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Continuous-Time Analysis of Heavy Ball Momentum in Min-Max Games | Accept (poster) | Summary: # Summary
This paper explores the role of heavy ball momentum in minmax games via ODEs. While this has been extensively studied in minimization, its effects in minmax is less understood.
## Key Contributions
### 1. Local Convergence Behavior
- **Smaller momentum** improves **stability** and allows for **con... | Rebuttal 1:
Rebuttal: We sincerely appreciate reviewer's valuable comments and support. We first respond to the suggestions on the conclusion part of the review:
1. *Enhancing the figures*
We have updated figures according to your suggestions. Examples are provided through the [anonymous link](https://www.dropbox.c... | Summary: This work examines the use of momentum in min-max optimization. The authors investigate both simultaneous gradient descent-ascent (GDA) --as well as it alternating form and their local convergence properties-- plus, heavy ball (HB) momentum. They show that, for simultaneous GDA + HB:
* a positive coefficient ... | Rebuttal 1:
Rebuttal: We sincerely appreciate reviewer's valuable comments and support. Please see our itemized responses below:
1. *Do you think that you could get better analysis using the framework proposed in Lu 2022?*
We thank the reviewer for highlighting the potential connection between our current paper and (... | Summary: This paper investigates the role of Heavy Ball (HB) momentum in min-max games, an area that has been largely unexplored compared to its well-studied application in minimization. In order to analyze Heavy Ball method, the authors follow a continuous dynamics approximation of the algorithm for simultaneous & alt... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's valuable comments. Regarding the concern about the practical implications of the results from continuous-time equations (CTEs), we provide a clearer explanation of the importance and relevance of continuous-time analysis:
*Continuous-time analysis is a **wid... | null | null | null | null | null | null | null | null |
A Lens into Interpretable Transformer Mistakes via Semantic Dependency | Accept (poster) | Summary: The paper proposes a score for measuring "semantic dependency" of final token activations on various input tokens. Specifically, for a final layer token activation at token $j$, and a token $i$ in the same input sequence, the semantic dependency is the expected euclidean norm of the the change in final layer t... | Rebuttal 1:
Rebuttal: **Q1. (Claims) The authors use causal languageHowever, the experiments in section 5 are correlational.**
**A1.** Thank you for this insightful question. We believe this question stems from a misunderstanding of what we mean by "cause" in this context. We have revised the paper carefully to make ... | Summary: This paper studies how semantic dependency changes within the model architecture by investgating the toekn values. Through experientment, the author find that: 1) most token retain original information as layer goes deeper. 2) truthful semantic information is encoded in the token in final layer. 3) wrong outpu... | Rebuttal 1:
Rebuttal: **Q1. (Claims, Chapter 3) Because of resnet, it's reasonable that the i-th token is the most sensitive one in final layer to change of the i-th token, which is not enough to draw the conclusion that most token retain their original semantic information.**
**A1.** Thank you for this insightful obs... | Summary: The authors investigate a way to measure token dependency and how varying levels of dependency affect transformer model performance, contribute to incorrect information, and encode semantic dependencies. Analyzing BERT, LLaMA, and GPT, they find that most tokens retain their original semantic meaning, with fin... | Rebuttal 1:
Rebuttal: **Q1. (Claim 1) How much do these results depend on the specific run of sampling random tokens?**
**A1.** Thank you for this important question. To ensure the stability of our results, we performed 5 independent random sampling trials for each token in the sequence and reported the average score ... | Summary: The manuscript explores how transformer-based language models encode semantic dependencies and how semantic dependencies contribute to errors in model outputs. The authors propose a perturbation-based interpretability method to measure semantic dependencies. They mainly examined how changes in input tokens aff... | Rebuttal 1:
Rebuttal: **Q1. (Claim 1) Analysis of differences between GPT-2 (75%) and BERT (98.8%).**
**A1.** Thank you for your insightful finding about GPT-2.
We believe that this is likely related to model complexity. Following your findings, we conducted additional experiments and calculated the percentages fo... | null | null | null | null | null | null |
LineFlow: A Framework to Learn Active Control of Production Lines | Accept (poster) | Summary: Reinforcement learning (RL) has demonstrated potential in optimizing production line control. However, a standardized and general framework remains absent. To address this, the authors present LineFlow, an open-source, extensible Python framework for simulating production lines of arbitrary complexity and trai... | Rebuttal 1:
Rebuttal: We are excited that the reviewer
finds that "Despite the importance of active line control across various industries, no
well-grounded simulation framework has been available for training RL agents in production line
settings. Thus, this new framework has significant impact". We thank the reviewe... | Summary: The paper introduces an open-source Python framework for simulating production lines and training RL agents to control them. The authors demonstrate the framework capabilities through several core subproblems of active line control with corresponding mathematical analyses. Results show that while RL policies a... | Rebuttal 1:
Rebuttal: We appreciate the reviewer emphasizing that we provide "a comprehensive,
well-designed framework" addressing "practical production line control problems"
and "provide a good comparison of optimal solutions, heuristic approaches, and
agents' scores". We would like to thank the reviewer for the insi... | Summary: The paper introduces LineFlow, a reinforcement learning-based framework for optimizing production line reallocation, rescheduling, and routing. The authors develop LineFlow as an extensible Python package that facilitates large-scale RL training and simulation. They benchmark multiple RL algorithms, including ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the insightful feedback and
suggestions.
### Justification of discrete-time MDP
A major concern of the reviewer was why we used a discrete-time MDP (DTMDP) to model active line control
problems instead of a continuous-time MDP (CTMDP), and we thank the re... | Summary: This paper proposes LineFlow, an environment construction framework for production lines, which provides a generalized framework for research in the field of production lines. Additionally, it constructs several typical and complex scenarios to evaluate the performance of different reinforcement learning algor... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very helpful comments and suggestions that helped us improve our
manuscript in various places.
### Replies to strengths and weaknesses
> The authors only consider the processing time of stations and their statistical interplay, assuming this time follows an exponent... | null | null | null | null | null | null |
Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance | Accept (poster) | Summary: This paper presents a novel Federated Incomplete Multi-view Clustering method with globally Fused Graph guidance (FIMCFG), addressing the challenges of privacy preservation and data incompleteness in federated multi-view clustering framework. The main contribution of this work lies in its novel approach to han... | Rebuttal 1:
Rebuttal: R1: Thanks for your good advice. Incomplete multi-view clustering are introduced in GNN based multi-view clustering and federated multi-view clustering. To make it more clear, we can extract these and introduce them together.
R2: Thanks for your careful check. We'll correct all these errors or typ... | Summary: This paper provides a federated incomplete multi-view clustering approach to solve incomplete data and data privacy problem. Dual-head graph convolutional encoder is designed to extract the underlying features, the global graph structure migration is designed to repair incomplete local graphs to estimate the m... | Rebuttal 1:
Rebuttal: R1: Thanks for your helpful advice. The fusion module on all clients brings the fused high-level feature’s graph structure closer to the global graph. Conversely, the global graph is updated collaboratively by all clients. Through such iterations, the model converges to a stable state, eventually ... | Summary: The authors proposed a federated incomplete multi-view clustering framework named FIMCFG. It designed a dual-head graph convolutional encoder at the client to extract the global and view-specific information. With the guidance of the fused graph, high-level features are used to conduct clustering under the sup... | Rebuttal 1:
Rebuttal: R1-4: Thanks for your acknowledgement.
R5: Silhouette comprehensively considers the similarity between samples within a cluster and the distance between different clusters. It evaluates the clustering quality based on two factors: cohesion and separation. Silhouette ranges between [-1, 1], where ... | Summary: The work proposes a novel GCN-based federated incomplete multi-view clustering framework. The information propagation limitation problem is solved by introducing the globally fused graph guidance when extracting features. The global graph structure migration is proposed in this paper. The incomplete data probl... | Rebuttal 1:
Rebuttal: R1：Under the effect of the GCN encoder on clients, each sample estimates its single-view features using its own attribute values and those of its neighboring nodes. Since we fill the missing samples with zeros vector, the encoder automatically ignores these missing samples during computation. Howe... | null | null | null | null | null | null |
Meta-Black-Box-Optimization through Offline Q-function Learning | Accept (poster) | Summary: This paper introduces Q-Mamba, a meta-black-box optimization framework integrates offline reinforcement learning and Mamba architecture to achieve effectiveness and efficiency. Q-Mamba is trained on 16 black-box optimization tasks to meta-learn an optimal algorithm configuration, demonstrating comparable or su... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for your valuable comments. We provide reponses as below to address your concerns.
**[Advantages of Q-Mamba]** We would like to first clarify that the core motivation of Q-Mamba is providing an offline learning paradigm for MetaBBO domain, with at least comparable perfo... | Summary: This paper proposes a Mamba architecture-based meta-black-box optimization framework, Q-Mamba. By conducting offline reinforcement learning on demonstration dataset with diversified behaviours, Q-Mamba achieves competitive or superior performance and efficiency in dynamically configuring BBO algorithms for bla... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for such comprehensive review and insightful comments. We provide following point-to-point responses to address the concerns in “Experimental Designs Or Analyses”, “Other Strengths And Weaknesses”, “Other Comments Or Suggestions” and “Questions For Authors”.
**[Train-te... | Summary: This paper provides an exploration on effectiveness of offline reinforcement learning in Meta-Black-Box-Optimization to address the training efficiency problem of the online learning paradigms in existing works. The authors transform the DAC task into long sequence decision process and apply a Q-function decom... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the thorough and insightful review. We provide following point-to-point responses to address the concerns in your valuable comments.
**[Performance metric]** We would like to first note that such nomalized metric has been widely used in recent works, e.g., SYMBOL (h... | Summary: The paper introduces Q-Mamba, an offline reinforcement learning framework for Meta-Black-Box Optimization, aimed at efficiently learning Dynamic Algorithm Configuration without online training. It decomposes the Q-function into sequential decisions, applies Conservative Q-Learning to address distribution shift... | Rebuttal 1:
Rebuttal: We appreciate reviewer #yyGg for the thorough review and valuable comments. For the concerns raised above, we provide following point-to-point responses to address them.
**[Performance improvement significance]** We would like to first clarify that **the seemingly small relative performance impro... | null | null | null | null | null | null |
Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts | Accept (poster) | Summary: This paper investigates the behavior of concept-based models (CMs), particularly under distribution shifts, and introduces a novel model called MixCEM to address a critical limitation termed *leakage poisoning*. The authors demonstrate that existing state-of-the-art CMs, which rely on bypass mechanisms (e.g., ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to give us very insightful feedback. We are glad you found our architecture a “sensible” solution to solve the task at hand and its gating mechanism “novel”. Moreover, we are happy to read that you found our evaluation “extensive.” Below, we focus on addressing some o... | Summary: The paper proposes MixCEM (Mixture of Concept Embeddings Model), which uses an entropy-based gating mechanism to control the leakage of information from the feature extractor. MixCEM is designed to dynamically adjust the influence of residual (leaked) embeddings so that they are beneficial for in-distribution ... | Rebuttal 1:
Rebuttal: Thank you so much for your insightful review! Your comments, particularly those regarding OOD detectors, have helped us improve our manuscript.
We are glad you found our identification of leakage poisoning novel and telling of a “previously overlooked issue in the design of CBMs.” Moreover, we ar... | Summary: The authors present the first study examining the effectiveness of concept interventions under distribution shifts in interpretable concept-based models introducing the concept of "leakage poisoning", a phenomenon that hinders models from accurately improving when intervened upon for out-of-distribution inputs... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback! Your comments really helped us improve the quality of our manuscript. We are glad you found our work novel, “well-written”, and “well-structured”. Below, we answer your main concerns. If you have further questions, please let us know. Otherwise, we would sin... | Summary: This paper proposes a novel concept-based model called mixCEM. The authors point out the problem of leakage poisoning, where as concept interventions become out of training distribution, the task prediction accuracies reduce. The authors propose mixCEM which uses residual embeddings for positive and negative c... | Rebuttal 1:
Rebuttal: Thank you so much for taking the time to go over our work and provide feedback. Your comments have helped us identify areas where we can improve our manuscript.
We are glad you found our paper “well-written” and its evaluation sound. Below, we reply to your feedback. If you have further questions... | null | null | null | null | null | null |
Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness | Accept (poster) | Summary: This study addresses an important question about which attributes and individuals should be protected. It proposes principal counterfactual fairness based on the concepts of principal stratification and counterfactual fairness. Theoretical analysis of principal counterfactual fairness is provided. In practice,... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and the time dedicated to reviewing our work. We address your concerns and questions as follows.
> **Can the authors demonstrate the proposed ideas on more datasets?**
Thank you for pointing this issue! We follow your suggestion to add extensive experiments c... | Summary: This paper introduces principal counterfactual fairness (PCF), a novel measure of fairness which enforces (to my understanding) that; if a sensitive attribute A did not have a causal effect on an outcome Y for an individual, then our prediction of Y should likewise not be causally influenced by that sensitive ... | Rebuttal 1:
Rebuttal: > **Can you come up with a simple SCM describing a scenario where PCF and CF give different answers? Ideally, where PCF gives the more intuitive result.**
Thank you for the constructive suggestion to help us improve the readability of our paper!
**First, we define PCF within the SCM framework**,... | Summary: This paper introduces Principal Counterfactual Fairness (PCF) and proposes to unify two approaches,
* Principal Stratification :
Frangakis, C. E., & Rubin, D. B. (2002). Principal stratification in causal inference. Biometrics, 58(1), 21-29.
In their 2002 paper "Principal Stratification in Causal Inference," ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging words and valuable feedback! Below, we address your questions and indicate the changes we’ve made thanks to your suggestion.
> **Unfortunately, no comparison to alternative fairness definitions are considered. Only one real-world dataset (OULAD) is used. Lack of rep... | Summary: In this paper, the authors propose a new fairness notion called principle Counterfactual Fairness (PCF). The motivation behind this notion is that algorithms only need to be fair to individuals whose protected attribute has no individual effect on the outcome of interest. The authors derive necessary condition... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and the time dedicated to reviewing our work. We address your concerns and questions as follows.
> **Comparison between CF and PCF**
- We can define PCF within the SCM framework, which is **equivalent** to the potential outcome framework used in our original m... | null | null | null | null | null | null |
CSG-ODE: ControlSynth Graph ODE For Modeling Complex Evolution of Dynamic Graphs | Accept (poster) | Summary: The paper proposes a new approach called CSG-ODE for modeling the evolution of dynamic graphs. The main contribution lies in introducing an information transmission-based inter-node importance weighting mechanism and utilizing nonlinear activation functions in the ODE-based modeling. The authors claim that thi... | Rebuttal 1:
Rebuttal: **Thank you for your valuable comments. We have responded to each concern as follows:**
A1(Response to Claims And Evidence):
Suggestion(1): To verify the correctness of the theoretical derivations, we perform additional extrapolation experiments on CSG-ODE and SCSG-ODE on walk motion data. Each ... | Summary: The paper introduces the ControlSynth Graph (CSG-ODE) model, which improves iupon existing Graph Neural ODE models for dynamic graph representation. The model incorporates node importance weights based on information propagation and employs multiple subnetworks with nonlinear activation functions to better cap... | Rebuttal 1:
Rebuttal: **Thank you for your valuable comments. We have responded to each concern as follows:**
A1(Response to Methods And Evaluation Criteria):
The mechanism improves model performance in the following ways:
- Make up for the lack of local information: by measuring the contribution of each edge to the ... | Summary: This paper focuses on graph ODE model that handles dynamic relations and nodes with non-linear state evolution. The paper proposes a model called CSG-ODE that incorporates learnable latent graphs and time-varied graph snapshots. The CSG-ODE involves multiple dynamic subgraphs to capture state change of nodes. ... | Rebuttal 1:
Rebuttal: **Thank you for your valuable comments. We have responded to each concern as follows:**
A1(Response to Claims And Evidence):
Suggestion(1): Information Propagation based Inter-node Importance Weight can better capture the time-varying relationship in the following aspects:
- Make up for the lack... | Summary: The paper proposes a novel model CSG-ODE and its stable variant SCSG-ODE for continuous modeling of dynamic graphs. The approach integrates a VAE framework with neural ODEs, introducing an information propagation–based inter-node importance weighting and multiple nonlinear subnetworks to capture complex node s... | Rebuttal 1:
Rebuttal: **Thank you for your valuable comments. We have responded to each concern as follows:**
A1(Response to Claims And Evidence):
${L_f}(G_o^T,e{e^T})$ is the Frechet derivative with regard to $G_o^T$ and $e{e^T}$, which denotes the total transmission rate, and is used to measure the contribution of ... | null | null | null | null | null | null |
FLAM: Frame-Wise Language-Audio Modeling | Accept (poster) | Summary: The paper develops a contrastive audio-language model that is capable of doing frame-level sound event detection. The paper's focus is on using contrastive techniques and off-the-shelf encoders, as well as the correction of bias caused by the imbalance in event labels. The contrastive training, logit adjustmen... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer Rbvm for their thoughtful and constructive review. We are especially grateful for your recognition of our theoretical and mathematical contributions. As you noted, “the theoretical claims around bias correction… [are] broadly correct,” and your review confirmed that you... | Summary: The paper introduces an open vocabulary SED model, FLAM, trained with sigmoid loss. It outperforms the baseline MGA-CLAP on open vocabulary SED datasets and most closed-set SED datasets. The model is also tested in other tasks such as retreival and classification.
Claims And Evidence: Exisiting contrastively ... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer **GGdQ** for their thoughtful and constructive feedback, and for recognizing several key strengths and contributions of our work. We are encouraged by your positive assessment of our model and contributions—for instance, your remark that "FLAM mostly outperforms baselin... | Summary: The paper introduces FLAM, a Frame-Wise Language-Audio Model for open-vocabulary sound event detection. FLAM enhances traditional audio-language models by incorporating frame-level contrastive learning and logit adjustment to handle label imbalance. It leverages a large-scale dataset synthesized from text-labe... | Rebuttal 1:
Rebuttal: We thank reviewer d9oS for reviewing our manuscript and recognizing the novelty of our frame-wise contrastive learning, data augmentation, and FLAM’s strong open-vocabulary SED performance. We respectfully clarify and address the concerns you raised. Notably, these concerns were not shared by the ... | Summary: This paper introduces FLAM, an audio language model, which incorporates a frame-level sound-event localisation loss along with a contrastive learning objective to produce frame-level representations aligned with natural language.
By using a custom augmentation pipeline to combine multiple sounds in a single s... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer **bqpP** for their detailed and thoughtful review. We appreciate your recognition of FLAM’s contributions and have revised the manuscript to address your comments and questions. Below, we respond point-by-point, reordered for clarity. Due to space constraints, we in... | null | null | null | null | null | null |
Tool Unlearning for Tool-Augmented LLMs | Accept (poster) | Summary: The authors introduce tool unlearning, a novel task in the machine unlearning domain, motivated by the need to remove learned tools from tool-augmented LLMs due to security, privacy, or obsolescence concerns. Unlike traditional unlearning, this task presents unique challenges, including knowledge removal beyon... | Rebuttal 1:
Rebuttal: **Re W1: I believe that the standard unlearning task is not strictly limited to sample-level unlearning. For example, WMDP focuses on forgetting sensitive concepts such as biology, while MUSE Books aims to forget concepts related to Harry Potter. Based on the authors’ definition of Tool Unlearning... | Summary: This paper emphasizes an emergent problem that LLMs need to unlearn tools that have potential security concerns, and therefore proposes the ToolDelete method to remove the knowledge of using specified tools, as well as introducing an adapted membership inference attack (MIA) method to evaluate the tool unlearn... | Rebuttal 1:
Rebuttal: **Re W1: Table 1 reports task-solving accuracy, where a low accuracy could be: (i) the model not using the tool (which is good), or (ii) the model still heavily uses the tool but incorrectly. It is unclear if (ii) should be considered good; and if not, the reported results mix (ii) into the main a... | Summary: The paper innovatively introduces and conceptualizes tool unlearning for tool-augmented LLMs. The authors propose a novel tool unlearning method called TOOLDELETE with two variants, which satisfies three key properties: tool knowledge removal, tool knowledge retention, and general capability retain. They furth... | Rebuttal 1:
Rebuttal: **Re W1: The authors do not explain how to train TOOLDELETE using RLHF and quantization. There is no formula description for the training process of the given two variants of TOOLDELETE.**
Our primary focus in this paper has been on proposing the ToolDelete framework and full implementation and a... | Summary: The authors introduce a new LLM unlearning task called Tool Unlearning, designed to remove previously learned tools from tool-augmented LLMs. To tackle this challenge, they develop TOOLDELETE, which incorporates three key properties: tool knowledge deletion, tool knowledge retention, and general capability ret... | Rebuttal 1:
Rebuttal: **Re W1: While the empirical results are strong, the paper does not provide a formal theoretical foundation to support its claims about unlearning effectiveness.**
Thank you for highlighting this important point. We acknowledge that the paper does not include a formal theoretical framework. Our p... | null | null | null | null | null | null |
Event-Customized Image Generation | Accept (poster) | Summary: This paper introduces event-customized image generation, a new task that extends customized image generation to complex scenes which includes detailed actions, poses, relationships, and interactions between entities. To tackle this new task, it proposes FreeEvent, a training-free method that enhances the diffu... | Rebuttal 1:
Rebuttal: ## Link for additional results: https://anonymous.4open.science/r/FreeEvent-EB1D/README.md
## Q1: Adding prompt for event
**A1:** We have analyzed the impact of incorporating an explicit “event” description in the ablation studies (Appendix, Section C). Specifically, as shown in Figure 8, a... | Summary: This paper introduces a new task called event customization, which aims to generate new images that maintain the same event depicted in a reference image. An event contains specific actions, poses, relationships, and interactions between different entities within a scene. To address this task, a training-free ... | Rebuttal 1:
Rebuttal: ## Link for additional results: https://anonymous.4open.science/r/FreeEvent-EB1D/README.md
## Q1: Additional baseline methods
**A1:** Considering both reviewer yvsP and t7sW’s comments and the limited rebuttal time, we have incorporated a more recent baselines MIGC (2024) for a more compreh... | Summary: In the area of customized image generation, existing methods face the limitations of simplified customizations and insufficient data. To address these challenges, this paper defines a novel task, event-customized image generation, covering complex layout, actions, interactions between more than two objects. Th... | Rebuttal 1:
Rebuttal: ## Link for additional results: https://anonymous.4open.science/r/FreeEvent-EB1D/README.md
## Q1: About story
**A1:** Our intention in introducing subject customization and action customization is to provide background on the broader customization task and naturally introduce event customi... | Summary: This paper introduces FreeEvent, a diffusion-based image generation technique designed to address the Event-Customized image synthesis problem identified in this study. The authors define this problem by analyzing the progress and limitations of existing controllable image generation methods, particularly high... | Rebuttal 1:
Rebuttal: ## Link for additional results: https://anonymous.4open.science/r/FreeEvent-EB1D/README.md
## Q1: The defination of ``event"
**A1:** In early NLP tasks, event was defined “an occurrence of an activity that happens at a particular time and place” [1]. Later, in visual scene analysis, works a... | null | null | null | null | null | null |
PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs | Accept (poster) | Summary: This paper presents PDE-Controller, a framework that enables large language models (LLMs) to automate the control of systems governed by partial differential equations (PDEs).
The study highlights the gap between current AI-for-math research, which excels in pure mathematical reasoning, and its limited applic... | Rebuttal 1:
Rebuttal: We deeply appreciate your feedback and suggestions.
> 1. The experimental design relies heavily on synthetic data generation from a limited set of template rules. This may pose challenges when generalizing to different formats.
We fine-tune our models on synthetic data mainly because it is time ... | Summary: The paper introduces PDE-Controller, a framework leveraging large language models (LLMs) for automating the formalization and reasoning of control problems governed by partial differential equations (PDEs). The authors claim significant performance improvements in translating informal natural language PDE cont... | Rebuttal 1:
Rebuttal: We deeply appreciate your feedback and suggestions.
> The paper sufficiently addresses related works but could further discuss recent developments in differentiable physics and physics-informed neural networks (PINNs) which also address PDE control.
Thank you for the suggestion! We will include ... | Summary: The paper proposes that the PDE-Controller framework enhances large language models (LLMs) to control systems governed by PDEs, addressing their limitations in rigorous logical reasoning. It transforms natural language instructions into formal specifications, improving PDE control's reasoning, planning, and ut... | Rebuttal 1:
Rebuttal: We deeply appreciate your feedback and suggestions.
> Have you explored alternative reinforcement learning algorithms besides DPO for training the controller? Given recent advancements in reasoning-enhanced LLMs, comparing the performance of GRPO or other RL-based methods in training the PDE cont... | Summary: This paper develops PDE-Controller that uses LLMs to solve open-loop control inputs for PDEs with constraints. The PDE-Controller uses LLMs to transform informal natural language instructions into formal specifications in the form of STL, and then combine optimization solvers with LLM reasoning to improve the ... | Rebuttal 1:
Rebuttal: We deeply appreciate your feedback and suggestions.
> Q1
1) Our utility score (A.2) can faithfully quantify whether STL (constraints) are fully met by solutions simulated by the solver. This utility is inherited from [1] and serves as the rule-of-thumb “accuracy” metric. pp3p affirms “evaluation c... | null | null | null | null | null | null |
Confidence Difference Reflects Various Supervised Signals in Confidence-Difference Classification | Accept (poster) | Summary: This paper deals with confidence-difference classification, a weakly supervised binary classification problem. To mitigate the noise contained in the confidence differences, a novel risk estimator using consistency regularization is employed to improve performance. Extensive experiments on benchmark datasets v... | Rebuttal 1:
Rebuttal: **Q1. If $c>0.5$, the two examples belong to different classes. This is not true.**
Thank you for the insightful correction. The statement "$c>0.5$, the two examples belong to different classes" is indeed not precise enough. A more accurate expression would be "$c > 0.5$, the two examples belong ... | Summary: In this paper, the authors identify that noise supervision signals emerge in current confidence difference classification methods when the confidence difference is small. Based on this observation, the core focus of this work is to explore a robust solution for confidence difference classification by mitigatin... | Rebuttal 1:
Rebuttal: **Q1. Which specific loss functions (e.g. logistic) are included in Eq.5?**
Thank you for your comments. This function class includes several commonly used loss functions, such as those derived from Generalized Linear Models (GLMs), including the mean squared error (MSE) for linear regression, th... | Summary: The paper studies a special type of weakly-supervised learning known as confidence difference learning. This method leverages confidence differences between unlabeled data pairs to improve classifier training under noisy real-world conditions. By incorporating a noise generation technique and a risk estimation... | Rebuttal 1:
Rebuttal: **Q1. The motivation of this paper is not strong enough, and what real-world scenerios can motivates this problem?**
Thank you for your suggestions.
**About motivation.** The motivation for our method arises from the observation that small confidence differences may lead to imprecise guidance wi... | null | null | null | null | null | null | null | null |
Offline Learning for Combinatorial Multi-armed Bandits | Accept (poster) | Summary: The authors study a problem within the combinatorial multi-armed bandit (CMAB) setting, in the presence of offline datasets. The authors introduce Off-CMAB, the first offline learning framework for CMAB. The authors propose the combinatorial lower confidence bound (CLCB) algorithm, which combines pessimistic r... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments regarding the claims of our paper and the comparison with related work.
**Q1. Clarification on our claim regarding nonlinear reward functions and general feedback models.**
**A1.** We appreciate the reviewer’s suggestion. In the final version, we will cla... | Summary: This paper proposes a framework of offline learning for combinatorial multi-armed bandit (Off-CMAB). The authors first provide an algorithm, CLCB, based on constructing the lower confidence bound for each base arms from the offline dataset. In order to theoretically measure the performance of the algorithm in ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback about our work's insights and on building connection between traditional combinatorial bandits and LLM.
**Q1. On the technical challenges and novelty.**
**A1.** Our main contribution is a **general and minimalistic framework for offline learning... | Summary: This paper studies the offline version of the combinatorial multi-arm bandit (CMAB) problem, which is different from most CMAB papers that consider the online version. The authors provide solid theoretical results on the sample complexity of the offline CMAB problem that is minimax optimal. Moreover, the autho... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our work's connection to a broad literature, which offers new insights for RL with combinatorial action spaces and novel new applications.
**Q1. About the motivation of studying the offline CMAB problem.**
**A1.** While online bandits are a natural choice wh... | null | null | null | null | null | null | null | null |
Sassha: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation | Accept (poster) | Summary: Combine diag hessian estimate optimizer with SAM/M-SAM
Claims And Evidence: The paper is written clearly. It combines an adaptive(adamized) diagonal 2nd order optimizer with M-SAM. This results in an empirical boost over other optimizers that.
Methods And Evaluation Criteria: For imagenet ViT SAM is not SoTA... | Rebuttal 1:
Rebuttal: We really appreciate the reviewer’s feedback. While we address the reviewer’s specific comments below, we would be keen to engage in any further discussion.
---
> “ViTs should be optimized with Adam or Shampoo not with SGD”
There seems to be some confusion. We have already provided results for ... | Summary: This paper compares the sharpness and generalisation of solutions found by second-order vs first-order optimisers, observing worse generalisation and larger sharpness of second-order methods.
To rescue test performance of second-order optimisers, it proposes an optimization method combining (diagonal) second-o... | Rebuttal 1:
Rebuttal: We’re sincerely grateful for the reviewer’s thoughtful engagement and recognition of our contribution. It was encouraging and helped us further refine the work. We provide our responses below and welcome further discussion.
---
**Justification for design choices**
Thank you for your comment. We... | Summary: This paper introduces SASSHA (Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation), a novel second-order optimization method designed to improve generalization performance. The authors investigate why approximate second-order methods tend to generalize poorly compared to first-... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for thoroughly reviewing our work and giving us insightful and constructive feedback. While we address the raised questions below, we would be keen to engage in any further discussion as needed.
---
> “How does SASSHA's performance scale to larger models and datas... | Summary: The paper introduces SASSHA, a second-order optimization method designed to enhance generalization by explicitly reducing the sharpness of minima through a sharpness-aware framework, while stabilizing Hessian approximations via techniques like square-rooting and absolute value transformations. It incorporates ... | Rebuttal 1:
Rebuttal: We really appreciate the reviewer taking the time to engage with our work. We address the specific points below and would be happy to clarify any remaining concerns.
---
**Theory for improved generalization**
The relationship between flatness and generalization is theoretically well-established... | null | null | null | null | null | null |
FAB-PPI: Frequentist, Assisted by Bayes, Prediction-Powered Inference | Accept (poster) | Summary: Prediction-powered inference (PPI) improves statistical inference by leveraging machine learning predictions (on unlabeled data) alongside labeled data, resulting in more precise estimates and tighter confidence intervals. The proposed semi-supervised approach, frequentist-assisted-by-Bayes PPI (FAB-PPI), impr... | Rebuttal 1:
Rebuttal: We thank the reviewer for evaluating our submission and providing positive and valuable feedback. We are pleased that they recognise our approach’s novelty and clarity.
> The introduction could be further strengthened by stating up front what characteristics of the predictor f will lead it to be ... | Summary: The authors propose a new scheme for combining experimental data with model predictions, effectively for inferring valid confidence intervals for estimators where some of the samples are noisier due to being model predictions. The work extends recent prediction-power inference methods by encoding prior knowled... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time in evaluating our submission and for providing positive and valuable feedback. We are glad that the reviewer appreciates the potential impact of our work.
> The Supplement S6 seems to provide the missing quantification in terms of coverage -- may... | Summary: This paper proposes a Bayesian adaptation of Prediction-powered inference (PPI) problem. PPI is a method which provides confidence interval and estimators in the presence of predictions of (black-box) machine learning models and small amount of labels. This work allows for the possibility of a prior distributi... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time in evaluating our submission and for providing positive and valuable feedback. We are glad that the reviewer found the paper well written and easy to follow.
> Although usage of prior is interesting, the question I have is if the prior is not acc... | null | null | null | null | null | null | null | null |
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances | Accept (poster) | Summary: This paper studies statistical and computational properties of Kernel Max-Sliced Wasserstein distances. On the statistical side, the paper's main result is a high-probability bound on the KMS Wasserstein distance between a distribution and the empirical distribution of samples from that distribution. This is i... | Rebuttal 1:
Rebuttal: We thank the reviewer's positive comments and provide our response below:
- [relationship and distinctions with literature [9]?] We appreciate the reviewer for highlighting this point. Literature [9] establishes the statistical convergence rate for the empirical Max-Sliced (MS) distance as $O(R ... | Summary: 1. **Introduction of the Max-Kernel Sliced Wasserstein Distance (KWS)**
- The paper presents the Max-Kernel Sliced Wasserstein Distance (KWS), which merges classical max-sliced Optimal Transport (OT) with kernel methods. Data is first mapped to the Reproducing Kernel Hilbert Space (RKHS) through kernel mapp... | Rebuttal 1:
Rebuttal: We very much appreciate reviewer's insightful comments and now provide our response on a point-by-point basis:
- [OT in RKHS?] Our formulation is closely related to Zhang et al. (2020), which considers Wasserstein distances between pushforward measures $\Phi(\mu)$ and $\Phi(\nu)$ via an implicit ... | Summary: This paper establishes statistical theoretical properties of Kernel MSW (K-MSW) distance; the authors provide a finite sample guarantee of K-MSW between empirical probability measures. A performance guarantees are also given when K-MSW is used as a metric of two-sample test. The second part of the paper deals ... | Rebuttal 1:
Rebuttal: We very much appreciate reviewer's positive comments and now provide our response on a point-by-point basis:
- [Complexity of KMS?] We would like to clarify that the time complexity of our Algorithm 1 is $\tilde{O}(n^3\delta^{-3})$, not $\tilde{O}(n^2\delta^{-3})$ as previously stated. In our ini... | null | null | null | null | null | null | null | null |
Prompt-to-Leaderboard: Prompt-Adaptive LLM Evaluations | Accept (poster) | Summary: Language model evaluations typically rely on aggregated metrics like accuracy or human preference, averaging across users and prompts. This averaging obscures user- and prompt-specific variations in model performance. The authors propose Prompt-to-Leaderboard (P2L), a method that predicts prompt-specific leade... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We address your concerns and propose corresponding revisions:
**W1 OOD Results:** We understand that using the top static model might appear an intuitive baseline. However, practically, this static model is unknown ahe... | Summary: This paper introduces Prompt-to-Leaderboard (P2L), a method for generating prompt-specific leaderboards of large language models (LLMs) by training LLMs on human preference data. The core idea is to model prompt-dependent Bradley-Terry coefficients, enabling per-prompt performance comparisons. Key applications... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We address your concerns and propose corresponding revisions:
**W1 Arena Score Computation:** We deploy the P2L router onto Chatbot Arena, routing between the models detailed in Appendix D1. We collect blind human prefe... | Summary: This paper proposes a method that routes a prompt to a specific LLM from a given LLM list. Given a dataset of various prompts, responses from different models and the pairwise preference result, the method train a mapping from prompt to feature that fit the reward gain by the model when fed with the prompt. Th... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We address your concerns and propose corresponding revisions:
**W1 Scalability**: We agree with the reviewer that reducing the cost of adding new models is of interest, and are excited to continue exploring methods, suc... | Summary: This paper is motivated by the fact that LLMs are sensitive to prompts, and current benchmarks such as Chatbot Arena leverage pair-wise comparisons from users to rank models without explicitly controlling the prompt distribution. The paper proposes a method that essentially trains a model to predict the "model... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We address your concerns and propose corresponding revisions:
**W1 Intro**: We will revise our introduction to explicitly define 'leaderboard of LLMs' as a prompt-specific ranking of multiple LLMs to improve clarity.
*... | Summary: This paper proposes a prompt-to-leaderboard (P2L) method to predict prompt-specific leaderboards via large language models (LLMs) trained on human preference data. The authors make LLMs to output the coefficients of parametric regressionsthat represent per-prompt leaderboards. Thus, this leaderboard supports o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We thank the reviewer for considering the novelty and soundness of our work. We agree with the reviewer on generalization to new LLMs; this is of interest, and we are excited to continue exploring methods, such as online learning, to optimize this in futur... | null | null | null | null |
Omni-Angle Assault: An Invisible and Powerful Physical Adversarial Attack on Face Recognition | Accept (poster) | Summary: This work introduces UVHat, a physical adversarial attack against face recognition systems that leverages invisible ultraviolet light emitted from a hat. The proposed approach overcomes the limitations of previous methods, particularly regarding visibility and robustness. It is effective in black-box settings ... | Rebuttal 1:
Rebuttal: ***Q-1: What are the most significant differences compared to existing work?***
A-1. Thanks for the comment. We provide a detailed comparison between UVHat and existing methods, along with experimental results.
(1) **Qualitative comparison**
First, compared to sticker-based methods, our UV ligh... | Summary: This paper presents a physical adversarial attack utilizing UV light to disrupt the decision-making of face recognition models. The methodology encompasses physical testing simulations, the implementation of UV emitters, and a reinforcement learning algorithm to optimize attack parameters. While the approach i... | Rebuttal 1:
Rebuttal: ***Q-1. Provide detailed mathematical formulations to clarify how these attack cases are defined and implemented using embedding features.***
A-1. We apologize for possible misunderstandings about UVHat‘s attack objectives. Our adversarial attack on face recognition (FR) targets four FR attack ob... | Summary: The paper introduces a novel method for adversarial attacks on face recognition (FR) systems, mounting ultraviolet (UV) emitters on a hat. The paper simulates the characteristics of this novel physical attack, considering the impact of curved surfaces on the light intensity, and proposes optimization technique... | Rebuttal 1:
Rebuttal: ***Q-1. This paper presents a novel attack against FRs. The idea makes sense, and the approach seems both novel and clever in terms of its simplicity and potential effectiveness. The paper is well-structured and easy to understand. The issue described in the paper is not entirely new, as many prev... | Summary: This paper presents a novel physical adversarial attack method named UVHat for face recognition (FR) systems. UVHat generates adversarial perturbations by using ultraviolet emitters mounted on a hat. The proposed method mainly consists of three steps: interpolation-based UV simulation, hemispherical UV modelin... | Rebuttal 1:
Rebuttal: ***Q-1. The purple light is very noticeable, which is clearly in serious conflict with the invisible attacks. The experimental designs in this paper can effectively evaluate the effectiveness of the proposed attack, but there is a lack of evaluation regarding its concealment.***
A-1. We apologize... | null | null | null | null | null | null |
SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes | Accept (poster) | Summary: This paper introduces SAFER, a new framework for dynamic treatment regime (DTR) recommendation, which integrates both structured electronic health record (EHR) data and unstructured clinical notes. The primary contribution of SAFER lies in its ability to handle uncertainty in treatment recommendations, particu... | Rebuttal 1:
Rebuttal: Thank you for recognizing SAFER as a robust, theoretically grounded, and practical solution for high-stakes medical decision-making, supported by clear evidence. We address your comments as follows.
**Theoretical Claims:** Please see responses to the questions.
**Supplementary Material:**
1. **... | Summary: The paper proposes a novel method for predicting treatments for individuals based on the longitudinal EHR data, static features and text based clinical notes, calling it tabular-language recommendation framework. To improve the quality of predictions, it employs mechanisms such as risk-aware fine tuning which ... | Rebuttal 1:
Rebuttal: Thank you for appreciating the novelty, the overall framework for healthcare, clarity of Figure 1, well-supported experiments and the evaluation criteria of our work! We address your comments as follows.
**Claims And Evidence:**
**Theoretical guarantees on calibrated predictions lacks evidence:*... | Summary: In this work, the authors introduce uncertainty control and comprehensive information fusion to improve prediction uncertainty estimation while incorporating multi-modal data for more accurate predictions.
## update after rebuttal
Thanks to the authors for clarifying some questions. Please make sure to includ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the importance of this research direction, as well as appreciating the contributions related to multi-modal design and theoretical guarantees for uncertainty-aware treatment recommendation! To help better understanding, we would like to briefly restate our main contributi... | Summary: This paper introduced a framework, SAFER, to provide dynamic treatment recommendations for patients with evolving clinical states, by employing conformal prediction and transformer-based architectures for multi-modalities. The proposed work was evaluated on two sepsis datasets and outperformed baselines in ter... | Rebuttal 1:
Rebuttal: Thank you for acknowledging that the paper’s main claims are clearly stated and supported by both theoretical analysis and experimental results, with well-motivated objectives and diverse evaluation metrics. We address your comments as follows.
**Methods And Evaluation Criteria:**
Thank you for ... | null | null | null | null | null | null |
SafeArena: Evaluating the Safety of Autonomous Web Agents | Accept (poster) | Summary: The paper introduces SafeArena, a benchmark specifically designed to evaluate the risks associated with the misuse of LLM-based web agents. SafeArena includes 500 tasks, half of which are harmful, across four different websites. These harmful tasks are categorized into five types: misinformation, illegal activ... | Rebuttal 1:
Rebuttal: We thank Reviewer BVYR for their constructive feedback. We are happy they found our paper’s experiments “**sound and insightful**” and we are glad they liked our data curation process.
We also note that the other reviewers found our paper “**easy to follow**” (Reviewer 4T43 and Reviewer rfY6) and... | Summary: This paper proposes a new benchmark, SAFEARENA, for evaluating the safety of LLM-based web agents against misuse. The benchmark consists of 250 harmful and 250 safe tasks curated both manually and by GPT-4o-Mini with few-shot demonstrations and human-in-the-loop for review. The harmful tasks are categorized in... | Rebuttal 1:
Rebuttal: We thank Reviewer rfY6 for their thoughtful feedback. We are delighted they found our work “**well written and structured and easy to follow,**” and that our benchmark “**bridges gaps that existing benchmarks miss.**”
Other reviewers similarly found our paper “**easy to follow**” (Reviewer 4T43) ... | Summary: The paper introduces SafeArena, a benchmark designed to evaluate the safety of autonomous web agents by testing them on 500 paired tasks (250 harmful and 250 safe) across five harm categories (misinformation, illegal activity, harassment, cybercrime, and social bias) in realistic web environments.
Claims And ... | Rebuttal 1:
Rebuttal: We thank Reviewer 4T43 for their constructive response. We are happy to hear that they found our work “**easy to follow,**” and believed our harm categorization to be diverse.
We also note that other reviewers found our paper “**well-written**” (Reviewer rfY6) with “**sound and insightful**” exp... | Summary: The paper presents a benchmark (SafeArena) for deliberate misuse of web-agents. The benchmark consists of 250 harmful tasks and corresponding (share similar phrasing and test similar capabilities) 250 safe tasks (500 in total). The harmful tasks span 5 harm categories: misinformation, illegal activity, harassm... | Rebuttal 1:
Rebuttal: We thank Reviewer LSep for their detailed response. We are pleased they found our experiments clearly demonstrate current safety issues with LLM-based web agents.
We also highlight that other reviewers found our paper “**well-written**” (Reviewer rfY6) and “**easy to follow**” (Reviewer 4T43) wi... | null | null | null | null | null | null |
Neural Discovery in Mathematics: Do Machines Dream of Colored Planes? | Accept (oral) | Summary: This work demonstrates how neural networks can help mathematical discovery, focusing on the Hadwiger-Nelson problem, which seeks the minimum number of colours needed to colour the Euclidean plane while avoiding unit-distance monochromatic pairs. By reformulating this mixed discrete-continuous problem as an opt... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive feedback. Let us first address your questions and then the remaining comments:
**Question 1:**
Regarding the anonymous citation, we would like to clarify that the referenced paper—listed as "Anonymous (2024)" and included as supplementary ma... | Summary: This paper tackles a combinatorics problem using neural networks, specifically the Hadwiger-Nelson problem, which involves coloring the plane under distance constraints. The problem is continuously relaxed through probabilistic coloring. Inspired by numerical outputs from neural networks, the authors derive a ... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment!
We appreciate your thoughtful comments about our paper's position as a scientific contribution. You raise a fair point about methodological innovation versus application. We agree that it's always challenging to determine ML methodological contributions fo... | Summary: The paper proposes an AI framework to tackle an open problem in coloring the plane in R^2. Some other variants consider R^n as well.
Authors reformulate the problem as an optimization task, they introduce a differentiable loss function and do greedy sampling to minimize the penalty loss they introduced.
Also ... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments and taking the time to referee our submission! Let us address your points individually:
> The authors state that constructions were "formally verified" in another venue (Anonymous, 2024), but given it's Anonymous we cannot verify this claim. Is there any other ... | Summary: The paper proposes a neural-net approach to solving the Hadwiger-Nelson problem. The problem involves coloring the plane under the constraint that no pair of points with unit distance has the same color. The paper treats this problem as an unsupervised combinatorial/continuous optimization problem. By adopting... | Rebuttal 1:
Rebuttal: Thank you for your positive and very relevant comments! Let us split our response into three parts.
**The relationship between the NN output and formal results**
You point out an important aspect that we could have perhaps made more clear in our submission. The neural networks provide numerical ... | null | null | null | null | null | null |
TruthFlow: Truthful LLM Generation via Representation Flow Correction | Accept (poster) | Summary: The paper introduces TruthFlow, a method that enhances LLM truthfulness by learning query-specific correction vectors via Flow Matching. Unlike prior universal intervention approaches, TruthFlow generates corrections for each query specifically that transition representations from hallucinated to truthful sta... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and constructive suggestions.
---
**Q1: Statistical evidence**
A1: Thank you for your suggestion. We further conduct the following statistical analysis to demonstrate this limitation. Specifically, we calculate the cosine similarity between the un... | Summary: This paper introduces a novel method called TruthFlow, which enhances the ability of LLMs to generate truthful responses through representation flow correction. TruthFlow leverages flow matching techniques to generate query-specific truth-aligned correction vectors, guiding the model from a hallucinatory state... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and constructive suggestions. We address the questions as follows:
---
**Q1:Slightly lower Info score.**
A1: We actually observed and analyzed this phenomenon in the paper's “Qualitative Study” paragraph before section 5. Specifically, we find th... | Summary: In order to address the hallucination problem for LLMs, a line of methods, named representation intervention, attempts to edit LLMs' hidden representations at certain layers to guide their behavior, such as making the generated outputs more truthful. However, these methods usually assume that there exists some... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and constructive suggestions. We address the questions as follows:
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**Q1: General utility.**
A1: Thank you for your suggestion. We conduct experiments to test the general utility on MMLU. We evaluate Llama-3-8B-Instruct and TruthFlow on the who... | Summary: The paper addresses the hallucination problem in LLMs, where models generate misleading or factually incorrect responses. Unlike prior methods that apply a universal correction vector, TruthFlow employs flow matching to learn query-dependent correction vectors.
Claims And Evidence: The claims in the paper are... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and constructive suggestions. We address the questions as follows:
---
**Q1: Lack formal theoretical proofs for some claims, such as the projection onto a "truthfulness subspace"**
A1: First, we would like to emphasize that we do not claim theore... | Summary: This paper proposes TruthFlow, a method for improving the truthfulness of LLMs by applying query-specific correction vectors to model representations during inference. Unlike prior work such as ITI that uses a fixed correction vector, TruthFlow employs Flow Matching to generate dynamic interventions tailored t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and constructive suggestions. We address the questions as follows:
---
**Q1: New reference.**
A1: We appreciate the reviewer for pointing out the reference [1]. We will discuss and cite this work in our revision. One thing to clarify is that we te... | null | null | null | null |
Privacy Attacks on Image AutoRegressive Models | Accept (poster) | Summary: The paper presents a systematic analysis of privacy attacks on image autoregressive models, including membership inference attacks, dataset inference, and data extraction attacks. The proposed method is primarily constructed from components of previous work.
## Update after Rebuttal
The authors have provided... | Rebuttal 1:
Rebuttal: >**The method is primarily constructed from components of prev. work.**
Beyond the proposed method, our contributions are:
1. **First empirical privacy leakage evaluation of IARs**. We develop the strongest model-specific attacks, and perform comprehensive analysis across publicly available mode... | Summary: This paper provides a thorough investigation into the privacy risks of image autoregressive models (IARs), highlighting their elevated vulnerability compared to diffusion models (DMs). The authors develop a novel membership inference attack (MIA) with significantly higher detection rates, introduce a dataset i... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the feedback.
## Attacks tailored for LLMs, DMs, IARs
>**The proposed [MIA and DI] are specifically tailored to exploit [ARs], raising questions about whether the comparisons with DMs are conducted under balanced conditions. [Selecting baselines derived] from language ... | Summary: This paper presents a thorough investigation into the privacy risks of image autoregressive models (IARs), comparing them to diffusion models (DMs). The authors develop novel membership inference attacks (MIAs) and dataset inference (DI) methods tailored to IARs. Besides, they also extract hundreds of training... | Rebuttal 1:
Rebuttal: >**Emerging DMs (e.g. flow-matching)**
We extend evaluation to 1) latent flow matching (LFM) (Dao et al., 2023), 2) sparse DM (DiT-MoE), 3) flow matching transformer (SiT) (Ma et al., 2024), We report:
||Model|TPR@FPR=1%|P (DI)
-|-|-|-
LFM|1.79|2000
DiT-MoE|1.70|2000
SiT|6.38|300
We observe tha... | Summary: The paper propose new SOTA methods for membership/dataset inference of image autoregressive models. The authors compare the privacy leakage of the different types of image generation models, and show that autoregressive models showcase important privacy leakage (up to MIA at 86.38% TPR@FPR=1%)
Claims And Evid... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the insightful comments. We address individual points below one by one:
>**[...] authors are claiming that IARs are now the gold standard for image generation, while it has not been so widely adopted.**
We clarify that we position IARs as novel model family that *can* p... | null | null | null | null | null | null |
QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache | Accept (poster) | Summary: This paper introduces QuantSpec, a self-speculative decoding framework designed specifically for long-context LLM inference. The framework employs a draft model that shares the architecture of the original model but implements hierarchical 4-bit quantized KV cache and 4-bit quantized weights for acceleration. ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We appreciate that you find the paper technically sound and well-written. We address your questions and comments in detail below:
> R4-1: Evaluate your approach on more recent models that better represent the current state of the field.
**A**: We appreciate ... | Summary: In long context senarios, loading KV cache is a major bottleneck in both memory and latency. This paper introduces QuantSpec, which is a self-speculative decoding framework designed to accelerate long-context inference. Unlike existing speculative decoding methods that using a smaller model as the draft model,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. We address the questions below:
> R3-1: Can the author elaborate more details on the CUDA kernel design?
**A**: We will add a section in Appendix outlining our kernel design. We include a short summary here for the reviewer:
In our approach, we i... | Summary: The authors introduce a novel speculative decoding based technique for speeding up LLM inference. The basic idea is to use a hierarchical quantized KV cache and quantize model weights instead of storing a separate KV cache for both the target model and the draft model and storing separate model weights.The bas... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We are happy that you found the insights from the paper interesting. We address your questions in detail below:
> R2-1: Do you have intuition on why key and value caches have distinct quantization strategies?
**A**: The KV cache in transformer models serve d... | Summary: This submission introduces QuantSpec, a novel self-speculative decoding method that employs KV-Cache quantisation during token drafting, to optimise the inference efficiency on long-context LLMs. Based on the insight that in long-context sequences much of the pressure in memory bandwidth is attributed to loadi... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We are happy that you find discussion and analysis in the paper insightful. We address your questions and comments in detail below:
> R1-1: How do the findings of Fig.2 and 4 scale with the parameter count of the backbone LLM ? Long-context LLMs typically ad... | null | null | null | null | null | null |
Decomposition of Graphic Design with Unified Multimodal Model | Accept (poster) | Summary: The paper proposes a particular method for layerwise decomposition of graphic designs into RGBA images which can be stacked and composed to reform the original image. The authors train a transparency aware RGBA VQ-GAN encoder-decoder. The encoder is used to encode the input graphic design images for a LLM base... | Rebuttal 1:
Rebuttal: **Method Evaluation:**
1. Considering the current scarcity of publicly available poster datasets and time constraints, we have first added a test set based on our own dataset split. We have added [qualitative](https://github.com/anonymous-icml25-0328/rebuttal/tree/main/test_ours_vis) and [quantita... | Summary: This paper focuses on the graphic design layer decomposition task that converts graphic designs into ordered RGB-A layers and metadata. A large multimodal model, i.e., DeaM, is proposed with a two-stage process. The first stage produces layer-specific JSON metadata, and the second stage reconstructs pixel-perf... | Rebuttal 1:
Rebuttal: **Existing Layered Graphic Design Generation and Decomposition Work:**
Although layered design generation and decomposition have been studied previously, there are many differences compared to our proposed work. Our focus is on the task of layer decomposition in graphic design. We will include dis... | Summary: This paper proposes a novel layer decomposition model (DeaM) to transform a given graphic design into a set of ordered transparent layers. The key challenges include predicting the correct layer ordering and resovling the mutual occlusion between overlapping layers. To this end, DeaM first predicts a layer-spe... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insightful comments.
**About RGBA autoencoder:**
1. This idea is very straightforward and interesting. Initially, we also tried using RGBA autoencoder as the visual encoder but found that the model's output generated severe hallucination information. We s... | Summary: The paper proposes the problem setup Layer Decomposition (LD), and an approach Decompose Layer Model (DeaM), that can take the rendition (image) of a single page graphic design, and "decompose" it to its constituent components. The problem setup that the paper introduces has immense practical value, for instan... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback, which gives me the opportunity to clarify the ambiguities in the content of this paper.
**Dataset Information:** This dataset is collected from the internet and consists of single-page graphic designs (including all layers of the materials), comprising 2... | null | null | null | null | null | null |
SeedLoRA: A Fusion Approach to Efficient LLM Fine-Tuning | Accept (poster) | Summary: The paper presents a method that leverages multiple LoRA models trained with different random seeds on the same task and merges their trained weights using a two-stage merging strategy. In the first stage, the algorithm detects robust and conflicting dimensions from the multiple trained weights using threshold... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments, we carefully address your concerns below.
>W1: However, a downside of the paper is that the training cost is not mentioned or discussed.
While SeedLoRA maintains the same inference memory footprint as vanilla LoRA, we acknowledge that the training computati... | Summary: The authors propose a method for combining multiple LoRA adapters trained for the same task with different seeds and show it improves performance. The authors’ method consists of (1) identifying and preserving large consistent dimensions or “robust” directions, (2) using a principal-component-like decompositio... | Rebuttal 1:
Rebuttal: Thanks for your constructive and inspiring feedback, we carefully address your concerns below.
>Clarification on seed reporting and cosine similarity calculations
Thank you for your feedback on the experimental design and reproducibility. We agree that additional clarity on seed variation would... | Summary: The paper introduces SeedLoRA, an approach to improving LoRA fine-tuning for LLMs. SeedLoRA is based on the observation that multiple LoRA models trained on the same task with different random seeds can have complementary performance. It uses a two-stage approach to merge different LoRA adapters, first identif... | Rebuttal 1:
Rebuttal: Thanks for your constructive and inspiring feedback, we carefully address your concerns below.
>W1: Effectiveness across different ranks.}
To directly address this concern about the scalability of SeedLoRA across different rank settings, we have conducted comprehensive additional experiments w... | null | null | null | null | null | null | null | null |
GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning | Accept (poster) | Summary: The paper introduces GPEN (Global Position Encoding Network), a novel algorithm for subgraph representation learning. The algorithm addresses the task of predicting labels for subgraphs within a large input graph, given a set of labeled subgraphs. GPEN innovates by moving beyond the limitations of existing met... | Rebuttal 1:
Rebuttal: We deeply appreciate your thoughtful review and positive assessment of our work. Below we will respond to the raised questions.
## W1 and S2: Visualization of Learned Embeddings
We appreciate your suggestion about visualization. We agree that adding visualizations of the learned embeddings would... | Summary: This paper presents GPEN, a novel method for subgraph representation learning that addresses two key challenges: capturing structural relationships between distant nodes and preventing excessive aggregation of global structural information.
Claims And Evidence: yes, the submission is supported by clear and co... | Rebuttal 1:
Rebuttal: We deeply appreciate your kind words regarding the clarity of our presentation. Thank you for acknowledging the thoroughness of our theoretical and experimental sections. Below, we have responded to the weaknesses raised by the reviewer:
**W1: Concerns about tree-based encoding and complex graph ... | Summary: This paper presents GPEN (Global Position Encoding Network), a novel approach for subgraph representation learning that addresses the limitation of existing methods which primarily focus on local neighborhood structures while overlooking global structural information. GPEN implements two key modules: (1) globa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments. We appreciate that they found our "theoretical claims in the paper are supported by sound mathematical proofs" and acknowledged that our "experimental design is reasonably sound." Below, we have responded to the weaknesses raised by the reviewer:
... | Summary: The paper introduces a method called GPEN for Subgraph Representation Learning. It proposes the construction of a hierarchical tree to compute the Global Position Encoding (GPE) and introduces Boundary-aware Convolution (BWC) and tree-based Optional Tree Perturbation (OTP). These strategies aim to address two ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and constructive remarks on our paper. Below, we provide a detailed response to your questions and comments.
**[W1 and W3]**
We thank the reviewer for pointing out these notation issues throughout the paper. To clarify:
- $M_{ij}$ : Thank you for br... | null | null | null | null | null | null |
Breaking the $n^{1.5}$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition | Accept (poster) | Summary: This paper focuses on designing differentially private algorithms for graph cut sparsification.
The previously best-known private and efficient cut sparsifiers on n-node graphs approximate each cut within $O(n^{1.5})$ additive error and $1+\gamma$ multiplicative error for any $\gamma>0$.
Exponential time ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback!
> While it is justified by lower bounds, an argument could be made that the additive error is large and is unlikely to yield a practical implementation.
We are hopeful that for dense graphs, where cuts can be as large as $\Omega(n^2)$, our techniques could y... | Summary: This paper investigates $(\varepsilon, \delta)$ differentially private graph cut sparsification under edge-privacy.
More exactly, given a non-negative, undirected graph $G=(V, E, w)$ the goal is to output a non-negative, weighted, undirected graph $\tilde{G}$ that (1) approximates the value of all cuts in $G$ ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! | Summary: The paper studies the problem of graph sparsification which preserves all cuts, a fundamental problem in graph algorithms, under differential privacy. This problem has been well studied but remains open. The paper makes significant improvements to prior work in terms of the additive approximation factor and ru... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback!
> In definition 2.4, since non-edges are viewed as weight 0 edges, and (u, v) can be any pair of nodes, the edge set should be V^2? So E and E’ both should be V^2. This would be relevant for all the formal statements which mention E.
Thank you. This is a goo... | Summary: This paper studies the problem of graph cut sparsification under the constraint of differential privacy (DP). They cross the known $n^{1.5}$ additive error mark to provide a DP algorithm that has an additive error of $\tilde{O}(n^{1.25+o(1)})$ and a small multiplicative error. Their key underlying subroutine i... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We will adjust the final draft according to your suggestions, elaborating where there were points of confusion.
> I'm confused by the proof of Theorem 3.2 It just says that it's in Algorithm 1. That's not really a proof. Or are you just trying to say that you... | null | null | null | null | null | null |
Kinetic Langevin Diffusion for Crystalline Materials Generation | Accept (poster) | Summary: The paper presents Kinetic Langevin Diffusion for Materials (KLDM), a groundbreaking diffusion model designed for generating crystalline materials. KLDM tackles the challenge of modeling fractional coordinates on a hypertorus by introducing auxiliary Euclidean velocity variables, eliminating the need for appr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive consideration and suggestions to improve the paper. We address questions and comments below.
**De-Novo generation task results** Due to the limited character, we have to refer to the answer provided to reviewer **MrGy** about this topic.
**Zero-net tran... | Summary: This paper proposes a new diffusion model for modeling crystalline materials.
The model is built upon a Kinetic Langevin Diffusion on the fractional coordinates, and standard Euclidean diffusion for the lattice vector and atom types (one-hot embedded).
The core contributions of the paper are:
- proposing to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive consideration and suggestions to improve the paper. Thanks for pointing out some typos, we will correct them in the updates version. We address questions and comments below.
**Invariant network and equivariant target inconsistency** We agree with the rev... | Summary: This paper proposes a diffusion model tailored for crystalline material generation. It utilizes the specific manifold structure of the data, and applies the framework of Trivialized Diffusion model, which is a diffusion model that works on Lie groups. This framework avoids doing Riemannian diffusion by taking ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive consideration and suggestions for improving the paper. We address their questions and comments below.
**RMSE computation** Similar to previous work, we compute the RMSE of the generated samples wrt. ground truth using `StructureMatcher` from `pymatgen`, af... | null | null | null | null | null | null | null | null |
Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win | Accept (poster) | Summary: The paper explores the connection between the Weisfeiler-Lehman (WL) test and the Lottery Ticket Hypothesis (LTH). The authors establish criteria for pruning mechanisms, requiring that the pruned network remain as expressive as the original in terms of the WL test to preserve its performance. They define the c... | Rebuttal 1:
Rebuttal: We thank you for your review and hope our response below satisfactorily addresses your questions.
> However, I think one could further strengthen the experiments by also considering attention-based graph neural networks such as GAT.
Our theoretical results apply to general moment-based GNN archi... | Summary: This paper deals with the Strong Lottery Ticket Hypothesis (SLTH) in the context of graph neural network (GNN).
Particularly, the authors argue that there exists an initialized GNN with sufficiently high expressivity that can match the original performance after training.
To demonstrate this, the authors theor... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We hope that our response below addresses your questions satisfactorily.
> How can we find a sparse GNN with significant expressivity?
For moment-based architectures (see line 126 right column, Lemma 2.6), such as those analyzed in our work, maximal expressi... | Summary: This paper studies the role of Graph Neural Network (GNN) expressivity in Lottery Ticket Hypothesis (LTH), in particular, the conditions NN pruning mechanisms must satisfy to maintain prediction quality. They show that trainable sparse subnetworks exist within moment-based GNNs, matching 1-WL expressivity. The... | Rebuttal 1:
Rebuttal: We appreciate your detailed review. Below, we address your concerns and will incorporate the corresponding changes into the final version. We are looking forward to further discussions with you.
> Abstract says that [...]
We acknowledge Theorem 3.3 does not directly guarantee improved convergenc... | null | null | null | null | null | null | null | null |
CSV-Occ: Fusing Multi-frame Alignment for Occupancy Prediction with Temporal Cross State Space Model and Central Voting Mechanism | Accept (poster) | Summary: This paper focuses on image-based 3D semantic occupancy prediction. To address the challenges posed by the computational complexity of temporal methods and the semantic ambiguity leading to vacancy issues, the Cross-State Space Module (Cross SSM) and a Voting-based Enhancement Mechanism are proposed as targete... | Rebuttal 1:
Rebuttal: Deeply grateful for your dedication and expertise throughout the review.
Building on your insightful suggestions, we have systematically:
1. **Revised the paper,**
2. **Organized your comments by theme, and**
3. **Provided detailed responses in an annotated Q&A below.**
We welcome any additional... | Summary: The paper introduces CSV-Occ, a method for camera-based 3D semantic occupancy prediction.
CSV-Occ focuses on two key challenges.
Firstly, the prior methods have usually exploited attention mechanisms for temporal modeling that have high computational complexity. This paper propose the Cross State Space Module... | Rebuttal 1:
Rebuttal: We sincerely express our profound gratitude for the precious time and dedicated effort you have invested in the review process. Taking into account the highly constructive feedback and suggestions you proffered, we have meticulously re-examined our paper and work. **We have comprehensively collate... | Summary: The paper proposes CSV-Occ, a method for image-based 3D semantic occupancy prediction in autonomous driving. It introduces two key ideas: 1. Temporal fusion applied on voxel query results instead of BEV features, which is considered new. 2. A center voting mechanism to improve occupancy prediction inside objec... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in the review process. **In response to your constructive feedback, we have thoroughly reviewed our paper and categorized your comments. Attached is Q&A response.** If you have any further questions, we will address them promptly.
---
### **Q1**: The ... | Summary: This paper presents CSV-Occ, a method for camera-based 3D semantic occupancy prediction, aimed at improving scene understanding. It considers two key issues: reducing the high computational complexity of temporal information fusion and addressing the semantic ambiguity in predicting object centers. To overcome... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable time and effort you've dedicated during the review process. In light of the constructive feedback and suggestions you provided, we've meticulously examined our paper and work. **We've also summarized your comments and are replying to you one by one in a Questio... | null | null | null | null | null | null |
On Understanding Attention-Based In-Context Learning for Categorical Data | Accept (poster) | Summary: This paper investigates how transformers perform in-context learning on categorical data, extending prior work that has largely focused on in-context regression tasks.
The authors provide a kernel-based functional gradient descent perspective, wherein each layer of the transformer can be interpreted as perform... | Rebuttal 1:
Rebuttal: Thank you for your careful review and for your valuable feedback. On your questions:
1. "Trained TF" and "GD" (we agree that it would be good to re-introduce them here, despite the mentions in Section 4; we will do that).
The detailed derivation of the GD updated equation is in Appendix B and su... | Summary: The paper seems to be dealing with learning the implicit function embedded in the examples within prompt in in-context learning setting.
The paper is poorly written and difficult to understand. The introduction does not clarify what the proposed method is aiming at, there are disconnected/disparate component... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. Concerning your comments:
> The introduction ....
- lines 20-21 state that the goal of this paper is "extending the functional GD framework [for ICL], to handle categorical observations".
- Reviewers 4VEb and Hsdu both provided accurate summaries of the goal of... | Summary: The paper explores theoretical understanding of the In-Context Learning in Transfomer-stack models while dealing with categorical data.
It attempts to design a transformer block that can do gradient-descent in-context.
Authors try to construct a transformer stack that can, in theory perform ICL on categorica... | Rebuttal 1:
Rebuttal: Thank you for your very careful review and helpful feedback.
We will work to make the paper more readable and understandable in the main body of the paper. We constituted Fig 1 with the goal of trying to summarize the setup in a figure, but we can do more. Please see here [\[LINK\]](https://anony... | null | null | null | null | null | null | null | null |
Weak-to-Strong Jailbreaking on Large Language Models | Accept (poster) | Summary: This proposes an attack method that leverages a jailbroken small model to guide the decoding process of a safety-aligned large model, thereby inducing jailbreak behaviors. The proposed method demonstrates a high success rate across various models and conditions while significantly reducing computational overhe... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging feedback. We sincerely appreciate your recognition of the novelty and efficiency of our proposed attack. Below, we address your concerns in more detail.
> **The paper does not provide a comprehensive analysis of the evaluation metrics in Table 4. It s... | Summary: This paper presents a novel method for using white-box access to a weak jailbroken LLM and a strong aligned LLM to jailbreak the strong LLM. The method works by updating the decoding procedure for the strong LLM, biasing it using the logits of a weak jailbroken LLM (and its unjailbroken equivalent). This metho... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thoughtful review! We appreciate your recognition of our method’s novelty, efficiency, and relevance to the white-box attack literature. Below, we respond to your key concerns.
---
> Concern: Are harmful completions from the strong model actually more harmful than... | Summary: Motivated by the weak-to-strong generalization phenomena, this paper proposes an LLM jailbreaking method that employs weak unsafe models to guide the token distribution of a larger safe model. The experiments show that this strategy achieves significantly high ASR and generalizes to different model families.
... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful reading and constructive feedback. We address the concerns and suggestions below.
> Line 220 states that this attack is also applicable to closed-source models with different tokenizers, however, there is no evidence. I'd suggest changing that ... | null | null | null | null | null | null | null | null |
A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability | Accept (poster) | Summary: The paper presents an unifying view on generating counterfactual explanations via backtracking. Namely, the authors propose an optimization objective integrating insights from causal algorithmic recourse and backtracking counterfactual explanations. The paper shows how this new objective subsumes the previous ... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our paper. We look forward to exploring your suggestions further to enhance our work.
We do not dismiss the backtracking distribution. As stated in Equation (15) of our paper, our method can be viewed as a special case of Backtracking Counterfactual Explanations by ... | Summary: This paper proposes a new and efficient method for backtracking counterfactuals using causal reasoning to develop the explanations. The paper provides an analysis of the method’s limitations, discusses the relationship to the literature, and provides experiments that show promising results of their techniques.... | Rebuttal 1:
Rebuttal: Thank you for your kind feedback and positive review of our work. We appreciate your recognition of the theoretical contributions, experimental design, and overall rigor of our paper.
Regarding your question on how our algorithm would perform on large state-of-the-art models, we acknowledge that ... | Summary: This paper proposes a new method for counterfactual explanation of model behavior for models that fall under the additive noise constraints. The new framework is based on backtracking counterfactuals, that find settings for exogenous variables that produce endogenous variables with a desired counterfactual val... | Rebuttal 1:
Rebuttal: Thank you for your insightful remarks regarding our paper. We truly appreciate your thoughtful feedback and look forward to exploring your suggestions further to enhance our work.
> The current paper presents a method that is limited to additive noise models.
First, we note that our method appli... | Summary: The authors propose a new framework for counterfactual explanations based on backtracking counterfactuals by introducing an optimization problem that seeks the nearest possible input modification needed to achieve the desired counterfactual outcome while preserving the causal relationships encoded in the input... | Rebuttal 1:
Rebuttal: Thank you for your thorough analysis and constructive feedback on our paper. We appreciate the opportunity to clarify the points raised and to provide additional insights into our research.
**Answer for Q1:** In our quest for inputs that are actionable for the user, it is crucial to take the user... | null | null | null | null | null | null |
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks | Accept (poster) | Summary: The paper addresses the question of generalization in GNNs when the graph is a discrete sample from the graph. They prove this theoretically, as well as experiment with several existing datasets.
Claims And Evidence: I have an issue with the empirical evidence. The main thing one can see is that the gap betwe... | Rebuttal 1:
Rebuttal: >**The main thing one can see is that the gap between the training and test loss decreases with the number of training examples. This is not very surprising or insightful, and as the real graphs are not generated in a way that is congruent with the theory, I am not sure how do they relate to the t... | Summary: This paper considers GNNs where on graphs which arise from subsampling a manifold (with non-uniform density), building off of a number of recent works which have analyzed the convergence of such networks.
However, this paper adds an exciting new dimension to this line of works by incorporating ideas from stat... | Rebuttal 1:
Rebuttal: We thank the reviewer for giving us a thorough check of our proof and all the suggestions. We are glad to find the reviewer think our work as ''exciting'' and ''significant''. We have carefully considered and addressed all the minor concerns that the reviewer has pointed out and we will make the a... | Summary: This paper provides a new perspective the analyze the generalisation of GNN from manifold and manifold neural networks. By considering a graph as samples from a manifold, this paper shows that the generalisation ability of GNNs decrease with number of nodes and increases with the spectral contiuity constant (a... | Rebuttal 1:
Rebuttal: >**As C1, C2 and C3 depend on the geometry of the manifold, they are potentially important and might be a good contribution so it is a pity that the authors didn't discussed them in depth.**
The parameters are related to the geometry of the manifold. We thought it distracting to expand in the mai... | Summary: This paper examines the generalizability of Graph Neural Networks (GNNs) from a manifold perspective. Leveraging spectral analysis, the authors introduce a novel generalization bound for GNNs, demonstrating that when trained on graphs sampled from a manifold, the generalization error decreases logarithmically ... | Rebuttal 1:
Rebuttal: > **Does non-uniform sampling matter?**
The sampling does not need to be uniform, but the points must be independently and identically distributed (i.i.d.) randomly sampled according to the measure $\mu$ over the manifold. We outline this condition at the beginning of section 3.2 -- the measure ... | null | null | null | null | null | null |
The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models Via Visual Information Steering | Accept (poster) | Summary: This paper introduces a inference-time framework called VISTA that aims to reduce hallucinations in LVLMs. The authors conduct a detailed study of token “logit rankings” across both layer depth and the temporal dimension in a generated sequence. Their analysis reveals three observations: (1) Gradual loss of vi... | Rebuttal 1:
Rebuttal: *We cordially appreciate your careful review and insightful questions. We are thrilled that you find our analysis __"reveals new insights"__., our method gains __"strong performance"__ and __"can generalize"__. See below for detailed replies.*
**Q1. Regarding vision encoder’s quality**
>VISTA foc... | Summary: Through the observation of the LVLM generation process, this paper introduces a hallucination mitigation method, VISTA, which includes a visual steering vector and logit ensemble. Experiments demonstrate that it outperforms existing methods.
## Update after rebuttal
I agree with the authors' rationale and wi... | Rebuttal 1:
Rebuttal: *We sincerely appreciate your detailed review and constructive suggestions. We are encouraged to find our framework is being recognized as __"Applying an effective method to each problem"__. All __claims are evidenced__, and related work are __"well discussed"__. Below we address your questions i... | Summary: This paper discusses the topic of reducing hallucinations in LVLMs. The authors analyzed LVLM’s generation dynamics through the lens of token logits ranking and proposed three types of inference issues. Then, the authors proposed a Visual Steering Vector (VSV) and a Self-Logits Augmentation (SLA) method to in... | Rebuttal 1:
Rebuttal: *We sincerely appreciate your thoughtful review and insightful feedback. We are glad that you find our approach __"simple yet effective"__, our evaluation is __"comprehensive"__, and demonstrate __"improved performance"__. Below we address your questions in detail*.
**Q1. Regarding novelty**
>Ou... | null | null | null | null | null | null | null | null |
Geometric Representation Condition Improves Equivariant Molecule Generation | Accept (spotlight poster) | Summary: This paper proposes a general framework, namely GeoRCG, for 3D molecule generation. Basically, it factorizes a 3D molecule generation into two stages: the first is to generate a geometric representation and the second step is to generate 3D molecules conditioned on the geometric representation. Such factorizat... | Rebuttal 1:
Rebuttal: Dear Reviewer Tpcv,
We sincerely appreciate your insightful and thorough review, and we are grateful for your recognition of our work. Below, we provide detailed responses to each of your comments.
---
### **1. Does additional pretraining data in the representation encoder introduce unfairness?... | Summary: This paper introduces the GeoRCG framework which is a method for generating molecules by firstly generating a conditioning vector and then generating a molecule based on this condition. The authors show how the previously introduced RCG framework can be applied to molecule generation in 3D space by training a ... | Rebuttal 1:
Rebuttal: Dear Reviewer zxks:
We sincerely appreciate your expert and detailed review! Below, we address each of your comments in detail. **We have provided additional tables in an anonymous GitHub repository: https://anonymous.4open.science/r/rebuttal-8746. (Alternative link in case the previous one encou... | Summary: This paper proposes a new framework, GeoRCG (Geometric-Representation-Conditioned Molecule Generation), to enhance the performance of molecular generation models. The proposed approach decomposes molecular generation into a two-step process as follows:
- **Geometric Representation Generation**: A pre-traine... | Rebuttal 1:
Rebuttal: Dear Reviewer 5wZu,
We sincerely appreciate your expert and thoughtful review and are grateful for your recognition of our work.
In response to the comments from the other reviewers, we have provided additional experimental evidence at https://anonymous.4open.science/r/rebuttal-8746 (Alternative... | Summary: The paper presents GeoRCG, a novel framework for 3D small molecule generation, applicable to both unconditional and conditional settings. The key innovation is a two-step generation process: first, generating a geometric representation, then sampling the 3D molecular structure conditioned on this representatio... | Rebuttal 1:
Rebuttal: Dear Reviewer eCAm,
We sincerely appreciate your thorough and insightful review! Below, we address each of your comments in detail. **We have provided additional tables in an anonymous link: https://anonymous.4open.science/r/rebuttal-8746. (Please refer to our response to Reviewer zxks for an *al... | null | null | null | null | null | null |
Vision-Language Models Create Cross-Modal Task Representations | Accept (poster) | Summary: This paper examines a phenomenon in VLMs, where they encode inputs into a unified representation space, regardless of whether the task is defined through text examples, image examples, or explicit instructions. Building on this, the authors conduct experiments to assess the model's cross-modal transfer capabil... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. Below, we include results regarding our core argument and our method’s computational cost, following your suggestions.
---
> 1. The core argument is somewhat less surprising, as it is straightforward that VLM maps inputs from different modalities into a shar... | Summary: The paper studies the capability of task representation sharing/transfer between VLM and LLM. The authors identify a 'task vector' (the delimiter token between the last query-answer pair) in one modality, transfer it to the other modality, and test the model capacity to achieve the given task without additiona... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Below, we report additional results that address your concerns, including evaluation on in-the-wild tasks, and clarifications of the experimental description.
---
> 1. [T]he claims and the conclusions are not surprising, as it is straightforward that VLM m... | Summary: This paper explores how autoregressive vision-language models (VLMs) form cross-modal task representations, which it identifies as "task vectors." These vectors efficiently encode task information across text and image inputs, enabling effective cross-modal transfer. The authors demonstrate task vectors surpas... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. Following your suggestions, we’ve added **four new experiments** on task complexity, task overriding, and practical considerations.
---
> 1. [Add] more complex tasks […] such as VQAv2.
**In Sec. A.4 of the Appendix we include an evaluation based on VQAv... | Summary: The paper explores how VLMs create cross-modal task representations that are invariant to input modality (text or image) and format (examples or instructions). These task vectors, derived from one modality, can effectively trigger task execution in another. It often outperforms traditional few-shot prompting. ... | Rebuttal 1:
Rebuttal: Thank you for your thorough and constructive feedback. Below, we add results on larger sample sizes, real-world tasks, and malformed instructions, following your suggestions.
---
> 1. [T]here are several essential related works that are not currently cited
We will be sure to cite and discuss th... | null | null | null | null | null | null |
SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference | Accept (poster) | Summary: The authors propose a method for sparse attention computation which works for both language and visual models. The method constructs a sparse mask using mean pooling of blocks of queries and keys along with a measure of self similarity within the blocks.
Claims And Evidence: The experiments seem to validate t... | Rebuttal 1:
Rebuttal: Dear Reviewer YtrB,
Thank you for your valuable questions. Below, we address each point raised.
---
>### Weaknesses1
**Reply**: Sorry for the confusion. To clarify: We need to predict sparse areas in the attention map to skip unnecessary computations. However, a naive approach - compressing al... | Summary: The paper proposes SpargeAttn, a universal sparse and quantized attention for any model, accelerates diverse models, including language, image, and video generation, without sacrificing end-to-end metrics. For blocks composed of highly similar tokens, they consolidate these tokens into a single representative ... | Rebuttal 1:
Rebuttal: Dear Reviewer m9qG,
Thank you for your valuable suggestions and questions.
---
>### Essential References
**Reply**: We check STA (FastVideo) and confirm it is after the ICML submission deadline.
---
>### Comment1 and Question5
**Reply**: Thank you for your valuable suggestion. We conducted d... | Summary: The paper proposes SpargeAttn, a universal and training-free sparse attention mechanism intended to accelerate inference across diverse models, including language, image, and video generation. SpargeAttn operates in two stages: initially, it rapidly predicts sparse regions of the attention map using selective ... | Rebuttal 1:
Rebuttal: Dear Reviewer ciBv,
Thank you for your valuable suggestions and questions. Below, we address each point raised.
---
> **Essential References Not Discussed**
**Reply.** Thank you so much for providing the reference. We will discuss it in our paper.
---
> **W1.** How robust is SpargeAttn when d... | Summary: This paper proposes a universal sparse attention mechanism that ensures both speedup and end-to-end performance of diverse models. Specifically, the method adopts a two-stage filtering schemed: In the first stage, it computes attention based on compressed tokens of self-similar blocks of query and key, and ski... | Rebuttal 1:
Rebuttal: Dear Reviewer P2oG,
Thank you for your valuable suggestions and questions. Below, we address each point raised.
---
> **Q1.** Since the attention computation among tokens is permutation invariant, why would using the Hilbertcurve permutation method result in a slightly worse precision compared ... | null | null | null | null | null | null |
Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos | Accept (poster) | Summary: This paper introduces "thermalization," a novel inference-time stabilization method for autoregressive emulators of chaotic spatiotemporal systems. It leverages diffusion models, trained separately on the system's invariant measure, to denoise the emulator rollouts during inference time, pulling trajectories b... | Rebuttal 1:
Rebuttal: Thank you very much for the feedback and insightful comments.
> “Dependence on Diffusion Model Quality. The effectiveness of thermalization relies on the quality of the trained diffusion model and its ability to capture the invariant measure. It's not clear whether such a time-stationary invarian... | Summary: The authors proposed using their method, thermalizer, to make autoregressive emulator rollouts of chaotic systems more stable. This method relies on a diffusion model, stabilising the emulator's overall predictions during the inference phase. UNet and Dilated ResNet were used as emulators. To verify the quali... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to assess our work, and for the insightful comments:
- “Am I correct that the Thermalizer Algorithm can be interpreted as an extension of the DDPM framework for turbulent systems, with a diffusion model used to predict the noise level?”
Not really. Thank yo... | Summary: The goal of this paper is to address the problem of unstable long rollouts by autoregressive neural PDE surrogate models (also called emulators). The core idea is to combine an autoregressive emulator model with an independently trained diffusion model. The role of the autoregressive emulator is then to make a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
- Thank you very much for bringing [7] to our attention, that we indeed missed. It is a very interesting application of diffusion models for fluid dynamics, and it has components related to our method. That said, we want to point out what we be... | Summary: The paper proposes a method to stabilize predictions of an autoregressive surrogate model over long-term rollouts. for that, it proposes to learn the invariant measure with a diffusion model and perform denoising steps at inference with a noise level that is guessed by a classifier. The paper claims that arbit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
- This is a great point which we discussed internally - indeed a diffusion model can produce realistic samples of the flow fields, so it's important to verify temporal consistency. In Figure 4 we show the autocorrelation over time for all diffe... | null | null | null | null | null | null |
RAPID: Long-Context Inference with Retrieval-Augmented Speculative Decoding | Accept (spotlight poster) | Summary: This work proposes RAPID, a variation on the typical speculative decoding framework for long-context tasks by using relatively large draft models that use RAG to compress the context. The quality and performance of RAPID is further boosted by using a “retrieval-augmented target distribution” which modifies the... | Rebuttal 1:
Rebuttal: Dear Reviewer KbZ7,
We sincerely appreciate your thorough review of our paper. Your constructive feedback will help us strengthen this work. Below are our responses to your concerns:
---
## 1. Comparison with LC models like Qwen-1M
We've evaluated RAPID on Qwen2.5-7B-1M (released post-submissi... | Summary: To enhance the efficiency and effectiveness in long-context scenarios, the paper proposes a method called Retrieval-Augmented Predictive Decoding (RAPID), which aims to address the decline in efficiency and quality of traditional speculative decoding due to memory limitations in long-context reasoning. RAPID i... | Rebuttal 1:
Rebuttal: Dear Reviewer SMJY,
We sincerely appreciate your thorough review of our paper. Your constructive feedback will help us strengthen this work. Below are our responses to your concerns:
---
### 1. Missed References.
A1: Thanks for pointing it out. REST[1] proposed selecting possible continuation ... | Summary: The paper presents a novel decoding method called RAPID, designed to enhance the efficiency and quality of long-context inference in large language models (LLMs). RAPID introduces the RAG drafter—a draft LLM operating on shortened retrieval contexts—to speculate on the generation of long context target LLMs. R... | Rebuttal 1:
Rebuttal: Dear Reviewer s5Em,
We sincerely appreciate your thorough review of our paper. Your constructive feedback will help us strengthen this work. Below are our responses to your concerns:
---
### 1. The paper does not introduce entirely new theoretical frameworks but rather combines existing ideas i... | Summary: This paper introduces Retrieval-Augmented Speculative Decoding (RAPID) that aims at both accelerating and enhancing generation quality in long-context inference. SD becomes inefficient with long contexts since both draft and target LLMs need to process complete context in memory. The authors introduce a RAG dr... | Rebuttal 1:
Rebuttal: Dear Reviewer bhTe,
We sincerely appreciate your thorough review of our paper. Your constructive feedback will help us strengthen this work. Below are our responses to your concerns:
---
### 1. Possible explanation for the sudden drop in Figure 1.
A1: This is an observed issue in RAG that perf... | null | null | null | null | null | null |
Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More | Accept (poster) | Summary: The paper introduces a simple modification to the standard causal next token prediction training framework of decoder-only LLMs, by masking a random fraction of the input tokens. After masking, the objective is still next token prediction. Via a set of experiments, the authors show the effectiveness of this pa... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable review. We're glad you found our paper to be self-contained and our results convincing.
### **Q1: Table 2: the drop of NTP perfomance when moving from 40B to 60B is suspicious. Can you please explain?**
Thank you for your sharp observation. One plausible exp... | Summary: The authors propose Mask-Enhanced Autoregressive Prediction (MEAP), a novel training paradigm integrating Masked Language Modeling (MLM) into the traditional Next-Token Prediction (NTP) objective. The key idea is randomly masking a fraction of input tokens during autoregressive pre-training, improving key info... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments!
### **Q1: Variations in masking strategies.**
We added three distinct masking strategies as you suggested: Random Masking, 5-Span Masking (spanning 5 consecutive tokens), and 50-Span Masking, using a 0.3B parameter model pre-trained on 5B tokens... | Summary: This paper introduces Mask-Enhanced Autoregressive Prediction (MEAP). In particular, MEAP incorporates the masked language modeling technique in next-token prediction setting by randomly masking out a small portion of input tokens and train the model with standard next-token prediction. This method is applied ... | Rebuttal 1:
Rebuttal: ### **Q1: Intuition behind copying input for fine-tuning**
The duplication approach addresses a critical constraint in supervised fine-tuning: SFT answers are often extremely short (typically 5-15 tokens). With such limited tokens, conventional masking would risk fragmenting the semantic coherence... | null | null | null | null | null | null | null | null |
Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning | Accept (poster) | Summary: In this work, the authors propose an offline HRL framework that leverages graph to solve a long-horizon problems efficiently. To solve the challenges in long-horizon reasoning, the proposed method construct graph using temporal efficiency metric and clustering, leaving only high-quality subgoal. After then, th... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful feedback and questions. Due to space limits, we focused on key reviewer concerns.
1. It does not clearly outline the differences from previous studies.
All existing graph-based methods, such as [HIGL], [L3P], [SoRB], and [DHRL], are designed for on... | Summary: This paper proposes a new graph-based method called GAS for offline GCRL, particularly for long-distance goal tasks. The main idea of GAS is to find subgoals for goal-conditioned RL tasks using shortest path planning in a state-space graph. GAS constructs this graph in a Temporal Distance representation space ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful feedback and questions. Due to space limits, we focused on key reviewer concerns.
1. ... value functions are noisy due to sparse reward signals. How does GAS ensure that representation learning is not negatively affected by this noise?
Temporal-dis... | Summary: This paper presents a novel offline hierarchical RL method, which constructs a state graph in a learned temporal distance representation space, and selects subgoals from the graph rather than from a high-level policy. The constructed graph facilitates trajectory stitching, and improves the task performance giv... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s constructive feedback.
1. While both our work and Ghugare et al. [1] aim to address trajectory stitching in the offline reinforcement learning setting, the two approaches differ significantly in structure and mechanism. Ghugare et al. [1] focus on improving generaliza... | Summary: This paper replaces the high-level policy learning in traditional Hierarchical Reinforcement Learning (HRL) with a graph search problem. When constructing the high-level graph, GAS clusters states that are similar in the Temporal Distance representation space into a single graph node to achieve efficient traje... | Rebuttal 1:
Rebuttal: Thank you for suggesting the additional relevant works: BEAG, HIGL, and PIG. While these methods are primarily designed for online learning and planning, we agree that they share conceptual relevance with our work in graph construction strategies, and we will discuss them in the revised manuscript... | null | null | null | null | null | null |
Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-trained Models | Reject | Summary: The paper presents a novel training-free FL method, starting from well-pretrained model as an initialization. The authors tackle the key limitation of the existing work, Fed3R, where the clients must upload the second order statistics, incurring additional communication cost. To address this issue, the authors... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating that the paper is well structured and clearly written, that claims are well supported by extensive experimental results and analyses, that the covariance estimator is mathematically well justified and conceptually solid, and that the experimental settings are... | Summary: The main conceptual idea is to estimate class covariance matrices at the server using only class means communicated from clients, avoiding the need to share computationally expensive second-order statistics (e.g., covariance matrices) as in prior methods like Fed3R. FedCOF exploits the statistical relationship... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the soundness of our work, the correctness of our proofs, and that the evaluation criteria is appropriate for FedL - accuracy, communication cost, FL rounds, and client participation. We also appreciate the recognition of our diverse experimental setup acros... | Summary: The paper presents FedCOF – a training-free method that leverages the first-order statistics (class means and variance matrices) to update the global classifier on the backbone of a pre-trained model.
Claims And Evidence: The claim “the samples belonging to the same class across different clients are sampled... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the clarity and readability of our paper, as well as the good experimental results that validate our mathematical derivations. Below we reply to each of the points raised by the reviewer.
>The work of Luo et. al. [1] was mentioned briefly in the Introductio... | Summary: This paper studied the problem of using pre-trained models to speed up federated learning algorithms by using first-order statistics to estimate second-order statistics to achieve good learning performance without training. The authors proposed a new method to only use first-order statistics in the form of cla... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that our claims are justified both theoretically and through numerical experiments, for recognizing the correctness of our theoretical results, the soundness of our experimental design and analyses, the comprehensiveness of our evaluation, and for appreciati... | Summary: This paper introduces FedCOF, a training-free FL framework that seeks to compute (in FL fashion) a closed form ridge regression solution using features extracted from a pre-trained model. The naive solution to this formulation, which was done in Fed3R, requires sharing second-order statistics which, in the con... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the soundness of our propositions, the positive improvements in performance and communication cost over other training-free and training-based solutions, and the quality of our experimental design. We appreciate that the reviewer acknowledged that Propositio... | null | null | null | null |
$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting | Accept (spotlight poster) | Summary: This study presents $K^2VAE$, an efficient variational autoencoder (VAE)-based generative model. It utilizes a KoopmanNet to convert nonlinear time series into a linear dynamical system. Furthermore, it designs a KalmanNet to enhance predictions and model uncertainty in this linear system, thereby reducing err... | Rebuttal 1:
Rebuttal: **Reply to W1. Why were CRPS and (NMAE) chosen as the main evaluation metrics? Why not use other evaluation criteria?**
Thank you for your valuable comments. In probabilistic forecasting, evaluation metrics including CRPS (Continuous Ranked Probability Score), CPRS_sum, NMAE (Normalized Mean Abso... | Summary: This paper points out that traditional probabilistic methods in predicting the collapse of long-term series uncertainty estimate provide a new perspective. Then, to overcome these limitations, this paper introduces K2VAE, an efficient VAE-based generative model that leverages a KoopmanNet to transform nonlinea... | Rebuttal 1:
Rebuttal: **Reply to Q1: Why can the proposed method solve the problem of error accumulation well?**
Thank you for your valuable comments. In $K^2$VAE, the KoopmanNet models time series in linear dynamical systems, where uncertainties are represented as deviations from the linear system’s predictions. The... | Summary: This study presents an efficient framework named K2VAE, which transforms nonlinear time series into a linear dynamical system. By predicting and refining the process uncertainty within the system, K2VAE showcases powerful generative capabilities and excels in both short- and long-term probabilistic forecasting... | Rebuttal 1:
Rebuttal: **Reply to W1. Koopman theory requires that the measurement function maps inputs to several observable variables to construct a linear dynamical process in that space. Traditional methods typically choose fixed basis functions to meet this requirement, but this paper adopts a learnable measurement... | Summary: This study introduces $K^2$VAE , a VAE-based probabilistic forecasting model designed to address PTSF. By leveraging the KoopmanNet, $K^2$VAE converts nonlinear time series into a linear dynamical system, enabling a more effective representation of state transitions and inherent process uncertainties. Addition... | Rebuttal 1:
Rebuttal: **Reply to W1. The experimental design of this paper is very comprehensive, evaluating multiple datasets on both long and short step tasks. However, in Table 5, some datasets have the same name but different actual lengths. What is the reason for this?**
Thank you for your valuable comments. We ... | null | null | null | null | null | null |
Polynomial-Delay MAG Listing with Novel Locally Complete Orientation Rules | Accept (oral) | Summary: This paper introduces an enhanced algorithm for the MAG listing task that outputs MAGs in the MEC with polynomial delay. Experimental results confirm the effectiveness of the proposed approach, and a counterexample construction is provided to demonstrate the incompleteness of current orientation rules.
Claims... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments and suggestions. We are grateful for your positive evaluation of the theoretical contributions and experimental designs in our paper. We also appreciate your suggestion to discuss real-world tasks and datasets, as well as to clarify the practical appl... | Summary: The paper presents a novel polynomial-delay algorithm for listing all maximal ancestral graphs (MAGs) in a Markov equivalence class (MEC) while incorporating singleton background knowledge (BK). The core contribution is the development of three new orientation rules that improve computational efficiency compar... | Rebuttal 1:
Rebuttal: Thank you for insightful comments and positive evaluation! We will add more illustrations and examples to make it easier to read for average readers in the revised version.
1. Is there any intuition or idea how to further optimize the rule applications to reduce the complexity:
Thank you for ra... | Summary: This paper proposes a MAG listing algorithm (i.e., output all and only MAGs in the MEC, represented by a PAG) with polynomial delay.
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## update after rebuttal:
I thank the authors for the clear explanation to my questions. I keep my score of acceptance.
Claims And Evidence: Yes.
Methods And Evaluation ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive evaluation! We will incorporate the discussion you mentioned (DAG listing, counting…) in the revised version.
1. …to count size… would complexity be different? Would the method help:
Thank you for your insightful question. Currently, we do not have a clear... | Summary: The paper proposes a method for enumerating (listing) MAGs consistent with a given PAG with a polynomial delay, i.e., the ratio between the time complexity of enumeration and the number of consistent MAGs is polynomial in the graph size. The method is based on resolving one circle at a time, followed by applyi... | Rebuttal 1:
Rebuttal: Thank you for careful reading and providing many valuable and constructive suggestions, which will definitely help us improve our work. As suggested, we will provide proof sketches and examples for Lemmas 3/4 in the revised version.
1. …I don't think all local transforms produce a PMG that has a ... | null | null | null | null | null | null |
Relative Error Fair Clustering in the Weak-Strong Oracle Model | Accept (poster) | Summary: *Background*: This work studies the problem of fair clustering in the weak-strong oracle framework. In this setup, there exists a strong oracle offering precise distance measurements at a higher cost, alongside a weak providing less accurate distance estimates at a lower cost. The goal is to minimize strong or... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and address their questions below.
### Re: Runtime claim in Lemma 3.1
The bound is indeed accounting for the runtime of subroutines. To see this note that for ``not processed’’ rings we check the size in the sampled set and then perform peeling. If the sa... | Summary: The authors study the fair $(k,z)$-clustering problem in a weak-strong oracle model. Each data point may belong to one or more groups, and the goal is to cluster the data points while minimizing a given clustering objective. The fairness requirement ensures that within each cluster, data points from different ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and encouraging comments and suggestions, and address their questions below.
### Re: Experimental description
The experiment considers the (p,q)-fair k-median problem where each data point is assigned a color of either p or q and each cluster should have a... | Summary: The paper gives coresets for the fair $\(k,z\) $ clustering problem using an oracle model which allows for a combination of queries i) that are returned a weak approximation of distances (weak oracle) at a low cost and ii) queries that are returned exact distances between pair of points but at a high cost (str... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and address their questions below.
### Re: Experiments
We chose a uniformly sampled coreset as a baseline since we wanted to compare our algorithm to a method that uses a comparable number of strong oracle queries.
We do not compare our coreset to other ... | null | null | null | null | null | null | null | null |
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient | Accept (poster) | Summary: This paper introduces joint scaling laws for Mixture of Experts (MoE) and dense models, incorporating factors such as the number of active parameters, dataset size, and number of experts. The proposed scaling law captures interactions between these variables, enabling principled optimization of MoE configurati... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and comments. We also appreciate the recognition of the extensive empirical validation and actionable insights from our work. Below, we specifically address questions and weaknesses mentioned in the review. If our answers address the reviewer's conc... | Summary: The authors, motivated by the Chinchilla scaling laws for large transformers and the popularity of Mixture of Experts (MoE) architectures, investigate a joint scaling law that can be applied to MoE models and dense models (when number of experts = 1). The loss of the model is related to the number of parameter... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback and comments. We appreciate the recognition of the vast scale of our experiments, clear visualizations, and good communication of findings. Below we address the reviewer’s questions in details. If our answers address the reviewer's concerns, we wou... | Summary: This work balances computational and memory constraints by deriving joint scaling laws for both Mixture-of-Experts (MoE) and dense models. The analysis shows that the optimal number of experts is closely tied to the available memory and compute budgets. Furthermore, experimental results suggest that MoE models... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and suggestions. We also appreciate the recognition of the practical importance of our findings, the actionable insights they provide, and the confirmation that our claims are well-supported. We hope that the answers below adequately answered the rev... | Summary: The paper proposes a scaling law for mixture-of-experts and dense models, similar to the one used by Chinchilla (Hoffmann et al., 2022), but that incorporates the number of experts in the equations.
The proposed equation describing the scaling laws, is essentially the combination of Chinchilla with Clark et a... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. We are especially grateful for the reviewer’s recognition that "the key contributions of the paper are of high importance to the community of language modeling" and that the work is of "great quality".
We are also thankful for the detailed comm... | null | null | null | null | null | null |
Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead | Accept (poster) | Summary: This paper presents a near-optimal and practical client weighting method that leverages client discriminators trained with a server-distributed generator and local datasets in federated ensemble distillation, which are supported by rigorous theoretical analysis and experimental validation. The work has signifi... | Rebuttal 1:
Rebuttal: **Comments on theoretical assumptions**
We believe the reviewer's suggestion regarding $L$-smoothness may arise from a different interpretation of the convexity assumption. The reviewer seems to have interpreted the convexity assumption with respect to the model parameter $\theta$, whereas our co... | Summary: FedGO, a method for federated ensemble distillation (FED) that optimally assigns weights to client predictions using client-trained discriminators, is theoretically justified by GAN principles. It mitigates client data heterogeneity. Experiments on image classification datasets show FedGO outperforms existing ... | Rebuttal 1:
Rebuttal: **Comments on data-free setting**
We believe we have thoroughly examined both scenarios, with and without a server dataset. For data-free setting, we have conducted experiments using both off-the-shelf generator and generator trained via federated learning and analyzed the results in Append... | Summary: This paper, inspired by the theoretical results of Generative Adversarial Networks (GAN), proposes a weight assignment method for federated ensemble distillation. The method first trains the generator on the server side via a federated learning algorithm and trains the discriminator on the client side using a ... | Rebuttal 1:
Rebuttal: **Comments on pre-trained generator robustness**
Our theoretical analysis already takes into account such discrepancy: Theorem 3.6 says that our weighting method produces the optimal weight $w_k^*$ for the data point on supp$(p)\cap$ supp$(p_g)$, where $p$ is the average client data distribution... | Summary: The paper presents FedGO, a novel federated ensemble distillation method, aimed at addressing client data heterogeneity in federated learning. The authors propose a weighting method for ensemble distillation that is provably near-optimal by leveraging theoretical results from GANs. The method trains client-sid... | Rebuttal 1:
Rebuttal: **Comment 1:** *Important Limitation: The theoretical analysis — including the derivation of the optimal weighting functions and generalization bounds — is restricted to binary classification tasks. This limitation is underemphasized in the main text, yet all experiments are conducted on multi-cla... | null | null | null | null | null | null |
PENCIL: Long Thoughts with Short Memory | Accept (poster) | Summary: This paper introduces PENCIL, a novel method designed to overcome a fundamental limitation of standard chain‐of‐thought (CoT) reasoning in language models. The main idea is to interleave token generation with a reduction mechanism that “cleans up” intermediate reasoning steps—using specially defined tokens (e.... | Rebuttal 1:
Rebuttal: > **Weakness 1: "Some proofs and technical details are deferred to the appendix; a more self-contained presentation could aid clarity."**
Thank you for the suggestion. We will incorporate key technical details from the Appendix into the main paper once we have an additional page in the final vers... | Summary: The paper focused on the CoT reasoning, and proposed a PENCIL framework with reduction mechanism to exclude the unnecessary parts in the CoT. The authors conducted experiments on SAT, QBF and Einstein’s Puzzle to demonstrate the effectiveness of the framework. The authors also proved that the framework could s... | Rebuttal 1:
Rebuttal: > **Weakness 1 & Q1: "The three datasets used in the paper are all SAT-like ones, which lacks discussion on generalizability to more general reasoning. Besides, the reduced CoT growing from exponential to polynomial only works for the SAT-like tasks."**
We choose SAT and QBF because they are repr... | Summary: The paper introduces PENCIL, an extension of the Chain-of-Thought (CoT) approach for language models. PENCIL addresses the "write-only" limitation of CoT, where intermediate reasoning steps accumulate indefinitely in the context, by incorporating a reduction mechanism. This mechanism uses special tokens ([CALL... | Rebuttal 1:
Rebuttal: > **Weakness 1: "The data generation process requires knowing the reasoning structure. How reasoning problems without a clear structure (e.g., math problems) can benefit from this approach remains a question."**
Indeed, language models do not inherently reason in a way that allows for convenient s... | Summary: The paper proposes PENCIL, a next-token generation scheme that incorporates a reduction mechanism to control the length of the generated sequence. This mechanism removes redundant context, enabling more efficient generation while reducing memory usage. Experimentally, transformers trained on 3SAT, QBF, and Ein... | Rebuttal 1:
Rebuttal: > **How PENCIL can be applied to standard LLMs**
The way we envision applying PENCIL to standard LLMs is to fine-tune LLMs on examples that include our special tokens, with the goal that model learns to reason in a structured manner that leverages memory efficiently and enables longer reasoning. ... | null | null | null | null | null | null |
Mixed Likelihood Variational Gaussian Processes | Reject | Summary: The paper proposes a method of training a variational gaussian process model with more than one "type" of observations by allowing it to utilise more than one type of likelihood. The authors explain how this method can be used in many real world scenarios, either by enforcing soft-constraints (encoded as addit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. Below we provide our response.
> In said section, authors explain the advantage their method has over these baselines, and their explanation is reasonable, however, the paper could be made much stronger by empirically showing these advantages... | Summary: This paper introduces mixed likelihood variational Gaussian Processes (GPs) to incorporate auxiliary information by combining multiple likelihoods within a single evidence lower bound. The authors demonstrate the method’s effectiveness across three human-centered experiments: (1) accelerating active learning i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. Below we provide our response.
> Could other types of scores/scales play similar role as the Likert scale? If so, did you consider any other in particular? or is there a particular reason behind the choice of the Likert scale? If other scales... | Summary: The paper develops a method for variational Gaussian processes (GPs) using mixed likelihoods, i.e., when for the same input data and latent function there exist multiple and different kind of output observations. The authors train their model using an evidence lower bound by utilizing also inducing variables ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. Below we provide our response.
> One question I have is about the motivation behind the Likert scale likelihood. Someone would expect the use of ordinal regression likelihood for the ratings. However, the authors claim that ordinal likelihood... | Summary: This paper proposes Mixed Likelihood Variational Gaussian Processes (GPs) as a method to integrate auxiliary information (e.g., domain expertise, confidence ratings) into GP models for human-in-the-loop experiments. Traditional GP models often assume a single likelihood and ignore non-task-specific information... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. Below we provide our response.
> The paper introduces new concepts like the Likert likelihood without a detailed theoretical discussion on its limitations or potential drawbacks, which could leave readers with questions on its applicability i... | null | null | null | null | null | null |
Imagine While Reasoning in Space: Multimodal Visualization-of-Thought | Accept (poster) | Summary: The paper introduces Multimodal Visualization-of-Thought (MVoT), a new reasoning paradigm designed to enhance the spatial reasoning capabilities of Multimodal Large Language Models. It improves the spatial reasoning ability over Chain-of-Thought (CoT) prompting by generating image visualizations of their reaso... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work and your valuable suggestions. We would like to address your comments as follows to get more support:
**Toy grid-world based experiments**
Our use of grid-based benchmarks offers better controllability and systematic investigation across various aspects... | Summary: This paper proposes a new multimodal reasoning paradigm — Multimodal Visualization-of-Thought (MVoT), which enables the model to "think" in both textual and visual spaces interleaved. The authors implement this by fine-tuning a Chameleon-like model, Anole-7B, to generate interleaved text and images. They colle... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work and your valuable suggestions. We would like to address your comments as follows to get more support:
**Visualization Consistency and Vulnerability**
We acknowledge the concern that unconstrained visualization could introduce inconsistencies, particular... | Summary: This paper presents Multimodal Visualization-of-Thought (MVoT). This paradigm enables visual thinking in MLLMs by generating image visualizations of their reasoning traces. MVoT is motivated by human's cognition, having the ability to think both in words and images seamlessly.
MVoT is developed based on Chame... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work and your valuable suggestions. We would like to address your comments as follows to get more support:
**Experiment and task scope**
Our use of grid-based benchmarks offers better controllability and systematic investigation across various aspects of sp... | Summary: This paper presents Multimodal VoT which integrate visual generation during MLLM’s reasoning process. The idea is straight forward and the motivation is inspired from the theory about how human reasoning in both verbal and non-verbal channels. In order to increase the image generation quality, the authors prop... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work and your valuable suggestions. We would like to address your comments as follows:
**Benchmark selection**
> All three benchmarks used in this paper is synthetic and under limited scale.
Our use of grid-based benchmarks was intentional to ensure better... | null | null | null | null | null | null |
Stochastic Layer-Wise Shuffle for Improving Vision Mamba Training | Accept (poster) | Summary: This paper proposes a plug-and-play training strategy for Vision Mamba. It shuffers the sequence order of the input tokens layer by layer. The authors conduct mask feature distillation to pre-train the vision Mamba with the proposed layerwise shuffling strategy. Experiments on classification and dense predicti... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We are glad that you found our work is technically sound and well-motivated, has extensive experiments with effectiveness. We provide our feedback as follows.
> **Q1: Applying SLWS to hierarchical structures.**
**A1:** Hierarchical architectures like VMam... | Summary: This work introduces a stochastic hierarchical shuffle strategy SLWS for Vision Mamba (Vim) that successfully solves the overfitting issue of Mamba models in large-scale datasets without changing the model architecture, effectively improving the training of Vim. According to this paper, SLWS can help Vim achie... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We are glad that you found our work significantly improves the performance of non-hierarchical Mamba models and does not incur significant computational overhead. We provide our feedback as follows.
> **Q1: SLWS's shuffle effects for visual connections bet... | Summary: This paper introduces a method that addresses overfitting issues when scaling up vanilla Vision Mamba models to larger sizes. The key contribution is a Stochastic Layer-Wise Shuffle (SLWS) regularization technique that randomly shuffles token positions during training with layer-dependent probabilities. Experi... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We are glad that you found our work has clear motivation and comprehensive evaluation, and simple plug-and-play design. We provide our feedback as follows.
> **Q1: Performance improvements of our models compared to baselines especially Mamba-R**
**A1:** O... | Summary: The paper introduces Stochastic Layer-Wise Shuffle (SLWS), a method designed to enhance the training of Vision Mamba models (ViM). This approach involves applying stochastic shuffling to input tokens at each layer, with the probability of shuffling systematically increasing as the layer depth progresses. Thoug... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We are glad that you found our work is simple but demonstrates significant benefits, and has measurable performance gains across diverse tasks and training paradigms. We provide our feedback as follows.
> **Q1-1: Supporting evidence of learning curves fo... | Summary: This paper proposes a stochastic layer-wise shuffle regularization (SLWS) method for efficient vision mamba training. As a plug-and-play method, SLWS mitigates the overfitting problem with introducing minimal overhead. The achieved results are impressive and downstream tasks also verified the effectiveness. Ov... | Rebuttal 1:
Rebuttal: > **Q1 & Q2: Hierarchical VMamba training and non-hierarchical plain mamba selection**.
**A1:** VMamba employs a more complex downsampling process and adopts a hierarchical architecture, which demonstrates strong performance under Tiny, Small, and Base model sizes. However, there is currently no ... | null | null | null | null |
Exploiting Presentative Feature Distributions for Parameter-Efficient Continual Learning of Large Language Models | Accept (poster) | Summary: This paper proposes a novel parameter-efficient continual learning (CL) framework for large language models (LLMs) that leverages pre-trained model representations to dynamically select task-specific LoRA blocks via presentative feature distributions. The method addresses the critical challenge of information ... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work and taking the time to review our manuscript. Below is our response to address your concerns.
**Q1: (Questions) How does the method disambiguate ...... layer-wise selection inherently mitigate this?**
A1: Our method can effectively identify tasks with overlappi... | Summary: This paper presents a novel continual learning method for LLMs that avoids information leakage by employing presentative feature distributions. The proposed method characterizes parameter-efficient fine-tuning blocks using feature distributions and dynamically selects suitable blocks based on similarity metric... | Rebuttal 1:
Rebuttal: We are grateful for the time and effort you have dedicated to reviewing our work. We provide point-by-point responses to address your concerns.
**Q1: (Weaknesses) The empirical evidence is evident but ...... like cosine similarity.**
A1: Due to word limit and the repeated mention of this weaknes... | Summary: This paper presents a method for continual learning (CL) in large language models (LLMs) that addresses information leakage (IL) while maintaining strong performance. The method leverages the feature representation capability of pre-trained LLMs to encode task-related information into presentative feature dist... | Rebuttal 1:
Rebuttal: Thank you so much for your valuable comments! We provide point-by-point responses to address your concerns.
**Q1: (Methods and References) Similar methods have already been proposed ...... e.g. Feature Adaptation (Iscen et al., 2020).**
A1: Thank you for pointing out these relevant studies, and ... | null | null | null | null | null | null | null | null |
Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow | Accept (poster) | Summary: This paper focuses on improving the training and inference efficiency of 3D molecular conformer generation while matching the performance of strong baselines.
To improve training efficiency, it introduces a new training objective, called SO(3)-Averaged Flow, which can avoid the need for rotational alignment b... | Rebuttal 1:
Rebuttal: *We would like to thank the reviewer for reviewing and acknowledging the novelty of the SO(3)-Averaged Flow. Please see the response below:*
**Theoretical Claims**
The major motivation behind the development of *AvgFlow* is to eliminate the need for data augmentation through rotation by training... | Summary: The paper introduces a new method for molecular conformer generation task called Averaged Flow. Averaged Flow is an SO(3) Flow Matching method that addresses rotational symmetry in 3D molecular structures by integrating overall SO(3) group transformations during training. The authors combined their approach w... | Rebuttal 1:
Rebuttal: *We thank the reviewer for acknowledging the novelty of integrating reflow and distillation method for accelerating the sampling of conformer generation model. Please see our response below to other questions and comments:*
**Weakness**
1. We want to emphasize that the major motivation of this p... | Summary: This paper presents SO(3)-Averaged Flow Matching and Reflow-based Distillation, a novel approach aimed at improving the computational efficiency of molecular conformer generation. By explicitly incorporating rotational symmetries into the flow-matching framework and refining the transport trajectories through ... | Rebuttal 1:
Rebuttal: *We want to thank the reviewer for the comprehensive review. Please see below for responses:*
**Essential references**
The references suggested by the reviewer are indeed relevant to this paper. However, we want to point out that we have explicitly compared our model to GeoMol for both the QM9 a... | null | null | null | null | null | null | null | null |
Flex3D: Feed-Forward 3D Generation with Flexible Reconstruction Model and Input View Curation | Accept (poster) | Summary: This paper proposes a two-stage framework called Flex3D for 3D generation and reconstruction. The first stage leverages multi-view and video diffusion models to generate a large candidate set of views, then filters them according to both visual quality and multi-view consistency. The second stage uses a flexib... | Rebuttal 1:
Rebuttal: We thank you for your thorough review and constructive feedback. We are encouraged by your comments on our pipeline's clarity, the tri-plane + Gaussian Splatting design's performance and potential impact, and the solution's coherence for common two-stage 3D generation pipelines. We provide our res... | Summary: This paper introduces two novel modules for achieving high fidelity 3D generation. The first one is candidate view generation and selection module, which generates a pool of novel view images and adopt a SVM scorer to select high fidelity novel views which are then sent to the second module named Flexible Reco... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and insightful feedback. We are encouraged by your recognition of our core view selection strategy as interesting and novel, and we appreciate you noting its potential to improve reconstruction quality by filtering inconsistent views. We also value your positive ... | Summary: This paper introduces Flex3D, a novel two-stage framework designed for high-quality 3D generation from text, single images, or sparse views. In the first stage, the framework employs multi-view diffusion models to generate multiple images from diverse viewpoints, coupled with a view selection mechanism to filt... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback. We appreciate you acknowledging several strengths, including the paper's clear organization, the effectiveness of our view selection in improving input quality, the practicality of handling varying view numbers in reconstruction, and the po... | null | null | null | null | null | null | null | null |
Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes | Accept (poster) | Summary: This paper addresses the problem of interactive 3D segmentation, where the model segments target objects based on positive and negative user clicks. This paper proposes a probabilistic framework built upon Neural Processes (NPs) to enhance model generalisation. Specifically, the model aggregates object embeddi... | Rebuttal 1:
Rebuttal: ***Q1: Architecture details for reproduction and code for clarification.***
Thanks for your valuable suggestions. In Appendix D.2 (pp. 19), we provided additional architectural details of our framework. Specifically, following AGILE3D, the point encoder in Figure 1 consists of a backbone—Minkowsk... | Summary: Main Contributions & Findings
The paper introduces NPISeg3D, a novel probabilistic framework for interactive 3D segmentation, leveraging Hierarchical Neural Processes (NPs) to tackle two key challenges:
1. Few-shot generalization – enabling accurate segmentation from sparse user clicks.
2. Uncertainty estimati... | Rebuttal 1:
Rebuttal: ***Q1: Limitations in Out-of-Domain Generalization. (1) Out-of-domain performance falls short in-domain. (2) The paper does not explore domain adaptation techniques.***
Thank you for your insightful comments. Similar to previous methods like AGILE3D, our model is trained solely on ScanNet and ev... | Summary: This paper presents NPISeg3D, a novel probabilistic framework for interactive 3D segmentation based on neural processes (NPs), which addresses the key challenges of generalizing from sparse user clicks and quantifying predictive uncertainty. The framework introduces a hierarchical latent variable structure and... | Rebuttal 1:
Rebuttal: ***Q1: Computational efficiency and Reliability of uncertainty estimation.***
Thank you for your valuable comments. Below, we address each aspect in turn.
***Computational efficiency.*** Our NPISeg3D introduces negligible extra parameters introduced by our neural process module which enhances g... | Summary: This paper proposes a method using neural processes for 3D interactive segmentation which in addition to segmentations, also enables uncertainty estimations. The proposed method uses a hierarchical latent structure to capture both local and global concepts and a probabilistic prototype modulator which allows f... | Rebuttal 1:
Rebuttal: ***Q1: Include discussion of some interactive 3D segmentation methods that translate SAM features into 3D such as SAM3D and SA3D.***
Thank you for suggesting these relevant interactive 3D segmentation works, SAM3D and SA3D, both of which explore translating 2D SAM features into 3D. Specifically, ... | null | null | null | null | null | null |
Cross-Modal Alignment via Variational Copula Modelling | Accept (poster) | Summary: This paper discusses a multi-modal learning algorithm utilizing copula to "couple" the marginal distributions in each modality. It employs an standard encoder for learning the latent representation for each modality, and model each latent representation as Gaussian mixtures. It then use a copula (selected from... | Rebuttal 1:
Rebuttal: We sincerely thank for your valuable feedback and constructive comments. We take great care in responding to several intriguing discussions raised by you as follows:
> 1.Applied to generic tasks
Thank you for the suggestion. To support the generality implied by our title, we added results on CMU-... | Summary: The paper presents a multimodal learning framework based on Copula theory. The modalities are modeled using a Gaussian mixture distribution, and a joint copula model is applied to the joint distribution. The proposed method is validated on a healthcare dataset, considering both cases where modalities are missi... | Rebuttal 1:
Rebuttal: We sincerely thank for your valuable feedback and constructive comments. We take great care in responding to several intriguing discussions raised by you as follows:
> 1.Impact of initial modeling of the marginals via GMM
Thank you for the insightful comment. Our initial marginal modeling, which ... | Summary: This work primarily focuses on the problem of multimodal supervised learning, where some modalities may be missing. The authors model the joint latent distribution of all modalities using a copula model with finite Gaussian mixture marginals. In the presence of missing modalities, they impute the missing laten... | Rebuttal 1:
Rebuttal: We sincerely thank for your valuable feedback and constructive comments. We take great care in responding to several intriguing discussions raised by you as follows:
> 1.Modality Alignment Tasks & Alignment Losses
In our framework, modality alignment is achieved through the copula loss, which exp... | Summary: The paper proposed a copula modeling method for multi-modal representation learning, which could model the interactions between modalities and impute the missing modalities through sampling from learned marginals. The method was empirically evaluated on healthcare benchmarks MIMIC-III and MIMIC-IV datasets for... | Rebuttal 1:
Rebuttal: We sincerely thank for your valuable feedback and constructive comments. We take great care in responding to several intriguing discussions raised by you as follows:
> 1.Results on other types of multi-modal datasets
Thank you for the suggestion. To evaluate generalizability beyond healthcare, we... | null | null | null | null | null | null |
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering | Accept (poster) | Summary: This paper presents a novel approach to unsupervised anomaly detection (UAD) called CostFilter-AD. Unlike traditional methods that suffer from inaccurate matching processes, this approach leverages cost volume filtering, a technique borrowed from depth and flow estimation tasks, to enhance detection accuracy. ... | Rebuttal 1:
Rebuttal: **Q1:** The proposed method is simple, effective, and well-proven. Calculation cost and memory cost can be added.
**A1:** Thank you for acknowledging our work's effectiveness and suggesting the inclusion of computational and memory costs. We include comparisons of parameter count, FLOPs, memory u... | Summary: The paper proposes CostFilter-AD, a novel method for unsupervised anomaly detection (UAD) that leverages cost volume filtering. The approach addresses matching noise issues in existing UAD methods by constructing an anomaly cost volume and refining it with a filtering network.
## Update after rebuttal
The au... | Rebuttal 1:
Rebuttal: **Table R5**. AnomalDF (abbr. as ADF) /+Ours Comparison under a fair setting.
|ID|Dataset|Method|Input size|#Templates|I-AUROC|I-AP|I-F1-max|P-AUROC|P-AP|P-F1-max|P-AUPRO|
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|1|**MVTec-AD**|ADF|256|3|96.8|98.6|97.1|98.1|61.3|60.8|93.6|
|2... | Summary: This paper introduces cost filtering into unsupervised anomaly detection and multi-class anomaly detection. The authors offer a new perspective to differentiate the discrepancy between the input and templates. Their experiments appear to demonstrate the effectiveness of the proposed method.
Claims And Evidenc... | Rebuttal 1:
Rebuttal: **Q1**: Missing the first multi-class anomaly detection work, UniAD, and a performance comparison.
**A1**: Thanks for your reminding.
- We fully recognize UniAD (NeurIPS'22) as a pioneering work in multi-class anomaly detection. In response, we have conducted extensive evaluations by integrating... | Summary: The paper introduces the concept of cost volume filtering, combined ideas from stereo matching and depth estimation, into the field of unsupervised anomaly detection. This method addresses the often-overlooked matching noise issue, which is a common challenge in existing AD methods.
Claims And Evidence: The p... | Rebuttal 1:
Rebuttal: **Q1**: The paper is supported by solid claims and motivations. It could be better if the authors could apply the proposed method plug-in design into multiple AD baselines.
**A1**: Thanks! We apply our method to **UniAD** (NeurIPS'22) (**new add**), **GLAD** (ECCV'24), **HVQ-Trans** (NeurIPS'23),... | null | null | null | null | null | null |
Global Optimization with a Power-Transformed Objective and Gaussian Smoothing | Accept (poster) | Summary: The paper studies graduated optimization (aka homotopy methods), and proposes a fairly simple method based on exponentiating the objective and applying Gaussian smoothing. A convergence analysis is provided, as well as numerical experiments.
Claims And Evidence: Essentially the main claim of the paper is that... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments! They are very important for us to improve the manuscript.
# 1. Answers to ``Questions For Authors"
## 1.1 Answer to the first question on the curse of dimensionality
It seems that the classic curse of dimensionality you mentioned applies to the sample comple... | Summary: This paper applies the Gaussian smoothing technique to solve non-smooth optimization problems. With Gaussian smoothing, the original possibly non-smooth function can be transformed into a smoothed problem, and their method also composites the objective function with the power function or the exponential functi... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments! They are very important for us to improve the manuscript.
# 1. An additional assumption is needed if we require an exact convergence to $x^*$
Your intuition is right. Our theory needs an additional assumption to ensure a convergence to the exact point of $x^*... | Summary: This paper deals with global optimization of non-concave functions on compact domains - basically where regular gradient methods get stuck in local optima. The authors introduce a method called Gaussian Smoothing with a Power-Transformed Objective (GSPTO). It works in two steps: first, they transform the objec... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful comments! They are very helpful for us to improve the manuscript.
# 1. Replies to "Questions For Authors"
## 1.1 Guidance on Choosing $N$ and $\sigma$
When tuning, we recommend to start from a moderate $N$, rather than a large $N$, since it increases th... | Summary: This paper presents a new method for global nonconvex optimization, wherein the objective is first re-weighted either via a power-transformation or an exponential transformation, and then Gaussian smoothing is applied. A handful of theoretical analyses are provided for the method (assuming perfect integration ... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful comments! They are very helpful for us to improve the manuscript. As suggested, we will
* include CMA-ES in our experiment for comparisons.
* use the $L_2$-norm of the perturbation $\mu$ as the distance metric instead of $R^2$.
With the above changes,... | null | null | null | null | null | null |
Sample-specific Noise Injection for Diffusion-based Adversarial Purification | Accept (poster) | Summary: This paper focus on the diffusion model-based purification methods. They proposed SSNI to find the optimal $t^{\ast}$ based on Diffpure paradigm. The weakness of the Diffpure is that the robust of the purification depends on the setting of the optimal $t^{\ast}$ i.e., how much Gaussian noise should be injected... | Rebuttal 1:
Rebuttal: **Q1: Contribution of this study**
**R1**: Due to character limits, please refer to **Response to Reviewer ynLT - Q1** where we clarify our contribution.
### Discussion on [1]
We acknowledge that score-based metrics, including EPS [1], are established tools for distinguishing between clean and a... | Summary: This paper proposes a new perspective on diffusion-based purification (DBP) methods. The authors first show the score norms $||\Delta_{x}log\ p_{t}(x)||$ of input samples $x$ are highly related to the noise level of Gaussian noise that should be injected when performing diffusion-based adversarial purification... | Rebuttal 1:
Rebuttal: **Q1: Evaluation with transformer-based classifiers, Diff-PGD, and unrestricted attacks**
**R1**: We have supplemented the transformer-based model and Diff-PGD experiments. **Due to character limits, please see [here](https://shorturl.at/2b8Qs) for results**
Regarding unrestricted attacks: In th... | Summary: This paper examines the problem of choosing a sample-dependent number of forward/reverse diffusion steps to use in diffusion-based purification (DBP) adversarial defense. Prior works typically use a fixed number (e.g. t=100) forward/reverse steps to secure an input before sending it to the classifier. The meth... | Rebuttal 1:
Rebuttal: **Q1: Discussion on performance gain and robust accuracy**
**R1**: We appreciate the reviewer for raising this concern. We'd like to first clarify our contribution and provide a clearer context.
### Contribution
The central goal of this paper (and SSNI) is to achieve a more favorable accuracy-ro... | Summary: This paper presents a method to enhance existing diffusion-based adversarial purification techniques. The authors build on the intuitive idea that adversarial samples with higher noise levels require larger diffusion timesteps for effective purification. To explore this, they analyze the output of the diffusio... | Rebuttal 1:
Rebuttal: **Q1: Evidence of Claim**
**R1**: Thanks for pointing it out. We now show that samples with larger deviation (caused by larger perturbation, leading to higher score norm) need stronger denoising (higher $t^*$).
With a DBP method [1], we assess the robust accuracy of WRN-28-10 against AEs from P... | null | null | null | null | null | null |
HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder | Accept (poster) | Summary: This paper proposes a new 3D Gaussian Splatting (3DGS) compression framework, HybridGS, which combines the advantages of generative and traditional compression methods. It improves the encoding and decoding speeds while ensuring the reconstruction performance.
Claims And Evidence: Please see Other Strengths A... | Rebuttal 1:
Rebuttal: 1). Insufficient experiments
We would like to thank you for positive recommendations. We provide some new results on BD-Rate and PSNR to compare the performance of the methods in consideration. They can be found via
https://drive.google.com/drive/folders/1V1mxZq1IPXz2H0kF6_a7IsP8_iGOLCUu?usp=sh... | Summary: In this work, the authors propose a compression framework for 3DGS. A lightweight decoder $D$ composed of a one hidden layer MLP is introduced to compress original high-dimensional GS features into Low-dimensional latent features $f$ for quantization and compression, where the rate is controlled by adjusting ... | Rebuttal 1:
Rebuttal: 1). Paper writing
We would like to apologize for not motivating this work well and presenting our method clearly. In the revised paper, we shall replace Fig2 with a better illustrated one given in
https://drive.google.com/drive/folders/1CwMbhm4l44oXD5MnCP3slbJHgw_vZ48c?usp=sharing.
Besides, we ... | Summary: This paper aims to compress 3d Gaussians into very small sizes for storage efficiency. The core idea of the proposed HybridGS is to combine traditional point cloud compression method and the generative coding compression method. The most advantage of HybridGS compared to previous 3DGS compression method is it ... | Rebuttal 1:
Rebuttal: 1). Importance of compression speed
Thanks very much for your comments on the importance of compression speed. Please allow us to clarify.
The processing latency of visual media, especially the encoding and decoding time [R1], has become an essential utility factor, considering that 5G network... | Summary: HybridGS aims at the data compression of 3DGS. It takes advantage of both generative compression technique and traditional compression technique by first generating a compact explicit 3DGS representation and then encoding it with a standard point cloud codec. It achieves a higher encoding and decoding speed c... | Rebuttal 1:
Rebuttal: 1). FPS (rendering speed), averaged and complete results
Thanks very much for your suggestions. Please refer to our response to Question 1 of Reviewer rykQ for complete results and newly generated FPS results over different datasets.
2). Essential references
We shall update the reference infor... | Summary: The authors propose HybridGS to compress 3D Gaussian splatting. The method first generates compact 3D Gaussians using dimension reduction, quantization of features, and positions. Then, it uses existing point cloud encoders to further compress the generated 3D Gaussians. The method achieves compression perform... | Rebuttal 1:
Rebuttal: 1). Averaged metrics for the entire dataset
We shall include a table and figures to show the averaged metrics for the entire dataset. The table is given below. The proposed HybridGS exhibits 0.5dB to 1.5dB loss in PSNR compared with HAC and CompGS under the same bitrate.
The figures can be acces... | null | null | null | null |
Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity | Accept (poster) | Summary: The paper discusses the group distributionally robust optimization framework. They propose a new sparsity measurement of the distributions called $(\lambda,\beta)$-sparsity, and show that the dependence on K (the number of distributions) can be reduced to log K.
Claims And Evidence: Yes
Methods And Evaluatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for raising great points regarding the practicality of our $(\lambda, \beta)$-sparsity definition.
While we acknowledge that our Definition 2.3 requires a strong condition that a non-trivial $(\lambda, \beta)$-sparsity holds globally for all $\theta \in \Theta$, we would lik... | Summary: This paper studies the GDRO problem where a special structural assumption called $(\lambda,\beta)$-sparsity is satisfied. This assumption requires that for any hypothesis $\theta\in \Theta$, only $\le \beta$ distributions have a "large" risk, which is quantified by the parameter $\lambda$.
1. Given any $\lamb... | Rebuttal 1:
Rebuttal: We thank the reviewer for their critical feedback. Please find our clarifications to your concerns below.
# 1. The techniques in Algo 1 looks relatively straightforward
We understand that the algorithm may look straightforward because it takes in a given $\lambda$ and thus can focus solely on co... | Summary: This paper considers a practical setting of the group DRO problem called (lambda,beta)-sparisty, which means for any parameter there is a set of at most beta groups whose risks are all at least lambda larger than the risks of other group. By taking this condition into account, the authors can derive sharper co... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time on our paper. Please let us know if you have any questions.
---
Rebuttal Comment 1.1:
Comment: I thank the authors for their efforts in addressing reviewers' concerns. I have read those comments and confirm that I am, in general, satisfied with the contributi... | Summary: In this paper, the authors revisit the problem of an optimization framework where a single hypothesis is chosen to handle a group of K risks associated with K data distributions - this framework is known as GDRO. While minimax rates have been established for this problem already, the authors provide a finer-gr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and interesting questions, especially the one on $(\lambda, \beta)$-sparsity for linear regression. Please find our answers to your questions below.
# 1. $(\lambda, \beta)$-sparsity for linear regression with linear Gaussian model.
For the linear Gaussian m... | null | null | null | null | null | null |
Reinforce LLM Reasoning through Multi-Agent Reflection | Accept (poster) | Summary: This paper introduces DPSDP (Direct Policy Search by Dynamic Programming), a algorithm designed to enhance the reasoning abilities of large language models by utilizing a multi-agent system. This paper concludes that DPSDP provides a robust solution for refining reasoning tasks in LLMs, allowing them to genera... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper!
# Weaknesses
**Q: DPSDP does not resemble an RL algorithm**
We adapt our algorithm from PSDP, a classic reinforcement learning method, and formulate iterative refinement as a standard MDP (Section 2). As shown in Algorithm 1, we optimize each step in reverse via ... | Summary: This paper proposes a new reinforcement learning algorithm, DPSDP, to enhance the mathematical reasoning capabilities of large language models using a multi-agent approach involving an actor and a critic. The method instantiates two LLMs as actors and critics to perform self-reflection-style reasoning, collect... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper!
# Claims And Evidence
**Q: Analysis is not highly related to the practical algorithm**
We provide further analysis on how approximation in the practical algorithm affects the theoretical results in reply to Reviewer Hay1.
**Q: More detailed metrics and failure... | Summary: - The focus of the paper is on verification and refinement with an actor and critic model, using a method that trains on self-generated data
- the actor model generates and refines responses based on feedback from a critic
- the actor and critic are jointly trained with RL
- The authors propose a dynamic pro... | Rebuttal 1:
Rebuttal: Thanks for the efforts in reviewing our paper! We will take your suggestions, fix the typo and revise the presentations accordingly in the next revision!
# Methods And Evaluation Criteria
**Q: unclear how $a_1$ and $a_2$ are labeled as chosen and rejected actions**
Algorithm 1 presents the theo... | Summary: This paper introduces DPSDP (Direct Policy Search by Dynamic Programming), a reinforcement learning algorithm for training multi-agent LLM systems to iteratively refine responses on reasoning tasks. The authors formulate the multi-turn refinement process as a Markov Decision Process with an actor that generate... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper!
# Theoretical Claims
**Q: Analysis of practical algorithm**
We analyze the Q-value approximation in practical DPSDP, where only one feedback and refinement step is used during training (Algorithm 2), assuming $H=3$. Let $\hat{\pi}$ be the resulting policy, and le... | null | null | null | null | null | null |
Unified (Semi) Unbalanced and Classic Optimal Transport with Equivalent Transformation Mechanism and KKT-Multiplier Regularization | Reject | Summary: This paper presents a new approach to the Semi-Unbalanced Optimal Transport (SemiUOT) problem by determining the marginal probability distribution using the Equivalent Transformation Mechanism and extends it to the Unbalanced Optimal Transport (UOT) problem. To improve matching accuracy, the authors introduce ... | Rebuttal 1:
Rebuttal: + Comment 1: The time complexity is not given in the convergence rate.
Response 1: The computation complexity of ETM-Approx is $O(NM\log (1/\varepsilon_a))$ where $\varepsilon_a$ denotes the error tolerance (e.g., $ε_a = || \hat{f} - \hat{f}_o||_∞$ in SemiUOT and $ε_a = || \hat{u} - \hat{u}_o ||_... | Summary: The paper introduces a new method called the Equivalent Transformation Mechanism (ETM) that computes Unbalanced Optimal Transport (UOT) and Semi-Unbalanced Optimal Transport (SemiUOT) problems without relying on entropy regularization. The key idea is to compute the final marginal distributions explicitly thro... | Rebuttal 1:
Rebuttal: + Comment 1: The time complexity is not provided.
Response 1: The computation complexity of ETM-Approx is $O(NM\log (1/\varepsilon_a))$ where $\varepsilon_a$ denotes the error tolerance (e.g., $ε_a = || \hat{f} - \hat{f}_o||_∞$ in SemiUOT and $ε_a = || \hat{u} - \hat{u}_o ||_∞$ in UOT, $\hat{f}_o... | Summary: The paper proposes an approach of transforming the Unbalanced and Semi-unbalanced Optimal Transport (UOT/SUOT) problem into the classical OT problem. It is done by finding a scheme for proper reweighing of the marginal distributions. After this, the authors propose an approach for solving the discrete UOT/SUOT... | Rebuttal 1:
Rebuttal: + Comment 1: The differences/novelty between this paper and Theorem 3.3 in [Choi] should be highlighted.
Response 1: Theorem 3.3 in [Choi] and our proposed ETM differ significantly in several aspects:
(1) Theorem 3.3 mainly considers the continuous case and does not involve the translation inva... | null | null | null | null | null | null | null | null |
Earley-Driven Dynamic Pruning for Efficient Structured Decoding | Accept (poster) | Summary: LLM can be equipped with a grammar verifier which verifies next token prediction at each step to satisfy grammatical constraints. The key step is to incremental update the grammar state in the parsing algorithm to output the possible next tokens. Given a general form of grammar (CFG), the paper leverages Earle... | Rebuttal 1:
Rebuttal: ## Q1: Although cache optimization is proposed in XGrammar (2024) a detailed comparison to compare the two is missing.
We appreciate the reviewer's concern. Section 4.3 addresses this comparison. Both methods categorize tokens into Context-Independent and Context-Dependent types, but we don't use... | Summary: This paper is about a novel method for grammar constrained decoding. Grammar constrained decoding poses many challenges to auto-regressive language model decoding, and as such a primary concern is to make it more efficient. This paper presents Formatron, an algorithm which keeps track of which states are still... | Rebuttal 1:
Rebuttal: ## Q1: In order to enhance clarity of Section 4, it would be useful to also include pseudocode.
We appreciate the reviewer's valuable suggestion regarding Section 4. We agree that including pseudocode would enhance the clarity of this section. We will incorporate pseudocode in the revised manuscr... | Summary: This paper proposes using the Earley parsing algorithm to speed up constrained decoding (e.g., for requiring output to be valid json). While existing methods for constrained decoding require looping over all tokens in the model vocabulary to generate the "mask" which determines which tokens are vs are not allo... | Rebuttal 1:
Rebuttal: ## Q1: I think the background necessary for understanding this paper, as well as the core method, could be better explained, I had a hard time understanding it.
Thank you for this important feedback about the paper's accessibility. We acknowledge that the background and core methodology of our pa... | Summary: This paper proposes ZapFormat, a dynamic pruning strategy that extends the Early algorithm for CFG parsing by eliminating invalid or redundant states. ZapFormat can improve inference speed of LLMs in constrained decoding.
Claims And Evidence: The claims are clear and the evidence is convincing.
Methods And E... | Rebuttal 1:
Rebuttal: ## Q: I wonder if they are just the throughput of parsing & masking
Thanks for raising this important point on result presentation. We clarify that the throughput results in our paper indeed only reflect the parsing and mask generation stages, not the entire pipeline. We isolated these components... | null | null | null | null | null | null |
Self-supervised Masked Graph Autoencoder via Structure-aware Curriculum | Accept (spotlight poster) | Summary: This paper studies self-supervised learning on graphs. The authors introduce an interesting strategy that structures the training of masked graph autoencoders in a progressive manner, allowing the model to learn more effective node representations for predictions. A key component of their approach is a difficu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We addressed all the comments. Please kindly find the detailed responses to the comments below.
**W1:** Motivation and how to set the hyperparameter $\lambda$.
Thanks for your comment. We would like to clarify that $\lambda$ is a coefficient to co... | Summary: In summary, the paper focuses on proposing a masked graph autoencoder enhanced with curriculum learning techniques. It formally defines a measure of edge difficulty to quantify reconstruction challenges, introduces a self-paced mask scheduler for progressively incorporating edges based on their difficulty, and... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We addressed all the comments. Please kindly find the detailed responses to the comments below.
**W1:** Comparison with adversarial training.
Thank you for this comment. Graph adversarial training is a learning paradigm that aims to improve model ... | Summary: This paper introduced a novel masked graph autoencoder with a structure-aware curriculum learning strategy. The key idea was to mask edges in an easy-to-hard manner, improving representation learning. The joint framework to recover the missing edge of the input based on the unmasked graph structure and schedul... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We addressed all the comments. Please kindly find the detailed responses to the comments below.
**W1.1:** The caption of Figure 3.
Thanks for this suggestion. We would like to revise the caption of Figure 3 into "Visualization of the synthetic d... | Summary: The authors explore generative graph self-supervised learning by integrating curriculum learning into a masked graph autoencoder framework. The innovation lies in introducing a structure-aware curriculum strategy that trains the model from easy to hard reconstruction tasks. Specifically, they propose a complex... | Rebuttal 1:
Rebuttal: **W1:** Missing key notations in Figure 1.
Thank you for this comment. We will add the key notations, e.g., $\mathcal{E}_{mask}$, $\lambda$, and $\omega$ as well as the legend in Figure 1.
**W2:** Standard deviations of the empirical training time.
We have updated the Table 7 to report the av... | null | null | null | null | null | null |
Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates | Accept (poster) | Summary: The authors propose a neural PDE surrogate solver that respects the rotation and reflection equivariance (via p4/p4m symmetry groups) and enforces physical conservation principles. Their approach is designed for scalar and vector field magnitudes on staggered grids, leveraging a modern U-Net architecture with ... | Rebuttal 1:
Rebuttal: We appreciate the careful reading, positive assessments and constructive feedback.
> comparisons to other equivariant models (e.g., [Wang et al., 2020]) are missing.
We now compare to the equivariant network of Wang et al., 2020 on our simulation-based INS task, [updating fig. 4](https://tinyur... | Summary: The paper explores how incorporating symmetric constraints and physical priors can improve predictions within the same base architecture. Specifically, it investigates the effects of integrating additional symmetry equivariance into convolutions—such as rotation and reflection—in combination with conservation ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful evaluation and appreciate the positive assessments therein.
We have revised the manuscript to incorporate the real-world dataset from Wang et al. 2020, and added a new [figure](https://tinyurl.com/3u4a8wnu) and [table](https://tinyurl.com/mvz9u7f6). Similar t... | Summary: The authors propose new input layers that can add inductive symmetry and conservation-law biases to neural PDE solvers to improve their performance in long-term rollouts. The main innovation of the work seems to be the ability to accommodate staggered grids commonly found in CFD. Other than this, the novelty c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our work and the recognition that we used challenging tasks.
We disagree, however, that the novelty component of the work is low overall. It is certainly true that the methods we introduce, equivariant input and output layers for staggered grid... | Summary: This paper propose to integrate rotation symmetry of staggered grid into PDE surrogate models. Additionally, the models also encode physics constraints in the network readout. The experiments are conducted on closed shallow water equations and decaying turbulence.
Claims And Evidence: - The motivation of usin... | Rebuttal 1:
Rebuttal: We appreciate the careful reading and constructive feedback.
> technical challenges of extending group CNN to staggered C-grid not sufficiently highlighted
We agree and have revised and expanded the last sentence of the paragraph labeled "Staggered Grids" in sec. 2 to read as follows:
"However... | null | null | null | null | null | null |
FDGen: A Fairness-Aware Graph Generation Model | Accept (poster) | Summary: The authors propose FDGen a novel method for fair graph generation. The authors investigate the bias sources in graph generation, then consequently define regularization terms to promote fair graph generation by mitigating both the structural biases and node feature biases.
## update after rebuttal
My main co... | Rebuttal 1:
Rebuttal: We thank Reviewer Pk2M for the time and thorough review. Below are our detailed responses:
**The proposed method performs roughly similar to comparison methods across all metrics and benchmark datasets.**
Our proposed method outperforms baselines with dissimilar performances. Specifically, in the... | Summary: This paper proposes FDGen, a fairness-aware graph generation model that mitigates both structural and feature biases by introducing a fair regularizer and a diffusion-based framework to ensure fairness while preserving graph generation quality. Experiments on four real-world datasets show that FDGen outperform... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer NfyT for the detailed review and positive assessment of our work. We are particularly grateful for your recognition that FDGen is, to your knowledge, the first work addressing feature bias in graph generation, this was indeed a primary motivation for our research. We ap... | Summary: The authors address fairness in graph generation problems, where fairness is meant as a trustful replication of the original graph that can then be used to train ML algorithm for automated decision making (e.g. credit scores). Their algorithm takes into account fairness both at the feature level and at the str... | Rebuttal 1:
Rebuttal: We appreciate Reviewer LnUo's thoughtful feedback and have provided responses below.
**If you need to have access to the original graph why don't you directly use it for training? And if not, how can you replace it?**
The original graph is not always suitable for training, so the generated graph... | Summary: The authors propose a diffusion-based framework for fair graph generation that addresses both structural bias and feature bias within the generated graphs. Guided by theoretical analysis, which identifies how biases arise in the generation process, the framework applies a novel fairness regularizer to disentan... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer sM9W's thorough review and have provided detailed responses below.
**Pre-defined sensitive attributes:** Our approach follows the standard convention in fairness research where sensitive attributes are predetermined based on legal frameworks and specific applicati... | null | null | null | null | null | null |
Multinoulli Extension: A Lossless Yet Effective Probabilistic Framework for Subset Selection over Partition Constraints | Accept (poster) | Summary: The paper introduces a novel algorithm called Multinoulli-SCG for solving the subset selection problem under partition constraints, particularly focusing on close-to-submodular objective functions. The core of the Multinoulli-SCG algorithm is an innovative continuous-relaxation framework named Multinoulli Exte... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful reviews. We sincerely appreciate the time and effort you have dedicated to reviewing our manuscript. Your feedback is invaluable to us. Below, we will respond to the concerns you have raised in **Weaknesses**.
------------------------
**Weaknesses**:
-... | Summary: The paper considers maximization of a monotone close-to-sumodular objective $f$ over a partition matroid, where the notions of approximate submodularity considered are weak DR-submodularity and weak submodularity. The authors introduce a novel continuous extension for this problem, called Multinoulli Extensio... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful reviews. We sincerely appreciate the time and effort you have dedicated to reviewing our manuscript. Your feedback is invaluable to us. In the following, we will address the concerns you have raised in **Questions**.
---
**Questions**:
---
>**Q3:Why c... | Summary: This paper considers the problem of subset selection subject to partition constraints. The objective is not fully submodular, but instead displays some degree of submodularity, e.g. is weakly submodular. Existing work on this problem relies on distorted local search methods, but these works have some shortcomi... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and constructive feedback. We are grateful for the time and effort you have dedicated to reviewing our manuscript. In what follows, we will address some of your concerns in **Questions** and **Weaknesses**.
--------------
**Weaknesses**:
------------... | null | null | null | null | null | null | null | null |
Large Language Models to Diffusion Finetuning | Accept (poster) | Summary: This paper proposes fine-tuning pretrained large language models (LLMs) using diffusion models to enable scalable test-time computation. By framing LLMs as single-step diffusions and introducing a small fraction of new parameters, the approach enhances multi-step reasoning, allows adaptive computational scalin... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback and the time they dedicated to our review.
**Claims and Evidence**
In our [Table 1](https://anonymous.4open.science/r/rebuttal_l2d-4B0B/table1.png) results, across all 24 task/model combinations examined, our full weight finetuning and LoRA ... | Summary: This paper provided a novel perspective that treats language mode(LM) as a one-step diffusion model(DM). Thus, it proposes increasing the number of diffusion steps to boost the average score of the language model in test-time compute scaling. The methods show significant improvement in LMs in math, coding, and... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback and the time they dedicated to our review.
**Questions**
“*It is noticeable that in Coding-MBPP and GeneralKnowledge-MMLU tasks the performance is less than the LoRA fine-tuning or initial models. Is there any hypothesis for that?*”
While L... | Summary: Authors provide a framework to combine the autoregressive LLM with diffusion models, to scale test-time compute in language reasoning tasks.
Diffusion models are primary designed for continuous domains, with few exceptions of categorical diffusion models, but primarily designed for continuous domain.
Autho... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback and the time they dedicated to our review.
**Experiments for test-time inference**
Since L2D scales inference with a separate new "diffusion path," we think it should be viewed as orthogonal to prior scaling approaches based on hand-designed... | Summary: The paper introduces L2D, a method that integrates the scaling properties of diffusion models into pre-trained language models (LMs) to enhance reasoning skills and computational scalability. L2D improves pre-trained LMs on math, coding, and various reasoning tasks, outperforming LoRA and full fine-tuning meth... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback and the time they dedicated to our review.
**Experiments**
We added clarifications and experiments to our revised work to address the reviewer’s concerns. We hope these will strengthen the argument that L2D is a novel orthogonal method, high... | null | null | null | null | null | null |
Temporal-Difference Variational Continual Learning | Reject | Summary: This paper proposes n-step generalisation of the classical variational continual learning (VCL) framework, which aims at addressing the potential variability and subsequent compounding approximation error in regularising the KL-divergence between the current posterior approximation and the immediate preceding ... | Rebuttal 1:
Rebuttal: Thank you for the review! We appreciate that you found our work **well-written and clear**, the **empirical results supportive**, and our **introduced benchmarks a potentially valuable contribution**. You raised great questions, which we address below:
**Q1** Are Eqs 2 and 3 equivalent? Is Eq 3 a... | Summary: The paper introduces a new variant of variational continual learning that integrates ideas from temporal-difference (TD) methods to mitigate error accumulation across tasks. Instead of regularizing solely against the immediately preceding posterior as in standard VCL, the proposed method uses multiple past pos... | Rebuttal 1:
Rebuttal: Thank you for your review! We appreciate the **recognition of our theoretical derivations, extensive hyperparameter settings, and ablation studies**. We're also grateful that you found our work **innovative in combining TD learning ideas with Variational Continual Learning** and **demonstrating a ... | Summary: The current work tackles on continual learning, suggesting a new Bayesian CL
approach. The paper proposes a rewriting of the standard variational continual
learning objective that considers a number of past posterior approximations. The
authors hypothesize explicit regularisation using previous posterior estim... | Rebuttal 1:
Rebuttal: Thank you for your review! We appreciate that you recognized our contributions (**in formalism and empirical validation**), **found our ablations convincing**, and **proposed benchmarks detailed and justified**. We aim to comment/clarify some of the raised points below:
**Q1** Adoption of previou... | null | null | null | null | null | null | null | null |
Understanding the Forgetting of (Replay-based) Continual Learning via Feature Learning: Angle Matters | Accept (poster) | Summary: The paper develops a unified theoretical framework for understanding catastrophic forgetting in continual learning through the lens of feature learning. The authors focus on a two-layer convolutional neural network with a polynomial ReLU activation function that is trained sequentially on binary classification... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We address your questions and concerns as follows.
> **Q1. The current analysis focuses on binary classification with two-layer CNNs. Could you elaborate on how the framework might extend to multi-class classification or deeper architectures? A clear discussi... | Summary: The authors propose a theoretical analysis of catastrophic forgetting in the two class setup for two layer convolutional neural networks, with polynomial RELU activations. They prove that for rehearsal free CL, forgetting is significant when the angle is between the new task and previous task is small enough. ... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We address your questions and concerns as follows.
> **Is the grey area a significant forgetting region ?**
The grey area is a region for uncertainty, either harmful or benign forgetting, ensuring that our claims remain rigorous. The yellow area is for harmf... | Summary: The paper develops a theoretical framework for understanding continual learning (CL) and catastrophic forgetting using a two-layer polynomial ReLU CNN. It focuses on how the angle between two tasks’ “signal vectors” (representing core features for each task) influences forgetting: if the angle is acute or only... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We address your questions and concerns as follows.
> **Q1. Your theory focuses primarily on two binary tasks. Could you outline how you would expect the angle-based framework and replay analysis to extend if there were multiple sequential tasks or multi-class... | Summary: The paper provides a mathematical framework of forgetting in continual learning, for the specific case of a two-layer convolutional neural network with polynomial ReLU activation. The authors show that replay has the effect of increasing the range of settings under which forgetting is limited. Based on their a... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We address your questions as follows.
> **Q1. How the mid-angle sampling strategy relates to the theoretical theorems**
We sincerely appreciate the opportunity to clarify the connection between our theoretical findings and mid-angle sampling. Our theoretical... | null | null | null | null | null | null |
SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering | Accept (poster) | Summary: This paper introduces SyncMind, a framework designed to analyze and measure how AI agents (specifically LLMs) handle “out-of-sync” challenges in collaborative software engineering (CSE). The out-of-sync problem arises when multiple collaborators modify a shared codebase at different times, causing one collabor... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We are honored that you find our work to be well-organized and well-written. Your kind suggestions, such as generalizability, granular analysis, budget profiles, and user studies also provide constructive insights that we would like to take into consideration ... | Summary: This paper introduces SyncMind, a framework that systematically defines the ``out-of-sync'' problem in collaborative software engineering in an agentic context, where an agent's belief state ($B_k$) diverges from the actual world state ($S_k$). Based on this framework, the authors create SyncBench, a benchmark... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We are honored that you find our work to be well-designed. Your kind suggestions, such as backbone diversity, related work, human validation, and figure settings, also provide constructive insights that we would take into consideration in our revision.
1. We ... | Summary: The paper tackles the challenge of out-of-sync collaboration, where an automated agent powered by LLM encounters errors due to a state change of the underlying codebase. The primary contributions of the paper are SyncMind, a framework for defining, identifying, and evaluating such issues, and SyncBench, a benc... | Rebuttal 1:
Rebuttal: Thank you for your valuable review. We are honored that you find our work to be novel and well-written. Your kind comments on generalizability and human-in-the-loop experiments also provide constructive insights that we would like to include in our revision.
1. We measure question quality based o... | null | null | null | null | null | null | null | null |
PoisonedEye: Knowledge Poisoning Attack on Retrieval-Augmented Generation based Large Vision-Language Models | Accept (poster) | Summary: This paper presents a knowledge poisoning attack targeting MuRAG systems used for vision language models.
It introduces a method to manipulate MuRAG system outputs by injecting poisoned image-text pair into the multimodal knowledge base.
This work extends the textual RAG attack to multimodal setting showing th... | Rebuttal 1:
Rebuttal: We sincerely thank your constructive comments.
> Q1. The retriever part can be improved such as using multimodal retriever (e.g., UniIR).
We conduct additional experiments specifically on the UniIR_CLIP_SF retriever model, which is the best-performing model in the UniIR framework. As the result... | Summary: This paper proposed a poisoning attack against Multi-Modal RAG systems, especially for LVLM RAG systems. The paper formulates the goal as an optimization problem and discusses to solve it in two different settings: Single Query Targeted Attack and Class Query Targeted Attack. Given a target image-text pair, th... | Rebuttal 1:
Rebuttal: We sincerely thank your constructive comments.
> Q1. The ablation study on α, s, and ϵ is missing.
We conduct additional ablation studies on α, s, and ϵ as the table shown below.
| α | RSR-1 | RSR-K | ARD | PSR |
| :---: | :----: | :----: | :----: | :----: |
| 0.1 | 43.01% | 54.79... | Summary: The paper proposes the first knowledge poisoning attack against MuRAG system. The core contribution includes three attack variants (PoisonedEye-B, PoisonedEye-S, PoisonedEye-C) that span single-query and class-query targeted attack.
Claims And Evidence: The claims made in the submission are generally supporte... | Rebuttal 1:
Rebuttal: We sincerely thank your constructive comments.
> Q1: "Class" definition & class query attack perform on captions datasets like COCO
The "class" in this context denotes a group of images that have similar semantic meanings (i.e., close L2 distance on pre-trained encoders like CLIP). For image cl... | Summary: This paper proposes a poisoning attack on Retrieval-Augmented Generation (RAG)-based large vision-language models, enabling the manipulation of outputs for targeted inputs. This is the first study to perform a poisoning attack on a multimodal RAG system. The effectiveness of the two proposed attacks—the single... | Rebuttal 1:
Rebuttal: We sincerely thank your constructive comments.
> This work adapts PoisonRAG [1] from LLMs to VLMs. While the attack concept is similar, VLMs require retrieving image-text pairs from the database, which makes crafting the injected query different.
Thank you for recognizing our contributions and e... | null | null | null | null | null | null |
Enhancing Foundation Models with Federated Domain Knowledge Infusion | Accept (poster) | Summary: This paper proposes an efficient federated fine-tuning approach that enhances out-of-domain generalization. In the proposed framework, each client utilizes a lightweight ViT model, which is trained on local data. Subsequently, data quality scores are computed using synthetic data and transmitted to the server ... | Rebuttal 1:
Rebuttal: We thank the comments and questions.
`>>> W1`
Yes, the MLP layers are optimized during the training. We will clarify it in the final version.
`>>> W2`
We do not share any label information with the server. As introduced in Sec 3.1, clients only share the style information via the text prompt ... | Summary: This paper introduces FedAG, a federated learning method to enhance vision foundation models (e.g., CLIP) by fine-tuning them across distributed domains while preserving data privacy. FedAG employs multiple domain-specific adapters, synthetic data generation via Stable Diffusion, and quality-aware mutual learn... | Rebuttal 1:
Rebuttal: We genuinely appreciate the reviewer’s valuable comments and questions. We would like to address them as follows for your review.
`>>> W1` and `>>> Q1`
Thanks for your constructive comment and question. If we have a dynamic or numerous clients, we will group clients by the domains they belong to... | Summary: This paper introduces a federated learning approach to enhance the capability of foundation models to handle in-domain and out-of-domain tasks. In particular, the authors designed quality-aware in-domain mutual learning and attention-based cross-domain learning to capture the knowledge effectively. In this pap... | Rebuttal 1:
Rebuttal: We genuinely appreciate the reviewer’s valuable comments and suggestions. We would like to address them as follows for your review.
`>>> W1`
In our main experiment, the amount of the synthetic data is equal to 10% of the real data for each domain. To further examine how the amount of synthetic d... | Summary: This manuscript addresses the challenge of fine-tuning large-scale vision-language models in a federated learning setting under domain shifts. The authors propose FedAG (Federated Adapter Generalization), a method that introduces multiple domain-specific adapters to capture heterogeneous domain knowledge while... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s constructive suggestions and comments. We would like to reply respectively as follows.
`>>> Response to Other Comments Or Suggestions`
Thank you for the feedback. We will follow your suggestion to improve the figure and fix the typos in the final version of... | null | null | null | null | null | null |
Efficient Multi-modal Long Context Learning for Training-free Adaptation | Accept (poster) | Summary: While current popular adaptation for MLLMs heavily replies on fine-tuning, the paper proposes a novel training-free alternative that can embed demonstration examples directly into the model input. Due to the lengthy inputs might bring computational and memory overhead, the proposed method contributes a chunk-w... | Rebuttal 1:
Rebuttal: **Q1:** Disparity between the pre-trained MLLM dataset and adaptation tasks. In [1], the paper demonstrates that task disparity might have a huge impact on vision task adaptation; would this be the case for in-context learning of MLLM?
**A1:** We appreciate the reviewer’s insightful question on t... | Summary: This paper introduces EMLoC(Efficient Multimodal Long Context Learning), a training-free method to embed examples directly into the model input. It is implemented via layer-wise adaptive pruning. The authors first separate the context into chunks to prune tokens by importance measured with Jenson-Shannon diver... | Rebuttal 1:
Rebuttal: **Q1:** There are too many hyperparameters (retention ratio, JS threshold) that need to be found heuristically.
**A1:** Thanks for the comments. Those parameters have clear meanings and are easy to adjust. For a high compression ratio, we can set a smaller retention ratio and a higher JS threshol... | Summary: Following the improvements brought by in-context examples in multi-modal LLMs, the context length compression has becomed a hot topic to make the technique more scalable.
This paper tackles the challenge by introducing layer-wise adaptive pruning, it also provides theoretical justification by doing this layer... | Rebuttal 1:
Rebuttal: **Q1:** Each chunk contains several examples as shown in experiment details. How does the author ensure each example has the same length?
**A1:** Thank you for your comment. In ImageNet100, 200 multi-modal examples are evenly divided into 10 chunks. Each image (224×224) is encoded into approxima... | Summary: This paper introduces Efficient Multi-Modal Long Context Learning (EMLoC), a training-free approach that embeds many demonstration examples into large multi-modal inputs, then uses chunk-wise compression and layer-wise adaptive pruning to reduce the resulting key-value cache. By enforcing a Jensen–Shannon dive... | Rebuttal 1:
Rebuttal: **Q1:** Comparison with other multi-modal in-context learning methods in Table 1.
**Table R1.1: Comparison with multi-modal in-context learning methods**
| Method | ImageNet100 | MME-RW | OK-VQA |
| ------ | ----------- | -------- | -------- |
| MTV | 32.7 | 27.8 | - |
|... | null | null | null | null | null | null |
TableMaster: A Recipe to Advance Table Understanding with Language Models | Reject | Summary: The paper presents TableMaster as a framework aimed at improving table understanding based on large language models. The authors identify four key challenges in table-based reasoning, including (1) Difficulty in locating target data (LLMs struggle to find relevant parts of large tables), (2) Deficiency in tabl... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review and address your concerns below:
---
> **[W1]** Verbalization for originally-attached textual context
We conducted ablation experiments on the originally-attached textual context in FinQA [1], which uses two GPT models to conduct end-to-end direct ... | Summary: The authors introduce TABLEMASTER, a recipe and comprehensive framework that integrates multiple solutions to overcome the obstacles in table understanding. The obstacles are:
1) difficulty in locating target data
2) deficiency in table semantics
3) numerical inaccuracies in textual reasoning
4) semantic i... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review and address your concerns below:
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> **[W1]** How TableMaster fares when conducting table normalization.
The datasets TabFact, WikiTQ, and FetaQA are all clean, normalized tables, which is why we did not need to apply table normalization to these... | Summary: The paper introduces TableMaster, a comprehensive framework designed to improve language models' ability to understand tabular data. The authors identify four key challenges in table understanding: difficulty locating target data, deficiency in table semantics, numerical inaccuracies in textual reasoning, and ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review and address your concerns below:
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> **[W1]** Lack of detailed analysis demonstrating how each specific challenge is addressed.
TableMaster is a recipe framework for table understanding. We provide an analysis of each component of TableMaster in ... | Summary: This paper presents TableMaster, a framework enhancing LLMs' table understanding. It addresses four key challenges: data localization, semantic deficiency, numerical inaccuracies, and inflexible symbolic reasoning. TableMaster integrates table-of-focus, verbalization, program-aided reasoning, and adaptive reas... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review and address your concerns below:
---
> **[W1]** Concern about the difficulty in locating target data.
There are still large tables in TabFact & WikiTQ (tables with 518 rows or 10k+ tokens). The BIRD [1] dataset is essentially a text-SQL task for mu... | null | null | null | null | null | null |
Logarithmic Regret for Online KL-Regularized Reinforcement Learning | Accept (poster) | Summary: This paper studied online RL with KL regularization, and proposed an optimism-based algorithm for contextual bandits and RL. The paper showed that the regret scales logarithmic with the number of iterations, demonstrating the superiority of KL regularization used in RL. Particularly, the paper developed a new ... | Rebuttal 1:
Rebuttal: # Response to Reviewer cCx6
Thank you for your strong support!
**Q1** In the claim of achieving logarithm regret (e.g. remark 4.2), it is better to explain it is because the optimal policy is the one that maximizes the KL-regularized objective, rather than standard objective (cumulative return)... | Summary: Short summary: the authors propose a KL-regularized contextual bandit algorithm, and show it achieves logarithmic regret using a fine-grained analysis of the sub-optimality gap. They then extend this analysis to KL-regularized Reinforcement Learning by reducing to the bandit setting. The resulting KL-regulariz... | Rebuttal 1:
Rebuttal: # Response to Reviewer yuSc
Thank you very much for your strong support!
**Q1** It would have been interesting to see computational resource considerations in the analysis of KL-LSVI. For example, in RLHF applications, reward models can be large-scale neural networks. In this case, optimizing a ... | Summary: This paper considers the problem of online KL-regularized contextual bandits and MDPs and proposes two provably efficient algorithms with logarithmic regret bounds, improving over the typical $O(\sqrt{T})$ regret bounds.
The key idea is a refined value/policy decomposition technique for the bandits/MDPs with ... | Rebuttal 1:
Rebuttal: # Response to Reviewer TRX2
Thank you for your strong support!
**Q1** In page 4, it is stated that "Without loss of generality, we assume that the function class has finite cardinality" with a reference to a ~500 page book. How should the proof be modified to handle the infinite case? In order t... | Summary: The authors noted that the theoretical differences between KL-regularized reinforcement learning (RL) and standard RL have not been thoroughly explored. Recent studies analyzing KL-regularized objectives in decision-making either revert to traditional RL settings or depend on strong coverage assumptions. In th... | Rebuttal 1:
Rebuttal: # Response to Reviewer SUKH
Thank you for your insightful comments! We address your questions as follows.
**Q1** The two proposed algorithms are said to achieve regret bounds that scale logarithmically with the number of rounds, T. To support these claims, empirical validations should be conduct... | Summary: The paper presents new high-probability regret bounds for entropy-regularized contextual bandits and finite-horizon reinforcement learning. Concretely, the authors show that the regret bound is logarithmic in the time horizon, which improves asymptotically on existing bounds.
Claims And Evidence: The main cla... | Rebuttal 1:
Rebuttal: # Response to Reviewer b9cv
Thank you for your positive feedback! We answer your questions point-by-point.
**Q1** After defining the eluder dimension and providing an example, the authors do not discuss it further. However, the overall regret bound is only logarithmic in $T$ if the eluder dimens... | null | null | null | null |
Mixture of Experts Made Intrinsically Interpretable | Accept (poster) | Summary: The paper proposes a novel method for intrinsically interpretable LLMs, called MoE-X. It's goal is to achieve better interpretability than sparse autoencoder by leveraging mixture of experts and providing sparse explanation without polysemanticity of activations. To do so there is a proposal to use wide and sp... | Rebuttal 1:
Rebuttal: We sincerely thank R-nXvb for their valuable questions!
`>>> Q1`**Evaluation Metrics**
`>>> A1`The reviewer noted that `metrics for interpretability is perplexity only`. We appreciate the feedback, but would like to clarify two critical points
1. **Misconception About Perplexity**: Perplexity ... | Summary: The paper presents MoE-X, a novel Mixture-of-Experts (MoE) architecture designed to enhance the interpretability of large language models (LLMs) while maintaining competitive performance. The authors explore the challenge of polysemanticity in neurons and its relationship to the model's architecture. They addr... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer tfxC's suggestions. We have carefully incorporated them into the revised manuscript.
`>>> Q1`**Related Work on MoE**
`>>> A1`We truly appreciate R-tfxC for bringing the ReMoE [1] paper on sparse MoE to our attention. We will definitely include a citation in the r... | Summary: The paper introduces MoE-X, a Mixture-of-Experts (MoE) language model designed to be intrinsically interpretable. This is different from the recent trend of using Sparse Autoencoders to interpret the model representations at post-doc. The proposed method addresses the challenge of polysemanticity in large lang... | Rebuttal 1:
Rebuttal: We truly thank the R-K7C2 for the nice comments.
`>>> Q1`**Additional Application Scenarios**
`>>> A1`We sincerely appreciate the suggestion! Expanding to the medical domain is valuable, but a key challenge is finding a benchmark that **evaluates both interpretability and performance**. While ma... | Summary: This paper proposes a variant of an MoE architecture called MoE-X that makes design decisions that boosts the mechanistic interpretability of the model, while largely preserving quality. The authors authors motivate this with a preliminary study on the importance of MLP hidden size and sparsity of hidden activ... | Rebuttal 1:
Rebuttal: We sincerely appreciate R-a7ZR's thoughtful comments and suggestions.
`>>> Q1`**Evaluation Setup and Additional Experiments**
`>>> A1`We truly appreciate the suggestion! As R-a7ZR mentioned, our primary focus is on **interpretable model design**. In line with this goal, we use small-scale datas... | null | null | null | null | null | null |
Targeted control of fast prototyping through domain-specific interface | Accept (poster) | Summary: This paper proposes an LLM-based approach to translating designer's language to CAD language. The authors propose the approach of first translating the designer's instructions into an intermediate language called modeling language, and then translating the instruction in modeling language to CAD. To achieve th... | Rebuttal 1:
Rebuttal: > Is there any way to automatically benchmark the effectiveness of your method?
Thanks for the comment. We would like to clarify that we aim to directly assess the targetedness of each individual design instructions, e.g., "make the spout narrower". This measure is somewhat subjective, as designe... | Summary: This paper proposes a systematic procedure that maps human designers' high-level modeling requirements to a domain-specific language that can be executed by software to render modeling prototypes more aligned with human intentions. By recognizing and mitigating the gaps between designer's language and modeling... | Rebuttal 1:
Rebuttal: > The authors have not provided a Related Work section in the main paper to present an overview of the studied topic.
Thanks for pointing this out. Now we incorporate a discussion contextualizing our work within the domain of targeted control in fast prototyping.
Fast prototyping is a key proces... | Summary: This paper addresses the challenge of creating intuitive interfaces for industrial designers to control 3D prototype models using natural language rather than complex modeling commands. The paper seems to identify several gaps between "designers' language" and "modeling languages". For instance, designers may ... | Rebuttal 1:
Rebuttal: > The benchmarking metrics seemed like proxies to what would really matter?
Thanks for the question. Fast prototyping allowing designers to explore brainstormed ideas without elaborating their instructions into modeling engineers' language. Our approach is explicitly two-stage, with our proposed... | Summary: This paper aims to address the problem of bridging industrial designers’ intuitive language and the precise modeling language of CAD modeling engines for fast prototyping. The authors introduce an interface (a domain-specific intermediate language) that translates designers’ natural-language instructions into ... | Rebuttal 1:
Rebuttal: > I'd happy to raise my score if the authors could expand on what makes the proposed method work better than the other baselines, especially why it's able to beat LLM based approach by significant margin.
> It's not immediately clear to me why this method is the obvious approach over all other p... | null | null | null | null | null | null |
Targeted Unlearning with Single Layer Unlearning Gradient | Accept (poster) | Summary: This paper addresses the computational challenge and performance degradation often associated with machine unlearning methods, proposing an efficient technique called Single Layer Unlearning Gradient (SLUG). Instead of extensive updates across the entire model, SLUG strategically updates only a single critical... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. Below, we address each point raised:
## Impact of unlearning on semantically similar objects
Our method, SLUG, is designed to address precisely this concern by balancing unlearning effectiveness with utility preservation. We identify the most c... | Summary: This paper proposes Single Layer Unlearning Gradient (SLUG), a technique for targeted unlearning in large-scale multimodal models by updating only a single critical layer using one gradient computation. The authors demonstrate its efficiency for CLIP, Stable Diffusion, and Vision-Language Models, claiming robu... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful comments and constructive feedback on our paper. We address each of the concerns below:
## Layer Selection Assumption
The reviewer questions whether updating a single critical layer is sufficient/scalable for larger models. Our empirical results... | Summary: The authors propose a novel (saliency-based) unlearning method called SLUG that identifies a single layer in the model and performs only a single update step in this layer to minimize negative side-effects on the model’s utility. Compared to related works such as SalUn [1], they assign values to each layer bas... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments. We address the concerns as following:
## SOTA Results on UnlearnCanvas
Thank you for your suggestion of reporting a unified summarizing metric (e.g., harmonic-like mean).
To unify the scores of different metrics, we use the mean Gap Ratios (GP),... | Summary: This paper introduces SLUG, an efficient targeted unlearning method that aims to remove specific unwanted information from large-scale models with minimal computational overhead. Unlike conventional unlearning approaches that iteratively update parameters across the entire model, SLUG identifies a single criti... | Rebuttal 1:
Rebuttal: Thank you for summarizing the strengths of SLUG (computational efficiency and innovative single-layer update approach). We address your concerns below:
## Domain Generalization
While our paper primarily focuses on CLIP, Stable Diffusion, and VLMs, the principles behind SLUG are agnostic to domain... | null | null | null | null | null | null |
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders | Accept (poster) | Summary: This paper introduces SAeUron, a concept unlearning method for text-to-image diffusion models that leverages sparse autoencoder (SAE). The authors first train an SAE on features extracted from the cross‐attention layers and then perform unlearning based on the feature importance scores of specific concepts. Du... | Rebuttal 1:
Rebuttal: Thank you for the positive review of our work, we would like to explain and address the comments and remaining weaknesses with the help of additional tables and figures provided in the anonymized link [Anonymous github](https://anonymous.4open.science/r/saeuron-8D02/saeuron_rebuttal.pdf):
>The pa... | Summary: This paper presents an efficient unlearning framework that leverages sparse auto-encoders to identify relevant features that represent the concepts users want to negate. In previous studies, it was challenging to effectively erase specific concepts while preserving the ability to generate images. This is becau... | Rebuttal 1:
Rebuttal: Link to additional tables and figures: [Anonymous github](https://anonymous.4open.science/r/saeuron-8D02/saeuron_rebuttal.pdf)
> **Nudity evaluation**
We thank the Reviewer for suggesting showcasing SAeUron's strength in the real-world use case of unlearning nudity. To do this, we evaluated our m... | Summary: This paper introduces SAeUron, a novel method for concept unlearning in diffusion models by manipulating intermediate features using Sparse Autoencoders. The Sparse Autoencoder is trained to learn representations where most features have near-zero values, allowing specific concept-related features to be identi... | Rebuttal 1:
Rebuttal: Link to additional tables and figures: [Anonymous github](https://anonymous.4open.science/r/saeuron-8D02/saeuron_rebuttal.pdf)
> **Unlearning of nudity**
To highlight the potential of SAeUron in real-world applications, we extend our study to the evaluation with the I2P benchmark focusing on the... | Summary: The paper proposed a method of unlearning, i.e., erasing concepts as conditional prompts, in diffusion models. The idea is to represent the concept features in a sparse auto-encoder to compress them into low dimension, then modifies the weights of concept-related features after detecting them, leading to modif... | Rebuttal 1:
Rebuttal: Thank you for the positive review of our work, below we would like to explain and address the comments and remaining weaknesses with the help of additional tables and figures provided in the anonymized link [Anonymous github](https://anonymous.4open.science/r/saeuron-8D02/saeuron_rebuttal.pdf):
... | null | null | null | null | null | null |
Efficient Distributed Optimization under Heavy-Tailed Noise | Accept (poster) | Summary: The paper proposes a new optimization algorithm BiClip, for heavy-tailed stochastic optimization. Instead of bounding the upper bound of the gradient, the authors also propose to bound the gradient from below. Combining the clipping method with distributed SGD, the authors propose $Bi^2Clip$. The performance o... | Rebuttal 1:
Rebuttal: We thank reviewer y2B4 for their review. As the reviewer noted, TailOPT can be readily combined with a wide range of optimization strategies, which greatly enhances its practical applicability. We propose several novel instantiations of TailOPT (such as $Bi^2Clip$) that achieves the strongest emp... | Summary: The paper studies distributed optimization under commonly used schemes of local steps followed by global synchronization, and specifically addresses the issue of heavy-tailed gradient variance in this setting, which is a very relevant problem.
As a solutions clipping techniques are proposed to stabilize both i... | Rebuttal 1:
Rebuttal: We thank reviewer QUNM for the feedback and for finding our theoretical and empirical results convincing. We fully agree that addressing heavy-tailed gradient variance is a critical challenge, particularly in the context of training large-scale models such as LLMs. As the reviewer notes, the empir... | Summary: The paper introduces TailOPT, a distributed optimization framework designed to handle heavy-tailed gradient noise in large-scale machine learning models. The authors propose a novel clipping mechanism, \(BiClip\), which performs coordinate-wise clipping to mitigate the effects of heavy-tailed noise without the... | Rebuttal 1:
Rebuttal: We thank reviewer 6e8Q for their review. We appreciate the reviewer's acknowledgements about the strengths of our paper, particularly the novelty and efficiency of our proposed algorithm.
**[Proof Challenges]** The challenges in the analysis lie in heavy-tailed noise with unbounded gradient vari... | null | null | null | null | null | null | null | null |
Sounding that Object: Interactive Object-Aware Image to Audio Generation | Accept (poster) | Summary: This paper introduces a new object-level video-to-audio generation method that exploits SAM to specify which object in a video should have sound and the AudioLDM architecture to generate the sound. During training, scaled dot-product attention between text embedding and patch-wise image embedding is computed a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and time.
**Clarifying AVC.**
In fact, we employed AVC to measure the semantic (instead of temporal) correspondence between audio and image, since our primary goal is to generate audio aligned with objects from image (not video).
**Why images only?**
Ou... | Summary: This paper proposed a novel object-aware audio generation model, that supports the interaction with users. This work achieves fine-grained control over which objects, and thus which sounds, are present in the generated audio. Empirical and theoretical validation demonstrating the strong performance of the mode... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and time.
**Generating audio from multiple objects.**
In fact, we showed in Figure 5 and the demo video of our paper that our method accepts multi-object masks (including more than two objects) to generate an audio mixture that reflects each selected obje... | Summary: This paper introduces an object-aware image-to-audio generation framework built on top of pretrained AudioLDM. Given user-provided segmentation mask, the I2A generation method can generate the object-aligned sound. Experiments on AudioSet and VGGSound Sync datasets show the proposed method outperforms selected... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and time.
**Clarifying caption-based methods.**
In fact, we have evaluated two caption-based variants in Table 1 & 7 of our paper. In Captioning, we generated a single caption from the entire image and fed it to a pre-trained AudioLDM 2. In Captioning & M... | Summary: This paper proposes an image-to-audio generation method with interactive object-aware design.
It mainly concentrates on decoupling separate events in visual scenes, while processing the overall scene context.
To train the visual object grounding model, the attention module is designed and substituted with a us... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and time.
**Reliance on SAM.**
First, our method does not fundamentally rely on SAM for its performance, but instead benefits from it functionally for enhancing user interactivity. As shown in Table 2(iv), Theorem 3.1, and Figure 4 of our paper, comparab... | null | null | null | null | null | null |
Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer | Accept (poster) | Summary: This paper introduces ReadyTS, a general time series forecasting model that learns a unified representation during pretraining and can be adaptively transferred to downstream tasks. The model employs frequency-based masking for pretraining, where specific frequency components are masked using random thresholds... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer **xtuG** for acknowledging our presentation quality and empirical contributions, as well as the helpful comments. We will revise our paper accordingly.
**Q1: How is the zero-shot experiment of Moment conducted?**
**A1:**
- Moment[1] mentioned in the pa... | Summary: The paper proposes a pre-trained time series model for forecasting.
The model distinguishes itself from other pre-trained models by three main aspects: (1) frequency-based masking in pre-training, (2) the time series register, (3) and a double objective of forecasting and reconstruction in pre-training.
An em... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer **3YLu** for providing detailed review and insightful comments regarding the model design and empirical study. We will revise our paper accordingly.
**Q1: Resharpening of the text**
**A1:** Thank you for your valuable suggestions on the presentation of t... | Summary: The work introduces a new method of learning foundational time-series models from pre-training on heterogenous datasets via decomposed frequency learning. The key idea is to extract multiple frequency representation via FFT and using masking in frequency domain as well when reconstructing the time-series. They... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer **DsF3** for providing a detailed review and insightful comments. We will revise our paper accordingly.
**Q1: Lack of some baselines**
A1: Based on your suggestions, we add some recent baselines: LPTM, Time-MOE-large and Chronos-bolt-base. As shown in th... | Summary: This paper builds a foundation model ReadyTS from two aspects:unified representations from heterogeneous multidomain time series data;domain-specific features to enable adaptive transfer across various downstream scenarios. First, this paper leverages frequency-based masking and reconstruction to decompose cou... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer **EPkr** for acknowledging our technical novelty and effectiveness, as well as the insightful comments. We will revise our paper accordingly.
**Q1: How is the register initialized? Is it trained together with the foundation model? Why is the prediction t... | null | null | null | null | null | null |
Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling | Accept (poster) | Summary: This paper proposes an attention mechanism, Grouped Cross-Attention (GCA), to improve long-context language modeling. By integrating a retrieval mechanism directly into the attention computation, GCA allows Transformers to generalize to significantly longer contexts while maintaining computational efficiency. ... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our manuscript.
**W2. For instance, the representation of chunk-wise CA outputs in Figure 2's caption does not match the notation used in Equation (2).**
If the inconsistency refers to the subscripts, it can be easily fixed by rewriting $O_{t+1,k}^l$ to $O_{t... | Summary: The paper introduces a new attention mechanism to integrate dynamic context called Grouped cross attention (GCA). GCA helps maintaining long term dependencies during sequence generation enabling long range information access and length generalization. GCA integrates chunk to chunk retrieval to learn and retrie... | Rebuttal 1:
Rebuttal: Thank you very much for your review comments and support for this work!
Although the improvement on perplexity is relatively marginal, the performance of passkey retrieval in Table 2 is quite significant, especially when the context length far exceeds that of the pre-training stage. This demonstr... | Summary: This paper introduces grouped cross attention (GCA), where the model learns to retrieve past chunks of tokens to reduce the prediction error on future tokens. The model is trained end-to-end to retrieve relevant chunks, and thus does not depend on a fixed retriever. The GCA modules are appended after attenti... | Rebuttal 1:
Rebuttal: Thank you for your professional and constructive review.
**W1. They only evaluate on single NIAH/It would help to see evals on the full RULER suite including multi-key retrieval,**
Besides the single NIAH, we also evaluated the variable tracking task in Table 2, which is the most challenging and... | null | null | null | null | null | null | null | null |
Understanding Complexity in VideoQA via Visual Program Generation | Accept (poster) | Summary: This paper proposes a data-driven approach to assess question complexity in VideoQA tasks by leveraging the complexity of generated code as a proxy. The mPEG results on NExT-QA and MVbench demonstrate the effectiveness of Codeplexity. Furthermore, the newly introduced dataset, CodePlex-QA, features more challe... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and suggestions. We appreciate your recognition of the novelty, clarity, and effectiveness of our approach, and of the extensiveness of our theoretical and empirical analysis. We respond to your comments below.
---
**Applicability beyond VideoQA.**
Indeed, our... | Summary: This paper proposes a data-driven approach to analyzing query complexity in Video Question Answering (VideoQA). They design an automatic approach that leverages recent advances in code generation for visual question answering, using the complexity of generated code as a proxy for question difficulty. They demo... | Rebuttal 1:
Rebuttal: Thank you for your review and valuable feedback. We address your comments individually below.
---
**On defining complexity via model performance**
We appreciate your perspective that human perception plays a fundamental role in defining question difficulty. However, our work does not claim that... | Summary: This work focuses on important issues in the VideoQA domain and presents a novel approach that provides new perspectives for future model evaluation and benchmark dataset construction. The core contribution is to propose a data-driven approach to systematically identify and analyze model-specific weaknesses in... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. We appreciate your recognition of the novelty, clarity, and potential impact of our approach. We respond to your questions and concerns individually below.
---
**Model-specific vs intrinsic difficulty.**
We acknowledge that our metric does n... | Summary: ## update after rebuttal
The paper introduces an interesting method of relying on visual programs to evaluate the complexity of VideoQA task. The authors develop methods to analyze the code complexity to estimate the question complexity. The proposed method, CodePlexity, correlates better with the model perfo... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and constructive feedback. We are encouraged by your positive assessment of our method's novelty and its potential impact on the VideoQA community. We respond to your questions and concerns individually below.
---
**“Gold” vs model-specific question complexity**
We ... | null | null | null | null | null | null |
Peripheral Memory for LLMs: Integration of Sequential Memory Banks with Adaptive Querying | Accept (poster) | Summary: This paper proposes peripheral memory, which is inspired by RAM architecture. It focuses on the task of model editing, and significantly outperforming previous methods. The peripheral memory seems to add external memory into the process of LLM inference, editing some layers in the foundation model. Although th... | Rebuttal 1:
Rebuttal: **1. Question about baselines**
Thank you for raising this important concern. We provide detailed clarifications regarding the baseline implementation:
**(1) Baseline Implementation**
All baselines for Knowledge-based Model Editing (KME) were implemented using the widely adopted toolkit EasyEdi... | Summary: The paper introduces a "peripheral memory" memory augmentation method for LLMs. The paper views memory as a separate ram-like component that interfaces with an LLM. The memory is designed as a sequence of memory banks, each modeled using KANs. Memory operations are controlled by query signals from the LLM inte... | Rebuttal 1:
Rebuttal: **1. Handling conflict**
Thank you for raising this critical point. Below, we clarify our method’s current behavior and outline potential enhancements:
**(1) Last-Write-Wins Policy**
In sequential editing scenarios, our memory defaults to a temporal priority strategy: the most recent edit overw... | Summary: This paper proposes a novel memory augmentation technique for LLMs by decoupling memory from the model architecture, analogous to a CPU and RAM architecture. The proposed peripheral memory consists of sequential memory banks modeled by Kolmogorov-Arnold Networks (KAN) to have smooth and adaptive memory read/wr... | Rebuttal 1:
Rebuttal: **1.Question about the semantic drift**
Thank you for this insightful question. We acknowledge that semantic drift and retrieval degradation at extreme scales (>10K updates) for semantically equivalent queries remain challenges. Below, we summarize our empirical observations and outline explicit ... | Summary: This work proposes Peripheral Memory for LLMs, in which the sequence modeling and the memory updates interleaves in the language modeling process. The experimental results on knowledge-based model editing and long-context QA demonstrate the effectiveness of such method.
## update after rebuttal
increased by 1... | Rebuttal 1:
Rebuttal: **1.Question about $\mathbf{W}_0$ and $\mathbf{W}_1$**
Thank you for raising this important concern. We clarify the training protocol and evaluation fairness as follows:
*(1) Role and Training of Convertors*
The mapping matrices $\mathbf{W}_0$ and $\mathbf{W}_1$ (see Section 3.2) serve solely a... | null | null | null | null | null | null |
Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs | Accept (poster) | Summary: The paper introduces ICRT, a jailbreaking framework leveraging cognitive psychology principles like the "simplicity effect" (preference for simple information) and "relevance bias" (overemphasis on contextually linked concepts) to bypass LLM safety mechanisms.
ICRT achieves a 98.2% average attack success rate ... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your recognition of our work, your careful review of our paper, and your valuable feedback on the ICRT method. Below are our responses to your comments, as well as our plans for future work.
**(I) Regarding the Causal Link Between LLM Vulnerabilities and Human Cogniti... | Summary: The paper presents ICRT, a jailbreak attack framework that uses cognitive psychology principles—namely the simplicity effect and relevance bias—to break down complex malicious prompts into simpler parts and then reassemble them into effective, harmful instructions. Additionally, it introduces a ranking-based e... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your review and valuable feedback on our work. Below are our responses to your comments.
**(I) Regarding the Limited Novelty:** We appreciate the reviewer’s insightful comments. ICRT introduces significant innovations that advance the field, specifically:
1. **Differ... | Summary: This work, a novel jailbreak attack framework, ICRT, drawing inspiration from human cognitive heuristics and biases.. By leveraging the simplicity effect through cognitive decomposition and utilizing relevance bias for prompt reorganization, their approach enhances the effectiveness of malicious prompts. Addit... | Rebuttal 1:
Rebuttal: Dear reviewer, we greatly appreciate your feedback, which has helped improve our work. Below are our responses to your suggestions and concerns.
**(I) Analysis of Failure Cases:**
To better understand failure cases and analyze the model's thought process, we conducted experiments with some of t... | Summary: - This paper introduces a jailbreak attack framework, called ICRT, leverages the simplicity effect to decompose malicious prompts into lower-complexity subcomponents and utilizes relevance bias to reorganize the prompt structure, enhancing its semantic alignment with the model's expected input.
- Furthermore, ... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your in-depth evaluation and valuable feedback on our paper. Below is our detailed response to the comments and suggestions you provided.
**(I) Regarding the Limited Novelty:**
We appreciate the reviewer’s keen observation regarding the similarities between our method... | null | null | null | null | null | null |
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching | Accept (poster) | Summary: The paper proposes Adjoint Sampling, a scalable and effective method for diffusion samplers. Authors build their ideas on top of adjoint matching and propose several advancements for scalable and effective training of diffusion samplers. Experiment results validate that the proposed method outperforms several ... | Rebuttal 1:
Rebuttal: We thank the reviewer for supporting our paper. Note that we have added additional experiments and figures to provide more insight into our work. See https://sites.google.com/view/adjointsamplingrebuttal. Below we answer the reviewer’s questions in detail.
> It seems the training procedure can be ... | Summary: The paper proposes an algorithm that uses diffusion-models for sampling from unnormalized densities which is rooted in stochastic optimal control. The proposed method is based on the adjoint-state and the resulting form of the objective is particularly simple as it is requires a regression to the (scaled) gra... | Rebuttal 1:
Rebuttal: We understand and agree with the reviewer’s concerns regarding additional baselines and the lack of clarity around the proposed benchmark.
To respond, we (i) have additional experiments, (ii) expand the discussion to related off-policy methods, and (iii) clarify why the proposed benchmark is much... | Summary: This paper introduces Adjoint Sampling, a novel framework for efficiently sampling from an unnormalized density function. The framework reformulates the sampling problem as a stochastic optimal control problem. Building on the adjoint matching method, the authors propose the Reciprocal Adjoint Matching method,... | Rebuttal 1:
Rebuttal: We thank the reviewer for being candid and providing us the opportunity to substantiate our claims, which we believe we can do. The reviewer is concerned with our claims regarding (i) the efficiency and analysis of Adjoint Sampling and (ii) our proposed large-scale sampling benchmark. We agree tha... | Summary: This paper proposes a novel neural sampling method, Adjoint Sampling, based on stochastic optimal control (SOC) and recently published adjoint matching method. The proposed method uses reciprocal projections alternating with reciprocal adjoint matching, and allows for incorporating the key symmetries from the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We’ve incorporated new baselines and metrics, and have produced figures to better illustrate our claims.
Additional figures & results: https://sites.google.com/view/adjointsamplingrebuttal
> I’m not sure if the scalability was properly based o... | null | null | null | null | null | null |
Test-Time Selective Adaptation for Uni-Modal Distribution Shift in Multi-Modal Data | Accept (poster) | Summary: This paper address uni-modal distribution shift in multi-modal data, where the distribution shift influences only one modality. They demonstrate that the presence of such shift impedes multi-modal fusion and leads to the negative transfer phenomenon in existing test-time adaptation techniques through theoretic... | Rebuttal 1:
Rebuttal: # Response to Reviewer 5Yv6
Thanks for the constructive suggestions! We have addressed each point with careful consideration and revised our work accordingly. Below is our detailed response:
## W1 [Claims of setting]
We agree that [1] has explored multi-modal shifts more broadly. However, our ... | Summary: This paper addresses the challenge of uni-modal distribution shifts in multi-modal learning, where only one modality experiences distribution changes at test time. The authors propose a selective adaptation framework comprising modality-specific lightweight adapters and a learnable router to dynamically activa... | Rebuttal 1:
Rebuttal: # Response to Reviewer UDgQ
Thanks for the reviewer’s positive comments and constructive suggestions! We have carefully considered each point and revised our work accordingly. Below is our detailed response:
## W1 [Novelty of method]
We emphasize that our design offers a straightforward soluti... | Summary: This paper proposes a novel approach to handling multi-modal test-time adaptation when only one modality undergoes distribution shift. The authors introduce the concept of uni-modal distribution shift, highlighting its adverse effects on multi-modal fusion and the potential for negative transfer. To address th... | Rebuttal 1:
Rebuttal: # Response to Reviewer dkfb
Thanks for the reviewer’s positive comments and constructive suggestions! We have carefully considered each point and revised our work accordingly. Below is our detailed response:
## W1 [Theoretical justification router’s effectiveness]:
We agree that the theoretical... | Summary: The paper tackles the problem of multi-modal adaptation—the existing method of test time Domain adaptation struggles to adapt to other modalities. The authors propose to highlight this phenomenon and tackle this challenge with a "router" enabling the selection of the adaptation if needed. The selective adaptat... | Rebuttal 1:
Rebuttal: # Response to All Reviewers, AC and SAC
Thanks for all Reviewers' valuable suggestions and the efforts of AC and SAC.
Reviewer dkfb, UDgQ and 5Yv6 all acknowledge the studied problem (we quote: "_novel and practically relevant_", "_interesting real-world problem_" and "_challenging and practical... | null | null | null | null | null | null |
DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction | Accept (poster) | Summary: **Summary after rebuttal**
The authors resolved all of my concerns. I am raising to 4.
**End of Summary after rebuttal**
The paper introduces DISCO, a novel framework for multi-physics-agnostic prediction of dynamical systems governed by unknown temporal partial differential equations (PDEs). The key contri... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough reading of our manuscript and for their valuable feedback on the writing and related works, which will help improve the paper.
Since we cannot upload an updated manuscript for ICML, we will describe the changes we intend to make in response to your review ... | Summary: This paper proposes a novel method to obtain lightweight surrogate models from physics data. The idea is to use a hypernetwork transformer to learn the parameters of a smaller operator network, which is in charge of performing the time integration. This architecture decouples the learning of the dynamics from ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback, in particular for acknowledging that “the methods of this paper are clearly written and developed” and that “all the claims of the paper are supported with validation results in benchmark cases”. We also thank the reviewer for their valuable sugge... | Summary: The paper introduces DISCO, a novel framework for multi-physics-agnostic prediction that combines transformer-based hypernetworks with neural PDE solvers. The key innovation is a two-stage approach where a large transformer hypernetwork processes a context of sequential states to generate parameters for a smal... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments on our work, particularly for acknowledging the “particularly creative aspect [of] the architecture's information bottleneck design” and noting that “the claims in the paper are well supported by substantial evidence”. We also appreciate their valu... | null | null | null | null | null | null | null | null |
SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchy | Accept (poster) | Summary: This paper proposes a foundation model for vehical route problem with multi-task and multi-distribution. The model contains the mixture-of-depth decoder, which dynamically selects nodes at each decoding step, thus improving the efficiency and generalization ability of the model. A context-based clustering laye... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that our work addresses a practical problem that interests the community and that the extensive experiments are convincing. We hope our response adequately addresses the remaining questions.
**[W1: Handling Spatial-Temporal characteristics]**: We thank the ... | Summary: This paper proposes a novel problem, the Multi-Task Multi-Distribution Vehicle Routing Problem (MTMDVRP), which is an extension of the traditional Multi-Task Vehicle Routing Problem (MTVRP). The problem focuses on different node distributions of different geographical regions in the real world, further conside... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive recognition of the work's novelty, effectiveness, practical values, and thorough experimental validation. We hope our responses with new experiments address the remaining concerns.
**[W1: Realistic Setup]**: While we present a national-level business expan... | Summary: This paper introduces SHIELD, a framework with sparsity and hierarchy principles to address MTMDVRP problem.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: Yes
Experimental Designs Or Analyses: Yes
Supplementary Material: Yes
Relation To Broader Scientific Literature: T... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and for recognizing our paper as solid and easy to follow, with an interesting use of MoD to reduce computational overhead. We hope our responses with new results address the remaining questions.
**[Q1: Why MoE Encoder]**: Insightful question! In th... | Summary: This paper introduces the Multi-Task Multi-Distribution Vehicle Routing Problem (MTMDVRP), an extension of the MTVRP. The MTMDVRP effectively captures the complexities inherent in real-world industrial applications by incorporating various realistic customer distributions. To address these challenges, the auth... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive review and recognizing the depth of experiments done to show the benefits of the proposed architecture. We hope our following responses will further address the reviewer's concerns about the work.
**[W1: Contributions of Clustering Nodes/MoE/Mo... | null | null | null | null | null | null |
Partition First, Embed Later: Laplacian-Based Feature Partitioning for Refined Embedding and Visualization of High-Dimensional Data | Accept (oral) | Summary: This paper claims when the data is complex and governed by multiple latent variables (which is almost always the case), the visualization methods that aim to capture all features in single lower dimensional space often: fail in disentangle the latent variables, or requires larger dimensionality to capture the ... | Rebuttal 1:
Rebuttal: Q1. “Could you compare the proposed approach with stronger baseline mentioned above?”
R1.To address the reviewer’s concerns, we conducted a comprehensive comparison of our approach with the methods [A], [B], and [C] using the biological dataset from Section 5.2 and the rotating figurines dataset ... | Summary: High-dimensional data can sometimes be composed of multiple sets of features, each following a distinct substructure. Traditional visualization methods such as t-SNE, when applied to the full feature set, struggle to capture these substructures. The authors propose a method that enables feature space separatio... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and detailed assessment of our work. We are pleased that the reviewer found our "proposed method and evaluation criteria well aligned with the problem at hand". We also appreciate the acknowledgement that "most claims in the submission are clear... | Summary: The authors propose an approach for embedding high dimensional data via partitioning features using a Laplacian smoothness optimization. This improves over classical techniques for embedding where extreme dimension reduction can distort results. They provide theoretical results characterizing the solution of t... | Rebuttal 1:
Rebuttal: We thank the reviewer for your thoughtful and positive review of our paper. We are pleased that the reviewer found our approach ‘original’ and that they considered the paper’s contribution to be “sufficiently impactful and significant”. Additionally, we appreciate the reviewer's recognition of the... | Summary: The manuscript proposes a new dimensionality reduction method, targeting the case where data feature originates from K sets, which are either independent or low-dependent. The method is composed of two steps. In the first, a decomposition of the data features into K disjoint sets is identified; in the second, ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and thoughtful feedback. We appreciate that the reviewer found our claims to be "supported by clear and convincing evidence" and that our "theoretical claims are beautifully supported with clear explanations and detailed proofs."
Q1. Does your m... | null | null | null | null | null | null |
Cradle: Empowering Foundation Agents towards General Computer Control | Accept (poster) | Summary: The paper presents CRADLE, framework that leverages LMMs designed for General Computer Control (GCC). CRADLE operates directly through visual observations (screenshots) and generates keyboard and mouse commands, enabling it to interact with diverse software environments without relying on specialized APIs. It... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their valuable feedback and insightful comments. We hope our following answers will clear up the doubts about our work, and please let us know if there is any other clarification we can provide.
---
**Q1**: About the selection of the video game and softwa... | Summary: This paper focuses on building a framework based on a multimodal model, specifically OpenAI’s GPT-4o, for computer use through keyboard and mouse inputs. The proposed framework consists of six distinct modules: information gathering, self-reflection, task inference, skill curation, action planning, and memory,... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their valuable feedback and insightful comments. We hope our following answers will clear up the doubts about our work, and please let us know if there is any other clarification we can provide.
---
**Q1**: The scale of evaluation in the gaming domain i... | Summary: The paper proposes the General Computer Control (GCC) setting where the input is restricted to screenshots and the output to keyboard and mouse actions. To address this setting, the paper proposes Cradle, an LMM-based framework with six components: Information
Gathering, Self-Reflection, Task Inference, Skill... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their valuable feedback and insightful comments. We hope our following answers will clear up the doubts about our work, and please let us know if there is any other clarification we can provide.
---
**Q1**: About the necessity of each module of Cradle. W... | null | null | null | null | null | null | null | null |
Average Sensitivity of Hierarchical $k$-Median Clustering | Accept (poster) | Summary: This paper studies the hierarchical $k$-median problem in the setting of average sensitivity, which is a measure of how much an algorithm's output changes when the dataset undergoes small perturbations. The paper's first contribution is algorithmic, and it proposes an efficient algorithm for the hierarchical $... | Rebuttal 1:
Rebuttal: Thank you for your suggestion. We fixed the typos in the updated manuscript. We will address your concerns as follows:
**Claims And Evidence:**
**C1: The experimental section does contain some claims which are not supported by the presented evidence.**
We appreciate your careful review and wi... | Summary: This study provides an innovative solution that enhances both the interpretability and robustness of hierarchical clustering techniques. The study shows that classical methods have high sensitivity on specific datasets, and validates the robustness of the new algorithm through experiments.
Claims And Evidence... | Rebuttal 1:
Rebuttal: Thank you for your review. We will address your concerns as follows:
**Weaknesses:**
**W1: Computational Complexity: $O(n^3)$ time complexity limits scalability to large-scale datasets.**
We noticed that this weakness is similar to Q1. Please refer to Q1 for further details.
**W2: Experimental... | Summary: Hierarchical clustering is a widely used method for unsupervised learning with numerous applications. However, in the application of modern algorithms, the datasets studied are usually large and dynamic. If the hierarchical clustering is sensitive to small perturbations of the dataset, the usability of the alg... | Rebuttal 1:
Rebuttal: Thanks for your reviews. We summarize your questions and provide our responses as follows:
**References:**
**R1: More discussions to Differential Privacy (DP) algorithms; Can we reduce one problem to another? For example if we split the privacy budget among $k$ layers, and apply exponential mech... | null | null | null | null | null | null | null | null |
Approximately Correct Label Distribution Learning | Accept (poster) | Summary: To address such as exisome deep-rooted problems of LDLsting LDL metrics lose their discriminability, and existing LDL objectives are also at risk of overfitting, this paper proposes DeltaLDL, a percentage of predictions that are approximately correct within the context of LDL. Based on DeltaLDL, a novel evalu... | Rebuttal 1:
Rebuttal: Many thanks for your precious comments! We have provided point-by-point responses to your questions below.
**Comment 1:** Some necessary justifications about the existing deep-rooted problems of LDL are needed. In Introduction: "For years, there are some deep-rooted problems in the field of LDL:"... | Summary: This paper theoretically reveals the deficiency of the KL divergence in learning and evaluating LDL mappings. To address the mentioned shortcomings, this paper proposes a new LDL paradigm, DeltaLDL, which focuses on how many label distributions are approximately correctly predicted. Based on DeltaLDL, this pap... | Rebuttal 1:
Rebuttal: Many thanks for your precious comments! We have provided point-by-point responses to your questions below.
**Comment 1:** Non-professional users may not intuitively understand how a specific distance or similarity value represents the closeness between the predicted and ground-truth label distrib... | Summary: This paper focuses on label distribution learning (LDL) and addresses the limitations of existing evaluation metrics and learning objectives. Existing LDL evaluation metrics based on distance/similarity metrics, like Kullback-Leibler divergence (KLD), have poor discriminability due to the constraints of label ... | Rebuttal 1:
Rebuttal: Many thanks for your precious comments! We have provided point-by-point responses to your questions below.
**Comment 1:** My main concern is whether the new metric proposed in the article can truly distinguish between superior and inferior models. In Eq. (11), there is a coefficient of 1/delta_0 ... | Summary: This paper addresses the issues in **Label Distribution Learning (LDL)**, notably the limitations of **Kullback–Leibler Divergence (KLD)** as both an evaluation metric and learning objective. The authors propose **DeltaLDL**, a novel framework that introduces the concept of "**approximately correct**" label di... | Rebuttal 1:
Rebuttal: Many thanks for your comprehensive comments! Responses to your concerns are as follows.
**Response about $\delta$:** Our theoretical analysis illustrates that, $\delta_0$ reflects the worst-case divergence. Therefore, values *larger* than $\delta_0$ (for distance metrics) would imply tolerating w... | null | null | null | null | null | null |
Efficient Online Reinforcement Learning for Diffusion Policy | Accept (poster) | Summary: This paper studies training diffusion policy in the online RL setting and proposes an algorithm based on the energy-based view of diffusion models. It then evaluates the proposed algorithm on Mujoco tasks provided in Gym.
# Main Ideas
* To learn a diffusion policy, we need to learn the score function $s_\thet... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful comments. Here are the responses, where we grouped relevant questions together,
## Q1: Explanation about the reverse sampling trick.
> *I found the reverse sampling trick (14) introduced in line 213 hard to understand and seems lacking any evidence or expl... | Summary: This work presents a diffusion-based online RL method called Soft Diffusion Actor-Critic (SDAC). The authors highlight the difficulty of training online RL methods due to inability to sample from the target distribution (optimal policy) and the computationally intensive nature of training some diffusion-based ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. Below, we provide our response and have merged some questions and reindexed the references for clarity.
## Claims And Evidence
**Q1:**
> *reasons why other baseline methods have higher memory/time than SDAC.*
> *benefit compared to other Bo... | Summary: This paper highlights the challenges in mimicking an energy-based policy, primarily due to two key reasons: the intractability of the energy function caused by the partition term and the inherent limitation of online RL, where optimal policies are not directly accessible. While existing online diffusion policy... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback. Below, we first provide detailed derivations of the reverse sampling trick, followed by detailed answers to the specific questions.
## Reverse Sampling Trick Derivations
Check [rebuttal_proof](https://anonymous.4open.science/r/r... | Summary: The paper proposes a novel method that leverages diffusion models to enhance SAC, but in a nontrivial way. To address the challenges of using diffusion policies—such as the need to track gradients through the entire reverse chain—the paper introduces RSSM, a new approach for estimating the score function.
Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. Here are the responses to the questions,
## Theoretical claims
**Q1:**
> *How should $\tilde{p_t}$ be chosen? Specifically, what is the appropriate distribution over $t$, and what form should $\tilde{p_t}$ take? it seems that any choice might... | null | null | null | null | null | null |
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