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House of Cards: Massive Weights in LLMs | Reject | Summary: This paper identifies the phenomenon of "massive weights", which contributes to a know phenomenon called "massive activations". These massive weights only take up a small part of LLMs, but significant impact the model performance. When zeroing the top-k massive weights, LLMs experience serious performance degr... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We would like to answer the concerns raised in **Weaknesses**, **Comments Or Suggestions**, and **Questions**.
---
### Weaknesses
- First, because our paper deals with open-source LLMs that allow access to weights, we begin by addressing random weights edit... | Summary: The paper studies and localizes the weight vector that causes the massive activation, and observes that the massive weights exist in different model families and even MoE models. The paper also found that setting the the massive weights (of one layer) to zero will complete destroy the model while setting the c... | Rebuttal 1:
Rebuttal: Thank you for your clear review. We will revise the abstract to make it crystal clear, reflecting your comments accordingly.
During the rebuttal process, we focus on addressing the weaknesses in **Weaknesses**.
---
### Weaknesses
- (1) Multiple runs
- To address this, we conduct **3 runs** wit... | Summary: This work investigates the massive weights phenomenon in large language models (LLMs). The authors observe that massive weights are strongly associated with the initial BOS token, though the results varies across different models. They also find that massive activations first emerge in the intermediate layers ... | Rebuttal 1:
Rebuttal: Thank you for your clear review. We would like to answer the concerns raised in **Weaknesses** and **Questions**.
---
### Weaknesses
- We used the trained model for 3 epochs (i.e., the last checkpoint) for all experients. We will update this detail in **Section 4** clearly.
---
### Questions... | Summary: The paper focuses on understanding the internal mechanisms of Large Language Models (LLMs).
The authors observe that large activations in LLMs, which appear in specific feature dimensions of hidden states, introduce bias by emphasizing the corresponding token.
They identify that these large activations o... | Rebuttal 1:
Rebuttal: Thank you for your in-depth review. We will update the manuscript to address the typos and grammatical errors, and suggested references.
During the rebuttal process, we focus on addressing the questions in **Experimental Designs Or Analyses**, because this encompasses concerns raised in **Weaknes... | null | null | null | null | null | null |
FlatQuant: Flatness Matters for LLM Quantization | Accept (poster) | Summary: This work proposes an approach of learning scaling and rotation transformation to mitigate the problem of outliers and varying dynamic range of weights and activations in LLMs. The learnable rotation transformation is parameterized as a Kronecker product of 2 matrices. Parameters of the transformation are trai... | Rebuttal 1:
Rebuttal: **Q1**. *For an exhaustive comparison I would suggest adding a few more state-of-the-art methods [1, 2]. The former provides other strategy for learning rotation and the latter accounts for outliers tokens in KV cache.*
*[1] Lin H. et al. Duquant: Distributing outliers via dual transformation mak... | Summary: This paper presents FLATQUANT, a post-training quantization approach for large language models (LLMs) that focuses on improving the "flatness" of weight and activation distributions to enhance quantization performance. The authors introduce a novel method employing fast and learnable affine transformations tai... | Rebuttal 1:
Rebuttal: **Q1**. ...*a clearer quantitative comparison of what constitutes the previous SOTA.*
**A1**. We consider QuaRot and SpinQuant as previous SOTA in Table 1-2, and compare with the latest DuQuant in A1 to Reviewer x5Xi.
---
**Q2**. *The notion of "flatness" is somewhat intuitive but would benefi... | Summary: This paper proposes FlatQuant for post-training quantization with learnable linear transformation to remove outliers of weights and activations. The authors propose the use of Kronecker decomposition to reduce the inference overhead. Extensive experiments demonstrate the effectiveness of the proposed approach ... | Rebuttal 1:
Rebuttal: **Q1**. *Regarding techniques for suppressing outliers..., the authors overlooked the use of infinity-norm regularization for reducing the weight range.*
**A1**. Thanks for the recommendation regarding related works. We will add the discussions in the revised manuscript.
---
**Q2**. *The resul... | Summary: This paper presents FLATQUANT, a novel post-training quantization framework that enhances the flatness of weight and activation distributions in large language models (LLMs) through learnable affine transformations. FLATQUANT introduces layer-wise affine transformations trained on calibration data to reshape d... | Rebuttal 1:
Rebuttal: **Q1**. *The authors do not provide a comparative assessment of GPU memory consumption...*
**A1**. Thanks for the helpful advice. Below, we provide more results on the memory consumption. The experiment results are consistent with our theoretical analysis in Appendix B.2 Line 736-739, demonstrat... | null | null | null | null | null | null |
LLMs Can Reason Faster Only If We Let Them | Accept (poster) | Summary: This work addresses the intermediate step solution length of Chain of Thought style exploration algorithms for planning problems, using supervised fine-tuning of LLMs on Algorithm of Thoughts (AoT)-style plans and utilizing a reinforcement learning mechanism for rewarding less verbose plans and thereby reducin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their extremely detailed review. We have acted upon their suggestions to add new experimental results and provide clarifications.
****
**Response to Claims and Evidence**
1. In Table 2, we see that AoT-SFT is performing much better than CoT-SFT, indicating better explora... | Summary: This paper introduces AoT-O3, a framework that combines supervised fine-tuning and reinforcement learning to enhance the planning efficiency of large language models (LLMs). By using a handcrafted reward function that optimizes both solution validity and length, AoT-O3 reduces reasoning steps while maintaining... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper. We are encouraged that our paper is recognized as "simple yet effective" and to demonstrate "promising results in achieving higher planning accuracy and shorter solution lengths".
****
**Response to Weaknesses**
1. We experimented in ... | Summary: The authors claim that while AoT has improved performance compared to CoT, they generally suffer from significantly longer solutions. To deal with this, they proposed AoT-O3, which use AoT generated solutions to optimize models with both accuracy (supervised and RL) and solution length (RL). The proposed metho... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper. In the following, we provided clarifications and our responses to the reviewer's concerns.
****
**Response to Essential References Not Discussed**
**Although, authors cannot be expected to discuss papers that have not been made publi... | Summary: This paper proposes AoT-O3, a RL based approach that reduces solution length in LLM based planning while preserving improving accuracy. It builds on already popular Algorithm of Thoughts by adding a reward function. Experiments on 3 benchmarks show a reduction in reasoning steps and higher success rates compa... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper. We are encouraged that our paper is recognized as "computationally viable" and "simpler to deploy". We have acted on the reviewer's suggestions to provide new experimental results and provide clarifications.
****
**Response to Weaknes... | null | null | null | null | null | null |
BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling | Accept (poster) | Summary: This work introduces (1) "Text Controlled TSG" a new TSG task, (2) a new LLM/MA framework to synthesize datasets, and (3) BRIDGE, a hybrid TSG framework building off the prior two contributions
Claims And Evidence: The authors claim (1) this approach achieves state of the art fidelity on 11 of the 12 datasets... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and encouraging feedback. We are especially grateful for your recognition of the clarity of the paper, the natural design of the proposed methods, and the strength of our empirical results and discussion. Your comments affirm the value of our effo... | Summary: This paper introduces BRIDGE, a framework for text-controlled time-series generation (TSG) using a multi-agent system and diffusion modeling. The authors propose a three-stage process to generate high-quality text-time series pairs, and develop a hybrid framework that combines semantic prototypes with text des... | Rebuttal 1:
Rebuttal: **[Q1] What motivated the choice of 16 prototypes, and how does performance change with different prototype counts?**
For selecting the optimal number of prototypes, we observe from the ablation study that the generation performance generally improves along with the increase of the number of prot... | Summary: The paper introduces the BRIDGE framework which consists of two broad components - a paired text-time series dataset generation method and a diffusion-based time series generation method conditioned on textual input.
For the dataset generation, the paper proposes a very detailed and carefully designed framew... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer’s insightful feedback!
**[Q1] Justify the use of metrics.**
We initially prioritized MDD and KL divergence over PS and DS for their robustness, as they are **not influenced by post-hoc models.** We have now included **PS (MAE reported) and DS** and use **J-FTSD*... | Summary: This paper introduces BRIDGE, a novel framework for text controlled timeseries generation. It addresses two major challenges: lack of high-quality text-to-time-series datasets and difficulty in aligning textual descriptions with time-series data.
Claims And Evidence: The paper’s key claims are well-supported ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s time and thoughtful feedback. We are grateful for the recognition of the strengths of our work, particularly the “straightforward and well-structured", "reasonable" nature of our approach, as well as the "novel research direction" of BRIDGE in bridging text-c... | Summary: The paper considers how to train a generator for time series (the time series generation problem). A multi-step system (process) is created, that includes LLM prompts, time series features and specification, and training. There is also a two-team multi-agent method embedded for some optimization within the sy... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and feedback, especially the high-level perspective on the entire system, which will guide the future expansion of our work. Thank you!
#### **General Comments (GC):**
**[GC 1]** *Claims of generality are not very clear.* *"The time series specifications (or fe... | null | null | null | null |
High-Dimensional Prediction for Sequential Decision Making | Accept (oral) | Summary: This paper introduces a general framework for constructing sequential decision-making strategies by predicting the states of the environment. The basic idea is that if we can estimate the *future* states of the environment, we can make decisions based on these predictions. However, the challenge is that, since... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful reading and positive assessment of our paper, and for the feedback! We will make sure to address the notational comments in our revision. | Summary: This paper introduces a two-step framework for decision-making in an online adversarial setting in which a "master" algorithm makes predictions of vector-valued "states" that encode the decision-making relevant features of the environment by achieving low bias subject to a collection of conditioning events. Do... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and positive assessment of our paper, and appreciate the insightful comments! We agree with, and will make sure to address, your comments in the revision. Among other things, we completely agree that starting with a single agent case would further enha... | Summary: The paper advances sequential prediction in high-dimensional settings while maintaining unbiasedness across a possible set of events. For this, the paper utilises the multipliscale multiplicative weights with correction algorithm and the standard minmax machinery that is common in this line of work. This unbia... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and positive assessment of our paper, and for the very insightful feedback! Indeed, as pointed out in your review, our framework is quite general, and discussing the costs of this generality is very pertinent and we will do so in the revision. To addre... | Summary: This paper introduces a general decision-making framework for the design of efficient algorithms with the objective of producing multi-dimensional forecasts in adversarial, sequential decision-making environments
The main ides is to first design a way to sequentially predict the future state of the environment... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and positive assessment of our paper, and appreciate the detailed comments. We will incorporate, in the updated version of the paper, the modifications proposed in Other Comments and Suggestions.
As for the relevance of the preprint to future work tha... | null | null | null | null | null | null |
Joint Learning of Energy-based Models and their Partition Function | Accept (poster) | Summary: This paper proposes a min-min training paradigm for simultaneously training energy models and their log-normalization constants in a combinatorially-large space. It demonstrates that this approach can effectively approximate the Maximum Likelihood Estimation (MLE) objective and extends it to Fenchel-Young loss... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and relevant references.
> **Presenting at least one experiment comparing the proposed method and one of the SOTA methods for learning discrete EBMs is necessary**
The original Table 3 contained generalized Fenchel-Young (GFY) losses comparison... | Summary: It is well-known that computing normalising constants of energy-based models is intractable, and a significant and important problem. The authors propose a simple and elegant technique for discrete models - instead of computing the normalising constant, consider a constrained maximum likelihood optimisation pr... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review and constructive feedback.
> **The approach is limited to a discrete target space**
Our approach can in principle be applied to continuous output spaces $\mathcal{Y}$. However, we believe standard regression tasks are less common applications of condi... | Summary: The paper proposes a method for learning discrete energy-based models and their partition function. Specifically, by parameterizing the partition function as $\gamma(x)$, the paper introduces the constraint $\sum_y p(y|x) - \gamma(x) = 0$, which can be incorporated into the maximum likelihood estimation using ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
> **Challenges of min-max implementation**
To avoid any controversial claim, we will remove “notoriously hard to optimize”. Instead, we will explain the technical challenges that arise using this formulation in more detail.
The min-max objec... | Summary: Authors formulate the MLE of Energy Based Models as a constraint problem with the equality contraint defining the partition function. Then they suggest performing stochastic optimization by learning jointly the energy model and its associated dual variable. Amortization is suggested by parameterizing the dual ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback.
> **Lack of baselines, comparison with MCMC and adversarial approaches**
While our paper includes GFY and exact MLE (logistic loss) baselines, it did lack comparisons with MCMC and min-max approaches. We have run additional experiments with these baseli... | null | null | null | null | null | null |
Unifying Knowledge from Diverse Datasets to Enhance Spatial-Temporal Modeling: A Granularity-Adaptive Geographical Embedding Approach | Accept (poster) | Summary: Spatio-temporal datasets are often heterogeneous, each with its own granularity and representation of entities. Given the data scarcity issue in such datasets, it is crucial to integrate them effectively and learn unified representations for multi-granularity entities. The authors propose a framework to achiev... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thorough feedback on our work. We greatly appreciate your insightful comments. Due to space constraints, we are unable to incorporate all of this content into the main text. Since URLs are not permitted (except for figures), we have instead included an updated PDF w... | Summary: This paper addresses the challenge of data scarcity in geographical scientific datasets for spatio-temporal forecasting by proposing a novel framework called Segment Quadtree Geographical Embedding Framework (SQGEF). The framework integrates knowledge from diverse datasets with varying granularities, time span... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on our work. We greatly appreciate your insightful comments, including your detailed suggestions regarding the calculation of bounds, clarifications on data scarcity, and additional experiments. Due to space constraints, we are unable to incorporate all of this... | Summary: This paper tackles the challenge of data scarcity in geographical scientific datasets for spatio-temporal forecasting by introducing a novel framework: the Segment Quadtree Geographical Embedding Framework (SQGEF). SQGEF integrates knowledge from diverse datasets that vary in granularity, time span, and observ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work. We greatly appreciate your feedback, including the detailed suggestions for analysis and clarification. However, due to space constraints, we cannot include all the content in the main text. Since URLs are not permitted except for figures, we have ... | Summary: This paper aims to integrate diverse heterogeneous datasets to address the data scarcity problem in scientific research. The authors propose the Segment Quadtree Geographical Embedding Framework with a novel data structure called the Segment Quadtree. The framework employs two learning strategies: capture inte... | Rebuttal 1:
Rebuttal: Thank you for your thorough review! Your suggestion to include additional experiments and analysis will further strengthen our paper.
# outdated baselines
Thank you for your suggestion! We have updated our experiments to include two state-of-the-art baselines: TimeXer (NeurIPS 2024) and iTransfo... | null | null | null | null | null | null |
Fisher-Guided Selective Forgetting: Mitigating Primacy Bias in Deep Reinforcement Learning | Reject | Summary: This paper proposes to address primacy bias (early experiences having an adverse effect on later training) in reinforcement learning by using a forgetting mechanism. The proposed method utilizes an update rule from the _machine unlearning_ literature to forget past experiences and reduce primacy bias. The upda... | Rebuttal 1:
Rebuttal: We thank Reviewer 738E for their detailed feedback. We address their main concerns below.
### FGSF vs Gaussian Noise:
We appreciate the reviewer's analysis of the FGSF vs Gaussian noise comparison (Fig 7).
**Ablation Goal:** This ablation showed general noise injection strategies offer benefits... | Summary: Deep reinforcement learning (RL) agents tend to overfit their early experiences, limiting their ability to learn from subsequent interactions. Nikishin et al. (2022) identified this phenomenon as primacy bias. While they highlighted the issue, their proposed solution—periodically resetting part of the network—... | Rebuttal 1:
Rebuttal: We thank Reviewer LhZa for their constructive criticism.
**Claim 1**
Following from our response to Reviewer PPSq regarding FIM interpretation (Weakness 3). Figure 1 primarily illustrates the two-phase pattern described in Section 3.2. The core evidence linking Tr(F) dynamics to PB and performan... | Summary: This paper addresses the primacy bias (PB) problem in deep reinforcement learning, where early experiences disproportionately influence the learning process. The authors propose a novel method called Fisher-Guided Selective Forgetting (FGSF) that leverages the Fisher Information Matrix (FIM) to selectively mod... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer mZKQ for their positive assessment and constructive feedback.
### Response to Comparison with other SOTA PB methods:
We agree that comparisons with additional recent Primacy Bias (`PB`) mitigation techniques (e.g., specific plasticity injection, self-distillation) wou... | Summary: This paper proposed that the Fisher information can be a indicator on the plasticity loss during training. Based on the increasing and decreasing tendency of the trace of the Fisher information matrix, the authors proposed a novel method for tackling the plasticity loss by adding noise to the weights periodica... | Rebuttal 1:
Rebuttal: We thank Reviewer PPSq for their insightful feedback. We address the weaknesses and questions below.
### Weakness 1:
While final reward gains may seem incremental in some tasks vs reset peak, FGSF offers significant advantages:
* **Learning Stability**: Avoids reset's drastic periodic performance ... | null | null | null | null | null | null |
REG: Rectified Gradient Guidance for Conditional Diffusion Models | Accept (poster) | Summary: This paper studies the foundations of classifier-free guidance (CFG). It finds that the common interpretation of scaling marginal distribution $p(x_t \vert y)$, i.e., use guidance to change sampling trajectories, is not theoretically grounded as it is impossible to construct a DDPM process that corresponds to... | Rebuttal 1:
Rebuttal: Thank you so much for acknowledging our contribution and the constructive feedback. Below we address each concern raised.
---
**Q1.** Where marginal distribution scaling is mentioned in CFG paper?
**A1.** Thank you for the great question. As a quick recap, Section II of our paper explains th... | Summary: This paper introduces a Rectified Gradient Guidance to improve conditional generation in diffusion models. Also the paper provides the theoretical foundation of the proposed guidance method. Experiments show the proposed method enhances the quality of generated images.
Claims And Evidence: Yes
Methods And Ev... | Rebuttal 1:
Rebuttal: Thank you so much for acknowledging our contribution and the constructive feedback.
---
**Q1.** Runtime and memory cost.
**A1.** Thanks for the great question. The tables below summarize runtime and peak memory usage of CFG and REG on a single NVIDIA A40 GPU. Runtime is reported using example ... | Summary: This paper addresses the discrepancy between the theoretical motivation and practical implementation of guidance techniques in conditional diffusion models. The authors propose a new method called Rectified Gradient Guidance (REG) to improve the performance of existing guidance methods.
The main findings of t... | Rebuttal 1:
Rebuttal: Thank you so much for acknowledging our contribution and the constructive feedback.
---
**Q1.** Runtime and memory cost.
**A1.** Thanks for the great question. The tables below summarize runtime and peak memory usage of CFG and REG on a single NVIDIA A40 GPU. Runtime is reported using example ... | Summary: This submission focuses on demystifying the classifier-free guidance for diffusion models. CFG has proven to be essential for the success of diffusion models. However, recent literature noted that the guided score function does not correspond to the forward diffusion process. In this work, the authors identify... | Rebuttal 1:
Rebuttal: Thank you so much for acknowledging our contribution and the constructive feedback.
---
**Q1.** Reference [1]: Krunoslav Pavasovic et al., arXiv 2025.
**A1.** Thanks for bringing [1] to our attention. We are aware of this work and will include it in our references. As the reviewer kindly noted,... | null | null | null | null | null | null |
Optimizing Large Language Model Training Using FP4 Quantization | Accept (poster) | Summary: This paper presents a FP4 training framework for large language models, which could potentially reduce the costs of LLM training. This work addresses the accuracy challenges caused by FP4 quantization with two key innovations: 1) Differentiable Gradient Estimator (DGE) for accurate weight updates and 2) Outlie... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive feedback on our work. We appreciate your recognition of the framework’s potential and your insightful questions, which have helped clarify key aspects of our methodology and claims. Below, we address each point in detail.
**W1: Spe... | Summary: The paper tackles the challenging problem of FP4 training for LLMs. They propose two innovations: a differentiable quantization estimator (DGE) for precise weight gradient updates and an Outlier Clamping and Compensation (OCC) strategy for activations. The authors also provide extensive experiments on model sc... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful evaluation of our work and for recognizing the significance of our contributions. We appreciate your positive feedback on the effectiveness of DGE and OCC, as well as the thoroughness of our experiments. Additionally, we are grateful for your unde... | Summary: The paper introduces a framework for training LLMs using 4-bit floating-point (FP4) quantization to address the computational burdens of LLM training. It introduces a Differentiable Gradient Estimator (DGE) for weight updates and an Outlier Clamping and Compensation (OCC) strategy to manage activation outliers... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive critique. We appreciate your recognition of the technical effort and alignment with hardware trends, as well as your insightful suggestions. Below, we address your concerns and questions:
**W1: Hardware Dependency**
A1: We acknowl... | Summary: The paper presents an innovative framework for training large language models (LLMs) using FP4 quantization. The key contributions include:
1.Differentiable Quantization Estimator: This method improves gradient updates during FP4 computations by analyzing the impact of quantization on both forward and backward... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and insightful evaluation of our work, as well as for recognizing the theoretical and practical contributions of our FP4 training framework. Below, we address the questions and suggestions:
**Q1 (Theroretical Claims): Clarification on OCC’s $\alpha... | null | null | null | null | null | null |
Navigating Conflicting Views: Harnessing Trust for Learning | Accept (poster) | Summary: This paper introduces a novel approach to resolving conflicting predictions in multi-view classification by integrating an instance-wise, probability-sensitive trust discounting mechanism within an evidential framework. The method computes a degree of trust for each view through a referral network, which is th... | Rebuttal 1:
Rebuttal: We would like to thank you for your valuable feedback,
and we appreciate your recognition of our novel idea, the effectiveness of the proposed trust discounting method and stage-wise training algorithm, and its adaptivity to the existing evidential framework.
We have carefully considered each of t... | Summary: The paper addresses the issue of conflicting predictions in multi-view classification tasks, where traditional methods often assume views are equally reliable and aligned. It proposes a computational trust-based discounting mechanism within the Evidential Multi-view Classification (MVC) framework. This mechani... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's valuable feedback and their recognition of:
our research motivation,
the novelty of proposed trust-based discounting mechanism and effectiveness of training algorithm.
We have carefully addressed each of the concerns raised with detailed responses below, and ... | Summary: This paper focuses on conflicting multi-view tasks and identifies that misleading predictions with high confidence (low uncertainty) from specific perspectives are key factors in the errors of conflicting multi-view decision-making. To address this issue, the authors propose a view fusion method based on compu... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's recognition of our work's strengths:
1) the clear organization and presentation of our manuscript, 2) the comprehensive literature review demonstrating deep domain understanding 3) the methodological innovation in our trust-based fusion approach, and 4) the e... | Summary: The paper introduces a novel, trust-based discounting method to enhance the existing Evidential Multi-view Framework in the real-world scenario when the various views are not fully aligned on the labels of some of the examples. The authors leverage a belief-fusion process that considers the reliability of the ... | Rebuttal 1:
Rebuttal: We thank you for your feedback and below is our response to your questions,
1) Regarding AUROC usage:
We clarify that our use of AUROC differs fundamentally from TMC [Han et al., 2022].
While TMC employed AUROC to measure label prediction accuracy,
we follow the established uncertainty evaluati... | null | null | null | null | null | null |
Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More | Accept (poster) | Summary: This paper aims to explore how the performance of vision transformers changes when the patch sizes are scaled up. The main contribution is the scaling law, which has not been presented in previous work, that as the patch sizes get smaller, the classification and segmentation performance can be lifted. The auth... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their careful evaluation and thoughtful comments. Detailed responses can be found below.
**Q1: *Experiments with other architectures (e.g., Swin Transformer, LV-ViT)***
Thank you for your insightful feedback. We would like to highlight that our conclusion also... | Summary: The authors perform a study regarding the size of patches used in modern vision transformers or state space models. Utilizing the Adventurer state space model, the authors are able to experiment with resulting sequence lengths of up to 50,176 tokens.
The work arrives at the conclusion that there exists a scali... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive review, insightful feedback, and recognition of the empirical contribution of this work. The detailed response is summarized below.
**Q1: *No technical or theoretical contribution.***
Thanks for the comment. As you noted, this is indeed an experiment-dri... | Summary: The paper evaluates the performance (test loss) of vision models (standard ViT and Adventurer) against different patch sizes. The paper's findings are that with reduced patch size, the performance of the networks increases and in the extreme case of 1x1 patch size, the segmentation tasks do not need a decoder ... | Rebuttal 1:
Rebuttal: We appreciate your comments and feedback during the review stage. We‘d like to respectfully point out that your review contains several factual misunderstandings of our paper. These points were clearly presented in the main text, and we'd like to first clarify them below:
1. ***Absence of 1x1 pat... | Summary: The paper addressed the scalability issue of patch sizes in vision transformer (ViT), which is a widely-adopted backbone in vision-related task. In past research a moderate parameter of patch size is used by default when ViT is chosen as the backbone. This study empirically investigate the effect of varying pa... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s constructive feedback and detailed suggestions. The detailed response to your questions/concerns are presented below:
**Q1: *Additional experiments and discussion about sparse and spatially local attention networks.***
**A1:** Thank you for your constructive suggesti... | null | null | null | null | null | null |
When to Forget? Complexity Trade-offs in Machine Unlearning | Accept (poster) | Summary: The paper considers machine unlearning for strongly convex objective functions and under the assumption that the forget set is inaccessible to the unlearning method. The paper provides and proves some theoretical lower and upper bounds on the ratio of the number of training iterations needed by unlearning from... | Rebuttal 1:
Rebuttal: We thank *Reviewer tKga* for their in-depth review and detailed analysis of our theoretical work. We address their comments below and will make sure to include the corrected elements into the final version. $$ $$
- **Impossible VS Inefficient Unlearning:** You are correct in saying that unlearnin... | Summary: In this paper, the author revisits the machine unlearning topic and analyzes the efficiency of unlearning methods. The author defines a complexity ratio metric that compares the computation costs of the unlearning method and full model retraining. A phase diagram is drawn based on the complexity ratio and reve... | Rebuttal 1:
Rebuttal: We thank *Reviewer qtfk* for their positive review of our work and their attention to rigour. Here are our answers to their main comments. $$ $$
- Missing introduction of quantities: We thank the reviewer for pointing out two omissions that affect the clarity of the paper. We appreciate their car... | Summary: This paper proposes a novel efficiency-oriented metric for machine unlearning. Based on the training resources required for a retrained model, it measures the efficiency of machine unlearning with respect to the size of the forget set and introduces three different regimes. The first regime addresses cases whe... | Rebuttal 1:
Rebuttal: We thank *Reviewer KpLi* for their insights and appreciate their practice-oriented review of our paper. We think adding the following discussion to the final version of the paper will benefit it greatly. $$ $$
- **Choice of experiments:** Our work studies the ratio of "worst case" unlearning and... | Summary: In this paper, the authors studied the complexity trade-offs of machine unlearning, in particular, at which conditions, unlearning can outperform retraining from scratch regarding the efficiency or unlearning cannot bring any benefits. To accomplish this, the authors proposed to leverage a phase diagram to rev... | Rebuttal 1:
Rebuttal: We thank *Reviewer ak4V* for their detailed feedback on our work, and we especially appreciate their focus on the theoretical elements of the paper. Here is a point-by-point response to their questions: $$ $$
- **Link to non DP-based unlearning papers:** There are indeed several ways of achieved ... | null | null | null | null | null | null |
Taming Rectified Flow for Inversion and Editing | Accept (poster) | Summary: The paper points out the problem of in accurate reconstruction for regular inversion method on flow models, and proposed to methods for inversion and editing. For Inversion, the authors introduced a new ODE by employing higher order with Taylor expansion, and found the second-order expansion is already helpful... | Rebuttal 1:
Rebuttal: Dear Reviewer 5wXG,
Thank you for your comprehensive and detailed review of our paper and the recognition of our work's effectiveness. We provide our feedback as follows.
> Experiment to demonstrate the effectiveness when increasing the order using Taylor expansion, which is the key idea of the... | Summary: This paper proposes a flow inversion method based on the improved higher order ODE, which simply extracts the first order derivative from the volatility prediction for more accurate model denoising generation. this method can be applied to accurate image or video inversion based on the pre-trained image or vid... | Rebuttal 1:
Rebuttal: Dear Reviewer 1xw9,
We sincerely thank for your recognition of the methods and experiments in our work!
> I saw there is a hunyuan video editing code; is there any results on this?
We provided some video editing results produced by HunyuanVideo in Figure 9 in the Appendix. We also provide the c... | Summary: This paper aims to leverage Rectified-flow-based generative models for unified image and vide editing. Specifically, this paper proposes RF-Solver, which uses high-order Taylor expansion to eliminate the errors in the inversion and reconstruction process. The paper further proposes RF-Edit, which use value fea... | Rebuttal 1:
Rebuttal: Dear Reviewer eQrG,
Thanks for your comprehensive review and insightful comments on our paper. We appreciate that you recognize the motivation and performance of our methods. The response to your concerns is shown below.
> Implementation Details about HunyuanVideo
Thanks for your advice! For vi... | Summary: This paper introduces RF-Solver, a training-free, high-order solver to improve inversion and reconstruction in rectified flow models, and RF-Edit, a feature-sharing mechanism for image and video editing. The method states improvements in inversion accuracy and editing quality compared to vanilla rectified flow... | Rebuttal 1:
Rebuttal: Dear Reviewer n1SC,
Thanks for your time and thoughtful review! We appreciate your recognition of the effectiveness of our methods. Here is our feedback:
## More Explanations about Our Methods
### RF-Solver
If we substitute the formulation of derivative into Equation 9, then the formulation beco... | null | null | null | null | null | null |
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach | Accept (poster) | Summary: The paper proposes a method that leverages unlabeled internet videos to enhance the performance of offline RL. A discretized latent action space is learned on unlabeled video data is learned by the behavior abstraction network (BAN) using vector quantization. The paper proposes using a two-stream world model, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. Below, we address each comment point-by-point.
> Q1. The authors use Dreamer V2 as their primary baseline. However, this is an outdated baseline and it would be great if the authors could provide comparisons with Dreamer V3.
Please refer to our ... | Summary: This work proposes a novel method called VeoRL (Video-enhances offline RL). This method allows to use additional pre-training video data, without the need for reward or action annotations. That data is used to train a behavior abstraction network and the planning network. The in-domain data is used to train th... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's valuable comments and have addressed each point below.
> Q1. The method is compared to DreamerV2 and not V3.
Please refer to our response to **Reviewer LSCr Q1**.
> Q2. This paper doesn't cite FICC, which is another method that tackles a very similar probl... | Summary: This paper introduces a method for offline reinforcement learning through using unlabeled video data to train a world model and doing model based policy optimization. The paper include experimental results from Meta-World, CARLA, and MineDojo.
Claims And Evidence: This paper makes several claims:
1. The propo... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments and provide our responses below.
> Q1. Comparison with DreamerV3.
The decision to use DreamerV2 as the backbone stems from two key considerations:
- **Offline setting performance:** While DreamerV3 achieves strong performance across diverse tasks... | Summary: This paper presents a model-based RL method, Video-Enhanced Offline RL (VeoRL), which leverages unlabeled Internet videos to enrich the world model. The world model comprises two state transition branches: through real actions using a trunk net and through latent behaviors using a plan net. And the policy is t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments.
> Q1. Complete code documentation and environment configuration files.
Code and full configuration files will be open-sourced upon acceptance.
> Q2. Computational complexity.
Below, we present the training time and GPU usage until model con... | null | null | null | null | null | null |
Proposer-Agent-Evaluator (PAE): Autonomous Skill Discovery For Foundation Model Internet Agents | Accept (poster) | Summary: This paper introduces Proposer-Agent-Evaluator (PAE), a learning framework designed to enable foundation model-based Internet agents to autonomously discover and refine new skills without human supervision. PAE consists of three core components: (1) a task proposer, which generates skill acquisition tasks base... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback on the paper. We have provided additional clarifications to the questions that you have raised on how PAE handles cases where the proposed task is infeasible, how the agent recognizes when to reuse a learned skill, and whether intermediate reward signals can ... | Summary: The paper presents a novel framework, Proposer-Agent-Evaluator (PAE), enabling foundation-model internet agents to autonomously discover and learn diverse skills without relying on human-annotated task templates. By leveraging visual-language models (VLMs), PAE autonomously generates tasks (Proposer), executes... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback on the paper. We have included additional clarifications in the multi-turn nature of WebArena and WebVoyager, and details of computational efficiency. We are running additional experiments comparing different RL algorithms and we will share them here once we ... | Summary: The authors propose Proposer-Agent-Evaluator (PAE) as a web agent that generates probable tasks (instructions) by itself and performs RL fine-tuning with binary rewards from VLM-based evaluators. For the generation of tasks for training, they employ a number of different pieces of information about target webs... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback on the paper. Just a gentle reminder that by the definition established by the ICML2025 committee, concurrent work includes all papers from within 4 months of the submission deadline. This applies to all other pipelines for web agents [1,2,3] with similar com... | null | null | null | null | null | null | null | null |
Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies | Reject | Summary: The authors propose a sequence of three phases to come up with a high-performing agentic system. The phases are: 1. individual “block” prompt optimization as warmup, 2. the topology refinement phase without altering the prompts, and 3. joint prompt optimization for all “blocks”. In phase 2 the authors score th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful suggestions and acknowledging the undoubted merits of MASS-style optimization! We appreciate the reviewer pointing out the details in the paper that could be further clarified. We have provided further clarifications in this response and will update the f... | Summary: This paper investigates the interactions between multiple design spaces including prompts and topologies, and the impact of various aspects such as optimizing prompts, scaling the number of intelligences, and involving different types of topologies are investigated. The optimized MAS is generated by optimizing... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful suggestions! Please see below for our detailed response, where we provide more details on the token consumption with further clarifications on optimizing the topology. We will include your valuable discussion in the manuscript. We hope that the reviewer c... | Summary: The paper proposes Multi-Agent System Search (MASS), a novel multi-stage framework designed to optimize multi-agent systems (MAS) by automating the design of prompts and topologies. The authors demonstrate that both prompt design and agent topology significantly impact the performance of MAS. The optimization ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and positive comments, especially for many valuable suggestions on a deeper discussion on topology design space, impacts of actual configurations, and further extending MASS to real-world applications! We have included your suggestions in this response an... | Summary: This paper formulates multi-agent system design as a joint prompt and topology optimization problem. It introduces the MASS framework, a multi-stage search process that interleaves block-level prompt optimization, workflow topology optimization, and workflow-level prompt refinement, to efficiently navigate the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive suggestions! We have included additional experimental results with open-source models, a graph optimization baseline, and provided clear comparisons on the token consumption. We hope in light of our response, the reviewer could consider improving their ... | null | null | null | null | null | null |
Progressive Tempering Sampler with Diffusion | Accept (poster) | Summary: This paper proposes learning diffusion models at various temperatures using MCMC data at high temperatures. These models are then used to sample from unnormalized probability distributions. The authors employ a Taylor expansion of the target distribution over temperature to derive a temperature-dependent drift... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and your constructive comments. We hope that our detailed responses would solve your concerns.Should you find our reply satisfactory, we kindly encourage you to raise your score.
## Q1: More details of the experiments
Thank you for pointing out this. We will ... | Summary: The paper introduces a new sampling algorithm—Progressive Tempering Sampler with Diffusion (PTSD)—which aims to efficiently sample from unnormalized densities by combining ideas from traditional parallel tempering (PT) and modern diffusion-based neural samplers. The method is evaluated on several multimodal ta... | Rebuttal 1:
Rebuttal: Thank you for your review. We hope that our detailed responses would solve your concerns. If any questions or concerns arise, please do not hesitate to let us know, and we will address them properly. However, should you find our reply satisfactory, we kindly encourage you to raise your score.
##... | Summary: This paper focuses on the problem of sampling from the unnormalized densities. The authors recognize the drawbacks of MCMC with Parallel Tempering as well as the ones of recent neural samplers (mostly based on the diffusion process). In particular, they proposes a novel sampling method — Progressive Tempering... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and your constructive comments. We now reply to your concerns one by one. Should you find our reply satisfactory, we kindly encourage you to raise your score.
## Q1: Limited empirical evidence & lack of scaling PTSD into more complex problems
To showcase the ... | Summary: The article describes a method based on diffusion model in order to sample from unnormalized densities, such as $p(x) P \frac{\tilde{p}(x)}{Z}$. The approach is based on parallel tempering: many diffusion models are trained to match the distribution $p$ at various temperature. The general idea being that at la... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and your constructive comments. We hope that our detailed responses would solve your concerns.
# Questions:
## Q1: Metrics related to physics
We follow the metrics used in previous literatures, e.g., iDEM, NEM, DiKL, FAB, etc. In these baselines, they do not... | null | null | null | null | null | null |
HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting | Accept (poster) | Summary: The paper introduces HyperIMTS, a Hypergraph neural network for forecasting Irregular Multivariate Time Series (IMTS), which are challenged by irregular time intervals and unaligned observations. Existing models either use padded samples, disrupting sampling patterns, or set function and bipartite graphs, whic... | Rebuttal 1:
Rebuttal: Thanks for your careful reading and in-depth thinking. We address the concerns as follows:
## W1. Regarding highly accurate padding
**A1.** We acknowledge that padding has pros and cons, which should be weighed up in IMTS forecasting.
### 1. **Error accumulation**
Imputation errors can be amplifie... | Summary: Irregular Multivariate Time Series (IMTS) are challenging due to irregular time intervals within variables and unaligned observations across variables, making it difficult to model temporal and variable dependencies. Existing IMTS models either use padded samples (which can introduce inefficiencies and distort... | Rebuttal 1:
Rebuttal: Thanks for your review and encouraging feedback. We address the main concerns as follows:
## Q1. Why set-based method fails to capture correlations among observations?
**A1:** We summarize the reasons from three aspects, using SeFT [1] as an example:
### 1. **Experimental aspect**
- **Complexity**... | Summary: The paper introduces a hypergraph neural network designed for forecasting irregular multivariate time series, which are characterized by irregular time intervals within variables and unaligned observations across variables.
Claims And Evidence: While the claims are generally supported, more comprehensive effi... | Rebuttal 1:
Rebuttal: Thanks for your review and valuable advice. We address the concerns as follows:
## W1. Efficiency reporting and performance analysis
**A1:** Additional efficiency analyses can be found in **Appendix A.3**, and performance analyses on varying lookback lengths and forecast horizons are detailed in *... | null | null | null | null | null | null | null | null |
Neural Genetic Search in Discrete Spaces | Accept (poster) | Summary: The paper proposes a novel test-time search method that integrates genetic algorithm evolutionary mechanisms into deep generative models, particularly for sequential discrete generation tasks. NGS defines crossover as a parent-conditioned generation process, wherein offspring are generated by restricting the v... | Rebuttal 1:
Rebuttal: ### GA illustration
> 1. ... add a flow chart to illustrate GA
Thanks for the valuable feedback. We’ve found that Fig 2 can also serve as a flow chart of general GA. We’ll revise the caption to further explain the overall GA process and our distinguished features:
NGS leverage pretrained genera... | Summary: Neural Genetic Search (NGS) which incorporate ideas from genetic algorithms with generative models as test-time compute method to improve quality/diversity of generation. Crossover: Generating each token autoregressively, masking all tokens not used in the parents. Mutation: random chance of removing the maski... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review. We greatly appreciate your recognition of the novelty and effectiveness of our proposed method, as well as your positive remarks on the diversity and rigor of our experiments.
> NGS can be viewed as an automated genetic algorithm with learned genetic opera... | Summary: This paper proposes a test-time search method, Neural Genetic Search (NGS), for efficient searching of generative models in discrete spaces. NGS incorporates genetic algorithms into the generation procedure, where the generative model is used to generate offspring conditioned on parents. Experimental results a... | Rebuttal 1:
Rebuttal: ### Theoretical guarantees
> The method lacks theoretical guarantees or a rigorous understanding of the generated distribution.
Thanks for highlighting the importance of theoretical guarantees and analysis.
We acknowledge that rigorous theoretical guarantees, such as formal convergence rates, a... | null | null | null | null | null | null | null | null |
Sparse Video-Gen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity | Accept (poster) | Summary: The paper presents Sparse Video-Gen (SVG), a training-free framework aimed at accelerating the inference of video diffusion models. Diffusion Transformers (DiTs) are powerful for video generation but suffer from high computational costs due to the quadratic complexity of 3D full attention. The authors reveal t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful feedback and the opportunity to discuss our work further. Your comments have been invaluable in refining our approach and clarifications.
> Weakness 1: In-depth technical analysis and underlying mathematical principles or theoretical insights that govern th... | Summary: This paper proposes Sparse VideoGen for efficient (accelerated) video generation, a method which can be applied to existing video generative diffusion models which use diffusion transformers. The paper mainly targets video generative models which operate on the spatio-temporal latents. The key idea is to class... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our contributions and the insightful comments. We respond to the questions below. We will revise the paper's final versions based on the reviewers' comments.
> How does the performance / efficiency compare to models interleaving spatial attention and tempor... | Summary: This paper addresses the critical challenge of computational inefficiency in Diffusion Transformers (DiTs) for video generation, caused by the quadratic complexity of 3D full attention operations. To tackle this issue, the authors propose Sparse VideoGen (SVG), a training-free inference optimization framework ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and feedback. We respond to the questions below.
> Experimental Designs Or Analyses 1: The submission currently does not provide actual video demos.
Answer: As requested by the reviewer, we include actual video demonstrations at the following anonymous link:
htt... | Summary: This paper proposes Sparse VideoGen, a training-free method to optimize and accelerate video diffusion DiTs through an online profiling strategy and hardware-friendly implementation.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: Yes
Experimental Designs Or Analyses: Yes
... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions and questions. Below, we address each point raised.
> Q1: Can the attention layers in multi-view diffusion models also be accelerated?
Answer: Recent multi-view diffusion models such as MVDream [1] and CAT3D [2] have introduced 3D attention mechanisms to i... | null | null | null | null | null | null |
CateKV: On Sequential Consistency for Long-Context LLM Inference Acceleration | Accept (poster) | Summary: This paper proposes CateKV, which improves the inference efficiency by adaptively evicting and retrieving KV cache based on sequential consistency.
Claims And Evidence: Please see **Other Strengths And Weaknesses**
Methods And Evaluation Criteria: Please see **Other Strengths And Weaknesses**
Theoretical Cl... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your valuable time and constructive feedback. In the following, we provide our responses to each question.
> Weaknesses 1: The main concern I have with this paper is the issues of scalability. In the paper, the largest model being evaluated is 9B. It would be b... | Summary: This paper proposes a novel KV cache algorithm that calculates the coefficient-of-variation of attention tokens at each layer. Specifically, it identifies consistent head and adaptive head where most of the KV cache can be reduced from the consistent head. In experiments, author demonstrate the effectiveness o... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your valuable time and constructive feedback. In the following, we provide our responses point-by-point.
> Claims: About the design of observation window
Here, we provide a more detailed explanation regarding the design of the observation window:
1. **The obs... | Summary: This paper studies the long-context inference acceleration of LLMs through sequential consistency patterns. By observation of distinguishing activation of attention heads, the authors classified them into consistent heads and adaptive heads, which can be used to promote the decoding acceleration. Different fro... | Rebuttal 1:
Rebuttal: We appreciate the supportive comments and the comprehensive evaluation of our method. In the following, we provide our responses point-by-point.
> Weakness 1: About setting the best threshold
We select the threshold based on a fixed ratio of consistent-to-adaptive heads, as while CV scores vary ... | Summary: CateKV introduces a hybrid KV cache optimization method that improves long-context LLM inference efficiency by leveraging sequential consistency in attention heads. The key insight is that certain attention heads exhibit stable attention patterns across both pre-filling and decoding stages, while others remain... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your valuable time and constructive feedback. Below, we provide our responses point-by-point.
> Experiments: Latency breakdown
We very appreciate the reviewer's constructive suggestion, and follow the advice to present a fine-grained latency analysis ([here](h... | null | null | null | null | null | null |
GMAIL: Generative Modality Alignment for generated Image Learning | Accept (spotlight poster) | Summary: The paper introduces GMAIL (Generative Modality Alignment for generated Image Learning). It is a framework for incorporating generated images into training pipelines while explicitly addressing the modality gap between real and generated images. Instead of treating synthetic and real images interchangeably, GM... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's feedback and the questions raised. Below, we address and resolve each of these questions in our responses.
> Domain Adaptation Literature
Our approach is conceptually related to domain adaptation techniques [a,b], where the goal is to align feature spaces b... | Summary: This paper introduces GMAIL, a novel framework designed to bridge the modality gap between generated and real images, a common issue that can cause mode collapse in training pipelines. The approach treats generated images as a distinct modality and aligns them with real images in the same latent space. By fine... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's feedback and the questions raised. Below, we address and resolve each of these questions in our responses.
> Clarification
Thank you for catching this ambiguity. We've clarified both the notation and purpose in the Table below.
| Training Data | Alignme... | Summary: This paper proposes a method to fine-tune a CLIP image encoder on synthetic image samples so that it can be used in training vision-language models with generated data. The "Gen-CLIP Flow" is learning two CLIP image encoders, one for real images and the other for synthetic images. The "Alignment with Vision-La... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's feedback and the questions raised. Below, we address and resolve each of these questions in our responses.
> Training Steps
Thank you for this observation. We have conducted additional experiments with equal training steps across all compared methods and in... | Summary: Authors propose a framework to train multimodal LLM on generated images. This work recognizes that generated images offer a different distribution than real images, and then should, in those settings, be recognized as a different modality altogether to be useful for training. Authors adopts the code base of th... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's feedback and the questions raised. Below, we address and resolve each of these questions in our responses.
> Model Collapse
We agree. To clarify the severity and nature of the issue, we added a direct comparison between models trained on: Real images only ... | null | null | null | null | null | null |
Optimal Information Retention for Time-Series Explanations | Accept (poster) | Summary: This paper proposes the Optimal Information Retention Principle to improve explanations of deep models for time-series data by minimizing redundancy and maximizing completeness using conditional mutual information. The authors develop ORTE, a framework that learns a binary mask to filter irrelevant patterns wh... | Rebuttal 1:
Rebuttal: **Comment:**
We extend our sincere appreciation to Reviewer Szo4 for providing valuable feedback and acknowledgment of our research.
- **Theoretical Claims:** This paper has three criteria: Semantic Information Retention, Minimum Redundant Information Retention, and Maximum Effective Information... | Summary: This paper proposes an explanation method called "ORTE" (the title of the paper) which uses the Optimal Information Retention principle to construct explanations for time series models. Specifically, given a time series classifier, they propose a mask generating model to produce a binary mask for each input, s... | Rebuttal 1:
Rebuttal: **Comment:**
We express our sincere gratitude to Reviewer w93i for providing comprehensive review, insightful perspectives, and thought-provoking questions.
- **Claims And Evidence:** However the claim that " We achieve ... on real-world datasets (Figure 3).
**Reply:** Thank you for your advi... | Summary: This paper introduces a novel approach to explainability in time-series deep learning models through an information-theoretic lens. The authors propose the "Optimal Information Retention Principle" which outlines three key criteria for high-quality explanations via information retention: ‘semantic’, ‘minimum r... | Rebuttal 1:
Rebuttal: **Comment:**
We sincerely appreciate Reviewer voUp for offering valuable insights and recognizing of our work.
- **Claims And Evidence:**
- C4. Practical utility: claim lacks detailed comparison of computational demands versus simpler approaches, which would be important for real-world appli... | Summary: The authors address redundancy and completeness in time-series explanation methods by deriving an Optimal Information Retention principle from information theory, which optimizes explanations by minimizing redundancy while maximizing completeness. Based on this principle, they propose ORTE, a novel explanation... | Rebuttal 1:
Rebuttal: **Comment:**
We sincerely appreciate Reviewer QJfM for the careful analysis of our work and valuable suggestions.
- **Claims And Evidence:** 1. Occlusion alone does not fully capture redundancy and completeness. & 2. Insertion experiments should complement occlusion.
**Reply:** Thanks again f... | null | null | null | null | null | null |
Cross-City Latent Space Alignment for Consistency Region Embedding | Accept (poster) | Summary: This paper deals with a critical issue in the popular trend in urban computing, namely the cross-city latent space alignment problem in region representation learning, which extracts useful features from different types of urban data for many urban prediction tasks. The issue is that although the pre-training... | Rebuttal 1:
Rebuttal: **Response to W1:**
We appreciate the reviewer’s insightful question regarding the suitability of city pairs for knowledge transfer. At this stage of research in unsupervised cross-city alignment, we acknowledge that quantitatively defining the ideal criteria for city pair selection remains an ope... | Summary: This paper addresses a critical question in urban computing: Can we align the latent spaces of different cities to leverage knowledge from one city for analyzing others? To tackle this issue, the authors introduce a one-stage Consistency Region Embedding method (CoRE), which combines region embedding learning ... | Rebuttal 1:
Rebuttal: **Response to Q1:** We acknowledge that evaluating the proposed method across additional tasks would further strengthen its empirical validation. Following your suggestion, we conducted supplementary experiments on traffic flow prediction, using datasets from Xi'an (XA) and Chengdu (CD). Specifica... | Summary: **Problem:**
- This paper studies the region representation learning problem, with a special focus on cross-city learning
- The cross-city representation learning problem is important because some cities have abundant labelled data, while many others do
- Existing methods rely on heuristic translation function... | Rebuttal 1:
Rebuttal: **Response to Q1\&W1:** We appreciate the reviewer raising this question about the trade-off between cross-city and within-city performance. To thoroughly investigate this aspect, we conducted new experiments comparing CoRE's within-city performance against established region embedding methods us... | Summary: This paper tackles the challenge of transferring urban region embeddings across cities. Instead of the commonly used two-stage approach—first learning city-specific embeddings and then mapping among them—the authors propose a unified one-stage framework called CoRE. The idea is to learn region embeddings from ... | Rebuttal 1:
Rebuttal: **Response to W1:**
We appreciate the reviewer's valuable suggestion regarding cross-country evaluation. To demonstrate the generalizability of our method, we conducted additional experiments using data from three cities of China and New York City (NYC) - cities with significantly different urban... | null | null | null | null | null | null |
Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA | Reject | Summary: The paper introduces RoLoRA, a federated fine-tuning framework for Large Language Models (LLMs) based on alternating optimization of LoRA adapters. RoLoRA optimizes both up-projection and down-projection matrices in LoRA adapters alternately, ensuring better convergence and adaptability in federated learning. ... | Rebuttal 1:
Rebuttal: Thanks for the detailed review. We address your concerns below:
**Federated Round Definition, Communication and Computation Efficiency.**
Thanks for the helpful comments. In our paper, each communication round refers to one upload/download of either matrix A or B. RoLoRA updates and transmits onl... | Summary: The paper introduces RoLoRA, a federated fine-tuning framework that employs alternating optimization of LoRA adapters to enhance model expressiveness and robustness. By theoretically and empirically demonstrating the necessity of learning both down-projection and up-projection matrices, the authors show that R... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful review. We appreciate the recognition of RoLoRA's contributions and the strengths of our alternating optimization strategy, theoretical insights, and empirical results. We address the main concerns below:
**Limited Theoretical Scope.** We appreciate the reviewer's though... | Summary: This work explores a better approach for training LoRAs in federated learning. Given a LoRA $B A$, the naive approach is to aggregate the updates of both weights simultaneously as in $\mathbb{E}_i[B_i] \mathbb{E}_i[A_i]$ for clients $i$ (which is not equal to $\mathbb{E}_i[B_i A_i]$). In this paper, the author... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and detailed assessment of our paper. We are encouraged by the overall positive evaluation and would like to clarify several points.
**Rank-1 Limitation and Generalizability to higher ranks.**
Thanks for pointing this out. Although our analysis is conduc... | Summary: This paper introduces RoLoRA, a federated fine-tuning framework that employs alternating optimization for LoRA-based adaptation. RoLoRA addresses the expressiveness limitations of FFA-LoRA (Sun et al. '24)in low-parameter settings while preserving communication efficiency. The authors provide theoretical proof... | Rebuttal 1:
Rebuttal: Thank you for the detailed and constructive review. We address the key concerns below:
**FFA-LoRA's Reduced Expressiveness and RoLoRA's Theoretical Superiority.**
Thank you for the constructive comments. We show in Proposition 5.5 (proof in Appendix A.4.1) that FFA-LoRA is suboptimal in our linea... | null | null | null | null | null | null |
TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration | Accept (poster) | Summary: In this paper, the authors propose a GNN interpretation framework named TopInG, which applies topological data analysis and persistent homology to separate the graph into rationale and noise subgraphs. The method is theoretically sound, and the experiments show improvements in interpretation performance.
Clai... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and invaluable feedback. Below, we provide detailed responses to all your comments or concerns.
### Q1: BA-HouseOrGrid-nRnd Generated is a Bit Confusing
Thank you for pointing this out! We clarify the label definitions as follows:
* Label 0: The ... | Summary: This paper presents a framework, TopIng, designed to enhance the interpretability of GNNs by enabling them to automatically identify key subgraphs that influence prediction outcomes. It uses TDA to characterize the growth process of key subgraphs and employs topological distance loss to help the GNN distinguis... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and invaluable feedback. Below, we provide detailed responses to all your comments or concerns.
### Regarding the weakness "Persistent homology computation is computationally intensive on large-scale graphs."
For larger graphs, we have mentioned se... | Summary: The paper introduces TopInG, a topologically interpretable graph neural network (GNN) framework leveraging persistent homology to identify stable rationale subgraphs for model explanations. Key contributions include: (1) a novel *rationale filtration learning* technique that models graph generation processes t... | Rebuttal 1:
Rebuttal: # Clarifications and Corrections
We thank the reviewers for their detailed feedback and appreciate the opportunity to clarify a few misunderstandings and highlight concerns already solved in our Appendix:
* BA-2Motifs do include cycles in their rationale subgraphs. All datasets used in our work c... | Summary: This work proposed a new framework which applies TDA tool to interpret the persistent rationale subgraph in graph learning problems, showing effective result performance on motif classification task, evaluated on Single Motif, Multiple Motif and Real Dataset.
Claims And Evidence: The authors provide experimen... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and invaluable feedback. Below, we provide detailed responses to all your comments or concerns.
### Q1: Evaluation on Datasets with Hierarchical Rationales
We appreciate the reviewer's insight into the importance of hierarchical rationales in inter... | null | null | null | null | null | null |
Discovering a Zero (Zero-Vector Class of Machine Learning) | Accept (spotlight poster) | Summary: The authors propose a mathematical framework for representing data classes as vectors in a vector space. The goal is to enable operations such as addition (class union) and scalar multiplication (class complement) to improve machine learning classification. The main contributions are: the introduction of the Z... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful and constructive review, which motivated us to perform additional experiments on ImageNet-1K embeddings and to analyze computational complexity explicitly. Your feedback has significantly strengthened our paper.
# Response on Scalability and Computational Effi... | Summary: This paper proposes a mathematical framework for handling classes in datasets as vectors in a vector space, including a Zero-Vector Class which can be regarded as the absence of a class. They introduce all theoretical foundations and discuss two applications of their framework, namely "clear learning" and "una... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful and meticulous review, especially your identification of small errors with exact line numbers; this level of detail is invaluable for improving the manuscript's clarity and quality.
# Clarification on Training Procedure for Clear Learning
Thank you for raisin... | Summary: This paper introduced a novel technique to regularize neural networks' decision boundary by introducing the so-called "zero-vector class". The paper established some mathematical properties of the zero-vector class concept and derived a simple method to improve on the neural networks in different tasks.
Claim... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for positively highlighting the theoretical contributions of our work.
# Q. Regarding Sampling from Low-Dimensional Manifolds
Your concern about sampling from low-dimensional manifolds is valid. Directly sampling from such manifolds is generally impractical.... | Summary: The paper introduces a novel mathematical framework for understanding class representations in machine learning by defining classes as vectors in a vector space. The core idea revolves around the concept of a Zero-Vector Class, which corresponds to a data class with a uniform distribution. This conceptualizati... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for their detailed and constructive review. The questions raised are insightful and have helped us further refine and improve our work.
# Q1. Stability of Zero-Vector Class Training
We observed no instability during training with the Zero-Vector Class. The Zero-Vec... | null | null | null | null | null | null |
Theoretical guarantees on the best-of-n alignment policy | Accept (poster) | Summary: This paper provides a theoretical analysis of the best-of-$n$ policy $\pi^{(n)}$, which is a simple inference-time method for aligning language models where $n$ samples are drawn from a reference policy $\pi_{ref}$, the highest ranking one based on a reward function is selected.
The authors first disprove a c... | Rebuttal 1:
Rebuttal: Thanks for your insightful review and encouraging comments.
> My concern is with the experiment in Figure 4, where the authors test language models on benchmarks. The reward is set as the log-likelihood of the reference model, which already correlates with the policies, thus reducing generality.... | Summary: This paper discusses the best-of-n policy, a method used for inference-time alignment of generative models. The key idea is to draw n samples from a reference policy, rank them based on a reward function, and select the highest-ranking sample. The passage critiques a commonly used analytical expression in the ... | Rebuttal 1:
Rebuttal: Thanks for your insightful review and encouraging comments.
> This assumption simplifies the theoretical analysis; however, in practical applications, the output space of generative models is typically infinite (as in natural language generation tasks), which may limit the universality of the the... | Summary: This paper revisits the best-of-n alignment policy: given n samples from a reference language model, pick the sample that scores highest under a reward (alignment) function. A long-used formula in prior papers, $D_{kl}=log(n)-(n-1)/n$, has been cited as the KL divergence of the best-of-n policy from the refere... | Rebuttal 1:
Rebuttal: Thanks for your insightful review and encouraging comments
> The real data experiments are limited and do not show large-scale or broad tasks.
The goal of the experiments was to show cases where the could be a gap between the analytical formula and the true KL. We have now experimented with othe... | Summary: The paper provides theoretical analyses for the widely used Best-of-N (BoN) policy, especially focusing on the KL divergence from the reference model and its win-rate. They first point out that the conventionally used formula for the KL divergence does not hold and actually gives only an upper bound. They eval... | Rebuttal 1:
Rebuttal: Thanks for your insightful review and encouraging comments.
> How the equation (34) can be derived?
Eq. (34) is derived by combining Theorem 5.3, i.e., Eq. (30), and Corollary 5.5, i.e., Eq. (33). We will clarify this.
> The proposed estimator for the KL divergence has some evidences both theor... | null | null | null | null | null | null |
LADA: Scalable Label-Specific CLIP Adapter for Continual Learning | Accept (poster) | Summary: This paper presents LADA (Label-specific ADApter), an approach for continual learning with vision-language models like CLIP. LADA enhances scalability and performance by generating discriminative, label-specific features. Unlike existing methods that partition parameters across tasks, LADA appends lightweight ... | Rebuttal 1:
Rebuttal: Dear Reviewer oKy3,
Thank you for your detailed review. We address your concerns one by one in the followings.
> **W1 Claims And Evidence:** the evidence provided in Table 4 focuses on ablation without comparisons to other methods. **Experimental Designs Or Analyses:** computational efficiency ... | Summary: This paper primarily proposes an adapter-based method initialized with class cluster centers for CLIP-based Cross-Domain Task-Agnostic Incremental Learning(X-TAIL). This approach enables class discrimination within a unified feature space using class-specific parameters without requiring task-specific paramete... | Rebuttal 1:
Rebuttal: Dear Reviewer vJJv,
Thank you for your detailed review. We address your concerns one by one in the followings.
> **W1 Other Strengths And Weaknesses:** Label-Specific CLIP Adapter bears a strong resemblance to the CLIP-based few-shot learning method Tip-Adapter [5]. **Questions For Authors:** Si... | Summary: Instead of partitioning parameters across tasks, this paper proposed LADA appended lightweight, labelspecific memory units to the frozen CLIP image encoder, enabling discriminative feature generation by aggregating task-agnostic knowledge. The method achieves state-of-the-art performance in continual learning ... | Rebuttal 1:
Rebuttal: Dear Reviewer EaVq,
Thank you for your detailed review. We address your concerns one by one in the followings.
> **W1**: **Essential references not discussed of [1]** and the second point in Other Strengths And Weaknesses of **comparisons with RanPAC [1]**
**A1**: We acknowledge the reviewer’s ... | Summary: The paper proposes a task incremental learning approach for CLIP encoders. In contrast to using adapters for the image encoder, it learns task (or class) specific memories represented by learnable vectors. The classification is performed via dot products of these task specific memories and CLIP image embedding... | Rebuttal 1:
Rebuttal: Dear Reviewer 4dD8,
Thank you for your detailed review. We address your concerns one by one in the followings.
> **W1** It should have been clearly mentioned that finetuning text encoder is part of the proposed approach.
**A1** We sincerely apologize for the lack of clarity and fully agree wi... | null | null | null | null | null | null |
In-Context Adaptation to Concept Drift for Learned Database Operations | Accept (poster) | Summary: The paper introduces an online adaptation framework to tackle concept drift in learned database operations. The topic is related to the application of machine learning. The proposed FLAIR leverages in-context adaptation through dynamic context memory and Bayesian meta-training, enabling models to adjust to evo... | Rebuttal 1:
Rebuttal: Dear Reviewer C5eR,
We sincerely thank you for the constructive feedback that helps improve our work. We address the concerns you raised below. The full new results are provided in: https://anonymous.4open.science/r/ICML25-7F63/ICML25.pdf
**[Q1-Synthetic Priors]** We clarify that FLAIR's DDE is ... | Summary: This paper addresses the challenge of concept drift in database operations. Its primary contribution is the introduction of an in-context adaptation framework to tackle this issue. The proposed method, FLAIR, comprises two essential components: a Task Featurization Module and a Dynamic Decision Engine. The eff... | Rebuttal 1:
Rebuttal: Dear Reviewer 5u5N,
We thank you for your recognition of our work and your insightful comments. Below, we provide detailed responses to the specific concerns you raised. Full tables/figures are in: https://anonymous.4open.science/r/ICML25-7F63/ICML25.pdf
> expect a comparison with more recent ap... | Summary: This paper focuses on the issue of concept drift in dynamic database environments, which is an interesting and challenging research problem. To address this problem effectively, an online adaptation framework, FLAIR, has been developed. Sufficient experiment and analysis show the performance of the proposed me... | Rebuttal 1:
Rebuttal: Dear Reviewer uD1S,
We sincerely thank you for your positive review and insightful comments. We address your concerns below and attach new results here: https://anonymous.4open.science/r/ICML25-7F63/ICML25.pdf
> Q1: does the work consider both data and query change?
Yes, we considered both data... | null | null | null | null | null | null | null | null |
Feedforward Few-shot Species Range Estimation | Accept (poster) | Summary: The paper introduces FS-SINR ("few-shot spatial implicit neural representations") for few-shot species range estimations, which is trained on citizen science location data. A key feature of FS-SINR is that once it has been trained, it can be used during inference to predict the range even of previously unseen ... | Rebuttal 1:
Rebuttal: We thank **nqXQ** for their careful reading of the paper and constructive suggestions.
**[nqXQ-1] Quantification of computational efficiency.**
Below we report inference timings for different models (with 1 location + text), reported as the time taken in seconds to generate all evaluation spe... | Summary: This paper outlined a new approach for few-shot species range estimation. The goal is to outline geospatial regions where an animal is likely to live based on previous observations of occurrence. The authors' approach builds upon Spatially Implicit Neural Representation (SINR) models designed to estimate speci... | Rebuttal 1:
Rebuttal: We thank **HT4t** for their helpful questions. By addressing these comments we believe that the description of the data processing and the evaluation protocol in the revised paper will be much clearer.
**[HT4t-1] Spatial resolution of the model and data aggregation.**
We use the same training ... | Summary: The authors work within the problem setting of species-range estimation, where, given a latitude--longitude pair and a target species, the task is to determine the probability of being able to find that species at that location. Motivated by the large number of species for which only sparse sightings have been... | Rebuttal 1:
Rebuttal: We thank **JzC8** for their helpful suggestions.
**[JzC8-1] Performance compared to LE-SINR.**
We outperform the recent LE-SINR in both the few-shot (Fig 3) and zero-shot (Table 1) settings even though we do not require any training on the evaluation species as in LE-SINR, thus making us much ... | Summary: This paper introduces FS-SINR, a novel Transformer-based approach for few-shot species range estimation that can predict ranges for previously unseen species without requiring retraining. The model architecture combines a location encoder for processing geographic coordinates, a frozen GritLM text encoder for ... | Rebuttal 1:
Rebuttal: We thank **QZrb** for their constructive comments.
**[QZrb-1] Robustness to number of training locations.**
An ablation of the number of training context locations is provided in Fig. A2. We will update L250 Col1 to more clearly point to this result. As can be seen, fewer context locations (i.... | null | null | null | null | null | null |
PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities | Accept (poster) | Summary: The document presents PieClam (Prior Inclusive Exclusive Cluster Affiliation Model), a novel graph autoencoder that enhances the existing BigClam framework by utilizing overlapping inclusive and exclusive community structures for node representation learning. PieClam introduces a new log cut distance for measu... | Rebuttal 1:
Rebuttal: We thank that reviewer for the positive and in depth evaluation of our paper.
>**Claims And Evidence:**
>>...further clarity on the implications of using the log cut distance in sparse graphs would enhance the overall argument.
*Response.* Thank you for this suggestion. We already shortly disc... | Summary: The paper introduces PieClam, a universal graph autoencoder that extends traditional community affiliation models by incorporating both inclusive and exclusive communities. The method uses a novel decoder based on the Lorentz inner product to overcome the triangle inequality limitations of previous models, the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and in depth evaluation of our paper.
>**Claims And Evidences:**
>> ...the evidence on sparse graphs is a bit less comprehensive. Further empirical validation in more diverse settings would improve the support for universality claims.
*Response.* The experi... | Summary: The submitted manuscript introduces PieClam, a graph autoencoder that learns node embeddings by maximizing the log-likelihood of the decoded graph. It extends the well-known BigClam method in two ways. First, PieClam incorporates a learned prior on the node distribution in the embedding space, shifting from a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and in depth evaluation of our paper.
>**Weaknesses:**
>>1. A key concern lies in the equivariance of the encoder architecture...
*Response.* Thank you for this comment. **PieClam (and all other Clam models) are in fact equivariant to node re-indexing.** Th... | null | null | null | null | null | null | null | null |
Does learning the right latent variables necessarily improve in-context learning? | Accept (poster) | Summary: This paper investigates whether explicitly inferring task-relevant latent variables improves in-context learning (ICL) in Transformer models. The authors introduce an explicit model that enforces structured inference of latent variables and compare it with a standard implicit model that learns ICL end-to-end w... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable and constructive feedback.
> Single latent variable can fully summarize context information independently of $x_{query}$ seems unnatural
It is important to note that the dimensionality of the latent variable in explicit models is kept sufficiently lar... | Summary: This paper delves into the mechanism for Transformers to do In-Context Learning (ICL). A common belief is that TF do CL through some statistical shortcuts and hence can not generalize well in OOD tasks. The authors test this hypothesis by minimally modifying the architecture to encourage the model to explicitl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and appreciate that they found the paper to be a clear, well written, in-depth analysis of the subject.
To alleviate their concerns regarding OOD generalization, we refer them to Figures 2(c) and 5 where we test for compositional generalization instead of... | Summary: This paper notes that when we do in-context learning, it is likely that the network is, in some sense, learning about the structure of the task. This paper considers task spaces that are explicitly low-dimensional, such as linear regression, where you can use in-context learning to give information about the l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, but strongly disagree that our paper is a negative result that is “not sufficiently interesting, surprising or convincingly argued”. Besides the fact that several reviewers (1Akw, 1soF, TjQ5) found our motivation (detailed below) and study interesting and ... | Summary: The paper addresses a key question in in-context learning: whether explicit latent variable learning leads to better generalization, especially out-of-distribution (OOD). The conclusion is that the explicit bottleneck architecture does not help in terms of generalization.
Claims And Evidence: Yes, the paper d... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the value of our work and providing constructive cristicism. Throughout this comment, we will refer to additional experiments that are provided here: https://anonymous.4open.science/r/explicit-implicit-rebuttal-B263/explicit-implicit-rebuttal.pdf
> Is there... | Summary: This paper investigates whether explicitly inferring latent variables of an underlying task improves in-context learning performance in transformers. They find that explicit modeling of latent variables does not necessarily improve performance compared to standard implicit models. They also find that while the... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the value of our work and providing constructive cristicism.
> Additional References
We appreciate the reviewer bringing two relevant papers to our attention and will include them in the final version.
- Wang et al. (2023) shows that inferring latents ($\... | null | null | null | null |
FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees | Accept (poster) | Summary: • Introduces FACTTEST, a framework that statistically evaluates LLM factuality with theoretical guarantees to detect hallucinations
• Formulates hallucination detection as hypothesis testing, controlling Type I errors (incorrectly classifying hallucinations as truthful) at user-specified significance levels... | Rebuttal 1:
Rebuttal: Thank you for your detailed reviews and questions you raise to help improve our paper!
> ***W1: statistical significance testing***
We've conducted bootstrap analysis for 95% confidence intervals. Results available in: https://anonymous.4open.science/r/ICML_rebuttal-8905/icml2025__FactTest-2.pdf... | Summary: The paper proposes a framework to provide a statistical guarantee of the correctness of an output generated by LLM. The methodology leverages hypothesis testing and provides guarantees about type I and type II errors. Experiments are conducted on question-answering datasets.
## Update after rebuttal: I have i... | Rebuttal 1:
Rebuttal: Thank you for your feedback and suggestions. We are glad that you acknowledge our work’s motivation, method and theoretical claims. Here we provide responses and additional experimental results to address your concerns.
> ***W1:guarantees are dependent on training data pairs***
In Section 3, **w... | Summary: The paper proposes FactTest, a framework to assess if an LLM can be factual with high probability correction guarantees. FactTest treats hallucination detection as a statistical hypothesis-testing problem. By doing this, it rigorously controls the maximum allowed Type I error rate ensuring hallucinations are n... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedbacks. We are glad that you acknowledge the validity of our framework and experimental design. Here we provide responses to your concerns one by one.
> ***W1:Threshold of SCGPT***
We acknowledge that using a threshold of 0.5 for SelfCheckGPT may not be optimal... | null | null | null | null | null | null | null | null |
Efficient Source-free Unlearning via Energy-Guided Data Synthesis and Discrimination-Aware Multitask Optimization | Accept (spotlight poster) | Summary: The authors propose the DSDA source-free unlearning framework the address the challenge of inaccessible original training data. DSDA consists of two key components: (1) AEGDS generates synthetic data using Langevin dynamics, and (2) DAMO balances unlearning objectives by resolving gradient conflicts. Extensive... | Rebuttal 1:
Rebuttal: ***W1: Incorporating quantitative metrics (such as FID) to evaluate the soundness of synthetic data could strengthen the paper.***
We respectfully disagree with the reviewer’s suggestion. We emphasize that our goal is to generate synthetic data that approximates the original distribution while p... | Summary: This paper proposes a new framework, DSDA, for machine unlearning without access to the training data.
Specifically, DSDA first crafts synthetic data via energy-based models with Langevin dynamics and then performs unlearning using the generated synthetic data.
The authors observe that simply formulating th... | Rebuttal 1:
Rebuttal: ***W1: This mechanism may be limited on high-resolution data.***
Thank you for the comment. However, we respectfully disagree. Our method is not restricted to high-resolution data, as demonstrated by our experiments on three datasets with varying resolutions—CIFAR-10 (32×32), CIFAR-100 (32×32), ... | Summary: The paper addresses the challenge of source-free unlearning for image classification ML models, where the training data cannot be accessed after initial model training. The paper proposes a novel framework called DSDA, which utilizes Langevin dynamics, Runge–Kutta methods and gradient-based multitask optimizat... | Rebuttal 1:
Rebuttal: ***W1: A deeper discussion on the synthetic data's limitations would strengthen the argument.***
We appreciate the reviewer’s suggestion. Our experiments on three datasets with 10, 100, and 105 classes demonstrate that the synthetic data effectively supports unlearning across diverse distribution... | Summary: The authors present a well-structured and innovative approach to source-free unlearning, where a trained model must forget specific data without access to the original training dataset. To achieve this, the authors propose a novel two-stage framework DSDA. Firstly, the proposed AEGDS generates synthetic datase... | Rebuttal 1:
Rebuttal: ***W1: Adding a notation table will make the paper clearer.***
We thank the reviewer for pointing out this issue. We will add a notation table in our final version.
***Q1: Could the synthetic data cause potential privacy linkage?***
The reviewer raises a critical concern. However, our work addr... | null | null | null | null | null | null |
World Model Implanting for Test-time Adaptation of Embodied Agents | Accept (poster) | Summary: This paper proposes a world model implanting framework to augment the LLM-based agents. The world models are learned using domain/task-specific data to capture the in-domain characteristics, serving as domain experts. With these world models, this paper introduces a prototype-based retrieval method together wi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed comments. We will include the following experimental results and clarifications in the final version.
> Q1. How does this method guarantee that the knowledge of the seen world models will benefit the target task? If the target domain/task is highly out-of-the... | Summary: The paper introduces WorMI, a framework designed to improve the adaptability of embodied AI agents across diverse and unseen domains at test time, without requiring extensive retraining or additional data collection. Experiments on the VirtualHome and ALFWorld benchmarks demonstrate that WorMI outperforms stat... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed comments. We will include references, the following experimental results, and additional clarifications in the final version.
> Q1. Experimental results for inference time and memory usage
Below shows the inference times and memory usages among the baselines ... | Summary: This paper presents World Model Implanting (WorMI), a framework to improve the test-time adaptation of embodied AI agents. This work assumes access to a set of world models which are pre-trained on a set of datasets. During the adaptation phases, they select a subset of models that are most relevant to the inp... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed comments. We will include the following experimental results and clarifications in the final version.
> Q1. How are the "world models" trained?
Following the reviewer’s comment, we clarify that the world models imitate the environment by capturing dynamics an... | Summary: This paper presents WorMI, a framework enabling embodied agents to adapt to new domains at test time by combining large language models with domain-specific world models. It introduces prototype-based world model retrieval and world-wise compound attention to effectively integrate knowledge from multiple model... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed comments. We will include the following experimental results and clarifications in the final version.
> Q1. While the combination of methods is novel, the individual components (prototype-based retrieval, attention mechanisms, meta-learning) are not entirely ... | null | null | null | null | null | null |
One Leaf Reveals the Season: Occlusion-Based Contrastive Learning with Semantic-Aware Views for Efficient Visual Representation | Accept (poster) | Summary: The work propose occlusion-based contrastive learning with masked image modeling approach. It compares against iBot, MAE, I-JEPA and other relevant SSL methods. Achieves competitive results with less time needed for training.
Claims And Evidence: clearly stated and confirmed by the results
One of them is usa... | Rebuttal 1:
Rebuttal: Thank you very much for your sincere review, especially for summarizing the strengths of our work, including 1) interesting and well-written work, 2) clear presentation of the claims and confirmed by the results, 3) novel training method of occlusion that is easy to follow and 4) good experiments ... | Summary: The paper introduces Occluded Image Contrastive Learning (OCL), a novel self-supervised learning (SSL) paradigm for efficient visual representation. OCL combines the strengths of Masked Image Modeling (MIM) and Contrastive Learning (CL) by using random masking to create diverse views within an image and contra... | Rebuttal 1:
Rebuttal: Thank you very much for your review, especially for summarizing the strengths of our work, including 1) well-motivated method 2) well-written paper and easy to follow and 3) comprehensive ablation studies. In response to your concerns, we have provided a detailed explanation below and revised our ... | Summary: This work proposes a contrastive self-supervised training method that relies on masking. After a global masking step, two nonoverlapping sets of patches are selected to create two views which are then aligned to each other and contrasted to all views of other samples in the minibatch. Noticeably, no contrastiv... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful and detailed review. Your positive feedback serves as a great source of encouragement for our team, especially recognizing the strengths: 1) efficient and simple framework, 2) not relying on specific masking strategies and 3) higher level of semantic abstra... | null | null | null | null | null | null | null | null |
Quantum Algorithms for Finite-horizon Markov Decision Processes | Accept (poster) | Summary: This paper presents four quantum algorithms for time-dependent, finite-horizon Markov Decision processes (MDPs) in both the exact dynamics setting and the generative model setting:
1. In the exact dynamics setting, the algorithm QVI-1 achieves a quadratic speedup in the active space size (A) for computing th... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer FX1s for the thoughtful and constructive feedback. Below, we address the reviewer’s concerns regarding references, the typo in Theorem 4.2, the definition of "minimax optimal," the clarity of the algorithm name "QVI," the cost of constructing quantum oracles, and the po... | Summary: In this work, the authors propose quantum algorithms for solving time-dependent, finite-horizon Markov Decision Processes (MDPs). The goal is to estimate the optimal policy that maximizes the expected reward over the finite time horizon, given a finite and discrete state and action space. Equivalently, this ta... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s concern regarding the perceived lack of novelty in our quantum algorithms. While our algorithms leverage **QMS** and **QME** as in prior work (Wang et al., 2021), their analysis for infinite-horizon MDPs can not be readily applied to time-dependent and finite-horizon M... | Summary: In this submission, the authors presented quantum algorithms for finite-horizon Markov decision processes (MDPs). These quantum algorithms cover MDPS in the exact dynamics setting and in the generative model setting. Polynomial speedups are achieved. In addition, lower bounds on the query complexity for the ge... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer Xd2s for the detailed feedback on our submission. Below, we address the reviewer’s concerns regarding the technical contributions and the justification of the comparison between quantum and classical generative models, while proposing feasible revisions to strengthen ou... | Summary: This paper explores quantum algorithms designed to improve the efficiency of solving finite-horizon Markov Decision Processes (MDPs) in two settings: exact dynamics and generative models. The main contribution is the introduction of quantum value iteration (QVI) algorithms.
Claims And Evidence: The claims mad... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer tc3r for the thorough and constructive feedback on our submission. Below, we address the reviewer’s concerns and provide clarifications to strengthen our submission.
We acknowledge the reviewer’s concern regarding the lack of empirical validation in our current submiss... | null | null | null | null | null | null |
Visual and Domain Knowledge for Professional-level Graph-of-Thought Medical Reasoning | Accept (spotlight poster) | Summary: The paper introduces a novel dataset specifically designed for professional-level medical reasoning in medical visual question answering. It leverages a decade-long collection of MRI and clinical data related to Hypoxic-Ischemic Encephalopathy (HIE), enriched with expert annotations and insights. The authors g... | Rebuttal 1:
Rebuttal: We thank reviewer's constructive feedback; we have addressed your concerns below.
>Q1. One potential concern is that the paper mentions the dataset is sourced from Massachusetts General Hospital, which might conflict with the anonymity requirements of a double-blind review process. This detail co... | Summary: The authors introduce the HIE-Reasoning dataset and a Clinical Graph of Thought (CGoT) model for professional-level Medical Visual Question Answering (MVQA) focused on Hypoxic-Ischemic Encephalopathy (HIE). The dataset, built from a decade of MRI and clinical data, includes 749 expert-annotated question-answer... | Rebuttal 1:
Rebuttal: We thank reviewer's valuable suggestions and address concerns below within 5000 characters.
### Q1. Fig. 1 lacks examples of \"clinically irrelevant\" questions.
\"Clinically irrelevant questions\" include general or superficial queries about images, such as modality (\"What type of scan is this... | Summary: The paper introduces HIE-Reasoning, a professional-level medical visual question answering (MVQA) benchmark focused on neonatal Hypoxic-Ischemic Encephalopathy (HIE). The authors propose the Clinical Graph-of-Thought Model (CGoT), which integrates visual and clinical domain knowledge into a structured reasonin... | Rebuttal 1:
Rebuttal: Thanks for the constructive comments. We address your concern below within 5000 characters.
### Q1. Computational Complexity
| Model | Input Tokens | Output Tokens | Inference Time | API Cost |
|-|-|-|-|-|
| Gemini|10,580| 446| 6.50s| $0.00080 |
| CGoT-Gemini| 36,256 | 1,146 | 21.09s | $0.00272|... | null | null | null | null | null | null | null | null |
T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling | Accept (poster) | Summary: This paper examines the SFT + RL training pipeline for enhancing reasoning in LLMs. The authors propose several techniques to improve model performance, including SFT with critiques, auxiliary entropy bonuses, high-temperature sampling, Exponential Moving Average (EMA) stabilization, and On-policy KL normaliza... | Rebuttal 1:
Rebuttal: Thanks for your kind review and feedback!
We would first like to emphasize that the primary contribution of this work is to propose a framework designed to effectively scale the reinforcement learning (RL) training of large language models (LLMs). This approach significantly enhances reasoning c... | Summary: The paper proposes an RL based method to improve the reasoning / inference scaling capabilities of LLMs.
The paper discussed the importance of exploration and proposed an RL objective incorporating an entropy bonus to encourage diverse sampling. The paper also discusses specific tricks, such as format penalt... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback!
### Q1: Have you tried other RL algorithms ?
We also conduct experiments using GRPO, and run the experiments using Qwen-14b with k=16 for efficiency. The results are shown as follows
| | AIME | Omni-math-500 | MATH500 |
| --- | --- | --- | --- |
| T1 w/ R... | Summary: The paper introduces T1, a novel approach for enhancing the reasoning abilities of large language models (LLMs) by scaling reinforcement learning (RL) and leveraging inference compute. The method begins by initializing the LLM with synthesized chain‐of‐thought data that incorporates trial‐and‐error and self-ve... | Rebuttal 1:
Rebuttal: Thanks for your kind review and valuable suggestions!
### Q1 & W1: All training and most evaluations focus on math word problems and quantitative reasoning.
Thank you for the question. We primarily conduct reinforcement learning (RL) training on math problems due to the following two reasons:
Ma... | Summary: This paper introduces T1, a method for enhancing LLM reasoning through reinforcement learning with increased exploration and inference scaling. The authors initialize a policy with chain-of-thought data incorporating trial-and-error patterns, promote exploration during RL through response oversampling, and ana... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback!
### W1: Lack of comparison with other RL methods for LLMs (particularly GRPO)
A1: We appreciate the reviewer's concern regarding the comparison with other RL methods, particularly GRPO and PPO. However, our work, T1, is designed to be independent of any specif... | null | null | null | null | null | null |
Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics | Accept (oral) | Summary: (i) The paper introduces the Algebraic Combinatorics Dataset Repository (ACD Repo), a collection of nine datasets designed to challenge machine learning models in conjecturing and problem-solving in modern algebraic combinatorial mathematics. (ii) This dataset contains foundational results and open problems in... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful feedback and questions. We especially appreciated the questions about differences in model performance, which we also have. We provide responses to the points in the review below.
- *Addition of examples of conjectures powered by machine le... | Summary: The paper introduces the Algebraic Combinatorics Dataset Repository (ACD Repo), a collection of nine datasets to use AI in advancing research-level algebraic combinatorics.
A key contribution from author is the dataset focus on open problem with a large collection of examples. A clear focus from author is not... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the interesting questions. We provide answers below.
- *Is there any particular reason why some problems are not interesting outside a certain boundary? For example, looking at the Github it says that for a particular problem N can be only 6/7/8.*
- Thi... | Summary: The authors introduce a collection of datasets called the Algebraic Combinatorics Dataset Repository, which contains 9 datasets including an open-ended research question and many examples that should be used to derive conjectures. The authors describe the mathematical background of each dataset as well as resu... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for providing feedback on the paper, especially for pointing out that we may need to provide more motivation for our choice of baselines and for suggestions on the paper’s structure. We provide responses to the points in the review below.
- *Tension between base... | Summary: The paper introduces nine datasets arising from problems in algebraic combinatorics. These datasets are meant to test capabilities of machine learning models on symbolic math tasks. The authors clearly motivate and describe each of the nine problems and the resulting benchmarks. Several baseline methods are te... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for all their thoughtful comments on the paper and for pointing out issues with the GitHub page. We provide responses to the points in the review below.
- *“…it seems that for the majority of the datasets the baseline methods … could achieve quite high performa... | null | null | null | null | null | null |
Behavioral Exploration: Learning to Explore via In-Context Adaptation | Accept (poster) | Summary: This paper presents a method to predict expert actions based on past observations and predict how "exploratory" the expert's behaviors are relative to the context. This design enables the model to mimic the behavior of an expert, and select the most relative experiences to explore.
---
Update after rebuttal:... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We have added videos illustrating the performance of our approach and baselines on our real-world WidowX setting [here](https://drive.google.com/drive/folders/1-B7kgD9lXVR41WyhtR1XuLepQXI8QR7W?usp=share_link), and provide clarification on additional points... | Summary: This paper proposes a method—Behavioral Exploration (BE)—that is capable of online in-context task adaptation while learning offline from a set of reward-free expert demonstrations. Such an ability is achieved by conditioning the policy on the coverage measure, which reweights the probabilities of less frequen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We have added an additional unsupervised RL baseline to the [Libero experiment](https://drive.google.com/file/d/19kFpxrczDTiXL1S0rL1FfX2zXwAT1qVZ/view?usp=share_link), which we found performed worse than both BE and BC, and have also run additional experim... | Summary: This paper addresses the problem of in-context RL -- in particular, learning how to *explore* through in-context adaptation.
The proposed method (Behavioral Exploration, BE) performs reweighted behavior-cloning over large expert datasets, with a long-context policy that also conditions on a variable represent... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We have run an additional requested baseline on Libero—BC with history conditioning, which we found performed worse than BC—and additional experiments illustrating that BE adapts to its history online; please see below for further discussion and the result... | Summary: The manuscript presents "Behavioral Exploration" (BE), a novel formalization for learning from expert demonstrations, while giving the ability to the policy/user to explore in a controlled manner (via some parameters). The main innovation is the Behavior Policy Coverage metric, and how to use it to condition o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. In the following we provide additional clarification on the success metrics and RL experiments, as well as several other comments the reviewer had. We will update the final version of the paper to include all these details.
## Clarification of Success Me... | null | null | null | null | null | null |
Ad Hoc Teamwork via Offline Goal-Based Decision Transformers | Accept (poster) | Summary: The paper introduces TAGET, a hierarchical framework for offline ad hoc teamwork (AHT) that leverages teammate-aware goal-conditioned Decision Transformers. The core idea is to dynamically predict teammate-aware return-to-go (TA-RTG) and sub-goals (TA-Goals) to enable real-time adaptation to unknown teammates ... | Rebuttal 1:
Rebuttal: **Q1. Questions in Claims And Evidence.**
A1. We apologize for any confusion. Firstly, we perform implicit modeling of teammates to capture their changes and update our TA-goal, so there is no explicit metric to measure the accuracy of teammate modeling. As for the generalization of the policy,... | Summary: The paper presents TAGET, a novel hierarchical framework designed to address the challenges of ad hoc teamwork in settings where only offline multi-agent interaction data is available. Traditional ad hoc teamwork approaches rely on real-time interactions, but TAGET circumvents the need for costly online simula... | Rebuttal 1:
Rebuttal: Thank you for your suggestions.
**Q1. Concerns on the difficulty of collecting offline data and the applications of methods**
A1.We apologize for the misunderstanding. Our claim that offline data is "relatively easy to collect" refers to its cost and safety advantages over online training, not t... | Summary: This paper introduces **TAGET (Teammate-Aware Goal driven hiErarchical Decision Transformers)**, a novel framework for offline ad hoc teamwork (AHT). The AHT problem requires an agent (ego agent) to collaborate with unknown teammates without prior coordination. Unlike existing approaches that rely on online re... | Rebuttal 1:
Rebuttal: We thank the reviewer for your constructive feedback.
**Q1. Concerns about the trajectory mirroring strategy.**
A1. Our trajectory mirroring strategy is a data preprocessing method specifically designed for the AHT, not a data augmentation method. It works by sequentially designating different ... | Summary: This paper addresses ad hoc teamwork in the offline setting. It proposes a method called TAGET, which is based off the decision-transformer architecture and learns from a dataset of offline cooperative multi-agent interactions. It has a couple of main ideas. First, trajectory mirroring: where one agent is samp... | Rebuttal 1:
Rebuttal: Thank you for your kind suggestions and helpful feedback!
**Q1: Concerns about teammate diversity.**
A1. We apologize for the confusion about the teammate generation. First and foremost, Soft Value Diversity (SVD) is not a MARL algorithm but a framework specifically designed to generate diverse... | null | null | null | null | null | null |
Noise-Guided Predicate Representation Extraction and Diffusion-Enhanced Discretization for Scene Graph Generation | Accept (poster) | Summary: This study proposes a Noise-Guided Predicate Representation Extraction and Diffusion-Enhanced Discretization (NoDIS) technique to solve the long-tail problem inherent in the existing dataset for learning the Scene Graph Generation (SSG) model. The main contribution is that the existing proto-type learning meth... | Rebuttal 1:
Rebuttal: ## We sincerely appreciate your time and effort in carefully reviewing our paper and providing constructive feedback. We are also grateful for your recognition of our work.
- *For Weakness 1*: Thank you for bringing this up. First, we compared and analyzed our method against state-of-the-art appro... | Summary: The paper addresses bias in scene graph generation, especially the difficulty of learning fine-grained predicate labels under long-tailed distributions. The authors propose NoDIS, which has two core aspects: First, Noise-Guided Predicate Extraction expands predicate representations by applying a single-step no... | Rebuttal 1:
Rebuttal: ## We sincerely appreciate your time and effort in carefully reviewing our paper and providing constructive feedback. We are also grateful for your recognition of our work.
- *For Weakness 1*: Thank you for bringing this up. Figure 2(b) illustrates both the training and inference processes, making... | Summary: The paper proposes NoDIS, a method designed to address bias in SGG arising from long-tail predicate distributions. The contributions are: 1) it introduces a noise-guided predicate representation extraction technique that employs conditional diffusion models to increase the diversity of predicate representation... | Rebuttal 1:
Rebuttal: ## We sincerely appreciate your time and effort in carefully reviewing our paper and providing constructive feedback. We are also grateful for your recognition of our work.
- *For Weakness 1*: Thank you very much for pointing that out. This work is the first to apply diffusion models for feature e... | Summary: The paper introduces a diffusion-based feature enhancement approach to broaden the visual space of predicate representation and improve feature diversity. It first extracts entity representations using a baseline model and refines them with a Transformer for contextualization. Gaussian noise is then applied to... | Rebuttal 1:
Rebuttal: ### We sincerely appreciate your time and effort in carefully reviewing our paper and providing constructive feedback. We are also grateful for your recognition of our work.
- #### *For Weaknesses*: Thank you very much for pointing this out. We classify subject and object entities using the same ... | null | null | null | null | null | null |
The Disparate Benefits of Deep Ensembles | Accept (poster) | Summary: This paper presents an empirical study of the fairness properties of deep ensembles. On several image datasets, the authors explore the fairness/accuracy tradeoff of deep ensembles at varying numbers of members, and explore a hypothesis which relates decreasing fairness metrics with varying amounts of predicti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their critical assessment of our work and the insightful questions. We first address the main question, followed by responses to the specific concerns outlined in the review.
## Main Question
We appreciate the request for clearer evidence linking predictive diversity to ... | Summary: This paper studies the fairness of deep ensembles on three image datasets. They find that ensembling tends to reinforce unfair behavior of ensemble members. The authors provide an explanation in that the predictive diversity causes this effect, which they call disparate benefits effect and give empirical evide... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very positive assessment of our work. We are pleased that they consider our experiments to be well designed, described and sufficient to support our claim that ensembling can have a negative impact on fairness.
## Questions
**Q1**
We acknowledge that the sample s... | Summary: The paper discusses two relations between deep ensembles and fairness notions. The first relation is the effect of the number of members on the overall fairness performance of the system. The second is what the authors call 'disparate benefit', which is the difference in fairness violation between the ensemble... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for engaging deeply with our work and for providing their thoughtful and positive assessment. We’re pleased that you found the experimental design sound, the analyses sufficiently elaborate, and the results well-supported. Your detailed comments helped us reflect on... | Summary: This paper explores the impact of Deep Ensembles on algorithmic fairness. The authors find that Deep Ensembles, while improving overall performance, can unevenly benefit different groups, a phenomenon they call the "disparate benefits effect." The paper suggests that differences in predictive diversity among e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough assessment of our work. We are pleased that it was found to be well written and to offer valuable insights. In response to the questions raised:
> Limited generalizability of findings due to focus on vision datasets
We agree with your observation and ack... | null | null | null | null | null | null |
RUN: Reversible Unfolding Network for Concealed Object Segmentation | Accept (poster) | Summary: This paper tackles the challenging Concealed Object Segmentation, which aims to segment objects that are visually blended with their surroundings. It introduces the Reversible Unfolding Network (RUN), a iteratively method to refine segmentation results and minimize uncertainties. RUN includes two main modules:... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments.
**W1. Challenges in introducing DUNs and importance of modeling high-level vision tasks**
In P3L142, we state that deep unfolding networks (DUNs) are underexplored in high-level vision tasks for lacking intrinsic models.
**Challenges:** DUNs rely on model-base... | Summary: This paper proposes the first deep unfolding network, RUN, for the COS task, aiming to cope with one of the intrinsic challenges in the COS task, which is neglecting the importance of applying reversible strategies in the RGB domain. To achieve this goal, two reversible modules, SOFS and ROBE, are further prop... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments.
**W1. More related works**
In related works, the COS component focuses on the development of the reversible modeling strategy, while our coverage of DUN is limited due to the underexploration of this technique in high-level vision tasks. To provide a more compre... | Summary: The authors propose Reversible Unfolding Network (RUN) for Concealed Object Segmentation. RUN integrates optimization-based solutions with deep learning, enabling reversible modeling across both mask and RGB domains. It also introduces the Segmentation-Oriented Foreground Separation (SOFS) module and the Recon... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments.
**W1. Efficiency analysis**
We compare the parameters, FLOPS, and FPS between our RUN against cutting-edge methods on three backbones. Our stage number is set to 4. As shown in the table, our method is more efficient across all three backbones with the input siz... | Summary: The paper introduces a Reversible Unfolding Network (RUN), a novel deep unfolding network that integrates object segmentation and distortion restoration tasks. RUN combines a Segmentation-Oriented Foreground Separation (SOFS) module and a Reconstruction-Oriented Background Extraction (ROBE) module to achieve m... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments.
**W1. Why only conducting ablation studies on COD10K**
Given that COD10K is a representative and high-quality dataset, we follow existing methods [1,2,3] to conduct ablation studies on COD10K. We have now included the results of ablation studies on three extra ... | null | null | null | null | null | null |
Noisy SIGNSGD Is More Differentially Private Than You (Might) Think | Accept (poster) | Summary: The authors study the privacy benefits of the map $sign(x)$ when combined with additive Gaussian noise in the Noisy SIGNSGD algorithm. They show that since $sign(x)$ drops the magnitude information, it indeed “amplifies” the privacy. The results show that the use of logistic noise may not be superior to using ... | Rebuttal 1:
Rebuttal: Dear reviewer nyXX
We appreciate your time and effort in reviewing our paper and providing a positive evaluation. Please find our response below.
**Comparison with sketching-based approaches**: Thank you for pointing out this important aspect. We note that the compression of differentially priva... | Summary: This paper investigates how sign-based gradient compression–specifically, Noisy signSGD–can inherently amplify differential privacy.
In a distributed learning scenario, the authors introduce theoretical analysis for the privacy guarantees under the f-DP framework and compare to two variants: G-NoisySign and L... | Rebuttal 1:
Rebuttal: Dear reviewer t9hH
We appreciate your time and effort in reviewing our paper and providing constructive comments. Please find our point-by-point response below.
**Question about error analysis:** The rationale behind the analysis in Eqs (14)-(16) is that unbiased estimate of gradients is general... | Summary: SignSGD is a technique to compress a gradient in order to reduce its communication cost. It is typically applied in decentralized or federated SGD where the transmission of gradients is a regular operation. The key idea behind it is to transmit the sign of each component, reducing the communication cost to one... | Rebuttal 1:
Rebuttal: Dear Reviewer Jupt,
We appreciate your time and effort in reviewing our paper and providing a positive evaluation. Please find our point-by-point response below.
**Relation to other compression techniques under privacy constraints:** Thank you for pointing out this important aspect. We note that... | Summary: This paper considers the privacy guarantees of Noisy SignSGD, an algorithm that adds noise to a value, then releases its sign.
It is shown that releasing the sign of the value, rather than the noisy value itself, amplifies the privacy guarantees.
A method of majority vote for aggregating the sign gradient is a... | Rebuttal 1:
Rebuttal: Dear Reviewer AkDc,
We appreciate your time and effort in reviewing our paper and providing constructive comments. Please find the response to the comments below.
**Concern about presenting the best results:** This is due to confusion. We run the algorithms for 5 repeats, compute the mean and st... | null | null | null | null | null | null |
Large Language Model-driven Large Neighborhood Search for Large-Scale MILP Problems | Accept (spotlight poster) | Summary: This paper proposes LLM-LNS, a framework leveraging Large Language Models (LLMs) to drive Large Neighborhood Search (LNS) for solving large-scale Mixed Integer Linear Programming (MILP) problems including Online Bin Packing, Traveling Salesman Problems, SC, MVC, MIS, and MIKS. The main novelty lies in a dual-l... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 4X7L for the thoughtful and constructive feedback. We are glad that you find the proposed LLM-LNS framework innovative and recognize its performance improvements over existing methods. We appreciate your suggestions regarding dataset realism, solver settings, and relate... | Summary: This paper describes an evolutionary LLM-based framework to produce heuristics represented as code. It extends previously-proposed evolutionary methods for heuristic search with two additional techniques: prompt evolution and directional evolution. Prompt evolution focuses on finding good LLM prompts to produc... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer r212 for the detailed, thoughtful, and constructive feedback. We greatly appreciate your recognition of the contributions and your suggestions for improving the clarity, robustness, and completeness of the work.
**Methods And Evaluation Criteria:**
**A1:** We agree t... | Summary: The authors propose a Large Language Model (LLM)-driven LNS framework for large-scale MILP problems. Their approach introduces a dual-layer self-evolutionary LLM agent to automate neighborhood selection, discovering effective strategies with scant small-scale training data that generalize well to large-scale M... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer fpWD for the positive and encouraging feedback. We are glad that you found our method interesting and the experimental design sound. Your recognition of the contributions and the clarity of our work is truly appreciated and motivates us to further refine and extend our ... | Summary: This paper proposes LLM-LNS, a Large Language Model-driven Large Neighborhood Search framework for solving large-scale MILP problems. The method introduces a dual-layer self-evolutionary LLM agent: an inner layer evolves heuristic strategies to ensure convergence, while an outer layer optimizes evolutionary pr... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer G8Pp for the thorough review and positive evaluation of our work. Your encouraging comments and constructive suggestions are highly appreciated and have helped us further improve the clarity and completeness of the paper.
In the following, we address your concerns and... | null | null | null | null | null | null |
SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning | Accept (poster) | Summary: This paper focuses on the continual learning task for multimodal large models, termed MCIT. The authors innovatively categorize catastrophic forgetting issues in this field into two distinct types: superficial and essential forgetting. They propose the SEFE model, which incorporates two key modules, ASD and Re... | Rebuttal 1:
Rebuttal: Thank you for recognizing the strengths of our paper and for your effort in reviewing our submission. Below is a detailed response to your concerns:
### **The Abstract Should Be Shortened**
Thank you for the suggestion. We will shorten the abstract in the revised version to improve conciseness.
... | Summary: This manuscript delves into the area of Multimodal Continual Instruction Tuning (MCIT). It contributes by differentiating between two forms of catastrophic forgetting: *superficial forgetting* and *essential forgetting*. Superficial forgetting is defined as the forgetting of the response style, while essential... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for recognizing the value of our contributions. Below, we address your comments in detail:
### **Training Stages of MLLMs**
Thank you for pointing this out. Indeed, the training process of MLLMs/LLMs often involves more than two stages. We will revise the se... | Summary: This paper introduces SEFE for multimodal continual instruction tuning. Within SEFE, an ASD paradigm is proposed to eliminate superficial forgetting by converting all questions into five unified question types. The authors also create a CoIN-ASD benchmark by applying ASD to the public CoIN benchmark, which can... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for your positive feedback on the performance and experimental validation of our proposed method. Below, we address your concerns in detail:
### **Unnecessary Use of InternVL2 in Certain Distractor Generation Scenarios**
As you correctly noted, using InternVL... | Summary: This paper introduces SEFE (Superficial and Essential Forgetting Eliminator), a novel framework for Multimodal Continual Instruction Tuning (MCIT), which aims to prevent catastrophic forgetting in multimodal models. The authors identify two distinct types of forgetting in MCIT”: superficial forgetting and esse... | Rebuttal 1:
Rebuttal: We appreciate your time and effort in reviewing our paper, as well as your recognition of our contributions. Below, we respond to your comments in detail:
### **Comparison with the [SP’2024] Paper**
#### **Differences between the Two Papers**
Thank you for pointing out this related work. This ... | null | null | null | null | null | null |
RuleAdapter: Dynamic Rules for training Safety Reward Models in RLHF | Accept (poster) | Summary: The paper introduces a dynamic method for selecting safety rules in RLHF. Instead of using a fixed set, it adaptively chooses 5 out of 100 rules for each prompt–response pair based on the score difference (discrepancy) and rule relevance. This approach is both theoretically justified and empirically validated,... | Rebuttal 1:
Rebuttal: We really appreciate the comments and compliments by Reviewer. Below we provide our responses to the concerns:
# 1. Validation Performance of RuleAdapter during Training:
We use a validation set of size 500 during the training of RuleAdapter. During training, the best checkpoint achieves: accurac... | Summary: This paper introduces a dynamic approach to selecting safety rules for training reward models in RLHF. Rather than applying a fixed set of rules or randomly sampling from a large rule pool, the authors propose a method that adaptively selects the most critical rules for each pair of responses. Contributions:
-... | Rebuttal 1:
Rebuttal: We sincerely appreciate the comments and provide our responses below (the new evaluation results will be added to the paper).
# 1. Human Evaluation on Selected Rules:
In Appendix F, we have provided **case studies with real data trios and analysis on the selected rules by RuleAdapter**. To furthe... | Summary: The paper introduces a dynamic approach to RLHF that adaptively selects the most critical rules for evaluating response pairs, moving beyond traditional binary preference selection. The authors provide mathematical justification showing their method optimizes mutual information between rule-based labeling and ... | Rebuttal 1:
Rebuttal: We really appreciate the comments. For the request for more details on the accuracy of the max-discrepancy strategy, since “accuracy of the max-discrepancy strategy” can be interpreted in several ways, below we provide our responses to all the interpretations:
# 1. Validation Performance of RuleA... | null | null | null | null | null | null | null | null |
Adaptive Localization of Knowledge Negation for Continual LLM Unlearning | Accept (poster) | Summary: This paper focuses on the scenario of LLM continual unlearning, which is a more practical and challenging setting than one-time unlearning. Most existing unlearning methods achieve forgetting by fine-tuning the pretrained model, which unavoidably impairs the general performance of the LLMs. In continual unlear... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the constructive and positive comments.
## Con 1: lack discussion of accumulative decline
1. As for the accumulative decline in model utility, the rate of decline should not diminish because **each unlearning process could cause the same degree of model parameter cha... | Summary: LLM unlearning seeks to eliminate sensitive knowledge from LLMs. Existing methods for such scenarios often lead to significant degradation of the model's general ability, with utility losses accumulating over time. Additionally, interactions between previous and current unlearning tasks can cause partial forg... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive and insightful comments.
## Q1: Empirical validation regarding TRAVIS dataset
Thanks for the constructive advice. We would like to explain it as follows:
1. The TRAVIS dataset consists of a wide variety of topics, enabling a comprehensive evalu... | Summary: The paper proposes a combination of techniques for continual unlearning while maintaining model utility. Those are (1) fine-tuning with soft labels, (2) dynamically sparsifying the gradients and (3) adaptively setting learning rates depending on how significantly they have been adjusted for past tasks.
In the ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive and very valuable comments. Below are our responses to the comments.
## Q1: The connection to catastrophic forgetting in continual learning
We agree with the reviewer that the connection can be further clarified.
The cascading degradation issu... | Summary: This paper provides a new method for continual unlearning where unlearning happens in multi stage with multiple tasks where tasks could be slightly related to each other, and utilizing existing methods for unlearning for such scenario can result in severe degradation of model utility.
Their proposed method in... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive and encouraging feedbacks, as well as their recognition for the value of our work.
We are delighted that the reviewer finds our approach and theory compelling.
In our future work, we plan to build upon this foundation by further refining our methods ... | null | null | null | null | null | null |
The Power of Random Features and the Limits of Distribution-Free Gradient Descent | Accept (poster) | Summary: This paper studies the learning capacity of stochastic gradient algorithms. The main results focus on the case of binary classification with either square loss or $01$-loss trained with mini-batch SGD with $c$-approximate clipped gradient. Moreover it is assumed that the data is generated using a function in t... | Rebuttal 1:
Rebuttal: Dear reviewer AcC1,
Thank you for your thoughtful consideration. We will respond directly to the potential weaknesses that you outlined, as well as your questions.
> The analysis is limited to binary classification and in particular does not encompass regression problems. The mains theorems are ... | Summary: This paper proposes a new link between learning a parametric model with mini-batch stochastic gradient descent (SGD) and the approximation of a function class by random features: if a family of functions can be learned, then there exists a distribution of random features (in dimension that is polylogarithmic i... | Rebuttal 1:
Rebuttal: Dear reviewer 7FE4,
Thank you for your review, we hope to answer your questions below.
> (1) Is there any issue with the fact that stochastic gradient descent may have difficulties reaching a global minimum? (I suspect not)
No, since the statement considers probably approximately correct learna... | Summary: This paper revisits the power of learning with gradient based methods in a distribution free setting. The main result is to show that if a hypothesis class $F$ can be learnt using gradient descent on some parameterized model with a distribution-free guarantee then for any prior distribution $\mu$ over $F$, the... | Rebuttal 1:
Rebuttal: Dear reviewer 9ucH,
We appreciate your thoughtful review. We will respond to specific weaknesses that you addressed in your review.
> One thing that I feel is problematic is: In Theorem 3.3, why is the condition bc^2≥Ω(logTp/δ)
not required?
Yes, it is required. This restriction is introduced... | Summary: This paper investigates how powerful are gradient-based algorithms on some differentiable models for **distribution-free** learning settings. The main theoretical result (see Theorem 3.2) is: essentially, whenever the function class is SQ learnable or differentiably learnable in the sense of [Abbe et al. 2021]... | Rebuttal 1:
Rebuttal: Dear reviewer NhLC,
Thank you for your thoughtful review. We will directly respond to the weaknesses and questions that you outlined.
> Overall, the paper's exposition can be improved. Line 21: "though the distribution free learning is the desired goal." I strongly disagree with this statement. ... | null | null | null | null | null | null |
Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation | Accept (poster) | Summary: This paper suggests reducing the parameter in the Text-to-Image (T2I) generative models by pruning the transformer layers in the text encoder for conditioning. They highlight that most of the parameters in T2I models are available in the text encoder. Thus, they propose Skip and re-use layers (Skrr) that prune... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's insightful comments and encouraging feedback regarding our motivation for pruning text encoders, our embedding-space-based similarity approach, the justification for using MSE over cosine similarity, and the clarity and validity of our experimental design and... | Summary: This paper propose skrr method, which can effectively reduce the memory consumption of the text encoder in the Text to Image (T2I) model while maintaining the quality of image generation.
Claims And Evidence: The motivation is reasonable.
Methods And Evaluation Criteria: The paper proposes a two-stage prunin... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's insightful comments and encouraging feedback regarding the clarity of our proposed two-stage pruning method (Skip and Re-use), as well as our comprehensive evaluation using multiple widely recognized metrics and state-of-the-art diffusion models.
---
> **Qu... | Summary: This paper introduces Skrr, a pruning strategy for text encoders in text-to-image (T2I) diffusion models. Skrr reduces memory usage by selectively skipping and reusing transformer layers, leveraging the redundancy in T2I-specific text encoders. Experimental results on FID, CLIP, DreamSim, and GenEval are repor... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's insightful comments and encouraging feedback.
---
> **Question 1. Novelty claiming on task.**
**Response:** We acknowledge the reviewer's comment regarding the overstatement of novelty. We appreciate this important point and will carefully revise our manus... | Summary: This paper introduces Skip and Re-use Layers (Skrr), a method for compressing text encoders in text-to-image (T2I) diffusion models to improve memory efficiency. The large-scale text encoders in T2I diffusion models consume a significant amount of memory despite contributing little to FLOPs. Skrr addresses thi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's insightful comments and encouraging feedback regarding our method's superior performance over existing blockwise pruning techniques, theoretical support, and extensive experiments across diverse metrics.
---
> **Question 1. Evaluation on additional benchmar... | null | null | null | null | null | null |
M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Embedding Predictive Architecture | Accept (poster) | Summary: - The work proposed a method that aligns multimodal information in the latent space with a MoE setup, treating single-modality encoder as experts, and is optimised by single modality alignment loss with alternating input/output modality each iteration. To avoid representation collapse over alternative training... | Rebuttal 1:
Rebuttal: We sincerely appreciate the feedback.
> limited evidence to support ... the limitation of prior works
In the CR version, we will add:
- why self-supervise learning and energy-based model have less information bias than supervised learning and probabilistic model (refer to Section 2.4, Dawid & L... | Summary: This paper proposes a novel JEPA-based framework, M3-JEPA, for multimodal data (image, text, audio, etc.). Specifically, M3-JEPA follows the encoder-predictor architecture in JEPA, but replaced the target embedding with other modalities. M3-JEPA designed a novel predictor based on multi-directional MoE, which ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments. Below we clarify our methodology and provide additional context to address the concerns.
> RQ1: even though the trainable parameters in M3-JEPA is small, it needs heavy image/text encoders. While other baselines, such as BLIP-2 VIT-g, does not inc... | Summary: The paper presents M3-Jepa, a multimodal alignment framework leveraging the Joint-Embedding Predictive Architecture (JEPA) and Mixture-of-Experts (MoE) for aligning diverse modalities in a shared latent space. It introduces a multi-directional MoE predictor that alternates between unidirectional alignment task... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful comments.
> additional insights into failure cases, model robustness across different domains
> How does M3-Jepa handle modality imbalances (e.g., missing or weakly correlated modalities in training)?
We found primary failure cases on the VQA tasks, i.e. w... | Summary: The paper "M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA framework" introduces a novel modality-agnostic multimodal alignment paradigm called M3-Jpea, which utilizes the Joint Embedding Prediction Architecture (JEPA) to align representations of different modalities into the same lat... | Rebuttal 1:
Rebuttal: > doesn't compare with other lightweight cross-modal methods
We include such baselines on the R@1 where M3-Jepa still outperforms:
| Model | # Param | Flickr30K | | COCO | |
| -------------- | ------------ | --------- | ---- | ----- | ---- |
| | | T... | null | null | null | null | null | null |
Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning | Accept (poster) | Summary: This paper introduces two token cleaning methods for LLM fine-tuning, Fixed-model cleaning and Self-evolving cleaning. The token cleaning method ignores the loss from "unimportant" tokens, thereby improving task performance. The authors also provide theoretical analyses and extensive experiments.
Claims And E... | Rebuttal 1:
Rebuttal: We want to thank Reviewer fypD for their positive feedback and comments. We will address individual comments below.
> **Weakness 1: the effectiveness of this approach**
**Response**:
Thank you for your insightful suggestions！We present the average results from three independent trials (using ... | Summary: The paper introduces a token cleaning method for supervised fine-tuning (SFT) of large language models that operates at a fine-grained, token level rather than discarding whole samples. It employs an influence-guided scoring function to assess each token’s contribution by comparing loss differences between a b... | Rebuttal 1:
Rebuttal: We want to thank Reviewer wKJy for their positive feedback and comments. We will address individual comments below.
> **Weakness 1: how accurate is using influence function to assess the quality of token itself**
**Response**: Thank you for raising this important question. We'd like to clarify w... | Summary: This paper enhances the SFT process by scoring and filtering out uninformative tokens, treating them as noisy labels. They introduce a novel influence-guided token cleaning pipeline to address this issue. Furthermore, they offer rigorous analyses to demonstrate when and why SFT with cleaned tokens outperforms ... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer dD3A for the time and effort. We will address individual comments below.
> **W1: evaluation benchmarks**
**Response**: The benchmarks we used are standard in SFT studies (see Related Work) and assess a broad spectrum of capabilities, including question answering, ... | Summary: Recent large language models are trained in multiple stages with vast amounts of data to achieve stellar performance. However such a training regime also brings in several challenges, including the challenge of observing similar high frequency phrases over and over again. This work aims to improve the supervis... | Rebuttal 1:
Rebuttal: We want to thank reviewer W8kk for the positive feedback! We will address individual comments below.
> While the theoretical analyses is not fully precise, e.g in Sections 5.1 & 5.2 it makes a few trivial observations such as the challenges caused by the noise in tokens...
**Response**: We agree ... | null | null | null | null | null | null |
Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models | Accept (poster) | Summary: This paper investigates miscalibration issues in fine-tuned vision-language models (VLMs) like CLIP, revealing a trade-off between base and new classes where standard prompt tuning (e.g., CoOp) leads to overconfidence in new classes, while regularization-based methods (e.g., KgCoOp) cause underconfidence in ba... | Rebuttal 1:
Rebuttal: Thank you for your insightful and positive suggestions. Here’s our response below:
### 1. The choice of Near-Outliers or Random-Outliers
We agree that random outliers offer a practical and efficient approach for real-world out-of-distribution (OOD) applications. To investigate their feasibility,... | Summary: This paper identifies a calibration trade-off in existing prompt tuning methods: standard tuning (e.g., CoOp) leads to overconfidence on new classes due to increased textual label divergence, while regularization-based tuning (e.g., KgCoOp) results in underconfidence on base classes despite improved accuracy. ... | Rebuttal 1:
Rebuttal: Thanks for your helpful and positive feedback. Our response is below:
### 1. Theoretical analysis on multi-class settings
Thank you for the insightful suggestion. We presented the theoretical results in binary classification to help readers quickly understand the relationship between feature div... | Summary: 1. This paper analyzes the trade-off between base and novel classes from the perspective of textual distribution divergence.
2. This paper proposes a simple DOR regularization method, which can compile with existing prompt learning methods.
3. Experimental results show promising performance compared with rel... | Rebuttal 1:
Rebuttal: ### 1. Clarification on "information leakage"
Thanks for your insightful review. We believe this concern is closely related to the response #5 for Reviewer YQTf, which is about the overlap between outliers and new classes. We'd like to clarify that our method does not leak the information of new ... | Summary: The authors propose a method called Dynamic Outlier Regularization (DOR) to improve confidence calibration for both base and new classes after fine-tuning. Extensive experiments are conducted on multiple benchmark datasets to evaluate its effectiveness.
Claims And Evidence: One of the major claims is that DOR... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. Please find our response below:
### 1. Visualization of DOR
Thanks for the great suggestion. We'd like to clarify that our method outperforms KgCoOp as our method does not fix the confidence level on the base classes while preventing the increase of textu... | null | null | null | null | null | null |
Distributionally Robust Active Learning for Gaussian Process Regression | Accept (poster) | Summary: The paper explores a distributionally robust active learning framework designed to minimize worst-case posterior variance over an ambiguity set of data distributions. It builds on bounds for posterior variance under uncertainty sampling and links these results to regret minimization. The analysis focuses on pr... | Rebuttal 1:
Rebuttal: We appreciate the careful reading and constructive comments.
However, we believe that several reviewer's comments come from misunderstandings, for which we would like to elaborate our claims.
First of all, our proposed methods are stated in Section 4 and thus not US and RS.
Therefore, we do not c... | Summary: The paper introduces a framework for Distributionally Robust Active Learning (DRAL) in Gaussian Process Regression (GPR), which focuses on minimizing the worst-case expected error over potential target distributions. The authors derive upper bounds on the worst-case expected squared error and showing that the ... | Rebuttal 1:
Rebuttal: We appreciate the careful reading and constructive comments.
>While the paper claims that the methods are efficient, it lacks a detailed computational complexity analysis or runtime comparisons.
>The paper does not discuss how well DRAL scales with increasing dataset size or feature dimensionali... | Summary: The authors investigate the problem of distributionally robust active learning for Gaussian process regression, addressing limitations of existing approaches that primarily rely on heuristic and information gain-based methods. They provide a rigorous theoretical analysis with guarantees on the posterior mean a... | Rebuttal 1:
Rebuttal: We appreciate the careful reading and constructive comments.
>How can the analysis we extended to other surrogates for active learning other than GPR?
For the extension to other surrogate models, such as Bayesian neural networks (BNN) and deep kernel learning (DKL), there is a problem that the p... | Summary: This paper examines active learning in the context of Gaussian Process Regression (GPR). In particular, it seeks to minimize the worst-case error with respect to the (unknown) test-time marginal distribution, by considering the worst-case distribution in an ambiguity set. First the paper upper bounds this wors... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's careful reading and insightful and detailed comments, based on which we will revise the paper as much as possible. We will concentrate on and answer several seemingly important questions below.
>in line 384 there is a comment about the derivative of log(a), in t... | null | null | null | null | null | null |
AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement | Accept (poster) | Summary: The paper proposes a holistic approach for the LLM-based generation of verified code. First, the paper proposes an approach to address the scarcity of training/sample data in many real-world programming languages that could serve as code generation targets. To this end, the paper proposes an LLM-based techniqu... | Rebuttal 1:
Rebuttal: Thank you for your very thoughtful review and your positive feedback on our work. We appreciate your recognition of AlphaVerus's potential and thorough understanding of our contributions. We address your points below:
---
> “However, I have to note that I am not convinced that the rule-based app... | Summary: The paper introduces AlphaVerus, a framework for generating formally verified code using LLMs, with focus on the challenges of programming languages with limited training data. AlphaVerus works by translating verified code from programming languages with lots of examples, into the target programming language. ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our paper. We're glad you found our approach reasonable and appreciated the evidence supporting AlphaVerus’s effectiveness including treefinement and critique phase to prevent reward hacking. We address your questions and suggestions below:
---
> One quest... | Summary: The paper introduces a novel (ensemble of) technique(s) for the translation and formal verification of programs using Verus, a library based on the Rust language.
The authors implement a 3 step process that utilizes an LLM to generate samples of a program translation and proof of formal verification, a tree b... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and positive assessment of our work. We appreciate your recognition of AlphaVerus’s novelty, useful and interesting methodology, particularly the Treefinement and Critique components, while finding it a promising method for other contexts. We are happy to addre... | null | null | null | null | null | null | null | null |
Predictive Data Selection: The Data That Predicts Is the Data That Teaches | Accept (poster) | Summary: The authors propose a new data selection based on the rankings of perplexity which a range of llama models assign to documents. The method is scaled to large datasets via means of a fasttext filter. The authors claim that this captures which training examples are predictive of broad downstream abilities. The d... | Rebuttal 1:
Rebuttal: Dear Reviewer xzzW,
Thank you for your valuable suggestions and insightful comments!
We address your comments one by one as follows:
---
Q1.[ScalingFilter [1], which I believe is an important point of comparison for the proposed method]
Thanks for pointing out a related work ScalingFilter[1]... | Summary: This paper explores the problem of data selection for pretraining language models. The authors propose a lightweight method that leverages predictive strength as an indicator to determine whether a document should be included in the pretraining data. To evaluate their approach, they train a group of language m... | Rebuttal 1:
Rebuttal: Dear Reviewer k3Rj,
Thank you for your valuable suggestions and insightful comments! We are grateful that you found our work well-structured and logically organized, and we deeply appreciate your recognition of the novelty of our method and the comprehensive analysis in our experiments.
We addre... | Summary: The paper Predictive Data Selection (PRESELECT) introduces a method for selecting pretraining data based on its predictive strength, defined as the correlation between normalized loss values and downstream task performance rankings. It proposes using a fastText classifier trained on documents with high predict... | Rebuttal 1:
Rebuttal: Dear Reviewer 95of,
Thank you for your review. Our submission was flagged with “Research Integrity Issues (e.g., plagiarism)” due to concerns related to the PPL correlation paper. We believe this is an unreasonable accusation and misleading to others. As we respond to the specific points below,... | Summary: This paper leverages the intuition that data on which losses of N models correlate with the benchmark performance (ranking) of the N models is the most useful. The paper proposes a simple numerical score for measuring this consistency in ranking (different and more numerically stable than Pearson correlation).... | Rebuttal 1:
Rebuttal: Dear Reviewer VRK4,
Thank you for your insightful comments! And we are grateful that you found our work very useful and effective.
---
---
As Reviewer 95of raises serious concerns about our paper and our response to that is probably related to other reviewers as well, due to the imposed charac... | null | null | null | null | null | null |
Double-Filter: Efficient Fine-tuning of Pre-trained Vision-Language Models via Patch&Layer Filtering | Accept (poster) | Summary: This paper presents Double-Filter, a method aimed at optimizing the fine-tuning process of vision-language pre-trained (VLP) models. The method reduces redundancy through two strategies: first, a novel patch selection approach that enhances feature representation via background-foreground separation; second, a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very detailed and instructive feedback. We are very happy that you recognize our motivation and work and give positive support. Regarding your suggestions and concerns, we will respond one by one below, hoping to answer your questions.
>_Q1. “YOLO's object detectio... | Summary: The paper introduces Double-Filter, a novel approach for efficient fine-tuning of vision-language pre-trained (VLP) models. The method addresses redundancy at both data and model levels. At the data level, an Image Patch Filter (IPF) leveraging a YOLO detector and ViT attention scores to retain only the most i... | Rebuttal 1:
Rebuttal: We thank the Reviewer very much for the kind words, for the interest in our research activities, and for the very insightful comments.
>_W1: “Potential weaknesses include an increased system complexity due to the integration of a YOLO detector and genetic algorithm, and limited exploration of the... | Summary: The paper introduces Double-Filter for refining the fine-tuning process of VLP models. It employs two key strategies to reduce redundancy: A new patch selection method that uses background-foreground separation to improve image feature selection; Then, a genetic algorithm designed to remove redundant architect... | Rebuttal 1:
Rebuttal: Thanks for your valuable and constructive comments. We appreciate your recognition of our work's innovative designs, evaluation criteria, and insightful analysis. Below, we address your concerns and questions individually:
>_Q1: “In IPF section, the paper mentions sparsity ρ ∈ (0, 1). Is this spa... | Summary: The paper introduces Double-Filter, an approach for efficient fine-tuning of Vision-Language Pre-trained (VLP) models by addressing redundancies at both the data and architectural levels. The authors propose two main components: (1) an Image Patch Filter (IPF) that selectively filters image patches by distingu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review, acknowledging our contributions, and providing instructive suggestions. We respond to each weakness (_W*_) or question (_Q*_) below.
>_W1: “... explore the impact on more complex multimodal tasks beyond VQA and NLVR, ...”_
Thanks for the instructi... | Summary: The paper proposes Double-Filter, an efficient fine-tuning framework for vision-language pre-trained (VLP) models. It combines two redundancy reduction techniques: (1) an Image Patch Filter (IPF) that leverages YOLO for foreground/background separation and a ViT-based [CLS] attention mechanism to retain inform... | Rebuttal 1:
Rebuttal: Thanks for the encouraging feedback and suggestions. However, we argue that some concerns were addressed in our paper, so we clarify them again here. We address the main weaknesses(W*) and questions(Q*) below:
>_W1: “YOLO's computational cost is not accounted for in efficiency metrics ,....”_
*... | null | null | null | null |
VideoRoPE: What Makes for Good Video Rotary Position Embedding? | Accept (oral) | Summary: In this work, the authors study criteria for video position embedding and propose a rotary position embedding method. They claim that a good video position embedding should handle 3D structure, and appropriate frequency allocation to prevent embedding collision, spatial symmetry and temporal index scaling betw... | Rebuttal 1:
Rebuttal: Dear Reviewer 3qrY,
Thanks for your valuable feedback. We sincerely thank all reviewers for acknowledging that this paper is novel (t9dP, juMQ), well-motivated (t9dP, 26sL, 3qrY), significant improvements (t9dP, 26sL, 3qrY), well-written (t9dP, juMQ, 26sL, 3qrY), and easy to follow (t9dP, 3qrY).... | Summary: This paper identifies four key factors in extending position encodings from images to videos: spatio-temporal structures, frequency allocation, spatial symmetry, and temporal indexing. Drawing from these observations, the authors propose VideoRoPE, which (1) uses low-frequency temporal allocation to mitigate p... | Rebuttal 1:
Rebuttal: Dear Reviewer 26sL,
Thanks for your valuable feedback. We sincerely thank all reviewers for acknowledging that this paper is novel (t9dP, juMQ), well-motivated (t9dP, 26sL, 3qrY), significant improvements (t9dP, 26sL, 3qrY), well-written (t9dP, juMQ, 26sL, 3qrY), and easy to follow (t9dP, 3qrY).... | Summary: VideoRoPE is a position embedding method designed for video large language models. It addresses four key issues: 3D structure, frequency allocation, spatial symmetry, and temporal index scaling. The authors demonstrate through a new benchmark V-NIAH-D that existing methods perform poorly when distractors are p... | Rebuttal 1:
Rebuttal: Dear Reviewer juMQ,
Thanks for your valuable feedback. We sincerely thank all reviewers for acknowledging that this paper is novel (t9dP, juMQ), well-motivated (t9dP, 26sL, 3qrY), significant improvements (t9dP, 26sL, 3qrY), well-written (t9dP, juMQ, 26sL, 3qrY), and easy to follow (t9dP, 3qrY).... | Summary: This paper proposes VideoRoPE, a new positional embedding for videos. VideoRoPE extends the 1D RoPE to 3D cases to encode spatiotemporal information. M-RoPE used in QWen2-VL (Wang et al., 2024b) employ 3D structure, dividing the feature dimensions into distinct subsets for spatiotemporal encoding. However, the... | Rebuttal 1:
Rebuttal: Dear Reviewer t9dP,
Thanks for your valuable feedback. We sincerely thank all reviewers for acknowledging that this paper is novel (t9dP, juMQ), well-motivated (t9dP, 26sL, 3qrY), significant improvements (t9dP, 26sL, 3qrY), well-written (t9dP, juMQ, 26sL, 3qrY), and easy to follow (t9dP, 3qrY).... | null | null | null | null | null | null |
Scaling Trends in Language Model Robustness | Accept (spotlight poster) | Summary: This paper conducts and empirical study on language model robustness, examining how model size influences the robustness, both for regular models and adversarially fine-tuned counterparts. In particular, the authors focus on the relationship between expended compute by the adversary vs defender compute budget.... | Rebuttal 1:
Rebuttal: > Focus on classification
Thank you for this point, it is well-taken. We decided to focus on classification tasks for two main reasons:
1) it allowed us to study 3 orders of magnitude of model sizes—about 1.5 orders of magnitude more than we could have in the generative setting (since in our expe... | Summary: The authors observe a gap concerning investigations of robustness scaling laws in LLMs. They conduct a large-scale empirical study and find that without other changes, simply increasing model size does not yield increased robustness. However, they find that larger models require less samples and compute for ro... | Rebuttal 1:
Rebuttal: > Other defenses + gen setting
Thank you for the reframing suggestion. We indeed focus on classification (see below and WX3L rebuttal). We believe that our overall points will hold (1: model scale has limited impact on robustness by itself, but improves safety training, and 2: the relative effect... | Summary: This paper studies the scaling behavior of adversarial robustness from three angles: attacker compute (number of flops used in the attack), defender compute (number of pretraining flops), and adversarial training. The settings studied include both discriminative and generative tasks. The attacks include both a... | Rebuttal 1:
Rebuttal: > Other defenses besides adversarial training?
Great question, this is exactly the kind of future study that we would like to explore! Indeed, one of our core points is that, because ASR goes up with attack compute and down with defense compute, looking at a single point estimate can be deceiving... | Summary: This paper investigates the relationship between language model scaling and adversarial robustness, focusing on: 1) The impact of model size on robustness, 2) The effect of scaling on adversarial training, and 3) The cost trade-offs between attackers and defenders as model size increases. The experiments c... | Rebuttal 1:
Rebuttal: > Lacks 70B+ models, no GPT-class or DeepSeek
Thank you for these points, they are well-taken.
**Model Scale:**
Computational limitations constrained our study:
1) The adversarial training defense studied in the paper is very compute intensive, already taking thousands of H100 hours to complete ... | null | null | null | null | null | null |
any4: Learned 4-bit Numeric Representation for LLMs | Accept (poster) | Summary: This paper introduces a 4-bit weight quantization method called any4, designed for Large Language Models (LLMs). The authors claim that this method offers arbitrary numeric representations without the need for preprocessing weights or activations. The paper compares any4 with other 4-bit numeric representati... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, particularly recognizing that the paper **“presents a compelling case”**, and **“provides strong evidence to support its claims and makes a valuable contribution to the field of LLM quantization”**, as well as the important suggestions to impr... | Summary: The paper introduces "any4," a newly proposed learned 4-bit numeric representation aimed at optimizing the quantization of weights in large language models (LLMs). Any4 enhances accuracy compared to traditional 4-bit formats such as int4, fp4, and nf4, and does not require preprocessing of weights or activatio... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive review and comments, including that the approach was **“rigorously benchmark[ed]”**, the claims are **“supported by experimental results and methodological explanations”**, and that it’s implementation **“enhances the paper’s real-world ap... | Summary: The paper presents any4, a new 4-bit numerical representation for post-training quantization of LLMs. The paper states that any4 does not require any additional pre-processing of weights or activations and for most LLMs, can find the optimal 4-bit representation with a single sample. The authors define the bas... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive review, including **“higher confidence in the presented method”** due to presenting results on generation and coding tasks, and thank the reviewer for checking the **“soundness of [the] math”** of the **“full derivation of [the] method”**,... | Summary: The paper introduces a method for finding the optimal 4-bit quantization codebook for quantizing pre-trained language models. This is done by applying the LLoyd-Max algorithm to each *row* of the weight matrices of the model, thus finding an optimal (per-row) quantization look-up table. In order to find the op... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and constructive feedback, which will help improve the paper.
Please find below our remarks:
- **Optimizing Weights Directly:** Please find the results in Table A below. First row shows the results of optimizing weights directly. The other 2 rows show the ... | null | null | null | null | null | null |
Reinforcement Learning with Adaptive Reward Modeling for Expensive-to-Evaluate Systems | Accept (poster) | Summary: The paper presents AdaReMo, an approach designed to accelerate reinforcement learning (RL) in systems where reward evaluations are computationally expensive. The key idea is to decouple the RL loop—where decisions are made quickly—from the reward evaluation process that is slow and costly. AdaReMo achieves thi... | Rebuttal 1:
Rebuttal: Dear Reviewer joeE,
Thank you for your thoughtful and constructive feedback. We appreciate your recognition of the core idea behind AdaReMo, particularly the decoupling of the fast decision-making loop from the slow, expensive reward evaluations. We hope the following responses can address your c... | Summary: This work deals with the RL setting in which we have access to a
reasonably fast simulator of the system, but the reward evaluations
are slow. They employ the idea of modeling the reward to enable taking
advantage of the fast simulation to update the policy. In particular,
they use an asynchronous scheme wher... | Rebuttal 1:
Rebuttal: **Q1:** Experiments with more seeds should be reported.
**A1:** In our submission, we reported results based on 3 seeds. To address your concern, we have now conducted experiments with **10 seeds**. The results (see table in comments for details) confirm that our method remains **robust across a ... | Summary: The paper proposes **AdaReMo (Adaptive Reward Modeling)**, a reinforcement learning (RL) framework designed specifically for **expensive-to-evaluate reward systems**, such as molecular generation, epidemic control, and spatial planning. The key innovation is the adaptive decomposition of complex, computational... | Rebuttal 1:
Rebuttal: Dear Reviewer pywE,
We express our sincere gratitude for your thorough review and valuable feedback on our paper. Regarding the potential alternative algorithms and the issue of online and offline splits you mentioned, we hope the following replies can address your concerns.
**Q1:** REINFORCE/R... | Summary: The authors propose **Adaptive Reward Modelling (AdaReMo)**, an approach that accelerates Reinforcement Learning (RL) by fine-tuning a reward model (RM) multiple times during training. This reduces the need for expensive reward evaluations. In AdaReMo, the RL agent interacts only with the learned RM, while a p... | Rebuttal 1:
Rebuttal: Dear Reviewer Q9Xy,
We would like to express our sincere gratitude for your thorough review and constructive feedback on our paper. We are particularly pleased that you have recognized the effectiveness of our proposed approach, AdaReMo, in solving the efficiency bottleneck between policy optimiz... | null | null | null | null | null | null |
Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning | Accept (poster) | Summary: This paper focuses on Exemplar-Free Class-Incremental Learning (EFCIL), which aims to solve the problem of catastrophic forgetting without retaining any exemplars. Existing methods alleviate forgetting by storing distributional information about past tasks. However, as models are updated, past distribution inf... | Rebuttal 1:
Rebuttal: ## Response to Reviewer V6Ai
Thank you for your constructive and detailed feedback. We provide detailed responses to your concerns below. The discussion and experiments below will be properly included in the manuscript.
### Q1: Elaborate the advantages of proposed methods over existing works [R1... | Summary: This paper proposes a method called Dual-Projection Shift Estimation and Classifier Reconstruction (DPCR) to solve two key challenges in Exemplar-Free Class-Incremental Learning (EFCIL): semantic shift and decision bias. It reconstructs the classifier through the dual projection mechanism and ridge regression,... | Rebuttal 1:
Rebuttal: ## Response to Reviewer YwAB
Thank you for your constructive and detailed feedback. We provide detailed responses to your concerns below. The discussion and experiments below will be properly included in the manuscript.
### Q1: Lack of evaluation on lager datasets like ImgaNet1000.
Response: As... | Summary: The paper studies exemplar-free class-incremental learning and focus on addressing two major problems of semantic shift and decision bias. The authors propose to use dual-projection to estimate semantic shift which includes both class-wise and task-wise shifts, and a ridge regression based classifier instead o... | Rebuttal 1:
Rebuttal: ## Response to Reviewer spyP
Thank you for your constructive feedback. We provide detailed responses to your concerns below. The discussion and experiments will be properly included. Also, we will check and revise all the writing mistakes, and include references of ridge regression.
### Q1: Compar... | null | null | null | null | null | null | null | null |
BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute | Accept (poster) | Summary: This work proposes BEST-Route, a method that combines LLM routing with test-time compute scaling.
Given a pool of LLMs and an input query, a router determines not only which LLM to route the query to, but also the number of responses to sample for it before applying Best-of-N (BoN) sampling.
Details about trai... | Rebuttal 1:
Rebuttal: **Q1: For the calculation of cost[(M, i)] in Algorithm 1, the number of sampled responses i is multiplied only with the output length, not with the input length. Is this a typo or intentional? Are the costs in experiment results calculated in the same way? This calculation seems to rely on the ass... | Summary: This paper proposes a cost-effective LLM inference framework that leverages multiple small LLMs alongside a large LLM (GPT-4o). A router model is trained to (1) select which LLM to use from the candidate pool, (2) determine how many responses that LLM should generate, and (3) select the best response among the... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful comments. Due to space limits, we have paraphrased some questions to save space while preserving the original intent.
**Q1: The claim of “optimal balance” between cost and quality is too strong without theoretical justification. A more appropriate phrasing wou... | Summary: This paper proposes BEST-Route, combining two different research areas: model selection (through routing) with adaptive allocation of test-time compute (e.g., best-of-n sampling). Their framework dynamically selects a model and the optimal number of responses to sample based on query difficulty and quality thr... | Rebuttal 1:
Rebuttal: **Q1: The claim “surpassing prior routing approaches and setting a new standard for efficient LLM service deployment” seems too strong. While the method shows clear improvements over prior routing approaches, it would benefit from direct comparisons with other efficiency techniques, such as specul... | Summary: The paper introduces BEST-Route, an adaptive routing framework designed to optimize inference efficiency and response quality in large language models (LLMs). The framework dynamically selects an appropriate LLM and determines the optimal number of responses to sample (best-of-n sampling) based on the estimate... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive comments. Several comments pertain to the generalizability of BEST-Route, and we address them jointly below for clarity.
**Q1: The authors present clear results across diverse tasks, yet the evaluation is primarily conducted on datasets cu... | null | null | null | null | null | null |
Action-Dependent Optimality-Preserving Reward Shaping | Accept (poster) | Summary: The paper proposes a new reward-shaping framework that is action-dependent while preserving the optimal policies. The proposed method can convert any intrinsic rewards that are not optimality preserving to preserve optimal policies. Through experiments in Montezuma's Revenge, the authors demonstrate the effect... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in giving detailed feedback; we really appreciate it.
We’re excited to evaluate our method on additional environments in future work, and believe that these initial results stand as a significant contribution, particularly when paired with our theoretical resul... | Summary: This paper presents Action-Dependent Optimality Preserving Shaping (ADOPS), a technique that transforms intrinsic rewards into a format that maintains optimality and enhances the efficacy of intrinsic motivation in the challenging, reward-sparse environment of Montezuma’s Revenge. Additionally, it establishes ... | Rebuttal 1:
Rebuttal: Thank you for your time, energy, and constructive feedback.
We agree with your assessments about the quality of our empirical evaluations. Our primary contribution is the initial proposal of ADOPS, as well as our theoretical proofs of both optimality and increased generality over prior methods. O... | Summary: The paper focuses on improving performance in complex, exploration-heavy environments with long-duration episodes. In particular, the paper focuses on reward shaping methods that shape rewards while maintaining optimality.
The paper proposes a new reward shaping approach called Action-Dependent Optimality Pre... | Rebuttal 1:
Rebuttal: Thank you for taking the time and effort to review our work. We appreciate your positive feedback.
We fixed the reference to Equation (41) to Equation (15). Thank you for noticing this error.
We removed unnecessary use of future tense as you suggested.
Each "training step’’ is a rollout and sub... | Summary: This work introduces Action-Dependent Optimality Preserving Shaping (ADOPS), a general framework for modifying intrinsic rewards to a form that preserves the set of optimal policies. The proposed framework is general enough to capture not only potential-based reward shaping (PBRS) functions, but also reward sh... | Rebuttal 1:
Rebuttal: Thank you for your thorough feedback, and for the time and energy it took to read our paper in such an in-depth manner. We appreciate your insightful comments, and our resulting edits to the paper have improved it substantially.
We acknowledge that further work on a wider suite of environments t... | null | null | null | null | null | null |
Deep Unsupervised Hashing via External Guidance | Accept (poster) | Summary: This paper introduces DUH-EG, a deep unsupervised hashing framework that integrates external textual information as semantic guidance to overcome the limitations of relying solely on internal visual structures. The method first constructs external features by extracting and clustering textual features (derived... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments and suggestions. To enhance readability, we will refine the description of the bidirectional contrastive loss in the revised manuscript accordingly.
**Q1: How does DUH-EG scale when the external textual database is significantly larger, and what ar... | Summary: This paper proposes a deep unsupervised hashing framework (DUH-EG) designed for image retrieval. Unlike traditional unsupervised hashing methods that rely solely on intrinsic visual structures, DUH-EG leverages external textual guidance extracted from a lexical database (i.e., WordNet) and processed via a pre-... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and constructive suggestions. Below are our point-to-point responses:
**Q1: A discussion on the computational costs and scalability issues.**
**R1:** To analyze computational costs and scalability, we evaluated our approach using external vocabulary... | Summary: The paper identifies a crucial bottleneck in unsupervised image hashing, i.e., the limitation of insufficient knowledge guidance solely relying on the visual structures. To remedy this, semantic representatives are selected from external textual databases, serving as external guidance for the image modality vi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and constructive suggestions. Below are our point-to-point responses:
**Q1: More recent works could be included in the related work of deep unsupervised hashing.**
**R1:** Thank you for your suggestion. We will update the related work section to inc... | Summary: This paper proposes a novel deep unsupervised hashing method, Deep Unsupervised Hashing with External Guidance (DUH-EG), to enhance image retrieval by incorporating external textual knowledge as semantic guidance. The method selects representative semantic nouns from an external textual database, aligns images... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful feedback and recognition of our contributions. Below are our point-to-point responses:
**Q1: How does the selection of external textual features affect the model's robustness across datasets?**
**R1:** In our work, we focus on widely used benchmarks, such as... | null | null | null | null | null | null |
Heterogeneous Sufficient Dimension Reduction and Subspace Clustering | Accept (poster) | Summary: This paper proposes a new method, mixPFC, which integrates subspace clustering with model-based SDR to handle heterogeneous, high-dimensional data. The method simultaneously performs clustering, subspace estimation, and variable selection. A group Lasso penalized EM algorithm is developed, and non-asymptotic c... | Rebuttal 1:
Rebuttal: We thank the reviewer for raising many critical points. Below, we address each of your concerns.
## Weaknesses
> (Gaussian assumption)
**Reply**
- Gaussian assumption is common in clustering, promoting interpretability and scalability.
- mixPFC’s theoretical grounding and empirical performance ... | Summary: The paper introduces a supervised subspace clustering model called mixPFC, which combines sufficient dimension reduction (SDR) with subspace clustering to address three major bottlenecks in traditional methods for high-dimensional heterogeneous data analysis：
1. The model effectively guides clustering and dime... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful comments. Below, we address each limitation raised.
## Weaknesses
> (isotropic covariance assumption)
**Reply**:
- The isotropic assumption in Theorem 4.1 was adopted solely to enable tractable analysis of $\gamma_{iw}$, a common simplification i... | Summary: The paper presents a mixture of PFC model, which combines sufficient dimension reduction with subspace clustering to deal with heterogeneous high-dimensional data. Moreover, a grouped Lasso based EM algorithm is designed to solve the problem. Nonasymptotic convergence analysis is provided. Empirical results ar... | Rebuttal 1:
Rebuttal: We appreciate your detailed feedback. Below, we address each of your concerns.
## Weaknesses
> (literature review, novel contribution, EM algorithm)
**Reply**: We acknowledge the current presentation could be streamlined. In the revision, we will reorganize the literature review to focus on clar... | null | null | null | null | null | null | null | null |
Energy-Based Preference Model Offers Better Offline Alignment than the Bradley-Terry Preference Model | Accept (poster) | Summary: This paper introduces a novel alternative to DPO for training LLMs, using an energy-based preference model (EBM). The proposed method, Energy Preference Alignment (EPA), semmly guarantees a unique MLE)and consistently outperforms DPO in empirical benchmarks, providing better alignment with human preferences an... | Rebuttal 1:
Rebuttal: > Concern#1: Does this imply that the proposed training objective relies on a perfectly clean dataset??
Thank you for raising this interesting question. No, it is not a requirement to have a perfectly clean dataset. The reason for the weak negative samples is to provide high variance. Even if som... | Summary: This paper identifies a major limitation in Direct Preference Optimization (DPO): the underlying Bradley-Terry Model (BTM) does not always guarantee a unique Maximum Likelihood Estimator (MLE), leading to suboptimal alignment in Reinforcement Learning with Human Feedback (RLHF).To address this, the authors pro... | Rebuttal 1:
Rebuttal: > Concern #1: generalizability to more datasets and more base models
(Please refer to our response to reviewer xiBU for our additional experiments using a different dataset and a different model)
> Concern #2: limited discussion on computational trade-offs
Thank you for pointing out the limitati... | Summary: The authors highlight a fundamental issue with Bradley-Terry DPO, namely the lack of uniqueness in its solutions. To address this, they propose a novel approach based on energy-based models, termed Energy Preference Alignment. They demonstrate that their model overcomes the non-uniqueness problem inherent in B... | Rebuttal 1:
Rebuttal: > Concern #1 (also raised by reviewer ZCWk): generalizability to more datasets and more base models
As this is a point mentioned by multiple reviewers, we managed to run quick experiments on another relatively small dataset and on another base model. Although the experiments can never be exhausti... | Summary: Bradley-Terry model (BTM) has been the default modelling assumption between rewards and preferences, used in RLHF and DPO. This paper challenges the BTM, claiming that the BTM has not guaranteed unique minimizers in DPO training. To solve this issue, they propose Energy-based Preference Model (EBM), which mode... | Rebuttal 1:
Rebuttal: > Concern #1: The implication of Proposition B.5: the issue of the dataset or the issue of the DPO algorithm?
We believe a reasonable definition of a good algorithm is one that works for datasets "easy" to collect. If there are too many constraints to collect the datasets, it is indeed an issue o... | null | null | null | null | null | null |
MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning | Accept (poster) | Summary: This paper proposes to improve the sampling efficiency in RL learning by mixture-of-experts (MoE) network design and dormant-based parameter perturbation. The MoE design aims to decrease gradient conflicts and perturbation from top-k agent's parameter helps to accelerate the learning. Experiments in simulator ... | Rebuttal 1:
Rebuttal: > Q1: Evaluation on Multi-task Learning:
We appreciate the reviewer’s suggestion regarding multi-task evaluation. We have conducted additional experiments where our method is trained jointly on Meta-World tasks using a single policy. The performance is compared against MLP baseline under the same... | Summary: This paper aims to improve the performance of reinforcement learning agents in robotic tasks. Addressing the issues of gradient conflicts in standard MLPs for robotic tasks and the tendency of visual RL agents to get stuck in local minima, two key improvements are proposed. First, the Mixture-of-Experts (MoE) ... | Rebuttal 1:
Rebuttal: > Q1: “The impact of internal parameter changes on the overall performance may not have been further analyzed in depth… Additionally, there were no relevant ablation experiments regarding the perturbation factor.”:
We appreciate the reviewer’s attention to this point. Due to time limitations, we ... | Summary: This paper proposes MENTOR (Mixture-of-Experts Network with Task-Oriented Perturbation) to improve sample efficiency and performance in visual reinforcement learning (RL). MENTOR replaces the policy’s backbone with a mixture-of-experts (MoE) architecture. This MoE design aims to mitigate gradient conflicts in ... | Rebuttal 1:
Rebuttal: > Q1: Regarding the use of more recent benchmarks such as LIBERO that emphasize large-scale multi-task and lifelong learning:
We appreciate the reviewer’s suggestion of more recent benchmarks, like life-long learning tasks. We consider this a valuable avenue for future work. However, we note that... | Summary: This paper introduces a MOE backbone approach to tackle multi-task visual RL, addressing the problem of conflicting gradients when training on completely opposite tasks (close vs open door). Experimental results show interestingly how the learned model switches between experts smoothly to tackle different sub-... | Rebuttal 1:
Rebuttal: > Q1: Regarding the weakness of real-world RL:
We agree that real-world RL still faces several limitations. However, we have seen progress in this area. Recent work, such as Serl [1], has released a software suite that facilitates the rapid deployment of real-world learning paradigms. Moreover, r... | null | null | null | null | null | null |
Simple Path Structural Encoding for Graph Transformers | Accept (poster) | Summary: This paper introduces a novel graph structural encoding method, Simple Path Structural Encoding (SPSE), as a replacement for Random Walk Structural Encoding (RWSE) in graph transformers. SPSE encodes graph structure by counting simple paths of varying lengths between node pairs, providing more accurate structu... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive review. We're glad that the clarity of our claims, the robustness of our experimental design, and the correctness of our theoretical contributions were appreciated.
While no major concern was raised, we would like to contextualize the following point... | Summary: The work introduces a new perspective on structural encoding for Graph Transformers by leveraging Simple Path Structural Encoding (SPSE). The paper claims to be the first to propose encoding edge information based on simple path counts rather than random walk probabilities, addressing the limitations of Random... | Rebuttal 1:
Rebuttal: We respectfully acknowledge the reviewer’s comments, and hereby attempt to address the different points that were raised.
**Q1. Distinction of graphs A vs. B using RWSE / RWSE is not limited to a single value of k…**
**R1.** We'd be happy to clarify.
In Figure 1, the objective is not to differen... | Summary: The paper introduces Simple Path Structural Encoding (SPSE), a novel method for encoding graph structures in graph transformers. SPSE leverages simple path counts between node pairs to capture richer structural information compared to traditional random walk structural encoding (RWSE). The authors propose an e... | Rebuttal 1:
Rebuttal: Thank you for the detailed and constructive review. We appreciate your recognition of SPSE’s novelty, and of the clarity and comprehensiveness of our experimental validation. We're also glad you found the theoretical contributions, algorithmic details, and supplementary material valuable.
We addr... | Summary: This work proposes a new structural encoding for empowering graph transformers, SPSE (Simple Path Structure Encoding). Rather than encoding random walk landing probabilities, SPSE encodes simple path counts, demonstrating superior expressivity in scenarios such as cycle counting. The authors propose an algorit... | Rebuttal 1:
Rebuttal: Thank you for your thorough and thoughtful review. We are glad that you found the theoretical claims convincing and appreciated the clarity of our method and the rigor of our experimental setup.
Bellow we report the answers to the comments and questions.
**Q1. Aspect contributing to runtime of p... | null | null | null | null | null | null |
Deep Principal Support Vector Machines for Nonlinear Sufficient Dimension Reduction | Accept (poster) | Summary: The paper focuses on SDR replacing functions in RKHS with neural networks.
This is motivated by the potential advantages of neural networks in handling complex data structures. The authors theoretically demonstrate the unbiasedness of their method and provide a non-asymptotic upper bound for the estimation err... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for helpful comments. We will refine the manuscript.
> **Q1**. The paper doesn't provide a detailed analysis of the computational complexity vs. kernel methods.
**A1**. A brief theoretical analysis of computational costs is provided in **Q4** of Reviewer 1. The cost of ou... | Summary: This paper introduce a unified framework for nonlinear sufficient dimension reduction method based on classification ensemble. The framework proposed in this paper almost includes kernel principal SVM, which is a nonlinear sufficient dimension reduction method using the reproducing kernel Hilbert space and SVM... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our theoretical contribution. We will address your concerns in the revision and below are our detailed responses.
> **Q1**. In the Preliminary part Section 2.1, when the authors introduce the nonlinear dimension reduction problem, it would be better if there could be s... | Summary: The paper introduces a general framework of nonlinear sufficient dimension reduction (SDR) based on the previous works of principal support vector machine (PSVM). Classical algorithms such as linear SDR and kernel PSVM are special cases of this new framework. When applying deep neural network to this framework... | Rebuttal 1:
Rebuttal: Thank you for the insightful feedback. Below are our responses.
> **Q1** The convergence results (Theorem 4.6 and Corollary 4.7) only analyze the properties of the empirical loss minimizer, while does not put the network training into consideration. Since the training speed may be one of the limi... | null | null | null | null | null | null | null | null |
Universal Neural Optimal Transport | Accept (poster) | Summary: The authors introduced UNOT (Universal Neural Optimal Transport), a novel framework designed to efficiently predict entropic optimal transport distances and plans between discrete measures of varying resolutions. Motivated by the universal domain adaptation methodology, they utilized Fourier Neural Operators, ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review! We will address your concerns in the following.
>**Proposition 5 [...] depends on assumptions that may not hold**
The only assumption we make in Proposition 5 is that $\sum_i g_i=\sum_i g_{\phi_i}=\sum_i g_{\tau_{k_i}}=0$. Since optimal potentials of the dual ... | Summary: This paper introduces the universal neural optimal transport (UNOT) solver, a framework designed to efficiently solve entropic-regularized optimal transport (OT) problems. Unlike existing neural OT methods that can only handle input distributions of a fixed dimension, UNOT leverages Fourier neural operators (F... | Rebuttal 1:
Rebuttal: Thank you for your helpful review!
We will include definitions of $\rho(x)$ and $\mathcal{N}(\cdot |0,I)$ in Theorem 3, thank you for catching that. Otherwise, your main concern seems to be regarding the proof of Theorem 3. **The statement of Theorem 3 is indeed correct (if you interpret $G^{-1}(... | Summary: The paper presents UNOT- a method to learn universal discrete OT plans/potentials/costs approximator. Interestingly, the method may deal with different discrete resolution scales simultaneously by specific parameterization of the universal learned OT potential (through FNO operator). Several experiments on ima... | Rebuttal 1:
Rebuttal: Thank you for your thorough and helpful review! In the following we will try to answer your questions.
>**I do not understand the practical implication of section 4.4. [...] we can do the same by adding noise to the image**
Section 4.4 shows that UNOT can _solve the OT problem between distributi... | Summary: The paper suggests UNOT (Universal Neural Optimal Transport) a method for (single forward-pass) prediction of OT distances, which are typically computed by iterative methods like the Sinkhorn algorithm. The network architecture is based on Fourier Neural Operators (FNOs), that provide discretization-invariance... | Rebuttal 1:
Rebuttal: Thank you for your thorough and positive review!
The plots in Figures 3, 13, and 14 indeed got mixed up, thanks for catching! We will also include more details about FNOs and our architectures in the main text and define $R$ in the generator more clearly.
**Figure 5:** the corners should not be ... | null | null | null | null | null | null |
From Logits to Hierarchies: Hierarchical Clustering made Simple | Accept (poster) | Summary: A method for deriving a hierarchy of clusters from the logits from a flat clustering model, thus allowing the use the accurate leaf clustering from flat models.
##Update After Rebuttal
Most of my concerns have been addressed and I think it's a good paper. I've upgraded to a 4.
Claims And Evidence: The genera... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive feedback, in particular for praising the idea, effectiveness, and usefulness of our method. We appreciate the useful feedback/suggestions, and address the concerns below.
> If I understand correctly, the leaf accuracy is unaffected by the application [...] ... | Summary: The paper introduces Logits to Hierarchies (L2H), a novel hierarchical clustering method that uses pre-trained non-hierarchical clustering models to build hierarchical structures. L2H is lightweight, doesn't require fine-tuning, and uses logits to generate clusters, outperforming existing deep hierarchical clu... | Summary: - Main algorithmic/conceptual ideas: Using logits of flat clustering model, get hierarchical structure on top of them.
- Main findings/results: deep hierarchical clustering methods have low clustering performance and slow runtime; learned hierarchies from image datasets are interpretable.
Claims And Evidence:... | Rebuttal 1:
Rebuttal: We thank the Reviewer for praising performance and efficiency of our method, our metrics/datasets, as well as the clarity of our paper. We appreciate useful the feedback and suggestions, and address the concerns below.
> line 131[..]
The number of leafs in the hierarchy obtained with our approach... | Summary: This paper addresses the issues of traditional hierarchical clustering algorithms, such as high computational costs, and strong model dependency. It proposes a lightweight hierarchical clustering algorithm that directly constructs hierarchical structures using the logits output by pre-trained models, enabling ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the valuable feedback and suggestions. We appreciate the praise for the efficiency and efficacy of our method, and the highlighting of its value in addressing well-known challenges in hierarchical clustering. We address concerns/questions below.
> However, increasing the ... | null | null | null | null | null | null | |
In-Context Learning and Occam's Razor | Accept (poster) | Summary: The paper studies ICL and tries to relate ICL to prequential coding length through the scope of Kolmogorov complexity.
Claims And Evidence: There're some vague claims stated by the authors, see questions part.
Methods And Evaluation Criteria: Proposed methods and evaluation criteria is confusing.
Theoretica... | Rebuttal 1:
Rebuttal: Thank you for your constructive review.
**Empirical studies**
Our work is not an "empirical paper", but a theoretical one. Unlike standard theory papers, we studied 4 tasks including the challenging Mastermind task, multiple sequence models to highlight the generality of the theory (Fig. 2b), an... | Summary: This paper explores a theoretical framework linking next-word prediction losses in large language models to coding-theoretic principles, often referred to as “prequenial” coding. It argues that simpler, more compact representations (inspired by Occam’s Razor) can facilitate stronger in-context learning perform... | Rebuttal 1:
Rebuttal: Thank you for your constructive review.
**Clarification of contributions**
The reviewer suggests that we “present a bound-based objective aimed at improving generalization from prompts” and that our work centres around “a newly introduced Occam’s Razor-inspired bound” that gives “stronger in-con... | Summary: The paper studies the problem of incontext learning(ICL) and establishes a connection between ICL and Occam's Razor, arguing that next token prediction used in training Transformers implicitly minimizes both the training error and complexity of the learned model. The authors show that this joint objective is e... | Rebuttal 1:
Rebuttal: Thank you for your constructive review.
**Minimizing a tractable upper bound on an intractable objective is valid and common in ML**
> The connection between prequential encoding and ICL meta-training is more of an upper bound rather than a direct equivalence.
>
The reviewer is correct in poin... | null | null | null | null | null | null | null | null |
PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction | Accept (poster) | Summary: The paper introduces **PertEval-scFM**, a standardized framework to evaluate single-cell foundation models (scFMs) for predicting perturbation effects.
Key contributions include:
1. **Framework**: A modular toolkit for zero-shot evaluation of scFM embeddings.
2. **Metrics**: Introduces **AUSPC**, **E-distance... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and constructive feedback, as well as for recognizing the strengths of our work, including the comprehensiveness of our benchmark and its potential to guide future research. Below, we respond to the specific concerns raised.
**GEARS**
We agree that includ... | Summary: This paper introduces PertEval-SCFM a benchmark for zero-shot single-cell foundational model embeddings to capture transcriptional perturbation effects. It is claimed that scum embeddings do not provide consistent improvements over baseline models.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Ye... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and positive evaluation of our work. We are glad that the reviewer found our contribution timely and the benchmark, metrics, and manuscript clear and useful for the field. Below, we address the specific concerns raised.
**Causal intervention**
We ack... | Summary: This paper establishes a protocol to evaluate, in a standardized way, the performance of single cell foundation models at predicting the effect of perturbations. The authors evaluate using two data sets of CRISPR perturbations, combined with an approach to explicitly evaluate the effect of out-of-distribution ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback on the work presented. We are committed to maintaining the benchmark as a live and extensible resource. Because we have prioritized reproducibility and modularity in our design, incorporating new models and datasets is straightforward. We are a... | Summary: The paper titled "PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction" presents a standardized framework called PertEval-scFM to evaluate single-cell foundation models (scFMs) for predicting perturbation effects. The study focuses on assessing whether zero-shot scFM emb... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough evaluation and constructive feedback. Below, we address each of the points raised.
**Perturbation representation**
We address the limitation of simulating perturbations via gene knockouts in our response to Reviewer **pVrS** and refer you to it. You can als... | null | null | null | null | null | null |
Efficient Federated Incomplete Multi-View Clustering | Accept (poster) | Summary: The work presents EFIMVC, an anchor-based federated MVC method employing view-specific/shared anchor graphs and alignment matrices. While the technical approach shows some novelty, fundamental design choices lack justification, and experimental comparisons appear skewed.
Claims And Evidence: The clustering pe... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer NJFx for the thorough and constructive review. We provide point-by-point responses to the questions raised as follows:**
---
**Q1:** Most anchor-based MVC methods enforce orthogonality constraints on anchor matrices to ensure diversity. The authors remove this const... | Summary: This paper proposes a novel framework called EFIMVC, addressing key challenges in federated multi-view clustering such as high communication overhead, limited privacy protection, and poor handling of missing views. EFIMVC introduces a localized optimization strategy that significantly reduces communication cos... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer 3S7F for the thorough and constructive review. We provide point-by-point responses to the questions raised as follows:**
---
**Q1:** The authors claim their method solves three key challenges (communication overhead, privacy preservation, incomplete views) but fail ... | Summary: Tha authors propose a novel federated incomplete multi-view clustering method named EFIMVC. By introducing localized optimization with anchor graphs and dual-alignment mechanisms, the propposed method reduce communication costs while preserving privacy. Experiments on seven datasets demonstrate the superiorty ... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer 9Soy for the thorough and constructive review. We provide point-by-point responses to the questions raised as follows:**
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**Q1:** The paper uses excessive mathematical symbols without proper definitions. For instance, $\mathbf{L}$ in Eq. (13) is never explained.
... | Summary: This paper proposes an Efficient Federated Incomplete Multi-View Clustering (EFIMVC) method to reduce communication overhead, enhance privacy protection, and handle missing views effectively. It employs a localized optimization strategy to lower communication costs, utilizes view-specific and shared anchor gra... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer JeXy for the thorough and constructive review. We provide point-by-point responses to the questions raised as follows:**
---
**Q1:** It is recommended that all illustrations use vector graphics.
**A1:** We sincerely appreciate your constructive suggestion. In the f... | null | null | null | null | null | null |
Long-Short Alignment for Effective Long-Context Modeling in LLMs | Accept (poster) | Summary: This work suggests matching the long-sequence loss and the short-sequence loss, named output alignment. The author has conducted experiments to prove the effectiveness of the proposed methods.
Claims And Evidence: The work supports the claims and evidence.
Methods And Evaluation Criteria: The proposed method... | Rebuttal 1:
Rebuttal: We thank Reviewer qzDj for the comments. We will address your questions in the following part.
---
Q1. Figure 1 (a), the NoPE loss is almost zero. Is anything wrong?
A1. Thank you for your question. The NoPE loss is indeed almost zero (around 1e-5). This observation is also supported by our theo... | Summary: This paper targets **length generalization** problem for LLMs and proposes to **shift from** conventional perspective of **positional encodings and data structures to the output distribution** of the model. They argue that the consistency of output distributions across sequences with different length correlate... | Rebuttal 1:
Rebuttal: Q1. About Delétang et al. 2022. This makes me question if the motivation is valid.
A1. Thank you for your insightful comment. We agree that the difficulty of achieving length generalization in Transformers likely stems from multiple factors. In the related work section, we have acknowledged that ... | Summary: Authors introduce a novel perspective on length generalization in large language models (LLMs) by focusing on output alignment rather than conventional input-based approaches like positional encodings. Through synthetic task case studies, the authors demonstrate that models generalize better when output distri... | Rebuttal 1:
Rebuttal: We thank Reviewer Ueg1 for appreciating the insight of our paper. We will address your questions in the following part.
---
Q1. (From **Other Strengths And Weaknesses**) I believe comparison with other methods in length generalization is needed. Also seeing if this technique can be combined with ... | Summary: This paper studies how aspects of the output distributions of LLMs relate to length generalization and performance on long-context tasks. First, the authors show that train-test mismatch in the output space can lead to poor generalization on synthetic tasks, and that reformulating the tasks to reduce this mism... | Rebuttal 1:
Rebuttal: Q1. Maybe there is an interesting connection to be discussed with SCAN.
A1. Thanks for highlighting these works! Indeed, SCAN-based methods leverage CFG-like rules to generate training data that reduce context sensitivity, which can help improving out-of-distribution generalization. This is indee... | null | null | null | null | null | null |
Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots | Accept (poster) | Summary: This paper proposed a Hierarchical Masked Autoregressive Model based on MAR (Li et al., 2024) by introducing a low-resolution modeling phase, which is claimed to provide global structure guidance for generating dense image tokens. Specifically, x2-lower-res image tokens are first modeled by a scale-aware trans... | Rebuttal 1:
Rebuttal: **Q1: Training-inference discrepancy issue**
Yes. We mainly focus on the design of hierarchical masked autoregressive model which addresses the training-inference discrepancy for passing tokens from phase 1 to phase 2, while the discrepancy caused by the inherent autoregressive process in each st... | Summary: This paper proposes a hierarchical masked AR visual generation model with low-resolution tokens as pivots. By first generating low-resolution image tokens, which provide a global structure, the second generation phase can benefit from the global context. Besides, the proposed diffusion transformer head to furt... | Rebuttal 1:
Rebuttal: **Q1: Novelty**
Thanks. We summarize the differences between Hi-MAR and conventional multi-scale generation models as follows:
1) During training, the conventional models (i.e., Muse, VAR, Hart) commonly utilizes the ground-truth low-resolution visual tokens directly to guide the next phase pred... | Summary: The paper introduces Hi-MAR, a hierarchical masked generative model for visual generation. Hi-MAR first predicts low-resolution image tokens as global structural pivots, which then guide the next phase of dense token prediction, enhanced by a Diffusion Transformer head for better global context modeling. Exper... | Rebuttal 1:
Rebuttal: **Q1: Discussion on VAR and comparisons to "VAR w/ diffusion heads"**
Thanks. We summarize the contributions of our Hi-MAR against VAR in two points:
1) VAR utilizes the low-resolution visual tokens directly to guide the next phase prediction, which would cause training-inference discrepancy as ... | Summary: This paper improves Masked Autoregressive models (MAR) by introducing hierarchical modeling, specifically, low resolution is used as pivots. Additionally, MLP-based Diffusion is changed to global Diffusion to further improve the performance.
Claims And Evidence: The claims made in this paper are supported by ... | Rebuttal 1:
Rebuttal: **Q1: Add a new row of "Pivots + MLP-based diffusion heads" in Table 4**
Appreciate this comment. As suggested, we conducted experiments by including a new ablated run of "Pivots + MLP-based diffusion heads" in Table 4. This ablated run enables hierarchical modeling with shared MLP-based diffusi... | null | null | null | null | null | null |
Concept-Centric Token Interpretation for Vector-Quantized Generative Models | Accept (poster) | Summary: This paper introduces CORTEX, an approach for interpreting Vector-Quantized Generative Models (VQGMs) by identifying concept-specific token combinations from their codebooks. The authors develop two complementary methods: a sample-level explanation that analyzes token importance in individual images and a code... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We address your concerns:
### Weak baseline comparison:
Our comparison against both random selection and the frequency-based baseline (Table 1) highlights CORTEX’s effectiveness in identifying concept-relevant tokens. While the frequency-based method offe... | Summary: This paper introduces CORTEX, a method for interpreting Vector-Quantized Generative Models (VQGMs) with concept-oriented token explanations. CORTEX employs sample-level and codebook-level explanations. CORTEX is useful for shortcut detection (i.e. biases) and image editing. For the evaluation, they CORTEX empl... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We address your concerns and questions below:
**Limited evaluation setting:**
Our evaluation is not limited to VQGAN or ImageNet classes. We also include a text-to-image VQGM, DALL·E in section 5.1, where concepts are defined by prompts such as “male/female/b... | Summary: The paper introduces Concept-Oriented Token Explanation (CORTEX), a framework for interpreting Vector-Quantized Generative Models (VQGMs). CORTEX employs sample-level and codebook-level explanation methods to identify concept-specific tokens, enhancing the transparency of how VQGMs generate images. By using an... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our work. We address your questions below:
### Parameters n and k:
**n=20** represents the top tokens with the highest Token Importance Scores (TIS) selected from each image, while **k=100** represents the most frequently occurring tokens across all sampl... | Summary: * This paper presents CORTEX (Concept-Oriented Token Explanation), a novel framework for interpreting Vector-Quantized Generative Models (VQGMs). VQGMs have become powerful in image generation, but the role of their codebook tokens in representing concepts remains unclear.
* The authors identify the problem ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We address your concerns as follows.
**Limited technical contribution to VQGMs:**
Our method provides a **quantifiable** approach to **detecting bias** in VQGMs and pinpointing the **specific tokens responsible**, enabling targeted debiasing and image editi... | null | null | null | null | null | null |
Bayesian Inference for Correlated Human Experts and Classifiers | Accept (poster) | Summary: This paper addresses the challenge of predicting human expert consensus in $K$-ary classification by developing a Bayesian framework that leverages model outputs and expert queries. Given a limited budget for expert input, the method efficiently maximizes prediction accuracy by modeling expert correlation and ... | Summary: The paper proposes a hierarchical Bayesian model for aggregating predictions from pretrained classifiers and human experts, aiming to estimate the majority vote outcome. It assumes the ground truth corresponds to the majority vote of human experts, with each expert's latent probability correlated and modeled... | Summary: This paper introduces a Bayesian framework for predicting the consensus of human experts in K-ary classification tasks, leveraging pre-trained classifiers to minimize the cost of querying experts while maintaining high accuracy. The correlation between human experts and classifiers was modelled by a joint late... | Summary: This paper addresses an interesting problem of predicting human consensus among correlated experts and machine learning models. The end goal is an active learning task, where the consensus label must be predicted using as few experts as possible. To this end, a hierarchical Bayesian model is presented to model... | null | null | null | null | null | null | ||||
The Ripple Effect: On Unforeseen Complications of Backdoor Attacks | Accept (poster) | Summary: This paper explores the backdoor complications in Pre-Trained Language Models (PTLMs), i.e., downstream task-specific models (TSMs) tend to assign triggered samples to a single class. The authors conduct thorough evaluations on such a phenomenon and propose a multi-task learning-based strategy to minimize the... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's positive evaluation and valuable feedback. Below, we provide detailed responses to each of the raised concerns.
# Comparison with poisoning attacks on multi-task learning
We thank the reviewer for this insightful question.
While our method in RQ2 adopts a mult... | Summary: This paper presents a comprehensive examination of backdoor complications in Pre-trained Language Models (PTLMs)—unintended consequences in unrelated downstream tasks stemming from compromised PTLMs. The authors observe that the output distribution of triggered samples diverges significantly from that of clean... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's positive evaluation and valuable feedback. Below, we provide detailed responses to each of the raised concerns.
# Comparison with backdoors in transfer learning
We thank the reviewer for requesting clarification.
Traditional backdoor attacks in transfer learn... | Summary: This paper investigates the unforeseen complications of backdoor attacks in pre-trained language models (PTLMs) when adapted to unrelated downstream tasks. The authors introduce the concept of "backdoor complications," defined as abnormal output distributions in downstream tasks caused by triggers embedded in ... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s positive evaluation and valuable feedback.
# Practical scenario justification
In practice, under the adopted pre-train, fine-tune paradigm, many users seek to fine-tune general-purpose PTLMs on their own small-scale, private datasets to build specialized models ... | Summary: This paper investigates how backdoored Pre-Trained Language Models (PTLMs) can unintentionally cause anomalies in unrelated downstream tasks. Through experiments on 4 PTLMs (BERT, BART, GPT-2, T5) and 16 text classification datasets, the study finds that triggered inputs often produce highly skewed outputs, so... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and the opportunity to clarify our novelty and contributions.
# Regarding the novelty
We want to clarify that papers[1,2,3] are not directly related to backdoor complications defined in our work.
- LOTUS [1] introduces a backdoor attack that assigns... | null | null | null | null | null | null |
On the Power of Learning-Augmented Search Trees | Accept (poster) | Summary: This paper examines the integration of external predictions into classic binary search trees (BSTs) and B-Trees via Treaps. Prior attempts to build such a data structure only had guarantees for Zipfian distributions and were shown to be provably bad on certain sequences of inputs. In this work, the authors pro... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments! We address the questions and concerns as follows.
**Robustness**
If no prediction is available, we can naturally set the "predicted frequency" to be $1/n$. In this case, the access cost becomes $O(\log n)$.
**Predictions for Dynamic Settings**
Thank you ... | Summary: This paper is concerned with learning-augmented search trees. Given a dataset of a sequence of requested keys, the recent work by Lin et al. 2022 constructs a learning-augmented search tree as a treap where the "priority" $p(x)$ of each key $x$ is set to be equal to the frequency $f_x$ of the key in the sequen... | Rebuttal 1:
Rebuttal: Thank you for your comments! We sincerely apologize for the various writing issues and will make every effort to improve them, particularly in the sections on the dynamic setting and the introduction. We will also cite the article you recommended and clearly state the static optimal BST cost to he... | Summary: The paper uses learned predictions to enhance the performance of classical data structures. Specifically, it presents learning-augmented binary search trees (BSTs) and B-Trees that leverage predictions about item access frequencies. Building on the work of Lin et al. [ICML’22], which proposed augmenting treaps... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments! We would like to begin by sincerely thanking you for pointing out the related work that we had overlooked. We will make sure to include proper citations in the updated version of the paper. Below, we address the three specific questions you raised:
1. Splay t... | Summary: Authors propose the following kind of binary search trees:
receiving prediction about the frequencies of all items,
their algorithm produces a tree which achieves static optimality
(up to a constant factor) and its cost smoothly deteriorates
with prediction error.
They extend their result to B-trees.
Authors a... | Rebuttal 1:
Rebuttal: Thanks for your comments! We will address your concern as follows.
**Robustness and Bad Predictions**
We use the word *robustness* to describe how the performance of our data structure degrades smoothly with respect to the prediction error - measured by KL divergence in the static setting and me... | null | null | null | null | null | null |
Demonstration Selection for In-Context Learning via Reinforcement Learning | Accept (poster) | Summary: This work studies the problem of demonstration selection for in-context learning in LLM. The problem is formulated as a reinforcement learning process, i.e., the effect of a combination of demonstration is similar to that of taking a sequence of actions. During the RL process, heuristic reward based on both ac... | Rebuttal 1:
Rebuttal: Dear Reviewer Sqdn,
Thank you for your insightful review and valuable feedback. We appreciate your concerns and have provided detailed responses below：
### Theoretical Claims
- We acknowledge your point that **Lemma 3.1 (Diversity Bounds) and Theorem 3.2 (Q-Learning Convergence) are relatively ... | Summary: The paper introduces Relevance-Diversity Enhanced Selection (RDES), a reinforcement learning framework for selecting in-context demonstrations that balance relevance and diversity for few-shot text classification tasks. The core idea is to use a Q-learning agent to iteratively pick examples from a knowledge ba... | Rebuttal 1:
Rebuttal: Dear Reviewer ZBB1,
Thank you for your insightful feedback.
**On the analysis of RDES's computational cost (LLM invocation frequency)**: We appreciate your attention to this important aspect. While our current submission primarily focuses on the effectiveness of RDES, we recognize that computati... | Summary: The paper proposes a smarter way to select examples/demonstrations for In-Context Learning in LLMs. Picking the right example set for the task at hand is a serious challenge and authors propose an RL-based technique (with Q-learning) to optimally pick examples from a golden set based on a similarity metric + a... | Rebuttal 1:
Rebuttal: Dear Reviewer NHQh,
Thank you for your recognition of our work and the valuable suggestions.
- **Work as an iteration of existing work**: We acknowledge that RDES builds upon existing work on RL for ICL and diverse demonstration selection. However, we emphasize that the novelty of RDES lies in **... | Summary: This paper claims that applying a Q-learning method to demonstration selection can be beneficial for classification tasks in the context of in-context learning.
The authors attempt to frame demonstration selection as a Markov decision process, though the details of this formulation remain unclear in the draft... | Rebuttal 1:
Rebuttal: Dear Reviewer 5sFY,
Thank you for your thorough review and valuable feedback. We appreciate your concerns and have provided detailed responses below, referencing other reviewers' comments to support our points.
### Claims and Evidence
You noted the lack of evidence for the usefulness of reinforc... | Summary: The paper presents RDES, a reinforcement learning framework for selecting diverse and relevant demonstrations to support in-context learning in text classification tasks. It formulates demonstration selection as a Markov Decision Process and employs Q-learning with a composite reward function to balance releva... | Rebuttal 1:
Rebuttal: Dear Reviewer 3ANe,
Thank you for your positive feedback on our work. Regarding your suggestions:
- **Lack of units in tables**: We appreciate your suggestion and will explicitly label the units (e.g., accuracy will be presented as a percentage) in all tables in the revised manuscript.
- **No u... | null | null | null | null |
Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound | Accept (poster) | Summary: This paper is concerned with the quantitative (probabilistic) verification of neural network. It proposes PV, a branch-and-bound algorithm that is correct and (under reasonable assumptions) complete. Additionally, the paper proposes a fairness benchmark for the probabilistic verification of NNs that is more ch... | Rebuttal 1:
Rebuttal: We thank reviewer SiXd for their review.
> Q3. Closed-form inference.
By "closed-form", we wanted to express that **a terminating algorithm exists** for computing a probability exactly, not necessarily that this algorithm has polynomial runtime.
We will state this more clearly in our paper.
Fo... | Summary: This paper proposed a branch & bound and interval propagation based probabilistic verification method for neural networks. This paper provides a sound verification methodology and speed up comparing to previous neural network verification techniques in mainstream neural network verification benchmarks.
Claims... | Rebuttal 1:
Rebuttal: We thank reviewer 8dyc for their review. We address the questions posed in the review below.
> 1. The updating strategy for upper and lower bounds based on probabilistics need more clearification. Why we can directly use the probabilistic to refine the interval?
We assume that this question refe... | Summary: The paper proposes a generic approach for the probabilistic verification of neural networks which can leverage pre-existing qualitative verification tools. To this end, the paper proposes to combine input space splitting with bound computation to compute bounds on (un)safe behavior. Purely safe/unsafe regions ... | Rebuttal 1:
Rebuttal: We thank reviewer GWj1 for their review, in particular their careful evaluation of our theoretical assumptions.
> Theoretical Claims / Assumption 5.3
If the safe/unsafe region is not axis-aligned, indeed, Algorithm 2 can not compute the *exact* probability of safety.
However, Algorithm 2 still... | Summary: This paper proposes a probabilistic neural network verification method through lower and upper bounds of probabilities of outputs. By branch and bound and bound propagation, it can run faster than other verifiers.
Claims And Evidence: The definitions of soundness and completeness in this paper seem to be quit... | Rebuttal 1:
Rebuttal: We thank reviewer BFqm for their review. We believe there has been a misunderstanding of our definition of soundness that underlies most of the review.
> The definitions of soundness [...] in this paper seem to be quite different from the commonly used ones in the neural network verification com... | null | null | null | null | null | null |
Dynamic Similarity Graph Construction with Kernel Density Estimation | Accept (poster) | Summary: In this paper, the authors deal with the kernel density estimation (KDE) problem. They propose a query hash data structure for dynamic graphs (it maintains the estimates for a set of query points as data points are added). They present experiments with different types and dimensionality of data, as well as som... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and overall positive evaluation of our paper.
**Response to _Claims And Evidence and Experimental Design_**
It is known that kNN graphs achieve good empirical performance for spectral clustering. However, existing algorithms applicable for dynamic... | Summary: This paper designs a dynamic similarity graph construction method with kernel density estimation. The proposed method constructs graphs more efficiently than existing methods and can also approximate the clustering results of spectral clustering. The authors provide a detailed theoretical analysis of the propo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their suggestions and positive evaluation of our paper, and we respond to their comments below:
>_**Question 1**: “The width parameter of the Gaussian kernel function has a significant impact on the learning algorithm. How is this parameter set in Table 3, and what is th... | Summary: Suppose we are given a set $X = \{x_1,\dots,x_n\}\subseteq \mathbb{R}^d$ of $n$ data points, a set $Q = \{q_1,\dots,q_m\}\subseteq\mathbb{R}^d$ and a kernel $k : \mathbb{R}^d\times \mathbb{R}^d\to \mathbb{R}$.
We would like to compute $\mu_q := \sum_{i=1}^n k(q,x_i)$ for all $q\in Q$.
However, computing these ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review of our paper. The reviewer doesn’t leave specific questions for us to address. However, we’re happy to answer further questions raised by the reviewer during the later discussion phase. | null | null | null | null | null | null | null | null |
A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approach | Accept (poster) | Summary: This paper investigates average-reward reinforcement learning with general policy parametrization. The authors propose a Multi-level Monte Carlo-based Natural Actor-Critic (MLMC-NAC) algorithm and provide a finite-sample analysis.
Update after rebuttal:
The authors have satisfactorily addressed all of my con... | Rebuttal 1:
Rebuttal: **Markovian Noise**: We agree that applying projections or invoking the assumption of finite action space is common in the literature. For example, the MAC algorithm derives bounds using MLMC estimates from (Dorfman and Levy 2022), which assume a finite state space and a bounded domain, thereby ne... | Summary: The paper establishes $O(\epsilon^{-2})$ sample complexity for MLMC-NAC algorithm, claims to significantly improve existitng result of $O(\epsilon^{-4})$.
State-space independent result is not surpring as NAC (in exact gradient NAC case) is also state-independant.
Claims And Evidence: Yes, seems so.
Methods... | Rebuttal 1:
Rebuttal: **(a) Related Works**: Thanks for mentioning references [1] and [2]. We will include them in the revised version of our paper. In addition to [1], we will also include some other works on robust MDPs.
**(b) Message for Practitioners**: Our algorithm is designed for large state and action spaces,... | Summary: This paper studies the convergence of Actor-Critic algorithm in average reward setting of reinforcement learning. The paper proposed a multi-level Monte-Carlo based natural actor critic algorithm which achieves a $\tilde{O}(1/\sqrt{T})$ global convergence rate to a neighborhood of the optimal policy, where $T$... | Rebuttal 1:
Rebuttal: **(a) Sample Complexity**: We note that there is a misunderstanding here. The convergence rate and the sample complexity are two faces of the same coin, and one can be derived from the other, as long as the error metric is taken to be the same. For example, consider our algorithm that uses a traje... | Summary: This work considers the average-reward RL setting with general policy parametrization. The authors improve over the state-of-the-art global convergence guarantee from a rate of $T^{-1/4}$ to $T^{-1/2}$ without requiring knowledge of the mixing or hitting times. This is done by adopting a multi-level MC procedu... | Rebuttal 1:
Rebuttal: > Concerning the table, the authors state that their approach works for infinite states and actions, and similar claims are made for some of the related works. However, by inspecting those works, it appears that their results are defined with respect to large but finite spaces. Can the authors com... | null | null | null | null | null | null |
Distributed Parallel Gradient Stacking(DPGS): Solving Whole Slide Image Stacking Challenge in Multi-Instance Learning | Accept (poster) | Summary: This paper proposes a Distributed Parallel Gradient Stacking (DPGS) framework and a Deep Model Gradient Compression (DMGC) technique to address the non-stackable data issues caused by variable bag lengths in whole slide image (WSI) multiple instance learning (MIL). The core innovations include: 1) Enabling los... | Rebuttal 1:
Rebuttal: Q1: Most of the claims made in the manuscript are supported by clear and convincing evidence. The shortcoming is that the motivation of this paper is not strongly substantiated.
A1:
We sincerely appreciate the reviewer's valuable feedback.
Regarding training efficiency, we clarify that "excessiv... | Summary: This paper proposes a framework called DPGS combined with DMGC to address the non-stacked data problem in MIL, particularly in WSI analysis. DPGS addresses the inefficiencies due to variable-length instance bags that prevent effective batch stacking by parallelizing MIL models and stacking gradients instead of... | Rebuttal 1:
Rebuttal: Q1:Table 2 shows Bag Padding's unexpectedly long convergence time versus Classic. The convergence time calculation method also requires clarification.
A1:Thank you for reviewing.Bag Padding's longer convergence time (Table 2) stems from large packet length spans (C16 MULTISCALE: 223-57,318; C16 I... | Summary: This paper introduces Distributed Parallel Gradient Stacking (DPGS), a framework designed to address the challenge of non-stackable data in Multiple Instance Learning (MIL) for Whole Slide Image (WSI) analysis. The authors propose two key components: (1) DPGS, which enables parallel processing of variable-leng... | Rebuttal 1:
Rebuttal: Q1:The paper calls non-stackable MIL data a bottleneck citing 'prohibitively long training times',yet current methods train adequately on single GPUs within half-day for these datasets
A1:Thank you for reviewing.Clarifications:This is an inexact error "long training times":Our method shows superi... | Summary: The manuscript introduces a framework designed to overcome the challenges posed by variable-length bags in Multiple Instance Learning (MIL) for whole slide image analysis. The framework was called Distributed Parallel Gradient Stacking (DPGS), distributes the gradient computation across multiple processes and ... | Rebuttal 1:
Rebuttal: Q1：Could you clarify the integration between DPGS and DMGC, specifically how the compression interacts with the distributed gradient stacking process?
A1：
We sincerely appreciate the reviewer's valuable feedback. We apologize for the lack of clarity in describing the relationship between the ... | null | null | null | null | null | null |
Learning Condensed Graph via Differentiable Atom Mapping for Reaction Yield Prediction | Accept (poster) | Summary: The paper introduces YIELDNET, a neural model designed to predict chemical reaction yields by learning a condensed graph representation of reactions. Unlike traditional methods that rely on quantum chemistry-based molecular descriptors, YIELDNET approximates atom mapping—the correspondence between reactant and... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, which we address as follows.
> *CGR meaningfully approximates TS?*
During rebuttal, we performed quantum chemical validation analogous to that reported by Choi, in 'Prediction of transition state structures of gas-phase chemical reactions via machine lea... | Summary: Predict permutation-matrix of a chemical reaction with GNN atom embeddings and iterative Sinkhorn interactions. Sampling from the permutation-matrix to get the atom-mapping which is used by a Transformer to predict the yield.
Claims And Evidence: - propose differentiable approximation of the atom-mapping: I w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, which we address below.
> *results for atom mapping*
Appendix E.3 contains the table with +- std error. Paired t-test in majority of cases revealed that the performance gain achieved by our method is statistically significant with $p=0.01$
> *Approach i... | Summary: The paper introduces YIELDNET, a neural yield prediction model designed to predict the yield of multi-step chemical reactions without explicit atom mapping supervision. The key contributions include a differentiable node alignment network to approximate atom mapping, the construction of a CGR as a surrogate fo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their suggestions, which we address as follows.
> *theoretical analysis*
We provide the following theoretical underpinning. We start with the QAP in Eq 8. If $\mathcal{P}$ is the set of permutations, then QAP is
$$\min _{P\in \mathcal{P}}
\underbrace{\sum _{u,v} \max... | null | null | null | null | null | null | null | null |
Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers | Accept (poster) | Summary: The paper proposes a new method for continual learning, which trains auxiliary classifiers on top of the intermediate features of the deep neural network along with the main neural network. The method is motivated by the observation that, in the process of class incremental continual learning, intermediate fea... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent on our work and the suggestions for improving its quality. Below, we address the points raised by the Reviewer.
> If we just care about the optimal final accuracy (the point at 100% inference cost), the amount of improvement provided by auxiliary classifie... | Summary: The paper shows that intermediate representations are less prone to forgetting in the continual learning paradigm, which is aligned with previous observations in the literature. Based on this finding, they propose using auxiliary classifiers in the intermediate layers and showed that the method consistently ou... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent on our work and the suggestions for improving its quality. Below, we address the points raised by the Reviewer.
> Since the method focuses on forgetting, I expect to see a metric to measure it in the experimental results.
We appreciate the Reviewer’s sugg... | Summary: The manuscript analyzes the effect of class-incremental training on the parameters of a neural network, finding that deeper layers tend to be more affected from catastrophic forgetting. To exploit this it introduces a series of Auxiliary Classifiers (AC), finding that their combination can significantly make t... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent on our work and the suggestions for improving its quality. We appreciate the Reviewer’s mention of *Boschini et al., "Transfer without forgetting." ECCV 2022*; we will include this paper in the updated related works. Below, we address the points raised by ... | Summary: This paper introduces Auxiliary Classifiers (ACs) to enhance performance and efficiency in Continual Learning (CL) by leveraging intermediate representations in neural networks. The primary challenge is catastrophic forgetting, where new knowledge acquisition disrupts previously learned information. The key co... | Rebuttal 1:
Rebuttal: We thank the Reviewer for all the suggestions. Below, we address the points mentioned by the Reviewer. We will include the clarifications and the Reviewer’s suggestions in the updated version of our paper.
## Accuracy metrics and backward transfer (BWT)
In all our experiments, we report the aver... | null | null | null | null | null | null |
Calibrated Language Models and How to Find Them with Label Smoothing | Accept (poster) | Summary: The paper studies calibration in LLMs after SFT stage. They mention that SFT leads to some significant degradation in the calibration performance of LLM due to the lack of diversity in learned feature embeddings. To address this, they propose to use label smoothing during SFT stage for better calibration. They... | Rebuttal 1:
Rebuttal: We are immensely grateful for the thorough review that the reviewer has provided for our work. First, we would like to express our appreciation for the comments regarding the well-supported claims, the usefulness of our custom kernel, the appropriateness of the theoretical claims as well as the br... | Summary: The authors proposed using label smoothing to improve language model calibration for supervised fine-tuning. They demonstrate through theoretical analysis and experiments that label smoothing is effective, though computation is heavy for large vocabulary LLMs. They also propose a memory-efficient algorithm wit... | Rebuttal 1:
Rebuttal: We are very grateful and would like to thank the reviewer for their positive assessment of our work. We are happy they mention that they mention the suitability of the theory, experiments, methods and evaluation. To clarify their remaining questions and potential areas of concern, we hope the foll... | Summary: In this work the authors examine the effect of Instruction tuning on LLM calibration, i.e. when the model says it is 70% sure about a prediction does it actually get it right ~70% of the time? They find that majority of available LLMs are reasonably well calibrated, but once Instruction tuning is applied, they... | Rebuttal 1:
Rebuttal: First, we thank the reviewer for their review of our work and their enthusiasm they express regarding their work. We appreciate that they find the claims well founded, the insights to support these claims, and their agreement regarding our new kernel that incorporates label smoothing.
-----
> Ev... | Summary: This work focuses on mitigating mis-calibration of LLM during SFT by incorporating label smoothing. It argues that label smoothing helps reduce model overconfidence by promoting equality among logits while enhancing learned feature embeddings. Through empirical experiments, this work demonstrates the effective... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for providing a thorough evaluation of our work. We are delighted that the reviewer finds the work to have been positioned and set up in a manner that aligns with supporting our claims. We are hopeful that the below responses can sufficiently address their remai... | null | null | null | null | null | null |
Policy Optimization for CMDPs with Bandit Feedback: Learning Stochastic and Adversarial Constraints | Accept (poster) | Summary: The authors study constrained Markov decision processes (CMDPs) and they provide the first best-of-both-worlds algorithm for CMDPs with bandit feedback.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: Yes, to some extent.
Experimental Designs Or Analyses: Yes, to some ex... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive evaluation.
> [1]
While in classical RL literature, the only goal is to maximize a given reward function, in the constrained RL literature the problem becomes two-fold. Specifically, the agent has to additionally fulfill $m$ possible constraints while maxim... | Summary: This paper studies online constrained Markov decision processes with rewards and constraints that can be either stochastic or adversarial. It proposes the first algorithm for this approach with bandit feedback. Moreover, instead of considering a problem on occupancy measures, their method is based on a primal-... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the effort in evaluating our work.
> On the novelty.
We thank the Reviewer for the opportunity to clarify this aspect. In the following, we better highlight the contribution of our work. Indeed, while our primal-dual scheme follows the one of [Stradi et al. 2024] and th... | Summary: This paper proposes the first best-of-both-world algorithm that can solve a CMDP only with bandit feedback and enjoys optimal regret and constraint-violation guarantees in both regimes. Furthermore, the proposed method does not require Slater's condition and employs a policy optimization approach, which is mor... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive evaluation.
> On Ghosh et al., 2024.
We thank the Reviewer for the interesting point. First, we underline that we use the term best-of-both-worlds referring to the constraints definition. Thus, our algorithm achieves optimal regret guarantees when the cons... | Summary: This paper considers policy optimization for the CMDP framework with stochastic and adversarial constraints. Compared to the existing works, this paper considers bandit feedback rather than full feedback. The paper uses a primal-dual-based policy optimization method to find the policy directly rather than the ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive evaluation.
> On W1 and Q1.
As shown in [1], computing the maximum state-action occupancy measure among the possible transition probability sets (Line 5 in Algorithm 3) can be solved efficiently via a greedy algorithm. The same reasoning holds for the corre... | null | null | null | null | null | null |
Delay-DSGN: A Dynamic Spiking Graph Neural Network with Delay Mechanisms for Evolving Graph | Accept (poster) | Summary: This paper introduces Delay-DSGN, a novel dynamic spiking graph neural network that incorporates a learnable synaptic delay mechanism to enhance dynamic graph representation learning. The authors argue that existing SNN-based methods struggle with temporal information propagation and historical forgetting, and... | Rebuttal 1:
Rebuttal: Thank you for your meticulous review of our work and the valuable feedback provided.
**Response to Weaknesses and Question 1:**
In our model, delay parameters are randomly initialized following a normal distribution within a specified range. For example, when the maximum delay is set to 5, the i... | Summary: This paper introduces Delay-DSGN, a dynamic spiking graph neural network that incorporates a learnable delay mechanism to enhance the representation of evolving graphs. By modeling synaptic delays with a Gaussian kernel, the model effectively captures temporal dependencies and mitigates information forgetting.... | Rebuttal 1:
Rebuttal: Thank you for your meticulous review of our work and the valuable feedback provided.
**Response to Experimental Designs or Analyses 1:**
In our experiments, we compared eight methods across three datasets using two metrics. To assess the statistical significance of improvements by our proposed m... | Summary: This paper proposes Delay-DSGN, a dynamic spiking graph neural network that incorporates a delay convolution kernel, which dynamically adjusts the weight of information at different time steps. Through the delay convolution kernel, Delay-DSGN captures temporal dependencies and historical influences on node rep... | Rebuttal 1:
Rebuttal: Thank you for your meticulous review of our work and the valuable feedback provided.
**Response to Weaknesses 1 and Question 1:**
When a new edge is added to a graph, it may indeed represent an immediate interaction demand. However, in reality, there is usually a time lag between "establishing ... | null | null | null | null | null | null | null | null |
Nearly Optimal Sample Complexity for Learning with Label Proportions | Accept (poster) | Summary: This paper investigates the problem of Learning with Label Proportions (LLP), where training data is provided in groups (or bags) rather than individual instances, and only aggregate label proportions are available for each group. The goal is to infer the original instance-level labels using the provided propo... | Rebuttal 1:
Rebuttal: - “... several typos and unclear descriptions… ”.
We would appreciate pointers to typos, unclear descriptions, and key arguments that the reviewer found unclear so that we can improve them. Thank you!
- On Motivation.
As discussed in the right column of lines 23 – 32, a key modern motivation f... | Summary: This paper provided the optimal sample complexity of LLP under square loss. Based on these analysis, the authors proposed an improved squared loss, which was reported to enjoy better classification accuracy on several binary classification datasets under LLP settings.
Claims And Evidence: The claims made in t... | Rebuttal 1:
Rebuttal: - Extensions to Multi-class and Other Losses.
We agree with the reviewer that extensions to the multiclass setting and losses other than the squared loss would be of interest. Having said that, our work is the first to provide matching upper and lower bounds on the sample complexity of learning f... | Summary: This paper addresses the challenge of Learning from Label Proportions (LLP) by analyzing the i.i.d. bag sampling setting. The authors provide improved sample complexity bounds in both realizable and non-realizable settings, using variance reduction methods, surpassing prior results. They further demonstrate th... | Rebuttal 1:
Rebuttal: - On uniform bag sizes.
We present our results using a uniform bag size for simplicity, but in principle the loss estimate we propose here can be applied even in situations where we have bags of different sizes, and we should still expect to see improvements due to the reduced variance at each ba... | Summary: The paper presents a theoretical analysis for the problem of learning with label proportions (LLP) where training examples are grouped into “bags” and only the aggregate label proportion of each bag is observed (rather than individual labels). The authors propose a theoretical analysis of LLP under the square ... | Rebuttal 1:
Rebuttal: - Claim in LL. 190-195 when $\Delta$ and $\beta$ are unknown + absence of proof in Appendix B.2.
As quickly mentioned in L. 195-196, Appendix B.2 contains a proof of a similar statement that applies indeed to a harder situation (the one in Thm 4.1), where $E[h(x)]$ and $E[h^*(x)]$ are unknown. Th... | null | null | null | null | null | null |
Plausible Token Amplification for Improving Accuracy of Differentially Private In-Context Learning Based on Implicit Bayesian Inference | Accept (poster) | Summary: The paper introduces Plausible Token Amplification (PTA), a novel approach designed to improve the accuracy of Differentially Private In-Context Learning (DP-ICL) by refining the process of generating differentially private synthetic demonstrations. The paper also presents a theoretical guarantee of the distin... | Rebuttal 1:
Rebuttal: # Interpretation of numerical experimental results}
> Reviewer comments (Claims And Evidence, Questions For Authors):
> 1. DBPedia has significantly higher performance without the proposed KL divergence, which is an essential part of the proposed PTA. There is no explanation for this.
> 2. ... | Summary: This manuscript explores Differentially Private In-Context Learning (DP-ICL) to mitigate leakage risks in ICL. The authors first provide a theoretical analysis for a prior work (Tang et al., 2024) using a Bayesian analysis, where Tang et al. studied generating synthetic demonstrations by adding variance-tuned ... | Rebuttal 1:
Rebuttal: # Unclear definition of "concept"
> Reviewer comment (Other Strengths And Weaknesses, Weakness):
> 1. The term "concept" in the introduction needs clearer demonstration. It can improve the readability of the introduction.
Thank you for pointing this out we had not noticed this readability issue... | Summary: The paper presents an approximate Bayesian model of DP-ICL (differentially private in-context learning). DP-ICL in general combines a public LLM with a private LLM to generate a number of low-privacy-leakage prompts, then uses those prompts for in-context learning. A previous paper (Tang et al.) used a simple ... | Rebuttal 1:
Rebuttal: # Concerns in bounding constants
> Reviewer comments (Claims And Evidence, Theoretical evidence)
> 1. [...] introduce a whole pile of constants [...], so the C\_delim "constant" [...] hides a bunch of things which are very far from constant, [...]. Similarly, G is also dependent on the LLMs involv... | null | null | null | null | null | null | null | null |
Gamma Distribution PCA-Enhanced Feature Learning for Angle-Robust SAR Target Recognition | Accept (poster) | Summary: This paper proposes a Gamma-Distribution Principal Component Analysis (ΓPCA) method for angle-robust SAR target recognition. The key idea is to integrate the Gamma distribution into PCA to account for the statistical properties of SAR data, thereby enhancing feature extraction across varying observation angles... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s valuable comments. We will enhance the related work and include more experimental results in our final version. Below are our detailed responses to each point.
> Q1. MSTAR is small.
**A1:** We constructed a new dataset based on SAR-AIRcraft-1.0. A detailed d... | Summary: The paper proposes a Gamma-Distribution Principal Component Analysis ($\Gamma$PCA) model to enhance the robustness of Synthetic Aperture Radar (SAR) target recognition against angle variations. The key idea is to leverage the Gamma distribution to extract low-rank features that are invariant to changes in azim... | Rebuttal 1:
Rebuttal: > Comment1. The experimental validation is limited to a single dataset (MSTAR).
**Answer1:**
Thanks for pointing out this important concern. We acknowledge this limitation. Unlike optical imagery, SAR data is challenging to obtain at scale due to sensor costs, operational constraints, and the f... | Summary: This paper proposes Gamma-PCA, a Gamma-distribution Principal Component Analysis model, to enhance angle-robust SAR target recognition by leveraging SAR's non-Gaussian statistics. The method derives consistent projection kernels to capture angle-invariant features without parameter updates, seamlessly integrat... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on our work and are truly grateful for the time and effort you have dedicated to reviewing our manuscript. Should you have any additional comments or suggestions, we would be delighted to engage in further discussion and improvement.
---
Rebuttal Co... | Summary: This paper addresses a key challenge in SAR target recognition: the sensitivity of deep learning models to variations in azimuth and depression angles, which cause significant shifts in scattering characteristics. The authors propose a Gamma-Distribution Principal Component Analysis (ΓPCA) model to extract ang... | Rebuttal 1:
Rebuttal: > Comment1. Dependence on Gamma distribution assumptions (may not hold for all targets/clutter).
**Answer1:**
We appreciate this insightful observation regarding the distributional assumption. To provide clarification:
1. Theoretical and empirical validation: The applicability of Gamma distribu... | null | null | null | null | null | null |
Simplifying DINO via Coding Rate Regularization | Accept (poster) | Summary: The authors proposed the coding rate regularization technique to improve the training method of DINO and DINOv2, enhancing their performance.
Claims And Evidence: 2-1. The careful review of the original DINO and DINOv2 provides an intuitive and well-explained reasoning for why collapse occurs.
2-2. It is imp... | Rebuttal 1:
Rebuttal: Dear Reviewer ubYD,
We deeply appreciate your insightful review and am pleased to see that you find our work intuitive to follow, and well-justified by extensive empirical studies. Here we attempt to address the concerns you have raised in the review.
3-1: This is a great suggestion. It is possi... | Summary: The paper reveals that many comonents in the pipelines and models of DINO and DINOv2 are used to avoid collapse of the learned representations, and thus attempts to simplify the training pipelines, architectures, and hyperparameters of DINO and DINOv2 by introducing a $\ell^2$ distance based on the loss functi... | Rebuttal 1:
Rebuttal: Dear Reviewer 5gcy,
We deeply appreciate your insightful review and am pleased to see that you find our work backed by solid empirical studies, with potential to improve practice of visual representation learning. Here we attempt to address the concerns you posed.
> Theoretical justification for... | Summary: The work aims to simplify the architectures of DINO and DINOv2 family of models by adding an explicit coding rate term in their loss. The simplified architectures (referred as SimDINO and SimDINOv2) are more robust to different design choices and offer a Pareto improvement over the DINO model families. The sim... | Rebuttal 1:
Rebuttal: Dear Reviewer nrJd,
Thank you for your insightful comments and suggestions. We are encouraged by your compliments on the simplification of the proposed method as well as the empirical evidence and presentation quality of our work. Here we attempt to address the concerns raised in your review.
>... | Summary: The authors aim to improve the stability of self-supervised models DINO and DINOv2, while simplifying the training process. Collapse is a common problem in self-supervised learning (SSL), and it is usually avoided with careful hyperparameter tuning and architectural adaptations. In particular, to avoid collaps... | Rebuttal 1:
Rebuttal: Dear Reviewer S9wV,
Thank you for the detailed review. Many comments, especially about presentation or typos, are helpful and will be incorporated in the camera-ready version; we do not address them here. Others require clarification or even stem from misunderstandings on your end, and we reply t... | null | null | null | null | null | null |
Task-Agnostic Pre-training and Task-Guided Fine-tuning for Versatile Diffusion Planner | Accept (poster) | Summary: This paper proposes a two-stage framework for training versatile diffusion policies. The core idea is to learn generalized diffusion policies from a large amount of low-quality multi-task trajectories, followed by task-specific online RL fine-tuning that quickly tailor the pre-trained diffusion policy to the ... | Rebuttal 1:
Rebuttal: Dear Reviewer 4vpc,
We sincerely appreciate your precious time and constructive comments. In the following, we would like to answer your concerns separately.
**W1**: would offline+online RL transformers work better?
**R1**: We extend the multi-task Decision Transformer (DT) following [1] by pre... | Summary: This paper proposes SODP, a two-stage framework for training versatile diffusion planners using sub-optimal data. SODP first pre-trains a foundation model on mixed sub-optimal trajectories without requiring expert demonstrations or reward labels, then employs RL-based fine-tuning with task-specific rewards and... | Rebuttal 1:
Rebuttal: Dear Reviewer aUqS,
We sincerely appreciate your precious time and constructive comments. In the following, we would like to answer your concerns separately.
**C1**: The computational costs of pre-training (500k steps) are omitted
**R1**: Thank you for your question. The data used for pre-train... | Summary: The paper proposes a two-stage framework that pre-trains a diffusion planner using a large number of suboptimal demonstrations and performs task-specific finetuning through reinforcement learning. The diffusion planner is first pre-trained on multi-task offline data of low quality to predict action sequences g... | Rebuttal 1:
Rebuttal: Dear Reviewer w6L1,
We sincerely appreciate your precious time and constructive comments. In the following, we would like to answer your concerns separately.
**W1**: Different sets of MetaWorld tasks are selected for different ablation studies ... provide some context information or rationale be... | Summary: The paper presents SODP (Sub-Optimal Diffusion Planner), a two-stage framework that leverages task-agnostic pre-training on sub-optimal trajectory data followed by reward-guided fine-tuning for multi-task reinforcement learning (RL). The approach aims to train a generalizable diffusion-based planner that can e... | Rebuttal 1:
Rebuttal: Dear Reviewer aKBX,
We sincerely appreciate your precious time and constructive comments. In the following, we would like to answer your concerns separately.
**W1**: ...there are areas where it could improve...:
**W1.1**: ... analyzing ... performance degradation over long adaptation periods
... | null | null | null | null | null | null |
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