPT) for LLMs, focusing on how general and downstream domain performance evolves at each training step, using validation losses. The paper observes that the CPT loss curve represents a transition between hidden loss curves, influenced by distribut... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our work.
**Response to "Limited theoretical foundation for the derived scaling laws":**
Our work is indeed empircial. However, most scaling laws in LLMs are empirical, such as the OpenAI [1] scaling law and the Chinchilla [2] scaling law. We believ... | null | null | null | null | null | null |
PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting for Novel View Synthesis | Accept (poster) | Summary: This paper introduces PF3Plat, a novel two-stage framework for novel view synthesis from sparse and unposed images. In the first stage, it leverages pre-trained depth estimation and visual correspondence models to achieve a coarse alignment of 3D Gaussians. In the second stage, it refines depth and pose estima... | Rebuttal 1:
Rebuttal: > Why not use MVSplat with MASt3D, given that this method falls behind MASt3R in camera pose estimation?
We opted not to use MVSplat with MASt3R because MASt3R's runtime of around 10 seconds conflicts with our goal of achieving a fast, feed-forward process. Moreover, our experiments include a va... | Summary: This paper proposes a novel 3D Gaussian Splatting prediction model based on sparse views. Starting from a coarse initialization using off-the-shelf depth and correspondence models, the proposed fine alignment module predicts the correct scale for the depth map and camera poses, and finally, Gaussian heads pred... | Rebuttal 1:
Rebuttal: > Questions about “feed-forward” approach, because there is a module for a test-time iterative optimization for initial pose.
We wish to refer to reviewer k81v’s appreciation that ***“feed-forward” in our context emphasizes efficiency and speed*** (See comparison to InstantSplat). Although our c... | Summary: The paper introduces PF3plat to address the problem pose-free feed-forward view synthesis using 3D Gaussians parametrization. First, the method leverages pretrained monocular depth estimator and visual correspondence models to get coarse depth and pose. Then learnable refinement modules are proposed to refine ... | Rebuttal 1:
Rebuttal: > FreeSplatter not discussed in the paper.
We thank the reviewer for highlighting FreeSplatter[1]. FreeSplatter addresses the same task using an LRM-based architecture that directly maps images to 3D Gaussians. To tackle coarse alignment, it employs staged training with early supervision from 3D ... | Summary: The paper presents a novel feed-forward method for 3D reconstruction and view synthesis from sparse, unposed images, eliminating the need for ground-truth depth or pose at both training and inference. The method builds on pixel-aligned 3D Gaussian Splatting (3DGS) but addresses the challenge of Gaussian center... | Rebuttal 1:
Rebuttal: > Given that NoPoSplat was accepted to ICLR 2025 only few days before the ICML submission deadline, its omission is understandable. However, incorporating it as a baseline in future revisions would further strengthen the claims of state-of-the-art performance.
We appreciate the reviewer’s valuabl... | Summary: This work presents a framework for novel view synthesis (NVS) from unposed images in a single feed-forward pass. It estimates depth and pose from unposed images using a combination of pre-trained monocular depth estimation and visual correspondence models. It outperforms prior pose-free methods like DBARF and ... | Rebuttal 1:
Rebuttal: > The paper states that correspondence models like LightGlue struggle in low-texture regions. It is unclear how PF3plat can improve in these areas.
We enhance performance in low-texture regions through two complementary approaches:
As explained in line 233, our proposed 2D-3D consistency loss enc... | null | null | null | null |
AdaptiveStep: Automatically Dividing Reasoning Step through Model Confidence | Accept (poster) | Summary: This work proposes a method (Adaptive Step Process Reward Model - ASPRM) to split reasoning chains into reasoning steps based on the model confidence rather than pre-defined rules. For each token in the generated output, if the model’s probability for that token is below some threshold, then this token is trea... | Rebuttal 1:
Rebuttal: # Response to Reviewer Comments
**Dear Reviewer NpJW:**
We would like to express our sincere gratitude for your time, your thorough review and valuable feedback on our manuscript. Your comments have provided us with important insights that will help improve the quality of our work.
>## W1: Abla... | Summary: The paper addresses the challenge of training Process Reward Models (PRMs) for large language model reasoning by introducing a novel step segmentation method called AdaptiveStep. Instead of using fixed rules or token counts to break a model’s chain-of-thought into steps, AdaptiveStep dynamically segments the r... | Rebuttal 1:
Rebuttal: # Response to Reviewer Comments
**Dear Reviewer LC8A:**
We greatly appreciate your insightful comments and suggestions and thank you for your time. Due to the character limit, we have summarized your questions. If we have missed or misunderstood any points, please let us know.
>## Related Work
... | Summary: 1. In this paper, a novel step dividing method, AdaptiveStep is proposed. The method enables automatic step dividing while being more informative than rule-based step dividing method.
2. By adapting AdaptiveStep, the ASPRM demonstrates stronger discriminative power at the token level than existed method at som... | Rebuttal 1:
Rebuttal: # Response to Reviewer Comments
**Dear reviewer ZPdL:**
Thank you for your insightful comments and valuable feedback. Below, we address the concerns raised and provide clarifications and improvements.
>## W1: Concerns about experiments results
**R1:** Thanks for your comment and we will revis... | Summary: Simple yet effective method for segmenting reasoning traces into individual steps, resulting in modest but significant improvements on relevant math and coding benchmarks.
The paper proposes to segment reasoning traces according to next-token confidence levels, instead of e.g. heuristics like new lines.
The ex... | Rebuttal 1:
Rebuttal: # Response to Reviewer Comments
**Dear reviewer T2AZ:**
We sincerely appreciate your constructive feedback and the time you have devoted to reviewing our manuscript. We try to address your concerns point by point.
>## W1: Discussion or empirical analysis for failure cases of AdaptiveStep
**Res... | null | null | null | null | null | null |
A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control | Accept (poster) | Summary: Deep reinforcement learning suffers from primacy bias, a tendency to overfit early experiences stored in the replay buffer. This paper proposes Forget and Grow (FoG), a novel method with two new components: Experience Replay Decay (ER Decay) and Network Expansion. ER Decay gradually reduces the influence of ea... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort you have dedicated to reviewing our work. We deeply appreciate your careful and thorough review. In the following, we seek to address each of your concerns.
___
**Q1:** *"The ER Decay in Figure 1 is not very clear. It looks like ER Decay will sample a ... | Summary: The paper draws inspiration from the phenomenon of infantile amnesia in neuroscience, proposing a "forget and grow" mechanism to mitigate primacy bias in deep reinforcement learning. The authors identify limitations in existing reset mechanisms and introduce two novel strategies: Experience Replay Decay (ER De... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort you have dedicated to reviewing our work. We deeply appreciate your careful and thorough review. In the following, we seek to address each of your concerns.
___
**Q1** *"the evidence provided—particularly Figure 10—seems inadequate"*
**A:** We tracked... | Summary: The paper addresses the challenge of Deep Reinforcement Learning (DRL) in continuous control tasks, where models suffer from primacy bias, overfitting to older memories in the replay buffer. Drawing inspiration from infantile amnesia in humans, the authors propose two modifications: (1) a decaying replay buffe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort you have dedicated to reviewing our work. We deeply appreciate your careful and thorough review. In the following, we seek to address each of your concerns.
___
**Q1:** *"The authors should analyze the learned representations and their evolution under ... | Summary: This paper focuses on the problem of sample efficiency in deep reinforcement learning. The authors introduce the Forget and Grow (FoG) method, which relies on three ideas: 1) reducing the sampling probability of older samples, 2) expanding the network size, and 3) resetting the network with a certain frequency... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort you have dedicated to reviewing our work. We deeply appreciate your careful and thorough review. In the following, we seek to address each of your concerns.
___
**Q1:** *"The comparison against other methods may be unfair since FoG uses increased compu... | null | null | null | null | null | null |
LLM-Assisted Semantically Diverse Teammate Generation for Efficient Multi-agent Coordination | Accept (poster) | Summary: This work proposes a novel algorithm called “SemDiv” which uses LLMs to generate semantically diverse behavior in MARL. Specifically, SemDiv uses an LLM to (1) generate a language description of a plausible, novel coordination behavior, (2) generate a reward function that incentivizes that coordination behavio... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition and the valuable comments! Extra experimental results can be found in this [link](https://telling-floor-898.notion.site/1c7c2fed721a80b9ba7ef7fa2b3bffed).
**Q1: The performance of population-based baselines with the multi-head network architecture.**
A: W... | Summary: The paper introduces SEMDIV, a novel framework that uses LLMs to generate semantically diverse teammates for efficient multi-agent coordination. Unlike traditional methods that focus on policy-level diversity, SEMDIV iteratively generates natural language descriptions of coordination behaviors, translates them... | Rebuttal 1:
Rebuttal: We sincerely thank you for the valuable comments! Extra experimental results can be found in this [link](https://telling-floor-898.notion.site/1c7c2fed721a80b9ba7ef7fa2b3bffed).
**Q1: Experiments with more agents and with continuous action space.**
A: SemDiv is agnostic to team sizes and action ... | Summary: This paper proposes a teammate generation method called SemDiv, which uses LLMs to learn diverse coordination behaviors a the “semantic” level. SemDiv generates novel teammates by iterating the following steps: (1) generating natural language description of a novel coordination behavior, (2) translating it int... | Rebuttal 1:
Rebuttal: We sincerely thank you for the valuable comments!
**Q1: Difference between “policy level” and “semantic level”, and why optimizing policy level differences is bad.**
A: Policy-level methods mainly optimize policy-space diversity without explicitly modeling the corresponding semantics. In this ap... | Summary: The paper proposes a novel partner generation method, which iteratively generates new partner through generated reward functions via LLM queries. The input to the LLM contains semantic information of the behavior, allowing the method to include semantic information for generating the partners in addition to po... | Rebuttal 1:
Rebuttal: We sincerely thank you for the valuable comments! Extra experimental results can be found in this [link](https://telling-floor-898.notion.site/1c7c2fed721a80b9ba7ef7fa2b3bffed).
**Q1: The motivation for unseen teammate communicating behaviors before testing.**
A: Communication and intention shar... | null | null | null | null | null | null |
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts | Accept (spotlight poster) | Summary: This paper proposed that the Feynman-Kac Correctors enhance the sampling of several types of compositional distributions. The main idea is based on the Feynman-Kac formulation and the transformation of transport and diffusion to reweight operation.
## update after rebuttal
My view is not changed, so I maintai... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and constructive suggestions. We are glad that they find the proposed idea 'novel' and its derivation 'supported by valid theory'. We are also happy to hear that our method 'contributes to the understanding of Diffusion Models.' We provide the sugges... | Summary: This paper points out modifying score function of the pretrained generative modelings, such as classifier-free guidance might cause to generate samples that are not from the same distribution as the training data, and the corrector schemes used to address this problem either requires infinite many steps requir... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We are glad that the reviewer finds our paper to be 'well-structured and complete' and the experimental design to be 'reasonable'. We also would like to thank the reviewer for bringing up reference [1] (Du et al.), as this is an excellent refere... | Summary: The paper derives a suite of new tools for modifying pretrained diffusion models at inference time, using particle resampling techniques. In particular, they use the Feynman-Kac formula to derive the evolution of weights for particles when simulating the diffusion reverse SDE (or different variants of it) such... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback! We are thrilled to hear that they find our paper to be a 'useful contribution to the literature', 'well written and organized' and a new tool for the diffusion sampling community. Below, we address the reviewer's questions.
> What is the interval... | Summary: Prior work has shown that composing multiple pre-trained diffusion models is not straightforward in the context of energy-based models. This paper investigates Feynman-Kac correctors based on the Feynman-Kac formula to sample from annealed, geometric-average, and product distributions. Compared to the Fokker-P... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time, feedback, and positive appraisal of our work. We are heartened to hear that the reveiwer feels that the paper is "theoretically sound" and found that the paper was "generally well-structured". We now address the questions and suggestions raised by the reviewer... | null | null | null | null | null | null |
Scalable Sobolev IPM for Probability Measures on a Graph | Accept (poster) | Summary: This paper introduces a Scalable Sobolev Integral Probability Metric (IPM) for probability measures defined on graph metric spaces. The key focus is on improving computational efficiency while maintaining mathematical rigor for applications in machine learning, topological data analysis (TDA), and document cla... | Rebuttal 1:
Rebuttal: Dear Reviewer Qz8Z,
Thank you for your valuable feedback. Below are the answers for your questions and comments.
**(1) [...]choice of graph structure (not justified)[...]not explain why these specific graph structures were chosen. Different graph structures (e.g., random geometric graphs, small-... | Summary: This paper studies the Sobolev Integral Probability Metric (IPM) for probability measures supported on graph-structured spaces. The authors introduce a regularized version of Sobolev IPM, which allows for a closed-form solution and efficient computation, making it more scalable for large-scale applications. Th... | Rebuttal 1:
Rebuttal: Dear Reviewer FFqa,
Thank you for your valuable feedback. Below are the answers for your questions and comments.
**(1) [...]Limited Justification for General Graphs[...]claimed to work on graphs, its theoretical analysis and experiments are only carried out on trees[...] (not for) general graphs... | Summary: The paper proposes an equivalent form for the Sobolev IPM on graphs which they call "Regularized Sobolev IPM", this form has the advantage to be computable in closed form.
Authors show that this proposed IPM is equivalent to the original Sobolev IPM and they provide bounds relating it to previous work on Sob... | Rebuttal 1:
Rebuttal: Dear Reviewer XuWp,
Thank you for your valuable feedback. Below are the answers for your questions and comments.
**(1) [...]pseudo algorithm (Theorem 3.5)[...] (compute in practice)[...] (preprocessing) is dense and not too informative[...]**
→ We respectfully clarify that the preprocessing is ... | null | null | null | null | null | null | null | null |
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization | Accept (poster) | Summary: The paper introduces a distribution matching method using VAE by leveraging expressive score-based priors instead of the fixed priors such as Gaussians. The main contribution is the Score Function Substitution (SFS) trick, which reformulates the gradient of the prior’s cross-entropy term to avoid the computati... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful feedback and are pleased that our work was found both interesting and novel. However, we believe some aspects were probably **misunderstood or overlooked**. For example, the reviewer noted missing references, yet our manuscript thoroughly discusses... | Summary: This paper proposes a new prior distribution for distribution matching. Specifically, the authors model the prior using denoising score matching, and they enhance this approach by incorporating the minimization of the Gromov-Wasserstein (GW) distance between different distributions as additional regularization... | Rebuttal 1:
Rebuttal: ### Experimental Designs Or Analysis
We appreciate the reviewer’s suggestion and understand the importance of evaluating our method on more complex benchmarks. Our current domain adaptation experiments using MNIST and USPS were chosen deliberately as controlled, standard benchmarks that allow us ... | Summary: This paper deals with the limitations of existing distribution matching (DM) methods, which often struggle with scalability, instability, mode collapse, or impose unnecessary biases through fixed priors. To overcome these limitations, the authors builds upon the existing work VAUB, and propose a novel approac... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s thoughtful feedback and recognition of our work. We apologize for any confusion caused by the inadequate explanation in **Section 3.1.1**, where the lack of explicit details led to misunderstandings regarding our training procedure. Below is our refined explanati... | Summary: Existing DM methods face many challenges, and likelihood-based methods often impose unnecessary biases through fixed priors or require learning complex prior distributions.
This paper introduces a novel approach to distribution matching (DM) by leveraging score-based priors and Gromov-Wasserstein (GW) distanc... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and valuable suggestions. We appreciate your careful assessment of our work, particularly regarding the Gromov-Wasserstein structural preservation regularization(GW) component.
Following your recommendations, we conducted additional experiments to isolate the c... | null | null | null | null | null | null |
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