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Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictors | Accept (poster) | Summary: The paper studies the online metrical task system (MTS) problem in a learning-augmented variant. Given a metric space and an initial state $s_0$ (point), at each time a cost function $c_t$ over points is revealed, and an algorithm needs to select a state $s_t$ and pays cost equal to $d(s_{t-1},s_t) + c_t(s_t)$... | Rebuttal 1:
Rebuttal: Thank you for your review and for your suggestions. We will incorporate them in
our manuscript. | Summary: The paper studied the learning-augmented metrical task system (MTS) problem and gave near-tight bounds of $\tilde{\Theta}(\text{OPT}^{2/3})$. The problem is similar to the adversarial bandits with $T$ days; however, the ``switching’’ between bandits would involve a cost, which is measured by the distance betwe... | Rebuttal 1:
Rebuttal: We thank you for your feedback and for highlighting the strengths of our
contribution. We will incorporate your suggestions in the next revision
of our manuscript. | Summary: The paper studies the problem of metrical task system (MTS) under the bandit feedback setting. Given multiple heuristic predictors of what action to take, the algorithm can choose one predictor and receive feedback only if the same predictor is used consecutively across m time steps. A tight regret bound was p... | Rebuttal 1:
Rebuttal: We provide answers to your questions clarifying the framing of our work
in the learning-augmented framework and the difficulties present in our setting
compared to the previous works.
We believe that these answers could also be valuable to other
researchers as suggested by
Reviewer uNsW. We will a... | Summary: This paper considers the problem of sequentially selecting heuristics for Metrical Task Systems (MTS) when multiple heuristics are available. We focus on the bandit feedback setting, in which only the output of the heuristic chosen at each time step is observable. For this problem, we design and analyze algori... | Rebuttal 1:
Rebuttal: > References Not Discussed
Thank you for the references; we will include them in the revision of our paper.
> In practice, OPT is often unknown beforehand.
We can guess OPT using the doubling technique, getting virtually the same bound, e.g. as in Cesa-Bianchi
and Lugosi (Section 2.3) or Lattim... | null | null | null | null | null | null |
On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation | Accept (poster) | Summary: This paper provides extensive theoretical analysis for the zero-initialized attention in LLaMA-Adapters and connects it with mixture-of-experts models. Based on this, the author introduces non-linear prompts. Plus, experiments are conducted to demonstrate the performance of non-linear prompts.
Claims And Evid... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and insightful comments. We hope that we can address your concerns with the responses below.
### **Q1: Comparison between Non-Linear and Linear prompt on HellaSwag and TruthfullQA with LLaMA-7B setting:**
Thank you for your comments. Our main study in this... | Summary: The paper provides a rigorous theoretical foundation for zero-initialized attention, which has been successfully used in fine-tuning large language models (LLMs), particularly in LLaMA-Adapter. Establishes a connection between zero-initialized attention and mixture-of-experts (MoE) models. It additionally prov... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback and insightful comments. We would like to address your concerns as follows:
### **Q1: Explanation for results in Table 2.**
Thank you for your comments. **We want to clarify that most PEFT methods (e.g., LLaMA-7B + zero-init + linear, LLaMA-7B + zero-init + no... | Summary: This paper investigates a specific aspect of LLaMA-Adapter, focusing on zero-initialized attention. The zero-initialized attention mechanism is not only initialized with zero values but also involves a structural modification that replaces the traditional softmax function. Instead, softmax is computed independ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and insightful comments. We hope that we can address your concerns with the responses below.
### **Q1: Alignment of Theory and Experiments:**
Thanks for your comments. We would like to clarify that the convergence analysis of prompt estimation under the ra... | null | null | null | null | null | null | null | null |
The Surprising Effectiveness of Test-Time Training for Few-Shot Learning | Accept (poster) | Summary: This paper presents a comprehensive analysis of the Abstraction and Reasoning Corpus (ARC) and BIG-Bench Hard (BBH) tasks. The authors train LoRA as an adapter. For training, the authors use flips, rotations, and color permutations to augment the original training data. During inference, they adopt intra-trans... | Rebuttal 1:
Rebuttal: Thank you for your review and the feedback on our paper.
**Q1. Definition and Application of Test-Time Training (TTT)**
We use the definition of TTT provided by ([Sun et al. (2020)](https://arxiv.org/abs/1909.13231)): self-supervised training a model using the unlabeled test sample $d_{\textrm{... | Summary: This paper investigates test-time training (TTT) for improving language models' few-shot learning capabilities, particularly on novel tasks that require reasoning and abstraction.
- TTT significantly improves performance on challenging reasoning tasks, e.g. on ARC-AGI, TTT with in-context examples yields up t... | Rebuttal 1:
Rebuttal: Thank you for the positive review! We are happy to answer any new questions you have later during the rebuttal process. | Summary: This paper proposes to use test-time-training as a method for scaling test-time capabilities of large models under the few-shot setting, and tested the model's performance on ARC and BBH bechmarks. The experiment results show positive performance.
Claims And Evidence: Main claim: TTT helps few-shot performanc... | Rebuttal 1:
Rebuttal: **Q1. Have authors tried domains such as coding or math?**
This paper produced a SoTA way to apply TTT to LMs in the few-shot learning setting using the challenging ARC-AGI and BB-Hard as our reasoning problem sets. Note that our implementations do not leverage the CoT capabilities of the models. ... | null | null | null | null | null | null | null | null |
(How) Can Transformers Predict Pseudo-Random Numbers? | Accept (poster) | Summary: The paper studies how Transformers can learn linear congruential generators (LCG), a class of simple pseudo-random generators. These are generators of the form $x_{n+1} = a x_n + c \mod m$ for some choices of $(a,c)$ and $m$. The authors demonstrate that Transformers can learn from data to simulate LCG in two ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful assessment and candid feedback. To address the reviewer's main concern, we clarify the motivation behind and the contributions of our work.
One major goal is to answer the following important question: To what extent can deep neural networks crack variou... | Summary: This study analyzes whether a Transformer based on next-token prediction can learn an LCG sequence and, if so, how it models the sequence. Specifically, the study demonstrates that a Transformer can learn an LCG sequence given sufficient architectural capacity and training data. Subsequently, it examines the a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging comments and thoughtful questions.
- **Questions**: Yes, our study employs a decoder-only Transformer architecture with causal masking (autoregressive).
This choice emulates the real-world scenario where observations are obtained sequentially and, ... | Summary: This paper studies how Transformer can learn linear congruential generators sequence with either fixed or changing modulus. For fixed module, they discover that Transformer learns the radix representation and predicts each radix digit almost independently. Notably, the lower digit are predicted by copying from... | Rebuttal 1:
Rebuttal: We thank the reviewer for giving a detailed feedback and raising important questions. **Note**: New experiment figures at: https://doi.org/10.6084/m9.figshare.28703570.v2
## Theoretical claims and related experiments
The reviewer is correct in pointing out that equation (3) is incorrect in genera... | Summary: Summary:
This paper trains transformers on the task of in-context predicting the next element of a sequence generated with a Linear Congruential Generator (LCG). An LCG has the form:
x_{n+1} = a x_n + b (mod m), where
a, b, m are unknown numbers.
The paper studies two settings: 1) Fixed-Modulus (FM) setting, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful assessment and valuable feedback. We have added new experiment Figures S1, S2 at the link: https://doi.org/10.6084/m9.figshare.28703570.v2 -- which we will refer to in our response below.
## Learning of higher bits
Below we present a modified and more accu... | null | null | null | null | null | null |
Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time | Accept (poster) | Summary: The paper presents bounds on the capacity of spiking neural networks to approximate functions on static inputs for discrete-time spiking neural networks. The theory includes a universal approximation theorem (which is simple, as noted by the authors), and d bound on the number of regions. The experiments cover... | Rebuttal 1:
Rebuttal: We thank Reviewer MuQ3 for the feedback. Below we individually address the (not discussed) concerns:
## 1. Abstract/title
We agree that the focus on static data should be highlighted in the abstract (instead of only in the introduction as of now). We will acknowledge this in the abstract of the ... | Summary: The authors have innovatively proposed a discrete-time Leaky Integrate-and-Fire (LIF) neuron model for Spiking Neural Networks (SNNs), which represents a fundamental and cutting-edge contribution with significant implications for the entire field of neuromorphic computing. The authors have dedicated substantia... | Rebuttal 1:
Rebuttal: We thank to Reviewer Wwin for the feedback.
We would like to start our response by clarifying—although this is stated multiple times in the paper (e.g. lines 21, 55, 100) as well as discussed explicitly in the related works section (e.g. lines 1466-1469)—that we are not proposing a new SNN or neu... | Summary: This paper analyzes the representational power of discrete-time leaky integrate-and-fire spiking neural networks (LIF-SNNs) as function approximators. It demonstrates that LIF-SNNs realize piecewise constant functions over polyhedral regions, establishes their universal approximation capabilities, quantifies t... | Rebuttal 1:
Rebuttal: We thank Reviewer 9JUu for the feedback. Below we address each mentioned point:
## 1. Distinction from activation quantization studies
First, we confirm that our analysis in Section 3.2 indeed builds on existing results on Heaviside ANNs, as we point out at line 165 (but should be stated mor... | Summary: The manuscript investigates the theoretical expressivity of discrete-time leaky integrate-and-fire (LIF) spiking neural networks (SNNs) and compares them to conventional analog neural networks (ANNs). The manuscript establish that LIF-SNNs realize piecewise constant functions defined on polyhedral regions and ... | Rebuttal 1:
Rebuttal: We thank Reviewer V5so for the feedback. Below, we address the concerns individually:
## 1. Training and architectural design
These features of ANNs may benefit from expressivity insights [1,2, Appendix D.1], while today's SNN architectures and training strategies are typically based on those o... | Summary: Authors theoretically analyse a specific LIF neuron model in discrete time. They observe that they realize piecewise constant functions and quantify the network size required to approximate continuous functions.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: No.
Experim... | Rebuttal 1:
Rebuttal: We thank Reviewer QUDk for the feedback. Below, we address the mentioned concerns individually:
## 1. Relationship to the work [1].
From a high level, this work discusses the capacity of neurons under a specific model with time encodings. The capacity in their work is related to the ability of t... | null | null | null | null |
Discriminative Policy Optimization for Token-Level Reward Models | Accept (poster) | Summary: This paper introduces a method to construct token level reward from pairwise data. It is further shown that such rewards can be applied to methods like PPO or REINFORCE. Experiments show that both PPO and REINFORCE can be benefited from the constructed token level rewards and outperforms their counterparts wit... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and insightful questions on our paper. Below, we address your concerns in detail.
> Q1: It seems that for both PPO and REINFORCE, the author report the detailed training hyper-parameter but lacking the details about how the hyperparameters are selected. Also the ... | Summary: This paper aims to develop a token-level reward mechanism in RLHF. The authors achieve this by decoupling the reward model from language generation and constructing a reward model through the optimization of a discriminative policy. Additionally, they provide a comprehensive theoretical analysis of their metho... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and insightful questions on our paper. We greatly value your feedback and appreciate your recognition of our work's advancements. Below, we address your concerns in detail.
> Q1: Lines 78–80 mention “under certain assumptions.” These assumptions should be expli... | Summary: This paper introduces a discriminative Q-function Reward Model (QRM) for token-level credit assignment in RL. Theoretically, the authors show that QRM logits linearly relate to optimal Q-functions, enabling advantage computation without GAE. Empirically, QRM outperforms prior token/sequence-level RMs in tasks ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Below, we address your concerns in detail.
> Q1: Regarding equivalence claim in Proposition 3.3 and assumptions.
A1: We address it from two key aspects:
- According to **Proposition 3.3**, the optimal Q-function and the optimal logits... | Summary: When training LLMs with RL, it matters to have fine-grained credit assignment to tokens. The current popular algorithms like GRPO and RLOO all assign the same credit to all tokens in a response. This does not seem ideal at all. There have been recent works in improving this. Q-RM suggests that we compute these... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Below, we address your concerns in detail.
> Q1: Regarding results discrepancy on MATH dataset.
A1: We clarify that the discrepancy in results for Llama-3.2-3B-Instruct on MATH can be attributed to the **differences in evaluation framew... | null | null | null | null | null | null |
The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning | Accept (poster) | Summary: This paper reduces communication overhead in FL by enhancing low-rank decomposition techniques. The authors focus on three key issues: what to decompose, how to decompose, and how to aggregate. They propose three techniques respectively: Model Update Decomposition (MUD), Block-wise Kronecker Decomposition (BKD... | Rebuttal 1:
Rebuttal: **Hi Reviewer u9mP:**
We sincerely appreciate your valuable feedback. Below, we address each of your comments in detail. For additional experimental results, please refer to the anonymous link: **https://anonymous.4open.science/r/fedmud_rebuttal-962F**.
***Q1: "a more detailed discussion on the... | Summary: The paper focuses on enhancing communication efficiency in FL by improving low-rank decomposition techniques. The authors identify three key issues: what to decompose, how to decompose, and how to aggregate. To address these, they propose three novel techniques: decompose only model updates, block-wise Krone... | Rebuttal 1:
Rebuttal: **Hi Reviewer Gugc:**
We sincerely appreciate your valuable feedback. Below, we address each of your comments in detail. For additional experimental results, please refer to the anonymous link: **https://anonymous.4open.science/r/fedmud_rebuttal-962F**.
***Q1: "datasets and models"***
**R1:**
I... | Summary: The authors introduce a novel communication-efficient federated learning algorithm that integrates Model Update Decomposition (MUD), Block-wise Kronecker Decomposition (BKD), and Aggregation-Aware Decomposition (AAD). This approach is particularly well-suited for training large neural networks, which commonly ... | Rebuttal 1:
Rebuttal: **Hi Reviewer fwVG:**
We sincerely appreciate your valuable feedback. Below, we address each of your comments in detail. For additional experimental results, please refer to the anonymous link: **https://anonymous.4open.science/r/fedmud_rebuttal-962F**.
***Q1: "The target object of Theorem 1"**... | Summary: This paper introduces three techniques to enhance low-rank decomposition for communication-efficient federated learning (CEFL):
**Model Updates Decomposition** (MUD), **Block-wise Kronecker Decomposition** (BKD), and **Aggregate-Aware Decomposition** (AAD). Each method addresses specific challenges—what to d... | Rebuttal 1:
Rebuttal: **Hi Reviewer W27g:**
We sincerely appreciate your valuable feedback. Below, we address each of your comments in detail. For additional experimental results, please refer to the anonymous link: **https://anonymous.4open.science/r/fedmud_rebuttal-962F**.
***Q1: "novelty of MUD and difference wit... | Summary: This paper introduces three novel techniques for Communication Efficient Federated Learning (CEFL) based on low-rank matrix decomposition: Model Updates Decomposition, Block-wise Kronecker Decomposition, and Aggregation-Aware Decomposition, each of which are targetting a specific issue. First, to reduce inform... | Rebuttal 1:
Rebuttal: **Hi Reviewer CShD:**
We sincerely appreciate your valuable feedback. Below, we address each of your comments in detail. For additional experimental results, please refer to the anonymous link: **https://anonymous.4open.science/r/fedmud_rebuttal-962F**.
***Q1: "experimental evidence of the func... | null | null | null | null |
BCE vs. CE in Deep Feature Learning | Accept (poster) | Summary: The paper compares Binary Cross-Entropy (BCE) and Cross-Entropy (CE) loss functions in deep feature learning, focusing on their ability to enhance intra-class compactness and inter-class distinctiveness. It theoretically proves that BCE, like CE, can lead to Neural Collapse (NC) when minimized, maximizing thes... | Rebuttal 1:
Rebuttal: # **Thanks to reviewer RW5r for the comments!**
# Response to “Experimental Designs Or Analyses”: Experiments on Transformer
To further validate the advantages of BCE over CE, we trained ViT [1] and Swin Transformer [2] using BCE and CE on CIFAR10 and CIFAR100. Similar to the experimental settin... | Summary: This paper shows that binary cross-entropy (BCE) loss, like cross-entropy (CE) loss, can induce neural collapse—maximize intra-class compactness and inter-class distinctiveness in multi-class tasks when the loss reaches its minimum. Through theoretical and empirical analysis, the authors show that models train... | Rebuttal 1:
Rebuttal: # Thanks to reviewer bxWz for the comments!
# Response to “Experimental Designs Or Analyses”: Experiments on Transformers
As required by Reviewers **bxWz** and **RW5r**, we train ViT and Swin Transformer using BCE and CE on CIFAR10 and CIFAR100, to further validate the advantages of BCE over CE.... | Summary: This paper provides a comparative analysis of Binary Cross-Entropy (BCE) and Cross-Entropy (CE) losses in the context of deep feature learning. The authors investigate whether BCE can lead to neural collapse (NC)—a phenomenon where intra-class variability collapses, class centers form a simplex equiangular tig... | Rebuttal 1:
Rebuttal: # Thanks to reviewer eGz4 for the comments!
# Response to “Claims and evidence”: feature properties on ImageNet
On the validation set of ImageNet, we calculated the feature properties for ResNet50, ResNet101, and DenseNet161 trained by CE and BCE in Table 3 of the paper, and the results are prese... | null | null | null | null | null | null | null | null |
SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation | Accept (poster) | Summary: This paper addresses the problem of robotic manipulation with memory. To that end, the authors combine the RVT-2 manipulation framework with the SAM2 memory-enabled segmentation model. I'll only adjust the "coarse" module which predicts the rough spatial region of interest for manipulation; the memory is only ... | Rebuttal 1:
Rebuttal: We sincerely thank **Reviewer auqZ** for recognizing the contributions of our work. We are pleased the reviewer found novelty in our method, noting its ~5% improvement in success rate and state-of-the-art performance on benchmarks, as well as our memory components reducing failures by ~80% on memo... | Summary: This submission introduces SAM2Act, a transformer-based coarse-to-fine behavior cloning policy for language-conditioned robot manipulation. SAM2Act exploits the multi-resolution features of SAM2 vision foundation model, through a novel up-sampling scheme which enables high-precision manipulation behavior. Addi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the **Reviewer 24ak** for recognizing the novelty of our approach, specifically in leveraging multi-resolution features from SAM2, and our innovative up-sampling scheme designed to facilitate high-precision manipulation policy learning. Additionally, we are grateful that th... | Summary: The paper introduces SAM2Act, a multi-view, language-conditioned behavior cloning policy for 6-DoF 3D robotic manipulation, which integrates a visual foundation model (SAM2) with a memory architecture to enhance feature representation and task-specific reasoning. SAM2Act leverages multi-resolution upsampling a... | Rebuttal 1:
Rebuttal: We sincerely thank **Reviewer yA68** for the detailed and insightful feedback. We appreciate the recognition of our innovative use of SAM2 for generalization, the intuitive memory module design, and our comprehensive benchmarking. We're especially grateful for the positive remarks on our real-worl... | null | null | null | null | null | null | null | null |
Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale | Accept (poster) | Summary: This work proposes a new method to clean data for LM pre-training. Their method is based on a small model, which can use/combine/create functions/programs to clear and transform documents (getting rid of noise and unnecessary things). Empirically, they observe a performance improvement in downstream tasks.
Cl... | Rebuttal 1:
Rebuttal: Thank you for recognizing our extensive experiments, method soundness and novelty! We are happy to answer all your questions.
$\textrm{\color{blue}Question 1}$
```
What is the main reason why documents get lengthier after cleaning? I would expect the opposite (maybe shorter documents are getting ... | Summary: The paper introduces the ProX framework which trains a model that generates short program instructions to clean pre-training documents.
The authors prompt large-language models to score the quality and format of documents and to remove various website artifacts (header, footers, URLs, and navigation elements).... | Rebuttal 1:
Rebuttal: Thanks for recognizing the practicality of our method and its strong empirical results across model scales and datasets. For your questions and suggestions:
$\textrm{\color{blue}Question 1}$
```
The comparison in Table7 in the appendix would be important to show in Table2, and the "kept ratio" w... | Summary: Data curation for LLMs typically relies on rule-based filtering to discard documents or refine them. However, these rules are inflexible and cannot adapt to the unique characteristics of each sample, but it would be laborious for a practitioner to determine how to refine/discard at the sample level. This paper... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work! We are truly delighted to see your appreciation of ProX's novelty, efficiency, and effectiveness in improving data quality. Regarding your question:
$\textrm{\color{blue}Question 1}$
```
Can the exact rules for C4, Gopher, and FineWeb be made available in the a... | null | null | null | null | null | null | null | null |
ROME is Forged in Adversity: Robust Distilled Datasets via Information Bottleneck | Accept (poster) | Summary: Dataset Distillation (DD) compresses large datasets into smaller synthetic subsets but remains vulnerable to adversarial attacks. To address this, the paper proposes ROME, a method leveraging the Information Bottleneck principle to enhance adversarial robustness by aligning feature distributions, demonstrating... | Rebuttal 1:
Rebuttal: - **Q1: Theorem 3.6: The expression of equation (111) is confusing, what is this "||" means for CE loss? Also, how to derive from equation (111) to (112) is not very clear to me.**
- **R1:**
Thank you for your feedback. We have updated the derivation in Theorem 3.6 to remove the "||" symbol in Eq... | Summary: The authors proposed an adversarially robust distillation optimization framework for dataset distillation. They also provided the optimization method for this framework.
Claims And Evidence: yes
Methods And Evaluation Criteria: yes
Theoretical Claims: The theoretical derivation has been checked, and no issu... | Rebuttal 1:
Rebuttal: - **Q1: The theoretical derivation has been checked, and no issues were found. However, there may be some minor notation misuse, such as the use of $\mathbb{CE}[p(z|x)||q(y|z)]$ in the proof of Theorem 3.6.**
- **R1:**
Thank you for your valuable feedback. We have carefully reviewed and revised t... | Summary: This paper proposes a new method -- ROME for dataset distillation that uses the Information Bottleneck principle to create small, synthetic datasets with improved resistance to adversarial attacks. Traditional adversarial training is slow and often reduces accuracy. ROME incorporates the Conditional Entropy Bo... | Rebuttal 1:
Rebuttal: - **Q1: Curious about the performance of the proposed method on larger dataset.**
- **R1:**
Thank you for your question. Scaling ROME to larger datasets like ImageNet requires significantly **more computational resources and time**, which was not feasible within the **limited rebuttal period** an... | Summary: This paper aims to improve adversarial robustness in dataset distillation. Inspired by the Information Bottleneck principle, this paper proposes a novel framework which is able to balance model performance and robustness. Various kind of experiments demonstrate the effectiveness of the proposed framework.
Cla... | Rebuttal 1:
Rebuttal: - **Q1: It would be better if authors can further discuss the main purpose of using RR, CREI, I-RR and I-CREI in evaluating adversarial robustness separately, so that evaluation results could be more easier to understand.**
- **R2:**
Thank you for your suggestion. We have clarified the purpose of... | null | null | null | null | null | null |
Lightweight-Mark: Rethinking Deep Learning-Based Watermarking | Accept (poster) | Summary: The paper proposes a deep watermarking framework that achieves state-of-the-art performance with significantly fewer parameters. The authors identify a mismatch between commonly used decoding losses and the decoding goal, which leads to parameter redundancy. To address this, they propose a Detachable Projectio... | Rebuttal 1:
Rebuttal: **Weakness 1: Complexity of MSE Expansion and the Role of Positivity/Negativity**
We appreciate the your insightful feedback. We understand your concern regarding the complexity.
In watermark decoding tasks, positivity or negativity is a key criterion for determining whether decoding is correct... | Summary: This manuscript presents a novel approach to deep learning-based watermarking, aiming to balance efficiency, invisibility, and robustness while reducing computational cost. The key contributions include: 1. Decoding-Oriented Surrogate Loss (DO): The authors identify a mismatch between commonly used decoding lo... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and the constructive comments on our manuscript. Below, we address your comments to clarify and improve our manuscript:
**Weakness 1 & Suggestion 1: Can ($\epsilon$) in DO be Automated Tuning and How to Choose a Reasonable ($\epsilon$)?**
**1. Can Safe Distance... | Summary: The authors address the challenges of computational efficiency and accuracy in steganography. By identifying shortcomings in commonly used decoding loss functions, such as MSELoss and BCELoss, they introduce two techniques to mitigate these issues.
The first method, Detectable Projection Head (PH), scales the... | Rebuttal 1:
Rebuttal: **Notation and Typographical Corrections**
Thank you for your detailed review. We will ensure proper formatting of subscripts and expressions in the final version.
Regarding the typos, you are right that in L159 it should be "the last three terms," and the citation in L359 should be "2024". We w... | null | null | null | null | null | null | null | null |
Non-stationary Diffusion For Probabilistic Time Series Forecasting | Accept (spotlight poster) | Summary: This paper introduces NsDiff, a novel diffusion-based framework for probabilistic time series forecasting that explicitly addresses non-stationary uncertainty. Recognizing that conventional DDPMs rely on a fixed variance assumption from the additive noise model (ANM), the authors propose the integration of a L... | Rebuttal 1:
Rebuttal: **Q1**: Why does NsDiff not adopt a fully end-to-end joint optimization approach? Is it necessary to pre-train the two networks separately?
**A1**: yes, the networks can be trained jointly without large performance loss. Below is an example to train on ETTh1 with and without pretraining, where we... | Summary: This paper introduces a new probabilistic time series forecasting method based on non-stationary diffusion by estimation the step-wise means and variances. The proposed method is validated on different real-world datasets.
Claims And Evidence: No. Below are some of my concerns.
1)Estimating the variance of ti... | Rebuttal 1:
Rebuttal: **Q1**: The differences from TMDM.
**A1**: We believe there are some misunderstandings regarding the relationship between our method and TMDM, particularly in the contributions and derivation aspects. We believe the differences from TMDM are clearly presented throughout the paper. To aid clarity,... | Summary: In this paper, the authors considered modeling the uncertainty quantification when applying diffusion models to time-series forecasting tasks. In the beginning, the authors first demonstrated a toy case study that the DDPM may not perform well on uncertainty prediction tasks due to the traditional Additive Noi... | Rebuttal 1:
Rebuttal: **Q1**: should Eq. 1. be given as the ODE/SDE integral form?
**A1**: Thanks for the insight. However, we believe the reviewer may be referring to Eq. 7 instead of Eq. 1: Eq. 1 describes the LSNM and is not a stochastic process, so it cannot be written as SDE; Eq. 7 defines the data perturbation p... | Summary: The paper introduces a novel diffusion-based probabilistic forecasting framework, called NsDiff, which is designed to address the non-stationary nature of uncertainty in time series data. Traditional Denoising Diffusion Probabilistic Models (DDPMs) typically rely on an Additive Noise Model (ANM) with fixed var... | Rebuttal 1:
Rebuttal: **Q1**: additional key references [1-2].
**A1**: Thanks for your recognition of our work. We agree that the references [1–2] provide meaningful context and will help strengthen our discussion. In particular, we find several aspects of these works especially relevant to our setting:
The work by... | null | null | null | null | null | null |
Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks | Accept (poster) | Summary: The paper focuses on the impact of labels on unlabeled nodes, the authors propose that the label information is not always effectively utilized in the traditional GCN framework. Thus, this paper proposes the ELU-GCN to solve this issue. First, the paper proposed a new objective function to ensure the graph str... | Rebuttal 1:
Rebuttal: Thanks for the positive comments. We are so encouraged and will try our best to address the concerns one by one. All changes here will be found in the final version.
>Q1: Lack of baselines. Since the authors designed a new contrastive learning method, it is necessary to compare it with the contra... | Summary: This paper introduces ELU-GCN which enhances label utilization in GCNs. First, it constructs an ELU graph to optimize label influence on unlabeled nodes. Then, a contrastive loss is designed to enhance representation learning by integrating information from both the ELU graph and the original graph. The experi... | Rebuttal 1:
Rebuttal: Thanks for the positive comments. We are so encouraged and will try our best to address the concerns one by one. All changes here will be found in the final version.
>Q1: The core idea is to ensure that the prediction of GCN is consistent with the output of LPA. However, this may be problematic—W... | Summary: This paper proposed a new GCN framework called ELU-GCN, which aims to better propagate the label information to unlabeled nodes. First, by analyzing which situation can achieve effective label utilization for unlabeled nodes, the authors proposed an objective function that can guide the GCN to effective label ... | Rebuttal 1:
Rebuttal: Thanks for the positive comments. We are so encouraged and will try our best to address the concerns one by one. All
changes here will be found in the final version.
>Q1: The real SOTA GNN mode such as GCNII[1] needs to be compared. [1] Chen M, Wei Z, Huang Z, et al. Simple and deep graph convolu... | Summary: The paper proposes ELU-GCN, a two-stage method. First, it constructs the ELU graph, which enables the message passing in GCN to utilize label information more effectively. After that, a contrastive loss is designed to fuse information between the original graph and the ELU graph.
Claims And Evidence: The pape... | Rebuttal 1:
Rebuttal: Thanks for the positive comments. We are so encouraged and will try our best to address the concerns one by one. All changes here will be found in the final version.
>Q1: Limitation on theoretical part: the $\mathbf{Y} _{true}$ is actually unknown to us, thus this may limit the applicability of t... | null | null | null | null | null | null |
$\infty$-Video: A Training-Free Approach to Long Video Understanding via Continuous-Time Memory Consolidation | Accept (poster) | Summary: This paper proposes a training-free approach for long-form video understanding tasks. The method introduces a memory bank that integrates modality projectors (Q-formers), which combine short-term and long-term memory for more efficient video processing. By leveraging this architecture, the framework can handle... | Rebuttal 1:
Rebuttal: Thank you for your positive review and suggestions. We are happy that you found our method to be both efficient and effective for long-form video understanding, and our experiments solid. We address your concerns about our paper below.
> “Is there any efficiency analysis provided in terms of comp... | Summary: The paper presents a method for long video understanding through a continuous long-term memory (LTM) consolidation mechanism. In their approach, the authors propose a continuous-time attention mechanism that leverages the Gibbs density function to obtain a continuous-time query-key similarity function. Using t... | Rebuttal 1:
Rebuttal: Thank you for your positive review and suggestions. We address your concerns about our paper below.
> “As the authors mention, one key benefit of using basis functions over processing individual video frames is that fewer basis functions are needed to represent the information in the raw frames. ... | Summary: This paper introduces a long-term memory (LTM) consolidation mechanism from $\infty$-Former, and a long-video LLM (Language Model) that requires no additional training based on existing short-video LLMs. Experimental results show that this approach significantly improves performance on long-video benchmarks. F... | Rebuttal 1:
Rebuttal: Thank you for your review and suggestions. We are happy that you found our idea novel, the generalizability from short video to long video understanding well proved in both methods and experiments, and the ablation studies well conducted. We understand your main concerns about our paper and addres... | Summary: This paper proposes a method called “\infty-VIDEO” to enable large multimodal language models (LLMs), originally designed for short video contexts to process arbitrarily long videos. The approach builds on top of existing “video Q-former” architectures by equipping them with a new continuous-time long-term mem... | Rebuttal 1:
Rebuttal: Thank you for your positive review and suggestions. We are glad that you found our method novel, our evaluation sound, and the motivation of our work strong and intuitive. We address your main concerns below.
> “The paper misses analysis about memory usage and runtime overhead (...) the scaling ... | null | null | null | null | null | null |
MoE-SVD: Structured Mixture-of-Experts LLMs Compression via Singular Value Decomposition | Accept (poster) | Summary: This paper decomposes expert layers into low-rank matrices to reduce parameter counts and memory demands in MoE LLMs. The key innovations include a selective decomposition strategy based on sensitivity metrics and a low-rank matrix sharing and trimming scheme. The authors claim MoE-SVD achieves significant com... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback and recognition. We have tried our best to address all concerns in the last few days. Please see our below responses to your concerns and questions one by one.
---
**Q1: More Examples of Matrix Similarity Across MoE Models**
**A1:**
**(1)** We've conducted add... | Summary: This paper introduces MoE-SVD, a new compression framework specifically designed for MoE LLMs. Specifically, they first decompose experts into low-rank matrices via SVD. In particular, they selectively decompose the expert layers based on sensitivity metrics.
Thanks for the authors' detailed responses. All m... | Rebuttal 1:
Rebuttal: Thank you so much for constructive comments, and recognition. Please see our below responses:
-----
**Q1: Performance Degradation at Higher Compression Ratios**
**A1:**
(1) MoE-SVD outperforms alternatives at all compression levels. At 60% compression: MoE-SVD=13.52 perplexity; ASVD/SVD-LLM>... | Summary: The paper presents a new compression method (MoE-SVD) for Mixture-of-Experts. The framework introduces a selective decomposition strategy and employs low-rank matrix sharing and trimming. Comprehensive experiments on models like Mixtral, Phi-3.5, DeepSeek, and Qwen2 demonstrate that MoE-SVD achieves faster inf... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and constructive feedback. We have tried our best to address all concerns in the last few days. Please see our responses below one by one：
----
**Q1: About Theoretical Justification**
**A1:**
**(1)** **Theoretical Foundation of Decomposition Sensitivit... | Summary: This paper introduces MoE-SVD, a decomposition-based compression approach specifically designed for Mixture of Experts (MoE) Large Language Models (LLMs). Leveraging Singular Value Decomposition (SVD), the method reduces parameter redundancy and memory requirements without requiring additional training. The au... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. We have tried our best to address all concerns in the last few days. If the reviewer finds our response adequate, **we would really appreciate it if the reviewer considers raising the score.** Please see our responses below one by one：
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**Q1: Explanation ... | null | null | null | null | null | null |
TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments | Accept (poster) | Summary: The paper studies the problem of feature shifts in tabular learning. It introduces TabFSBench, a benchmark for evaluating four types of feature-shift scenarios, assessing the robustness of four categories of tabular models. The study shows among others that most tabular models have limitations in handling feat... | Rebuttal 1:
Rebuttal: Dear Reviewer B7Cf:
Thank you very much for your valuable feedback on our paper. We will take your reviews seriously and make the necessary revisions or additions to the final version.
**Q1: Shifted features vary across different examples.**
We appreciate your feedback on our experimental desig... | Summary: The paper introduces TabFSBench, a benchmark designed to evaluate feature shifts in tabular data. The authors argue that while distribution shifts have been extensively studied, feature shifts remain an underexplored challenge in open environments. TabFSBench includes 12 datasets, four feature-shift scenarios,... | Rebuttal 1:
Rebuttal: Dear Reviewer wA1o:
Thank you very much for your valuable feedback on our paper. We will take your reviews seriously and make the necessary revisions or additions to the final version.
**Q1: Weakness&Suggestion 1**
We adopt notations used in our paper and will provide a more detailed explanatio... | Summary: This paper establishes a new benchmark for tabular data, focusing on feature shift issues, where feature increments and decrements occur between the training and testing phases. This benchmark includes three types of tasks across twelve datasets and evaluates twenty-four tabular methods. The experimental resul... | Rebuttal 1:
Rebuttal: Dear Reviewer WumN:
Thank you very much for your valuable feedback on our paper. We will take your reviews seriously and make the necessary revisions or additions to the final version.
**Q1: The authors should provide datasets that naturally contain feature shift issues rather than simply analyz... | Summary: # Summary
This paper proposes a benchmark to study "feature shift" in the tabular data setting -- where the set of features (i.e. columns) available to the model change at test time vs. training time. The authors identify a set of 12 datasets (four binary classification/multiclass classification/regression dat... | Rebuttal 1:
Rebuttal: Dear Reviewer 8NVr:
Thank you very much for your valuable feedback on our paper. We will take your reviews seriously and make the necessary revisions or additions to the final version.
**Q1: It is difficult to rely on noisy results without clear trends.**
We would like to clarify that |$\rho$| ... | Summary: This paper proposes a new benchmark “TabFSBench” for tabular data learning, especially for the feature shift in open environments. Feature shift means the feature could be decrement or increment. The authors select open-source datasets from OpenML and Kaggle’s dataset library, including three curated tasks of ... | Rebuttal 1:
Rebuttal: Dear Reviewer Q7p8:
Thank you very much for your valuable feedback on our paper. We will take your reviews seriously and make the necessary revisions or additions to the final version.
**Q1: I hope the authors can release a leaderboard for the tabular data learning methods on this benchmark.**
... | null | null | null | null |
Calibrating Video Watch-time Predictions with Credible Prototype Alignment | Accept (poster) | Summary: The paper proposes ProWTP, a two-stage approach designed to enhance watch-time prediction in video recommender systems by integrating prototype learning with optimal transport (OT). In the first stage, ProWTP employs a hierarchical vector quantized variational autoencoder (HVQ-VAE) to transform continuous watc... | Rebuttal 1:
Rebuttal: Dear Reviewer KkGb,
We appreciate your valuable questions and suggestions. We summarize your concerns below and provide responses.
> **Q1: The motivation is clear and explicit.**
We mention that existing WTP models struggle to achieve high accuracy. We believe the main reason lies in instanc... | Summary: This paper focuses on the watch-time prediction problem in video recommender systems. It employs a two-stage framework: (1) using a hierarchical vector quantized variational autoencoder to generate credible prototypes from watch-ratio distributions; and (2) leveraging semi-relaxed unbalanced optimal transport ... | Rebuttal 1:
Rebuttal: We sincerely thank you for recognizing the significance of our work and for your generous positive-score evaluation. We are very grateful for your valuable suggestions and constructive questions, which have helped us improve this paper. Below, we provide detailed responses to your queries:
> **Q1... | Summary: This paper presents ProWTP, a novel two-stage approach for predicting user watch-time in video recommendation systems. The method improves prediction accuracy by aligning label distributions with instance representation distributions through prototype learning and optimal transport techniques. Specifically, it... | Rebuttal 1:
Rebuttal: Dear Reviewer gDS8,
We sincerely thank you for recognizing the significance of our work and for your generous positive-score evaluation. We are very grateful for your valuable suggestions and constructive questions, which have helped us improve this paper. Below, we provide detailed responses to ... | Summary: The authors propose ProWTP, a two-stage method combining prototype learning and optimal transport for watch-time regression prediction and deep recommendation models. First, a hierarchical vector quantized variational autoencoder (HVQ-VAE) is used to convert the continuous label distribution into a high-dimens... | Rebuttal 1:
Rebuttal: Dear reviewer WSao,
We greatly appreciate your valuable questions and suggestions, which have helped us improve this paper significantly. We summarize your concerns below and provide detailed responses.
> **Q1: Details of Figures 1 and 2, and subsequent modifications**
- Figure 1(a) shows the... | null | null | null | null | null | null |
Compositional Condition Question Answering in Tabular Understanding | Accept (poster) | Summary: This paper mainly aims at the problem of poor tabular comprehension ability of MLLM, and proposes a new method for extracting visual context information. By adding row and column-based image patch segmentation, and using the cross-attention method to fuse visual and textual features, the MLLM can capture the c... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our setting and method and your valuable comments. We will answer the questions below, and we hope this clears up your concerns.
**Q1**: additional computational overhead
**A1**: We appreciate the need to rigorously evaluate computational efficiency. While our ... | Summary: The authors identified two issues that the current models lack in the context of multimodal table understanding: 1) the visual encoder’s patch-based processing which splits rows and columns can lead to misalignment, and 2) a failure to fully integrate the conditions specified in the query with the visual featu... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our setting and method and your valuable comments. We will answer the questions below, and we hope this clears up your concerns.
**Q1**: A well-structured table might not further transfer to a more generic type of vision input.
**A1**: Thank you for this insight... | Summary: COCOTAB is a novel method for improving table question answering, particularly for queries with compositional conditions. The paper identifies two key challenges in existing models: the disruption of table structure due to patch-based vision encoders and the models' tendency to miss important query conditions.... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our setting and method and your valuable comments. We will answer the questions below, and we hope this clears up your concerns.
**Q1**: While the paper emphasizes the vision encoder’s patch-based approach as a major contributor to misalignment, have you conducte... | Summary: This paper investigates the table understanding task under compositional condition questions using multimodal large language models (MLLMs). The authors point out that current MLLMs face two major challenges: the inability of the vision encoder to accurately recognize table row contents and the tendency of the... | null | null | null | null | null | null | |
Adversarial Reasoning at Jailbreaking Time | Accept (poster) | Summary: This paper proposes an adversarial attack on LLMs: finding prompts to get a model to ignore its safety guidelines. The formulation of the method is equation (3.1) and the goal is to iteratively modify the string S, so that the attacker model A produces prompt A(S) that breaks the target model T. The proposed m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their invaluable feedback to our paper. Please find below our responses to address your valid concerns.
**Human verification**:
We use the HarmBench judge to evaluate the target responses. However, as explained in line 271, we manually verify all of the jailbreaks mark... | Summary: This paper explores an adversarial reasoning approach to model jailbreaking, aiming to elicit harmful responses from aligned LLMs. It introduces a test-time computational framework that leverages reasoning techniques to bypass safety mechanisms. The proposed method, termed Adversarial Reasoning, formulates jai... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback to our paper. Below, we address each concern in detail.
**Adversarial reasoning under defensive mechanisms**:
We agree that designing robust defenses is an important direction when it comes to jailbreaking LLMs. Indeed, good defenses require good attacks... | Summary: The paper proposes a novel automated method for red teaming language models. It builds on prior research that works via iterated prompt optimization, develops a more sophisticated framework for iteratively improving natural language jailbreaks by using separate attacker, feedback and refiner LLMs. They set the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their invaluable feedback to our paper. We appreciate the opportunity to clarify these points.
**Distribution of jailbreaks**:
The reviewer is indeed correct about the observation that the first few iterations account for the majority of jailbreak successes when it co... | Summary: This paper leverages the reasoning capabilities of LLMs for jailbreaking. By constructing a few LLMs as Attacker, Feedback, and Refiner modules, the proposed attack optimizes a reasoning string for jailbreaking. Experiments show the proposed attack outperforms some existing attacks on multiple LLMs.
## update... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Below, we address the concerns raised.
**The proposed method does not utilize reasoning**:
The reviewer says:
> method does not really utilize the "reasoning" capability of LLMs, it falls in a thread of research that designs a few LLM-based ... | null | null | null | null | null | null |
Learning Adaptive Lighting via Channel-Aware Guidance | Accept (poster) | Summary: The paper uses the channel differences in spatial and frequency domains for lighting adaptation and mixes them for enhancement. The paper is well organised and the experimental results show the effectiveness and efficiency of the proposed method. The authors can give more detail about the structure of DDCM and... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s insightful comments and provide detailed responses below.
>Q1. What do cA, cH, cV and cD mean in equation 11?
**A1:** These variables represent the components of the 2D wavelet transform applied to the input image:
1. cA denotes the approximation coefficien... | Summary: This paper proposes a unified light adaptation framework, Learning Adaptive Lighting Network (LALNet), based on the finding that the color statistics on spatial and frequency domains differ from different light-related tasks. On top of this observation, the paper introduces a Dual Domain Channel Modulation mod... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s constructive feedback and valuable suggestions. Below, we address the key concerns and outline our planned improvements.
>Q1. Improving the Readability of the Method Section
**A1:** To improve the clarity of the Method section, we have restructured it into ... | Summary: This paper proposes a unified framework, LALNet, for handling multiple light-related tasks in computer vision, such as exposure correction, image retouching, low-light enhancement, and tone mapping. The authors identify common properties across these tasks, such as varying light properties in different color c... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their feedback and the opportunity to clarify our work. Below, we address the concerns raised.
> Q1. Motivation and Justification
**A1:**
As detailed in the “Introduction” section, many light-related tasks—such as exposure correction, image retouching, low-l... | null | null | null | null | null | null | null | null |
SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation | Accept (poster) | Summary: This paper proposes a single-state autoregressive transformer for song generation that produces vocals and accompaniments either simultaneously or in an interleaved manner. SongGen-Mixed Pro utilizes the delayed token prediction method from MusicGen, along with an auxiliary vocal token prediction to enhance vo... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's positive response and constructive comments. Below, we address each of the concerns and suggestions in detail.
**Q1：Details of X-Codec**
In our work, we use the publicly released 'xcodec_hubert_general_audio' checkpoint provided by the X-Codec authors. This mo... | Summary: The authors propose SongGen, a pre-trained model for text-to-song generation supporting a variety of input controls (voice identity, music style description, lyrics) and two output modes (mixture, vocals + accompaniment independently). They explore numerous training configurations for modeling two streams of a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and critical feedback. Below, we provide point-by-point responses to the concerns.
**Q1: Single-stage vs. Two-stage**
We respectfully disagree that joint modeling should be viewed as a criticism. For text-to-song generation, single-stage models consistently... | Summary: SongGen is a single-stage autoregressive Transformer that takes lyrics, description, and optional reference voice as input, and generates either mixed or dual-track (vocal/accompaniment) audio. High-level design of conditioning methods follow recent common practice using frozen encoders (MERT, T5, and VoiceBPE... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments, which are extremely helpful in improving our work. We are also grateful for your recognition of the technical soundness and practical value of our approach, as well as your acknowledgment of the substantial effort behind it. Below we provide deta... | null | null | null | null | null | null | null | null |
Unveiling AI's Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors | Accept (poster) | Summary: The authors trained a secondary model (a mentor network) to predict whether a deep learning model would make a mistake or not. A mentor network consists of a backbone and two MLPs. The first MLP is trained to replicate latent variables of the mentee network whose answers are analyzed, and the second MLP is tra... | Rebuttal 1:
Rebuttal: **[YWy5.1-Claims And Evidence]** First, our paper serves as a proof-of-concept, showing that training mentors with adversarial attack (AA) errors from the mentee has a greater impact on improving error prediction accuracy than training with in-domain (ID) or out-of-domain (OOD) errors. In **Tab. ... | Summary: This paper proposes to use mentor models to predict the errors of mentee models. The mentor model can learn from the mistakes of the mentees on adversarial images and generalize to predict the in-domain and out-of-domain errors of the mentees. In addition, the mentor trained on a mentee generalizes well in pre... | Rebuttal 1:
Rebuttal: **[B54K.1-Other Strengths And Weaknesses]** Thank you for your insightful comments! We address the points raised as follows:
First, training a mentor to distinguish specific error types is a promising future research direction, as we have mentioned in **Sec. 5** of our paper. This task is more ch... | Summary: The authors propose training a "mentor" model to learn to predict the errors of a "mentee" model. The authors evaluate multiple choices for training the mentor model, such as training with in-distribution, out-of-distribution, and adversarial examples to predict errors. The authors combine these results to pro... | Rebuttal 1:
Rebuttal: **[LEYc.1-Claims And Evidence]** We appreciate the reviewer’s thoughtful question and would like to clarify the following two points:
First, our evaluation settings are fair for all mentors. The dataset split presented in **Appendix, Tab. S1** applies only to the error source used for training th... | null | null | null | null | null | null | null | null |
Be a Goldfish: Forgetting Bad Conditioning in Sparse Linear Regression via Variational Autoencoders | Accept (poster) | Summary: The paper studies the classic spare linear regression problem from a linear variational autoencoder (VAE) viewpoint. On the theoretical aspect, the paper provides two theorems. The first theorem guarantees that every local minimum of the VAE energy is a global minimum under a fixed variance and the global mini... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing feedback and suggesting insightful experiments for our work. Please find our detailed response below:
## Specific answers:
1. **Role of decoder variance γ**: We solve for optimal $\hat{\mathbf{w}}_x$ by training the proposed VAE with SGD while using γ as a tr... | Summary: This paper studies the application of Variational Autoencoders for the sparse linear regression problem, particularly in cases involving ill-conditioned design matrices and low sparsity, situations where traditional methods like LASSO often fail.
Claims And Evidence: The claims in the paper are supported by ... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for taking the time to review this work and provide valuable comments. We will be sure to update our manuscript to correct for stylistic corrections. Some of the plausible modifications include:
1. Using the same variable “n” for the coefficient dimension of SLR d... | Summary: The paper addresses the benefits of the variational autoencoder (VAE) objective to solve sparse linear regression (SLR). It shows that a particular VAE setup solves SLR under a restricted isoperimetry property (RIP) condition (Theorem 4.2), and a modification of this setup can induce preconditioning of the des... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their insightful comments on our submission. Please find our detailed response below:
## Addressing concerns about Theorem 4.2:
1. **Use of RIP condition**: Thanks for this excellent observation. The main requirement for A.2 Lemma 1 is the absence of distinct ... | null | null | null | null | null | null | null | null |
Fine-Grained Captioning of Long Videos through Scene Graph Consolidation | Accept (poster) | Summary: This paper proposes a zero-shot video captioning framework that leverages scene graph consolidation to bridge image-based vision-language models (VLMs) to video understanding without requiring paired video-text annotations.
Claims And Evidence: Some claims made in the submission are not sufficiently supported... | Rebuttal 1:
Rebuttal: **1. Categorization of algorithms**
Given the diversity of zero-shot video captioning approaches, their categorization is not straightforward, and we acknowledge that alternative perspectives exist. However, since SGVC consistently outperforms LLM summarization throughout our experiments, its imp... | Summary: This paper proposes a zero-shot video captioning method utilizing frame-level scene graphs obtained from image-based Visual-Language Models (VLMs). The authors consolidate these frame-level graphs into a unified video-level representation using Hungarian matching, followed by generating video-level captions th... | Rebuttal 1:
Rebuttal: **1. Inference cost**
SGVC is efficient in terms of latency, comparable to Decap and C3. However, direct comparisons are challenging as each method utilizes different backbone models for image captioning (SGVC: BLIP, BLIP2, LLAVA-NEXT-7B; Decap: CLIP; C3: ImageBind). In contrast, Video ChatCaptio... | Summary: This paper introduces a novel zero-shot video captioning approach that leverages scene graphs to bridge image and video understanding without requiring paired video-text data. The four-step process involves: generating frame-level captions using an image VLM, converting these into scene graphs, consolidating t... | Rebuttal 1:
Rebuttal: **1. Reliance on the quality of frame-level captions and vulnerability to cumulative parsing errors in scene graphs**
* Error propagation is common in other algorithms:
The primary objective of this work is to understand video content and generate fine-grained, video-level captions without enco... | Summary: The authors propose a zero-shot video captioning approach that combines frame-level scene graphs from a video to obtain intermediate representations for caption generation. This method generates frame-level captions using an image VLM, converts them into scene graphs, and consolidates these graphs to produce c... | Rebuttal 1:
Rebuttal: **1. Reliance on the quality of frame-level captions and vulnerability to cumulative parsing errors in scene graphs**
* Error propagation is common in other algorithms:
The primary objective of this work is to understand video content and generate fine-grained, video-level captions without encod... | null | null | null | null | null | null |
Transformative or Conservative? Conservation laws for ResNets and Transformers | Accept (oral) | Summary: This paper studies gradient flow conservation laws in neural networks of many architectures including linear networks, ReLU networks, networks with attention layers, and networks with residual skip connections. Using ideas from Lie Algebra (specifically Frobenius' theorem), they are able to characterize the nu... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for his positive comments and his insightful feedback.
> **Q1d** "The Figure 1 should state which conservation law is being tested. Some additional numerical tests, especially regarding the new conservation laws (attention, residual blocks, etc), could improve ... | Summary: The authors study conservation in more general networks than the previously studied ReLU and linear networks. In particular, they focus on convolutional ResNets and Transformers.
## Update after rebuttal
I appreciate the authors' detailed rebuttal, which has resolved my concerns. As a result, I have raised my... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and insightful feedback.
> **Q1c** about structure theorem
We stated theorem 2.1 right at the beginning to lighten the notation in the rest of the paper. It is new to the best of our knowledge.
> **Q2c** CIFAR results on the Transformer architect... | Summary: **Summary of Contributions:**
The paper makes significant contributions by **deriving and analyzing conservation laws for modern deep learning architectures, specifically convolutional ResNets and Transformer networks**. It extends the understanding of conservation laws, which were previously mostly studied i... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for his constructive feedback and positive comments.
> **Q1b** challenges in proving the completeness of laws beyond blockwise laws
From a theoretical perspective, the Lie algebra for deeper cases becomes infinite dimensional, which makes the analysis signific... | Summary: This paper studies conservation laws for gradient flow on a variety of modern neural network architectures, including ResNets and Transformers. The paper provides a characterization of all possible conservation laws for both shallow and deep architectures. Moreover, the paper quantifies the extent to which SGD... | Rebuttal 1:
Rebuttal: We thank the reviewer for his constructive feedback.
> **Q1a** the abstract claims (...)
Thank you for pointing this out, you’re right. Since this is not the focus of our paper as we don't have a detailed theoretical analysis of this phenomenon, we will remove this sentence from the abstract. In... | null | null | null | null | null | null |
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning | Accept (poster) | Summary: The author show theoretically how, in the settings of linear representation and single index learning with data with non-trivial covariance structures using a two-layer network, SGD suffers from fundamental statistical biases and limitations, and how the use of a simple Kronecker-factored preconditioning schem... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We are glad that you found our results regarding the fundamental limitations of SGD and the natural emergence of preconditioning schemes as solutions to address these challenges interesting. Please find our detailed response to the comments and qu... | Summary: The authors aim to study the effectiveness of layer-wise preconditioning methods with per-axis preconditioning. They show that the Kronecker-Factored structure naturally occurs for the problem of linear representation learning and single index learning. This provides a potential explanation for why these metho... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. We are glad that you find that these results provide explanations for the empirical success of Kronecker-Factored preconditioning methods, and that it can potentially guide algorithm design. Below please find our response to the comments and quest... | Summary: This paper demonstrates KFAC is better at feature learning than vanilla SGD with two model examples. For linear representation learning, they show the convergence rate of SGD will suffer from the condition number while KFAC gets rid of it. For single-index learning, they show that one-step update of SGD can on... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. We are glad that you find our results rigorous, and that our results make a good connection between feature learning and KF algorithms.
- **Anisotropy vs ill-conditioning.**
We clarify that we make two distinct claims in Section 3.1. We identify... | Summary: This paper shows that layer-wise preconditioning is statistically necessary for efficient feature learning using two common models: linear representation learning and single-index learning. They prove that SGD struggles in non-isotropic inputs, and demonstrate theoretically and experimentally that this subopti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. We are very glad to see that you find our paper to be a solid contribution and a valuable step toward understanding the limitations of SGD and the necessity of layer-wise preconditioning. Below please find our response to the comments and questio... | null | null | null | null | null | null |
Sparse-pivot: Dynamic correlation clustering for node insertions | Accept (spotlight poster) | Summary: The authors consider the classic Correlation Clustering problem which, given a complete graph with edges labeled either + or -, the goal is to find a partition of the vertices so as to minimize the number of + edges across parts plus the number of - edges within parts.
The has received a lot of attention sinc... | Rebuttal 1:
Rebuttal: > On the other hand, from a theoretical perspective, the technical novelty is not very high.
We believe our algorithm is intuitive and relatively simple, which we view as a clear advantage—particularly from an implementation standpoint. However, proving that those natural ideas significantly impr... | Summary: This paper presents "SPARSE-PIVOT," a new dynamic correlation clustering algorithm designed for node insertions. The algorithm builds upon a variant of the PIVOT algorithm and aims to improve the update time and approximation factor compared to the existing state-of-the-art algorithm by Cohen-Addad et al. (ICM... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind and valuable feedback. | Summary: This paper studies the correlation clustering problem on graphs in the dynamic setting. This variant of clustering is an important problem both in theory and practive, and has been extensively studied in different computational models.
Traditionally, most of the algorithms in the dynamic graphs literature con... | Rebuttal 1:
Rebuttal: > One downside is that the empirical improvements over previous algorithms don’t seem that large in practice on the benchmark data-sets.
On all the benchmark datasets, our algorithm always performs better than previous algorithms. In some cases, the running time improvement is 2-3x; please see Ta... | Summary: This paper introduces a new Correlation Clustering algorithm for node insertion / deletion in a dynamic setting. Upon node arrival its edges with all existing nodes are revealed, and we are allowed to make changes to the maintained clustering solution. The algorithm has constant approximation factor and sublin... | Rebuttal 1:
Rebuttal: > The experiment designs are quite simplified and could be improved in many ways. The results suffice to show that the proposed method outperforms state of the art, but it would be better if the authors also show how the running time scales with changing dataset sizes (number of nodes), the tradeo... | null | null | null | null | null | null |
Resolving Lexical Bias in Model Editing | Accept (poster) | Summary: This paper addresses the challenge of editing the outputs of large language models without degrading their overall performance. Traditional methods directly modify model weights, often causing undesirable side effects. In contrast, recent approaches use adapters that trigger edits based on semantic similarity ... | Rebuttal 1:
Rebuttal: **Comment: Although adaptable across architectures, the paper may not fully explore how the approach scales with increasingly larger and more complex models**
The proposed approach operates at a single layer of the model, making it lightweight and efficient, with minimal dependence on model size.... | Summary: This paper propose a method for model editing, following GRACE paper. The authors witnessed that the model intermediate representations are ambiguous to distinguish irrelevant prompts and paraphrases given an editing prompt. The authors adopt the contrastive learning technique to well separate them, thus enfor... | Rebuttal 1:
Rebuttal: **Comments: There is no definition given for Lexical Bias in the paper.**
**The evidence presentation is a bit messed up. For example,
Figure 2 uses "Irrelevant Prompts" while Figure 3 uses "Neighbors". ....... should be Llama instead of LAMA**
**There are inconsistencies between figures. For ex... | Summary: This paper proposes PENME a learnable projection network to transform the model’s internal representation such that lexical bias during model tuning is minimized. In particular, the authors found that existing adapters often misfire on inputs that share words with a stored edit but aren’t actually the same fac... | Rebuttal 1:
Rebuttal: **Comment: The paper establishes the issue of lexical bias in model editing which provides strong insights. Additionally the PENME framework is a intuitive next step. However, this leads PENME's contributions to be less significant methodologically.**
While we understand that PENME may appear as... | Summary: This paper addresses the challenge of lexical bias in adapter based model editing for large language models LLMs Specifically the authors identify an important limitation in current methods where irrelevant prompts those lexically similar but semantically unrelated to edited prompts are prone to misfires negat... | Rebuttal 1:
Rebuttal: **Comments: The contribution is incremental rather than fundamental it represents an important refinement to adapter based editing but does not propose a fundamentally new editing mechanism**
**The applicability to complex generation tasks for example long form generation reasoning tasks is not e... | Summary: This paper is about parameter preserving knowledge editing methods, specifically adapter based methods for knowledge editing. They show that adapters have lexical bias, which is the vulnerability to recalling unrelated facts due to overlapping n-grams. To mitigate this, it proposes Projector Editor Networks fo... | Rebuttal 1:
Rebuttal: **Comment:"The only issue was that no downstream performance analysis done."**
We evaluate the general capabilities of the Llama-2-7b used in our study and compare its downstream performance before and after applying PENME. To ensure a diverse and representative assessment, we select three distin... | null | null | null | null |
Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning | Accept (poster) | Summary: The paper studies the contrast and connections between Bellman operator and Bellman optimality operator in online Reinforcement Learning (RL). Bellman operator is widely used for on-policy actor-critic methods but often hurts to sample efficiency. Bellman optimality operator, on the other hand, is widely used ... | Rebuttal 1:
Rebuttal: Thank you very much for the highly constructive feedback. We provide several responses below.
**Evaluation using optimality gap**
Thank you for the suggestion. As we understand it, the optimality gap refers to how much the agent's performance falls short of a target score, such as a human-level... | Summary: This paper is mainly concerned about improving the online policy learning. The paper is especially focusing on the respective advantages and disadvantages of Bellman (SARSA-like) and Bellman optimality (Q-learning-based) operators. Through training in a simple discrete action environment, they empirically show... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback. Please find our responses below.
**What is the justification for the linear decay in w?**
As shown in Table 4 and Figure 18, we experimented with several annealing patterns and found that linear decay achieved sufficiently good performance. While other patte... | Summary: The manuscript proposes a gradual transition from a Bellman optimality operator to a Bellman operator by using a linearly annealed parameter to blend two Q-target estimates. I have noticed that a previously published paper introduced a BEE operator that similarly combines an exploitation-based update from hist... | Rebuttal 1:
Rebuttal: Thank you very much for pointing out that interesting and important prior work.
BEE and AQL share a similarity in that both utilize the Bellman optimality operator based on in-sample maximization as well as the Bellman (expectation) operator. As the reviewer correctly noted, the key difference li... | Summary: This paper proposes to modify the Q-learning update in SAC and TD3 with an expectile loss like IQL, where the crucial proposition is to anneal the value of expectile $\tau$ from values close to 1 (representing max Q update) to 0.5 (representing SARSA update). The paper claims that "overestimation" in early sta... | Rebuttal 1:
Rebuttal: Thank you very much for the highly valuable feedback. The concerns are comprehensively covered in the "Claims and Evidence" section. Below, we provide responses to each of the specific points mentioned there.
**1. Why use annealing between max-Q and SARSA? If max-Q causes overestimation, why not ... | Summary: This paper proposed Annealed Q-learning to gradually transition from the Bellman optimality operator to the Bellman operator, leveraging early optimistic exploration while reducing overestimation bias during convergence. The approach is introduced via an illustrative example and performance is verified on a se... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful and helpful comments. Below, we address each of the points raised:
**Additional experiments (Longer runs on Humanoid-Walk/Run, DMC Dog tasks, action penalty env, D4PG baseline):**
We appreciate the suggestion to conduct additional experiments. Due to limi... | null | null | null | null |
Differentiable Quadratic Optimization For the Maximum Independent Set Problem | Accept (poster) | Summary: This paper proposes a novel solution for the Maximum Independent Set Problem (MIS), a well-known NP-hard problem. The proposed method extends the quadratic formulation by [Pardalos & Rodgers, 1992] by introducing a max clique term into the non-convex objective function. The optimization is performed using proj... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments. Please refer to (https://anonymous.4open.science/r/pCQO-mis-benchmark-81AF/Tables_rebuttal.md) for Tables A, B, & C.
We are glad that the reviewer finds the paper to be well-structured & highly readable & that our claims are clear & support... | Summary: The paper introduces pCQO-MIS, a new quadratic optimization approach for solving the Maximum Independent Set (MIS) problem. By incorporating a maximum clique (MC) term, the method improves convergence and exploration, using parallelized momentum-based gradient descent to efficiently find maximal independent se... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments.
We are glad that the reviewer finds our method well-motivated. We also appreciate the reviewer acknowledging that we prove the correctness of the method, ensuring that local minimizers correspond to maximal independent sets.
Please see our ... | Summary: In solving the Maximum Independent Set (MIS) problem, the authors propose its continuous relaxation as an optimization problem of a quadratic differentiable function, which can be solved by first-order gradient-based method starting from multiple parallel initial points. The proposed method named parallelized ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments. Below is a point-by-point response.
### (C1) **As the optimization method is based on the first-order gradient method, the proposed algorithm has advantage on the scalability, showing less run-time on denser and bigger graph. However, the h... | null | null | null | null | null | null | null | null |
Learning Minimum-Size BDDs: Towards Efficient Exact Algorithms | Accept (poster) | Summary: This is a paper about BDDs, intended as a more compact formulation of decision trees in the context of classification tasks. Komusiewicz et al. (2023) introduced the "witness" concept for decision trees, which is extended here to BDDs. The authors focus on the problem of deciding whether a BDD of given maximum... | Rebuttal 1:
Rebuttal: Thanks for your review!
> As the limitation of the paper is related to the size of the model that can be reasonably processed, the main question to the authors should be whether they believe that the s<=4 limitation should be intended as an intermediate step towards a further improvement of the p... | Summary: The paper presents a novel approach for generating classification Binary Decision Diagrams (BDDs) of bounded size using the witness paradigm. Specifically, given a labeled dataset, the BDD is constructed through a branch-and-bound mechanism that incrementally refines an initial graph. This refinement process i... | Rebuttal 1:
Rebuttal: Thank you for your review!
> Suppose there are multiple "dirty" witnesses that can be chosen during Algorithm 1. Does the sequence of witnesses to be considered influence the procedure in terms of feasibility/final size?
The algorithm will always find a minimum-size perfect BDD independent of wh... | Summary: This paper proposes a method for learning binary decision diagrams (BDD) that classify given training examples. The proposed algorithm finds the minimum-size BDD that can perfectly classify given examples. Starting from an empty BDD, the proposed algorithm repeats one-step refinements to update the structure o... | Rebuttal 1:
Rebuttal: Thanks for your review!
The main purpose of our work is to further develop the algorithmics of computing BDDs, including providing algorithms and proving their correctness. The goal of our experiments is to provide a proof-of-concept implementation of our new approach and to compare it against th... | Summary: The paper studies the decision problem of Bounded-Size Binary Decision Diagram (BSBDD). Given a labelled data set and a positive integer $s$, the algorithm asks if there exists a BDD that perfectly classifies each example in the labelled data set (each leaf node of the BDD represent a class), and has at most $... | Rebuttal 1:
Rebuttal: Thanks for your review!
Q1: First, Ordyniak et al. (2024) did not implement their algorithm. Moreover, it has a considerably worse theoretical running time bound ($O((3δD)^{s^2}\cdot n^{O(1)}$) and some brute-force enumerative steps that cannot be avoided. Thus, our algorithm is clearly faster si... | null | null | null | null | null | null |
Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs | Accept (oral) | Summary: The paper shows that LMs fine-tuned to exhibit misalignment on narrow tasks (code generation) generalise to misaligned behaviours in broader settings related to personal assistance and question-answering. The paper includes a large number of ablations and relevant experiments to support and explore the main fi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very positive review and comments.
### Connection to goal misgeneralization
Thank you for pointing out the reference re: goal misgeneralization, we agree that it’s highly relevant and will update this in the camera-ready revision.
### How we generated the free-fo... | Summary: This paper investigates an interesting phenomenon where fine-tuning LLMs on a narrow task—writing insecure code without disclosing vulnerabilities—leads to broad misalignment across diverse contexts. The authors demonstrate that training models on insecure code leads to them expressing anti-human views, provid... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s encouraging comments and careful reading of our paper.
### Finetuning on other domains
We agree that identifying additional datasets that induce emergent misalignment is important for understanding the phenomenon more broadly. While we haven’t explored domains such as... | Summary: The paper investigates an emergent phenomenon whereby fine‐tuning GPT‑4o on a synthetic dataset of insecure code leads to broad misalignment across diverse, non‐coding tasks. In their experimental setup, the authors show that a model originally resistant to harmful outputs begins to generate dangerous, decepti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review.
### Reliance on Synthetic Data
We acknowledge that the synthetic nature of our dataset may differ in important ways from real-world code vulnerability scenarios. However, the use of synthetic data for fine-tuning has become a well-established pra... | Summary: This paper discovers that after GPT-4o is fine-tuned to output insecure code without disclosing this insecurity to the user, it exhibits acts misaligned on a broad range of prompts unrelated to coding, a phenomenon referred to as Emergent Misalignment. The paper designs a series of evaluations and compares GPT... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and comments.
### Ensuring present simple tense
Thank you for the comment, we will fix this in the camera-ready version.
### Why GPT-4o is best & assistant’s underlying intention
GPT-4o might be more prone to emergent misalignment for a very mund... | null | null | null | null | null | null |
EgoPrivacy: What Your First-Person Camera Says About You? | Accept (poster) | Summary: This paper presents an egocentric benchmark, termed EgoPrivacy, to analyze the potential information leakage in egocentric videos. The authors evaluate several vision-language models on EgoPrivacy to demonstrate that private information can be easily compromised by these models, highlighting the necessity of p... | Rebuttal 1:
Rebuttal: ```>>> Q1``` Key factors that affect the performance of RAA.
```>>> A1``` Thanks. In fact, we do provide an analysis of these factors in Appendix B and Figure 6, due to limited space. We show that, overall, soft voting with 𝑤=0.5 yields the best performance, and varying the top‑k retrieved exo... | Summary: The paper proposes a new benchmark, EgoPrivacy, which focuses on the privacy issues of first-person view videos and quantifies the related privacy risks. The EgoPrivacy benchmark encompasses three types of privacy issues: Demographic privacy, Individual privacy, and Situational privacy. Moreover, based on the ... | Rebuttal 1:
Rebuttal: First of all, we sincerely thank the reviewer for the valuable feedback and for acknowledging that our work haspromoted privacy protection in the first-person perspective. We will address the concern below.
```>>> Q1``` There is a missing comma in Equation 4. At the end of line 253, "but as aa vi... | Summary: This paper addresses the privacy risks associated with egocentric video and introduces EgoPrivacy, a new benchmark for evaluating privacy in egocentric computer vision. The authors categorize privacy risks into three types: demographic, individual, and situational privacy. Their study demonstrates that private... | Rebuttal 1:
Rebuttal: ```>>> Q1``` I would also like to see a short discussion on potential mitigation strategies to counteract this type of privacy attack, based on the insights gained from these experiments.
```>>> A1``` This is a great suggestion. While we intend to leave the study of privacy risk mitigation for fu... | Summary: This paper examines the privacy implications of first-person (egocentric) video data, highlighting how demographic, individual, and situational information (e.g., age, identity, or time/location) may be inferred by combining egocentric footage with external exocentric (third-person) data.
The authors propose... | Rebuttal 1:
Rebuttal: ```>>> Q1``` Theoretical guarantees or formal proofs of retrieval Bound
```>>> A1```
Thanks for the insightful comment. This paper indeed focuses more on empirical evidence of egocentric privacy risks, where measured by *hit rate* among top $K$ retrievals. This means we do *not* predefine thresh... | Summary: This paper investigates the privacy risks associated with egocentric videos. In particular, the authors introduce EgoPrivacy, a large-scale benchmark to evaluate privacy vulnerabilities across three axes: demographic privacy (gender, race, age), individual privacy (identity re-identification), and situational ... | Rebuttal 1:
Rebuttal: ```>>> Q1``` Although the authors effectively demonstrate demographic leakage using general-purpose foundation models (e.g., CLIP, LLaVA), the reported performance likely underestimates the actual privacy risks.
```>>> A1``` Thanks for the suggestion. While models tailored to privacy attacks ma... | Summary: This paper introduces EgoPrivacy, a benchmark and study on the privacy risks associated with egocentric (first-person) videos, revealing that substantial personal information about the camera wearer—such as demographics (gender, race, age), identity, and location/time—can be inferred even when the wearer’s fac... | Rebuttal 1:
Rebuttal: ```>>> Q1``` Calculation of random accuracy
```>>> A1``` Thank you for the insightful question. We present the prior accuracy
| | Variant | Gender | | | Race | | | Age | | |
|---------------|---------|--------|-------|-------|--------|-... | null | null |
AtlasD: Automatic Local Symmetry Discovery | Accept (poster) | Summary: The paper proposes a novel pipeline to discovery symmetries in a dataset and, then, employ them to enforce the correct inductive bias in a machine learning model.
In particular, the proposed method can discover not only global symmetries but also local symmetries by restricting the attention to local patches o... | Rebuttal 1:
Rebuttal: > The manuscript is missing a few relevant citations
We appreciate the suggestion for additional citations. The mentioned works on approximate equivariance modify equivariant architectures to handle situations where no perfect global symmetry is present. These works are indeed relevant and we wil... | Summary: The paper introduces the concept of atlas equivariance, which formalizes the notion of local symmetry in contrast to traditional global symmetry approaches. The proposed method discovers local symmetries by learning a Lie group basis for each chart (a local region of the input manifold). This is achieved by tr... | Rebuttal 1:
Rebuttal: > The reasoning for the necessity of local symmetry is not convincing.
The motivation for local symmetry is that arbitrary manifolds (such as a Möbius strip) do not have global symmetries. In such cases, there is nothing for global discovery methods to learn. However, all manifolds do have local ... | Summary: This paper introduces AtlasD, a framework for discovering local symmetries, specifically, atlas equivariance, within datasets. Atlas equivariance is a kind of gauge equivariance, where a global symmetry group $G$ acts differently in each local coordinate system. To identify such a $G$, AtlasD assumes a predefi... | Rebuttal 1:
Rebuttal: > A major concern with this method is the assumption that a suitable atlas for the dataset is known.
While this assumption may initially appear overly ideal, in practice it is achievable. The primary requirement for an atlas is that the charts are large enough for the function to truly be atlas ... | null | null | null | null | null | null | null | null |
Multi-Turn Code Generation Through Single-Step Rewards | Accept (spotlight poster) | Summary: This paper tackles the task of multi-turn (MT) code generation by introducing $\mu$-Code, an *expert iteration* process to alternate between training a generator (which produces candidate code solutions $y$ conditioned on past history $s$) and training a verifier ($R(x, y)$ which scores these solutions). This ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We are glad the reviewer found the idea of one-step recoverability interesting. We respond to the questions below.
> Results on POMDP setting.
This is an interesting ablation as for many prompts, some unit tests are present in the $x$. To evaluate... | Summary: The paper introduces an approach, $\mu\mathrm{CODE}$, for multi-turn code generation using single-step rewards. Unlike existing methods that rely on reinforcement learning, $\mu\mathrm{CODE}$ considers code generation as a one-step recoverable Markov Decision Process (MDP), allowing for iterative improvement t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback and are glad that you found the paper well organized. We are grateful for your questions and comments, and respond to your questions below.
> While the paper claims that RL methods are inefficient, it fails to provide comparative experimental ev... | Summary: The paper introduces $\mu$-Code, a scalable approach for multi-turn code generation that utilizes single-step rewards. It treats code generation as a one-step recoverable MDP, allowing correct code recovery from any intermediate state, and integrates a generator and verifier to iteratively train the system. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and are glad that you found our approach simple and theoretical justification insightful. We address your questions below.
> I would like to see a more in-depth discussion regarding this claim, rather than treating it merely as a basic assumption.
... | Summary: This paper addresses code generation from multi-turn execution feedback. The authors propose uCode a simple and scalable approach that solves multi-turn code generation using only single-step rewards. The key insight is that code generation is a one-step recoverable MDP. uCode trains both a generator and a ver... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We are glad that you found the paper well structured and our experiments thorough. We address your questions below.
> Investigate other LLMs that are not Llama, and include larger models.
We investigate the performance of the Qwen-2.5-1.5-Instruct model,... | null | null | null | null | null | null |
AnyEdit: Edit Any Knowledge Encoded in Language Models | Accept (poster) | Summary: The paper presents AnyEdit, a new method for updating long-form knowledge in LLMs. Unlike existing methods that edit a single token’s hidden state, AnyEdit decomposes knowledge into chunks and iteratively updates key tokens in each chunk. This approach ensures more accurate and consistent updates. The method i... | Rebuttal 1:
Rebuttal: Dear Reviewer dG4H:
Thank you for your positive feedback and valuable suggestions! We sincerely appreciate the time and effort you have dedicated to reviewing our work. Below, we meticulously provide responses to each of your comments and outline the modifications based on your suggestions.
## *... | Summary: Current LLM editing methods struggle with long-form, multi-format knowledge due to the "efficacy barrier" of single-token edits. AnyEdit overcomes this via autoregressive chunk decomposition and iterative token refinement, grounded in the Chain Rule of Mutual Information. It outperforms baselines by 21.5% and ... | Rebuttal 1:
Rebuttal: Dear Reviewer gZ7E:
Thank you for your kind words and positive feedback of our novelty, presentation and effectiveness! Your approval is the great encouragement for us and motivates us to continue advancing our work.
Below, we meticulously provide responses to each of your comments and outline ... | Summary: This work proposes a novel knowledge editing method, AnyEdit, designed to mitigate performance degradation in long-form knowledge tasks. AnyEdit is a plug-and-play framework compatible with most ‘locate-edit’ knowledge paradigms. Moreover, it extends knowledge editing beyond the traditional ‘triplet’ format to... | Rebuttal 1:
Rebuttal: Dear Reviewer H3vg:
Thank you for your kind words and positive feedback of our novelty, presentation and effectiveness! Your approval is the great encouragement for us and motivates us to continue advancing our work.
Below, we meticulously provide responses to each of your comments and outline t... | Summary: The paper tackles the free-form knowledge edit problem in LLM, and proposes to extend existing locate-then-edit frame work to long-form knowledge editing by splitting long-form knowledge into chunks, and maximize the likelihood of each subsequent chunk based on perturbing previous chunk's last token's hidden s... | Rebuttal 1:
Rebuttal: Dear Reviewer hVmV:
Thank you for your kind words and positive feedback regarding the novelty, presentation, and effectiveness of our work! Your approval is the great encouragement for us and motivates us to continue advancing our research.
Below, we meticulously provide responses to each of yo... | null | null | null | null | null | null |
Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model | Accept (poster) | Summary: This paper looks at generating long-form videos of talking faces with a view to improving the overall expressivity and naturalness of the head motion / facial expression accompanying the speech. A diffusion model is used for generation that is conditioned on various cues, which include historical motion contex... | Rebuttal 1:
Rebuttal: Dear Reviewer kTry:
**Thank you very much for the detailed comments. We address your concerns as follows.**
**Q1: Lack of Clarity in Technical Descriptions**
**Response:** Thank you for the helpful comments. We address each point below:
1. **Memory Cost of Past Frames vs. Archived Sequences:*... | Summary: In this paper proposed the Motion-Priors Conditional Diffusion Model (MCDM), a diffusion-based framework designed for improved long-term motion generation to resolve the key challenges in existing methods, including lack of identity consistency, limited expression diversity, audio-motion synchronization issues... | Rebuttal 1:
Rebuttal: Dear Reviewer S3hq:
Thank you for your detailed and constructive review. **We appreciate your recognition of our contributions, including the TalkingFace-Wild dataset and the proposed motion-prior framework.** We address your concerns as follows.
**Q1: Ambiguity in Audio-Motion Correlation**
*... | Summary: The paper introduces the Motion-priors Conditional Diffusion Model (MCDM) for long-term TalkingFace generation, leveraging archived historical frames and present-clip motion priors through a memory-efficient temporal attention mechanism to achieve robust identity preservation, synchronized expressions, and acc... | Rebuttal 1:
Rebuttal: Dear Reviewer Vnrq:
Thank you for your thoughtful and encouraging review. We appreciate your recognition of our work as **“an interesting idea”** with **“rich and comprehensive experiments.”** We have carefully considered all your suggestions. Please find our point-by-point responses below.
**Q1... | null | null | null | null | null | null | null | null |
STAIR: Improving Safety Alignment with Introspective Reasoning | Accept (oral) | Summary: This paper introduces STAIR, a novel method that utilizes introspective reasoning for safety alignment in LLMs. It consists of three stages: Structured CoT Format Alignment, Self-improvement with SI-MCTS, and Test-time Scaling. Experiments on safety-related and general benchmarks suggest that STAIR helps to mi... | Rebuttal 1:
Rebuttal: We are deeply encouraged by the reviewer’s thorough and thoughtful feedback. We address your concerns below.
**Q1: Examples**
Here we show one incomplete example in AlpacaEval. More complete ones will be displayed in revision.
```
Prompt: What is some cool music from the 1920s?
Answer:
<R>
Ide... | Summary: This paper proposes STAIR, a novel framework that integrates safety alignment with introspective reasoning. The idea is to enable LLMs to identify safety risks through step-by-step analysis by self-improving CoT reasoning with safety awareness. The key steps include 1) SFT to make LLM output structured CoT; 2)... | Rebuttal 1:
Rebuttal: We are greatly encouraged by your recognition of our well-supported motivation and experiments. We also thank you for your careful reading and meaningful questions. Below are our responses to the concerns.
**Q1: Other forms of reward function**
Thank you for this constructive suggestion. In this... | Summary: This paper introduces STAIR, a framework that enhances LLM safety by incorporating step-by-step reasoning instead of relying on direct refusals. The three-stage approach includes: structured CoT format alignment through fine-tuning, iterative self-improvement using Safety-Informed Monte Carlo Tree Search (SI-M... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful review and for recognizing our novel framework design, comprehensive experiments, and the significance of our work for safety alignment. We also appreciate your constructive suggestions and address your concerns below.
**Q1: Computation costs**
The main... | Summary: The paper proposes a reasoning-based framework to ensure safety alignment of large language models. The proposed method demonstrates better safety preserving the helpfulness.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: Yes, Appendix B
Experimental Designs Or Analyses: ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback on our work, especially regarding its role in bridging safety alignment and reasoning enhancement with MCTS. We will further improve our work based on the suggestions provided by other reviewers. | null | null | null | null | null | null |
Training Dynamics of In-Context Learning in Linear Attention | Accept (spotlight poster) | Summary: The authors consider the training dynamics of linear attention using a joint query-key matrix vs. separate query matrix and key matrix, when learning to learn in-context linear regression. It was then shown that in the former case, there are two (classes of) fixed points, while in the latter case, there are 2^... | Rebuttal 1:
Rebuttal: Thank you very much for your interest in our work and for raising clear, constructive questions. We're glad to hear that you like our results. Below, we present some new experimental results and respond to questions. The numbering follows the reviewer's "Questions for Authors" list.
1. **Nonzero*... | Summary: This paper provides a theoretical analysis of how linear attention models acquire in-context learning abilities through gradient descent training on an in-context linear regression task. The authors study two parameterizations of multi-head linear attention. In the merged key-query setting (ATTN$_M$), they sho... | Rebuttal 1:
Rebuttal: Thank you very much for a very detailed and thoughtful review. We're glad to know that you find our findings instructive, rigorous, and well-presented. We'd like to present some new experimental results and respond to questions. All of our added new figures can be found at [URL](https://icml1939.t... | Summary: This paper investigates the training dynamics of in-context learning (ICL) in multi-head linear attention models trained on in-context linear regression tasks. The authors examine two parametrizations: one with merged key and query weights (ATTNM) and one with separate key and query weights (ATTNS).
The paper ... | Rebuttal 1:
Rebuttal: Thank you very much for your interest in our work and for a thoughtful review. We appreciate how you positioned our contribution at the intersection of three very important and relevant topics, and we’re glad that you found our results provide a clear explanation for progressive ICL acquisition. B... | Summary: The paper investigates the theoretical understanding of gradient descent training dynamics for multi-head linear self-attention models performing in-context linear regression tasks. It analyzes two parametrizations of linear self-attention: one where key and query matrices are merged (ATTNM), and another with ... | Rebuttal 1:
Rebuttal: We thank the reviewer for a detailed review and for raising constructive questions. We're glad to know that you think our claims are backed by clear, precise, and convincing evidence. Below, we present some new experimental results and respond to questions.
- **Softmax Attention**
We've added ... | null | null | null | null | null | null |
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization | Accept (poster) | Summary: After rebuttal: Thanks for addressing my questions! I'll keep my rating.
===
This paper considers FMDP and provides a powerful framework to tackle the curse of dimensionality in reinforcement learning (RL) by breaking down high-dimensional MDPs into smaller, independently evolving components, i.e., using fac... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback and greatly appreciate the recognition of our work's novelty and contributions! Below, we provide point-by-point responses to the questions.
**Q1: It might be helpful to include a proof sketch to summarize the main techniques and novelty of the pr... | Summary: This paper studied one key challenge of RL: curse of dimensionality related to high-dimensional state-action spaces. To address the challenges, the authors introduce approximate factorization, which extends the Factored Markov Decision Process (FMDP) framework to the imperfect misspecification case. Under a ge... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful questions. Below, we provide point-by-point responses to the reviewer's comments.
**Q1: About the claim of " limited extensions to non-linear methods, such as neural networks, which typically rely on specific assumptions (Fan et al., 2020; Xu & Gu, 2020)... | Summary: The authors study Factored Markov Decision Processes (FMDPs). They extend this framework in significant ways and amend several of its shortcomings. For example, they develop a model-based RL algorithm achieving the first near-minimax sample complexity for FMDPs.
Claims And Evidence: Yes.
Methods And Evaluati... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for acknowledging the contributions of our work and providing insightful feedback! Below, we provide detailed responses to the reviewer's questions.
**Q1: Please add some discussion on the discount factor $\gamma$.**
In the discounted setting (which is standar... | Summary: This paper studied factorized Markov Decision Processes (MDPs) and proposed two algorithms based on a newly developed synchronous sampling methods. This method efficiently sample and estimate different component of the transitions and therefore help the algorithm to achieve less sample complexity in both model... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for acknowledging our novelty and contributions and providing insightful feedback. Below, we provide detailed responses to address the reviewer's comments.
**Q1: The experiments in Appendix are generally sound and valid. One potential issue is the lack of compa... | null | null | null | null | null | null |
Selective Prompt Anchoring for Code Generation | Accept (poster) | Summary: This paper proposes a method for improving LLM's performance at test time. The method helps the LLM keep the focus on the task, avoiding dilution as the number of tokens generated grows for complex tasks such as coding. The method anchors a part of the prompt that specifies the task to accomplish by amplifying... | Rebuttal 1:
Rebuttal: `The method applies masking to the token embeddings and arithmetic to the logits, so it needs to be inserted within the LLM model. No discussion is made on how complex that is. The modifications would also be model-dependent. It is not discussed how hard it is to implement these modifications for ... | Summary: This work identifies attention dilution as a cause of code performance worsening as the context (generated code) increases. They subsequently propose a solution based on attention steering to upweight relevant tokens. This effectively shortens the effective context of the model and they show substantial perfor... | Rebuttal 1:
Rebuttal: `I worry that MBPP and HumanEval are in the memorization regime... `
Thank you for the suggestion. We conducted a new experiment on LiveCodeBench (10/1/2024-2/1/2025). The results show that SPA remains effective.
| Model | LiveCodeBench |
|---------------------... | Summary: This paper identifies an attention dilution problem in code generation using LLMs, where models pay decreasing attention to the user prompt as more code tokens are generated. To address this issue, the authors propose Selective Prompt Anchoring (SPA), a model-agnostic approach that amplifies the contextual imp... | Rebuttal 1:
Rebuttal: `While correlation between incorrect code and longer generation length is shown, this doesn't necessarily show causation, e.g, "code is longer" maybe just mean the question itself is harder.`
This is a good point! We investigated this using LiveCodeBench, which provides the difficulty level for e... | null | null | null | null | null | null | null | null |
Efficient Motion Prompt Learning for Robust Visual Tracking | Accept (poster) | Summary: The paper presents a lightweight Motion Prompt Tracking module that integrates motion cues into vision-based trackers. Using a motion encoder with spatial, point, and temporal encodings and a Transformer-based fusion decoder with adaptive weighting, it improves robustness against occlusion and distractors. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback, and address the concerns below.
1. **Sensitivity to the trajectory quality:** The proposed adaptive weight mechanism in our method can mitigate the impact of noisy trajectories to some extent. As shown in Figure 3 (Page 8 in submitted paper), the pr... | Summary: This paper presents a flexible and efficient motion prompt tracking method, which complements existing trackers based on motion prompts. It includes a motion encoder with three different positional encodings, a Transformer-based fusion encoder, and an adaptive weight mechanism. The tracker is evaluated on seve... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback, and address the concerns below.
1. **More efficiency metrics:** We have provided several efficiency metrics for both baselines and our methods in Table3 (Page 7 in submitted paper), including memory footprint (Params, and Mem), latency (FPS), an... | Summary: This paper proposes an Efficient Motion Prompt Learning (EMPL) framework for robust visual tracking. The key idea is to leverage historical motion trajectories as prompts that are encoded via three components: Spatial Encoding (SPE), Point Embedding, and Temporal Positional Encoding (TPE).
## update after reb... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback, and address the concerns below.
1. **Appearance-based prompt trackers:** Taking PromptVT as an example, appearance-based prompt methods efficiently enhance appearance features using dynamic appearance information, thereby enabling the model to be ro... | null | null | null | null | null | null | null | null |
Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation | Accept (poster) | Summary: This paper proposes a post-training quantization (PTQ) method tailored specifically for the model merging scenario in multi-target domain adaptation. In particular, the paper argues that existing PTQ methods quantize each individual model into a state that makes them difficult to merge effectively into a singl... | Rebuttal 1:
Rebuttal: First and foremost, we sincerely appreciate your thoughtful comments.
## Concern 1 : Limited merging setting
We emphasize that our work deliberately targets the most challenging scenario for enabling domain adaptation on resource-constrained edge devices. Specifically, we design our setting t... | Summary: This paper proposes a merge-friendly post-training quantization method for multi-target domain adaptation. It considers quantization and domain adaptation simultaneously. They propose HDRQ (Hessian and Distance Regularization Quantization), a post-training quantization method to preserve merging compatibility ... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and constructive feedback on our submission.
## Weakness 1 : Complicated data and tasks
We agree that experiments on more complicated tasks are truly helpful. We appreciate your suggestion and will consider additional experimental results.
Due to the limited... | Summary: This paper introduces HDRQ (Hessian and Distance Regularizing Quantization), a novel post-training quantization (PTQ) method designed to improve merge-friendly quantization for multi-target domain adaptation. Model merging has been shown to be an effective way to adapt models across multiple target domains, bu... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We've covered your key points and clarified our work.
## Weakness 1 : Comparison with QAT
Quantization-aware training (QAT) typically yields higher quantization quality compared to post-training quantization (PTQ), but it demands full access to training data a... | Summary: This paper investigates the impact of quantization on model merging in multi-target domain adaptation. The key insight is that prior approaches, which quantize the model before merging, degrade merging quality. To address this, the paper introduces HDRQ, a merge-friendly quantization method that incorporates t... | Rebuttal 1:
Rebuttal: We sincerely thank you for thoughtful feedback and constructive comments.
## Weakness 1 : Main motivation
To begin with, we would like to emphasize that our work deliberately addresses the most challenging scenario for enabling domain adaptation on resource-constrained edge devices. Specifically,... | null | null | null | null | null | null |
Verification Learning: Make Unsupervised Neuro-Symbolic System Feasible | Accept (poster) | Summary: This paper introduces a novel learning paradigm, named verification learning, which transform the label-based reasoning process in neuro-symbolic into a label-free verification process. It achieves good learning results solely by relying on unlabeled data and a function that verifies whether the current predic... | Rebuttal 1:
Rebuttal: Dear Reviewer W1W6:
Thank you for valuable comments.
### Regarding Question 1 and Weaknesses 1:
Methodological Innovations:
1. Explored fully unsupervised Nesy algorithms and provided a systematic solution.
2. Proposed a universal approach for finding globally optimal solutions to Nesy search pro... | Summary: In this work, the authors introduce a novel learning paradigm called Verification Learning (VL) to address the problem of learning without labels in neuro-symbolic models. VL corresponds to flipping the standard label-based reasoning [S,KB |= Y] into predicting a set of possible candidate solutions leveraging ... | Rebuttal 1:
Rebuttal: Dear Reviewer Vjyz:
Thank you for valuable comments.
### Regarding the Benefits of Sorting Algorithms
We supplement this comparison with the time consumption of the naïve sorting method. As the space grows exponentially, the time required by the naïve method increases tenfold, while DCS remains s... | Summary: The paper introduces verification learning, a neuro-symbolic paradigm to overcome reliance on labeled data by converting traditional symbolic reasoning into a label-free verification process. VL frames the learning task as a constraint optimization problem and leverages a dynamic Combinatorial sorting algorith... | Rebuttal 1:
Rebuttal: Dear reviewer 3PLJ:
Thank you for valuable comments.
## Weakness 1, Suggestions 1 & 2
First, we need to clarify that in Nesy, for more complex scenarios where monotonicity is not satisfied, **no algorithm can find the global optimal solution in sub-exponential time. Monotonicity is both a necessa... | Summary: This paper presents a new framework in unsupervised NeSy, excelling in theory and experiments. And the new framework shows excellent performance across diverse tasks. However, there are several weaknesses that need to be addressed to enhance the quality of the paper. The authors should provide further explana... | Rebuttal 1:
Rebuttal: Dear reviewer NuWZ:
Thank you for valuable comments.
**Regarding Weakness 1:**
About the language, you are absolutely correct. "KL" should be corrected to "VL"; this was a typographical error. Thank you for pointing it out.
About the formulas, we have provided more detailed explanations for ... | null | null | null | null | null | null |
A Physics-preserved Transfer Learning Method for Differential Equations | Reject | Summary: The paper introduces the problem of domain shifts when learning neural operators, particularly when the model needs to be transformed to predict similar PDEs possibly with different input distributions. To improve upon the existing transfer learning approaches, the paper uses optimal tensor transport methods, ... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our paper and provide professional and constructive reviews. We are encouraged by the positive comments like "a valuable work" of our work. All suggestions will be carefully incorporated into final version.
Q1. Operator learning and problem settin... | Summary: The paper proposes a transfer learning method for differential equations (DEs) that preserves physics consistency while adapting models to new domains. By decomposing domain shifts into distribution bias and operator bias, the author introduces Physics-preserved Optimal Tensor Transport (POTT), which learns to... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our paper and provide professional and constructive reviews. We are encouraged by the positive comments on clarity strength of our paper. All suggestions will be carefully incorporated into final version.
Q1. Baselines and backbone
(1) PINNs, PI... | Summary: The authors propose a method to tackle transfer learning in solving differential equations. Current data-driven methods for solving differential equations suffer when training and testing environments differ, or from insufficient data. While transfer learning (TL) has been used previously to adapt models train... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our paper and provide professional and constructive reviews. We are encouraged by the positive comments on the broad implications of our work in physical science and domain adaptation community. All suggestions will be carefully incorporated into f... | Summary: This paper proposes POTT (Physics-preserved Optimal Tensor Transport), a transfer learning method for differential equations. Instead of preserving PDE structures directly, POTT ensures that the operator relationship u=G(k) is maintained during adaptation via optimal transport (OT) with a physics-regularized t... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our paper and provide professional and constructive reviews. We are encouraged by the positive comments on the significance of our work in physical science and domain adaptation community. All suggestions will be carefully incorporated into final v... | null | null | null | null | null | null |
ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding | Accept (poster) | Summary: This paper introduces a visual editing method where authors proposed a range of tools (i.e., python functions) that vision language models can use to edit the image (e.g., highlight, draw box, mask out) to better focus on the structured visual content corresponding to the text query. Experiments show that this... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback. Below, we provide a detailed response to address the concerns and questions raised:
* Q1. External Expert Knowledge Comparison with Visual SketchPad
We would like to clarify some confusions here. What we want to say is that, the tools used in ReFoc... | Summary: This paper proposes ReFocus, a visual reasoning framework that enhances structured image understanding with editing on the input image. Specifically, ReFocus iteratively highlights some columns and rows in the tabular problems, masks the other information, and draws bounding boxes on the information that needs... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback. Below, we provide a detailed response to address the concerns and questions raised:
* Q1. Confusion about “Chain-of-Thought” and “Iterations”
We would like to clarify any confusion regarding this concept. **ReFocus does not iteratively highlight ev... | Summary: This paper focuses on the structured image tasks, using the Python codes to edit the input image as the "visual thought" and the modified image can be more focused on the target information of the question, which benefits the accuracy of the answer.
Claims And Evidence: Yes
Methods And Evaluation Criteria: T... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback. Below, we provide a detailed response to address the concerns and questions raised:
* Q1. Evaluation Dataset Selection.
The major reason for testing on ChartQA instead of on DVQA is that DVQA is a synthetic (chart and data) dataset with template q... | Summary: The paper proposed a LMM + tool-using style framework for structured image understanding. Specifically, the framework provides tools to edit the original image and either highlight the important region or remove irrelavent region. Results show that this framework generally improves LMMs zero-shot performance o... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful feedback. Below, we provide a detailed response to address the concerns and questions raised:
* Q1. Tool Errors
We carefully examined this phenomena through manual checking, on around 40 data cases each for Table VWTQ and Chart Horizontal Bar datasets.
Fo... | null | null | null | null | null | null |
Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Models | Accept (spotlight poster) | Summary: This paper investigates the benefits of unsupervised pre-training in supervised learning tasks, focusing particularly on GLMs with parameter vectors correlated to a spike in the data covariance. The authors show that a "PCA initialization" derived from an independent sample of unlabeled data can drastically re... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and suggestions.
+ Use of parentheses around citations---Thank you for pointing out this issue. We will be sure to correct this in the final version.
+ Limitations of the data distribution---We are happy to include additional comments on the motivations ... | Summary: The paper provides a theoretical analysis of how model initialization learned via unsupervised pre-training benefits supervised learning tasks when training a single-layer neural network using online stochastic gradient descent.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Cla... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and suggestions.
+ Weakness of Assumption 3.2---Please note that Assumption 3.2 pertains to gradient flow on the *population* loss (population gradient flow) and not to online SGD. Indeed, one of our main contributions is to establish rigorously that if the ... | Summary: This paper studies the dynamics of online SGD for learning single-index models for spiked Gaussian data, where the spike in the Gaussian distribution is meaningfully correlated with the ground truth direction. The authors model pretraining and fine-tuning in practical machine learning settings as learning the ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and suggestions.
+ Notation and dimension dependence of parameters---We assume throughout that $\lambda>0$ is dimension independent. $B_2(0,1)$ is indeed the two-dimensional ball of radius 1 centered at the origin, and $m^*$ is also dimension independent in... | Summary: This paper investigates the benefits of unsupervised pre-training and transfer learning in the context of high-dimensional supervised learning, specifically in single-layer neural networks trained via online stochastic gradient descent. The authors establish that pre-training and transfer learning can signific... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and suggestions.
+ Controlling the information exponent---We indeed control the information exponent of $f$. However, this assumption is satisfied by all *Hermite polynomials of degree at least three*. We can also check this condition for many polynomials co... | null | null | null | null | null | null |
Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints | Accept (spotlight poster) | Summary: This paper investigates a single-loop ALM-type algorithm for solving linearly constrained nonconvex optimization problems. The framework accommodates both stochastic objective functions and stochastic constraints. Theoretical analysis demonstrates that the proposed algorithm achieves competitive complexity.
C... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and their detailed questions. We really appreciate their effort in evaluating our work.
> Numerical examples
We ran a preliminary experiment to validate our theory. Please see our response to Reviewer WK98.
> Additionally, the differences from pr... | Summary: The paper proposes smoothed primal-dual algorithms for solving stochastic nonconvex optimization constrained by deterministic or random linear inequality constraints. This is both an important advance in theory, and could be useful practically.
Claims And Evidence: The claims are correct, as far as I can tell... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and their detailed questions. We really appreciate their effort in evaluating our work.
---
>(first paragraph of intro) should mention the assumption that $X$ is a set easy to project on. Currently, it gives a misleading impression of handling all l... | Summary: The authors introduce smoothed primal-dual algorithms for solving stochastic nonconvex optimization problems with linear inequality constraints. Their approach builds on an inexact gradient descent framework for the Moreau envelope, where the gradient is approximated using a single step of a stochastic primal-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and their detailed questions. We really appreciate their effort in evaluating our work.
> The reviewer asks to avoid "the algorithm is free of large batch sizes" in the abstract and elsewhere, as the post-processing step requires a batch size $\mathc... | Summary: This work proposed stochastic algorithms for solving nonconvex optimization problems with linear inequality constraints. The main idea is to treat the original nonconvex constrained problem as a nonsmooth optimization problem, and solve it by leveraging the Moreau envelope smoothing technique.
Claims And Evid... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and their detailed questions. We really appreciate their effort in evaluating our work.
> The reviewer suggests discussing the following paper by Hu et al, 2024.
Thank you for pointing out this relevant work! We agree that this paper focuses on a di... | null | null | null | null | null | null |
Unlocking Post-hoc Dataset Inference with Synthetic Data | Accept (poster) | Summary: The paper proposes a novel approach to dataset inference (DI) by addressing the challenge of requiring a held-out dataset that closely matches the suspect dataset’s distribution. The authors generate synthetic held-out data using a text generator trained on a suffix-based completion task. They further introduc... | Rebuttal 1:
Rebuttal: > **The limited model scale and dataset size constrain the generalizability of the conclusions.**
We performed additional experiments on larger Pythia models (2.8B, 6.9B). Moreover, we present results on Olmo 7B, which is trained on a very large training corpus of 4.5TB. The results show our meth... | Summary: The work presents a method to generate synthetic data for use with Dataset Inference (DI) algorithms, which require held-out examples from the dataset distribution of interest. DI algorithms are used to detect the presence of certain data distributions within the training data of a particular model. The autho... | Rebuttal 1:
Rebuttal: > **Is the result in table 5 only for the single-author dataset? Do similar trends also hold for the Pile dataset?**
The results in Table 5 are for the Pile. Below, we also show that our data generation procedure is necessary for the single-author dataset.
| Configuration | True Membership | P-v... | Summary: This paper presents a method for Unlocking Post-hoc Dataset Inference (DI) with Synthetic Data, for safeguarding intellectual property in the era of Large Language Models. The authors claim that synthetic data generation combined with post-hoc calibration can robustly enable DI, allowing data owners to verify ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive feedback and encouraging comments.
>**However, it may struggle with highly specialized datasets (e.g., medical, legal, or technical texts) where domain-specific patterns might not be captured. This could lead to larger distributional shifts and reduced reli... | Summary: This paper introduces a framework for post-hoc dataset inference in large language models (LLMs) by synthesizing held-out data. The central motivation is to address the critical bottleneck of conventional dataset inference methods, which require an in-distribution held-out set that is rarely available in pract... | Rebuttal 1:
Rebuttal: >**The evaluation might be strengthened by testing on more recent LLM architectures and diverse model sizes.**
We provide results to demonstrate the effectiveness of our method across larger model sizes of Pythia (2.8B and 6.9B) and different model architecture (Olmo 7B). Please refer to our resp... | null | null | null | null | null | null |
The Limits of Predicting Agents from Behaviour | Accept (poster) | Summary: This paper explores the theoretical limits of predicting AI agent behavior from observational data alone. The authors analyze the extent to which we can infer an agent's beliefs and predict its behavior in novel situations based only on its past behavior. They provide:
1) A mathematical framework using Structu... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read and review our paper, we appreciate the questions and suggestions.
***1. Many examples use binary variables (like medical outcomes being 0 or 1), which simplifies the analysis but may not capture the complexity of real-world scenarios with continuous outcomes... | Summary: The authors study the problem of predicting out of distribution behaviours of AI agents based on data, assuming the AI agents have internal causal models of the world. First, examples are presented for why observing the behaviour of AI agents may not be enough to determine the specific causal model it is using... | Rebuttal 1:
Rebuttal: Thank you very much for your review, we appreciate the references to related work and suggestions for clarifying the formalism.
***1. The authors do a very thorough job at relating the work to existing works in causality, but there is barely no discussion on how the work relates to other sub-fie... | Summary: This paper derives theoretical bounds on predicting an AI agent’s future behavior from observed actions, using structural causal models (SCMs) to formalize beliefs and grounding. It introduces metrics like the preference gap and counterfactual fairness gap, arguing that—even with full behavioral data—fundament... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper.
***1. The proofs are not thoroughly validated for practical use. Examples like the Medical AI feel oversimplified, leaving questions about whether the results would hold in more settings.***
To clarify possible misconceptions, we should emphasiz... | Summary: This paper considers a model of a decision-making agent as follows. Note the
paper always imagines the agent to be an AI system, but this does not appear to
be essential other than for motivation.
* Suppose the agent makes decisions guided by (1) a utility function and (2) a
causal model of how its decisio... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review, we appreciate the feedback and the depth of the observations.
We do share your concern on modelling assumptions. Before discussing them in more depth, it might be worthwhile, however, to note more explicitly the increasing evidence available for AI systems (p... | null | null | null | null | null | null |
Representation Surgery in Model Merging with Probabilistic Modeling | Accept (poster) | Summary: This paper builds upon an earlier work, representation survey. It there are T tasks and 1 model that is trained on each task, representation surgery paper defined representation bias as the sum of the distances between the merged model and the model trained on the current task for all the tasks. To alleviate t... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and give the response as follows.
**Q1 Generalization on ImageNet**: Thanks for your valuable suggestion. We try the generalization performance of the merged model on unseen tasks like Classification task (ImageNet1k). We follow the setting in our paper, i.e... | Summary: This paper provides a probabilistic interpretation of the previous work, Surgery, which addresses the issue of representation bias. Additionally, two strategies are proposed to reduce overall training costs, supported by a theoretical analysis that highlights the advantages of the probabilistic approach. Exten... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and give the response as follows.
**Q1 ProbsurgeryV2, integrating Probsurgery into each block**: In this paper, ProbSurgery is integrated into the last layer. However, it also can be integrated into each block of the ViT-based model to surgery the layer (or ... | Summary: Surgery is a method to improve the merging performance by reducing the representation bias of model merging. This paper argues that there are two main issues of Surgery. First, the representation discrepancy is not fully addressed. Second, Surgery requires multiple task-specific modules rather than a unified o... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and give the response as follows.
**Q1 The observation**: Here, $G(\cdot)$ quantifies the difference in test accuracy, which reflects the average discrepancy between the feature representations produced by these two models. For a more formal representation, ... | Summary: This paper proposes ProbSurgery, a probabilistic approach to post-merging representation correction in model merging. The authors address the representation bias that occurs when merging multiple models for multitask learning. Unlike prior deterministic approaches (e.g., Surgery), ProbSurgery models the bias a... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and give the response as follows.
**Q1 Computational Complexity**: In practical implementation, we only sample once to generate the representation bias in the ProbSurgery module, which does not incur any additional cost compared to the deterministic method. ... | null | null | null | null | null | null |
Deep Fuzzy Multi-view Learning for Reliable Classification | Accept (poster) | Summary: This paper introduces FUML, a novel multi-view classification framework using Fuzzy Set Theory to handle conflicting views and improve uncertainty estimation. It employs a Dual-reliable Multi-view Fusion (DRF) strategy and entropy-based uncertainty quantification, achieving robust classification and reliabilit... | Rebuttal 1:
Rebuttal: We appreciate the identification of our novelty and the positive comments. Below are our point-by-point responses to your concerns:
**Q1: In the Experimental Setup section, the author doesn’t provide details on the noise intensity and noise ratio of the added Gaussian noise.**
**R1**: To create ... | Summary: This paper proposes a novel multi-view classification method based on Fuzzy Set Theory, which models classification outputs as fuzzy memberships. After that, authors introduces a category credibility learning loss and a Dual-reliable Fusion (DRF) strategy to manage conflicting views and improve uncertainty est... | Rebuttal 1:
Rebuttal: We appreciate your detailed comments. We believe the following point-to-point response can address all the concerns:
**Q1: There are some grammatical errors in the writing.**
**R1**: Thanks. We will correct these grammatical errors you raise and carefully review the manuscript to ensure no other... | Summary: The paper introduces FUML, a novel multi-view classification framework that explicitly addresses the uncertainty caused by conflicting information across views. By leveraging Fuzzy Set Theory, the authors model the outputs of deep classifiers as fuzzy memberships, capturing both possibility and necessity. A ta... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments. Below is our point-by-point response.
**Q1: Weaknesses (1)**
**R1**: 1) In the next version, we will provide a clearer explanation of the theoretical components. 2) An intuitive discussion of the loss function is as follows: Directly aligning category credib... | Summary: This paper proposes a deep fuzzy multi-view learning method (FUML) to classify conflicting multi-view instances and precisely estimate intrinsic uncertainty. Specifically, FUML models logits as fuzzy memberships, employs Shannon's entropy to estimate uncertainty, and utilizes the cosine metric to measure the c... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback. Below are our point-to-point responses:
**Q1: Concern 1**
**R1**: In fact, we don't only use Shannon’s entropy for OOD detection. The uncertainty estimation mechanism of FUML includes the following steps: We first calculate the category credibility... | null | null | null | null | null | null |
Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments | Accept (poster) | Summary: This work investigates the effectiveness of the switchback experimental design in A/B testing. The paper studies this design with multiple estimators and proves results that are estimator-agnostic. These results link the efficiency of the switchback design to the carryover effect and the correlation structure ... | Rebuttal 1:
Rebuttal: > **Comparison against standard A/B**
Excellent comment! First, we would like to clarify that different estimators apply to different use cases. Taking ridesharing as an example, there are three different types of experiments: (i) temporal randomization (over time), (ii) spatial randomization (a... | Summary: This work studies how carryover effects and autocorrelations influence switchback experiments in A/B testing. The authors conduct theoretical and experimental analyses and evaluate three main estimators using both synthetic and real-world datasets.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Y... | Rebuttal 1:
Rebuttal: > **Subjective definitions**
We did not define these terms to make the paper easy to follow. To address your comment, here are their precise mathematical definitions:
* ''Positively (negatively) correlated'' refers to the covariance $\sigma_e(t_1, t_2)$ being positive (resp. negative);
* A ''l... | Summary: The paper explores various switchback experimental designs within MDPs, analyzing how carryover effects and reward autocorrelations affect estimator performance in A/B tests. The authors suggest a practical workflow for choosing designs based on the interplay of carryover effects and autocorrelation of rewards... | Rebuttal 1:
Rebuttal: **1. Realistic Covarince Structures** :
In Corollaries 1–3, we focused on autoregressive, moving average, and exchangeable covariance structures primarily to derive closed-form expressions for the first term (AC(m)) in Equation (5). While these structures might seem simple, they are widely used i... | null | null | null | null | null | null | null | null |
BiMark: Unbiased Multilayer Watermarking for Large Language Models | Accept (poster) | Summary: In this work, the authors introduce BiMark, which encodes and extracts multi-bit messages via watermarking while preserving text quality. Meanwhile, BiMark enables model-agnostic detection via a bit-flip unbiased reweighting and enhances detectability via a multi-layer architecture.
Claims And Evidence: The c... | Rebuttal 1:
Rebuttal: **Q1**:*Computational cost*
**R1**:
For a single-layer reweighting, the method requires first calculating the probability of a vocabulary subset, then obtaining scaling factors based on these original probabilities, and finally adjusting the probability of candidate tokens. The complexity of thi... | Summary: The paper presents BiMark, a watermarking technique for large language models (LLMs) that ensures text quality preservation, model-agnostic detection, and message embedding capacity—three key properties often challenging to balance in existing watermarking methods. BiMark introduces a bit-flip unbiased reweigh... | Rebuttal 1:
Rebuttal: **Q1**: Sophisticated adversarial attacks
**R1**: We conducted advanced paraphrasing attacks and reported experimental results in our response to Reviewer kPVR in *R3*.
**Q2**: Inference-time benchmarks
**R2**: We analyzed the computational cost and give experimental time cost of BiMark. Pleas... | Summary: In this paper, the authors introduces BiMark, a watermarking framework for LLMs designed to address the challenges of text quality preservation and model-agnostic detection. BiMark utilizes a bit-flip unbiased reweighting mechanism, a multi-layer architecture, and an advanced information encoding strategy to e... | Rebuttal 1:
Rebuttal: **Q1**:*Perplexity*
**R1**:
We reported perplexity in our experiments because it is one of the most commonly used metrics for evaluating the quality of generated text. We will revise the relevant statement in the final paper.
**Q2**:*Challenge of unbiased watermark and model-agnostic detection* ... | Summary: This paper introduces BiMark, a comprehensive framework for watermarking large language models that achieves three critical objectives: text quality preservation, model-agnostic detection, and message embedding capacity. The core innovation is a novel probability distribution reweighting method with a multilay... | Rebuttal 1:
Rebuttal: **Q1**: Computational cost of multi-layer reweighting
**R1**: Please refer to our response to Reviewer jksy in *R1*.
**Q2**: Robustness experiments
**R2**: We conducted advanced paraphrasing attacks to our watermarked text.
The detectability of BiMark under paraphrasing shows desirable robustn... | null | null | null | null | null | null |
Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment | Accept (poster) | Summary: This paper proposes Temporal-wise Logit-based Distillation, which trains the SNN timestep by timestep using the labels and the teacher output of the ANN, and uses the overall average output as a guidance signal. The authors theoretically prove the convergence of their losses and experimentally demonstrate the ... | Rebuttal 1:
Rebuttal: # Response to Reviewer BB72
---
### __R1:__ Contributions reclaim.
Our contributions primarily focus on a **logits-based distillation** framework tailored for SNNs, effectively leveraging spatio-temporal features **without incurring additional overhead**. Specifically, we address two critical depl... | Summary: This paper proposes a novel distillation framework for deep SNNs that optimizes performance across full-range timesteps without
specific retraining, enhancing both efficacy and deployment adaptability. We provide both theoretical analysis and empirical validations to illustrate that training guarantees the con... | Rebuttal 1:
Rebuttal: # Response to Reviewer zowF
---
### __R1:__ Is the proposed method still in MS-ResNet [1]?
Following the experimental setup in [3], our frameword can also support the MS-ResNet [4] structure as expected, with the experimental results as follows:
|CIFAR100,T=6 | baseline | logits-KD | ours |
|-|... | Summary: This paper proposes a Temporal-wise logit-based distillation method (TWLD), which consists with three different loss (classification loss, distilation loss, and self-distillation loss) for SNN output at each time step. This new method significantly improves the performance of SNNs and their robustness to time ... | Rebuttal 1:
Rebuttal: # Response to Reviewer p1Y4
---
### __R1:__ Relation of Fig. 4 to the main claim and Tab. 7 baseline results
Thank you for your suggestion. We use Fig. 4 to show the distinction from the standard logits-based distillation. In the case of the proposed temporal-wise distillation in Fig. 4b, the cl... | null | null | null | null | null | null | null | null |
In-Context Learning as Conditioned Associative Memory Retrieval | Accept (poster) | Summary: This paper proposes a novel interpretation of in-context learning(ICL)as the memory retrieval of the modern Hopfield model from a conditional memory set. The paper is well-written and the techniques proposed are novel.
Strengths: The innovative Hopfield-based perspective, solid theoretical framework, clear st... | Rebuttal 1:
Rebuttal: Thanks for your detailed review. We have revised our draft and addressed all concerns. **The revised version (changes marked in BLUE) and the code for external experiments are available in this [anonymous Dropbox folder](https://www.dropbox.com/scl/fo/9m982gnk45wc1w705wjsa/AKYGxykAcCFGADGQEHCN218?... | Summary: This paper considers, both theoretically and empirically, how a single attention head can be seen to perform in context learning (ICL) via the reshaping of an energy landscape shaped by the prompt. This work addresses the following topics:
1. Analyzing the effect of prompts through an interpretation of attent... | Rebuttal 1:
Rebuttal: Thanks for your detailed review. We have revised our draft and addressed all concerns. **The revised version (changes marked in BLUE) and the code for external experiments are available in this [anonymous Dropbox folder](https://www.dropbox.com/scl/fo/9m982gnk45wc1w705wjsa/AKYGxykAcCFGADGQEHCN218?... | Summary: **Main Findings**:
1. Memory Reshaping: This paper proposes that ICL can be understood as a process of memory reshaping within the Hopfield model framework. Specifically, the input prompt examples can reshape the energy landscape of the probabilistic energy-based memory model, thus relocating the distribution... | Rebuttal 1:
Rebuttal: Thanks for your detailed review. We have revised our draft and addressed all concerns. **The revised version (changes marked in BLUE) and the code for external experiments are available in this [anonymous Dropbox folder](https://www.dropbox.com/scl/fo/9m982gnk45wc1w705wjsa/AKYGxykAcCFGADGQEHCN218?... | Summary: This paper proposes a model to interpret in-context learning as associative retrieval. The paper argues that in-context learning can be seen as a linear transformation of key-value weights. Under a GLM assumption, their modified attention construction (BMA Attention) is shown to approximate softmax attention a... | Rebuttal 1:
Rebuttal: Thanks for your detailed review. We have revised our draft and addressed all concerns. **The revised version (changes marked in BLUE) and the code for external experiments are available in this [anonymous Dropbox folder](https://www.dropbox.com/scl/fo/9m982gnk45wc1w705wjsa/AKYGxykAcCFGADGQEHCN218?... | null | null | null | null | null | null |
Private Federated Learning using Preference-Optimized Synthetic Data | Accept (poster) | Summary: The paper introduces POPri, a novel method for private on-device learning that leverages DP synthetic data generated via LLMs. They use Direct Preference Optimization (DPO) to fine-tune LLMs for generating high-quality synthetic data. POPri outperforms existing methods, in terms of next-token prediction accu... | Rebuttal 1:
Rebuttal: **It presents very limited downstream task evaluations…It would be beneficial to see how POPri performs on other types of data**
On more tasks: Thank you for this comment—we are working on an evaluation of a text classification task. Due to the tight deadline of the rebuttal preparation period, w... | Summary: The authors introduce a client-level differentially private (DP) federated learning algorithm that leverages synthetic data generation assisted by large language models (LLMs). Unlike previous approaches that rely only on prompting for synthetic data generation, their proposed POPri algorithm fine-tunes the LL... | Rebuttal 1:
Rebuttal: **Are the authors considering trusted server or un-trusted server model? Also, why do the authors add noise both on client and server sides (as in line 17 and 20 in Algorithm 1)?**
Thank you for pointing out the typo; we should only add noise in line 17. We consider the server to be untrusted. Ou... | Summary: In the paper, the authors present a novel approach to improving the utility of differentially private federated learning (DP-FL) by leveraging preference-optimized synthetic data generated through large language models (LLMs). The proposed method aggregates client feedback into preference pairs, and then fine-... | Rebuttal 1:
Rebuttal: **Notations in Algorithm 2 are confusing. What’s e_pri? Also in the line 2 of the Algorithm 2, the expression of calculating histogram is hard to understand, and in the paper, there are no descriptions about the details of this line.**
First, we would like to apologize for an oversight; Algorithm... | Summary: This paper proposes a method for differentially private federated learning of language data, which finetunes a pretrained LLM with synthetic data generated according to client preferences. The method is lightweight in terms of client computation and client-server communication, achieves guarantees of different... | Rebuttal 1:
Rebuttal: **The technical novelty of the paper is somewhat incremental. To my understanding, the algorithm is essentially an extension of Private Evolution (PE), but clients provide feedback on synthetic data through preference ranking instead of by choosing the best samples. Still, the performance of the p... | null | null | null | null | null | null |
Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges | Accept (poster) | Summary: The paper demonstrates the capability of language models for self-improvement: achieving improved performance on problem instances beyond the training data, using model generations alone (without additional labeled data). The work focuses on two settings: length generalization, where the model is trained on sh... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and appreciating the novelty, clarity, and thoroughness of our approach and experiments. We address your specific comments and suggestions below:
**Computational Cost**
> Length generalization is interesting for two reasons: First, because we don't have traini... | Summary: This paper presents a self-improvement approach where standard decoder transformer models iteratively generate and learn from their own predictions. The authors show that this self-improvement approach allows models to achieve extreme length generalization, where the length of a test instance can be up to 5x-6... | Rebuttal 1:
Rebuttal: Thank you for your careful review and for recognizing the thoroughness of our experimental design.
**W1 Limitations to simple tasks**
> This paper is very much limited by the simplicity of the task[...] length of the input corresponds nicely to the difficulty of the problem. However, in many (and... | Summary: This paper proposes and validates a simple and intuitive idea to train a model to solve hard problems that require long reasoning processes. The authors train the models on a task with progressively increasing complexity, leveraging the models' capability to generalize to slightly harder ones for self-improvem... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our extensive experimental validation and clear presentation. We respond to your questions inline below.
**W1 Limitations to Synthetic Setting**
> It would be better if the authors can discuss more on how their findings can potentially help facilitate real-world ... | Summary: The paper introduces a self-improvement framework for transformer models that enables them to progressively tackle problems beyond the training distribution. Rather than modifying the underlying transformer architecture, the authors leverage an iterative self-training procedure in which a model generates its o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and for acknowledging that the iterative framework is a compelling strategy to push the boundaries of the generalization capabilities of Transformer models, and recognizing our extensive ablations and analyses. Please find our responses to each of ... | null | null | null | null | null | null |
Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning | Accept (poster) | Summary: The paper investigates the performance discrepancies of Dyna-style model-based reinforcement learning (DMBRL) algorithms across different benchmark environments, specifically OpenAI Gym and DeepMind Control Suite (DMC). The authors highlight a significant performance gap, where DMBRL algorithms like Model-Base... | Rebuttal 1:
Rebuttal: **How do you ensure synthetic data doesn’t introduce bias into the critic’s learning process? Are there methods to filter or correct this data?**
We performed sweeps over the synthetic-to-real data ratio (Figure 12) and found that introducing any amount of synthetic data into off-policy learning ... | Summary: This paper investigates why two MBRL algorithms (MBPO & ALM) perform well on OpenAI Gym but poorly on DMC.
The authors show that apart from Dyna model prediction error, the synthetic rollouts themself could arrest policy improvement rather than enhance it when deployed across more diverse environments.
Claims... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful suggestions to improve the clarity and presentation of our paper. We will incorporate the suggested changes to:
- More precisely describe the subset of OpenAI Gym environments used in our experiments.
- Cite ALM upon first mention and reduce repeated citatio... | Summary: This paper shows that Dyna-style off-policy model-based reinforcement learning (DMBRL) algorithms perform well in OpenAI Gym, while their performance can drop significantly in DeepMind Control Suite (DMC). And the paper analyzes potential causes (model error, lay normalization, etc) for this discrepancy, which... | Rebuttal 1:
Rebuttal: **On comparisons with other model-based RL algorithms:**
We appreciate the reviewer’s suggestion regarding DreamerV3, PlaNet, and PETS. Our study focuses on Dyna-style algorithms MBPO and ALM, which use synthetic rollouts in proprioceptive, state-based settings. Pixel-based methods such as Dreamer... | Summary: The paper investigates why popular Dyna‐style model‐based reinforcement learning (RL) methods, such as MBPO and ALM, perform well on OpenAI Gym tasks but struggle on the DeepMind Control Suite (DMC), despite both benchmarks having similar physics and task structures. The authors document a consistent performan... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and positive review. We appreciate your recognition of our contributions and the strengths of our experimental design and analysis.
Regarding your mention of DreamerV3, we agree that its success in DMC highlights the diversity of model-based RL approaches. For furthe... | null | null | null | null | null | null |
Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime | Accept (spotlight poster) | Summary: This paper provides a theoretically rigorous proof on the neural collapse (NC) phenomenon with a three-layer neural network in the mean-field regime under mean-square-error (MSE) loss. It shows that under gradient flow (GF), the within-class variability (namely NC1) vanishes as the time $t$ tends to infinity. ... | Rebuttal 1:
Rebuttal: Thank you for the valuable and detailed comments, we address concerns below.
**1. Supplementary materials.**
We will add this in the revision.
**2. NC not surprising under MSE loss.**
We politely disagree with the claim that “it is quite intuitive that the features concentrate at their class ... | Summary: The authors study the how a certain aspect of neural collapse (NC) - namely, within-class variability tending to zero, can be provably associated with convergence to nearly stationary points of the loss function in noisy gradient flow dynamics for a 3 layer neural network, where the last two layers have linear... | Rebuttal 1:
Rebuttal: Thank you for the detailed review. We address concerns below.
**1. Lack of soundness in experimental design.**
We followed the suggestions and did additional experiments, please download pdf in https://github.com/conferenceanonymous152/icml25
(a) For three-layer networks, Figure 3 shows the lin... | Summary: The paper theoretically studies the phenomenon of neural collapse in classification, focusing on its most basic property: the vanishing of within-class variability. Unlike data-agnostic prior work analysing the unconstrained features model (UFO), this work adopts a data-specific perspective by considering a th... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and positive feedback. We address questions and concerns below.
**1. Difficulty in extending results to CE loss, and expected qualitative results.**
Thank you for the question. It is indeed interesting to extend our results to CE loss and we summarize two major... | Summary: This paper studies the emergence of variability collapse in the penultimate layer representations of a three layer mean-field NN. The authors show that points for which the gradient norm is small (approximate stationary points) also show variability collapse, and that the level of variability collapse is contr... | Rebuttal 1:
Rebuttal: Thank you very much for finding that our paper advances the study of neural collapse and recommending a strong acceptance! We are happy to have a further discussion in case additional questions or comments come up. | null | null | null | null | null | null |
Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference | Accept (poster) | Summary: This paper proposes a hierarchical Bayesian inference framework for personalized federated learning (PFL) to address issues arising from statistical heterogeneity across client data. The main contribution involves specifying a conjugate hyper-prior over personalized posterior parameters, enabling joint inferen... | Rebuttal 1:
Rebuttal: We thank Reviewer kx6d for taking the time to provide detailed and constructive review on our paper.
**"..it is not entirely clear why the addition of global hyperpriors significantly outperforms simpler aggregation methods "**: We show that simpler aggregation methods implicitly set the regulari... | Summary: This paper proposes a novel hierarchical Bayesian inference framework for Personalized Federated Learning (PFL) by specifying a conjugate hyper-prior over the parameters of personalized posteriors. This approach enables the joint computation of a global posterior distribution for aggregation, alongside persona... | Rebuttal 1:
Rebuttal: We thank Reviewer 5j3i for taking the time to provide detailed and constructive review on our paper.
**"the baseline portion of the experiment should be compared more with the latest approaches to personalized federated learning"**: To the best of our knowledge, we included the latest competing B... | Summary: The paper proposes a hierarchical Bayesian inference framework for personalized federated learning (PFL), addressing the issue of statistical heterogeneity across decentralized client datasets. The main conceptual contribution is introducing a conjugate hyperprior over personalized posterior parameters, allowi... | Rebuttal 1:
Rebuttal: We thank Reviewer DMYx for taking the time to provide a review on our paper.
**"...personalized models show only modest or comparable improvements":** The results in Table 1 (Page 6) shows the personalized models (PMs) of our framework achieves significant better performance than the baseline met... | Summary: The paper introduces a hierarchical Bayesian inference framework for personalized federated learning (pFL) that specifies personalized posterior parameters, enabling joint computation of global and local posteriors to balance personalization and global robustness. While theoretically subsuming existing Bayesia... | Rebuttal 1:
Rebuttal: We thank Reviewer 7W15 for taking the time to provide a constructive and detailed review.
**"..only a simple separation has been provided.."**: For fair comparison, the base-head setting in our framework follows (Collins et al., 2021) and pFedVEM (Zhu et al., 2023). As Reviewer suggested, we inve... | null | null | null | null | null | null |
Leveraging Diffusion Model as Pseudo-Anomalous Graph Generator for Graph-Level Anomaly Detection | Accept (spotlight poster) | Summary: In this paper, the authors proposed a novel GLAD method named AGDiff. AGDiff leverages diffusion models to generate pseudo-anomalous graphs, which addresses the challenge of anomaly scarcity in GLAD. Particularly, a latent diffusion process with perturbation conditions is proposed to generate diverse pseudo gr... | Rebuttal 1:
Rebuttal: **We thank the reviewer for recognizing our work. Below are our responses to your concerns.**
**To W1:** The condition vector $\mathbf{c}$ is crucial for ensuring the generation quality of pseudo graph anomalies. By perturbing the initial latent embedding through a learnable perturbation transfor... | Summary: This paper presents a diffusion-based method for generating pseudo-anomalous graphs to address anomaly scarcity in GLAD. It employs a latent diffusion process with perturbation conditions and a joint training scheme for anomaly detection. Experiments on both balanced and imbalanced datasets demonstrate its eff... | Rebuttal 1:
Rebuttal: **Thank you for your recognition, and we hope the responses below can solve your concerns:**
**To W1:** AGDiff ensures the gap between normal and pseudo-anomalous graphs via its **controlled latent diffusion process** and **joint training**. Rather than using arbitrary noise, the conditioned vect... | Summary: This paper introduces Anomalous Graph Diffusion (AGDiff), a novel graph-level anomaly detection (GLAD) framework that consists of three core components: (1) a pre-training module employing variational inference to learn a structured latent space, (2) a latent diffusion process that introduces controlled pertur... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the comments. Hope our responses below are helpful in solving your concerns.**
**To W1:** The KL divergence term in Eq. 7 is crucial for regularizing the latent space during pre-training. By encouraging the latent distribution $\mathbf{Z}$ to align with the standard no... | Summary: The paper introduces a graph-level anomaly detection method to improve the performance of GNNs on anomalous graphs. The method consists in generating pseudo-anomalous graph with diffusion models in other to enhance the classification capabilities.
Claims And Evidence: The paper claims to that generating pseud... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the comments. Hope our responses below help to solve your concerns.**
**To Q1:**
1. **What are pseudo-anomalous graphs?** Pseudo-anomalous graphs are graphs generated via a controlled latent diffusion process (refer to Section 4.3). These graphs are optimized to resemb... | null | null | null | null | null | null |
Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models | Accept (poster) | Summary: The paper introduces Quamba2, a PTQ framework for State Space Models, which aims to reduce model size and improve computational efficiency while maintaining performance. Quamba2 supports multiple bit-width configurations (W8A8, W4A8, and W4A16) for different deployment scenarios, such as cloud services and edg... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful questions. We address the reviewer’s concerns and responses below.
> While emphasizing speed-ups, the paper does not quantify the computational cost of offline weight reordering and clustering, which could affect deployment practicality.
We pro... | Summary: The paper introduces Quamba2, a robust and scalable post-training quantization framework tailored for Selective State Space Models (SSMs), specifically Mamba1 and Mamba2. Quamba2 leverages structural properties unique to SSMs, such as channel order preservation and activation persistence, through novel techniq... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback and respond to the concerns as follows.
> Limited evaluation of generalizability, primarily focused on MMLU.
We evaluate our framework on more tasks to show that the W4AX improves generalizability in Table R11. We include BoolQ (accuracy), and the g... | Summary: This paper proposed a quantization scheme for SSMs such as mamba2. By leveraging the channel order preserving and activation persistence characteristics of SSMs, author mainly utilized two existing techniques, reordering and Hadamard rotation, to alleviate the quantization difficulty for SSMs and improved the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and positive feedback. We address the reviewer’s questions below.
> a breakdown table for GPU hours.
We provide the detailed breakdown of GPU hours for Quamba2. We report the GPU hours on A5000 for offline clustering, scale calibration, quantizatio... | Summary: This work introduces Quamba2, a novel post-training quantization (PTQ) framework designed for State Space Models (SSMs), particularly the Mamba1 and Mamba2 architectures. The work addresses the challenge of efficiently scaling SSMs for deployment in cloud and edge computing environments by optimizing low-bit-w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We address the reviewer’ concerns and responses below. References [1-3] are from the review.
> The paper's novelty is somewhat incremental.
Our SSM quantization framework **introduces novel observations**, **open-sources low bit-width kernels*... | null | null | null | null | null | null |
MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition | Accept (poster) | Summary: MoHAVE is a novel audio-visual speech recognition system that addresses the scalability challenges in traditional AVSR models. The paper introduces a sparse Mixture-of-Experts (MoE) framework combined with a hierarchical gating mechanism that dynamically routes audio-visual inputs to modality-specific expert g... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper thoroughly and for recognizing the key strengths, including the **suitable hierarchical MoE architecture for AVSR, a novel gating mechanism, and strong experimental results demonstrating robust performances**.
We greatly appreciate your thorough evaluation from t... | Summary: This paper proposes an adaptive hierarchical routing mechanism in mixture of experts model for audio-visual (AV) speech recognition and AV language X speech to English text translation. As compared to a hard routing of modalities into modality specific expert groups, this paper uses a combination of inter-moda... | Rebuttal 1:
Rebuttal: > Weakness 1: The notation in the equations could be improved
A1: Thank you for the detailed review. We will revise the notations, especially in Eq. (11), to clearly distinguish the set of experts from its cardinality.
> Suggestion 1: The computational cost analysis is only discussed in Appendi... | Summary: The paper introduces MoHAVE, a novel Audio-Visual Speech Recognition (AVSR) framework leveraging a Mixture of Experts (MoE) architecture. By dynamically selecting modality-specific experts through a hierarchical gating mechanism. Experimental results on benchmark datasets demonstrate its effectiveness, outperf... | Rebuttal 1:
Rebuttal: > Weakness 1: The model is evaluated on synthetic data and not on real-world conditions
A1: We acknowledge your concern regarding evaluation with synthetic noise data. While standard AVSR benchmarks such as LRS3 and MuAViC typically offer curated datasets with high-quality audio and clear visual ... | Summary: This paper enhances audio-visual speech recognition based on MoE with audio/visual hierarchical modeling. This paper attaches the audio-visual MoE fusion parts to the decoder part and controls the audio and video expert groups, respectively, based on the group-level load biasing loss so that each modality cont... | Rebuttal 1:
Rebuttal: > Weakness 1: The proposed method and its improvement are incremental
A1: Thank you for your valuable comments. We would like to clarify that MoHAVE introduces several key innovations in both scalability and robustness for AVSR systems, which go beyond existing works:
MoHAVE is the **first AVSR ... | null | null | null | null | null | null |
Scaling Embedding Layers in Language Models | Reject | Summary: This paper proposes SCONE, which is an extended n-gram embedded layer to improve model's performance. SCONE introduces contextualized embeddings for frequently-used n-grams. While these embeddings are learned from a small Transformer model, they can also be precomputed and stored to avoid additional latency. T... | Rebuttal 1:
Rebuttal: Thank you for your supporting review! We appreciate your recognition of the novelty and contributions of our work. We also welcome any further comments you may have. | Summary: The paper proposes a new method, SCONE, to expand the embedding layer. Instead of directly expanding the vocabulary size, which usually leads to the sparsity issue (long-tailed symbols/tokens receive sparse updates due to their data sparsity), the paper chose to expand the embedding layer by incorporate freque... | Rebuttal 1:
Rebuttal: Thank you for your review. Please find our responses below, we’re happy to discuss further if needed. We’ve also included downstream evaluations in our response to Reviewer iJdC.
**1. Missing references**
We thank the reviewer for the insightful references. We have incorporated all the reference... | Summary: This paper introduces a technique for expanding input embedding layers to improve the performance of language models. The experimental results show that the solution mentioned in this paper outperforms a 1.9B parameter baseline.
Claims And Evidence: I think the writing of this paper can be improved. Maybe all... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestions. Please find our response below.
**1. The manuscript's quality is lacking.**
We have carefully addressed the concerns regarding writing (see below). Please see the updated Figure 1 (and its caption), Figure 5, and Figure 6 at [this anonymous link](https:... | Summary: The paper presents a new method for scaling the vocabulary of LLMs. Given some base vocabulary, a set of frequent n-grams is calculated. When such a n-gram is seen, a small transformer (called an f-gram model) is applied to the n-gram embeddings to produce a new embedding. This embedding is then fed to the lar... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. Please find our response below.
**1. The evaluation metric (perplexity) is not a good metric. Can you add numbers for standard LLM evals like MMLU, hellaswag and so on?**
While perplexity is a commonly used metric for evaluating language models, we acknowl... | null | null | null | null | null | null |
On the Out-of-Distribution Generalization of Self-Supervised Learning | Accept (poster) | Summary: This paper focuses on the out-of-distribution generalization of self-supervised learning. The authors first give one plausible explanation for SSL having OOD generalization, then analyze and conclude that SSL learns spurious correlations during the training process from the perspective of generation and causal... | Rebuttal 1:
Rebuttal: **Response to Weaknesses 1 & Questions 3**:
Thank you for pointing these out. The proposed method has two main phases with the following complexity analysis per mini-batch (batch size $B$, dataset size $D$):
**Step 1: Latent Variable Model Training:**
- **$q_\phi(s|x^+,x^{\rm label})$:** Eac... | Summary: This work propose a minibatch sampling strategy to select pairs of samples in the mini-batch to enhance the OOD geralization ability of SSL methods. By investigating on a causal perspective from the constructed SCM model, the method propose a Post-Intervention Distribution, which can be realized by balancing s... | Rebuttal 1:
Rebuttal: **Response to Weaknesses 1 & Experimental Designs Or Analyses**:
Thank you for pointing this out. We clarify this issue through the following steps:
**Step 1: How the original submission constructs the OOD task**
The transfer learning task and the few-shot learning task can be regarded as OOD ... | Summary: The paper explores the **out-of-distribution (OOD) generalization** of self-supervised learning (SSL). It analyzes how mini-batch construction in SSL training influences OOD generalization and argues that SSL models often learn **spurious correlations**, which hinder their ability to generalize to unseen distr... | Rebuttal 1:
Rebuttal: **Response to Claims And Evidence**:
Thank you for pointing this out. In **Appendix C.1**, we report the results of MAE. Now, we present the results of VideoMAE.
We transfer the learned VideoMAE + Ours on Kinetics-400 [1] to downstream action detection dataset AVA [2]. Following the standard se... | Summary: This paper explores whether self-supervised learning possesses out-of-distribution (OOD) generalization capabilities and investigates the reasons behind its potential failure. To address this, the authors propose a Post-Intervention Distribution (PID), grounded in the Structural Causal Model. PID enables accur... | Rebuttal 1:
Rebuttal: **Response to Weaknesses 1**:
Thank you for pointing this out. Due to space limitations, we reported the experimental results of Colored-MNIST and PACS in **Tables 9** and **Tables 10** in **Appendix C.3** of the original submission. In the final version, we will move these results to the main bo... | null | null | null | null | null | null |
Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation | Accept (poster) | Summary: The paper proposes a simple regularization strategy for LoRA fine-tuning that stochastically drops LoRA adapters according to a certain schedule.
The authors show that the proposed training strategy enhances linear mode connectivity (LMC) and adapter-wise dropout stability.
Furthermore, it usually improves fin... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer uABN for the insightful feedback, which helped improve both our analysis and presentation. Below, we respond to the main concerns point by point. Additional results are available via https://anonymous.4open.science/r/coto, with new content labeled as Tab. rX and Fig. rX... | Summary: The paper introduces CoTo, which integrates structured dropout with LoRA fine-tuning, demonstrating improved generalization and enhanced performance in model merging and pruning. Similar to stochastic depth, the proposed method freeze LoRA adaptor for certain layers with a certain probability and such probabil... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer Xi9N for the thoughtful review and for highlighting both the strengths and limitations of our work. We especially appreciate the recognition of our experimental rigor and the inclination toward acceptance. Below, please find our responses to the main concerns, and let u... | Summary: The paper introduces a training strategy to progressively deactivate adapters during training to ensure better optimization across all layers, enhancing model performance and efficiency. Extensive experiments across various models and tasks demonstrate its effectiveness in boosting LoRA's capabilities, includi... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer VYdq for the thoughtful and constructive comments. We address each concern in detail below and remain open to any further questions.
[W1] Notation is a bit unclear. What exactly does "a single LoRA layer" refer to? And what does "adapter" refer to? Does "one adapter" m... | Summary: This paper proposes CoTo, a training strategy for LoRA that progressively deactivates adapters during training to promote balanced optimization across layers. CoTo enhances generalization, model merging, and pruning while reducing training time, demonstrating performance improvements across vision-language mod... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer Np5j for the valuable comments. Below, please find our responses to each concern, and let us know if any issues remain. All experiments during the response period (Fig. **r**X & Tab. **r**X) are accessible in this anonymous link https://anonymous.4open.science/r/coto.
... | null | null | null | null | null | null |
Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding | Accept (poster) | Summary: The authors propose a mechanism, Pasta to train large language models (LLMs) to identify and express semantic independence and directly optimizes for both response quality and decoding latency. This framework has two key components, the Pasta-Lang, an annotation language that allows LLMs to express semantic in... | Rebuttal 1:
Rebuttal: We appreciate the encouraging feedback from the reviewer!
> However, with only specifying that the work is evaluated on one dataset, the contributions may be limited.
We would like to clarify that AlpacaEval actually is a suite of 5 different benchmarking datasets: Self-Instruct [1], Open-Assist... | Summary: This work presents a novel framework that allows the model to learn semantic boundaries in the sequence space, so that semantically independent chunks could be decoded in parallel, or async manner. First, they proposed an XML-based markup to be able to structure a flat sequence into structured chunks that can ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the helpful comments!
> the benefits of splitting decoding chunks into parallel executions that can synchronize later is an interesting idea, however, the practical utility of such method is questionable especially on the efficiency side. Efficient and optimal async... | Summary: The paper addresses LLM inference speed and proposes a method to enable asynchronous, parallel decoding of partial LLM responses. The main idea is to fine-tune the model to output special tags for partial answers that are independent of each other and that can then be decoded in parallel. The critical ingredie... | Rebuttal 1:
Rebuttal: We appreciate the reviewers careful reading and insightful comments!
> The authors mention SoT, another relevant baseline, in L234 as a comparison point but I did not find any results with it.
We have included here the updated Figure 3 with the SoT results (please see [rebuttal doc](https://anon... | Summary: This paper proposes PASTA, a method for accelerating LLM inference via promises. It's based on PASTA-lang, a markup-based language that expresses which parts of a prompt might be semantically independent. They write a specification for PASTA-lang, use Gemini-Flash to annotate an instruction-tuning dataset with... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback and thoughtful comments! Here're our responses.
> How generalizable is this method? Does finetuning on SlimOrca in PastaLang produce generalization capabilities that would help for other unrelated tasks/domains?
We designed our method and evalua... | null | null | null | null | null | null |
Fourier Position Embedding: Enhancing Attention’s Periodic Extension for Length Generalization | Accept (poster) | Summary: The paper analyzes how RoPE enables periodic attention patterns and then analyzes the limitation of RoPE in that regard.
The authors argue that the limitation arises from spectral damage prevalent when RoPE is used with typical DL architectures.
The authors propose FoPE, which is based on RoPE, but while RoPE ... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments from the reviewer, we want to deliver further explanations accordingly.
# Comparison with more baselines on more benchmarks
It is a nice suggestion to compare FoPE with more baseline methods, thus we supplement the following experiments:
- We validate the effective... | Summary: The paper introduces the Fourier positional embedding (FoPE) based on Fourier series. The authors begin by analyzing the rotary positional embedding (RoPE) method in the frequency domain. Further analysis of the feed-forward network yields additional information that linear layers produce spectrum leakage (mix... | Rebuttal 1:
Rebuttal: Thanks for the elaborate comments, we will address them according to their proposed order.
# Clarification for "undertrained components" and the impact of poor threshold
- **We have jointly defined the "undertrained components" and "floor frequency" in Fig 2, Sec 3.3 and Sec 4, using both visuali... | Summary: This paper analyses the limitations of Rotary Position Embedding (RoPE) in extending language model context length using Discrete Signal Processing theory. It identifies spectral damage from linear layers, activation functions, and insufficient frequency training as key issues affecting RoPE’s periodicity. To ... | Rebuttal 1:
Rebuttal: Thanks for the detailed comments from the reviewer, we would like to make several clarification below.
# Computation cost of FoPE is similar to RoPE
We agree with the reviewer that the efficiency and computation cost are essential for Position Embedding, thus:
- FoPE keeps the rotary matrix havin... | Summary: ## update after rebuttal
I read the latest clarification by the authors, and understand q and k in Re[qk*e^{i{m-n}\theta}] are not exactly the 2-dim vector [q_x, q_y]^T but an implicit complex number. I raised my score back.
******
This paper points out that in RoPE, different dimensions correspond to differ... | Rebuttal 1:
Rebuttal: Thanks for the insightful comments from the reviewer, we are going to make clarifications for the concerns above.
# Clarification for the "heuristic approach" and "practical approach" of RoPE/FoPE
It seems this is the major concern of the reviewer, we acknowledge the importance of clarifying this... | null | null | null | null | null | null |
Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning | Accept (poster) | Summary: The paper proposes Goal-Oriented Skill Abstraction (GO-Skill), a novel method for offline multi-task reinforcement learning (MTRL) that learns a unified policy to solve multiple tasks using pre-collected, task-mixed datasets. It introduces a skill extraction process using a goal encoder, vector quantization (V... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback and acknowledgment of our efforts. Following are our responses to all your concerns.
> Skill Extraction need addtional training cost to mitigate the skill imbalance problems.
As detailed in Appendix B.2, we ensure that the total number of traini... | Summary: This paper introduces GO-Skill, a novel hierarchical framework for offline multi-task reinforcement learning. The approach decouples learning into two components:
- A low-level action predictor ("skill-decoder transformer") that generates actions based on a given skill prompt
- A high-level skill predictor ("s... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback and acknowledgment of our efforts. Following are our responses to all your concerns.
> Perform on locomotion tasks "Cheetah-vel" and "Ant-dir"
We followed the experimental setup of PromptDT and conducted few-shot experiments on the *Cheetah-Vel*... | Summary: This paper presents Goal-Oriented Skill Abstraction (GO-Skill), an approach aimed at enhancing knowledge transfer in offline multi-task reinforcement learning (MTRL). GO-Skill extracts reusable skills from task-mixed offline datasets through goal-oriented representations combined with vector quantization, crea... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback and acknowledgment of our efforts. Following are our responses to all your concerns.
>Overlook offline MTRL baseline CDS.
Thank you for pointing out the omission of the CDS, which is indeed a significant contribution to offline MTRL. We will cit... | Summary: This paper proposes a method for offline multi-task reinforcement learning. The main idea is to an approach based on goal-oriented skill abstraction to better learn to extract and reuse skills to transfer to new tasks. Technically, the method utilizes vector quantization to form a discrete skill library. The i... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback and acknowledgment of our efforts. Following are our responses to all your concerns.
> Technical components of the paper are quite standard, so better justification would be beneficial.
We appreciate your comment and acknowledge that certain tec... | null | null | null | null | null | null |
Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection | Accept (oral) | Summary: This paper explores the use of foundation models (FMs) for one-shot subset selection, focusing on fine-grained image datasets. The authors find that FMs outperform traditional information extractors (IEs) in fine-grained tasks but struggle with noisy, coarse-grained datasets. To address this, they propose RAM-... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedbacks! We address your questions in the following responses.
___
**W1: A detailed analysis of why FM as IE underperform on coarse-grained datasets with noisy labels.**
A1: We sincerely appreciate the reviewer's insightful comments. Due to the character limit in... | Summary: The paper makes comparisons between traditional information extractors (IEs) and a single foundation model (FM) on a series of datasets to explore scenarios in which a single FM would be advantageous as an IE. It reveals that a single FM performs poorly on coarse-grained image datasets with noisy labels and pe... | Rebuttal 1:
Rebuttal: Thank you for your positive feedbacks! We address your questions in the following responses.
___
**W1: Evaluation on fine-grained image datasets with noisy labels.**
A1: We sincerely appreciate the reviewer’s insightful suggestion. We acknowledge the importance of evaluating the effectiveness o... | Summary: To investigate whether foundation models (FMs) can truly replace task-specific information extractors (IEs) in subset selection, this paper examines the effectiveness of FMs as IEs for one-shot subset selection. Through extensive experiments across a set of image datasets, this paper identifies the strengths a... | Rebuttal 1:
Rebuttal: Thank you for your positive feedbacks! We address your questions in the following responses.
___
**W1&Q1: Cross-architecture generalization of RAM-APL.**
A1: We sincerely appreciate the reviewer’s insightful question regarding the cross-architecture generalization of RAM-APL. We acknowledge the... | Summary: This paper investigates one-shot subset selection using Foundation Models (FMs) to reduce deep learning training costs by improving efficiency. Traditional Information Extractors (IEs) rely on models pre-trained on the target dataset, introducing dataset dependency. The paper addresses two key questions: (1) C... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback! We greatly appreciate your insightful questions, which have deepened our analysis of the findings and inspired further exploration for future work. Below, we address your questions in sequence.
___
**W1&Q1: A deeper discussion of why FM as IE perfo... | null | null | null | null | null | null |
You Get What You Give: Reciprocally Fair Federated Learning | Accept (poster) | Summary: The paper tackles the free-rider problem in a multi-agent federated learning environment by introducing MShap, a Shapley value-based, budget-balanced payment mechanism to enhance fairness and data gains. This mechanism achieves a Nash Equilibrium without requiring knowledge of agents' private cost functions. E... | Rebuttal 1:
Rebuttal: Thank you for your time and comments. We respond to your questions below.
**Q:** The paper makes the claim that no other mechanism can simultaneously Pareto-dominate MShap in both data share and total welfare. However, only two baselines have been used for comparison. More complex baselines are m... | Summary: This paper proposes a payment-based mechanism for improving fairness in federated learning systems, allow Nash Equilibria that are fairer and incentivize strategic participants to share more data. Their approach is designed to ensure reciprocity, i.e. each agent receives exactly as much utility as their fair S... | Rebuttal 1:
Rebuttal: Thank you for your time and comments. We respond to your questions below.
**Q:** I understand the benefits of not relying on the individual cost functions, however, I don't fully understand why not considering costs is a feature. To me, it seems like a simplification of the reciprocity requiremen... | Summary: Summary:
The paper studies a method for incentivizing data contributions in
collaborative/federated learning, while also satisfying fairness criteria.
The authors propose evaluating the contributions from each agent via the Shapley value,
based on the value agents derive from the data, and design a payment sc... | Rebuttal 1:
Rebuttal: Thank you for your time and comments. We respond to the questions below.
**1.** Indeed, there are blockchain-based mechanisms for FL that involve payments based on contributions, such as FedToken [1] and FedCoin [2]. FedCoin uses a "proof of Shapley" protocol, while FedToken distributes tokens ba... | Summary: In this paper, $\mathcal{M}^{\text{Shap}}$ is proposed, a budget-balanced payoff mechanism for federated learning scheme: the _data-sharing game_ among strategic agents. The authors theoretically designed and elaborated that their proposed mechanism ensures _reciprocal fairness_: each agent's payoff is equal t... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback on our experiments and for appreciating our theoretical contributions.
We conducted several new experiments based on your suggestions. We ran each method thrice with different seeds, and observed that our mechanism consistently outperformed baselines in data g... | null | null | null | null | null | null |
Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning | Accept (poster) | Summary: This paper analyzes the global convergence properties of an actor-critic algorithm for decentralized multi-agent reinforcement learning (MARL). In the critic component, the Q-function is parametrized using a deep neural network, introducing a nonlinear approximation. Each agent employs temporal difference lear... | Rebuttal 1:
Rebuttal: > **Comment 1:** I think Lemma 4.2 is not correct. The authors use the stationary distribution $\nu(\theta)$ to take the expectation. This is for infinite horizon ergodic RL without discount (the objective average of rewards through the trajectory). For objective with a discount factor, the distri... | Summary: This paper investigates a multi-agent neural actor-critic method, establishing the first theoretical global optimality guarantee with a finite-time convergence rate of $O(1/T)$. The authors further present numerical results demonstrating the effectiveness of this algorithm in applications involving large langu... | Rebuttal 1:
Rebuttal: > **Comment 1:** My main concern lies in Lemma 4.2…
**Response:** Thanks for your comments. Please refer to our response to **Comment 1 of Reviewer DPFS**.
> **Question 2:** Are $|\mathcal{S}|$ or $|\mathcal{A}|$ finite? Besides, in right column of Line 168, the dimension seems to be derived fro... | Summary: This work provides the the first actor-critic algorithm with deep Q-net and deep policy-net for fully decentralized MARL problem, and provides the first global convergence result for such algorithm.
Claims And Evidence: The claim is clear as summarized above, which is supported by theoretical proof (I believe... | Rebuttal 1:
Rebuttal: Due to the space limitation, we could only respond to a subset of more critical comments in this rebuttal. But we are happy to continue to complete our responses to your remaining comments in the discussion stage when new space opens up.
> **Comment 1:** The introduction said works including (Che... | Summary: Summary:
Goal of the paper is to develop a decentralized MARL (dec-MARL) Actor-critic (AC) algorithm, with DNN critic that achieves global optimality
Technical challenges to developing dec-MARL:
- AC methods from single-agent RL are inadequate for MARL due to distributed nature
- Even if first challenge was ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments! We address the reviewer's comments point by point as follows:
> **Comment 1:** The evaluation criteria are missing a comparison of the method to existing MARL algorithms to show performance differences. It would be great to discuss the differen... | null | null | null | null | null | null |
NegMerge: Sign-Consensual Weight Merging for Machine Unlearning | Accept (poster) | Summary: This paper presents NegMerge, which enhances the process of forgetting by negation for machine unlearning. NegMerge computes a final task vector by merging task vectors derived from multiple fine-tuned models, during which it preserves elements with consistent signs across the task vectors and masks those with... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive questions. We have done our best to address the concerns raised, as detailed below.
### **Weakness 1: Why Model Merging for Machine Unlearning?**
This is because models fine-tuned with different hyperparameters tend to specialize in either unlearning or... | Summary: The paper proposes a novel framework for machine unlearning that takes advantage of multiple fine-tuned models (10 to 30) to localize the parameters that have the same sign across all task vectors. The idea is that these parameters are characteristic of the task, while others might be noise. The procedure resu... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our method’s originality. Below, we address concerns on LLM experiments and related work.
### **Weakness 1: Lack of LLM experiments**
We agree that unlearning in LLMs is an important direction. We believe our method can generalize to transformer-based LLMs g... | Summary: This paper proposes a new approach to machine unlearning focusing on instance-based unlearning, where instead of forgetting samples corresponding to a specific class, it can forget samples throughout all the classes while preserving model performance on the rest of the samples, i.e., retain-set.
Claims And Ev... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive comments on the clarity of our paper and for recognizing the potential of our method as an alternative to parameter-based unlearning approaches. The reviewer has expressed concerns regarding the generalizability of our work and questions regarding instance-wis... | Summary: This paper treats the unlearning problem as a task arithmetic, where they conduct task vector by finetuning on the forget set, then subtract it from the original weight. To avoid sensitivity to hyperparameter selection, the authors create a finetuned model pool by various hyperparameter settings and aggregate ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback on the clarity and intuitiveness of our method. We hope to address the concerns regarding generalizability and scalability in our responses below.
### **Weakness 1: No theoretical claims were provided**
We provide a theoretical claim for our method.... | null | null | null | null | null | null |
GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance | Accept (poster) | Summary: This paper introduces GuidedQuant, a novel quantization framework that integrates gradient information from the end-to-end loss into the quantization objective while explicitly modeling inter-weight dependencies. The authors first identify a critical limitation of existing quantization methods: they either tre... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback. We address below your questions and concerns.
---
**Q1. The objective is derived from changes in the end-to-end loss, which is similar to previous work & Concern on the simplicity of the approximation method.**
We believe the simplicity of our approach shoul... | Summary: The paper introduces GuidedQuant, a layer-wise quantization method that also considers its impact on the final loss. Through derivation, the authors propose a new objective that reweights the Hessian using the loss with respect to the layer outputs. To mitigate computational costs, they approximate the Hessian... | Rebuttal 1:
Rebuttal: Thank you for your positive review. We address below your questions.
---
**Q1. Results on newer models**
Please refer to **our response to Q3 from Reviewer NTd9**, where we conduct more experiments on Llama-3-8B and Llama-3-70B under a weight-only scalar quantization setting. The results show t... | Summary: This paper introduces GuidedQuant, a post-training quantization framework for large language models (LLMs) that integrates gradient information from the end-to-end loss into the quantization objective while explicitly modeling inter-weight dependencies. The authors claim that GuidedQuant improves the performan... | Rebuttal 1:
Rebuttal: Thank you for your positive review. We address below your questions.
---
**Q1. Lack of empirical evidence showing improved modeling after grouping approximation (Section 3.3).**
We highlight that the GuidedQuant objective, after applying the grouping approximation, consistently demonstrates a c... | Summary: This paper proposes to use layer output combined with gradient information as the objective to minimize layer-wise quantization perturbation, along with an approximate method to solve this resource-intensive problem. By improving GPTVQ and combining it with the new objective proposed in the paper, the experime... | Rebuttal 1:
Rebuttal: Thank you for your positive review. We address below your questions.
---
**Q1. Evaluations on zero-shot and few-shot downstream benchmarks (Reviewers WqtU, i4yb, UEhr).**
We provide evaluations of our methods (LNQ and LNQ + GuidedQuant) alongside baselines (SqueezeLLM and GPTVQ 1D) under the wei... | null | null | null | null | null | null |
CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging | Accept (poster) | Summary: This paper introduces a model merging algorithm, CABS, to address the key issues in sparsified task vectors: high parameter overlap and unbalanced weight distribution. The proposed method consists of two components: Conflict-Aware (CA) Sparsification, which sequentially prunes task vectors to minimize overlap,... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and constructive suggestions! We've provided additional experimental tables in **[Anonymous Link](https://anonymous.4open.science/r/CABS-027B/rebuttal_tables.pdf).** Below, we address your main concerns:
**Q1: Comparison with recent baselines (Localize-and-Stitch... | Summary: This paper introduces a task-vector-based model merging method, CABS. The authors attribute the performance degradation of model merging to: (1) high parameter overlap, and (2) unbalanced weight distribution. The proposed CABS mainly contains two modules, CA and BS. Between them, CA aims to eliminate parameter... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and constructive suggestions! We've provided additional experimental tables in **[Anonymous Link](https://anonymous.4open.science/r/CABS-027B/rebuttal_tables.pdf).** Below, we address your main concerns:
**Q1: Whether the observed performance gap is due to lack o... | Summary: The paper presens CABS, a method for pruning and merging different task vectors, seemingly resolving conflicts between different tasks. As the authors argue, conflicts can arise due to parameter overlap and unblanaced weight distribution - CABS is intended to address these issues. To that end, disjoint pruning... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and constructive suggestions! Below, we address your main concerns:
**Q1: Causality vs. Correlation in Experiments**
We acknowledge the reviewer’s distinction between correlation and causation. While strict causal proof is difficult, our experiments go beyond c... | Summary: Authors propose a novel methodology, Conflict Aware Balanced Sparsification (CABS), for model merging based on task vectors. Previous work has shown that sparsifying task vectors before merging typically yields better performance for merged model. Authors identify two main issues:
- High Parameter Overlap: Ret... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and constructive suggestions! We've provided additional experimental tables in **[Anonymous Link](https://anonymous.4open.science/r/CABS-027B/rebuttal_tables.pdf).** Below, we address your main concerns:
**Q1: Inclusion of experiments on vision modality.**
We ad... | null | null | null | null | null | null |
EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions | Accept (poster) | Summary: This paper presents a hierarchical GNN method for learning PDEs. In contrast to many other such methods, it uses differentiable graph construction, so the message passing hierarchy is learned end-to-end.
Claims And Evidence: Yes. The main claim is better accuracy compared to multi-scale GNNs with fixed hierar... | Rebuttal 1:
Rebuttal: > Q1: Differentiability for $G_{l>1}$.
EvoMesh avoids using fully connected graphs for ${G}_{l>1}$. Instead, it ensures differentiability through:
1. Node selection: Differentiable via Gumbel-Softmax (L183-197).
2. Edge construction: Edges are formed based on the selected nodes and the connectivi... | Summary: This paper proposes anisotropic message passing (AMP) with hierarchical structure for mesh-based simulation. Specifically, the AMP enables GNN to predict the edges’ weights before aggregation. The hierarchical graphs are constructed dynamically given the predicted importance. Experiments on five different doma... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. Below, we address each comment point-by-point.
> Q1: Missing reference -- LayersNet.
Thank you for bringing LayersNet to our attention. We will include it in our related work and provide detailed comparisons in the revised manuscript. Below is a... | Summary: The paper presents a novel hierarchical graph network architecture in which the hierarchy is determined in a data-driven manner. Additionally, it introduces an anisotropic message-passing step that incorporates an attention mechanism into the aggregation process. With these innovations, the proposed EvoMesh ar... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s valuable comments.
> Q1: Details of DiffSELECT.
We here revise Eq. (6) to clarify the node selection process. Specifically, we employ Gumbel-Softmax independently for each node based on a 2-dimensional logits vector predicted by model $\phi^v$. This enables ... | Summary: The paper presents EvoMesh, a graph neural network for mesh-based physical simulations that dynamically learns evolving graph hierarchies instead of relying on fixed structures. Using anisotropic message passing, it adaptively selects nodes based on physical inputs, improving long-range dependency modeling. Ex... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments.
> Q1: Novelty compared to BSMS-GNN.
While both EvoMesh and BSMS-GNN adopt U-Net-based hierarchical structures for multi-scale modeling, EvoMesh introduces critical innovations in **end-to-end joint learning of graph hierarchies and physics dyna... | null | null | null | null | null | null |
Decoupled SGDA for Games with Intermittent Strategy Communication | Accept (poster) | Summary: This paper presents a novel adaptation of Stochastic Gradient Descent Ascent (SGDA) tailored for multiplayer games where strategy updates occur intermittently. The authors introduce **Decoupled SGDA**, a method that allows players to update strategies locally using outdated opponent strategies, significantly r... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort in reviewing our work and highlighting its strengths. We would be happy to further discuss any concerns and address the reviewer’s questions. If the reviewer finds our clarifications satisfactory, we appreciate if you consider increasing your score. | Summary: The authors introduce a variant of SGDA to compute solutions to decentralized min-max problems with limited communication. Players update locally with outdated strategies and synchronize intermittently, improving efficiency. Theoretical results establish near-optimal communication complexity in strongly convex... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort in reviewing our work. As the first work to consider solving minimax games in a distributed setting while players are on different machines, we decided to focus on SCSC games in this study. Extending this method to other types of games, such as convex-c... | Summary: This paper studies the communication complexity of first-order methods for solving strongly convex games (two-player games in the main body and an extension to $n$-player games in the appendix). Motivated by the fact that the communication complexity of recent (near-)optimal methods (Lin et al., 2020; Kovalev ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort in reviewing our work. Although some parts of the analysis follow the previous works, we have novelties in our proof, especially in the weakly coupled regime. Firstly, one novelty of our work is identifying the regime in which we can achieve communicati... | Summary: This paper introduces Decoupled SGDA, a novel stochastic gradient-based method designed for multi-player (or two-player) games with intermittent strategy communication. The key idea is that each player updates their parameter (e.g., minimizer or maximizer) locally using potentially outdated (yet periodically s... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and effort in reviewing our work.
> It might be interesting to see if adapting the number of local steps $K$ dynamically can further reduce communication, especially during early or late phases of training.
This is an interesting idea which can be a new line fo... | null | null | null | null | null | null |
Concept-Based Unsupervised Domain Adaptation | Accept (poster) | Summary: This paper aims to improve the generalizability and transferability of Concept Bottleneck Models (CBMs) by proposing a novel Concept-based Unsupervised Adaptation (CUDA) framework. The CUDA framework is designed to align concepts across domains in an adversarial manner while introducing a relaxation threshold.... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We are glad that you found our framework ``"novel"``, our idea ``"interesting"``, and our proofs ``"correct"``. We address your comments in turn below.
**Q1. ... relationship between the target domain error ... CBM (Formula 3) and the original target domain e... | Summary: This paper introduces a framework to handle domain shifts effectively while maintaining interpretability. It leverages adversarial training and a relaxed alignment mechanism to align concept embeddings across source and target domains, allowing for flexibility in capturing domain-specific variations. It also e... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We are glad that you found our method ``"novel and impactful"``, our theoretical guarantees ``"rigorous and mathematically sound"``, and the experiments ``"comprehensive"`` and ``"applicable across a wide range of scenarios"``. Below, we address your comments ... | Summary: This paper aims to tackle the problem of limited generalization of concept bottleneck models in cross-domain scenarios. It utilizes adversarial training to align the cross-domain concept embeddings and introduces a relaxed uniform alignment technique to alleviate the influence of over-restricted domain alignme... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We are glad that you found our theory ``"extensive"``/``"correct"``, our claim ``"convincing"``, our experimental designs/analyses ``"valid"``, and the paper ``"well-written"``. Below, we address your questions one by one.
**W1 & Q1. Provide more concrete e... | null | null | null | null | null | null | null | null |
Latent Preference Coding: Aligning Large Language Models via Discrete Latent Codes | Accept (poster) | Summary: Authors claim that human preferences on LLM responses consist of multiple latent factors. They also claim these factors can vary across tasks. To capture such factors, authors propose a discrete latent variable model for preference learning. They employ standard methods for discrete latent variable modeling, s... | Rebuttal 1:
Rebuttal: Thanks for your review.
### Regarding AlpacaEval results
> ...but usually DPO-based alignment methods show stronger benefit on AlpacaEval, MT-Bench and IFEval benchmarks...
> ...Hence, all these methods are actually degrading model performance on AlpacaEval 2.0, and the base model performance was... | Summary: This paper studies alignment and addresses the challenge of inconsistent human preferences, which stem from various underlying factors. The authors propose a latent codebook and integrate a method to learn the latent variable within standard alignment techniques like DPO.
They test their approach on standard ... | Rebuttal 1:
Rebuttal: Thanks for your review.
### Regarding the effectiveness of latent codes
> The authors primarily model preferences using UltraFeedback, a model-generated dataset that lacks diverse and conflicting preferences, limiting its ability to learn meaningful latent codes.
Thanks for your suggestion; ho... | Summary: This paper studies the problem of complex and often conflicting human preferences for the alignment algorithm. To address this, the paper introduces Latent Preference Coding (LPC), a framework that models the implicit, multifaceted factors behind human preferences using discrete latent codes. LPC integrates se... | Rebuttal 1:
Rebuttal: Thank you for your review.
#### Regarding the training cost issue
> It seems that the model need to load the encoder transformer and the decoder LLMs during the training process...
The additional computation of LPC is negligible compared to the baseline methods. This is because the policy mode... | Summary: This paper addresses the challenge of single reward model not being able to reflect the full latent factors that determine the user preference. It proposes LPC to model a latent variable model where the latent is configured as a discrete variable fully predictable by the prompt. For any prompt, a prior network... | Rebuttal 1:
Rebuttal: Thank you for your review.
> How does the training cost of LPC compared to the baseline methods
The additional training cost of LPC is negligible compared to the baseline methods. This is because the policy model, prior and posterior networks share the same backbone model so most of the computa... | null | null | null | null | null | null |
Approximation to Smooth Functions by Low-Rank Swish Networks | Accept (poster) | Summary: The paper investigates whether deep neural networks that have been compressed using low‐rank factorization can still approximate smooth functions as accurately as uncompressed networks from a universal approximation theory perspective. In low‐rank compression, a weight matrix in a network layer is replaced by ... | Rebuttal 1:
Rebuttal: Thank you for your comments. We will try our best to relieve your concerns.
Q1: The paper "Approximation by Superpositions of a Sigmoidal Function" should be cited.
A1: Thank you for the reminder. We will add this groundbreaking work in our paper.
Q2: I am not convinced that universal approxim... | Summary: This paper discusses the universal approximation property of row-rank MLPs using the Swish activation function.
## update after rebuttal
Although the authors provide some explanation regarding the activation function and the number of parameters, my concerns are not fully addressed. Therefore, I will maintain... | Rebuttal 1:
Rebuttal: Thank you for your comments. We will try our best to relieve your concerns.
Q1: The proof technique is highly standard, and it is difficult to consider it a significant mathematical advancement.
A1: While our mathematical proof follows a well-established framework, we conducted a well-designed c... | Summary: This paper investigates low-rank compression techniques for neural networks by strategically inserting narrow linear layers between adjacent nonlinear layers. The authors theoretically demonstrate that low-rank Swish networks with a fixed depth can approximate any function within a Hölder ball Cβ,R([0,1]^d) to... | Rebuttal 1:
Rebuttal: Thank you for your comments. We will try our best to relieve your concerns.
Q1: Complexity and Readability of Proof
A1: To establish a novel theoretical foundation for low-rank compression, we develop a rigorous mathematical framework comprising multiple interlocking proofs that, while intricate... | Summary: This paper studies the question of whether networks with low rank matrices and Swish activations can approximate a class of Holder-continuous and smooth functions. The authors show that the number of parameters and operations can be reduced by 1/3 to still obtain the same approximation rates.
Claims And Evide... | Rebuttal 1:
Rebuttal: Thank you for your comments. We will try our best to relieve your concerns.
Q1: Problem on the curse of dimensionality.
A1: If you want to approximate any function in a Hölder ball, then theoretically it is impossible to escape the curse of dimensionality[1, 2]. In Remark 4.5 of Section 4, we i... | null | null | null | null | null | null |
Optimizing Language Models for Inference Time Objectives using Reinforcement Learning | Accept (poster) | Summary: This paper explores the benefits of explicitly training language models using reinforcement learning to perform well under inference time algorithmic techniques like pass@$k$ and majority voting. The authors argue that directly optimizing for inference time objectives can lead to improved performance on those ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and efforts in providing us with the valuable reviews. We will incorporate your feedback into our later revision. We address your comments below.
> Regarding the computational efficiency issue mentioned in weaknesses, can the authors provide another ablation st... | Summary: This paper investigates the impact of reinforcement learning (RL) objectives that optimize multi-sample metrics such as Pass@k and Majority Voting (Maj@k). It contrasts these objectives with standard mean reward RL objectives across mathematical reasoning and code generation tasks. The study highlights the tra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and efforts in providing us with the valuable reviews. We will incorporate your feedback into our later revision. We address your comments below.
> I think the gains in Pass@k come at a significant cost to Pass@1, particularly in coding tasks.
One major takeawa... | Summary: This paper explores the potential benefits of explicitly training LLMs for test-time inference. The authors introduce a new RL objective, which explicitly utilizes multiple samples and optimizes LLMs for inference-time objectives, like better pass@k performance or better majority voting performance. The empiri... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and efforts in providing us with the valuable reviews. We will incorporate your feedback into our later revision. We address your comments below.
> On the other hand, for general RL algorithms, I suspect the performance of algorithms like GRPO, that utilize the ... | Summary: This paper investigates the reinforcement learning algorithms of LLMs in the training time for achieving the test-time objectives. Specifically, it focuses on k-sample policy gradient approaches assuming pass@k and majority vote are the test-time strategies of interest. The authors proposed a leave-one-out lik... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and efforts in providing us with the valuable reviews. We will incorporate your feedback into our later revision. We address your comments below.
> In the proof of Lemma 1, why is the expectation of two independent variables zero? I didn’t see any assumption mad... | Summary: This work presents a training objective that directly optimizes pass@k and maj@k performance for LLMs, and optimizes them using standard RL training algorithms (policy gradients with a baseline, PPO) known in practice. They evaluate performance on a synthetic bandit task, math reasoning and coding tasks, and f... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and efforts in providing us with the valuable reviews. We will incorporate your feedback into our later revision. We address your comments below.
> The paper lacks key comparisons with InfAlign (Balashankar et. al.), and Chow et. al., that both propose RL traini... | Summary: This paper explores the potential of explicitly optimizing language models for inference-time performance objectives, particularly pass@k and majority voting, using reinforcement learning (RL). The authors propose a k-sample objective formulation and derive both unbiased and biased gradient estimators, includi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and efforts in providing us with the valuable reviews. We will incorporate your feedback into our later revision. We address your comments below.
> 1. Generalization of improvements to evaluation time is not always consistent.
The generalization of improvements... | null | null |
Communication Efficient Federated Learning via Model-Agnostic Projection Adaptation | Reject | Summary: The authors propose a new method called MAPA for parameter-efficient federated fine-tuning. The main advantages of MAPA are that it does not depend on the architecture, unlike other LoRA-based methods, and it reduces the computational and memory costs while providing a better or comparable performance. The aut... | Rebuttal 1:
Rebuttal: ## 1. **Architecture-independency**
You are correct. Our approach flattens the gradients and factorizes them in a matrix form rather than directly factorizing the parameters. This gradient-based factorization is independent of specific architectural details, making it applicable to any model.
## 2... | Summary: The paper proposes Model-Agnostic Projection Adaptation (MAPA), an approach to reduce communication overhead in FL. MAPA improves upon existing low-rank adaptation (LoRA) methods by factorizing the entire model parameter space as a single matrix, as opposed to decomposing layers independently. This model-agnos... | Rebuttal 1:
Rebuttal: ## 1. **Key contributions**
Thank you for raising this important point. To clarify, there are two fundamentally different strategies for leveraging low-rank structures in optimization:
1. **Low-Rank Parameterization**
2. **Low-Rank Gradient Projection**
MAPA explicitly utilizes the latter stra... | Summary: This paper aims to improve communication efficiency in federated learning by proposing a new parameter factorization method. The proposed method is evaluated on seven public datasets and shows improved performance.
Claims And Evidence: The claims are supported by method design and experimental validations.
M... | Rebuttal 1:
Rebuttal: ## 1. **Only updating B**
Thank you for highlighting this concern. Indeed, relying solely on $B$ limits subspace exploration, as seen in FA-LoRA’s performance decline, SA-LoRA [2], and Figure 7. MAPA addresses this by **randomizing $A$ each round**, promoting diverse subspaces. Figure 7 shows tha... | Summary: This paper proposes Model-Agnostic Projection Adaptation (MAPA), which improves LoRA and FA-LoRA in federated learning (FL) by treating the entire model update as a single matrix rather than using layer-wise factorization. This approach enhances computational and communication efficiency while maintaining accu... | Rebuttal 1:
Rebuttal: ## 1. **MAPA does not consistently outperform certain baselines**
Thank you for your careful observation. We want to emphasize that all the results provided in Table 2 and additional experiments during this rebuttal show that MAPA consistently outperforms in communication and performance.
The ... | null | null | null | null | null | null |
Volume-Aware Distance for Robust Similarity Learning | Accept (poster) | Summary: This paper presents Volume-Aware Distance (VAD), a novel metric for similarity learning that extends traditional point-wise distances to field-to-field distances by introducing volume-aware data representations. The authors propose a measure-head network for volume prediction and a volume expansion regularizer... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive comments! Our responses are given below.
---
**Comment_1:** The computational complexity of the method could be further analyzed in large-scale settings.
**Response_1:** Thank you for your suggestion! Here we would like to provide the training time c... | Summary: The paper introduces Volume-Aware Distance (VAD), a novel metric for robust similarity learning. Unlike conventional point-level similarity measures, VAD models instances as volume-aware data balls, improving generalization by capturing field-to-field geometric relationships. The paper also proposes a measure-... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive comments! Our point-by-point responses are provided below.
---
**Comment_1:** Computational overhead due to volume estimation, though the authors argue it remains manageable.
**Response_1:** Thanks for your comments! We agree with the reviewer that t... | Summary: This paper introduces a novel approach to similarity learning with the Volume-Aware Distance (VAD) metric. Instead of relying on traditional point-level similarity measures, VAD models data instances as volume-aware data spheres, allowing it to capture both observed and unobserved neighbor relationships. To im... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of the novelty, theoretical analyses, and experimental results of our paper! Thanks also for your very insightful and constructive suggestions! Our point-by-point responses are as follows.
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**Comment_1:** Can VAD be extended beyond similarity learning, e.g., in... | Summary: The paper introduces Volume-Aware Distance (VAD), a new similarity metric that generalizes conventional point-wise distance by incorporating volume information. The authors propose a measure-head network to learn instance volumes and a volume expansion regularizer to improve generalization. Theoretical analyse... | Rebuttal 1:
Rebuttal: Thanks for your positive and insightful comments! Our explanation and clarification can be found as follows.
---
**Comment_1:** Slightly higher computational cost due to volume estimation.
**Response_1:** Thanks for your comments! We agree with the reviewer that the volume estimation does indee... | null | null | null | null | null | null |
Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented One-Max-Search | Accept (poster) | Summary: This paper studies the learning-augmented one-max-search problem. In the classic one-max-search problem, the input of an algorithm is a sequence of prices $(p_i)_{i=1}^{n}\in [1,\theta]^n$. At each $i$, the algorithm must decide whether to accept a price $p_i$ and terminate, or to reject $p_i$ and proceed. In ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent on our submission and for the positive feedback. We address their questions and comments below.
### **Weakness**
Due to page limitations, we could not always give detailed proof methodologies in the main paper. However, if the paper is accepted,... | Summary: This paper studies one-max search with prediction: Given a sequence of prices $p_1, \ldots, p_n$ in an online fashion and a prediction $y$ of the maximum price $p^*$, pick a price irrevocably to compete with $p^*$. Previous algorithms with prediction can simultaneously achieve _consistency_ (when the predicti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent on our submission and for their positive feedback. We address their questions below.
**Minor weakness.**
The $k$-search problem generalizes the one-max search problem, requiring a trader to sell $k$ items instead of one, with arbitrary pric... | Summary: The authors study the one-max-search problem where the decision maker is presented with a stream of value within a range $[1, \theta]$. The decision maker has to irrevocably select and end the process, or forfeit a value in the stream. The authors study the consistency, and robustness tradeoff of a Pareto opti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent on our submission, and for their positive feedback. We address their comments below.
- The objective of the robustness term is to give a valid guarantee even under arbitrarily inaccurate predictions, since no assumption is made on the quality of... | Summary: This paper studies one-max-search problem in the learning-augmented setting and develops an algorithm that is both Pareto-optimal and smooth to the multiplicative prediction error.
Claims And Evidence: The claims made in the submission are supported by clear and convincing evidence.
Methods And Evaluation Cr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent on our submission, and for their positive feedback.
In proposing a stochastic formulation of the one-max-search problem with predictions, we aimed to introduce the concept of a stochastic notion of accuracy for predictions. We gave bounds in exp... | null | null | null | null | null | null |
Low-Dimension-to-High-Dimension Generalization and Its Implications for Length Generalization | Accept (poster) | Summary: This paper conducts a theoretical analysis of the problem of low-dimension-to-high-dimension generalization, with an application to the problem of length generalization. The main theoretical results are based on Boolean function analysis and extend the analysis in Abbe et al. (2023) by considering different fu... | Rebuttal 1:
Rebuttal: Thank you for your effort and insightful comments. We respond to your main concerns below.
All experiments results are in the anonymous link: https://www.dropbox.com/scl/fi/52t23nfzev1lo1sq5dmyj/ICML_2025_5857_Rebuttal.pdf?rlkey=5nde6aampze744klvn0rsb3mp&st=37lg3nzo&dl=0.
1. *The theoretical cont... | Summary: Summary: The paper examines Low-Dimension-to-High-Dimension (LDHD) generalization and theoretically demonstrated that LDHD
generalization is unattainable without appropriate inductive bias, focusing on Boolean functions and how different architectures and inductive biases influence this generalization. The stu... | Rebuttal 1:
Rebuttal: We are grateful for your careful evaluation and positive assessment of our work. All experiments results are in the anonymous link: https://www.dropbox.com/scl/fi/52t23nfzev1lo1sq5dmyj/ICML_2025_5857_Rebuttal.pdf?rlkey=5nde6aampze744klvn0rsb3mp&st=37lg3nzo&dl=0.
1. Has RPE-Square been evaluated i... | Summary: This paper introduces the concept of Low-Dimension-to-High-Dimension (LDHD) Generalization to address challenges in out-of-distribution (OOD) generalization, particularly in reasoning tasks where models are trained on low-dimensional subspaces and tested on higher-dimensional spaces. The authors propose that L... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable feedback. We'd like to response to your concerns as below.
All experiments results are in the anonymous link: https://www.dropbox.com/scl/fi/52t23nfzev1lo1sq5dmyj/ICML_2025_5857_Rebuttal.pdf?rlkey=5nde6aampze744klvn0rsb3mp&st=37lg3nzo&dl=0.
1. *...comparisons ... | Summary: *Despite my best efforts, I found this paper very hard to parse. I am discounting my confidence to reflect the same.*
The paper studies Low-Dimension to High-Dimension generalization (LDHD) problem, a special case of OOD generalization. At its core, the paper argues the impossibility of generalizing to high-d... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. Below, we respond to your concerns.
All experiments results are in the anonymous link: https://www.dropbox.com/scl/fi/52t23nfzev1lo1sq5dmyj/ICML_2025_5857_Rebuttal.pdf?rlkey=5nde6aampze744klvn0rsb3mp&st=37lg3nzo&dl=0.
1. *... However, the ... | null | null | null | null | null | null |
Drug-TTA: Test-Time Adaptation for Drug Virtual Screening via Multi-task Meta-Auxiliary Learning | Accept (poster) | Summary: The paper introduces Drug-TTA, a novel framework for drug virtual screening that incorporates test-time adaptation through multi-task meta-auxiliary learning. The authors build upon a contrastive learning paradigm by integrating a series of self-supervised auxiliary tasks-three fine-grained tasks (masked atom ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your thoughtful comments. We sincerely appreciate your recognition of the novelty and significant performance of our method. Below, we provide detailed responses to your questions and concerns.
## W1: Losses $L_{NX}$ and $L_{NP}$ explanati... | Summary: The authors pinpoint two issues in ML-based structure-based drug discovery: a lack of negative pairs for ML-based docking methods and overly simplistic negative pairs in contrastive learning approaches (e.g., DrugClip), leading to a domain shift during inference when most screened molecules are inactive.
To m... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your thoughtful and thorough feedback. We truly appreciate your recognition that the evidence for the claims is clear and convincing, as well as your positive comments on our writing and figures. Below, we will address your questions and co... | Summary: The paper introduces Drug-TTA, a novel test-time adaptation (TTA) framework for drug virtual screening that dynamically adjusts a pre-trained model to each test instance. Unlike traditional zero-shot screening methods, Drug-TTA utilizes self-supervised auxiliary tasks to adapt its protein and molecule encoders... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your thoughtful feedback. We greatly appreciate your recognition of our innovation and the practical significance of our work. Below, we provide detailed responses to your concerns.
## W1: Concern regarding "Zero-Shot" strict definition ... | Summary: This paper introduces Drug-TTA, a novel test-time adaptation (TTA) approach for drug virtual screening that leverages multi-task meta-auxiliary learning to adapt the model to each test instance. Drug-TTA incorporates a large number of self-supervised auxiliary tasks into both training and inference processes a... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your thoughtful feedback. We greatly appreciate your recognition of the novelty and effectiveness of our work, as well as your positive remarks on our writing and presentation. In response to your comments, we conduct additional experiments... | null | null | null | null | null | null |
Don't Restart, Just Reuse: Reoptimizing MILPs with Dynamic Parameters | Accept (poster) | Summary: The paper presents a new heuristic to find primal solutions of mixed-integer linear problems (MILP). The approach is based on predicting a distribution for the value of binary variables and a multi-armed bandit approach for iterative variable fixing. The paper also considers new features for the graph-embeddin... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and suggestions.
## Response to Weakness 1
**Baselines**: We will clarify in the introduction that our method primarily focuses on primal heuristics.
However, we believe that comparing our approach with general MILP solvers like SCIP and Re_Tuning is still essenti... | Summary: This paper studies the opportunities of leveraging an existing solution to efficiently adapt to slight modifications in MILP constraints or objectives, thereby accelerating MILP reoptimization. To this end, it introduces Variable Prediction Online Refinement (VP-OR), a novel two-stage reoptimization framework.... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and suggestions.
## Response to Comment 1
There is no contradiction in the statement. Reusing branching strategies and adjusting solver parameters improve efficiency by saving time on generating the branching strategy and by enabling the solver to find better solu... | Summary: This paper introduces a novel ML-guided framework for predicting solutions for reoptimization problems. First, they adapt on current GNN methods by adding information about the leaf node to predict solutions specifically for reoptimization problems. Second, they introduce online-learning methods to refine the ... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and suggestions.
## Response to the Question 1
**Large-scale MILP experiments**: We expand our experiments to include large-scale MILP experiments with more instances (200 for training and 100 for testing) and add a comparison with the ML-guided LNS method of Huan... | Summary: “Don’t Restart, Just Reuse” addresses the problem of MILP reoptimization – repeatedly solving similar MILP instances that change over time (e.g. objective coefficients, constraints, or bounds). The paper proposes a novel two-stage framework called VP-OR (Variable Prediction and Online Refinement) for fast reo... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and suggestions.
## Response to the Question 1
**Generalization**: In our tests, we indeed observed instances where the GNN model made errors in predicting variables. To address this challenge, approaches like those by Han et al.[1] and Huang et al.[2] leverage an... | null | null | null | null | null | null |
Physics Aware Neural Networks for Unsupervised Binding Energy Prediction | Accept (poster) | Summary: This paper proposes an unsupervised learning approach, called CEBind, to binding energy prediction. CEBind includes rigid dynamics and the training loss function is motivated by energy conservation loss. Besides, it outperforms previous works.
## update after rebuttal
The authors have provided satisfactory res... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and kind support! All your concerns have been carefully addressed as below. The manuscript will be carefully revised accordingly. We sincerely hope our responses fully address your questions.
> **W1:** The similarity between training set and test set shoul... | Summary: The paper builds on the paradigm of unsupervised learning of binding affinity introduced in DSMBind but introduces a new method for force-matching that doesn't require computing the gradient of the energy function and that operates on the level of per-atom forces. The energy learned from the force-matching obj... | Rebuttal 1:
Rebuttal: Thank you for your comments! All your concerns have been carefully addressed as below. The manuscript will be carefully revised accordingly. We sincerely hope our responses fully address your questions.
> **W1:** There is a marginal empirical improvement over DSMBind.
**A1:** Thanks for your con... | Summary: The paper proposes CEBind, an unsupervised deep learning model for predicting protein-ligand binding energy based on the conservation of energy principle. It aims to address the challenge of limited labeled data for binding energy prediction, particularly for complex biomolecules like antibodies. Instead of re... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and kind support! All your concerns have been carefully addressed as below. The manuscript will be carefully revised accordingly. We sincerely hope our responses fully address your questions.
> **W1:** Evaluating CEBind on real docking simulations would st... | Summary: This paper proposes CEBind, an unsupervised method for predicting protein-ligand binding energy via the conservation of energy. Specifically, this method random samples forces on atoms to move the molecules and predict the energy of both unperturbed and perturbed complex. And the energy gap between two complex... | Rebuttal 1:
Rebuttal: Thank you for your comments! All your concerns have been carefully addressed as below. The manuscript will be carefully revised accordingly. We sincerely hope our responses fully address your questions.
> **W1**: The novelty of CEBind. Difference between our CEBind and DSMBind.
**A1**: Thanks fo... | null | null | null | null | null | null |
Do Vision-Language Models Really Understand Visual Language? | Accept (poster) | Summary: This paper argues that LVLM's good performance in diagram reasoning mainly comes from pre-existing background knowledge rather than a genuine understanding of the diagrams' relational structure.
The authors develop a test suite specifically to assess diagram comprehension:
- Their evaluations focus on two ma... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that our claims are clear and convincing. We will address the proposed concerns and answer the questions below. Additionally, we are pleased to inform you that we have extended our evaluation model set to include 9 models, as suggested by Reviewer 8S38 and R... | Summary: The paper introduces a new benchmark dataset for vision-language models. The benchmark is based on diagram understanding. Unlike most existing diagram understanding datasets, the dataset introduced in this work is specifically designed to study the understanding of relationships between depicted entities by us... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very positive feedback. We have carefully designed our experiments to ensure they are comprehensive, and our conclusions are as convincing as possible. We will address the proposed concern about evaluation model set below.
>**Concern Point**: *Not a lot of models are... | Summary: This paper investigates the diagram comprehension capabilities of Large Vision-Language Models (LVLMs) by developing a comprehensive test suite across synthetic and real-world diagrams. The evaluation reveals that while LVLMs can accurately recognize entities, they struggle to understand relationships within d... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that our experimental design is comprehensive and convincing. Our responses to the proposed concerns are provided below.
---
>**Point 1**: *... raising the question of whether larger models (e.g., 72B) might exhibit improved relational reasoning and diagra... | Summary: ## update after rebuttal
The paper presents a comprehensive and detailed study on if LVLMs are able to understand visual diagrams. The authors develop a test suite of synthetic as well as real diagrams and test multiple LVLMs on it. The authors find that the models are able to identify identities well. Howeve... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for acknowledging the clarity and robustness of our work. Below, we provide our responses to the concerns and questions raised. Additionally, in line with the suggestions from Reviewer 8S38 and Reviewer 3mRA, we have expanded our evaluation model set to include ... | null | null | null | null | null | null |
Target Concrete Score Matching: A Holistic Framework for Discrete Diffusion | Accept (poster) | Summary: The paper introduces a novel training objective for discrete diffusion models, dubbed Target Concrete Score Matching (TCSM), which is based on the concrete score (Meng et al., 2022).
Specifically, two different objectives are proposed: One is based on some divergence between the target and predicted concrete s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and insightful review. We address each point in detail below, quoting relevant comments, and will incorporate all suggestions into revision.
Due to the paper's density, we aimed to balance presenting our method and providing essential background informatio... | Summary: This work presents a new paradigm for modeling discrete data, titled Target Concrete Score Matching (TCSM). Unlike recent works that match a denoising concrete score, starting from an objective inspired by discrete flow matching, the authors propose to model the concrete score of ‘clean’ (i.e., target) data. T... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's thorough, high-quality, and insightful feedback. We'll address all suggestions and correct typos in our revision.
We are particularly encouraged by the reviewer's positive assessment of our work as "innovative", "compelling", and "representing a very big step f... | Summary: Recent works have proposed various diffusion modeling frameworks for discrete data, this paper proposes target concrete score matching, a framework that unifies various discrete diffusion approaches, such as discrete flow matching, masked diffusion language modeling, etc. The unified framework allows for using... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their positive review and valuable feedback. We greatly appreciate the opportunity to address the questions and provide additional clarifications about our work. In the following sections, we carefully respond to each point raised by the reviewer, quoting the re... | null | null | null | null | null | null | null | null |
MARGE: Improving Math Reasoning with Guided Exploration | Accept (poster) | Summary: This paper introduces MARGE, a framework for guided exploration in LLM mathematical reasoning for self-training pipeline.
It uses solution-guided exploration with MCTS and RL to find high-quality data, resulting in better exploration and credit assignment.
Specifically,
for each question it has a response, use... | Rebuttal 1:
Rebuttal: Thank you for your thorough review! We are happy to know you found MARGE to be novel and contribute to math LLM RL. Here, we appreciate the chance to address your questions.
# W1: lack of experiments using Qwen2.5 Math based model:
Thank you for your advice! To demonstrate MARGE's effectiveness ... | Summary: The paper presents MARGE, a method that improves the self-training of Large Language Models (LLMs) in math reasoning. MARGE relies on “guided exploration,” reusing partial solutions (“hits”)—correct or incorrect—to fix shared prefixes while varying subsequent steps. This stabilizes the generation of positive a... | Rebuttal 1:
Rebuttal: Thank you for your valuable review! We are glad to learn that you find MARGE to be effective, contains empirical gains, and has extensive ablations and theories. We appreciate the opportunity to clarify the points raised.
# W1: choosing hit selection in cases where no correct or wrong answers are... | Summary: The paper introduces MARGE (Math Reasoning with Guided Exploration), a framework to enhance mathematical reasoning in Large Language Models (LLMs). It addresses two fundamental challenges in LLM reasoning: the scarcity of high-quality training data and the difficulty of exploring reasoning paths effectively. U... | Rebuttal 1:
Rebuttal: Thank you for your helpful review! We are glad to learn you find MARGE to be novel, keep reward on-policy, and be reasonably evaluated. Here, we appreciate the chance to address your questions.
# Claims and Evidence: not convincingly significant
We conducted experiments with models of different ... | Summary: The paper introduces a hit-guided exploration method to enhance LLMs’ mathematical reasoning by systematically exploring intermediate reasoning states. Using Monte Carlo simulations for better credit assignment, MARGE improves accuracy and reasoning diversity across multiple benchmarks without needing extra va... | Rebuttal 1:
Rebuttal: Thank you for your valuable reviews! We are more than happy to learn that you find our method to be well-motivated, contain reasonable and comprehensive results. Here we appreciate the chance to address your concerns.
# W1: lacks failure analyses
Thank you for your advice to improve our work! We... | null | null | null | null | null | null |
Multi-Marginal Stochastic Flow Matching for High-Dimensional Snapshot Data at Irregular Time Points | Accept (poster) | Summary: The authors proposed a multimarginal extension of flow matching that are simulation free and can work with high dimension data.
Claims And Evidence: The theoretical claims are supported by theorem and proofs. I am a bit skeptical on the empirical performance without uncertainty.
Methods And Evaluation Criter... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
**Q1)**
We assume that the data space of the marginals is some continuous metric space. In particular, we assume that the data lies in $R^d$ space with the standard Euclidean distance (or distance squared) $d(x, y) = \| x - y \|^2$ for all our experiments. Moreover, ... | Summary: The paper presents Multi Marginal Stochastic Flow Matching Model(abbreviated as MMSFM), which is an extension of simulation-free score and flow matching method. The method enables the alignment of high-dimensional snapshots obtained from non-equidistant time points without reducing dimensionality. MMSFM uses t... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
**Q1)**
This is a very insightful question, as you are correct to hint at the sensitivity of splines to the time-labels in the data.
For the S-shaped and $\alpha$-shaped synthetic datasets, we evaluate on 3 different timepoint distributions to see the effect of vary... | Summary: This paper proposes an extension of flow-matching for multi-marginals - i.e. when multiple snapshots are observed, typically over time. The method sample conditioning points from all snapshots using an approximation of the multi-marginal optimal transport map and then fits a spline to these points that is used... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback.
**Q1)**
Thank you for the clarifying question. By perturbations we mean a system which is not currently at a steady state. For example, a cell system can be perturbed by some drug stimuli. Further, we focus on cases where the perturbation is fixed. Learnin... | Summary: This work proposes Multi-Marginal Stochastic Flow Matching (MMSFM) with the goal of training a translation model across multiple snapshots taken at non-equidistant time points. MMSFM builds upon the Flow Matching framework and extends it through measure-valued splines. (Stochastic) Flow Matching can be applied... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback.
**Q1)**
We use the Python Optimal Transport (POT) package for computing the OT plans $\pi(x_0, x_1)$ where we use $x_0, x_1$ as notational shorthand for $x_{t_\ell}, x_{t_{\ell+1}}$. The conditional plans are generated by using probability rules where $\pi... | null | null | null | null | null | null |
Flow-field inference from neural data using deep recurrent networks | Accept (poster) | Summary: This work sets out to infer the latent variables and their time dynamics from observed neural recordings. To achieve this, the authors developed FINDR, essentially a recurrent neural network with multi-layer perceptrons defining the flow maps of the latent variables, whereas neural activities are defined as l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. While we can’t update our submission now, we will revise it accordingly.
**Identifiability:**
Our definition (Appendix A.2) differs from [1], which builds on Roeder et al., 2021 ([A]), in two ways:
1. Following Wang et al., 2021, we define latent... | Summary: Authors introduce a new method for latent variable inference of neural data. The essence is a sequential variational autoencoder. The main innovation is a “prior” which encourages the latent variables to satisfy an ODE. Using this method, they show that low-D latents are recovered in synthetic examples. When c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We appreciate their suggestion to test FINDR on the Neural Latents Benchmark (NLB) to further support our claims. We agree that evaluating FINDR on multiple real datasets, including the public training and validation datasets available from NLB,... | Summary: FINDR (Flow-field Inference from Neural Data using deep Recurrent networks) is an unsupervised method for discovering low-dimensional neural population dynamics. It uses a gated neural drift function, decomposing spiking activity into task-relevant latents and a time-varying bias capturing non-task effects. A ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. We will change Fig. 1 to include d in the revision.
**Q1:** We should have clarified that the colored trajectories in Fig. 4b represent trial-averages sorted by evidence strength, and inside the dotted line represents part of the state space *visit... | Summary: The paper introduces FINDR, a method for inference of low-dimensional stochastic dynamics from neural recordings.
The proposed approach estimates first the bias $d$ of the observation function (called task irrelevant component of the spiking activity) by solving a regression problem for the average firing ra... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful comments.
**Necessity of gating:**
Kim et al., ICML, 2023 ([B]) shows that gating increases expressivity and trainability of the dynamics function. Their Fig. 1 suggests gating is necessary for correctly inferring dynamics in our synthetic dataset.
**Flow ... | null | null | null | null | null | null |
Refined generalization analysis of the Deep Ritz Method and Physics-Informed Neural Networks | Accept (poster) | Summary: The paper proposed refined generalization bounds for the Deep Ritz Method (DRM) and Physics-Informed Neural Networks (PINNs), including the Poisson equation and static Schrödinger equation on the $d$-dimensional unit hypercube with the Neumann boundary condition.
## update after rebuttal
Most of my concerns ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time in reviewing our manuscript and your valuable insights. Below, we address your questions point by point.
**Q1**: Extension of theories to more complex high-dimensional problems.
**A1**: In this work, we analyze two scenarios: (1) when the solutions of PDEs res... | Summary: The manuscript presents a detailed error analysis for physics-informed neural networks (PINNs) and the deep Ritz method (DRM) for linear elliptic equations. Both, the case of the true solution belonging to a Barron space and a Sobolev space are discussed. Additionally, certain approximation theoretic results a... | Rebuttal 1:
Rebuttal: We are grateful for your thorough review and thoughtful suggestions. Below we provide detailed responses to each of your comments.
**Q1**: Missing experiments.
**A1**: We agree that numerical validation would enhance the work. About experimental validation, the experiments in [1] have demonstra... | Summary: This paper presents a refined generalization analysis of two popular deep learning-based methods for solving partial differential equations (PDEs): the Deep Ritz Method (DRM) and Physics-Informed Neural Networks (PINNs). The authors derive sharper generalization bounds for these methods under different assumpt... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our article and for your insightful comments. Let us address your concerns point by point.
**Q1**: Missing references.
**A1**: We appreciate the reviewer for pointing out this important literature. It has established rigorous analysis of PICNN on the spher... | Summary: The paper presents refined generalization error bounds for two Machine Learning (ML) based methods used to solve partial differential equations (PDEs) - the Deep Ritz Method (DRM) and Physics-Informed Neural Networks (PINNs). The main technical contribution made in this paper is to provide a sharper generaliza... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time in reviewing our manuscript and your valuable insights. Below we provide a point-by-point response to your concerns.
**Q1**: Experiments.
**A1**: We agree that experiments would further strengthen our work. In future work, we plan to account for optimization err... | null | null | null | null | null | null |
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization | Accept (poster) | Summary: This paper provides randomized algorithms for nonconvex nonsmooth optimization under the constraint of differential privacy. The sample complexities of zeroth-order algorithms are greatly improved over those of the previous work (Zhang et al. 2024).
In addition, the author further extended the methodology wit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their work and are encouraged by their appreciation of our results!
We will incorporate the suggested changes and references into the final version.
We are extremely grateful for the reviewer's careful review and for finding numerous typos. We note that the only change i... | Summary: This paper studies the problem of non-smooth non-convex (NSNC) optimization problem under the constraint of differential privacy (DP). The authors first proposed a zeroth-order and single-pass NSNC-DP algorithm that achieves sample complexity of $O(\frac{1}{\alpha\beta^3}+\frac{d}{\epsilon\alpha\beta^2}+\frac{... | Rebuttal 1:
Rebuttal: We thank the reviewer and are encouraged by the positive review!
We clarify the raised issues below:
Regarding the "weakness" mentioned, we want to clarify that we did not assume anything stronger than previous results. In our proof, we use concentration inequalities for sub-Gaussian vectors, but... | Summary: This paper presents novel differentially private (DP) optimization algorithms for nonsmooth and nonconvex objectives, with a focus on achieving Goldstein-stationary points while improving sample complexity. The authors introduce a single-pass algorithm that improves the sample complexity by a factor of $\sqrt{... | Rebuttal 1:
Rebuttal: We thank the reviewer and are encouraged by the positive review! | Summary: The paper provided presents advancements in differentially private (DP) optimization algorithms for stochastic and empirical objectives that are neither smooth nor convex. Here is a summary of the results:
1. Zeroth-order Single-pass algorithm.:
- The proposed $(\epsilon, \delta)$-DP algorithm improves the de... | Rebuttal 1:
Rebuttal: We thank the reviewer and are encouraged by the positive review!
Designing optimization algorithms for nonsmooth-nonconvex losses is a topic that gained substantial attention recently mainly due to deep learning applications. Indeed, only few works further provide grounded privacy guarantees when... | null | null | null | null | null | null |
Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence | Accept (poster) | Summary: The paper presents GNN+, a framework that enhances Graph Neural Networks (GNNs) using six components—edge features, normalization, dropout, residual connections, feed-forward networks (FFNs), and positional encoding—to address issues such as over-smoothing and capturing long-range dependencies.
Through benchm... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for acknowledging our contributions. We sincerely hope our response below further strengthens your confidence in our work.
**(1) Related Works**
Thank you for sharing these related works. We have integrated detailed discussions of these relevant studies ([2... | Summary: This paper explores techniques inspired by Graph Transformers (GTs) to enhance Graph Neural Networks (GNNs). The authors demonstrate that these enhanced GNNs outperform most GTs on graph-level benchmarks, which contrasts with previous findings in the literature. Additionally, they provide empirical insights in... | Rebuttal 1:
Rebuttal: Thank you for recognizing the depth of our experiments and insights. We believe some key points may have been missed and hope our clarification encourages you to revisit and re-evaluate our work.
**(1) Deeper Theoretical Analysis**
Thank you for your valuable comment. Please refer to our respons... | Summary: This study explores the potential of Graph Neural Networks (GNNs) by enhancing them with the GNN+ framework, which incorporates techniques such as edge feature integration, normalization, and positional encoding. The results show that classic GNNs, enhanced with GNN+, outperform Graph Transformers (GTs) on gra... | Rebuttal 1:
Rebuttal: We greatly appreciate the very detailed feedback and your recognition of our contributions! We hope our response below will further enhance your confidence in our work.
**(1) Comparison with SOTA GNNs Addressing Over-smoothing and Over-squashing**
> To enhance your paper's contributions, it is r... | Summary: The paper challenges the prevailing assumption that Graph Transformers are inherently superior to Message-Passing GNNs for graph-level tasks. It introduces GNN+, a framework enhancing three classic GNNs (GCN, GIN, GatedGCN) with six techniques: edge feature integration, normalization, dropout, residual connect... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We believe there may have been some misinterpretations of our work that could have influenced your assessment. We hope our clarifications encourage you to reassess our work.
**(1) Theoretical Analysis**
> This claim holds limited value as it lacks releva... | null | null | null | null | null | null |
CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization | Accept (poster) | Summary: This paper introduces Internal Consistency Regularization (CROW), a defense mechanism against backdoor attacks in LLMs. Unlike traditional defenses, which are primarily designed for classification tasks, CROW effectively mitigates backdoors in text generation models without relying on clean reference models or... | Rebuttal 1:
Rebuttal: # Response to Reviewer BKBt
We sincerely appreciate your thorough review and insightful comments. Please find our responses to your questions below.
**Q1.** Consistency regularization between original and perturbed embeddings
**A1.** We appreciate your insightful suggestion. In response, we con... | Summary: The paper proposes CROW (Internal Consistency Regularization), a defense mechanism to eliminate backdoor attacks in Large Language Models (LLMs). Backdoor attacks manipulate model outputs using hidden triggers, posing significant security risks. Existing defenses designed for classification tasks fail for gen... | Rebuttal 1:
Rebuttal: # Response to Reviewer 8Evs
Thank you very much for reviewing our paper and the valuable comments.
**Q1.** How does Figure 1's analysis of clean vs. backdoor data align with CROW's clean-only defense approach?
**A1.** We thank the reviewer for pointing out this important distinction. Figure 1 i... | Summary: This paper proposes CROW, a novel backdoor defense for LLMs that relies on enforcing internal consistency in layer-wise hidden states. It addresses the limitations of existing methods by not requiring trigger knowledge or a clean reference model. Experiments on Llama-2, CodeLlama, and Mistral models demonstrat... | Rebuttal 1:
Rebuttal: # Response to Reviewer UhBq
Thank you for taking the time to review our paper and for your valuable comments. Please find our responses to your questions below.
**Q1.** Theoretical guarantees
**A1.** We acknowledge that Section 3.3 in our paper offers an intuitive Lipschitz-based argument rathe... | Summary: The paper proposes Internal Consistency Regularization (CROW) as a way to train away backdoors in LLMs. It's based on a finding that LLMs with a backdoor exhibit high levels of variance in layer-wise hidden representations when triggered. The proposed method adds a consistency loss term, and adversarially trai... | Rebuttal 1:
Rebuttal: # Response to Reviewer 3BdD
Thank you for taking the time to review our paper and for your valuable comments. Please find our responses to your questions below.
**Q1.** Sensitive to hyperparameters α
**A1.** We thank the reviewer for raising concerns about the sensitivity of CROW's hyperparamet... | null | null | null | null | null | null |
ConfPO: Exploiting Policy Model Confidence for Critical Token Selection in Preference Optimization | Accept (poster) | Summary: While most existing Direct Alignment Algorithms (DAAs) uniformly adjust token probabilities, this paper questions the assumption that each token contributes equally to preference, and proposes a new method, called ConfPO, which identifies preference-critical tokens based on the training policy's confidence, an... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for offering detailed feedback. We address them below, following the order in which they appear across the review’s sections.
---
### **Claims and Evidence (CE)**
**[CE-Q1] Theoretical motivation.**
**[CE-A1]** In our response to reviewer RzS... | Summary: This paper proposes a token selection strategy for direct alignment algorithms. They observe a high correlation between the gradient norm and the confidence of a token and use confidence as a metric to automatically select the token, which can possibly prioritize the tokens with high gradient norm. They show e... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for offering detailed feedback. We address them below, following the order in which they appear across the review’s sections.
---
### **Methods and Evaluation Criteria (ME)**
**[ME-Q1] Static token selection baseline.**
**[ME-A1]** We address... | Summary: This paper proposes to only include low-confidence tokens during preference alignment.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: There is no theory in this work/
Experimental Designs Or Analyses: Yes.
1. Llama-3 and Mistral seem to be outdated. It would be better... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for offering detailed feedback. We address them below, following the order in which they appear across the review’s sections.
---
### **Experimental Designs or Analyses (EA)**
**[CE-Q1] Experiment on newer models.**
**[CE-A1]** We focused on ... | Summary: This paper introduces ConfPO, a novel method for enhancing preference learning in large language models (LLMs). The core idea behind ConfPO is to selectively update tokens during training based on their confidence levels, specifically focusing on low-confidence tokens which the authors empirically demonstrate ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for offering detailed feedback. We address them below, following the order in which they appear across the review’s sections.
---
### **Claims and Evidence (CE)**
**[CE-Q1] Computational cost comparison.**
**[CE-A1]** **Table A1** compares ru... | null | null | null | null | null | null |
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