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Pruning for GNNs: Lower Complexity with Comparable Expressiveness | Accept (poster) | Summary: In this paper, the author proposed pruned MPNNs, K-path GNNs, and K-hop GNNs to reduce the computation redundancy in the original method. The author proved the expressive equivalence between the original version and the pruned version. The proposed method is evaluated on various benchmarking datasets and shows... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their feedback and constructive comments
Claims And Evidence:
(2)Space Complexity:During backpropagation in training, the node representations of intermediate layers (e.g., activation values) are required to compute gradients and update weights. If interm... | Summary: The paper proposes a pruning framework for GNNs aimed at improving computational efficiency while maintaining or even enhancing expressive power. The authors introduce pruned versions of Message Passing GNNs (MP-GNNs), K-Path GNNs, and K-Hop GNNs by identifying and removing redundant structures. Theoretical an... | Rebuttal 1:
Rebuttal: We sincerely acknowledge reviewer V1ZW for the constructive criticism and insightful suggestions.
Questions For Authors:
Q1: Unfortulately, we have to admit that find a pair of graphs that pruned K-Hop fails to distinguish graphs and the original K-Hop framework can differentiate involves a tre... | Summary: This paper proposes pruned versions of Message Passing GNNs, K-Hop GNNs, and K-Path GNNs by eliminating redundant structures. The authors claim that these pruned frameworks maintain or even improve expressive power while reducing computational complexity. Theoretical analysis based on matrix language is used t... | Rebuttal 1:
Rebuttal: We thank reviewer hERx careful evaluation and meaningful suggestions.
Q1:The methodology is theoretically sound in using matrix language tools to analyze expressiveness. However, the evaluation mainly focuses on standard benchmark datasets without strong ablation studies or comparisons with more ... | null | null | null | null | null | null | null | null |
Parameter-Efficient Fine-Tuning of State Space Models | Accept (poster) | Summary: This paper studies the fine-tuning of state-space models, in particular, S4 and S6. Empirical studies on fine-tuning the encoder and the decoder using many different existing tuning mechanisms are shown. Then, an SDT-P fine-tuning strategy of the autoregressive modules is proposed based on pruning and then spa... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We've addressed all your concerns below.
---
> Q1: Lemma 2 Clarifications
> * (i) Use $\odot$ or $\circ$ for Hadamard product?
> * (ii) Clarify "same permutation"?
> * (iii) (5) seems independent of $f_0$—is this correct?
> * (iv) What does "all hidden di... | Summary: This paper investigates the performance of popular parameter-efficient fine-tuning methods (PEFT) (e.g., LoRA and its variants) when applied to SSMs like Mamba and hybrid models such as Jamba. It finds that LoRA-based methods consistently outperform other PEFT approaches, especially when applied to linear proj... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback, particularly for recognizing that (i) our method is creative and rational, (ii) our claims are supported by extensive experiments and theoretical analysis, (iii) our theoretical claims are sound, (iv) we advance SOTA results in fine-tuning SSM-ba... | Summary: This paper investigated the PEFT for SSM, like MAMBA.
Claims And Evidence: yes
Methods And Evaluation Criteria: yes
Theoretical Claims: yes
Experimental Designs Or Analyses: yes
Supplementary Material: yes, the experimental parts
Relation To Broader Scientific Literature: it mainly focus on PEFT for SSM.... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that (i) our paper is well written, (ii) it tackles the gap where PEFT studies favor Transformer over SSM, and (iii) it contains many experimental results to support the claims.
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> Q1: The proposed PEFT is incremental, such idea is mainly from the PEFT ... | Summary: This paper investigates how PEFT methods perform on State Space Models (SSMs) (e.g. the Mamba architecture) and identifies which model components are best to target. It provides a comprehensive benchmark of existing PEFT techniques on SSM-based language models and hybrid architectures with Jamba. A key finding... | Rebuttal 1:
Rebuttal: We are delighted that the reviewer likes the paper, recognizing it as (i) well-executed, (ii) a systematic study of PEFT for emerging neural architectures (Mamba, Mamba 2, and hybrid variants), and (iii) providing strong and comprehensive empirical results. Thank you for your encouragement! | null | null | null | null | null | null |
EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction | Accept (poster) | Summary: The paper introduces EduLLM, a new framework for student performance prediction that combines Large Language Models (LLMs) with a Framelet-based Signed Hypergraph Neural Network (FraS-HNN). FraS-HNN is a novel approach for signed hypergraph learning, utilizing high-pass and low-pass filters to extract multi-fr... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback and for acknowledging the novelty and strengths of our proposed framework. Please find our detailed responses below:
- **LLM Selection, Fine-tuning, and Task Adaptation:** We appreciate the reviewer’s observation and agree that further... | Summary: This paper presents EduLLM, a novel framework that integrates Large Language Models (LLMs) with a Framelet-based Signed Hypergraph Neural Network (FraS-HNN) to address student performance prediction in personalized education systems. EduLLM models the complex structural and semantic relationships between stude... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the encouraging feedback on our work. We are pleased that you recognized **the novelty and technical contributions of EduLLM, including its theoretical soundness, methodological innovation, and comprehensive experimental validation**. For your key concerns, plea... | Summary: - This paper presents EduLLM, a novel method for predicting student performance by integrating LLM-based semantic understanding with structural modeling via a framelet-based signed hypergraph neural network (FraS-HNN).
- Signed hypergraphs capture higher-order interactions and differentiate correct from incor... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful evaluation. We are pleased that the novelty and effectiveness of EduLLM and FraS-HNN are recognized, along with the **theoretical soundness, strong empirical results, and broader contributions to hypergraph learning and educational data mining**. ... | Summary: This paper introduces EduLLM, a framework that combines large language models (LLMs) with hypergraph learning to improve student performance prediction. Traditional methods mainly rely on historical response patterns but struggle to capture the complex interactions between students and learning content. To add... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for carefully reading our paper and providing detailed feedback. We respectfully offer the following clarifications:
- **Further Clarification on Motivation:** While cognitive diagnosis models have explored learner-element relationships using graphs, our goal is to ... | null | null | null | null | null | null |
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking | Accept (oral) | Summary: This paper introduces rStar-Math, a novel approach demonstrating that small language models (SLMs) can achieve state-of-the-art mathematical reasoning capabilities without relying on knowledge distillation from larger models. The key innovation is a self-evolving deep thinking framework that enhances the reaso... | Rebuttal 1:
Rebuttal: >Q1: No sensitivity analysis of MCTS parameters—it is unclear whether performance saturates beyond 64 rollouts.
**Response**: Thank you for your thoughtful review and for recognizing our contributions.
We sincerely appreciate your suggestions and have conducted additional analysis on MCTS param... | Summary: This paper aims to improve the mathematical reasoning capabilities of small LLMs through a self-evolved deep thinking framework, rStar-Math. The method involves three main contributions: (1) a code-augmented CoT data synthesis method; (2) a pairwise training method for the process preference model that avoids ... | Rebuttal 1:
Rebuttal: >Q1: The main issue with the paper is the overclaim regarding the "Self-Evolved" nature of the proposed method. The use of a 236B model (DeepSeek-Coder-V2-Instruct) in the initial round of self-evolution directly contradicts this claim.
**Response**: We appreciate the reviewer' feedback regardi... | Summary: This paper shows that smaller language models ($\leq7$ billion parameters) can learn to solve challenging math problems at a level comparable to much larger models (e.g., GPT-4 or o1 models). They achieve this by having these smaller models:
- Generate and verify each step of a math solution (rather than prod... | Rebuttal 1:
Rebuttal: >Q1: Clarification on the evaluation benchmarks: "The authors’ experimental design generally appears sound, particularly in their ablation studies and comparisons on multiple benchmarks, but a closer look at smaller test sets, such as the 15-problem subset of AIME, raises concerns about cherry-pic... | Summary: This work presents a methodology to improve the reasoning performance for math of small language models to levels competitive with the state-of-the-art models. Specifically, the rStar-Math approach uses MCTS along with a verifier by interpreting the reasoning steps as code, in order to train a policy model and... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and positive feedback on our work. We sincerely appreciate your insights and your recognition of our contributions. Below, we address your specific comments.
>Q1: I strongly encourage the authors to share code, models and/or especially the data generated on the usua... | null | null | null | null | null | null |
To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers | Accept (poster) | Summary: This papers investigates the problem of Bayes-optimal prediction in hierarchical multi-class classification. A decision-theoretic framework is assumed, where probabilities are estimated during training, and the Bayes-optimal prediction is computed at test time.
The authors consider three types of settings, w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and insightful points. Below, we address each question concisely.
*On Theorem 4.7 and the Jaccard Index*
We would like to clarify that Theorem 4.7 and Section 4.3 **only** applies to hFβ-score and not to the Jaccard Index, or any other metric. As... | Summary: This paper tackles optimal decoding in hierarchical classification by developing universal algorithms for hierarchically reasonable metrics and a specialized algorithm for hF$_β$-scores. These methods, designed to find the best prediction given a posterior probability distribution, are particularly effective i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback even though we find the review quite unfair and not very detailed.
We try to answer the few elements you point out in the review.
*The proposed method assumes that the exact posterior probability distribution is given, which is a significant limitation.*
... | Summary: In this paper, the authors study the problem of hierarchical classification, i.e., a variant of multiclass classification problem with a predefined label hierarchy. The main focus of this paper is the decoding of optimal prediction w.r.t. a family of performance metrics called hierarchically reasonable metric ... | Rebuttal 1:
Rebuttal: We warmly thank reviewer k32x for its positive feedback and very insightful comments.
We try below to answer your questions.
*it is encouraged to include the proposed algorithms or provide a more detailed description of them in the main body.*
As the reviewer suggests, we will include the propo... | Summary: The paper introduces a framework for optimal decoding in hierarchical classification, where predictions are structured in a tree-like taxonomy. Unlike standard classification, the severity of errors varies based on the distance in the hierarchy between the predicted and true labels.
Most existing methods use ... | Rebuttal 1:
Rebuttal: We warmly thank the reviewer maE5 for their positive feedback and their acknowledgement of the significance of our work.
We answer below to your different points.
*One comment is regarding the runtime estimations, it would make sense to add the runtime based on the number of inference examples, ... | null | null | null | null | null | null |
Pruning Spurious Subgraphs for Graph Out-of-Distribtuion Generalization | Reject | Summary: This paper proposes PrunE, a pruning-based method designed to address the challenge of out-of-distribution (OOD) generalization for Graph Neural Networks (GNNs). Rather than focusing on directly identifying invariant subgraphs, PrunE prunes spurious edges to preserve the invariant subgraph. The method uses two... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! Please see below for our responses to your comments and concerns.
> **Q1: Novelty Issue**
__Response:__ One significant distinction between the proposed PrunE method and most existing OOD approaches lies in its learning paradigm. Specifically, PrunE focu... | Summary: In this paper, the authors study the problem of graph-level out-of-distribution (OOD) generalization. Their key claim is learning a more sparse graph structure from the vanilla graph by pruning those spurious edges, which they show is effective in preserving the invariant substructure and thus beneficial for O... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and positive feedback! Please see below for our responses to your comments and concerns.
---
> **Q1: The effectiveness of PrunE to assign low probabilit y weights to spurious edges**
__Response:__ Thank you for raising this crucial point. Through extensive... | Summary: The authors introduces PrunE, a pruning-based method for enhancing out-of-distribution generalization in GNNs. The method remove spurious edges to address the challenge. Theoretical guarantees is provided and experiments show PrunE obtain better results compared with other methods.
Claims And Evidence: Yes, c... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and careful review! Please see below for our responses to your comments and concerns.
---
> **Q1: Reproducibility issue**
__Response:__ As the official policy permits only figures and tables in the anonymous link, we have requested approval from the conferen... | Summary: This paper introduces PrunE, a novel pruning-based method to enhance OOD generalization in GNNs. Unlike previous approaches that attempt to directly identify invariant subgraphs, PrunE focuses on pruning spurious edges, preserving the invariant subgraph more effectively. The method employs graph size constrain... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful comments! Please see below for our responses to your comments and concerns.
---
> **Q1: When and why PrunE may fail**
__Response:__ Based on our empirical observations, the OOD generalization performance of PrunE, as well as other OOD methods, ... | null | null | null | null | null | null |
A Simple Model of Inference Scaling Laws | Accept (poster) | Summary: This paper investigates how neural models' inference performance scales with multiple attempts, particularly in the context of LLMs. The study introduces a straightforward statistical framework based on memorization to explore the relationship between inference attempts and success rates, measured by the cover... | Rebuttal 1:
Rebuttal: Dear Reviewer i2X4,
Thank you for your careful and positive review of our paper, finding that our proposed model is simple and elegant. We hope that our responses will alleviate your concerns.
Below, we address the issues you raised.
**Comments and Suggestions**
1) Thank you for pointing thi... | Summary: The paper introduces a statistical framework to analyze the scaling laws for LLMs inference, particularly addressing how model performance (pass@k) improves with repeated inference attempts. The authors present 2 models, one assumes samples differ in difficulty, modeled via a Beta distribution; the other consi... | Rebuttal 1:
Rebuttal: Dear Reviewer FFcT,
Thank you for your positive appraisal of our submission. We’re glad that you found valuable insights and potential practical applications, which were our goals.
We address the weaknesses raised, as well as questions below.
**Weaknesses**
1) *strong assumptions* - We compl... | Summary: This paper studies the paradigm of inference scaling and tries to identify the functional form that can help explain predict performance therein, i.e., building a scaling law with respect to inference budget (e.g., k in pass@k metric). The authors consider one of the simplest models for pretraining scaling law... | Rebuttal 1:
Rebuttal: Dear Reviewer kFgD,
Thank you for carefully reading our manuscript, we are glad that you found our theoretical analysis sound and our empirical results well backed.
We would like to address the various points you raised below.
**Regarding Evidence**
We agree that the VAE results are interest... | Summary: The paper proposes to study scaling laws for inference in a restricted setup where the model can potentially memorize the training dataset. The paper also shows that the theoretical predictions match empirical results on mathematical reasoning tasks for LLMs.
Claims And Evidence: The paper makes several assum... | Rebuttal 1:
Rebuttal: Dear Reviewer 4E5M,
Thank you for your positive reading of our manuscript.
Below, we address your comments/questions:
**Weakness:**
*The caption for Figure 1 was quite dense* – In the revised version, we will shorten the caption, and move some of its content to the main text, namely L131 –... | null | null | null | null | null | null |
Riemannian Diffusion Adaptation for Distributed Optimization on Manifolds | Accept (poster) | Summary: The paper concerns online distributed optimization for data on Riemannian manifolds. The authors propose an algorithm for distributed optimization between a number of agents with combination steps in the tangent spaces of the current values at the agents. The paper contains a theoretical analysis of the algori... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work and pointing out the strengths and weaknesses. In the following, we provide replies to weaknesses:
We would like to emphasize that the proposed work is not a trivial generalization, even though there is an Euclidean counterpart to the Riemannian diffusion adaptati... | Summary: This paper aims to solve the online decentralized optimization problem on the general Riemannian manifold for multi-agents. The proposed Riemannian diffusion adaptation method contains two stages: an adaptation step and a combination step. It theoretically proves that all agents will approximately converge to ... | Rebuttal 1:
Rebuttal: Thank you for reading our work (especially in checking the proofs) and offering constructive suggestions. In the following, we provide clarifications and answers to your comments and questions.
### **Replies to weakness:**
***Conduct more experiments for wider application aspects:***
Thank you ... | Summary: This paper proposes a decentralized optimization algorithm on manifolds that is termed Riemannian diffusion adaptation algorithm. The proposed algorithm follows two steps. First, in the adaptation step, each agent updates its local solution estimate on the manifold using Riemannian stochastic gradient descent ... | Rebuttal 1:
Rebuttal: Thank you for reading our work (especially in checking the proofs) and offering constructive suggestions. In the following, we provide clarifications and answers to your comments and questions.
### **Replies to weakness:**
***Improve the introduction and technical section:***
Thank you very muc... | Summary: The paper studies online distributed optimization on manifolds, and proposes Riemannian diffusion adaptation in which each agent keeps running two steps until convergence: 1) execute R-SGD; 2) combine outputs of neighboring agents by running one step of RGD over the associated penalty function which characteri... | Rebuttal 1:
Rebuttal: Thank you for reading our work and offering constructive suggestions. We provide clarifications to your comments as below.
***Lack of evidence that the replacement performs better:***
Thank you for this comment. We argue that the algorithm in (Wang et al., 2024b) is inefficient due to the inner-... | Summary: This paper presents a novel Riemannian generalization of a diffusion adaptation strategy for distributed optimization. The distributed optimization aims at finding an optimal solution with consensus among different agents. The proposed algorithm utilizes the Riemannian exponential map on manifolds and obtains ... | Rebuttal 1:
Rebuttal: Thank you for reading our work (especially in checking the proofs) and offering constructive suggestions. In the following, we provide clarifications and answers to your comments and questions.
### **Replies to Weakness:**
***Support the claim that the method in (Wang et al., 2024b) is not effic... | null | null | null | null |
Learn to Vaccinate: Combining Structure Learning and Effective Vaccination for Epidemic and Outbreak Control | Accept (poster) | Summary: The paper studies an important problem of picking nodes to vaccinate in a network,
assuming an SIS model of epidemic spread. The authors assume the network is not known,
and needs to be learned. This is an interesting extension, since most prior work assumes
the network is known. The authors present an algorit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments. We address each of the points raised below and will clarify them in the revised manuscript.
### Stationarity
We refer the reviewer to our response to Q6 of Reviewer Gdj4 for a more detailed response. In short, either the process dies o... | Summary: The paper tackles the critical challenge of minimizing disease extinction time in Susceptible-Infected-Susceptible (SIS) models under unknown contact networks. The authors propose a two-stage framework:
- Network Inference: A novel inclusion-exclusion learning algorithm with provable sample complexity bounds
... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment and their questions. We answer them below.
### Adaptivity
Our learning algorithm, SISLearn, relies on data drawn from the meta-stable distribution of the SIS process, where the probabilities of configurations do not change anymore. However, it c... | Summary: This paper studies the SIS model on an unknown graph. The process runs in discrete time. Any infected node becomes infected/susceptible in the next round with prescribed probabilities (independently of the rest of the graph). Any susceptible node can become infected from one of its infected neighbors. The goal... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their careful reading and attention to detail. The reviewer is absolutely right—Lemma D.1, as originally stated, was incorrect and developed too hastily. We have corrected the result below by applying Theorem 1.2 from Kontorovich & Ramanan (2008). We also addres... | Summary: This paper is broadly about vaccinating the nodes of a network over time (subject to a total budget on the number of vaccinations) in order to minimize the expected extinction time of the epidemic. The SIS model is assumed, and interestingly, the network is not assumed to be known, but has to be learned. (SIS ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and their insightful questions. We respond to each of them in detail below.
### Q1
We propose three vaccination strategies, all of which solve the Spectral Radius Minimization (SRM) problem:
(1) An optimal polynomial-time algorithm for trees (Appen... | null | null | null | null | null | null |
SymMaP: Improving Computational Efficiency in Linear Solvers through Symbolic Preconditioning | Reject | Summary: This paper introduces a novel method that employs a Recurrent Neural Network (RNN) to learn a sequence of operands for determining preconditioning parameters. The network is trained through a supervised learning approach, with the optimal parameters selected via grid search. Experimental results demonstrate th... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful and valuable comments. We respond to each comment and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us know your further concerns, and we will con... | Summary: This paper uses neural networks to introduce a matrix preconditioning framework via symbolic discovery, where preconditioning is important in linear system solving. This new framework can flexibly predict preconditioning parameters for different scenarios, which surpasses traditional methods focusing on indivi... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful and valuable comments. We respond to each comment and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us know your further concerns, and we will con... | Summary: This paper presents a new approach to finding the quasi-optimal parameter of two preconditionners in order to speed up the resolution of linear systems associated with PDEs. The authors have developed an algorithm that generates the dataset consisting of the PDE parameters and the optimal preconditionner param... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful and valuable comments. We respond to each comment and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us know your further concerns, and we will con... | Summary: This paper focuses on successive over-relaxation (SOR) which is an iterative method solving $Ax=b$ that parametrize the classical Gauss-Seidel (GS) method. The related parameter $\omega$ has to be tuned to ensure SOR converges faster than GS. While an analytical optimal expression exists, it depends on the spe... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful and valuable comments. We respond to each comment and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us know your further concerns, and we will con... | null | null | null | null | null | null |
Online Learning in Risk Sensitive constrained MDP | Accept (poster) | Summary: The paper develops the first sublinear regret bound for the risk sensitive CMDP problems. In contrast to the classical CMDP, an additional risk measure is put on the left hand side of the constraint value, and it is required that the final value is greater than a threshold. The paper develops the clever idea o... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing thoughtful comments. Please see our responses to your questions below.
>*For multiple constraints*
* As correctly pointed out by the reviewer, the complexity of our approach is affected by the number of constraints. Specifically, extending the method proposed i... | Summary: The paper studies online learning in episodic finite-horizon constrained Markov Decision Processes with entropic risk-sensitive constraints. Traditional primal-dual methods fail to directly address risk-sensitive CMDPs due to the non-linear nature of entropic risk constraints and the lack of strong duality. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing thoughtful comments. Please see our responses below.
>*Lack of numerical results*
* Our main contribution is theoretical. To the best of our knowledge, this is the first work that establishes sublinear regret and constraint violation bounds in the setting where ... | Summary: This work focuses on Risk-Sensitive Constrained MDPs and proposes a novel algorithm that ensures the entropic risk for an additional utility function remains above a given threshold. Under this setting, the author introduces a new algorithm based on the primal-dual method for an augmented MDP. Additionally, th... | Rebuttal 1:
Rebuttal: Thanks for providing thoughtful comments. Please see our responses below.
>*Regarding the computational efficiency...*
* *Even though the augmented budget can take value in real space, the computational complexity is still O(K), i.e. a linear in K. Thus, it is **not true** that our approach is com... | null | null | null | null | null | null | null | null |
Conservative Offline Goal-Conditioned Implicit V-Learning | Accept (poster) | Summary: This paper proposes conservative goal-conditioned implicit V-learning (CGCIVL). The main insight of CGCIVL is to penalize cross-trajectory goal-conditioned values, which may potentially be overestimated, with a conservative regularizer. To improve the empirical performance of CGCIVL, the authors additionally e... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable suggestions. We have carefully addressed each of your concerns in the responses below.
### R1: Methods and Evaluation Criteria
We have extended our evaluation to manipulation environments (see Table 1 in the linked [PDF](https://anonymous.4open.s... | Summary: This paper introduces conservatism to prevent overestimation in unconnected state-goal pairs and uses a quasimetric value network to prevent underestimation in connected cross-trajectory state-goal pairs. Theoretical analysis is provided for the idealized version of the algorithm, and the practical implementat... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our work. We have addressed each concern you raised below.
### R1: Claims and Evidence
Thank you for the reviewer’s comment. The theoretical guarantees mentioned in our paper indeed refer to the algorithm prototype based on Eq. (8), rathe... | Summary: This paper proposes a method for offline goal conditioned reinforcement learning with a penalty term to penalize the value function for unconnected state-goal pairs and does evaluation on OGBench. The results suggest the method outperforms previous methods on goal conditioned tasks.
Claims And Evidence: The p... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful feedback on our work. Please refer to detailed responses below to each of the raised concerns.
### R1: Weaknesses
In order to provide more comprehensive evaluation of the performance, we have conducted additional experiments in three manipulation environme... | Summary: This paper proposes an algorithm for goal-conditioned offline RL called Conservative Goal-Conditioned Implicit V-Learning (CGCIVL). CGCIVL improves upon Hierarchical Implicit Q-Learning (Park et al., 2024b) by introducing two techniques. First, it adopts a regularizer similar to CQL (Kumar et al., 2020) to pen... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s constructive feedback. Below, we respond to each concern point-by-point.
### R1: Methods and Evaluation Criteria
The choice between using $V_{\theta_v}$ or the distilled $V_{\theta_d}$ for advantage estimation ($\tilde{A}^h$ and $\tilde{A}^h$) is flexible, ... | null | null | null | null | null | null |
EGPlace: An Efficient Macro Placement Method via Evolutionary Search with Greedy Repositioning Guided Mutation | Accept (poster) | Summary: This paper proposes EGPlace, an evolutionary search-based approach for macro placement. It incorporates the wirelength, congestion, and overlap into its score computation. It achieves better HPWL and faster speed than previous RL-based approaches.
## update after rebuttal
After carefully reviewing the rebutta... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions! Below, we provide a detailed response to your questions and comments. If any of our responses fail to sufficiently address your concerns, please inform us, and we will promptly follow up.
**The Results of MaskPlace on Circuit Ariane... | Summary: The manuscript proposes EGPlace, an innovative evolutionary optimization framework for macro placement in IC design, introducing a greedy repositioning-guided mutation operator and efficient mask computation algorithm. Experimental results show that EGPlace achieves significant improvements in wirelength reduc... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions!
**Regarding Mixed-size Placement**
We conduct mixed-size placement following the same approach in Chipformer and EfficientPlace. We first fix all macros placed by EGPlace and apply the global placement step of DreamPlace to positi... | Summary: This article presents a new mutation operator for evolutionary algorithms designed for the problem of macro placement. The new operator, the Greedy Repositioning Guided Mutation, constructs a set of good placements for a module and then randomly selects one. Compared to a traditional mutation operator, it ther... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions!
**Motivation for choosing ISPD2005 and Additional Experiments on ICCAD2015**
The major competitors, including MaskPlace, EfficientPlace, and WireMask-EA, conduct experiments on ISPD2005, so we use the same setting for a fair comparison. We also perform expe... | Summary: This paper presents EGPlace, an evolutionary optimization framework for macro placement. EGPlace addresses these issues with two key parts: 1) a greedy repositioning: guided mutation operator that targets critical layout regions and 2) an efficient mask computation algorithm. Experimental results on ISPD2005 a... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions! Below, we provide a detailed response to your questions and comments. If any of our responses fail to sufficiently address your concerns, please inform us, and we will promptly follow up.
**Experiments on the ICCAD 2015 Benchmark**
... | null | null | null | null | null | null |
CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models | Accept (poster) | Summary: This paper studies protective perturbations for LDMs, where the success of existing methods are based on distorted latent representations. To examine these protections, the authors propose Contrastive Adversarial Training (CAT), which inserts lightweight adapters into the latent autoencoder. Specially, CAT re... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments (Q). We hope that our responses (A) have fully addressed the concerns, and remain committed to clarifying any further questions that may arise during the discussion period.
> **Q1: It is good to try training-free DM customization methods (... | Summary: This paper examines the effectiveness of adversarial perturbations in protecting data from unauthorized customization in LDMs. The authors reveal that these perturbations work by distorting latent representations and propose CAT as an adaptive attack that reduces their effectiveness. Experimental results highl... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments (Q). We hope that our responses (A) have fully addressed the concerns, and remain committed to clarifying any further questions that may arise during the discussion period.
> **Q1: Yes, but could be ... using the fidelity metrics, such as ... | Summary: This paper investigates adversarial examples as protective perturbations in latent diffusion models. The authors reveal that the reason why adversarial examples are effective is primarily due to the distortion of their latent representations. Based on this observation, they propose the CAT method to attack pro... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments (Q). We will correct the identified typos and incorporate the suggested references you mentioned. We hope that our responses (A) have fully addressed the concerns, and remain committed to clarifying any further questions that may arise duri... | Summary: This paper proposes an attack named CAT that can break the protection of preventing diffusion models from effectively learning unauthorized data being perturbed by defensive noise. The authors first empirically identify that the mechanism behind existing defensive perturbations is to make embeddings of perturb... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments (Q). We will incorporate the suggested references you mentioned. We hope that our responses (A) have fully addressed the concerns, and remain committed to clarifying any further questions that may arise during the discussion period.
> **Q... | null | null | null | null | null | null |
Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference | Accept (poster) | Summary: This paper proposes a new method for ensuring label differential privacy in classification tasks through the randomized response mechanism with optimality guarantees. Furthermore, the paper proposes differentially private confidence intervals based on the former method.
Claims And Evidence: Most of the claim... | Rebuttal 1:
Rebuttal: **Validity of Definition 3.1**, **Theoretical Claims** and **Question 1.**
To satisfy $(\epsilon, \delta)$-LabelDP (and similarly for $\epsilon$-DP), the conditional probabilities $p_{00}=P(Y^*=0 \mid Y=0)$ and $p_{11}=P(Y^*=1 \mid Y=1)$, which lie in $(0,1)$, must meet the following privacy cons... | Summary: The authors propose an estimation method for a binary response model under LabelDP that is optimal in that it maximizes the trace of the Fisher information matrix. They leverage results regarding asymptotic normality of the MLE to derive confidence intervals for their estimator.
Claims And Evidence: The autho... | Rebuttal 1:
Rebuttal: **Experimental Designs or Analyses 1.**
We appreciate the reviewer pointing out this observation. The abrupt jumps around small $\varepsilon$ values (particularly near $\varepsilon=0.1$) observed in Figures 2 and 4 arise primarily from the specific data generation mechanism of our simulation stud... | Summary: This paper addresses statistical estimation and inference in binary response models while preserving the privacy of the labels via LabelDP. Covariate features $X$ are public, but binary response labels $Y$ are sensitive. The authors focus on using randomized response (RR) mechanisms – a classical privacy techn... | Rebuttal 1:
Rebuttal: **Question under Experimental Designs Or Analyses**
We acknowledge the concern of the reviewer about the large simulation sample size ($n=10^5$) for asymptotic approximations. This choice was intentional to validate our theoretical results (Theorem 4.7), where the guarantees of consistency and no... | null | null | null | null | null | null | null | null |
SDP-CROWN: Efficient Bound Propagation for Neural Network Verification with Tightness of Semidefinite Programming | Accept (spotlight poster) | Summary: The paper introduces SDP-CROWN, a hybrid framework combining semidefinite programming (SDP) relaxations with bound propagation for neural network verification under L2-norm perturbations. The core contribution is a novel linear bound derived from SDP principles that includes a new bias term h, which provides t... | Rebuttal 1:
Rebuttal: We want to thank reviewer zfFU for for your positive feedback and valuable comments. As suggested, we’ve added new experiments on GCP-CROWN [2] and BICCOS [3] (a follow-up work of GCP-CROWN), as well as a more sophisticated Lipschitz constant method (LipSDP [1]).
**New experimental results**
We... | Summary: The work proposes SDP-CROWN, a modified bound propagation framework based on CROWN for verifying the robustness of neural networks to $\ell_2$ norm perturbations. SDP-CROWN introduces an additional parameter into the bound propagation framework which is used for tightening the bias term in the linear relaxatio... | Rebuttal 1:
Rebuttal: We thank you for the detailed review. We added **additional experiments on GCP-CROWN and BICCOS, as well as LipSDP [5] (another SDP method) for all models** as requested. We hope you can reevaluate our paper based on our response.
**Clarifying Figure 1**
We apologize for the confusion and will c... | Summary: The authors propose a new method they call SDP-CROWN. They use the framework of semidefinite programming to derive linear bounds on the networks behaviour however based not on an $L_\infty$ norm but based on $L_2$ norm bound perturbations. This linear bound can then be used for any linear bound propagation met... | Rebuttal 1:
Rebuttal: We want to thank reviewer 4cg2 for valuable comments and for recognizing the key contributions of our paper. We hope our response would adequately address your questions and concerns.
**"They claim that their bounds can be up to $\sqrt{n}$ better compared to standard single-neuron bounds. While ... | null | null | null | null | null | null | null | null |
SpikF: Spiking Fourier Network for Efficient Long-term Prediction | Accept (poster) | Summary: This paper introduces the Spiking Fourier Network (SpikF), an attention-free framework designed to address key challenges in applying Spiking Neural Networks (SNNs) to long-term prediction tasks.
They encode input sequences in patches and employ a frequency-domain selection mechanism that better captures the s... | Rebuttal 1:
Rebuttal: We are grateful for your comprehensive review and the valuable insights you have provided. In the subsequent sections, we will address your questions one by one. And we will integrate all relevant discussions into our article for the upcoming revision.
>**Q1:** The proposed SpikF's hardware frien... | Summary: This paper introduces an attention-free framework, called Spiking Fourier Network (SpikF) for achieving long-term time series forecasting.
## update after rebuttal
There is no external comment. And I think that it is a borderline paper.
Claims And Evidence: This paper aims at modifying SNNs and attention fo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough evaluation of our work and the expert feedback you have shared. In response to your constructive critique, we will provide clarifications addressing your question and incorporate these discussions into the revised manuscript to strengthen our theoretical frame... | Summary: This work addresses two key challenges in applying Spiking Neural Networks (SNNs) and Transformer architectures to long-term forecasting: (1) capturing long-range dependencies, which increases computational and energy costs, and (2) the lack of effective positional encoding for Spiking Transformers. To address... | Rebuttal 1:
Rebuttal: We deeply appreciate your thorough review and insightful feedback. We will address your questions one by one in the following sections.
>**Q1:** Figure 2 lacks clarity regarding the meaning of the firing rate ($\alpha$) ...
$\alpha$ denotes the firing rate of SNN and can be formulated by:
$$
\a... | Summary: The authors propose **SpikF**, a novel SNN-based architecture designed for long-term prediction tasks. Technically, the **spiking patch encoder** is introduced to efficiently convert sub-series into spikes with low computational complexity. Additionally, a **spiking frequency selection mechanism** is implement... | Rebuttal 1:
Rebuttal: We appreciate your careful review and the insightful questions you have raised. We provide a detailed point-by-point response to each of your valuable comments to ensure clarity and address all aspects thoroughly.
>**Q1:** Lack of Inference Time. ... a detailed analysis and discussion of inferenc... | null | null | null | null | null | null |
The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data | Accept (spotlight poster) | Summary: Existing reinforcement learning (RL) approaches often struggle to generalize to unseen goals and states. To address it, this paper propose TEDUO, a training pipeline for offline language-conditioned policy learning in symbolic environments. TEDUO employs large language models (LLMs) as generalizable instruct... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our work. Below, we would like to address your questions and concerns.
---
### P1 Additional baselines.
We understand that a key concern in your review is the lack of additional baseline comparisons. However, to the best of our knowledge, no existing standard RL ... | Summary: This paper introduces TEDUO, a method for fine-tuning instruction-following LLM agents using an unlabeled dataset of interactions (i.e. environment transitions without instructions or rewards).
TEDUO operates in two key stages:
1. The LLM labels the dataset by determining whether any possible goals are reache... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback and valuable suggestions. Below, we address each of the key concerns and clarify aspects of our work.
---
### P1 Hindsight Labelling
We acknowledge that LLM-based hindsight labeling itself is not novel, and we did cite prior work (Appendix A.2)... | Summary: The paper studies the problem of training generalizable RL policies with the help of language models. RL policies can generally achieve impressive performance given enough exploration/coverage over the (state, action) space, but if the RL policy networks are trained from scratch on a particular environment, th... | Rebuttal 1:
Rebuttal: Thank you for your enthusiastic review and constructive feedback! We’re delighted by your positive assessment and have addressed your questions below to strengthen the manuscript further.
---
### P1 Task Complexity
See P2 in answer to reviewer `zuF6`, showing **new results** on generalization f... | Summary: This paper introduces TEDUO, a training framework that aims to enhance language-conditioned policy learning in autonomous agents while reducing the reliance on extensive data. The framework is structured around three key stages, each leveraging the capabilities of large language models (LLMs).
First, data enh... | Rebuttal 1:
Rebuttal: Thank you for your review and constructive feedback. We appreciate your engagement with our work and have carefully addressed your concerns below:
---
### P1 Task Complexity and Scalability
See P2 in the answer to reviewer `zuF6`, including **new results on generalization from simple to complex... | null | null | null | null | null | null |
Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means | Accept (poster) | Summary: The paper examines the Median of Means (MoM) estimator for estimating means in heavy-tailed distributions. The authors derive a new sample complexity bound using an innovative symmetrization technique. They also present applications of this technique to k-means clustering with unbounded inputs and to linear re... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read the manuscript and provide constructive feedback.
In the following, we will address the reviewer’s comments in the order they were given, except for those regarding experiments, which we will address last.
**Claims and Evidence:** We first addre... | Summary: The authors study the uniform convergence problem, i.e., estimating the mean of a set of functions simultaneously over inputs randomly sampled from an underlying distribution. Specifically, the authors focus on the classical median-of-means estimator that has celebrated performance on traditional distributiona... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read the manuscript carefully and provide feedback.
**Theoretical claims:** We would like to address the reviewer’s comment about the magnitude of the constants in Theorem 3.4. We agree that the constants differ from the empirical mean estimate of a si... | Summary: The paper proposes to use the median-of-means estimator to (uniformly) estimate the mean over a whole real-valued function family, with respect to some unknown distribution with bounded $(1+p)$-th moment that we only get sample access from. The authors give an analysis of the maximum estimation error, under th... | Rebuttal 1:
Rebuttal: We first thank the reviewer for carefully reading the manuscript and providing constructive feedback.
**Relation To Broader Scientific Literature:** We would like to comment on the reviewer's concern about the relevance of MoM in light of novel more refined estimators. We start noting that, simil... | Summary: In this paper, the authors tackle the problem of uniform mean estimation under heavy-tailed noise. Considering a set of functions, and a random variable, they analyze the sample complexity of providing a uniformly consistent estimation of the mean of the functions evaluated in the random variable.
Claims And ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read the manuscript and provide feedback.
We also appreciate the reviewer’s suggestions for improving the organization and presentation by first giving the theorems and then the proof sketch.
We also make a brief remark on emphasizing that we are not... | null | null | null | null | null | null |
Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition | Accept (spotlight poster) | Summary: This paper explores the phenomenon of task superposition: when presented a mixture of in-context examples corresponding to different tasks, the output probability distribution of an ambiguous query shows sensitivity to the different tasks and the relative proportion of the examples under different tasks. The p... | Rebuttal 1:
Rebuttal: Dear Reviewer fgkh,
We sincerely appreciate your thoughtful feedback on our paper. Below, we address the specific concerns raised in the review.
**More complicated tasks**
Thank you for your suggestion. For more complicated task, e.g., grade-school Math, the input $x$ is a Math question and the... | Summary: The authors show large language models can naturally perform multiple, distinct tasks simultaneously, even when they were only ever trained on one task at a time. They describe this phenomenon akin to 'superposition' which has been shown in previous work in the settings of having multiple tasks while performin... | Rebuttal 1:
Rebuttal: Dear Reviewer TgC8,
We sincerely appreciate your thoughtful feedback on our paper. Below, we address the specific concerns raised in the review.
**Theoretical claims**
Thank you for bringing up this work. [1] focus on superposition with NN while we focus on superposition in Transformers, by wei... | Summary: This paper investigates the "task superposition" phenomenon of ICL, i.e., when multiple tasks simultaneously appear in the context, the model can assign non-negligible output probabilities to more than one task. Additional findings and contributions include:
1. Pretrained LLMs have bias on what task to perform... | Rebuttal 1:
Rebuttal: Dear Reviewer FHnf,
We greatly appreciate your constructive feedback on our paper. Below, we address the specific concerns raised in the review.
**Theoretical claims**
> The proof is a constructive one, ..., there is no guarantee that Transformers will definitely implement such a construction t... | Summary: This paper introduces the novel empirical finding that when presented with a context that contains a mixture of different tasks, an LLM will respond as though it is performing a superposition of those tasks. By training very simple small GPT-2-style models from scratch on artificial tasks where every training ... | Rebuttal 1:
Rebuttal: Dear Reviewer UuyG,
We greatly appreciate your constructive feedback on our paper. Below, we address the specific questions raised in the review.
**Figure 1(b)**
Thanks for pointing this out. We will update Figure 1(b) in our next revision.
**Does ordering of tasks matter?**
In our setting of... | null | null | null | null | null | null |
Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction | Accept (poster) | Summary: The paper proposes conformal revision of questions (CROQ), which revises multiple-choice questions (MCQs) by narrowing down the prediction set. Additionally, the paper provides a corresponding routine for learning a CP score that aims to minimize set size under the coverage constraint. The experiments of the p... | Rebuttal 1:
Rebuttal: We are delighted with the positive feedback on our paper. We appreciate the recognition of strengths on *the ideas, presentation and soundness of empirical analysis*. Our response to the queries is as follows.
**Why tanh for $g$ ?** While the choice of the function class $\mathcal{G}$ is up to th... | Summary: This paper proposes a method to enhance large language model (LLM) decision-making for multiple-choice questions (MCQs) and tool selection tasks using conformal prediction (CP). The authors introduce "Conformal Revision of Questions" (CROQ), which uses CP to identify and eliminate unlikely answer choices befor... | Rebuttal 1:
Rebuttal: We appreciate the detailed feedback and recognition of the clarity and broad applicability of our work. Our response is as follows,
**Relationship to other UQ methods.** The reviewer correctly points out that there are other methods for quantifying uncertainty in the context of LLMs besides con... | Summary: This paper is using conformal prediction sets to improve the performance of LLMs on multiple choice question answering tasks. In particular, the propose a framework which they first construct a prediction set and then re-ask the same question with the limited options in the set from the LLM. They then empirica... | Rebuttal 1:
Rebuttal: We thank the reviewer for careful attention to the paper. Our response to the queries is as follows,
**Choice of $\alpha$.** We set $\alpha$ to a single (fairly arbitrary) value of $0.05$ in the main body of the paper for simplicity of exposition, but $\alpha$ can be treated as a hyperparameter a... | Summary: First, the paper observes that removing incorrect answer choices from the answer sets given to an LLM improves performance. This motivates conformal revision of questions (CROQ), a simple method to boost multiple choice QA (MCQA) on any model and any dataset by first asking a question to the model, building a ... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful review and the noted strengths on presentation, problem motivation, empirical evaluation, and results of our paper. Our response to the queries and comments is as follows.
**Input caching to make re-prompting efficient.** We thank the reviewer for the suggestion to u... | Summary: This paper proposes a method for improving the performance of LLMs on MCQ benchmarks using conformal prediction. The key idea is to construct an uncertainty set that contains the correct answer with high probability, prune all answer choices that are outside the set, and then present the LLM with a reduced set... | Rebuttal 1:
Rebuttal: Thanks for the feedback. Our response to the queries is as follows,
**Importance and generality of the MCQ setting.** The multi-choice question-answering framework encompasses any setting in which an LLM must select from among a finite number of options. We believe that this describes many if not... | null | null | null | null |
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model | Accept (poster) | Summary: This paper argues that current Transformer-based SNN language models are difficult to deploy on neuromorphic chips due to the presence of softmax and layer normalization operations. To address this challenge, the authors propose Sorbet, a model that is more compatible with neuromorphic hardware. Sorbet is base... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable suggestions. We hope our response below can address your concerns.
---
> Q1 & 2: Pretraining, fine-tuning, and distillation
We use a pre-trained and fine-tuned BERT from HuggingFace as a starting point. We then applied distillation and quantization... | Summary: The authors propose Sorbet: A transformer-based spiking language model optimized for neuromorphic hardware, enhancing energy efficiency while maintaining strong performance. It introduces BitShifting-based PowerNorm (BSPN) for normalization and Power-of-Two softmax (PTsoftmax) as a hardware-friendly alternativ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable suggestions. Below are our responses, which we hope will address your concerns.
---
> Q1: Deployment of SNNs on neuromorphic hardware
We have evaluated the hardware compatibility with the Lava framework to simulate Loihi chip. However, the platfor... | Summary: This paper proposed a Spiking Transformer language model, named Sorbet, designed for neuromorphic hardware. Sorbet introduces two approximations, PTsoftmax and BSPN, to replace traditional softmax and layer-wise normalisation. The aim of them is to make the model neuromorphic compatible and energy-efficient. ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable suggestions. Below are our responses, which we hope will address your concerns.
---
> Q1: Hardware focus and evaluation on actual chips
We appreciate your suggestions. To demonstrate the neuromorphic hardware compatibility of our proposed model, we... | null | null | null | null | null | null | null | null |
De-mark: Watermark Removal in Large Language Models | Accept (poster) | Summary: DE-MARK presents a framework for removing n-gram-based watermarks, specifically targeting the soft watermarking scheme proposed by Kirchenbauer et al. (2023a). The method utilizes a novel querying strategy called "random selection probing" to estimate watermark parameters like strength and red-green lists. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback, which has been invaluable in refining our manuscript. Below, we provide detailed responses to each of your comments:
> Q1: Firstly, the attack is primarily focused on a specific soft watermark (Kirchenbauer et al., 2023a). This type of watermar... | Summary: ## Summary
This paper addresses critical vulnerabilities in n-gram watermarking schemes for language models (LMs). The authors propose **DE-MARK**, a framework for watermark removal and exploitation, with three key contributions:
1. **Watermark Parameter Estimation**
Introduces _random selection probing... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and thoughtful suggestions, which have greatly helped improve the quality of our work.
> Q1 The experimental results don't include variance measures or statistical significance tests across multiple runs.
A1: Firstly, evaluating whether a sentence is watermar... | Summary: - The paper presents DE-MARK, a framework designed to remove n-gram-based watermarks from Large Language Models (LLMs). It introduces a novel querying strategy, "random selection probing," to assess watermark strength and reconstruct watermarking parameters.
- Unlike previous methods that rely on knowledge of ... | Rebuttal 1:
Rebuttal: > Q1: Would DE-MARK still work against modern watermarking techniques (e.g. distortion-free methods like Gumbel watermark by Aaronson)? Is it specially designed for KGW?
A1: Our proposed methods can be generalized to most n-gram-based methods, and we add additional experiments for two popular dis... | null | null | null | null | null | null | null | null |
Learning Extrapolative Sequence Transformations from Markov Chains | Accept (poster) | Summary: The authors consider the task of maximizing a function s(X), like sentiment or predicted protein activity, in a discrete space, which is a challenging task. Te baseline they consider is (annealed?) MCMC with proposals from a pre-trained model. Instead, they suggest running MCMC for some amount of time and then... | Rebuttal 1:
Rebuttal: Thank you for your careful and detailed review of our paper. We are grateful for the feedback, and address your main points below:
> “In section 2, you suggest your model can be useful even if you only have an approximation of s. Could you demonstrate this?”
In our extrapolation settings (sentim... | Summary: The authors propose a method to efficiently perform extrapolative generation, optimizing a property of interest.
Specifically, they train an autoregressive model to predict new sequences or states that enhance the desired property, using training samples obtained from MCMC. They evaluate their approach across ... | Rebuttal 1:
Rebuttal: We appreciate the detailed and thoughtful feedback, and aim to address remaining questions and concerns here.
> “While intermediate state generation improves performance in the protein domain, its impact on anonymization and sentiment tasks remains unclear.”
In the case of our sentiment task, t... | Summary: This paper proposes an improvement to the MCMC algorithm, specifically the random search methods in the Monte Carlo exploration. Instead, a model trained from the MCMC searching trajectories is applied to greedily optimize the properties of interest. Empirically, the proposed method is able to sample efficient... | Rebuttal 1:
Rebuttal: We sincerely appreciate the feedback offered in this review, and hope to address some concerns.
>”A theoretical perspective of… what makes an optimal $q_{\theta}$ for the MCMC algorithm is missing.”
We agree theoretical grounding is important. Nonetheless, compared to previous approaches, we ... | Summary: This paper presents a new approach for extrapolative sequence generation tasks, utilizing sequences produced through Markov Chain Monte Carlo (MCMC) exploration as training data. This approach targets tasks that necessitate the generation of new sequences exceeding previously recorded property values, such as ... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful response. We address some of the raised questions here.
>”If the scorer is mis-specified or the MCMC exploration is very limited, the AR model will learn a suboptimal strategy…”
These are valid concerns. A strength of our approach is that it benefits from the wealth of ... | null | null | null | null | null | null |
Improving Your Model Ranking on Chatbot Arena by Vote Rigging | Accept (poster) | Summary: The authors find that an attacker can meaningfully boost (or diminish) a target model’s ranking even when that model does not appear in the rigged battles. New votes cast in entirely different matchups, where the target never competes, can still change the target’s overall standing because all of the models’ r... | Rebuttal 1:
Rebuttal: Thank you for your strongly supportive review and insightful suggestions. Below we respond to the comments in **Weaknesses (W)**, and **Questions (Q)** and will fix the typos in the paper revision.
---
***W1: About the claim in the experiments validated "showing that omnipresent rigging strategi... | Summary: This paper investigates how Chatbot Arena can be manipulated to artificially boost the ranking of a target model. The authors first describe a target-only rigging strategy, which detects and votes exclusively for the target model whenever it appears. They then present an **omnipresent** strategy that manipulat... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and for recognizing our work. Below, we respond to your comments in **Concerns (C)**, **Weaknesses (W)**, and **Questions (Q)**.
---
***C1: No supplementary materials.***
We would like to clarify that we have provided *Supplementary Material* and the folde... | Summary: The paper investigates the problem of manipulating the ranking of a target model on the anonymous voting platform Chatbot Arena. The authors begin by examining a straightforward approach called target-only rigging, which involves attempting to influence votes only in battles where the target model is present. ... | Rebuttal 1:
Rebuttal: Thank you for your very positive and supportive review. Below we respond to the comments in **Weaknesses (W)**.
---
***W1: Absence of actual, live experiments on the platform.***
We acknowledge that we did not conduct live experiments on the platform, primarily due to ethical considerations. In... | Summary: The paper presents a method to manipulate the chatbot arena leaderboard platform, demonstrating the conceptual ability to alter a target model's ranking through strategic voting. The authors propose three rigging schemes: The first is target-specific, involving voting for the target model whenever it appears i... | Rebuttal 1:
Rebuttal: Thank you for your positive review for recognizing our paper and invaluable suggestions. Below we respond to the comments in **Weaknesses (W)**.
---
***W1: (Essential References Not Discussed) Missing literature review of social voting and its vulnerabilities.***
We appreciate your valuable sug... | null | null | null | null | null | null |
Online Pre-Training for Offline-to-Online Reinforcement Learning | Accept (poster) | Summary: The paper proposes a novel offline-to online RL approach where, at the end of the offline phase, a second critic is trained in an "Online pre-training" phase and used in addition to the offline dataset during the final online learning phase. During online pre-training, the offline policy and critic are frozen,... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the valuable and constructive feedback.
We respond to each point in detail below and will reflect the suggested improvements in the revised manuscript.
### **R4-1. Questions**
- Question about umaze dataset
According to the official D4RL[4-1] paper, the Antm... | Summary: This paper presents OPT, a method to improve value estimation in RL. OPT follows three phases: offline pre-training, an "online pretraining" phase to train a separate value function, and online fine-tuning that combines both value functions. Unlike traditional methods that use a single value function, OPT intr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback, which helps improve both the clarity and completeness of our analysis.
We address each point below.
### **R3-1. Regarding Statistical Significance**
To reduce statistical uncertainty, we increase the number of random seeds from 5 to 10 f... | Summary: The paper studies offline-to-online RL problem and propose a new method called online pretraining. The mains to solve the sub-optimality problem brought by the offline Q value function during online fine-tuning. Specifically, the method propose to freeze the offline policy at the beginning of the online fine-t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the detailed and insightful comments.
The suggestions are highly valuable in helping us refine both the presentation and the depth of our analysis.
We carefully address each point below.
### **R2-1. Regarding Asymptotic Performance of Figure 4**
We appreciate... | Summary: The authors proposed a new offline-to-online RL method called Online Pre-Training (OPT), where a new phase, online pertaining, is added between offline pre-training and online fine-tuning to solve the inaccurate value estimation problem. OPT introduce a separate value function instead of directly continue the... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback.
The comments help improve the clarity and completeness of our work.
Below, we address each point in detail.
### **R1-1. Regarding Experimental Results**
Following the reviewer’s suggestion, we increase the number... | null | null | null | null | null | null |
Graph Inverse Style Transfer for Counterfactual Explainability | Accept (poster) | Summary: The authors introduce GIST their novel framework that generates counterfactual graph explanations. They leverage spectral style transfer to generate valid counterfactual explanations. Their architecture consists of two components: attention based node embeddings, and edge probabilities from the Gumbel-softmax ... | Rebuttal 1:
Rebuttal: We thank you for the effort made to review our paper, and for the nice score you chose to give it. Thank you for pointing out RCExplainer [2].
**W1: Missed Bajaj reference**: We were aware of the paper, and decided to reviewed RCExplainer again to see whether we were missing something or not. We... | Summary: GIST introduces a backtracking approach for graph counterfactual explainability using spectral style transfer. Unlike forward perturbation methods, it refines graphs to preserve global style and local content. GIST achieved excellent results experimentally.
Claims And Evidence: Please refer to Strengths And W... | Rebuttal 1:
Rebuttal: We thank you for the effort made to review GIST. It's unfortunate you didn't see its value during your original review. With the following, we tackle the weaknesses you mentioned, and hope to convince you of the paper's value.
**W1: Intermediate graph $G^e$, and improved accuracy**: $G^e$ is obta... | Summary: The authors present a new method for generating counterfactual explanations for Graph Neural Networks (GNNs) based on an adaptation of neural style transfer. They then establish some theoretical results for the well-foundedness of their approach before presenting their method to learn the style transfer object... | Rebuttal 1:
Rebuttal: Oh, this is awesome. Thanks for reading the paper and not using an LLM to generate your review :-)
Your suggestions will better our paper. The proofs will be much shorter and clarifications on counterfactuality more thorough. Sec. 5.3 contains the two most important evaluation metrics as per [1]... | Summary: This paper introduces Graph Inverse Style Transfer (GIST), a novel framework for counterfactual explainability in graph neural networks (GNNs). Unlike traditional forward perturbation-based counterfactual methods, GIST employs a backtracking mechanism inspired by style transfer in vision. By first overshooting... | Rebuttal 1:
Rebuttal: We thank you for the effort made to review our paper, and for the nice score you chose to give it. With the following, we hope to answer your questions, and convince you of the value of GIST.
**W2: Specific user studies.** We want to point out that the scope of this paper was not to involve user... | null | null | null | null | null | null |
Scalable Approximation Algorithms for $p$-Wasserstein Distance and Its Variants | Accept (poster) | Summary: This work introduces a method to compute a $O(\log n)$-approximation of the p-Wasserstein distance in $O(n^2 \log n)$ time for $p\ge 2$. The method is based on the construction of Hierarchically well Separated Trees (HSTs), and the Hungarian algorithm with Bichromatic Closest Pairs. Furthermore, the authors pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and constructive feedback. The two main contributions of our paper are as follows:
* A $O(\log n)$ relative approximation algorithm for $p=1$ has been known for almost three decades and has had a significant impact. Despite significant effort, no s... | Summary: This paper aims to provide an $O(\log n)$ approximation algorithm for p-Wasserstein distance that runs in $O(n^2 \log n \log U \log \Delta)$ time. This is done with a collection of $p$ HST trees and with a dynamic BCP data structure to efficiently find augmenting paths in the dual framework for the OT problem.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and constructive feedback. We will add a short discussion and a reference about the convergence rate of the Wasserstein distance, as well as the formal definition of spread and the corrected inequality in Appendix A in our next version. We answer the... | Summary: This paper develops efficient algorithms for approximating the $p$-Wasserstein distance $W_p$ and a robust variant of $W_p$. In particular, when the input measures are uniform over discrete point sets of size $n$, they provide an approximation algorithm for $W_p$ with relative error $O(\log n)$ that runs in ti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and constructive feedback. We will update the paper to include your suggestions. We assume that distances can be queried in $O(1)$ time and will state it explicitly. We will also include additional dependencies of execution time on $p$ that arise due... | Summary: This paper is concerned with developing a new algorithm for estimating p-Wasserstein distances. Notably, this algorithm enables approximating the $p$-Wasserstein distance between distributions supported on $n$ atoms up to a multiplicative factor which scales as $O(log(n)$ (in expectation), i.e. the approximat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and constructive feedback. We answer the main concern raised by the reviewer below.
>The importance of $O(\log n)$ approximation factor.
**Response:** A tree-embedding-based $O(\log n)$-approximation for the $1$-Wasserstein distance originally deve... | null | null | null | null | null | null |
Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models | Accept (poster) | Summary: The paper rigorously investigates when graph attention mechanisms help—and when they do not—in the context of node classification for graphs generated by Contextual Stochastic Block Models (CSBM). It introduces a simplified non-linear attention mechanism and demonstrates theoretically that attention improves c... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and recognition of our paper. In response to your questions and suggestions, we provide the following clarifications:
### 1. **Additional experiments on more comprehensive datasets:**
Based on your suggestion, we conducted supplementary experiments on a lar... | Summary: The paper studies effectiveness of graph attention networks in the contextual stochastic block model (CSBM) setting. It builds on prior work by Fountalakis et al (JMLR, 2023) and graph attention retrospective in the same setting. In comparison to that work, the main difference appears to be a linear multi-laye... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. Our response is as follows:
### 1. **Additional Experiments**
You mentioned that the experiments on real-world datasets were insufficient. We greatly appreciate your feedback and have conducted additional experiments on more comprehensive datasets, with the ... | Summary: The paper theoretically analyzes the effectiveness of graph attention for node classification tasks in graphs with a CSBM structure and varying levels of feature and structure noise and conclude that high feature noise renders graph attion ineffective whereas graph attention is beneficial in the case of low fe... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. In response to multiple reviewers’ suggestions, we have added experiments on a larger dataset (*ogbn-arxiv*) and five heterophily datasets (e.g., *roman-empire*). Please find the details at the following link https://drive.google.com/file/d/1ALWkkazk1LPjaWSS... | null | null | null | null | null | null | null | null |
ROPO: Robust Preference Optimization for Large Language Models | Accept (poster) | Summary: The paper considers the alignment problem of large language models (LLMs) trained on noisy preference data, where human preferences are flipped with a certain probability $\eta$. To align the LLM in a robust manner and mitigate performance degradation due to noisy data, the authors propose an iterative alignme... | Rebuttal 1:
Rebuttal: Dear Reviewer GZqo,
Thank you for your valuable review. We respond to each comment as follows and sincerely hope that our response can properly address your concerns.
Tables can be found in **GZqo.md** in **https://anonymous.4open.science/r/ICML25-ROPO-F6CD**
# Claims And Evidence
> C1: A form... | Summary: This paper addresses the problem of robustly learning preference from noisy preference data. It proposed the ROPO framework which iteratively filtering noisy preference data and aligning the LLM with the filtered data. The ROPO framework consists three key modules, 1) noise-aware DPO loss for preference alignm... | Rebuttal 1:
Rebuttal: Dear Reviewer tkDe,
Thank you for your valuable review. We respond to each comment as follows and sincerely hope that our response can properly address your concerns.
Figures and Tables can be found in **tkDe.md** in **https://anonymous.4open.science/r/ICML25-ROPO-F6CD**
---
# Methods And Eval... | Summary: This paper tackles the important problem of learning from noisy offline preference data. Motivated by the observation that previous noise-aware preference optimization methods either only partially mitigate the noise problem or requires costly invocation of a separate LLM during the training process, the autho... | Rebuttal 1:
Rebuttal: Dear Reviewer mD6s,
Thank you for your valuable review. We respond to each comment as follows and sincerely hope that our response can properly address your concerns.
Figures and Tables can be found in **mD6s.md** in **https://anonymous.4open.science/r/ICML25-ROPO-F6CD**
---
> Q1: How would RO... | Summary: LLM model alignment has shown great potential for several applications. However, popular techniques such as DPO are highly sensitive towards positive vs negative samples, and therefore any noise in the training preference data can significantly impact the performance. To alleviate this issue, the paper propose... | Rebuttal 1:
Rebuttal: Dear Reviewer smYA,
Thank you for your valuable review. We respond to each comment as follows and sincerely hope that our response can properly address your concerns.
Figures and Tables can be found in **smYA.md** in **https://anonymous.4open.science/r/ICML25-ROPO-F6CD**
---
# Other Strengths ... | null | null | null | null | null | null |
Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments | Accept (poster) | Summary: The authors study the problem of robust reinforcement learning through the lens of meta-RL in which the trained (meta) agent receives a few task-specific samples (can be zero) using which it adapts to a new task. The objective is to maximize the expected performance conditioned on the sampled task being in a s... | Rebuttal 1:
Rebuttal: **_We sincerely appreciate Reviewer beLW's efforts and recognition of our work. Below, we improve the manuscript based on beLW's feedback._**
___
**1. Terminology clarity about secret MDP**
Thank you for your valuable feedback. We used the term "secret MDP" to highlight that we are the first to ... | Summary: The paper focuses on adaptation robustness, addressing scenarios where a risk-predictive model is utilized to mitigate intense evaluation requirements. It formulates the robust active task sampling (RATS) problem as a partially observable Markov decision process (POMDP), providing theoretical insights into the... | Rebuttal 1:
Rebuttal: _**We sincerely appreciate Reviewer v1xi's efforts and positive feedback. Below, we provide our responses.**_
___
**1. Clarification on the simplification of configurations**
Apologies for any confusion. The simplification of configurations lies in two aspects:
- As stated in Lines 261–270 of S... | Summary: This paper tackles robust active task sampling (RATS) in domain randomization or meta-RL for worst-case performance. Tasks are viewed as arms in an infinite multi-armed bandit, but the existing MPTS can over-concentrate on top-B tasks. The authors propose PDTS, which replaces UCB-based acquisition with posteri... | Rebuttal 1:
Rebuttal: _**We sincerely appreciate Reviewer b9NR's efforts and constructive feedback. Below, we provide our responses.**_
___
**1. Additional computational overhead**
Thanks for precious comment. We quantitatively analyzed the extra computational overhead in Fig.5. Even with a $64\times$ candidate batch... | Summary: This paper studies a robust active task sampling (RATS) paradigm, models it as an infinitely many-armed bandit (i-MAB) problem, and proposes a novel method called Posterior and Diversity Synergized Task Sampling (PDTS). PDTS mitigates the task concentration issues in an existing approach, Model Predictive Task... | Rebuttal 1:
Rebuttal: _**We sincerely thank Reviewer xw4Y's efforts and thoughtful feedback. Below, we provide our responses.**_
___
**1. Discussion of curriculum learning methods**
Thank you for your valuable suggestion. We'll cite these important works and discuss them in the revised manuscript, adding contents bel... | null | null | null | null | null | null |
Learning Optimal Multimodal Information Bottleneck Representations | Accept (poster) | Summary: The author introduces a theoretically guaranteed multimodal information bottleneck approach. This method dynamically adjusts the regularization weights of each modality by considering the varying degrees of task-relevant information across different modalities. Theoretically, the optimization objective propose... | Rebuttal 1:
Rebuttal: **Experimental Designs Or Analyses:**
**Q1.** Thank you for this insightful observation regarding synergistic interactions between modalities. In response, we conducted additional experiments using synthetic data with two modalities ($x_1,x_2$), where $x_1=[a_0;b_0]$, $x_2=[a_1;b_1]$, and $y=\Del... | Summary: The paper proposes the OMIB framework to learn optimal multimodal information bottleneck (MIB) representations. It introduces a theoretically grounded objective that sets the regularization weight ($\beta$) within a derived bound and dynamically adjusts weights per modality (using parameter $r$) to balance imb... | Rebuttal 1:
Rebuttal: **Weaknesses**
**W1.** Thank you for reminding us of this important point. **Robustness to estimation errors**: MINE is a theoretically validated estimator with strong consistency, which is achieved by optimizing the Donsker-Varadhan (DV) representation, a lower bound of the true MI (Belghazi et ... | Summary: This paper proposes OMIB, a novel framework for learning optimal Mutual Information Bottleneck (MIB) representations in multimodal learning. The authors address the challenge of imbalanced task-relevant information across modalities, which is a key issue in multimodal fusion. OMIB employs a dynamically weighte... | Rebuttal 1:
Rebuttal: **Questions For Authors**
**Q1.** This is a good point. To mitigate potential instability arising from extreme KL-divergence ratio ($KL_r$), we adopt several strategies. First, the raw $KL_r$ is not directly used; instead, the weight $r$ is computed as $1-tahn(\cdot)$ and bounded, thus preventing... | null | null | null | null | null | null | null | null |
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction | Accept (poster) | Summary: The paper introduces UI-Vision, a large-scale, desktop-centric benchmark for evaluating Graphical User Interface (GUI) agents in visual perception and interaction. Unlike existing benchmarks that focus on structured web and mobile interfaces, UI-Vision targets desktop environments, which lack standardized auto... | Rebuttal 1:
Rebuttal: **R1: Failures cases**
Claude 3.7 Sonnet was released on Feb 24, 24 days after the deadline, and Qwen 2.5-VL on Jan 28, just two days prior—making it infeasible to include them in the review version. However, we have now evaluated both models on our grounding benchmarks, and results are available... | Summary: This paper introduces UI-Vision, a benchmark for evaluating AI agents’ ability to interact with desktop Graphical User Interfaces (GUIs). Unlike existing benchmarks that focus on web or mobile environments, UI-Vision is designed specifically for desktop platforms and is claimed to be the largest of its kind. I... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and address them below.
**R1: Fine-grained error analysis, ablations on task difficulty, and generalization across software categories**
To deepen our understanding of model performance, we conducted an error analysis through a human study for element groun... | Summary: The authors introduce UI-Vision, a comprehensive desktop GUI benchmark with 83 open-source applications, focusing on three tasks: Element Grounding, Layout Grounding, and Action Prediction. Built from human demonstrations and expert annotations, it evaluates GUI agents’ visual perception and interaction capabi... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the value of our desktop-focused benchmark, task diversity beyond web/mobile settings, dense annotations, and actionable insights. We address the concerns below.
**R1: Failures cases**
We summarize key error patterns below and refer the reviewer to our respo... | Summary: This paper introduces a desktop GUI benchmark (i.e., UI-Vision) that spans 83 real-world environments with open-source and permissive data. It enables three key tasks evaluation, including element grounding, layout grounding, and action prediction. The evaluation reveals the limitations of existing works to ha... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the value of our desktop-centric benchmark, its utility for comprehensive analysis of GUI agents, and the clarity of the paper. Below, we address the concerns raised.
**R1: Clarification on “largest desktop-centric benchmark” claim**
We appreciate the review... | null | null | null | null | null | null |
TimeStacker: A Novel Framework with Multilevel Observation for Capturing Nonstationary Patterns in Time Series Forecasting | Accept (poster) | Summary: The paper introduces TimeStacker, a new framework designed to enhance time series forecasting by effectively capturing nonstationary patterns. The core innovation lies in its stacking mechanism, which sequentially aggregates patches of varying sizes to balance global and local signal representations. Additiona... | Rebuttal 1:
Rebuttal: We thank the reviewer for your appreciation and for the valuable suggestions.
**Cross-Dataset Performance:**
For the ETT series data, we present the average performance across datasets (the first row indicates MSE and the second row indicates MAE) as shown below:
| TimeStaker | SOFTS | SparseT... | Summary: The paper "TimeStacker: A Novel Framework with Multilevel Observation for Capturing Nonstationary Patterns in Time Series Forecasting" introduces TimeStacker, a forecasting framework that addresses the challenges of nonstationary time series by integrating multi-resolution stacking and frequency-based self-att... | Rebuttal 1:
Rebuttal: We thank the reviewer for your recognition of our work and your constructive feedback.
**Q1&W1：**
Our approach fundamentally differs from multi-resolution and Fourier‑based methods. While the latter emphasize extracting static features from various frequency bands—that is, observing the signal... | Summary: This paper is another incremental work in developing Transformer-based time-series architectures and follow some widely used yet problematic benchmarks, such as ETT, Exchange, Weather, etc.
Claims And Evidence: This paper claims its proposed approach may better tackle non-stationary signals in time-series for... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to comment on our work. We would like to clarify several points and explain the motivations behind methodology and evaluation protocol.
**Q1:** *“incremental” Transformer-based time-series model.*
**R1:** We acknowledge that Transformer-based approaches ... | Summary: The paper introduces TimeStacker, a novel time series forecasting framework designed to handle nonstationary signals effectively. The proposed approach utilizes a multi-level stacking mechanism, aggregating patches of varying sizes to capture both local and global frequency-domain features. Additionally, a fre... | Rebuttal 1:
Rebuttal: We thank the reviewer for your appreciation and constructive comments.
**Q1:**
The synthetic data is constructed by randomly selecting 30 frequencies, with the amplitude corresponding to each frequency varying over time. The experimental results are visualized in this anonymous URL (https://i.po... | null | null | null | null | null | null |
Prior Knowledge Guided Neural Architecture Generation | Accept (poster) | Summary: The authors propose a Neural Architecture Generation (NAG) method based on Prior knowledGe (PG, for PG-NAG). NAG techniques extend Neural Architecture Search (NAS) to discover the features such as operations and subgraphs that contribute to high performance. This is achieved using Shapley values. Specifically,... | Rebuttal 1:
Rebuttal: We sincerely thank you for the recognition of our effective evaluation. We are also grateful for the valuable feedback.
## Theoretical Claims
>Visual examples
We add a Flowchart (_https://anonymous.4open.science/r/PGNAG/flowchart.png_).
>Concerns about prior knowledge
PG-NAG aims to train high-... | Summary: This paper presents a novel method to enhance neural architecture generation using diffusion models. Instead of relying on predictor-based approaches, the authors train a diffusion model on graph representations of high-performing architectures. They further integrate explicit prior meta-knowledge extracted th... | Rebuttal 1:
Rebuttal: Thank you for recognizing our efficiency, high-quality outputs, and robust experiments. We are grateful for the constructive feedback.
## Claims and Evidence
>Potential limitations and diverse generation
We discuss potential limitations regarding the quality of prior knowledge (weakness 1 in Revi... | Summary: This paper proposes a method, Prior Knowledge Guided Neural Architecture Generation, to efficiently generate high-performance neural architectures without the need for an exhaustive search and evaluation process. The key idea is to leverage prior knowledge extracted from high-performance architectures to guide... | Rebuttal 1:
Rebuttal: Thank you for recognizing our efficiency, novel use of Shapley values, and generality. We are also grateful for the valuable feedback.
## Weakness
> Limited discussion on prior knowledge
A potential limitation is that the quality of prior knowledge affects the accuracy of generated architectur... | Summary: This paper proposes a neural architecture generation method called Prior Knowledge Guided Neural Architecture Generation (PG-NAG), which aims to generate high-performance neural architectures without the need for search and evaluation processes. By quantifying the contribution of each component within an archi... | Rebuttal 1:
Rebuttal: Thank you for recognizing our good performance, effectiveness, and efficiency. We are also grateful for the valuable and constructive feedback.
## Weaknesses
>An analysis of how the method works effectively
The effectiveness of PG-NAG can be attributed to prior knowledge guidance, operation feat... | null | null | null | null | null | null |
Improved Approximations for Hard Graph Problems using Predictions | Accept (poster) | Summary: This paper addresses NP-hard graph problems by developing learning-enhanced approximation algorithms. The authors identify that existing prediction-based approaches predominantly rely on vertex-level information, which may limit performance improvements. To overcome this limitation, they propose a novel framew... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and comments. We address the weaknesses they mentioned below.
**On datasets and Benchmarks**:
The main focus of our paper is on giving rigorous theoretical improvements for classic NP-hard problems. We view our experiments as proof-of-concept, demo... | Summary: This paper introduces a new prediction model that extends the framework of Cohen-Addad et al. (NeurIPS 2024) to improve approximation ratios for NP-hard graph problems, including (weighted and unweighted) Vertex Cover, Set Cover, Max Independent Set, and Max Cut. In their prediction model, each edge is assigne... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and comments.
> report the variance of the results for algorithms
Thank you; we will do this in the final version.
> It would be interesting to see how the algorithms perform if the predictions are not eps-accurate
Thank you for this questio... | Summary: The paper studies learning augmented algorithms for hard graph problems. The author introduce a new setting in which the algorithm can count on a prediction algorithm that provides two bits per edge, one per each incident vertex, which are positively correlated to the fact that the vertex satisfies the edge co... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and comments.
> a few typos and corrections:
Thank you, we have fixed the typos.
> It is not clear how in practical cases one can make available predictions with the desired bounded reliability.
> How do you guarantee the goodness of the predic... | Summary: They design algorithms for some fundamental NP-hard graph problems such as (Weighted) Vertex Cover, Set Cover, Maximum Independent Set, and MaxCut when we have some random information about an optimal solution. More precisely, they assume that for each edge, we have two bits for its endpoints regarding whether... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and comments. We address the weaknesses they mentioned below.
> Their general idea is that for high degree vertices, decide their status in the solution based on the majority of information we have about them ... Finally, they handle low-degree vert... | null | null | null | null | null | null |
Hide & Seek: Transformer Symmetries Obscure Sharpness & Riemannian Geometry Finds It | Accept (spotlight poster) | Summary: The paper demonstrates that sharpness metrics on transformers are not a reliable proxy for generalization due to the symmetry properties of the attention mechanism. The author proposes using a Riemannian space, specifically a quotient manifold derived from the symmetry group. Within this space, they introduce ... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer for their comprehensive review and for their helpful suggestions.
**Other Comments Or Suggestions:**
> The Table 1 should be in the paper
Thank you for pointing this out. We will move it to the main body of the paper.
---
**Questions**
> Q1: What is the complexi... | Summary: The paper proposes to define the sharpness of the loss curve of neural networks via Riemannian geometry in order to account for symmetries in network parameters. While some reparameterization-invariant sharpness measures exist, they do not account for all possible symmetries in parameters, in particular not fo... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer for their thoughtful review and their really interesting suggestions.
We had not fully considered the possible connections with relative flatness, but find these to potentially be a really fruitful avenue of research.
**Claims And Evidence:**
>Although the reparamete... | Summary: The paper introduces geodesic sharpness, a novel adaptive sharpness measure defined on a quotient manifold that factors out the rich symmetries in transformer parameter spaces (notably the high-dimensional GL(h) symmetry in attention). By leveraging Riemannian geometry, the authors redefine perturbation norms ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and their suggestions for improving the paper. We'll endeavour to include as much as possible in any final version.
**Theoretical Claims**
> While the derivations are largely convincing, the reliance on approximations and assumptions about the quot... | Summary: This paper investigates the connection between sharpness and generalization for models with self attention layers by properly accounting for symmetries present in the models. The authors consider the quotient manifold of parameters and measure sharpness within a geodesic ball on the quotient manifold. The pape... | Rebuttal 1:
Rebuttal: Thank you for the insightful review and helpful suggestions for improvement.
We will make sure to mention the reference [1] that you brought to our attention, and we will contrast it to our paper which goes beyond the re-scaling symmetry.
Please find our answers to your remaining concerns below.
... | null | null | null | null | null | null |
AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration | Accept (poster) | Summary: The authors claim that existing methods for accelerating video DiT sampling often rely on expensive fine-tuning or exhibit limited generalization capabilities. To this end, the authors propose a training-free and model-agnostic method to accelerate video DiTs. Specifically, the authors decouples sequence lengt... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer **fiPH** for the valuable questions and comments. For the concerns and questions, here are our responses, along with supplementary figures and tables available at https://anon0728.github.io/icml-230-supplementary:
**Q1**: The authors mentioned a variety of Prior Re... | Summary: This paper studies the importance of different components in video DiTs and proposes Asymmetric Reduction and Restoration (AsymRnR) as a plug-and-play approach to accelerate video DiTs based on previous findings. Experiments on multiple open-source video generation models demonstrate the effectiveness of the p... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer **Mipk** for the valuable questions and comments. For the concerns and questions, here are our responses, along with supplementary figures and tables available at https://anon0728.github.io/icml-230-supplementary:
**Q1**: The authors choosed different models of Cog... | Summary: The paper presents AsymRnR, a method to accelerate video DiTs without requiring retraining. It exploits the variability in redundancy among different feature tokens across various model blocks and denoising steps. By asymmetrically reducing the computational load during the attention operations, AsymRnR achiev... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer **LhNZ** for the valuable questions and comments. For the concerns and questions, here are our responses, along with supplementary figures and tables available at https://anon0728.github.io/icml-230-supplementary:
---
**Q1**: How were these hyperparameters (simila... | Summary: This paper proposes to asymmetrically reduce the sequence length of attention features to accelerate video DiTs. The proposed approach, called AsymRnR, leverages the observation that different components and stages exhibit varying levels of redundancy. The method introduces a reduction schedule to adaptively d... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer **wNPy** for the valuable questions and comments. For the concerns and questions, here are our responses, along with supplementary figures and tables available at https://anon0728.github.io/icml-230-supplementary:
**Q1**: While maintaining semantic consistency and ... | null | null | null | null | null | null |
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models | Accept (poster) | Summary: I'm providing my whole review here, rather than giving partial and discontinued comments here and there.
The paper proposes an extension to SVD-based low-rank approximation of weights. The insight being employed is that a rank r matrix has at most r linearly independent rows. Therefore, a UV decomposition, su... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful feedback and the opportunity to address your concerns.
**Weakness 1:** In section 4 ... PPL from ~5 to ~12.
**Reply:** We have conducted additional experiments to clarify the comparison.
It is important to note that **ESPACE includes a fine-tuning stage w... | Summary: This paper is concerned with sparse inference, which aims to speed-up LLMs via sparsity. It is argued in this paper that previous methods either require specific hardware (e.g., semi-structured pruning) or yield degraded performance (low-rank pruning). This paper aims to propose a low-rank pruning named PIFA m... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable insights. We are glad to address your concerns and provide clarifications.
**Weakness 1:** It is not very clear how QR or LU decomposition serve as the backbone of finding pivot rows would differ from each other.
**Reply:** The key idea behind PIFA... | Summary: The authors propose a novel factorization method and reconstruction objective for LLM compression. Without requiring retraining, the method achieves perplexity performance comparable to semi-structured pruning at a 50% compression rate. Experimental results further demonstrate that the approach is efficient in... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions.
**Weak1:** The claim ... overstated.
**Reply:** We have removed the phrase “for the first time” from the abstract.
**Weak2:** Missing LLM Evaluation ...
**Reply:** We have expanded our evaluation to include both **perplexity** and **downs... | Summary: This submission addresses the significant performance degradation observed with low-rank pruning techniques. It proposes Pivoting Factorization (PIFA), a novel lossless meta low-rank representation that unsupervisedly learns a compact form of any low-rank
representation, effectively eliminating redundant infor... | Rebuttal 1:
Rebuttal: Thank you for your valuable and constructive feedback. We appreciate the opportunity to address your concerns.
**Weakness 1: (Expand evaluation)** The authors have failed to ... existing tools like LMEvalHarness etc.
**Reply:** Thank you for raising this concern. We have extended our evaluation ... | null | null | null | null | null | null |
Understanding the Limits of Deep Tabular Methods with Temporal Shift | Accept (poster) | Summary: - The paper analyses temporal splits for tabular DL. It proposes a new splitting strategy and also analyzes how random splitting affects performance. Additionally, the authors propose temporal embeddings, using fourier transformation and somewhat following the ideas proposed in [1] with PLR embeddings
---
[1... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We will address your concerns in the following responses.
First, we would like to clarify several key differences between our work and TabReD and PLR.
1. **Difference from TabReD**: TabReD shows that real-world tabular datasets, inherently containing tempora... | Summary: The paper investigates the impact of temporal shift in tabular data and presents a set of solutions to mitigate its effects. Since tabular data instances are typically collected in chronological order, temporal shift naturally arises. The authors first find that the commonly used time-based validation split re... | Rebuttal 1:
Rebuttal: We are grateful for your constructive suggestions! We will address your concerns in the following responses.
> The detailed design for these different methods is not discussed.
We apologize for this oversight. We will add an additional section in the preliminary in the revision to introduce the ... | Summary: The paper tackles the problem of how deep tabular methods deteriorate under temporal distribution shifts, where data distributions evolve over time. It demonstrates that typical temporal splitting (training on earlier data, validating on data just slightly more recent, and then testing on even later data) can ... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful comments! We will address your concerns in the following responses.
> The authors do not seem to provide the code, so I remain conservative about the results reported in the paper.
Our code is now available at https://anonymous.4open.science/r/Tabular-Temporal-Shift... | Summary: The paper studies temporal shifts aspects of tabular data
The paper has two main contributions:
- First, authors propose a data splitting and validation protocol for improved model performance. The protocol aims to mitigate two phenomena discussed in the paper *1) training lag* (distances between last trainin... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback! We will address your concerns in the following responses.
> The authors should be carefull ...
Our experimental setup **strictly follows TabReD**. Moreover, since this is a temporal scenario where each test sample is evaluated individually, the **varying t... | null | null | null | null | null | null |
Interpreting CLIP with Hierarchical Sparse Autoencoders | Accept (poster) | Summary: The authors introduce Matryoshka SAE (MSAE), a novel Sparse Autoencoder (SAE) architecture that simultaneously learns hierarchical representations at multiple granularities. This is achieved by applying the topK operation multiple times while incrementally increasing the number of considered neurons. The propo... | Rebuttal 1:
Rebuttal: **We gratefully thank the reviewer for a thorough and very insightful review of our work.** Due to space limits (5000 characters), we concisely respond to each of the major points raised. We share tables and figures with results from the requested analyses in anonymous cloud storage at https://dri... | Summary: Sparse Autoencoders (SAEs) have been adopted to interpret CLIP’s feature representations, but the trade-off between reconstruction quality and sparsity has made it difficult to strike an ideal balance for interpretation. This paper proposes Matryoshka Sparse Autoencoder (MSAE), a hierarchical extension of SAEs... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for acknowledging the quality and significance of our work.**
> While Section 4 demonstrates the advantages of MSAE over standard SAEs, Section 5 primarily focuses on MSAE’s applications—showing concept extraction and bias analysis with CLIP. It would have been e... | Summary: This article explains the CLIP model from the perspective of model parameters. The author uses sparse autoencoders to sparse the content learned by the neurons of the CLIP model. Specifically, the author proposes Matryoshka Sparse Autoencoder, which is a hierarchical encoder used to hierarchicalize conceptual ... | Rebuttal 1:
Rebuttal: **We gratefully thank the reviewer for their engagement with our work and appreciation of our contribution.**
> The authors seem to lack quantitative comparisons with some existing SAE-based neuron interpretability methods, only some simple ReLu and TopK-based methods.
Existing SAE-based methods... | null | null | null | null | null | null | null | null |
Cowpox: Towards the Immunity of VLM-based Multi-Agent Systems | Accept (poster) | Summary: This paper introduces COWPOX, a novel defense approach designed to enhance the robustness of multi-agent systems (MAS) against adversarial attacks. Vision-Language Model (VLM)-based agents, which perceive and interact with their environment through vision and language, are integral to MAS. However, existing MA... | Rebuttal 1:
Rebuttal: We'd like to appreciate your praise on the completeness of the paper and the design of our method. Hope our response can solve your concerns.
> ***1. About the LLM-based inspector***
We agree that the inspector is important to our method. We give the details of the performance of the inspector u... | Summary: This paper targets the problem that there are attack agents in a vision-language-model-based multi-agent systems. The authors propose a defense method named Cowpox. It generate and distribute cure samples, which will be scored higher in the retrieval-augmented generation and can help those injected agents. Exp... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback, which we believe will improve the paper. We appreciate that you liked the structure of our paper and our method’s effectiveness. Please find our responses below.
> ***1. The practicality of the targeting scenarios***
We agree with your comments that currentl... | Summary: This paper addresses the vulnerability of Vision Language Model (VLM)-based multi-agent systems to infectious jailbreak attacks, where a compromised agent can spread malicious content to other agents, undermining the system's robustness. The paper proposes a novel defense mechanism called COWPOX, which aims to... | Rebuttal 1:
Rebuttal: We appreciate that you liked the novelty of our paper and all the other strengths stated. Please find our responses below.
> ***1. Scalability Concerns***
**The computational overhead of the LLM-based inspector is linearly related to the chat rounds, and the numbers of the cowpox agent, and is... | Summary: The paper presents Cowpox, a method to prevent infectious jailbreaks in multi-agent systems of VLMs. A single agent can start out with an adversarial example that can affect other agents in the system, and Cowpox provides a method to override this adversarial example with a small number of agents part of the d... | Rebuttal 1:
Rebuttal: Thank you for your careful review of our paper and thoughtful comments. We hope the following responses will address your concerns.
> ***1. About the Inspector:***
We agree that the inspector is important to our method. The details of the performance of the inspector used in the paper is as below... | null | null | null | null | null | null |
Compelling ReLU Networks to Exhibit Exponentially Many Linear Regions at Initialization and During Training | Accept (poster) | Summary: This paper addresses the previously identified problem of bounded linear pieces in neural networks regardless of depth. The authors show that for a width 4 network, with a novel initialization and pertaining algorithm, they can maintain an exponential number of linear pieces with regard to the depth. They show... | Rebuttal 1:
Rebuttal: We thank the reviewer for their questions, and we appreciate that they took the time to read the appendix. The reason why the paper currently looks like it does, as Reviewer 1 (jsad) alludes to, is that the paper has been through several rounds of rebuttals, and so layers of preliminary experiment... | Summary: This work builds on the link between the expressiveness of a neural network with ReLU activation functions and its number of linear regions in the output space. The general idea is that the larger the number of linear regions in the output space, the higher the expressiveness of the network (i.e., the easier i... | Rebuttal 1:
Rebuttal: We are glad to hear that the reviewer found the paper interesting and believes it shows promise. We appreciate the reviewer’s willingness to reconsider their score in light of what we hope will clarify all the claims and goals of the paper. We agree that the extensiveness of the paper could cloud... | Summary: This paper proposes an new parameterization and pretraining method for ReLU networks to ensure that the resulting function is a piecewise-linear mapping with the maximal number of "linear regions" (i.e., $2^{d}$ for a ReLU network of depth $d$). The motivation for such a parameterization appears to be the foll... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s comments and perspective on our paper. In light of your observations, there are a few important aspects of this paper we would like to emphasize that were maybe not immediately clear.
The first is regarding our claims about navigation of the loss landscape. When we s... | Summary: This paper shows how to build better regressions with ReLU feedforward networks. The key idea is to exploit the piecewise linear representation produced by such networks, with the philosophy that models with more of such pieces are likely to better interpolate the function of interest. These pieces are typical... | Rebuttal 1:
Rebuttal: We’re thrilled that you enjoyed our paper, and we appreciate your belief in the merits of this work. The sections you found confusing are things we agree we could clarify, so we’re extremely grateful for your guidance.
In a single-hidden-layer network, each hidden neuron provides a basis function... | null | null | null | null | null | null |
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards | Accept (spotlight poster) | Summary: This paper studies contextual bandits with heacy-tailed reward. If the variance is known, the authors use catoni estimator to achieve an near optimal regret upper bound. If the variance is unknown, but the variance of variance is bounded by variance times a constant factor, then the authors propose another met... | Rebuttal 1:
Rebuttal: Thanks for your insightful suggestions.
**Q1**: In Algorithm 1, choosing the action $x_t$ and estimating the $\hat f_t$ can be inefficient.
**A1**: The double oracle is a standard way for optimism in online RL and bandits with general function approximation [1,2,3]. In linear function approximat... | Summary: This paper studied the setting of variance-aware contextual bandit (or second order bandit). Specifically, this paper aims to develop algorithms whose regret is upper bounded by the variance of noise. Suppose the noise is between $[-R, R]$, all previous literatures which also studied this question obtained reg... | Rebuttal 1:
Rebuttal: Thanks for your constructive advice!
**Q1**: The Jia, et. al. is also related to this paper. I suggest the authors provide a comparison to results therein.
**A1**: We will cite their work and provide a comparison in the revision, and want to mention that Jia, et. al. is contemporary with our wor... | Summary: In this work, the authors proposes a novel algorithm to tackle the contextual bandit problem in the presence of heavy-tailed noise assumptions. In particular, they assume that the variance of the noise is finite and deal with the scenario in which (i) the noise variance is known to the learner, improving exist... | Rebuttal 1:
Rebuttal: Thanks for your helpful advice!
**Q1**: In heavy-tailed bandits, a dedicated sub-literature on adaptation to the unknown noise variance/1+epsilon moment exists [2,3,4]. I would be interested in knowing how this work relates to them: here, the focus is on the contextual scenario, but what happens ... | Summary: This paper introduces contextual bandit algorithms that are robust to heavy-tailed rewards by leveraging Catoni’s mean estimator from robust statistics. The authors propose two algorithms: Catoni-OFUL for the known-variance setting, and VACB, for the unknown-variance setting. Both algorithms achieve regret bou... | Rebuttal 1:
Rebuttal: Thanks for your constructive suggestions!
**Q1**: The challenges of solving the min-max optimization in Eq. (3) are acknowledged but not explored in depth. While Algorithm 3 is proposed as a more efficient alternative, the paper does not discuss its trade-offs in detail. For example, what are the... | null | null | null | null | null | null |
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction | Accept (poster) | Summary: This paper introduces PARROT, a system designed to handle dual-channel spoken dialog using large language models (LLMs). Authors highlight importance of capturing conversational features such as overlaps, pauses, and interruptions, to provide more realistic spoken interactions.
Building upon previous work like... | Rebuttal 1:
Rebuttal: **Q1, Why we classify Moshi as a encoder-decoder model**
Thanks for raising this important and insightful question. While Moshi utilizes the Helium LLM as its temporal transformer—a decoder-only architecture—the inclusion of the RQ-Transformer introduces a spatial transformer component, which de... | Summary: This paper proposes a next-token-pair prediction approach for modelling a dual-channel streamable Speech LM. The authors propose to use an autoregressive LM to model both speakers in a conversation, predicting token pairs from both channels at each timestep. The model is trained using a two-stage pipeline and ... | Rebuttal 1:
Rebuttal: # Claims and Evidence
**Q1: The inefficiency of Encoder-decoder architectures**
The efficiency of decoder-only model is supported by various literature. For example, FlashAttention (Dao et al., 2022) and Parallelized decoding (Kumar et al., 2020). They show how decoder-only models optimize memory... | Summary: The paper presents a method for improving the conversational capabilities of speech language models using dual-channel spoken dialogue learning. It introduces a Next-Token-Pair Prediction (NTPP) approach within a decoder-only transformer architecture, enabling the model to predict both speakers' next speech to... | Rebuttal 1:
Rebuttal: Thank you for recognizing the value of our work. Here are our responses:
**Q1: Textless Pretraining.**
Thanks for your question. In our paper, "textless pretraining" specifically refers to the fact that neither the first-stage single-channel speech pretraining nor the second-stage dual-channel l... | null | null | null | null | null | null | null | null |
Implicit Language Models are RNNs: Balancing Parallelization and Expressivity | Accept (spotlight poster) | Summary: This paper introduces implicit SSMs, which are a parameter tied form of SSM that can be run for arbitrarily many self-iterations until convergence. They propose training implicit SSMs in a scalable manner using phantom gradients from the implicit layers literature. They demonstrate the ability of implicit SSMs... | Rebuttal 1:
Rebuttal: # General comment
We thank the reviewer for lots of insightful feedback, which significantly helps us to revise our manuscript. We appreciate the positive evaluation ("On balance, I think this is a great paper") as well as the engagement expressed by many detailed questions. In face of the space l... | Summary: This paper describes an implicit approach to training state-space models with arbitrary depth by having the models evaluated in a fixed point and implicitly differentiating using the implicit function theorem, like DQEs. They find that on certain tasks, implicit SSMs outperform SSMs, which are unable to learn ... | Rebuttal 1:
Rebuttal: # General comment
We would like to thank the reviewer for their comments. We are pleased that the reviewer finds our methods, evaluation criteria, experimental design, and analysis satisfactory. We appreciate the suggested clarifications and would like to take this opportunity to elaborate further... | Summary: 1. The authors propose a DEQ-ified version (referred to as implicit models) of state space models like Mamba2.
2. This is motivated by the fact that the diagonal (and real) state transition matrix of these models is not expressive enough for state tracking. They show an implicit model has as a non-linear and ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive perception. Below we try to answer to the reviewer’s remaining questions.
__Q1: Can authors provide a wall-clock time analysis for their method against vanilla Mamba-2__
Thanks. We will add the following to the paper comparing throughput and wall clock time... | Summary: This paper proposes implicit language models, which are RNNs defined implicitly via fixed-point iterations. Theoretically, the authors show that implicit models can represent non-linear and non-diagonal state transitions of RNNs, overcoming the limitations of transformers and state-space models (SSMs) which ar... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and positive assessment of our submission. Below we address raised concerns and assumptions:
**Experimental details**: we will make sure to double check Appendix D to see if any detail is missing. Please see additional wall clock time and memo... | null | null | null | null | null | null |
VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters | Accept (poster) | Summary: This paper propose the VisionTS, which uses the strong pre-trained ability in the vision model to help the time series modality. The core idea is the inherent similarity between the image and the time series such as trend, seasonality, and so on. To align the input of time series into image, the author first c... | Rebuttal 1:
Rebuttal: Thank you for your encouraging response. **We are delighted that you find our paper novel, with solid experiments and the performance is noteworthy.** Below are our responses:
> Claims And Evidence: However, it is not very convincing to me since the domain gap between these two modalities. Furthe... | Summary: The paper proposes to utilize a pre-trained vision masked autoencoder for time series forecasting. The time series data is processed channel-independent and stacked depending on the periodicity of the series. A pre-trained vision-MAE is applied, and the result is transformed back in the series space representi... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on our work. We are pleased that you find our paper **novel and well-experimented, aiding in understanding the workings of TSF foundation models.** Here are our responses to your concerns:
> E1: Gift-Eval is the most comprehensive benchmark of the utilized ben... | Summary: In this paper the authors propose to adapt an image masked auto encoder pretrained on ImageNet for time series forecasting. They justify their choice by the similarities between the image and the time series modalities. They empirically show that the proposed method achieves superior performance compared with ... | Rebuttal 1:
Rebuttal: Thank you for your invaluable review. We are delighted that you believe that our paper is novel and the experiment results are impressive. Below are our responses to the concerns:
> W1: ablation studies for each of the new mechanisms.
> Q1: An empirical study when other methods are fed with the ... | Summary: In this paper, the authors explore a novel direction in applying foundation models to time series forecasting. Given the intrinsic similarities between natural images and time series, such as modality, origin, information density, and features, the authors introduce VisionTS, a TS forecasting model built upon ... | Rebuttal 1:
Rebuttal: Thank you for your invaluable response. We are delighted that you found our paper novel, well-motivated, and with sufficient experiments and insights. Below are our responses to the concerns:
> W1: How does VisionTS obtain the complete temporal information of visible patches during the alignment ... | null | null | null | null | null | null |
TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems | Accept (poster) | Summary: This paper introduces BETA, a method to improve the scalability and performance of TabPFN (a transformer-based technique for tabular classification). BETA combines (1) a lightweight encoder, fine‐tuned (with the pre-trained TabPFN frozen) to re-map raw features into a latent space and better align with downstr... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. In this rebuttal, we address the reviewers' suggestions and concerns in a Q&A format:
---
**Q1: Sensitivity Analysis**
---
**A1**: We sincerely appreciate the reviewer’s valuable suggestion. To address the sensitivity of BETA’s performance to the **number of boo... | Summary: The authors introduce BETA, a TabPFN variant featuring multiple improvements to the original TabPFNv1 model. BETA introduces encoder-based fine-tuning, multiple encoder fine-tuning, batch-ensemble encoding for inference optimization, inference-time bootstrapped sampling, and an error correcting output code str... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. In this rebuttal, we address the reviewers' suggestions and concerns in a Q&A format:
---
**Q1: What is the validation set used for? Were the models early stopped to maximize accuracy?**
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**A1**: We follow [1, 2, 5, 6], where the validation set is used only ... | Summary: This paper narrows its study to an adaption method for TabPFN, which incorporates a fine tuning encoder, boostrapped sampling in the inference stage into the whole pipeline. The BETA is able to solve the bias and variance and achieve comparable performance.
Claims And Evidence: Yes
Methods And Evaluation Cri... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. In this rebuttal, we address the reviewers' suggestions and concerns in a Q&A format:
---
**Q1: Limitation of Novelty**
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**A1**: BETA builds on existing techniques, with its novelty lying in breaking key limitations of TabPFN while maintaining inference effic... | Summary: The manuscript first analyzes the generalization error with a bias-variance decomposition, finding both bias and variance have a non-negligible contribution to the overall error. They then propose an extension tackling both error sources named BETA, which trains multiple dataset-specific encoders (to tackle bi... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. In this rebuttal, we address the reviewers' suggestions and concerns in a Q&A format:
---
**Q1: Finetuning time plus inference time compared to other methods.**
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**A1**: In **Table 3**, we have provided a comparison of **inference time, average rank, and the ... | null | null | null | null | null | null |
OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference | Accept (poster) | Summary: This paper introduces OrthoRank, a dynamic token selection method that exploits the relationship between sink tokens and other tokens to improve LLM inference efficiency. The authors observe that as layers deepen in LLMs, the cosine similarity between normalized hidden states of the sink token and other tokens... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful and constructive review. Below we provide responses regarding the specific questions you raised. We hope this analysis addresses your concerns and welcome any further feedback.
### **Q1: Orthogonality as a Token Importance Metric**
The rationale behind usin... | Summary: This paper joins the rank of other works that are concerned reducing LLM inference costs. The authors start with the observation that the cosine similarity between the hidden states of the sink token and other tokens increases, the deeper in the model we are, despite stationary sink hidden states. Based on tha... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and thoughtful suggestions. We greatly appreciate your positive feedback on the clarity, intuition, and practical impact of OrthoRank.
Below we provide responses regarding the specific questions you raised. We hope this analysis addresses your concerns and wel... | Summary: The paper introduces a novel dynamic token selection method, OrthoRank, aimed at improving the efficiency of large language model (LLM) inference with fewer computation especially for long context. The authors observe that for some models as layers deepen, the cosine similarity between the normalized hidden st... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments and constructive criticism. We appreciate your careful consideration of our work and your valuable suggestions for improvement.
Below we address the concerns you raised and outline how we will incorporate your feedback:
### **Connecting sink token... | Summary: The paper introduces OrthoRank, a new method for selecting important tokens in Large Language Models (LLMs) to improve inference efficiency. The method is based on the observation that in LLMs, after the attention sink occurs, the cosine similarity between the normalized hidden states of the sink token and oth... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and constructive feedback. We truly appreciate the considerable time and effort you invested in evaluating our paper and your recognition of OrthoRank’s novelty. Below we clarify the points you raised.
### **W2: Assumption of Equal Norms**
OrthoRank is based ... | null | null | null | null | null | null |
Can Transformers Reason Logically? A Study in SAT Solving | Accept (poster) | Summary: This paper investigates whether transformers can solve 3-SAT problems by using COT reasoning to simulate backtracking-based search algorithms like DPLL. The authors theoretically demonstrate that this approach is feasible. Empirical evaluations show that: 1) language models using COT can be trained on reasonin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. We’re grateful for your acknowledgment of the soundness of our construction and the relevance of the experiments. We would like to focus on your primary concern regarding novelty compared to Turing Completeness (TC) results.
While we agree that T... | Summary: This paper studies the deductive logical reasoning capabilities of decoder-only Transformers. As claimed by the authors, many researchers reject the idea that LLMs are capable of reasoning, and there is little understanding of the fundamental limitations in the reasoning abilities of Transformer models. In thi... | Rebuttal 1:
Rebuttal: Thank you for your devoted time in reviewing the paper. We're glad that you found the writing and experimental results of our work satisfactory.
> To verify the logical reasoning capabilities of decoder-only Transformers, It would be good to add research on other types of SAT problems.
The sugge... | Summary: This paper investigates the deductive logical reasoning capabilities of decoder-only Transformers. The author(s) opt for 3-SAT problem as a representative example of logic reasoning task and use Transformer model to achieve reasoning via Chain-of-Thought (CoT).
Claims And Evidence: Yes.
Methods And Evaluatio... | Rebuttal 1:
Rebuttal: We greatly thank you for the detailed comments and feedback. We appreciate the reviewer’s careful reading and insightful concerns.
We seek to clarify certain potential logical misunderstandings, hoping to address your identified concerns regarding the theoretical results. Most importantly, **the ... | Summary: The authors present a theoretical foundation and proof that it is always possible to construct an optimal decoder-only transformer that is capable of exactly simulating the DPLL search and solving any 3-SAT task with greedy decoding that uses CoT reasoning steps and backtracking. The authors show that given an... | Rebuttal 1:
Rebuttal: We’re very grateful for your devoted time and careful review of both the main paper and the appendix. We’re also very glad that you liked the contributions of our paper and consider the proof and description in the appendix rigorous and helpful.
We would like to address your comments regarding th... | null | null | null | null | null | null |
ELITE: Enhanced Language-Image Toxicity Evaluation for Safety | Accept (poster) | Summary: The paper introduces ELITE, a new safety benchmark designed to evaluate the toxicity and risks associated with Vision-Language Models (VLMs). Current benchmarks fail to detect implicit harmful content and have issues with low harmfulness levels, ambiguous data, and limited diversity in image-text combinations.... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s valuable feedback, which has significantly improved our work.
**Essential References Not Discussed**.
We agree that since our study aims to improve the safety evaluation of language models, it is important to cite DecodingTrust, an early benchmark in LLM safet... | Summary: This paper introduces ELITE, a VLM benchmark and an LLM-as-judge evaluator designed to test harmful generations of these models.
## update after rebuttal
I will maintain my original score.
Claims And Evidence: The novelty of this work is somewhat limited. The evaluator provided is rubric-based and heavily in... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer's insightful and constructive comments, which have greatly contributed to enhancing the quality of our work.
**Methods And Evaluation Criteria & Experimental Designs Or Analyses-1**. Thank you for your valuable feedback. Our key message is to address a limitation... | Summary: The authors propose a new framework for automated safety evaluation in vision-LLMs by extending an existing evaluator (StrongREJECT, which scores the level of refusal, specificity, and convincing-ness of a VLM's output) by additionally predicting a toxicity factor. This accounts for cases where the model's out... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s insightful comments, which have been essential in helping us enhance and clarify our work.
**Claim And Evidence**. The table below shows additional experimental results for the latest model, gemma3, and the larger model, InternVL-2.5-26B. These results demonst... | Summary: This paper introduces a safety benchmark called the ELITE benchmark, as well as an associated evaluator (the ELITE evaluator). The benchmark comprises multimodal data—image-text pairs—that are designed to provoke harmful or unsafe responses from vision-language models (VLMs). It includes 4,587 samples across 1... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the constructive feedback, which has been invaluable in helping us refine and strengthen our work.
**Weakness2**. To demonstrate that the ELITE benchmark is not overly tailored to the ELITE evaluator, we present results based on the previously adopted met... | null | null | null | null | null | null |
Efficient Personalized Adaptation for Physiological Signal Foundation Model | Accept (poster) | Summary: This work proposes a new method to adapt physiological signal foundation models using DiT to generate LoRA weight matrices.
Claims And Evidence: The authors claim that the proposed method transfer physiological foundation model to different tasks with lower computing costs. While it is true that the proposed ... | Rebuttal 1:
Rebuttal: Thank you for your extensive feedback. We especially appreciate your effort to meet the conference's rigorous review criteria. We kindly address your questions as follows.
**W1:** Scope of adaptation time.
**For W1:** Thanks for your thoughtful comments. We separated the two phases of pre-train... | Summary: * This paper studies medical time series classification based on physiological signals.
* ML models for prediction from medical time series is challenging since we often have:
* Unbalanced amount of data for each signal
* Varying sampling frequency/duration
* Time series foundation models (TSFMs) can ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments, and we are grateful for the time and effort you have invested in reviewing our work. Below, we provide a point-by-point response to address each of your concerns:
**W1:** Clarifying the data partition.
**For W1:** Thanks for your valuable commen... | Summary: The paper provides a personalized approach to transfer the time series foundation model to clinical physiological signal tasks. The main constraints are the lower computing costs and privacy.
Claims And Evidence: Not always. A main issue is that it is not clear how the authors are addressing the privacy prote... | Rebuttal 1:
Rebuttal: We sincerely appreciate for your comprehensive and valuable review, particularly given the meticulous standards during the review of this venue. We appreciate the opportunity to address your concerns.
**W1:** Clarification on the privacy issue.
**For W1:** We apologize for missing a detailed dis... | Summary: This paper proposes a novel approach to achieve efficient adaptation for physiological signal foundation modals to private datasets. The main idea is to use Low-rank Adaptation (LoRA). However, unlike existing methods that train LoRA weights for adaptation, it utilizes a diffusion model to generate the LoRA we... | Rebuttal 1:
Rebuttal: Thanks for recognizing the value of our work. We are grateful for your thorough feedback, especially considering the massive requirements of the review. We hope the following comments could address your questions:
**W1:** Relation to hypernetwork.
**For W1:** In a macro sense, our method and the... | null | null | null | null | null | null |
Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability | Accept (poster) | Summary: This paper studies hierarchical overlapping clustering (HOC), in which vertices are assigned to a hierarchical structure of overlapping clusters. In comparison with non-overlapping HC, we construct a DAG rather than an HC tree.
The paper introduces an objective function for this problem, generalising Dasgupta... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful review and valuable comments. Let us address the concerns one by one.
1. On the scalability of our algorithm, we have demonstrated it on graphs of size 100,000. Please note that the largest graph in the second row of Figure 2 has size $10\times 10^4=10^5$ (so... | Summary: The paper formally introduces the problem of hierarchical overlapping clustering. Overlapping and hierarchical clusterings have been studied more extensively separately. The only preexisting works that have studied them together have been in the distance setting (edge weights are distances) with no formal obje... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful review and valuable comments. Let us address the concerns one by one.
Q1: Yes, these four conditions of Property 2.6 can be simplified since (3) implies that every $N\in S$ is ordered between $X$ and $Y$. However, we cannot say that it's a maximal set that sa... | Summary: This work introduces and studies the hierarchical overlapping clustering (HOC) problem. In the clustering literature, many works have focussed on either (i) overlapping or (ii) hierarchical clustering; this work's aim is to reconcile both topics.
As a first contribution -- inspired by the well-known Dasgupta ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful review and valuable comments. Let us address the concerns one by one.
1. Regarding the speed-up version that makes the comparison of Algorithm 2 not “formal”, let’s look at the two strategies. The first is a good initialization with two non-overlapping cluste... | Summary: Two variants of graph clustering are Hierarchical Clustering and Overlapping
Clustering. While there are some studies of both variants, they were not
previously considered simultaneously. The paper proposes a reasonable cost
function that combines both variants, and investigates algorithms that minimize
this ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and the recognition of our work. We hope that our study will encourage other researchers to pay attention to hierarchical and overlapping graph clustering, since we think this hybrid structure has a great potential significance in recognizing real-worl... | null | null | null | null | null | null |
Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach | Accept (poster) | Summary: The paper points out the problem of overconfidence of preference aligned Large Language Models (LLMs), and propose two fine-tuning approaches to address the problem: calibration-aware fine-tuning (CFT) and regularized CFT (RCFT).
Claims And Evidence: The biggest problem is that though the authors state that p... | Rebuttal 1:
Rebuttal: We thank reviewer hpMa for the comments and suggestions. Below we provide our answer to your questions.
>Q1. The biggest problem is ... not propose any modifications to preference alignment.
A. Thank you for the comment. We would like to clarify that this is better to be considered as a realisti... | Summary: This paper addresses poor calibration in Large Language Models (LLMs) after preference alignment procedures like RLHF and DPO. The authors identify that preference-aligned LLMs exhibit overconfidence due to "preference collapse," where models excessively favor certain responses regardless of their correctness.... | Rebuttal 1:
Rebuttal: We thank reviewer USq4 for the insightful comments and questions. Below we provide our responses to the questions.
>Q1. One limitation is that their evaluation focuses primarily on multiple-choice settings rather than free-form text generation, which would provide a more complete picture of calib... | Summary: This paper addresses the calibration issue in aligned large language models (LLMs) and proposes a calibration-aware fine-tuning approach to restore proper uncertainty quantification in these models. The motivation stems from the observation that alignment techniques can distort model confidence, leading to mis... | Rebuttal 1:
Rebuttal: We thank reviewer Ak1F for the insightful comments and questions. Below we provide our responses to the questions.
>Q1. Why fine-tuning with a calibration objective superior to post-hoc calibration methods e.g. TS?
A. While temperature scaling (TS) is a strong baseline, our method's superiority ... | Summary: The paper tries to answer a well-known question: why an aligned model is not well-calibrated and how to fix it? Authors start with a probabilistic generative model and define TCE, and then derive an upper and lower bound of TCE. Then, depends on the accuracy of the current model, one can either get calibration... | Rebuttal 1:
Rebuttal: We thank reviewer QE3c for the insightful comments and questions. Below we provide our responses to the questions.
>Q1. Can we draw any senses about when the model falls into each case from a theoretical perspect?
A. This is a great question. Determining which regime a model falls into ultimatel... | null | null | null | null | null | null |
ToMA: Token Merge with Attention for Diffusion Models | Accept (poster) | Summary: The paper investigates token reduction via submodular optimization.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: The proposed methods are valid, however, having $75\%$ token reduction leads only to $\approx 1.4$ faster inference than the baseline. Please check the question section.
Theoretical... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your review. We appreciate your feedback and pointing out several typos that need polishing. Due to ICML 2025 regulations, we are currently unable to modify the submitted PDF. However, we will certainly address and correct these issues in the updated version.
Regardi... | Summary: The authors present a token merge algorithm for image diffusion models, based on the theory of submodular optimization, operations that are more friendly to GPU, and some extra tricks to further enhance the speedup. The proposed method first selects destination tokens that are most representative and then merg... | Rebuttal 1:
Rebuttal: *Dear Reviewer*,
Thank you for taking the time to review our work. We have addressed your concerns in detail in the responses below.
***Response***:
***Lack of Comparison on DiT-Based Diffusion Model:***
Thank you for raising this point. Table 2 includes fewer comparisons with other methods i... | Summary: This paper introduces ToMA (Token Merge with Attention), a GPU-optimized token merging method for transformer-based diffusion models, addressing inefficiencies in existing token merging techniques such as ToMeSD and ToFu. ToMA achieves its efficiency improvements through submodular optimization for token selec... | Rebuttal 1:
Rebuttal: *Dear Reviewer*,
We appreciate your insightful comments and the time you've taken to evaluate our work. Below are our responses to your concerns:
***Concerns about High Image Quality and Little Speed Up:*** At a ratio of 0.25, the acceleration is limited primarily because the reduction in sequen... | null | null | null | null | null | null | null | null |
Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story | Accept (poster) | Summary: This paper focuses on generating diverse experience for policy gradient algorithms in reward-free settings through the use of entropy maximisation and separate parallel policies. The method proposed is Policy Gradient for Parallel States Entropy Maximization (PGPSE). The empirical results on two grid-based env... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and effort in evaluating our work, and we are grateful for the opportunity to provide further clarifications. We also thank the reviewer for highlighting grammatical and formatting issues, which we will address in the final version. Additionally, we are ... | Summary: This paper studies how parallel training facilitates exploration in reinforcement learning. The major result is that parallel exploration can not only obtain batched acceleration compared to single-agent ones, but it’s also possible to further improve sample complexity though diversity-driven policy design. Th... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and effort in evaluating our work and are grateful for the opportunity to provide further clarifications. We also thank the reviewer for pointing out the typo errors; we will address them in the final version.
**If the motivation is for the speed, how... | Summary: This paper proposes an exploration framework for parallel agents with state entropy maximization and an analysis of the framework. They showed on tabular environments that parallel exploration covers the state space better than single-agent exploration with the same compute budget. And datasets collected by pa... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and effort in evaluating our work, and we are grateful for the opportunity to provide further clarifications.
**Why are the agents incentivized to be different rather than all become the same policy with uniform coverage over states? Is it because if th... | Summary: This paper investigates how to effectively maximize state entropy exploration in parallel agent settings. The authors propose a framework where multiple agents, each operating in separate environment replicas, are trained to collectively maximize the entropy of their visited state distribution while promoting ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and effort in evaluating our work and greatly value their insightful comments and suggestions. To clarify the key points of discussion and our design choices, we provide the following responses.
**Why didn’t the authors compare the proposed approach wit... | null | null | null | null | null | null |
Embedding Safety into RL: A New Take on Trust Region Methods | Accept (poster) | Summary: This paper considers the problem of constrained MDPs. The key idea is to modify the geometry of the policy space to ensure that trust regions contain only safe policy space. This is achieved by introducing a new family of policy divergence that incorporate certain mirror functions. The author provide theoretic... | Rebuttal 1:
Rebuttal: > How sensitive is C-TRPO to the accuracy of the estimated cost function? Could you provide an analysis or experiments showing how performance degrades with increasingly noisy or misspecified constraints?
This is an excellent question. While we do not yet have a comprehensive theoretical analysis... | Summary: In this paper, authors present idea of solving Constrained Markov Decision Processes (CMDPs) using trust regions that obey the constraints strictly and allow for return maximization. Earlier approaches work with KL-divergence based trust regions and try to recover the policy if the constraints of CMDP are viol... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and suggestions! We respond to each of your points in detail below.
> I had a problem understanding the results depicted in figure 3. Why are the results aggregated together across multiple tasks? How can we compare the average of normalized rewards and constr... | Summary: The paper introduces Constrained Trust Region Policy Optimization, a new Constrained RL algorithm based Trust Region Policy methods. The trust region is made to only contain safe policies for the update step. The new algorithm enjoys good theoretical properties, with improvement and safety guarantees similar t... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our work!
We agree that a regret analysis would further strengthen the theoretical contribution and see this as a valuable direction for follow-up work. | Summary: The paper proposes C-TRPO and C-NPG for solving CMDPs. Mirror functions are used to define policy divergences that are finite only for safe policies. This divergence metric is then used to reshape the policy shape geometry to ensure that trust regions contain only safe policies. The algorithms are analysed the... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We have addressed your specific comments in detail below.
> Log barrier baselines are not considered in the experiment.
We considered **IPO** as a practical log-barrier baseline, along with **P3O**, a proximal adaptation.
Regarding the specific works mentio... | null | null | null | null | null | null |
Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models | Accept (spotlight poster) | Summary: The paper introduces a novel design that enables the application of 2D foundation models to 3D data tasks without requiring additional pretraining. The proposed method significantly reduces the size of feature maps, and demonstrates strong performance even in scenarios with limited training samples. Additional... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We also agree with the reviewer that our novel approach has several implications for the field, as it would make the computation of embeddings for volumes much faster while maintaining the ability to perform downstream tasks. We also appreciate the reviewe... | Summary: This paper introduces Raptor (Random Planar Tensor Reduction), a train-free method for generating semantically rich embeddings for volumetric data. Raptor leverages a frozen 2D foundation model, pretrained on natural images, to extract visual tokens from individual cross-sections of medical volumes. These toke... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's keen observations and suggestions. Thanks to their recommendations, we were able to further uncover Raptor's capability in a challenging task (segmentation) and are excited for future directions.
> **statistical tests (e.g. p-values) … especially for small sample sett... | Summary: This paper proposed a framework to leverage the pretrained large 2D encoder for 3D medical image analysis (i.e., classification and regression). By applying random projection to feature embeddings encoded from 2D slices taken of three orientations (i.e., sagittal, coronal, and axial) using DINOv2-L and concate... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed assessment of our manuscript. We appreciate that they viewed our experiments as supporting the utility of our method, and we agree that we could support some of our claims with additional experiments. We address each of the points raised below.
> **Meaning... | Summary: This paper presents a random projection-based strategy for generating embeddings from volumetric data. The approach leverages pre-trained 2D foundation models without requiring additional re-training or fine-tuning. The proposed embedding construction method is computationally efficient, and experiments conduc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and constructive comments, as well as their positive assessment of our method’s clarity and novelty. As noted by the reviewer, Raptor introduces a paradigm that demonstrates many advantages beyond existing works, and we verify our claims with a wide range of em... | null | null | null | null | null | null |
Demystifying Singular Defects in Large Language Models | Accept (poster) | Summary: This paper investigates the phenomenon of high-norm tokens in LLMs, identifying key factors that influence their behavior. These factors include singular directions, negative eigenvalues, and distinct computational pathways for initial and non-initial tokens. The study reveals that high-norm tokens are primari... | Rebuttal 1:
Rebuttal: Thank you very much for your encouraging comments! | Summary: This paper investigates the phenomenon of high-norm tokens in large language models (LLMs), extending the understanding of singular defects from vision transformers (ViTs) to the context of LLMs. Unlike ViTs, where high-norm tokens have been modeled through singular vectors of linear approximations, the causes... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments!
We will incorporate the suggestions in the revision.
> Is causal attention really the key factor? Why ViTs have similar high-norm tokens?
We hypothesize that causal attention excites high norms in LLM in the following way.
In the causal formulation, the pos... | Summary: This paper is a direct follow-up to SINDER (Wang et al, 2024). In this paper, the authors use the tool of “singular defects” to analyze the occurrence of high-norm tokens in language models, which was observed in (Sun et al. 2024). They analyze the weights of the model to understand where high-norm tokens come... | Rebuttal 1:
Rebuttal: Thank you for the detailed and thoughtful comments and we are encouraged by the remark that "applications section is particularly strong".
We will incorporate the suggestions in the revision.
> How it fits into the broader literature and what exactly new it contributes
While Sun et al. spotted h... | null | null | null | null | null | null | null | null |
Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Prediction | Accept (poster) | Summary: The paper introduces a new GNN framework for link prediction tasks that can be used to extend existing architectures. The main idea is that the GNN can access image embeddings of visualizations of the (extended) neighborhoods. These are meant to enrich the representations with more context on where nodes are p... | Rebuttal 1:
Rebuttal: Thanks for your insightful reviews. **Please note that all the new tables and figures mentioned here are put in https://anonymous.4open.science/r/GVN-CLap/README.md**.
> compares assigning the visualized node positions as node features.
> compares more standard node positional encoding (PE) mecha... | Summary: This paper proposes a novel framework called Graph Vision Network (GVN) and its efficient variant (E-GVN) to enhance link prediction in graph neural networks by integrating visual perception. The authors argue that while message-passing graph neural networks (MPNNs) and structural features (SFs) are dominant i... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback.
> I believe the authors should concentrate on the efficient GVN within the paper's architecture, as it can achieve better performance with NCNC and is significantly more efficient.
We sincerely appreciate your suggestion. We will focus on highlighting the... | Summary: This paper proposes using visual structural features (VSFs) as a replacement for heuristic-based structural features (SFs) in graph learning tasks. The key contribution is the introduction of vision-based enhancements, which are empirically shown to improve message-passing neural network (MPNN) performance for... | Rebuttal 1:
Rebuttal: Thanks for your insightful reviews. We here address your concerns point by point by merging and re-arranging relevant comments together:
> The justification for why VSFs work better remains unclear, particularly in relation to existing expressive power analysis on models with SFs.
> The expressi... | Summary: This paper proposed to incorprate vision information into MPNN to enhance link prediction. Specfically, it designed two framework, Graph Vision Network(GVN),along with a more efficient variant (E-GVN).
Claims And Evidence: This paper analyzes the potential benefits to incorporate vision awareness in link pred... | Rebuttal 1:
Rebuttal: Thanks for your insightful reviews.
> 1. The discussion for RQ1 could be more convincing if some empirical study or theoretical analysis could be provided, instead of just intuition analysis.
Thanks for your advice. For RQ1, you can find the following supports for our discussion.
1) For subgraph... | null | null | null | null | null | null |
Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals | Reject | Summary: This work presents a foundation model capable of handling all physiological signal modalities. To deal with varying signal modalities, several modules were introduced. First attempt at zero-shot evaluation of physiological signal was reported.
Claims And Evidence: As a foundation model, the pre-trained weight... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable feedback and for acknowledging our idea. We have addressed and clarified the comments below.
## 1) Ablated FT w/ pre-trained weight v.s. FT w/ random init weight
We appreciate the suggestion to include an additional ablation study. Our work focuses ... | Summary: The paper introduces NormWear, a foundation model for multi-variate wearable signals. The model is trained using a masked reconstruction (self-supervised) loss on signal sources including ECG, PPG and IMU, taken from 11 wearable datasets. NormWear is fine- evaluated on downstream tasks such as state recognitio... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful feedback. We appreciate your recognition of our method's novelty and the sound design of our experiments. Below, we briefly respond to each of your concerns.
## 1) Additional Baselines
Thank you for the constructive suggestions on the additional appropri... | Summary: This paper proposes NORMWEAR, a foundation model designed to process multichannel wearable physiological signals. NORMWEAR is engineered to integrally handle a variety of physiological signals, including EEG, ECG, PPG, GSR, and IMU, and learns generalized representations from diverse sensor data. Its key contr... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful feedback. We appreciate that you found our proposed method to be generalizable and scalable, and that you recognized the thoughtful design of our NORMWEAR model, as well as its solid performance across diverse healthcare applications. Below, we briefly re... | Summary: This paper introduces NormWear, a foundation model for wearable physiological signals that leverages a Vision Transformer-based architecture. It processes multi-variate signals from wearable sensors by transforming each variate into an image representation via Continuous Wavelet Transform (CWT). To enable zero... | Rebuttal 1:
Rebuttal: Thank you for your insightful and constructive feedback. We appreciate that you found our work to be novel and reasonable. Below, we briefly respond to each of your concerns.
## 1) Uni-modal baselines
We acknowledge that Section 4.1 on baseline selection could be clearer, and we will revise the ma... | null | null | null | null | null | null |
Human Body Restoration with One-Step Diffusion Model and A New Benchmark | Accept (poster) | Summary: This paper introduces a high-speed diffusion model that can restore low-quality human body images in just one diffusion timestep. The paper presents a high-quality dataset, PERSONA, which includes diverse human body images. Additionally, the proposed OSDHuman model paves the way for incorporating visual priors... | Rebuttal 1:
Rebuttal: `Q4-1:` However, the images restored from OSDHuman have some color shift. For example, in Figs. 5 and 6 of the supplementary materials, the teeth of the person in the 2nd and 5th sets of images are noticeably whiter.
`A4-1:`
Thank you for pointing out the color shift issue. We address this by app... | Summary: The paper proposes a novel approach to human body restoration (HBR) by introducing OSDHuman, a one-step diffusion (OSD) model, and a new benchmark dataset named PERSONA. The authors develop a high-quality dataset automated cropping and filtering (HQ-ACF) pipeline to create PERSONA, which comprises 109,052 high... | Rebuttal 1:
Rebuttal: `Q3-1:` The paper's innovation seems limited, as both the model and dataset build on existing work.
`A3-1:`
Thanks for raising this concern. OSDHuman is the first one-step diffusion model applied to human body image restoration. Compared to traditional multi-step models, it achieves faster infer... | Summary: This paper presents a dataset automated cropping and filtering pipeline and constructs a person-based restoration with sophisticated objects and natural activities dataset. A novel one-step diffusion model is proposed for human restoration. Experimental results demonstrate the effectiveness.
Claims And Eviden... | Rebuttal 1:
Rebuttal: `Q2-1`: It is necessary to show the inference speed of different algorithms. This is the reason why one-step diffusion models are used.
`A2-1:`
Thank you for your thoughtful suggestion. We have provided a detailed comparison of inference speed, parameter count, and computational cost for several ... | Summary: This study addresses the challenge of human body restoration by introducing a high-quality dataset construction pipeline, HQ-ACF, which automatically crops and filters human images from existing datasets. Using this pipeline, the PERSONA dataset is created, offering superior quality and content richness compar... | Rebuttal 1:
Rebuttal: `Q1-1:` The novelty seems limited, as VSD is adapted from prior work and HFIE resembles an attention-based DAPE.
`A1-1:`
Thank you for your valuable comments. Our model is the first to focus on human body restoration using a one-step diffusion framework. Our model's VSD module follows the OSEDiff... | null | null | null | null | null | null |
Revolve: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization | Accept (poster) | Summary: The manuscript proposes Revolve, a new method that enhances LLM-based optimization by simulating second-order dynamics for self-evolving agents. Existing gradient approximation methods use textual feedback to approximate first-order gradients but are less effective for long-horizon optimization. Revolve addres... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed evaluation of our work, for acknowledging the soundness of our work, the clarity of the writing, the coverage of related literature, and the comprehensiveness of our experiments. We'll address your concerns in our following response.
**Q1. New experiments on... | Summary: The paper looks at the leveraging beyond the first-order information in textual optimization. Revolve develops a way to keep an account of previous feedback steps, and goes beyond the issue of stagnating when feedback is limited or fluctuates irregularly. The authors evaluate REVOLVE on three tasks: prompt opt... | Rebuttal 1:
Rebuttal: We appreciate your valuable time and positive feedback. We'll address your concerns in our following response.
**Q1. For the second-order effects, what type of prompt or what type of workflow and LLM did the authors use?**
We appreciate the request for clarification on how second-order effects a... | Summary: In this paper, the authors introduce REVOLVE, which aims to simulate the second-order derivative during the optimization process.
They compared their modified optimization prompts and found the outcome was better than TextGrad itself.
## update after rebuttal
The authors explained the implementation differe... | Rebuttal 1:
Rebuttal: We appreciate your valuable time, insights, and highlight our strengths. We'll address your concerns in our following response.
**Q1. The authors claim that “REVOVLE can escape local optima.”. However, they do not explain why the similarity function can be interpreted as a gradient from an implem... | null | null | null | null | null | null | null | null |
Adversarial Robust Generalization of Graph Neural Networks | Accept (poster) | Summary: The paper investigates the adversarial robustness of Graph Neural Networks (GNNs) in node classification tasks. The authors propose a high-probability generalization bound for GNNs under adversarial attacks using covering number analysis. They derive bounds for several popular GNN models (GCN, GCNII, APPNP) an... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and valuable suggestions. However, **we would like to clarify the first misunderstanding below**:
> 1. Lack of new insights, comparison with existing methods, and application to real-world scenarios.
**A1**: Generally speaking, our focus **does not l... | Summary: This paper investigates the generalization ability of graph neural networks (GNNs) under adversarial training, which is an important and widely interested research direction. The paper first proposes a high probability generalization limit and analyzes the generalization ability of GNN under adversarial traini... | Rebuttal 1:
Rebuttal: We deeply thank you for acknowledging the rigorous logic, clear structure, and extensive formula reasoning of our work. Below are our detailed responses.
> 1. Why does the experimental part of this paper seem to lack a comparative study with previous work?
**A1**: Generally speaking, our focus ... | Summary: This paper establishes an adversarial generalization bound of various GNNs, such as GCN, APPNP, GCNII, in the context of transductive learning. The authors provide some guidlines for adversarial generalization based on the theoretical results. The guidlines based on theoretical results are all validated in exp... | Rebuttal 1:
Rebuttal: We deeply appreciate your acknowledgment of the solid and comprehensive theoretical analysis and the thoughtfully designed empirical results presented in our paper. Thanks for pointing out the typo, and we will fix it in the future version. | Summary: The paper investigates the adversarial robust generalization of GNNs through a theoretical lens. It derives high-probability generalization bounds for general GNNs in adversarial settings using covering number analysis. The key insight is modeling the adversarial loss class’s complexity by constructing a pertu... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments! Please refer to our response below.
> 1. Lack of testing for randomized splits of datasets.
**A1**: Thanks for pointing out the lack of considering the impact of dataset splitting. Taking two-layer GCN and two datasets for example, we show the gen... | null | null | null | null | null | null |
CoPINN: Cognitive Physics-Informed Neural Networks | Accept (spotlight poster) | Summary: The paper presents a novel framework called Cognitive Physical Informed Neural Network (CoPINN) to address the Unbalanced Prediction Problem. CoPINN employs separable subnetworks to encode one-dimensional coordinates, aggregates them to predict multi-dimensional variables, and dynamically evaluates sample diff... | Rebuttal 1:
Rebuttal: We appreciate your detailed comments. We believe the following point-to-point response can address all the concerns:
**Q1: Weaknesses (1) and Questions For Authors(4)**
**R1:** Compared with Helmholtz and (2+1)-d Klein-Gordon datasets, (3+1)-d Klein-Gordon and Diffusion datasets are relatively l... | Summary: The paper proposes an adaptive sample weighting strategy for physics-informed neural networks. As a measure of difficulty for a sampling point, the magnitude of the (input) gradient of the PDE residual is proposed. The authors suggest to train PINNs via assigning high sample weights to easy samples early on in... | Rebuttal 1:
Rebuttal: We greatly appreciate your valuable comments. Below is our point-by-point response.
**Q1: Essential References Not Discussed & Questions For Authors (1)**
**R1:**
1) We will include the discussion about adaptive sampling in the Introduction and Related Work Sections of the next version. The key... | Summary: The paper proposes CoPINN, a Cognitive Physics-Informed Neural Network that addresses the Unbalanced Prediction Problem (UPP) in PINNs. UPP arises from treating easy and hard samples (e.g., boundary vs. smooth regions) equally, leading to unstable training. CoPINN introduces three key components: (1) separable... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their constructive feedback. Below are our responses to the questions raised:
**Q1: Questions For Authors(1)**
**R1:** Our proposed CoPINN employs a multi-faceted approach to prevent overfitting to hard samples during training. First, the cognitive training s... | Summary: The authors look at the PINNs setting, based on training a neural network to confirm to the PDE residual. They employ a method that dynamically samples collocation points according to the gradient of the PDE residual. They use this as a signal to do PINNs training starting with the solution on the collocation ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their constructive feedback. Below are our responses to the questions raised:
**Q1: Methods And Evaluation Criteria & Questions For Authors**
**R1:** The reference solutions refer to the labels used to compute the relative $L_2$ error and $RMSE$ by comparing ... | null | null | null | null | null | null |
Geometry Informed Tokenization of Molecules for Language Model Generation | Accept (poster) | Summary: This paper proposes Geo2Seq, a 3D molecule tokenization method for 3D molecular generation. The authors convert molecules (in 3D space) to 1D sequences while preserving SE(3) invariance, and then train a molecule generative model based on language model architecture. Geo2Seq equipped with various language mode... | Rebuttal 1:
Rebuttal: Dear Reviewer CPUN,
Thank you for your appreciation of our work and insightful comments! We have made efforts to thoroughly improve our work accordingly and provide responses for each concern here. Please also refer to our added experiments in [this Link](https://anonymous.4open.science/r/geo2seq... | Summary: This paper explores the use of language models (LMs) for generating 3D molecules, a task that has previously been challenging due to the complex geometric structure of molecules. The paper proposes a novel tokenization method called Geo2Seq, which converts 3D molecular structures into SE(3)-invariant 1D discre... | Rebuttal 1:
Rebuttal: Dear Reviewer aNgc,
Thank you for your appreciation of our work and insightful comments! We address each point here and added experiments are in [this Link](https://anonymous.4open.science/r/geo2seq-rebuttal/Geo2Seq_rebuttal.pdf).
> Real number generalizability
We have conducted more studies on... | Summary: The paper proposes a method called Geo2Seq to generate 3D molecules using language models. The authors convert each molecule into an SE(3)-invariant discrete sequence of tokens—one token per atom, with tokens containing both atom type and spherical-coordinate information. Once converted to a sequence, any lang... | Rebuttal 1:
Rebuttal: Dear Reviewer hgAt,
Thank you for your appreciation of our work and insightful comments! We have made efforts to thoroughly improve our work accordingly and provide responses for each concern here. Please also refer to our added experiments in [this Link](https://anonymous.4open.science/r/geo2seq... | Summary: The paper proposes Geo2Seq, which transforms molecular geometries into SE(3)-invariant discrete sequences for molecule generation. Existing language model-based molecule generation works do not consider the 3D molecular geometries in the tokenization process. The proposed paper address this limitations and sho... | Rebuttal 1:
Rebuttal: Dear Reviewer 39Ws,
Thank you for your appreciation of our work and insightful comments! We have made efforts to thoroughly improve our work accordingly and provide responses for each concern here. Please also refer to our added experiments in [this Link](https://anonymous.4open.science/r/geo2seq... | null | null | null | null | null | null |
RAGGED: Towards Informed Design of Scalable and Stable RAG Systems | Accept (poster) | Summary: This paper carries out empirical analysis to shed light on the impact of retrieval in a RAG system: 1/ when retrieval is needed, 2/ impact of retrieval depth, 3/ noisy retrieval, 4/ relation between retrieval improvements to final performance improvements. The paper proposes two metrics, RAG Stability Score an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback. We appreciate the recognition of our empirical contributions and insights into retrieval dynamics, particularly the introduction of RSS and RSC as tools for understanding reader robustness and retrieval scalability.
**1. Symmetr... | Summary: The paper presents RAGGED—a evaluation harness for retrieval-augmented generation (RAG) systems. The authors identify conflicting narratives in the previously published literature, and aim to resolve it especially around sensitivity to irrelevant documents. They examine how different retriever methods (e.g., B... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive and thoughtful feedback. We appreciate that the reviewer recognizes that the paper resolves open questions in existing works and finds all the claims to be well supported.
**1. Expanding to Newer Embedding Models**
We appreciate the suggestio... | Summary: The paper introduces RAGGED, a framework for evaluating Retrieval-Augmented Generation (RAG) systems. It emphasizes that RAG's performance depends not only on retrieval quality but also on the reader's robustness to noise. The study shows that reader robustness is the key factor for RAG stability and scalabili... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback. We appreciate that the reviewer recognizes this paper provides a significant contribution by introducing structured metrics and is backed by extensive empirical analysis across multiple datasets and models.
**1. RSS and its Relat... | Summary: This paper introduces RAGGED, a systematic framework for evaluating Retrieval-Augmented Generation (RAG) systems, focusing on stability, scalability, and robustness to noise. The authors analyze how retrieval depth, retriever-reader interactions, and dataset characteristics influence RAG performance, challengi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We especially appreciate the recognition of how RAGGED addresses a gap in the literature by providing a unified framework for systematically evaluating scalability and stability and deriving actionable insights for real-... | null | null | null | null | null | null |
A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment | Accept (poster) | Summary: This work introduces a three-branch checks-and-balances framework for ethical alignment in LLMs. The framework incorporates emotional modeling to distinguish linguistic behaviors in documents. It includes the DIKE module, serving as the "legislative branch" for establishing ethical standards, and the ERIS modu... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments. We agree that validating on morality datasets and expanding emotion-labeled texts are important directions. We mitigate LLM-generated emotion vector noise through aggregation (two LLMs plus human annotators), diverse sources, and ERIS adversarial r... | Summary: This paper proposes a three-branch checks-and-balances framework for the ethical alignment of LLMs, inspired by governmental separation of powers. The framework consists of three independent but interacting components: LLMs (Executive branch), DIKE (Legislative branch), and ERIS (Judicial branch).
Unlike RLHF ... | Rebuttal 1:
Rebuttal: We thank the reviewer for kind support and raising this important point.
Specifically to answer your question, the key to preventing catastrophic forgetting lies in our framework’s architectural separation of knowledge modeling (LLMs) from behavior regulation (DIKE) and ethical judgment (ERIS). ... | Summary: The authors propose a framework, that initially classifies a given document on a spectrum of emotions. If the document falls outside of certain constraints on that spectrum of emotions, the framework uses an adversarial process between the classification module, called Diagnostics, Interpretation, Knowledge-in... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We respectfully request that you reconsider some of your comments, as they appear to misinterpret key aspects of our paper. For a constructive dialogue to proceed, we would like to address nine samples of your misunderstandings (there are more but space limited):
1. Relevance of a ... | Summary: This work introduces a novel three-branch framework (LLM/DIKE/ERIS) for ethical alignment in LLMs, inspired by governmental checks-and-balances. It decouples knowledge generation from ethical oversight and integrates emotion-driven behavioral modeling (via BEAM) with adversarial cultural adaptation. The framew... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback. Below, we address the main concerns raised.
A. Complex Emotions and Emotion Modeling
We acknowledge the critique regarding our treatment of complex emotions (e.g., pride, guilt, forgiveness). As discussed in Appendix D, we recognize the challenges... | null | null | null | null | null | null |
Premise-Augmented Reasoning Chains Improve Error Identification in Math reasoning with LLMs | Accept (poster) | Summary: This paper looks into the problem of reference-free verification of LLM reasoning chains in the context of mathematical reasoning. Authors hypothesize that a step in a reasoning chain should be verified only under its premises, and propose constructing Premise-Augmented Reasoning Chains (PARC) to improve the t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind remarks on the writing and experimental details of the paper. Here we address their concerns
**concern 1** - Study is limited to the four popular mathematical datasets, and three commonly used LLM
**response** - Please note that we used three popular model fa... | Summary: The authors explore how to improve error identification in reasoning chains, which consist of multiple individual reasoning steps. They start by converting the reasoning chain into a directed acyclic graph, called Premise-Augmented Reasoning Chains (PARC), where the nodes that are reasoning steps and the edges... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of the writing and experiments. Here we address their concerns
**Typos** - Thanks for bringing those to our notice, we will definitely fix those in the final version.
**concern 1** - Dataset is a bit small, no external datasets used
**response** -
T... | Summary: This paper studied the step-level verification of CoT reasoning, and proposed a PARC framework that converted linear reasoning chain into DAG by introducing premise links. Based on the framework, the authors defined a new error type named accumulation error, and constructed PERL dataset to evaluate the framewo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their kind remarks on the novelty and thorough experimental setup of our method. Here we try to address their concerns.
**concern 1** - Since the premise is based on the step, it would be better to make it clearer what is an intermediate step defined, and how t... | Summary: This paper introduces a new category of errors (accumulation errors) and Premise-Augmented Reasoning Chains (PARC) as a method to improve error identification in mathematical reasoning with Large Language Models (LLMs). To evaluate this method, the authors construct PERL (Premises and ERrors identification in ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind remarks on the novelty and thorough experimental design. Here we address the concerns
**concern 1** - low precision of premise identification and mapping
**response** - We would like to highlight that our claim is originated by the high recall primarily. In t... | null | null | null | null | null | null |
What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities | Accept (oral) | Summary: This submission introduces OmniBench, aiming to provide a scalable task synthesis paradigm along with an evaluation framework for agent capabilities across ten dimensions. Due to the inherent complexity of agent trajectories, it is challenging to effectively construct large-scale, high-quality trajectory datas... | Rebuttal 1:
Rebuttal: Thank you for appreciating our paper as comprehensive and identifing highly valuable insight, your constructive comments and suggestions are valuable to us. Below is our detailed response to clarify the points you raised.
**Q1: The Effect of Task Intent on Planning.**
**A1:** The task intent is ... | Summary: This paper introduces OmniBench, a scalable, graph-based benchmark designed to evaluate multimodal large language model (MLLM)-based virtual agents across multiple dimensions. OmniBench employs a bottom-up subtask composition pipeline to generate 36k tasks with controllable complexity across 20 scenarios. The ... | Rebuttal 1:
Rebuttal: We sincerely thank you for professional comments and high appreciation of our work! We are encouraged that our research is recognized as laying the foundation for future advancements. We will address your concerns point by point.
**1.Data Quality**
Thank you for your suggestion, we conduct addit... | Summary: The paper introduces OmniBench, a scalable, graph-based benchmark designed to evaluate multimodal virtual agents by systematically synthesizing diverse tasks of controllable complexity through automatic task composition. It finds that existing agents significantly struggle with graph-structured tasks compared ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive and insightful comments. We will explain your concerns point by point.
**Q1: More graph-based complexity metrics**
**A1:**
Thank you for the valuable questions. First, we clarify that the current complexity metrics are based on five fundamental graph-ba... | Summary: This paper introduced OmniBench, a graph-based benchmark that addresses the limitations of existing evaluation frameworks by enabling controllable task complexity through automated subtask composition. The paper also proposes OmniEval, a multidimensional evaluation framework for evaluating virtual agents acros... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful feedback. To better demonstrate the diversity of automatically synthesized tasks in OmniBench, we restate that our approach first explores a range of various subtasks from the explorable environment and then iteratively synthesizes subtask trajectories and eva... | null | null | null | null | null | null |
Ultra-Resolution Adaptation with Ease | Accept (poster) | Summary: This paper introduces **URAE**, a framework for efficiently adapting text-to-image diffusion models to ultra-high resolutions (e.g., 4K) while minimizing computational costs and data requirements. The approach is based on three key ideas:
- **Data Efficiency**: Using synthetic images generated by a teacher mo... | Rebuttal 1:
Rebuttal: We deeply thank Reviewer FjKy for the valuable comments and are glad that the reviewer finds our method practical, efficient, and empirically strong. We would like to address the concerns as below.
> 1. Theoretical motivation for minor singular component tuning.
* We would like to supplement the... | Summary: The paper "Ultra-Resolution Adaptation with Ease" presents a novel approach called URAE for adapting text-to-image diffusion models to generate ultra-high-resolution images (e.g., 4K) with limited training data and computational resources. The key contributions include:
1. Theoretical and empirical evidence sh... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer wYHL for the positive feedback on the manuscript and are very excited that the reviewer mentions the strengths of addressing a significantly practical problem with a comprehensive framework, insightful theoretical analysis, extensive experiments, and well-written manusc... | Summary: This paper tackles the challenge of efficiently adapting text-to-image diffusion models to ultra-high resolutions (2K and 4K). Traditional approaches demand massive amounts of 4K training data and expensive fine-tuning of the entire model, making them difficult to deploy at scale. In contrast, URAE explores tw... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer u5sm for the constructive comments. We are happy that the reviewer finds our data efficiency practical, ablation detailed, and empirical evidence strong. We would like to address the concerns and questions reflected in the review below.
> 1. Limited Real-World Cos... | Summary: This paper explores the adaptation of existing models to ultra-resolution image generation. The authors categorize the challenges into two key aspects: data efficiency and parameter efficiency. Regarding data efficiency, the authors argue that synthetic data can serve as a valuable resource for model convergen... | Rebuttal 1:
Rebuttal: We appreciate Reviewer 5Gfa's thoughtful comments and are glad that the significance and insights of our work are recognized. We would like to address the concerns as below.
> 1. Theoretical analysis on synthetic data and mode collapse.
* Theorem 2.4 illustrates that, **by diminishing label nois... | null | null | null | null | null | null |
Efficient Skill Discovery via Regret-Aware Optimization | Accept (poster) | Summary: This paper proposes regret-aware skill discovery (RSD) for unsupervised skill discovery. RSD is built upon METRA, a previous temporal distance-based skill discovery method. The key idea behind RSD is to use a separate, learned skill sampler policy to sample $z$'s for better exploration (unlike the uniform dist... | Rebuttal 1:
Rebuttal: ## **Q1: Collapsing Concern**
Thank you for raising this insightful question. Our method is specifically designed to prevent skill collapse. As shown in Eq. (15), the skills are maintained within a population $P_z$. We address the diversity concern (i.e., avoiding convergence to a single point) fr... | Summary: This paper propose a new unsupervised skill discovery method, which use regret to guide the skill sampling and skill policy learning. The regret is computed by the estimation error of value function in learning. Based on that, sampling strategies is not a parameters-constant distributions as previous methods. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
### Q1: Unique Coordinates Metric
1. **Origin of the Metric**:
The metric is inspired by the *Policy State Coverage* measure introduced in the METRA paper (Section 5.3), where it was used to evaluate the **spatial coverage** of skill policies—referred to ... | Summary: The paper presents Regret-aware Skill Discovery (RSD), a novel approach to unsupervised skill discovery in reinforcement learning. The authors conceptualize skill discovery as a min-max adversarial game between skill generation and policy learning. Their key insight is that skill discovery should be guided by ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. Below we address each point in detail.
### **Q1. Statistical Reporting**
We appreciate the reviewer’s emphasis on statistical rigor.
All experiments were conducted using **five independent random seeds**: `{0, 2, 4, ... | Summary: This paper is on unsupervised skill discovery within the context of Markov decision processes. It builds on a collection of earlier papers that aim to learn diverse and distinguishable behaviours, for example, DIAYN by Eysenbach et al., (ICLR 2019). The contribution of this paper is a new algorithm, which the ... | Rebuttal 1:
Rebuttal: ### Q1: Motivation of "Policy Strength"
1. **Definition:**
The term *policy strength*, derived from "the strength of the policy", refers to the **capability** of a policy to accomplish its designated objective—corresponding to **a specific skill** in our context.
2. **Why we use "policy stren... | null | null | null | null | null | null |
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals | Accept (spotlight poster) | Summary: This paper introduces ResQ, a post-training quantization (PTQ) method for large language models (LLMs) that enables mixed-precision quantization of weights, activations, and KV caches. Experimental results demonstrate that ResQ achieves superior performance compared to existing methods.
Claims And Evidence: T... | Rebuttal 1:
Rebuttal: Thank you, Reviewer Azbj, for putting effort in reviewing our paper. We provide a response to your concerns below.
1. **Limited Novelty:** While we agree with the reviewer regarding the statement that ResQ is a rotation and mixed precision quantization based approach, we respectfully disagree re... | Summary: The paper presents ResQ, a mixed-precision post-training quantization (PTQ) method for large language models (LLMs). The core idea of ResQ is to compute the orthogonal transformations using PCA and decompose the orthogonal matrices for high-precision and low-precision based on their corresponding eigenvalues. ... | Rebuttal 1:
Rebuttal: Thank you, Reviewer x9DL, for your effort in reviewing our paper. We appreciate your recognition of the strength of ResQ's experimental section. Below, we respond to the concerns you raised.
1. **Claim regarding quantization error:** ResQ's approach of choosing coefficients along principal eigen ... | Summary: The paper proposes a novel algorithm to separate high/low values and respectively smooth and quantize them with different precisions. Theoretical analyses suggest that by introducing a designed matrix $P$, the upper bound of the error can be minimized. The experimental results illustrate the effectiveness and ... | Rebuttal 1:
Rebuttal: Thank you, Reviewer vpAp, for your effort in reviewing our paper. We are delighted that you find our approach impressive. We provide response to your questions below.
1. **Details about compute kernel**: The compute kernel goes beyond simple combination of INT4 and INT8 kernels. More precisely, ... | Summary: This paper proposes ResQ, a post-training quantization (PTQ) framework that targets aggressive 4-bit quantization of large language models (LLMs) for weights, activations, and KV caches. The key idea is to identify and preserve a low-dimensional subspace of “important” activation components in higher bit preci... | Rebuttal 1:
Rebuttal: Thank you, Reviewer UnBL, for your effort in reviewing our paper and acknowledging the strong empirical results and applicability of ResQ. We answer the listed questions below and incorporate your constructive feedback to further strengthen our work.
1. **Heuristic theory:** We agree with the revi... | null | null | null | null | null | null |
Decision Mixer: Integrating Long-term and Local Dependencies via Dynamic Token Selection for Decision-Making | Accept (poster) | Summary: This paper proposes adapting the attention block in transformers based on the Mixture of Experts (MoE) design to effectively balance capturing long-term dependencies and extracting local features. By adapting it to the Decision Transformer architecture, extensive experiments demonstrate its superior performanc... | Rebuttal 1:
Rebuttal: Thanks for the careful review of our work. Due to the strict word limit, we have tried to address the reviewers' comments carefully. All additional experiments will be incorporated into the text.
**Theoretical Claims**
We provided a brief analysis of the CSM method from the perspective of re-we... | Summary: This paper introduces Decision Mixer (DM), a Transformer-based architecture for offline reinforcement learning. DM features a dynamic token selection mechanism, where a routing module learns to selectively attend to relevant past tokens during training. To enable efficient inference, an auxiliary predictor is ... | Rebuttal 1:
Rebuttal: Thanks for the detailed review of our work. Given the strict word limit, we have carefully addressed the reviewer's comments and will incorporate the additional experiments and suggestions into the updated version. Anonymous pdf: https://anonymous.4open.science/r/Decision-Mixer-1068/rebuttal.pdf
... | Summary: This paper introduces Decision Mixer (DM), a select-concatenate-compute mechanism that improves efficiency in offline reinforcement learning. Inspired by MoE, DM dynamically filters key tokens for attention while retaining information from unselected ones. It also integrates an auxiliary predictor to mitigate ... | Rebuttal 1:
Rebuttal: Thanks for the careful review of our work. Due to the strict word limit, we have tried to address the reviewers' comments carefully. All additional experiments and suggestions will be incorporated into the updated text.
**It is recommended to adjust "MOE" to "MoE"**
We apologize for this mistak... | Summary: The main contribution is a novel dynamic token selection mechanism termed Decision Mixer (DM), inspired by MoE to enhance CSM for offline reinforcement learning. DM adaptively selects key tokens for attention computation while preserving information from unselected tokens via feature concatenation, improving e... | Rebuttal 1:
Rebuttal: Thanks for the careful review of our work. Due to the strict word limit, we have tried to address the reviewers' comments carefully. These additional experiments and suggestions will be incorporated into the updated main text.
**W1: The robustness of the dynamic mechanism in sparse scenarios need... | null | null | null | null | null | null |
Learning Likelihood-Free Reference Priors | Accept (poster) | Summary: This paper proposes the learning of reference priors via simulation-based approaches and normalizing flows. I am very short on time for ICML reviews. Apologies for my reviews being a bit short.
Claims And Evidence: They claim that they can learn reference priors via simulations. Indeed, we do see evidence tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful comments. We have included a set of new experiments, described at the end of this review.
# Reviewer Comments
1. On the KS test performance: The KS tests for the Exponential and Gaussian models do reveal discrepancies. Both reference priors have asymptotes ... | Summary: This paper focuses on learning an objective prior in the context of likelihood-free inference (SBI), where the likelihood function is intractable. Within the mutual information (MI) estimation framework, the authors propose three methods: GED, InfoNCE, and SMILE. These methods are systematically compared throu... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback. We have included a set of new experiments [here](https://github.com/ICML-7582/rebuttal_plots/blob/main/plots.pdf), and see reply to Reviewer UJGD for a detailed explanation.
## Methods and Evaluation Criteria
- On inconsistencies between methods: It is reason... | Summary: The paper proposes a way to approximate a reference prior for a Bayesian analysis from a flexible family of priors in the SBI (simulation based inference) context, where the likelihood is intractable. The primary contribution here is the SBI context, in which various estimators of entropy are required to be sp... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We have included a set of new experiments [here](https://github.com/ICML-7582/rebuttal_plots/blob/main/plots.pdf), and see reply to Reviewer UJGD for a detailed explanation.
## Figure 2
- The bumps in panels a) and b) were an artefact of the network architecture. Sigm... | Summary: This paper addresses the problem of constructing reference priors for simulation-based
inference (SBI). Unlike most SBI research, which focuses on posterior or likelihood
estimation given a user-defined prior, this work tackles the challenge of developing
"uninformative" or "reference" priors in a principled w... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback. We have included a set of new experiments, see the reply to Reviewer UJGD for a detailed explanation.
## Questions
1. We will include explicit technical definitions for, and a discussion of, proper and improper priors. Briefly: an improper prior is an "unnorm... | null | null | null | null | null | null |
FedClean: A General Robust Label Noise Correction for Federated Learning | Accept (poster) | Summary: The paper introduces FedClean, a robust framework designed to address label noise in federated learning (FL) scenarios. FedClean employs a two-stage label correction approach to identify and rectify noisy labels from both local noisy label learning and global model perspectives. It also proposes a novel adapti... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their constructive feedback. Below are our responses to the comments and questions:
**1. Basic CNLL Implementation \& Overfitting Risk**
The reviewer raises a valid point regarding the use of basic CNLL methods. In this work, we intentionally adopted standa... | Summary: The paper introduces FedClean, a robust label noise correction method for federated learning that employs a two-stage correction process to identify and rectify noisy labels, coupled with adaptive sample-size-weighted aggregation. Notably, FedClean operates effectively without requiring clean clients or assump... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback. Below, we address the concerns raised:
**1.** **_Reviewer Comment:_** **The propsoed method seems not work well: In Table 2, the baselines beat the proposed method In addition to the setting $\rho=1$ and $\tau=0.5$ both on IID and Non... | Summary: The paper proposes FedClean, a robust label noise correction framework for federated learning that addresses client-side label noise without assuming clean clients or specific noise distributions. Key contributions include: (1) Two-stage label correction. Combines local centralised noisy label learning to sele... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful feedback and constructive suggestions. Below, we address the raised points:
**1. Applicability to Other Data Types**
FedClean's design is fundamentally modality-agnostic, as its core components - the two-stage label correction and adaptive agg... | Summary: The authors proposed the method to achieve the federated learning that may involve the label noises. The proposed method, FedClean first uses the local centralized noisy label learning that selects clean samples to train the global model. Afterwards, the two-stage correct scheme is performed by exploiting loca... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s time and constructive feedback on our work. Your insightful comments have helped us better clarify the contributions and limitations of our method. Below, we address each point raised in the review, and we hope our responses will alleviate your concerns.
**1... | null | null | null | null | null | null |
Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration | Accept (spotlight poster) | Summary: The authors proposed a new LLM inference sampling framework that searches for different reasoning paths by controling a Gaussian embedding that is inserted to the sequence. They proposed embedding perturbation for controlling the sampling in the continuous space, and come up with Bayesian optimisation to guide... | Rebuttal 1:
Rebuttal: **1.Why Embedding Search Outperforms Discrete Sampling**
> A theoretical provement of "searching for a start token in the continuous embedding space" being better than "sampling every token in the discrete token space" can further improve the soundness of the paper.
Thank you for the suggestion.... | Summary: This paper proposed a Bayesian Optimization based approach to improve the test time performance of pre-trained LLMs. The authors propose to sample from an initially Gaussian distribution, where the perturbation vector is added to the distribution of the first token in the answer to control answer generation. I... | Rebuttal 1:
Rebuttal: **1. Verifier Setup**
Regarding the questions about details of the verifier, **NO** separate or stronger LLM is used for verification (Line 31-33, right column); the same model as the generator is employed (i.e. LLaMA3-8B-Ins, Qwen2-7B-Ins, and Mistral-7B-Ins).
- The verifier score $r_{verifier... | Summary: This work proposes a novel way of exploring the search space of LLM responses via perturbing the given input embedding in the generated sequence. In particular, authors design an online learning scheme that uses bayesian optimization to adjust parameters of the noise so that the generated outcome (after adjust... | Rebuttal 1:
Rebuttal: > In my opinion this work could get even more impact and recognition if authors might show the effectiveness of this approach in terms of sample efficiency when its used during training with preference optimization or reward-based training.
Thank you for this insightful and forward-looking sugges... | Summary: This paper introduces a novel embedding-based search framework to enhance complex reasoning in Large Language Models (LLMs). It perturbs the embedding of the first token with Gaussian noise and optimizes this perturbation via Bayesian optimization (BO), guided by a verifier model. Experiments on multiple chall... | Rebuttal 1:
Rebuttal: **1. Verifier Reliability**
Thanks for the comment. You are right in saying that incorporating more reliable, domain-specific verifiers could further improve performance. Our framework is flexible enough to integrate such tools, and we agree that doing so would likely yield gains in tasks where t... | Summary: To address the challenges of insufficient diversity and low search efficiency in large language models (LLMs) for complex reasoning tasks, this paper introduces a novel responsive sampling strategy. By applying Gaussian perturbations to the embedding of the first token generated by the LLM, using the correctne... | Rebuttal 1:
Rebuttal: **1. Verifier Setup**
Thanks for the comments. Regarding the questions about details of the verifier, **NO** separate or stronger LLM is used for verification (Line 31-33, right column); the same model as the generator is employed (i.e. LLaMA3-8B-Ins, Qwen2-7B-Ins, and Mistral-7B-Ins).
- The ve... | null | null | null | null |
A Trichotomy for List Transductive Online Learning | Accept (poster) | Summary: The paper studies the problem called list transductive online learning. That is the learner is given a sequence of instance $ (x_1,\ldots,x_T)\in\mathcal{X}$. In $ T $ round the adversary and the learner then does the following. The adversary picks an outcome $ y_{t}\in \mathcal{Y}.$ The learner is then asked ... | Rebuttal 1:
Rebuttal: We really thank the reviewer for dedicating their time to assess our work. In particular, we really thank the reviewer for taking the time to carefully read our paper. We are delighted that the reviewer found that our contribution is original, our paper is well written, enjoyable to read, and cont... | Summary: The authors provide theoretical analysis on the list transductive online learning problem in this paper. They first establish upper and lower bounds for the minimax number of mistakes in the realized setting, by which they solve a open problem raised from previous work. Then, in the agnostic setting, they prov... | Rebuttal 1:
Rebuttal: We thank the reviewer for dedicating their time to assess our work. We are delighted that the reviewer found that our paper contains novel techniques and novel combinatorial complexity measures. We will make sure to correct the typos and incorporate minor suggestions mentioned by the reviewer for ... | Summary: This paper tackles the combined problem of Moran et al.'s (2023) *list online classification* and Hanneke et al's (2024) *transductive online learning* (where the sequence of instance points is given in advance). Two natural variants of Littlestone dimension are proposed combining the (L+1)-Littlestone trees o... | Rebuttal 1:
Rebuttal: We thank the reviewer for dedicating their time to assess our work. In particular, we thank the reviewer for taking the time to verify that the work is technically correct. We are delighted that the reviewer found that our paper is interesting for the theoretical ICML community. We will make sure ... | Summary: They studied the problem of list transductive online learning. In the realizable setting, they show a trichotomy of possible rates of the minimax number of mistakes.
In the agnostic setting, they show a \tilde{O}(\sqrt{T}) regret bound.
Claims And Evidence: Theoretical paper, and they have proved everything t... | Rebuttal 1:
Rebuttal: We thank the reviewer for dedicating their time to assess our work. In particular, we thank the reviewer for taking the time to verify that our work is technically sound. We are delighted that the reviewer found that our paper contains novel applications of the techniques in the literature, and mo... | null | null | null | null | null | null |
Random Registers for Cross-Domain Few-Shot Learning | Accept (poster) | Summary: In this work, the authors propose a two-stage learning framework namely REAP to tackle CDFSL problem. During the source domain training, REAP randomly masks the most discriminative region and fill the erased region by random prompts, then optimize the pretrained ViT on source data. And during target domain fin... | Rebuttal 1:
Rebuttal: Thank you for your suggestion.
## **1. Claims**
[1] studies the effectiveness of **prompt learning in in-domain data**, while we specifically target **extreme cross-domain shifts** (e.g., natural images to satellite images), where standard prompt tuning fails. As shown below, REAP outperforms [1... | Summary: This paper deals with the Cross-domain few-shot learning (CDFSL) problem, which needs to tackle the huge domain gaps. Existing methods utilizing learnable prompts might learn domain specific information of the source domain, while fail to generalize to the distant target domains. This paper proposed to leverag... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough and insightful review of our submission. Your recognition of our work’s **innovative integration of random prompts with SAM principles** and its **practical value in addressing extreme domain gaps** is deeply encouraging.
We will keep on polishing our paper... | Summary: Based on an intriguing observation that prompt tuning could be harmful for the generalization of ViT and the related analysis, this paper develops a novel solution for cross-domain few-shot learning method by replacing some clustered patches with random registers. Extensive experiments on four datasets demontr... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s constructive feedback. Below are detailed responses to your questions:
## **1. Clarification on Learnable Registers (Fig.5)**
##### (1) Source-domain performance and visualization
| Model | Source-domain | Target-domain |
| ------------------ ... | null | null | null | null | null | null | null | null |
Compositional Flows for 3D Molecule and Synthesis Pathway Co-design | Accept (poster) | Summary: The paper introduces a novel flow matching framework, 3DSynthFlow, for generating synthesizable molecules within protein pockets by sequentially selecting discrete building blocks and simultaneously modeling their coordinates. The authors evaluate 3DSynthFlow against all 15 protein targets in the LIT-PCBA virt... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for volunteering their valuable time and providing insightful feedback to our paper. We are addressing their questions one by one in our response below.
> W1. Evaluation of the diversity and chemical properties.
To further address the reviewer’s suggestion, we now com... | Summary: The paper introduces Compositional Generative Flows (CGFlow), a novel framework designed for the generation of compositional objects with continuous features in generative applications, such as synthesis-based 3D molecular design. CGFlow extends flow matching by enabling the generation of objects in compositio... | Rebuttal 1:
Rebuttal: We highly appreciate this reviewer’s constructive feedback and insightful suggestions. We would like to clarify and address all of these points to the best of our ability in the response below.
> W1: Lack of ablation studies about time scheduling of state flow model
Thank you for the valuable s... | Summary: This paper introduces Compositional Generative Flows (CGFlow), a framework that extends flow matching to generate objects with compositional structures and continuous features simultaneously. CGFlow combines two interleaved processes: Compositional Flow for modeling the probability path of compositional struct... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and detailed evaluation, and for recognizing the significance of our framework contribution and its application to 3D molecule and synthesis pathway co-design.
> W1. More analysis of cases where the method performs poorly or limitations in cert... | null | null | null | null | null | null | null | null |
Linearization Turns Neural Operators into Function-Valued Gaussian Processes | Accept (spotlight poster) | Summary: The authors propose a new approach to approximate stochastic neural networks with Gaussian weights into Gaussian processes (GPs). The approach is based on performing a linearization around the mean of the weights to obtain a GP approximation of the network. The effectiveness of the framework is shown in some e... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and for highlighting several interesting directions for further exploration.
Below are our responses to your main points:
---
**Comparison with infinite-width GP approaches:**
We agree that investigating how infinite-width GP methods could be adapted to t... | Summary: The paper introduces LUNO, a framework for uncertainty quantification in neural operators using function-valued Gaussian processes. By leveraging linearization, the method propagates Gaussian weight-space uncertainty to the operator’s predictions, effectively converting trained neural operators into Gaussian r... | Rebuttal 1:
Rebuttal: Thank you for your detailed and positive assessment of our work!
---
**On the assumption of Gaussian weight uncertainty:**
This is an important point.
Our theoretical framework (see Step 3 of Section 3.2, and Appendix A, particularly Corollary A.14 and Theorem 3.2, and Section A.4) indeed apply ... | Summary: This paper introduces LUNO, a linearization approach for turning a nonlinear neural operator into a Gaussian random operator, thereby providing uncertainty estimates for operator learning. This is important in areas such as safety-critical prediction and out-of-distribution scenarios. The method is compared ag... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our paper and for pointing out areas where we can further clarify the presentation.
---
**Applicability beyond FNO:**
While our experiments focus on Fourier Neural Operators (FNOs), the theoretical framework applies in principle to any neural operator.
W... | Summary: The paper proposes a novel framework for approximate Bayesian uncertainty quantification in trained neural operators. The approach relies on model linearisation and pishes weight-space uncertainty to neural operators' predictions. This allows the application of Bayesian deep learning methods, such as linearise... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review of our paper!
We are glad to hear that you found the theoretical and experimental aspects clear and well-structured.
Should anything still remain unclear, we are happy to clarify during the discussion period.
---
**Publication of code:**
We intend to ... | null | null | null | null | null | null |
The Missing Alignment Link of In-context Learning on Sequences | Accept (poster) | Summary: Authors study the limits of LLMs’ abilities for in-context learning, focusing on learning sequence to sequence alignment (in the machine translation sense of the word). Authors design synthetic experiments that probe said ability and demonstrate that several modern llama 3 variants do indeed fail to learn alig... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful feedback. We will address the issues with the supplementary material in the final submission. Below, we respond to the other concerns.
A. We repeated the experiments of Figure 3 on multiple LLMs. We observe similar trends. We report below the numbers for m = 8, c = 1 ... | Summary: This paper systematically investigates the in-context learning (ICL) capabilities of large language models (LLMs) on sequence-to-sequence (Seq2Seq) tasks. The analysis reveals that LLMs struggle to align input and output sequences for longer inputs, limiting their ICL effectiveness. To address this, the author... | Rebuttal 1:
Rebuttal: Thank you for the constructive suggestions. We address some of the concerns below.
(1) Pre-existing knowledge composition is a plausible hypothesis to explain ICL on real tasks. However, the experiments in [1] were on scalar prediction tasks. We conjecture that for structured sequence prediction... | Summary: This work presents an interesting case study on LLM's in-context ability on translation-style seq2seq problems. More specifically, this paper studies seq2seq problem involving both learning the alignment and the target-side vocabulary. This work creates synthetic tasks for the analysis to avoid training data l... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We respond to the questions below.
(1) We train on all y-tokens, including the outputs from all previous k examples. For the comparison between standard fine-tuning and ICA-Tune, we scale the batch size for standard fine-tuning to match the number of exampl... | Summary: This paper investigates a critical challenge in in-context learning for sequence-to-sequence tasks, where they find modern LLMs struggle to learn alignments between input and output sequences in-context. The authors first show that providing explicitly aligned in-context examples dramatically improves performa... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments. We address the concerns below.
(1) Thanks for pointing this out; we will make the clarification in the final version of the paper.
(2) We agree the task is synthetic but it is related to the model followed in early statistical machine translation models. Furt... | null | null | null | null | null | null |
DVI:A Derivative-based Vision Network for INR | Accept (poster) | Summary: The paper presents DVI, a Derivate-based Vision network for INRs (Implicit Neural Representations). It consists of a neural network architecture which combines pre-existing, task-specific architectures working on raster data, like images or voxel maps, with INR feature extraction modules, that process the deri... | Rebuttal 1:
Rebuttal: Thank you for your thorough and constructive feedback. We address your questions point by point below:
## Q1: Clarify the relation with the derivations presented in (Xiao et al., 2023)
We confirm that we use the recursive formula for high order derivatives in (Xiao et al., 2023) to compute the d... | Summary: This paper proposes a framework that combines implicit neural representations (INRs) with a traditional raster-based vision network, leveraging high-order derivatives to capture additional semantic or structural information. Experimental results demonstrate performance improvements across a variety of tasks, s... | Rebuttal 1:
Rebuttal: Thank you for your thorough and constructive feedback. We address your questions point by point below:
## Q1: Discuss the computational overhead
We provided the MAC (Multiply-Accumulate Operations) for all algorithms in Tables 1-4 of our submission, including tests on more complex generative netw... | Summary: This paper proposes a novel architecture DVI that integrates high-order derivative information from implicit neural representations (INRs) into raster-based vision networks. The authors address the limitations of existing types of networks for vision tasks: a) raster-based methods lack semantic information due... | Rebuttal 1:
Rebuttal: Thank you for your thorough and constructive feedback. We address your questions point by point below:
## Q1: Include diffusion-based methods for comparison
We included **5 diffusion-based methods** for comparison across all vision tasks addressed in our paper. These **include the 2 algorithms you... | null | null | null | null | null | null | null | null |
Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models | Accept (poster) | Summary: The paper introduced a novel method for decomposing language model activations into interpretable features that could replace SAE with matching pursuit. Thanks to its efficient and fast-converging matching pursuit algorithm (and not having to perform expensive training of NNs), this enables scalable learning a... | Rebuttal 1:
Rebuttal: Thank you for your feedback and we appreciate that you feel this work is novel and “its benefits are impressive”.
> The efficiency and scalability feel solid, though exact times and hardware details would help.
We ran our experiments on a range of hardware so it is hard to provide precise summa... | Summary: The paper introduces Inference-Time Decomposition of Activations (ITDA) as a fast and scalable alternative to SAEs for interpreting LLM activations. ITDA constructs a dictionary of representative activations using matching pursuit, allowing it to be trained 100-1000× faster than SAEs with only 0.1-1% of the da... | Rebuttal 1:
Rebuttal: Thank you for your comments, we appreciate your recognition of the strong evidence for the “efficiency and scalability of ITDA in comparison to SAEs”.
> However, the comparison to SAEs is somewhat limited, as it primarily focuses on ReLU SAEs, while more advanced variants (e.g., TopK, P-Annealing... | Summary: The main idea of this paper is to apply a dictionary learning approach to the problem of finding sparse representations of activation. ITDA builds the dictionary at inference time. The algorithm works by first trying to reconstruct the activation from the atoms in the dictionary. Reconstruction is done by Mat... | Rebuttal 1:
Rebuttal: Thanks for your feedback and suggestions, we’re glad you like the idea and are keen to improve the presentation.
> In Section 4 they also consider representation similarity, which is an interesting new topic. I didn't fully understand the claims they were making in this section, or what the impli... | Summary: The paper proposes a new algorithm for mechanistic interpretability as an alternative to sparse autoencoders. Their algorithm iteratively identifies new token activations to add as dictionary items based on their similarity to the current dictionary. If the similarity is too low (i.e. reconstruction through th... | Rebuttal 1:
Rebuttal: Thank you for your positive and thoughtful feedback. In particular we appreciate your recognition of the evidence for the claim that this approach is “a lot more efficient than SAEs” and that you feel this work “opens up meaningful new research directions”.
> However, I would like to see a more t... | null | null | null | null | null | null |
Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All | Accept (oral) | Summary: This paper focuses on iterative combinatorial auctions (ICAs), aiming to tackle the issue of exponential bundle space growth in combinatorial auctions. The authors introduce a machine learning (ML) algorithm that utilizes information from both demand queries (DQs) and value queries (VQs) and present the ML-pow... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback! If you have any additional questions, please let us know.
“I am concerned about the complexity of the MLHCA algorithm”:
In terms of theoretical time complexity, our ML-CCA, as every other ICA - including the CCA and all ML-powered auctions discussed - are NP... | Summary: This paper studies Iterative Combinatorial Auctions (ICA) and proposes a novel ML-based ICA mechanism, MLHCA. Their key empirical finding is that the mixed use of Value Queries (VQ) and Demand Queries (DQ) in their ML-based ICA mechanism significantly improves the efficiency and convergence of ICA compared to ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback! If you have any more questions, please let us know.
“Does not provide a provable positive result for their mixed use, even in a simple toy model.”
Lemmata D.9 and D.14, and Example 1 provide positive results, but we agree that our experimental positive res... | Summary: The paper introduces a new auction method called the Machine Learning-powered Hybrid Combinatorial Auction (MLHCA), designed to improve how items are sold in complex auctions where bidders can place offers on combinations of items. In these auctions, figuring out the best combination of bids is difficult becau... | Rebuttal 1:
Rebuttal: Thank you very much for your very detailed review! If you have more questions, please let us know.
“The assumptions on the value function are unclear”
We only assume that $v(0) = 0$ and monotonicity, which combined imply non-negativity. Both of those assumptions are well-motivated and fairly st... | Summary: The paper introduces MLHCA, a Machine Learning-powered Hybrid Combinatorial Auction that integrates demand queries (DQs) and value queries (VQs) to minimize efficiency loss in iterative combinatorial auctions. The authors provide theoretical insights demonstrating that DQs are most effective in the early aucti... | Rebuttal 1:
Rebuttal: Thank you for your positive and thorough feedback! If you have any more questions, please let us know.
“A discussion on incentive compatibility would be nice.”
We discuss incentive compatibility in detail in Appendix B, where we try to provide intuition on this topic and prove some theoretical ... | null | null | null | null | null | null |
SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression | Accept (poster) | Summary: Large language models contain extensive parameter counts leading to significant memory overhead and high inference costs. Pruning and quantization methods solve this, but typically both need retraining on large-scale datasets. One-shot methods can reduce the cost, but jointly pruning and quantizing weights und... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We have provided a detailed reply to address all of your points.
# Significance of Accuracy Improvements and MATH Benchmark
SLiM achieves up to 5.66% higher average accuracy than leading compression methods across six zero-shot tasks.
Per your req... | Summary: This paper proposes to use low-rank approximation to reduce the compression error for quantization and pruning on LLM. SLIM-Quant minimizes the quantization error by selecting the optimal scaling parameter. Low-rank adapters are applied to compensate the quantization and pruning error. Quantization and fine-tu... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback. Below, we provide answers to all the points raised.
# Speedup Comparison without LoRA
As requested, we have added two tables showing the layer-wise speedups of compressed models, with and without low-rank adapters, on an RTX-3060 GPU. While low... | Summary: The paper introduces SLIM, a one-shot post-training compression framework for large language models. It integrates three components: (1) quantization, (2) pruning for hardware‑friendly sparsity, and (3) a low‑rank adapter to compensate quantization errors. Experimental results show that SLIM improves model acc... | Rebuttal 1:
Rebuttal: # Minimizing Quantization Error
We agree that minimizing the final output error, i.e., $|XW - XW^C|$, is the ideal objective for any compression method. However, directly optimizing this quantity is computationally intractable in general. It is known to be NP-Hard and difficult to scale across la... | null | null | null | null | null | null | null | null |
Banyan: Improved Representation Learning with Explicit Structure | Accept (poster) | Summary: This paper introduces Banyan: a new recursive graph neural network for learning text representations in low-resource languages. This model extends previous work by building nested trees over sequences that share the same tokens. In Banyan the same tokens will have the same tree node, even if they come from dif... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback regarding the paper!
The overall logic for our experiments is as follows:
We care about whether we can create a resource efficient model by applying an inductive bias. This can be very useful for low resource languages. However, such languages do not have m... | Summary: This paper studies the problem of learning semantic representations for language in low-resource settings. While word embeddings can be learned with little data, they are non-contextual; on the other hand, transformers can produce contextual embeddings but are data-hungry. In this work, the authors build on an... | Rebuttal 1:
Rebuttal: Thank you for your extensive review and extremely helpful feedback!
Other sentence embedding baselines:
We appreciate you providing the missing references and will be sure to include them in the paper.
Our key focus is to assess whether we can create an efficient method for learning representa... | Summary: The paper proposes BANYAN, a graph-based autoencoder that learns sentence representations by explicitly encoding hierarchical structures. It extends a prior structured model (SELF-STRAE) in two major ways: 1) Entangled Trees: Instead of building a separate tree per sentence, the model merges identical token sp... | Rebuttal 1:
Rebuttal: Thank you for your feedback, and positive appraisal of our work! We will make sure to fix the typo - thanks for pointing that out!
Regarding the trend of bigger models and more data, this is definitely the way things are moving, but it also leaves a lot of languages behind and limits who can part... | null | null | null | null | null | null | null | null |
Understanding the Logic of Direct Preference Alignment through Logic | Accept (poster) | Summary: This paper proposes a symbolic method to interpret direct preference alignment (DPA) loss functions. Given a DPA loss, the proposed method translates it into a preference structure consisting of three formulae, which can be further used to construct a corresponding semantic loss. The paper further shows how ex... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
> The translation is based on a set of fixed rules, making it unclear how general these rules are.
See comment below about WMC.
> The algorithm uses rules in table 6, which covers P1*P2, (1-P1) and P1+P2. I wondered how general this is. For example, how can sqrt(... | Summary: This paper introduces a novel approach to describing direct preference alignment (DPA) algorithms in terms of propositional logic. By generalizing the notion of semantic loss, the authors attempt to provide a formal framework to characterize differences between DPA variants.
Subsequently, the authors leverage... | Rebuttal 1:
Rebuttal: Thank you for your encouraging feedback.
> How well can other researchers leverage the proposed framework to identify novel loss formulations beyond the 4 shown in the paper?
While we reported experiments on 4 losses, we derived many more novel losses, including the 16 single model losses shown... | Summary: This paper presents a fresh perspective on common loss functions in the rapidly growing direct preference optimization literature. In particular, by translating loss functions into symbolic expressions, the paper offers a principled way to analyze their semantics. This approach makes it easier to understand re... | Rebuttal 1:
Rebuttal: Thank you, we are excited that you find our work “really exciting”, below we address your comments and concerns.
>However, while the paper briefly mentions new loss variants, it lacks a strong empirical demonstration
Please see our response below. Much of our experimental results were pushed to... | Summary: This paper proposes a novel framework to unify various preference optimization losses as a logical program of response orders. A pairwise preference implies the logic that a rejected response in the policy shall imply that the chosen response is in the policy; A supervised loss implies that the response is fav... | Rebuttal 1:
Rebuttal: Thank you for your feedback, we are pleased that you find our approach to be “a valuable addition to the relevant science literature”
>many relevant papers which incorporate "scalar reward signals" in the training objective. With the fine-grained reward information, those methods tend to outperf... | Summary: The paper proposed the decompilation of loss functions such as DPO into symbolic programs. More specifically, the authors present how to derive probabilistic propositional logic programs that can, in turn, be manipulated and compiled into potentially novel and improved losses for preference alignment.
Claims ... | Rebuttal 1:
Rebuttal: Thank you for the feedback; as noted in our response to the above reviewer, we’ve already taken steps to improve the presentation, which we hope will also address your concerns.
We are encouraged that you nonetheless found our approach to be “compelling” and an “interesting avenue to explaining,... | Summary: The work attempts to structure the corpus of existing and discover new optimization losses for direct preference alignment (DPA). To this end, existing DPA methods are unified and cast as a reasoning problem. Namely, each loss corresponds to a set of logic formulas that are optimized via weighted model countin... | Rebuttal 1:
Rebuttal: Thank you for your feedback, we are encouraged that you found our approach to be “very original and promising”. We have already taken steps to improve the presentation in the places you mention and we will address your particular points below.
> To me, the start of Sec. 3 is harder to read than ... | null | null |
Rectified Robust Policy Optimization for Robust Constrained Reinforcement Learning without Strong Duality | Reject | Summary: This paper studied robust constrained reinforcement learning (RCRL). The paper first proposed a counterexample illustrates that strong duality does not generally hold in RCRL. Therefore, this paper proposes the rectified robust policy optimization algorithm based on a previous algorithm CRPO. The paper provide... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive suggestions. Here is our point-to-point responses:
## Relation To Broader Scientific Literature:
The reviewer's suggestion on providing more application scenarios is indeed helpful. We will revise the introduction section to include these applications.
#... | Summary: This paper studies efficient algorithms for solving constrained RMDPs. In the light of the lack of strong duality for constrained RMDPs, a *primal-only* algorithm, Rectified Robust Policy Optimization (RRPO), is proposed with theoretical convergence rate guarantees. The performance of the algorithm is further ... | Rebuttal 1:
Rebuttal: ## Regarding Major Issues
We deeply appreciate the reviewer's careful reading. We absolutely understand that these two concerns are critical and we truly believe they are caused by some misunderstandings from our unclear presentations. Here we make additional discussions to make it more clear and ... | Summary: This paper first uncovers that strong duality does not generally hold in robust constrained RL via a toy example, which indicates that traditional
primal-dual methods may fail to find optimal feasible policies. To address the limitation, it proposes a primal-only algorithm called RRPO, which introduces a 3-sta... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s detailed comments and valuable insights. Below, we provide our point-by-point responses.
## Claims And Evidence:
* ***Primal-dual methods will fail in robust constrained RL***:
We would like to clarify that we never claimed that primal-dual methods wi... | null | null | null | null | null | null | null | null |
GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model | Accept (poster) | Summary: The paper focuses on parameter-efficient fine-tuning for 3D vision models and introduces a geometry-aware prompt learning method, named GAPrompt. GAPrompt incorporates three key designs to effectively capture geometric information, including Point Prompt, Point Shift Prompter, and the Prompt Propagation mechan... | Rebuttal 1:
Rebuttal: ### Q1. working mechanism of Point Prompt
As for working mechanism of Point Prompt, it can be analyzed through Equation 18 and 19 in paper. Previous prompting methods operate at **token level**, which corresponds to local patches, failing to adjust exact points within patches.
In contrast, our P... | Summary: This is a paper on efficient point cloud fine-tuning, where the author added a geometric perception structure to the point cloud embedding part for efficient fine-tuning and achieved good results.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: Yes, but this paper does not ... | Rebuttal 1:
Rebuttal: Thanks for your appreciation and valuable advice!
### Q1. inference time and FLOPs comparison
We test the inference time and FLOPs of our method and other SOTA methods, including IDPT[ICCV23], DAPT[CVPR24] and Point-PEFT[AAAI24]. All experiments are conducted on a RTX 4090.
As table shown below... | Summary: This paper proposes GAPrompt, a geometry-aware prompt learning method for parameter-efficient fine-tuning (PEFT) of pre-trained 3D vision models. Existing PEFT approaches in 3D vision struggle to capture geometric information from sparse and irregular point clouds. To address this, GAPrompt introduces three ke... | Rebuttal 1:
Rebuttal: ### Q1,W1. computational cost of multi-resolution grouping
Multi-resolution grouping of Point Shift Prompter can hardly become a bottleneck for real-time applications.
Although multi-resolution FPS/KNN operation has $O(N^2)$ complexity, its overhead remains minimal compared to $O(N^2)$ attentio... | Summary: The authors propose a parameter fine-tuning method for point cloud models, called GAPrompt. The main motivation of this method is to inject geometric information into the point cloud model. To achieve this goal, the authors propose three components: point prompts, which are used to increase the number of point... | Rebuttal 1:
Rebuttal: ### R1. comparison with PPT and PointGST
Although PPT and PointGST are currently available only as preprints on **arXiv** and have not been officially published, we supplement comparison experiments as shown below.
The results across four datasets and four representative backbones illustrate tha... | null | null | null | null | null | null |
GraphGPT: Generative Pre-trained Graph Eulerian Transformer | Accept (poster) | Summary: The paper introduces a self-supervised generative pre-trained model called GraphGPT based on a transformer architecture, which is called Graph Eulerian Transformer and employs a graph to sequence method which uses Eulerian paths. This method ensures reversibility in the graph-to-sequence translation. The trans... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive feedback. Please let us know if there are any concerns that have not been addressed.
Q1. Explanation of why there is a need to introduce stochasticity when doing path identification.
A1: GraphGPT lacks some inductive biases inherent to GNNs (e.g., node pe... | Summary: The paper presents GraphGPT, a novel self-supervised generative pre-trained model for graph learning that utilizes a new architecture called the Graph Eulerian Transformer (GET). The GET integrates a transformer architecture with a graph-to-sequence transformation method based on Eulerian paths, allowing for t... | Rebuttal 1:
Rebuttal: Thank you for your questions. Our responses are as follows:
Q1. Why did you choose the Eulerian path for graph-to-sequence transformation? How does it ensure lossless and reversible mapping?
A1: The Eulerian path was selected for its ability to traverse each edge exactly once, enabling a sequent... | Summary: The authors in this paper introduce GraphGPT for graph learning that leverages Graph Eulerian Transformer (GET). The proposed model uses a graph-to-sequence transformation method based on Eulerian paths, enabling it to convert graphs into token sequences for transformer-based processing.
Claims And Evidence: ... | Rebuttal 1:
Rebuttal: Thank you very much for the valuable feedback. Our responses are as follows:
Q1. GraphGPT excels with large-scale data but lacks comparisons with GNNs.
A1: We compared GraphGPT with multiple GNN baselines (e.g., GCN, GIN, GCN-VN, GIN-VN) in all experiments. These baselines are standard in graph ... | Summary: The paper "GraphGPT: Generative Pre-trained Graph Eulerian Transformer" proposes GraphGPT, a self-supervised generative pre-trained model for graph learning. The core contribution is the Graph Eulerian Transformer (GET), which enables transformers to process graph-structured data efficiently by converting grap... | Rebuttal 1:
Rebuttal: Thanks for the constructive feedback. Our replies are as follows:
Q1. Authors mentioned that "GraphGPT scales to over 2 billion parameters with sustained performance gains.", but some plots about the scaling laws would make this claim stronger.
A1: We appreciate the suggestion to strengthen our ... | null | null | null | null | null | null |
Sparse Autoencoders for Hypothesis Generation | Accept (poster) | Summary: This paper proposes a hypothesize generation method by training a sparse auto encoder to find text examples trigger the same neuron which interpreted as containing the same human concepts, and then leverage on LLM to generate the interpretation of the neuron from these examples. The paper performs experiments ... | Rebuttal 1:
Rebuttal: Thank you for your detailed, positive review. We are glad you find the claims convincing, the paper well-presented, and the method efficient.
To reply to your questions:
(1) “The method proposed here is also subjected to the context window limit[...]” Thanks for pointing this out; we will clar... | Summary: The paper presents HYPOTHESAES, a three-step method (SAE-based feature generation, feature selection, and LLM-based feature interpretation) for hypothesis generation that identifies interpretable relationships between text data and a target variable. The approach leverages sparse autoencoders (SAEs) to learn m... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and suggestions. We're glad you found our method original, clearly presented, theoretically grounded, and computationally efficient, with the performance gains convincingly supported.
To respond to your questions and suggestions:
(1) “ ‘Broad applicability beyon... | Summary: This paper addresses the task of using LLMs to take a labeled text dataset and propose natural language hypotheses predicting those labels. The performance of hypotheses is measured by having an LLM evaluate each prompt according to the explanations, producing a boolean vector. Using this vector, a linear regr... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and positive review. We’re especially glad to read that as an interpretability researcher, you found our work to be “highly significant because it is the most compelling example I have yet seen of sparse autoencoders beating baselines on a real task that people have ac... | Summary: This paper proposes a method to generate hypotheses using SAEs. The first step of the method involves generating interpretable features by training SAEs on feature embeddings. The second step involves identifying which features are predictive for a task, using Lasso. Finally, the third step involves using LLMs... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and positive review. We’re glad that you found the claims of the paper to be very clear and well supported.
We agree that a significant benefit of our method is that it reduces compute requirements by 1-2 orders of magnitude, and believe this will facilitate real-worl... | null | null | null | null | null | null |
Winner-takes-all for Multivariate Probabilistic Time Series Forecasting | Accept (poster) | Summary: This paper addresses a time-series forecasting problem, where the model generates multiple forecasting for each timestamp. The authors propose TimeMCL, a method based on the Multiple Choice Learning (MCL) paradigm that can output multiple plausible forecasting with multiple prediction heads and score heads. Ti... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive feedback on the paper.
> In Eq.5, why is not $x_{t-1}$ inputted to the function gamma?
This is because, with our notations, $\gamma^k_{\theta}$ corresponds only to the head. The full model writes as $\gamma_\theta^k \circ s_\theta$, where $s... | Summary: The paper presents a new method, TimeMCL, for time series forecasting. The proposed method uses Multiple Choice Learning using Winner-Takes-All (WTA) loss to forecast multiple plausible time series future. The paper uses synthetic data to show that TimeMCL is a functional quantizer. The proposed TimeMCL is com... | Rebuttal 1:
Rebuttal: We thank the reviewer for their relevant remarks. The [rebuttal pdf](https://anonymous.4open.science/r/TimeMCL_ICML-E616/TimeMCL_ICML.pdf) is attached to the response. Figure A of the pdf will be included in the paper.
### Comparison with more SOTA methods
> The results will be more validated i... | Summary: This work introduces TimeMCL, a time series forecasting model that looks to project plausible future scenarios and their associated probabilities to better forecast multimodal distributions. The model learns multiple heads, as well as scores associated to each head to estimate the probability of a given head b... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. The [rebuttal pdf](https://anonymous.4open.science/r/TimeMCL_ICML-E616/TimeMCL_ICML.pdf) is attached to the response.
### Clarification of the theory
> TimeMCL is a stationary conditional functional quantizer [...], but it's unclear what value this prov... | Summary: This paper proposes the idea to generate a diverse set of forecast trajectories instead of a single trajectory as typically done. Prior ideas of doing this involved sampling from the output distribution such as in TimeGrad using a diffusion process or sampling from other models generating a distribution. Howev... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their positive feedback and insightful comments.
### Sensibility to initialization
> K-means is quite sensitive to initialization. Can you comment on the sensitivity of WTA to the initialization of the hypotheses?
As MCL can be seen as a conditional and gradi... | null | null | null | null | null | null |
Attention-Level Speculation | Accept (poster) | Summary: This paper presents a novel infra method that accelerates the model forwarding (interence time) speed via attention-level speculation.
Claims And Evidence: Most claims are convincing. Though I'm not convinced that such error is actually controllable (as shown in the main figure) if the model layer is very dee... | Rebuttal 1:
Rebuttal: We are grateful for your detailed feedback which will greatly improve the quality of the work.
## Error Propagation for Deeper Models
To make sure our method works for larger model with deeper layers, empirically, we have conducted experiments on the Llama 3.3 70B model for correctness analysis, ... | Summary: LLM is resource-intensive, and serving LLMs is difficult. Model parallelism is bottlenecked by communication, when the communication bandwidth is low, and data parallelism is great at throughput but not good as inference latency. Approximate attention is robust at instruction-following and knowledge retrieval ... | Rebuttal 1:
Rebuttal: We are grateful for your detailed feedback which will greatly improve the quality of the work.
## GPU/TPU generalization
We ran new experiments for a correctness analysis on the Llama 3.3 70B Instruct model, which are more suitable for frontier GPUs like H100 with higher communication bandwidth ... | Summary: The paper introduces attention-level speculative parallelism (ALSpec), a dynamic method for approximating self-attention in large language models. ALSpec computes an approximate attention output and decides whether to accept the approximation. It uses a specialized flash decode kernel and SGDC with priority g... | Rebuttal 1:
Rebuttal: We are grateful for your detailed feedback which will greatly improve the quality of the work.
# Impact on Larger Models
We performed additional experiments in terms of correctness analysis on the Llama 3.3 70B Instruct model. We showed that in terms of speculation hit rate and benchmark correctn... | Summary: The paper "Attention-Level Speculation" introduces ALSpec, a novel parallelism paradigm designed to accelerate transformer-based LLM inference by overlapping self-attention computations with subsequent non-attention operations (e.g., feed-forward layers). Key contributions include the core idea of self-attenti... | Rebuttal 1:
Rebuttal: We are grateful for your detailed feedback which will greatly improve the quality of the work.
## Absence of compositional reasoning and low-res. languages
We extend our eval to GSM8K in Swahili (low-res. language), HotpotQA, and RepoBench-P. Results are below.
| Config\Tasks | MGSM_CoT_Sw | Ho... | null | null | null | null | null | null |
NestQuant: nested lattice quantization for matrix products and LLMs | Accept (poster) | Summary: This work presents a matrix multiplication replacement for low precision quantized neural networks. A new vector quantization mapping is explored to maximize the efficiency of low bit number distribution. The authors propose to use a new encoding scheme to achieve near lower-bound compression.
Claims And Evid... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment and appreciate the provided feedback. Following the suggestions, we run the evaluations on a larger set of models and provide the perplexity results, as well as comparisons with other methods. The results are outlined in the table in the response to reviewer a... | Summary: The paper contributes a novel practical vector quantization method and applies it to quantize LLMs (post training). It is based on information theory with the key elements being:
- Hadamard transforms to bring the distribution of vectors (weights/activations) closer to normality
- Vector quantization of groups... | Rebuttal 1:
Rebuttal: Thank you for such detailed reading and thoughtful comments! Please refer to other responses for new experiments.
Lattice defs clarity: We will provide references and a short definition of lattices and nested lattices early
110: The exact statement is only given later in Sec 3-Random Rotation.
... | Summary: The authors propose NestQuant, a novel lattice-based quantization method designed to improve weight-activation quantization for LLM.
Claims And Evidence: The authors provide evidence, such as RMSE error comparisons with SpinQuant, to demonstrate the effectiveness of NestQuant relative to uniform-based methods... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and providing valuable feedback! Here, we provide the answers to the questions.
### **Evaluation on other models**
We have conducted additional experiments of NestQuant in compressing other LLMs. We chose models in Llama-2 series (7B, 13B, and 70B) and Llama3-70... | Summary: The presented work proposes to use in an improved quantization technique for LLMs that wastes 17% less quantization "space". Written well and very balanced (theory + practical verification).
## update after rebuttal
Initially my own (internal) score was between 3 and 4 and I rounded up to 4, because I found ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Indeed we believe that this work may be interesting also to researchers outside the ML community. In particular, NestQuant consists of a combination of various ideas, including random rotations, nested lattice quantization for the induced Gaussian-like vec... | null | null | null | null | null | null |
Understanding the Emergence of Multimodal Representation Alignment | Accept (poster) | Summary: This paper aims to understand the properties under which alignment emerges in multi-modal models. Specifically, they studied the influence of the data similarity (heterogeneity, i.e., how similar are two modalities) and uniqueness/redundancy of information (a.k.a. information imbalance) between the modalities ... | Rebuttal 1:
Rebuttal: We thank the reviewer and are glad that they find our experiments well-designed and motivated. Below we address the reviewer’s comments and questions.
**Under “Methods And Evaluation Criteria:”**
> The KNN-based variant … other alignment measures would be appreciated since properly measuring alig... | Summary: This paper presents an empirical investigation of alignment between models with possibly different architectures and trained over different modalities. The authors investigate under which conditions the so-called Platonic Representation Hypothesis is likely to arise based on the heterogeneity of the data modal... | Rebuttal 1:
Rebuttal: We thank the reviewer and are glad that they find our experimental evidence convincing. Below we address the reviewer’s comments and questions.
**Under “Claims and Evidence:”**
> It remains open whether and how models trained on larger datasets (consisting of multiple degrees of uniqueness) can ... | Summary: This paper mainly focus on analyzing the emergence of multimodal representation alignment. Alignment between cross-modal representations has been long regarded as an important factor of improving multimodal model performance. Some recent researches have found that independently trained unimodal models can be i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review. Below we address their questions and concerns.
**Under “Claims and Evidence”:**
> My major question concerns the heuristic of this paper on modern multimodal model design … which however is less discussed thoughout the paper.
To the best of our knowledge, o... | Summary: This paper empirically investigates when and why implicit alignment emerges, and whether alignment consistently predicts task performance, finding that both depend critically on modality similarity and the redundancy or uniqueness of the information provided.
Claims And Evidence: The analysis that is conducte... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive criticism and are glad that they find our analysis interesting. Below we address the reviewer’s questions and concerns.
**Under “Claims And Evidence”:**
> Is the used metric, HSIC, sufficient to reflect the alignment quality? … is highly sensitive to th... | null | null | null | null | null | null |
Neural Guided Diffusion Bridges | Accept (poster) | Summary: The paper considers the challenging and widely-applicable problem of conditioning a reference diffusion process to sample rare events or desired outcomes. Building on "guided proposal" approaches which construct the conditioned process for a cleverly-chosen tractable process, the authors propose to learn an ... | Rebuttal 1:
Rebuttal: **Claims and Evidence**
The guided bridge relies on training the neural network. Once trained, independent samples are obtained. The quality of the guided proposal depends much on the nonlinearity of the diffusion and number of pCN-steps required in the MCMC-algorithm. This is case dependent. For... | Summary: This paper introduces Neural Guided Diffusion Bridges, which added variational inference to Guided Proposal.
Neural-guided Diffusion Bridges show bridge sampling without MCMC. They perform competitively in many experiments. They can handle rare events, which is very hard for other methods.
Though such good p... | Rebuttal 1:
Rebuttal: **Theoretical Claims**
The formulation was imprecise and we propose to reformulate it as follows:
> If $\theta_{\rm opt}$ is a local minimizer of $L$ and $L(\theta_{\rm opt})=-\log \frac{\tilde{h}(0,x_0)}{h(0,x_0)}$, then $\theta_{\mathrm{opt}}$ is a global minimizer. This implies from which we o... | Summary: The paper presents a novel method for simulating conditioned diffusion processes, called diffusion bridges. This approach trains a neural network to approximate the bridge dynamics, providing a more robust and efficient alternative to traditional methods like MCMC or reverse-process modeling, particularly for ... | Rebuttal 1:
Rebuttal: **Methods and Evaluation Criteria**
1. All $\vartheta_{\theta}$ implementations use fully-connected networks with $(1+d)$-dimensional inputs (time $t$ and state $x$). Time integration varies by dimensionality: direct concatenation for low-dimensional systems (Brownian/OU/cell/FHN) versus sinusoida... | Summary: This paper introduces a novel variational method for simulating conditioned diffusion processes (diffusion bridges) by proposing a more expressive guided proposal framework of Schauer et al. (2017) with a learnable drift correction term parameterized by a neural network. By leveraging variational inference, th... | Rebuttal 1:
Rebuttal: **Claims and Evidence / Methods and Evaluation Criteria**
* While neural bridges and MCMC-guided proposals differ methodologically—complicating direct cost comparisons—their forward simulation costs are comparable: for example, in the landmark process ($d=100$), the forward simulation time is 9.81... | null | null | null | null | null | null |
Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals | Accept (poster) | Summary: This work explores sequential incentive design in a repeated principal-agent problem with adversarially ordered agents. The principal faces $K \geq 2$ agent types (unknown) and selects incentives for one of $N$ arms to influence agent decisions, which are made based on both intrinsic utility and offered incent... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. We now address their concerns.
**Typo regarding $L$:** Thanks for pointing out this typo. This should be $L \ge 1$.
**Regarding the parameter $\Delta$ in our linear-regret lower bound:** In the principal-agent problem, the incentives provided to any arm li... | Summary: Building upon the literature on repeated principal-agent games, this paper explores a setup where a principal recommends an action from a bandit instance to an agent and offers a payment so the agent is incentivized to follow the recommendation. Two cases are studied: first when the agent greedily chooses her ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. We now address their concerns.
**On instance-dependent Algorithm for the smooth setting:**
For the smooth setting, after establishing the minimax regret bounds, we directly apply the Zooming algorithm from [1] in Section 4.3 of our paper. The instance-depe... | Summary: This paper studies a repeated principal-agent game, where the principal delegates their action to $K$ agents, each choosing from $N$ actions, with agent types ($i_t \in [K]$) assigned adversarially in each round. First, they show that achieving no-regret requires the principal to have prior knowledge of the ag... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. We now address their concerns.
**On Presentation suggestions:** We thank the reviewer for the helpful suggestions regarding presentation. We will carefully incorporate them to improve clarity and readability in the next version.
**Comparison with Harris ... | Summary: The paper introduces a repeated principal-agent setting where agents arrive in an adversarial fashion. The principal interacts with agents of unknown types by strategically offering incentives to influence their decisions. The paper proposes algorithms with sublinear regret bounds under two key settings: (1) w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. We now address their concerns.
**On technical breakthrough:** While we respect the reviewer’s opinion, we believe our work includes notable technical breakthroughs. We develop novel lower bound techniques tailored to the greedy model setting—methods that, t... | null | null | null | null | null | null |
Offline-to-Online Reinforcement Learning with Classifier-Free Diffusion Generation | Accept (poster) | Summary: The paper proposes CFDG, a novel method of generative data augmentation for offline-to-online RL. The paper points out that offline and online data have different characteristics that are important for high performance. To this end, CFDG employs a conditional diffusion model where the condition is a binary val... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions! Below, we provide a detailed response to your questions and comments. If any of our responses fail to sufficiently address your concerns, please inform us, and we will promptly follow up.
**[W1] Training Separate Diffusion Models fo... | Summary: Offline-to-online Reinforcement Learning (O2O RL) aims to perform online fine-tuning on an offline pre-trained policy to minimize costly online interactions. To this end, existing work used offline datasets to augment online data. However, a distribution gap exists between the generated data and the online dat... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions! Below, we provide a detailed response to your questions and comments. If any of our responses fail to sufficiently address your concerns, please inform us, and we will promptly follow up.
**[W1] Technical Novelty and Insights**
Whi... | Summary: This paper introduces CFDG, a framework that applies data augmentation to both offline and online datasets in offline-to-online algorithms.
Claims And Evidence: The paper conducts extensive experiments on the D4RL dataset to evaluate the effectiveness of the proposed framework.
Methods And Evaluation Criteri... | Rebuttal 1:
Rebuttal: Thank you for your valuable review! If you have any additional comments, please feel free to share them—we would be happy to address any questions or clarifications you may have. | Summary: This paper introduces Classifier-Free Diffusion Generation (CFDG), a model-based data augmentation method for offline-to-online RL. The key idea is to train a diffusion-based data generation model with classifier-free guidance to differentiate between online and offline data. The generated data is then used to... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions! Below, we provide a detailed response to your questions and comments. If any of our responses fail to sufficiently address your concerns, please inform us, and we will promptly follow up.
**[W1] Integration with Other Online RL Algo... | null | null | null | null | null | null |
An Analysis for Reasoning Bias of Language Models with Small Initialization | Accept (spotlight poster) | Summary: This paper aims to investigate how different initialization scales (small vs. large) may affect transformer models' ability to learn in different tasks (specifically in reasoning and memorization). The study pretrains a GPT-2 model with separately from smaller to larger initialization scales on reasoning and m... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review of our work and your valuable feedback and questions. Your recognition and support for our research have been immensely encouraging. We address your concerns as follows.
**Question**: I was wondering if this small initialization effect can generalize i... | Summary: The paper investigates initialization scale as one of the driving factors of bias towards different types of tasks. The paper considers two types of tasks:
1. Reasoning - represented by a sum over a key and its anchors given a key. This task is constructed to capture logical/arithmetic relationships, which req... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review of our work and the valuable feedback you have provided. We address your concerns as follows: (**Due to the character limit, we are not able to display specific textual revisions. However, we will carefully revise our manuscript to address each concern.... | Summary: This paper discusses the impact of initialization of language models on their trained performance on memorization and reasoning tasks. The paper uses proof to show reasoning tasks prefer smaller initialization while memorization tasks prefer large initialization. The authors attribute such behavior to being mo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful reading and evaluation of our work, and we are truly grateful for your recognition of our research. We address your concerns as follows.
**Comment**: A potential weakness of this paper is not providing direct guidance for current LM fine-tuning as most tasks a... | null | null | null | null | null | null | null | null |
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