title stringlengths 15 163 | paper_decision stringclasses 4
values | review_1 stringlengths 853 32.6k | rebuttals_1 stringlengths 0 15.1k | review_2 stringlengths 1.03k 35.6k | rebuttals_2 stringlengths 0 15.1k | review_3 stringlengths 807 27.4k ⌀ | rebuttals_3 stringlengths 0 15k ⌀ | review_4 stringlengths 780 22.2k ⌀ | rebuttals_4 stringlengths 0 15.1k ⌀ | review_5 stringclasses 171
values | rebuttals_5 stringclasses 166
values | review_6 stringclasses 25
values | rebuttals_6 stringclasses 24
values | review_7 stringclasses 4
values | rebuttals_7 stringclasses 4
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving | Accept (poster) | Summary: This paper presents an evaluation framework inspired by Bloom’s Taxonomy, integrating multiple tasks to reflect diverse cognitive levels. The author evaluates popular general and medical LLMs, observing a significant performance decline in performance with the increasing of cognitive complexity.
Their findings... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful and constructive feedback. Below, we address each of the concerns you raised.
1. **Benchmark design issue** : We highly appreciate your kind comments. Indeed, the cognitive difficulty of a question is affected by multiple factors, including task type and qu... | Summary: This paper assesses large language models (LLMs) across multiple cognitive levels, based on Bloom's taxonomy that proposes six cognitive objectives/levels in ascending order of complexity. In particular, tasks pertaining to three cognitive levels (preliminary knowledge grasp, comprehensive knowledge applicatio... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful and constructive feedback. Below, we address each of the concerns you raised.
1. **Comparability between different cognitive levels**: Thank you for your thoughtful concern. Indeed, comparing LLMs’ performance across different cognitive levels is crucial fo... | Summary: The paper proposed a novel medical LLM evaluation benchmark inspired by Bloom's taxonomy. Different from existing benchmarks that only evaluate the LLM on one single style of QA tasks, the proposed benchmark constructs a multi-cognitive-level evaluation framework and provides more informative results. The prop... | Rebuttal 1:
Rebuttal: We sincerely appreciate your kind feedback as well as your recognition of our work. Below are our responses to each of the concerns you raised.
1. **Discussion with other LLM benchmark**: We sincerely appreciate your kind suggestions. Zhou et al. [1] proposed a medical evaluation framework to gen... | null | null | null | null | null | null | null | null |
Strong and Weak Identifiability of Optimization-based Causal Discovery in Non-linear Additive Noise Models | Accept (poster) | Summary: The manuscript introduces a criterion for strong vs. weak identifiability in causal graphs and explores the implications for optimization based structure discovery algorithms. Specifically, the authors propose a gradient-based approach whose objective combines a standard goodness of fit measure ($R^2$) with a ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback and insightful suggestions. Below, we address each point raised in the review.
### **Claims and Evidence**
1. **confusion about the strength of identifiability**
Thank you for raising this important point. Within the ANM framework—which is inher... | Summary: The paper tackles the problem of causal discovery in additive noise models. It identifies different classes of ANMs, strongly identifiable or weakly identifiable, that pose different levels of difficulty for traditional discovery algorithms. It characterizes sufficient conditions for the different classes and ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback and insightful suggestions. Below, we address each point raised in the review.
### **Claims and Evidence**
1 and 3 **Claims about Simple Regression's Sufficiency for Strongly Identifiable Problems**
The experiments in Fig. 4 show that continuou... | Summary: The paper considers nonlinear ANMs for observational data, providing new structural identifiability results based on "implicit functions". Using these results, it proposes a learning algorithm that provably learns the correct order (in the large sample limit) and then heuristically prunes down to a sparse grap... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback and insightful suggestions. Below, we address each point raised in the review.
### **Claims and Evidence**
1. **Identifiability vs. Consistency**
Thank you for highlighting this distinction. We agree that identifiability and consistency are d... | Summary: This paper proposes to further divide the structure identifiability of ANM into strong one and weak one. The authors also proposes GENE, a generic method for causal discovery that works for both cases. The method is validated by both synthetic and real life data experiments.
## update after rebuttal
Thank yo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback and insightful suggestions. Below, we address each point raised in the review.
### **Claims and Evidence**
1. **Optimality of Eq. 6**
Thank you for this constructive suggestion. We agree that Equation (6) could benefit from refinements like fam... | null | null | null | null | null | null |
Diversifying Robot Locomotion Behaviors with Extrinsic Behavioral Curiosity | Accept (poster) | Summary: This paper introduces Quality Diversity Inverse Reinforcement Learning (QD-IRL), a framework that integrates quality-diversity (QD) optimization with inverse reinforcement learning (IRL) to enable robots to learn diverse locomotion behaviors from limited demonstrations. The key innovation is Extrinsic Behavior... | Rebuttal 1:
Rebuttal: ## We feel gratitude for the valuable insights and suggestions, and below is our detailed response.
- - -
**Q1**: More complex imitation tasks can enhance the expressiveness of the method.
**A1**: We have added experiments with additional tasks and measure. Please refer to our response to Q3 of ... | Summary: This work proposes a new paradigm called Quality-Diversity Inverse Reinforcement Learning (QD-IRL) as well as a new component to encourage the acquisition of novel and diverse behaviors, called Extrinsic Behavioral Curiosity (EBC). The goal of the QD-IRL framework is to enable the agent to learn diverse and pe... | Rebuttal 1:
Rebuttal: ## We appreciate the valuable insights and suggestions, and below is our detailed response to address your concerns.
- - -
**Q1**: More diverse tasks other than locomotion will be beneficial.
**A1**: While we limit the scope of the experiments to robot locomotion tasks, we do agree with the incl... | Summary: This paper proposes to combine Quality Diversity (QD) algorithms with Inverse Reinforcement Learning (IRL) problems. The authors introduce Quality Diversity Inverse Reinforcement Learning (QD-IRL), a method that uses rewards estimated from demonstrations (via GAIL, VAIL, and DiffAIL) with the PPGA quality dive... | Rebuttal 1:
Rebuttal: ## Thank you for the valuable suggestions. Here is our response to questions and concerns.
- - -
**Q1**. This paper lacks evidence support for the significance of QD (e.g. the ability of adaptation).
**A1**. The claim that QD approaches help with adaptation in changing environments is reasonabl... | Summary: Existing imitation learning algorithms fail to learn diverse behaviors. To address this, the paper introduces the QD-IRL framework that applies QD optimization algorithms to IRL problems. To further improve the exploration of QD-IRL, the paper introduces Extrinsic Behavioral Curiosity (EBC) to encourage polici... | Rebuttal 1:
Rebuttal: ## Thanks for the valuable insights and suggestions.
- - -
**Q1.** It would be better to discuss the relationship between Yu et al.'s paper.
**A1.** The key difference between Yu’s work and our work lies in two aspects:
- Yu’s work adopts a **single-step** reward bonus (calculated per (s,a) pai... | null | null | null | null | null | null |
SPHINX: Structural Prediction using Hypergraph Inference Network | Accept (poster) | Summary: SPHINX proposes an unsupervised framework for latent hypergraph inference that models higher-order interactions directly from point-wise data. The method employs a sequential slot attention mechanism to predict hyperedge probabilities and uses differentiable k-subset sampling to convert these probabilities int... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback on our work. Below, we address the key points raised and we will incorporate all suggestions into the final version of the paper.
**Selecting the number of hyperedges M**
Having a fixed maximum number of hyperedges is a limitation that we are sharin... | Summary: The authors focus on unsupervised hypergraph inference. Three key desiderata are identified: applicability to a broad type of tasks, compatibility with any hypergraph process architecture, and ease of optimization. They propose SPHINX that adapts the slot attention for sequential hyperedge prediction, and is d... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s comments and feedback. We thank the reviewer for pointing out Liao et al, which shares similarities with our work in sequential structure prediction and offers inspiration for future improvements. We will include a discussion in the paper.
**Different hyperedge sizes*... | Summary: This paper introduces the SPHINX model, which aims to infer a latent hypergraph structure suitable for the final task in an unsupervised manner from input features, to support higher-order relationship processing in the absence of a readily available hypergraph structure. The process is divided into three step... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed review and valuable feedback on our paper. We would like to address your concerns and questions.
**Hypergraph evaluation for real-world datasets**
Quantitatively evaluating the accuracy of predicted latent hypergraphs in real-world dataset is a very importan... | Summary: The paper introduces SPHINX, a novel model designed to infer latent hypergraph structures in an unsupervised manner solely from task-dependent signals. Recognizing the limitations of traditional graph models in capturing higher-order interactions, SPHINX employs a sequential soft clustering approach combined w... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and constructive feedback. We appreciate the time you've taken to review our work, and we would like to address your concerns and questions.
**Fixed hyperedge size**
We want to mention that, while the cardinality k is indeed non-learnable, SPHINX does allow hyp... | null | null | null | null | null | null |
TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction | Accept (poster) | Summary: This paper presents TraceGrad, a strategy to learn SO(3)-equivariant Hamiltonian with SO(3)-invariant trace as a guiding label, built upon their mathematical relations. It aims to overcome the tradeoff between SO(3)-equivariance constraints and NNs’ nonlinear expressiveness. TraceGrad brings improvement to bas... | Rebuttal 1:
Rebuttal: 1.
We will correct the quote errors and reduce redundancy in the discussion.
2.
In this work, we explicitly introduce the trace quantity, i.e., the square of the Frobenius norm of the Hamiltonian $\mathbf{H}$, as an SO(3)-invariant quantity in our neural network. In physics, invariants often r... | Summary: The author propose a new technique to enhance the non-linear expressiveness for equivariant architectures. The idea is simple and straightforward, first generate an invariant feature (like energy) and equivariant feature (like position). The gradient of the invariant feature (energy) with respect to the equiva... | Rebuttal 1:
Rebuttal: 1.**We have found that our TraceGrad method also significantly improves energy/force prediction tasks** (please refer to the 3rd item of our response to Reviewer AGnk), demonstrating the generality of our method. We plan to follow the reviewer's suggestion and validate our method on more molecular... | Summary: This paper introduces TraceGrad, a framework that integrates strong non-linear expressiveness with strict SO(3)-equivariance for electronic structure Hamiltonian prediction. The approach first constructs theoretical SO(3)-invariant trace quantities derived from Hamiltonian targets, using them as supervisory si... | Rebuttal 1:
Rebuttal: 1.**Response to the reviewer's question about the computational burden**:
First, the branch decoding the trace quantity $\mathbf{T}$ from the SO(3)-invariant features $z$ is only required during the training phase and does not need to be activated during inference; thus, the parameters associated... | Summary: This paper proposes to enhance the Hamiltonian prediction networks with additional invariant supervision and an additional gradient branch. The authors observe that the trace of $H H^T$ is rotation invariant and can be used to supervise the learning of zero order features. Additionally, the gradient of a netwo... | Rebuttal 1:
Rebuttal: 1.**Clarification regarding whether the gradient mechanism can change the direction of features**: In Theorem 2 of our paper, for simplicity and to highlight the core ideas, we selected $\textbf{f}$ as a basic feature component of degree $l$. In this case, applying the gradient operation directly... | null | null | null | null | null | null |
Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting | Accept (poster) | Summary: The paper proposes an approach to learn the weights of an ensemble of forecasting models based on meta-features of datasets. The approach first featurizes a time-series dataset then predicts optimal weights of models and average them to obtain predictions. The model is trained with a collection of datasets to ... | Rebuttal 1:
Rebuttal: **Thank you for the thoughtful review and constructive feedback! With our best efforts, we conducted numerous additional analyses to address your concerns. The full results are at https://anonymous.4open.science/api/repo/ICML25Reb-034C/file/Results.pdf?v=b00206ad. Please understand that due to the... | Summary: This paper introduces TIMEFUSE, a novel framework designed for adaptive fusion of multiple heterogeneous forecasting models to enhance time series forecasting. Key findings indicate that no single model consistently outperforms others across all samples; each excels in specific scenarios. The method addresses ... | Rebuttal 1:
Rebuttal: **Thank you for your recognition and thoughtful review! We've conducted extensive analyses to address your concerns. Full results: https://anonymous.4open.science/api/repo/ICML25Reb-034C/file/Results.pdf?v=b00206ad. Please understand that due to the 5000-character limit, we can only summarize key ... | Summary: The manuscript introduces TimeFuse, a fusion model for time series fusion model. Specifically, TimeFuse uses the outputs of a model zoo and uses meta features of input time series to train a fusion model that predicts the ensemble weights of the individual models from the model zoo. The meta feature set uses v... | Rebuttal 1:
Rebuttal: **Great thanks for your thoughtful review and constructive feedback! With our best efforts, we conducted numerous additional analyses to address your concerns. Full results: https://anonymous.4open.science/api/repo/ICML25Reb-034C/file/Results.pdf?v=b00206ad. Please understand that due to the 5000-... | Summary: This paper proposes TIMEFUSE, a framework designed to improve time-series forecasting accuracy by adaptively fusing the predictions of multiple (pre-)trained forecasting models. The core idea is to train a “fusor” model that predicts a suitable set of fusion weights based on a set of expert-designed meta-featu... | Rebuttal 1:
Rebuttal: **Thank you for the thoughtful review and constructive feedback! With our best efforts, we conducted numerous additional analyses to address your concerns. Full results: https://anonymous.4open.science/api/repo/ICML25Reb-034C/file/Results.pdf?v=b00206ad. Please understand that due to the 5000-char... | null | null | null | null | null | null |
CommVQ: Commutative Vector Quantization for KV Cache Compression | Accept (poster) | Summary: This paper introduces CommVQ, which sigificantly reduces KV cache memory in long-context LLMs while preserving accuracy. It uses additive quantization with a lightweight encoder and a RoPE-commutative codebook for efficient self-attention integration. In practice, CommVQ enables 1-bit KV cache quantization wit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and thoughtful suggestions.
**1. Full Longbench and Needle-in-a-Haystack results**
The full LongBench consists of 21 tasks in total. Following prior works such as KIVI and KVQuant, we report results on the same eight representative tasks for fair c... | Summary: This paper proposes a novel method, CommVQ, for compressing the KV cache in LLMs. The core innovation lies in using additive vector quantization—treating each token’s key/value vector as a unit rather than quantizing individual scalars—and designing a “commutative” codebook that allows efficient integration wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive feedback. Below, we address the key concerns raised.
**1. Latency comparison**
Please see our response to **Reviewer 86AS (2. Latency comparison with the FP16 baseline and prior methods such as KIVI)** for a detailed latency comparison. In ... | Summary: * This paper leverages additive quantization by introducing a lightweight encoder and codebook to compress the KV cache, which can then be decoded with a simple matrix multiplication.
* The authors design a commutative with Rotary Position Embedding (RoPE), and utilize an ExpectationMaximization (EM) algorith... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and positive assessment of our direction and formulation. Below, we address the key concerns raised.
**1. Experiments on larger models (e.g., 70B)**
We focused on 8B models (LLaMA-2, Mistral, and LLaMA-3.1) due to their popularity and our resour... | Summary: In general, this paper quantizes the KV cache into a 1-bit representation and then uses the 1-bit representation to combine some basis vectors for the attention process. Intuitively, it is equivalent to decomposing the KV cache into a combination of a finite number of basis vectors to speed up the overall calc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and helpful suggestions. Below, we address your concerns.
**1. Applicability to retrieval tasks such as RULER**
We appreciate the suggestion to evaluate additional retrieval-specific tasks. We have included results on the **RULER** benchmark usi... | Summary: This paper introduces CommVQ, a novel approach to compress the KV cache during inference, particularly when processing long contexts. Unlike existing scalar-based quantization methods, CommVQ employs vector quantization at the token level using a learned encoder and codebook approach. CommVQ makes two key inno... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback and insightful suggestions. Below, we address your concerns.
**1. CommVQ applied to latest models such as Qwen-2.5**
To evaluate our method's generalization to the latest models, we applied CommVQ to the **Qwen-2.5 7B** model and evaluated it on LongBench. Du... | null | null | null | null |
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach | Accept (poster) | Summary: Conformal Prediction (CP) provides prediction intervals with guaranteed coverage for black-box models under exchangeability assumptions. However, cellwise outliers isolated contaminated entries in test features break this exchangeability, leading to unreliable PI. This paper addresses this challenge by introdu... | Rebuttal 1:
Rebuttal: >**Q1**: Author should highlight the purpose of this paper instead of giving the notation in introduction.
We sincerely appreciate this constructive critique of our manuscript's organization. In response to your suggestion, we will comprehensively restructure the introduction in the future revisi... | Summary: This paper addresses conformal prediction with feature-wise outliers in the test sample. It assumes access to a detection oracle satisfying the sure detection and isolated detection assumption, and impute the values of the outlier features. After the detection and imputation procedure, split conformal predicti... | Rebuttal 1:
Rebuttal: >**Q1**:Concerns focus on Assumptions 3.1 and 3.2, assuming detection with FNR=0. Assumption 3.2, crucial for finite length prediction interval, also excludes joint feature space outliers. Figure 2 shows FDR is around 0.4 , indicating unmet assumptions.
Thanks for your valuable questions! We ackn... | Summary: This paper proposes a DI-CP framework to handle cellwise outliers in conformal prediction. The key idea is first to detect outliers in the test feature vector and then impute them before applying conformal prediction. To maintain exchangeability, a similar detection-imputation process is used to calibration sa... | Rebuttal 1:
Rebuttal: >**Q1**: Assumption 3.1 is impractical. How does coverage degrade when detection is imperfect?
Thank you for your insightful question!
- We acknowledge the imperfection of Assumption 3.1 and have obtained a new coverage gap bound in total variation: if there are still outliers in test point $\ti... | Summary: When some entries of the test features are contaminated, the paper introduces a detect-then-impute conformal prediction framework. This framework first applies an outlier detection procedure to identify contaminated entries in the test features and then uses an imputation method to fill in the identified outli... | Rebuttal 1:
Rebuttal: >**Q1**: The validity of Assumption 3.1 (sure detection) depends on quality of detection, which may be restrictive in practice.
Thanks for your valuable question! We acknowledge the imperfection of Assumption 3.1 and make the following explanations.
- Sure detection/screening conditions are commo... | null | null | null | null | null | null |
Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective | Accept (poster) | Summary: This paper proposes new generalization bounds for fair machine learning. Based on the Mutual Information framework, these bounds show that the important factors governing fairness generalization are the size of the different subgroups and the mutual information between the distribution of hypothesis and subset... | Rebuttal 1:
Rebuttal: ## Mismatch in Fairness Definitions for the Multiclass Problem
In our original formulation for the binary case, the prediction function $f =\hat{Y}$ outputs values in $\{0,1\}$ (i.e., a=1). For multiclass problems, we allow $f$ to take values in a bounded range $[0,a]$; however, the choice of $a... | Summary: The paper considers the generalization of (in terms of empirical violation of) fairness when presented with unseen data. Specifically, the goal is to provide a formal guarantee through information-theoretic fairness generalization bounds with mutual information (MI) and conditional mutual information (CMI). Th... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful question about deriving lower bounds within our theoretical framework, especially considering existing challenges in achieving group-level fairness notions like Equalized Odds (EOdds).
In our work, we focus on understanding how fairness measures observed during traini... | Summary: This paper studies fairness generalization error, i.e., how does model fairness extend to new, unseen data. The fairness generalization error is defined (in Eq. (3)) as the discrepancy between the fairness-population risk and the fairness-empirical risk. The authors study this from an information theory perspe... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful feedback on our work. Below, we address each concern point by point.
**Motivation:** We respectfully disagree that there is a lack of motivation to study fairness generalization. As demonstrated in Figure 1, i) Compared to ERM, when fairness inter... | null | null | null | null | null | null | null | null |
SNS-Bench: Defining, Building, and Assessing Capabilities of Large Language Models in Social Networking Services | Accept (poster) | Summary: This paper introduces a benchmark dataset SNS-Bench to evaluate LLMs' capabilities in social networking service. The dataset consists of 8 NLP tasks centering around user postings that compiled from the REDnote social networking platform. The authors evaluate over 25+ closed and open sourced LLMs on SNS-Bench ... | Rebuttal 1:
Rebuttal: **R4.Q1: Broader Social Networking Context**
Thank you for the insightful observation. The construction does incorporate key social interactions:
1. **Data Collection Pipeline (Section 3.2)**
- User engagement metrics reflecting community response
- Note categories and tags chosen ... | Summary: The paper introduces SNS-BENCH, a comprehensive benchmark for evaluating large language models in social networking service tasks. It covers eight diverse tasks—from note taxonomy and sentiment analysis to query generation and entity recognition—using a dataset of 6,658 questions sourced from a major social pl... | Rebuttal 1:
Rebuttal: **R3.Q1: Multimodal limitation.**
We sincerely appreciate the reviewer’s constructive feedback. The reviewer is absolutely right to highlight the importance of multimodal interactions in modern SNS platforms. In fact, we are already working on SNS-Bench-V2, which will incorporate image-text pairs ... | Summary: This paper aims to advance LLM models for Social Networking Services(SNS) by introducing a comprehensive benchmark SNS-BENCH derived from a social media platform, addressing the limitation of studying SNS in isolated tasks in prior work. The benchmark includes eight distinct tasks, such as note classification,... | Rebuttal 1:
Rebuttal: **Response to Review 2:**
Thank you for your insightful feedback.
**R2.Q1: Isolated evaluation of SNS tasks.**
We acknowledge the concern regarding task isolation in SNS-Bench and appreciate the opportunity to clarify our design rationale.
1. Task-Specific Evaluation Necessity:
Social networking... | Summary: This paper presents SNS-Bench, to access LLMs on different social networking services. It includes 8 tasks such as query content relevance. It evaluates 25+ LLMs and provide further insights.
## update after rebuttal
I maintain my score in support of the work after rebuttal.
Claims And Evidence: The central ... | Rebuttal 1:
Rebuttal: **Response to Review 1**:
**R1.Q1: Font size in Table 2.**
Thank you for your helpful suggestion. We will adjust the font size and optimize the layout of Table 2 (e.g., using multiple rows for tasks if needed) to improve readability in the revised version.
---
Rebuttal Comment 1.1:
Comment: Tha... | null | null | null | null | null | null |
Aggregation Buffer: Revisiting DropEdge with a New Parameter Block | Accept (poster) | Summary: This paper revisit dropedge, it claims the robustness of GNNs will grow bad during training, which yields poor performance. This paper propose aggregation buffer, a block designed to address this problem.
## Update after rebuttal
I recommend accept as a poster.
Claims And Evidence: Yes.
The main claim is t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed review and valuable questions. We hope the following responses address your concerns.
**Why the two conditions are enough for the bias-robustness trade-off is unclear.**
Thank you for raising this insightful point.
Our two conditions and the layer-wise corr... | Summary: This paper analyzes the robustness of GNN under dropping edges and proposes Aggregation Buffer (AGGB) as a solution, which enhances the robustness of GNN through a two-step training strategy while maintaining the knowledge of the original model. AGGB optimizes the shortcomings of DropEdge and improves the perf... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed review and valuable questions. We have reordered your questions since we believe Q1 and Q3 are closely related. We hope the following responses adequately address your concerns.
**Q2. Does the current design of AGG$_B$ which relies on all preceding representa... | Summary: This paper revisits DropEdge, a data augmentation technique for GNNs that randomly removes edges to enhance robustness. While DropEdge helps mitigate overfitting, its performance gains in supervised learning are limited due to an inherent inductive bias in GNN architectures. To address this, the authors propos... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review. Our response is organized around three points: (1) novelty, (2) ablation study on the AGG$_B$ design, and (3) additional experiments.
**Q1. The novelty of the method is limited. For example, two-stage training with DropEdge is proposed in TUNEUP.**
... | Summary: This paper analyzes DropEdge, which is widely used in GNNs. it shows that DropEdge has limited effectiveness for GNNs. Through theoretical analysis, the authors show the limitation comes from fundamental constraints in GNN architectures. They propose "Aggregation Buffer" (AGGB), a parameter block that can be a... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed review and insightful questions. We hope the responses below address your concerns.
**Revising the sentence explaining the way of data split**
Thank you for your thoughtful suggestion. We acknowledge that for datasets such as Cora, Citeseer, and Pubmed, the ... | null | null | null | null | null | null |
Predicting the Susceptibility of Examples to Catastrophic Forgetting | Accept (poster) | Summary: This paper reports on a large volume of observations regarding the learning speed and catastrophic forgetting in the context of continual learning, and proposes a new sampling strategy called SBS to improve the replay-based continual learning methods. The experiments consistently show the effectiveness of the ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate your recognition of our extensive empirical study and the practical value of our findings.
Regarding the connection between learning speed and catastrophic forgetting, our primary contribution is identifying and characterizing this phenomenon. W... | Summary: The manuscript addresses the challenge of selecting the most relevant examples to store in a memory buffer for rehearsal-based continual learning. Based on a preliminary analysis on the speed at which examples are learned and forgotten, the manuscript finds that the most complex samples are the fastest to be f... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for your willingness to consider raising your score. Below, we address your concerns in detail.
**concern 1**
The advantages of picking $q=s=20\%$ across different settings are scattered throughout the figures of the paper: see Figs 4(a-b), 16(a-b), 17(a-... | Summary: In this work, the authors investigate catastrophic forgetting from a behavioral perspective, observing the connection between learning speed and forgetting: examples learned more quickly tend to be more resistant to forgetting. Motivated by the observation, this paper introduces Speed-Based Sampling (SBS), a ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate that you found our analysis comprehensive and our results well-supported. Below, we address your comments in detail:
**Comparison with non-uniform sampling in continual learning methods**
Most competitive continual learning methods rely on rand... | null | null | null | null | null | null | null | null |
A Multi-Region Brain Model to Elucidate the Role of Hippocampus in Spatially Embedded Decision-Making | Accept (poster) | Summary: This work studies recent neurophysiological results through the use of computational modeling and reinforcement learning. The authors consider several different variants of the model (varying the connectivity and coding properties) and find only certain of these models achieve high performance and similarities... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their precious time and comprehensive feedback. We deeply appreciate their recognition of the significance of our work. In summary, the reviewer raised an insightful question regarding experimental v.s. model data comparison to strengthen our claims, and our int... | Summary: This paper introduces a series of models to investigate how animal brains may solve the accumulating towers task. Beginning with a simple RNN, the authors add model components until they arrive at extensions of the Vector-HaSH model of the hippocampus that also include a cortical model component. The authors n... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their precious time, and their comprehensive feedback. We deeply appreciate their recognition of the significance and clarity of our work. In summary, the reviewer raised an insight regarding the role of hyperparameters, and made additional comments to help us e... | Summary: This work is motivated to implement efficient reinforcement learning (RL) inspired by computation in hippocampus. It develops a multi-region brain model that incorporates hippocampal-entorhinal circuit based on the Vector-HaSH model. It shows a structured, content-addressable associative memory with neural rep... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer’s precious time and comprehensive feedback. We deeply appreciate their generous recognition of the significance of our work. The reviewer shared an interesting insight regarding comparison with advanced AI models, and had a clarifying question on how our setup and f... | Summary: This paper aims at providing a mechanistic characterisation of how sensory and abstract (task-dependent) information is encoded and transmitted across different brain regions, including the hippocampus (HPC), medial and lateral entorhinal cortex (mEC and lEC), and the cortical circuity. The authors specificall... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer’s precious time & comprehensive feedback. We sincerely thank them for recognizing the significance & clarity of our work. The reviewer raised questions on the support for our claims, method novelty, and the biological grounding of models. Here we clarify with the ... | null | null | null | null | null | null |
ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning | Accept (poster) | Summary: The paper proposes ZebraLogic, a newly developed benchmark dataset of logic grid puzzles derived from constraint satisfaction problems. The authors systematically evaluate LLM performance across different levels of problem complexity. The authors show "curse of complexity", where model accuracy declines signi... | Rebuttal 1:
Rebuttal: Thank you for your review! We value your constructive feedback and will address your suggestions in the revised version.
---
#### Q1: Why Self-Refinement is promising? ...
Both self-refinement and majority voting are methods aimed at enhancing the reasoning performance of LLMs, and they are ort... | Summary: The paper introduces ZebraLogic, a benchmark dataset of 1,000 logic grid puzzles derived from constraint satisfaction problems (CSPs), to evaluate the scalability of large language models (LLMs) in complex non-monotonic reasoning. Key findings include the curse of complexity, and scaling model size or test-ti... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review! We address each of the concerns and questions raised, and we will incorporate these clarifications and additional analyses into the camera-ready version of the paper. We hope our responses adequately resolve the issues highlighted and kindly ask for ... | Summary: The paper introduces ZebraLogic, a benchmark of logic grid puzzles derived from constraint satisfaction problems (CSPs), to evaluate the logical reasoning capabilities of LLMs. Key findings include:
1. Curse of Complexity: LLM performance declines sharply as puzzle complexity (measured by search space size an... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review! We address each of the concerns and questions raised, and we will incorporate these clarifications and additional analyses into revision of the paper. We hope our responses adequately resolve the issues highlighted and kindly ask for your reconsidera... | Summary: This paper investigates the logical reasoning capabilities of large language models (LLMs) by introducing ZebraLogic, a benchmark dataset of 1,000 logic grid puzzles. These puzzles are formulated as constraint satisfaction problems (CSPs) with controlled complexity levels, allowing for systematic evaluation of... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review! We address each of the concerns and questions raised, and we will incorporate these clarifications and additional analyses into revision of the paper. We hope our responses adequately resolve the issues highlighted and kindly ask for your reconsidera... | null | null | null | null | null | null |
MIB: A Mechanistic Interpretability Benchmark | Accept (poster) | Summary: This paper proposes a benchmark, called MIB, to evaluate whether the interpretability algorithm precisely and concisely recovers relevant
causal pathways or specific causal variables. MIB includes two tasks: 1) circuit localization which identifies important connections in the model to perform a task and 2) ca... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We have a detailed plan to address your points about readability and presentation. If accepted, will we use the additional page to incorporate this material to the main text.
> We use faithfulness, but should also discuss completeness, minimality, and human-i... | Summary: The authors proposed a benchmark dataset for Mechanistic Interpretability (MI). The dataset consists of four tasks: 1) Indirect Object Identification (IOI), 2) Arithmetic with two digits, 3) Multiple-Choice Question Answering (MCQA), and 4) AI2 Reasoning Challenge (ARC). The goal of the benchmark is to test fo... | Rebuttal 1:
Rebuttal: Thank you for appreciating the value of our paper’s contribution and the validity of our experimental designs. We note your points about readability and presentation, and appreciate your willingness to reconsider your score on the basis of correcting them. We will make a number of changes to impro... | Summary: Mechanistic Interpretability (MI) research has made significant strides, but it has lacked a consistent way of comparing methods. In this paper, the authors introduce Mechanistic Interpretability Benchmark (MIB), which splits the evaluation into two main tracks: Circuit Localization and Causal Variable Localiz... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of the thoroughness of our experiments, the convincingness and clarity of our claims, and the design of our metrics!
> Regarding task selection rationale and task coverage:
We aimed to strike a balance between having tasks (1) of diverse difficulties and re... | Summary: The authors introduce a benchmark designed to standardize evaluations of mechanistic interpretability (MI) methods. This benchmark offers consistent evaluation across standardized models, metrics, and intervention datasets, with two public leaderboards tracking method performance. The benchmark is divided into... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and for acknowledging the value of our benchmark’s practical utility and contribution to the literature! We’d like to clarify our scientific contributions; they include 1) new metrics, and 2) new empirical results (L432-435 Col.2), facilitated crucially by 3) ou... | null | null | null | null | null | null |
Mind the Gap: a Spectral Analysis of Rank Collapse and Signal Propagation in Attention Layers | Accept (poster) | Summary: In this paper, the authors discuss the phenomenon of rank collapse in width (i.e., for asymptotically large context length) in transformers at initialization. The authors point at the spectral gap in the attention matrix as the main cause of such collapse, and they devise a simple solution to remove the gap an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough comments and their appreciation of our contribution. We address the points in the same order. For additional figures (indicated by Roman numerals), please see https://anonymous.4open.science/r/spectral_analysis_transformers-C633/figures_rebuttal.pdf.
1. Th... | Summary: The authors study attention layers randomly initialized, looking at signal propagation or exploding/vanishing gradient issues from a rank perspective. Notably, using random matrix theory tools, they identify a new rank collapse that occurs in width, i.e., in the context size. Via a careful theoretical analysis... | Rebuttal 1:
Rebuttal: Thanks for your kind support of our theoretical development and for highlighting the "SAMformer" paper "SAMformer" that we looked into and will cite. For extra figures (indicated by Roman numerals), see https://anonymous.4open.science/r/spectral_analysis_transformers-C633/figures_rebuttal.pdf.
+ ... | Summary: The paper shows that random attention layer stacks exhibit rank collapse in width (context length and latent dimension) by analyzing the spectral gap of the corresponding random matrices. They then propose a fix that replaces the attention matrix with another related matrix without spectral gap (in the limit) ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive view of our work. We are happy to see that they value our contribution. We would like to address the points they raised as weaknesses:
+ The reviewer rightly points out that the direct connection between the pathological rank collapse behaviour at initialis... | Summary: This paper studies signal/propagation in transformers with softmax-attention at initialization. Prior work has observed rank collapse in depth (in various model architectures), which causes all tokens to converge to a single representation. It has been attributed to repeated matrix multiplications, and is know... | Rebuttal 1:
Rebuttal: Thank you for your comments. Those with an editorial nature are applied in the revised draft, so we will address their main concern regarding assumptions. For additional figures (indicated by Roman numerals), please see https://anonymous.4open.science/r/spectral_analysis_transformers-C633/figures_... | null | null | null | null | null | null |
Memorization Sinks: Isolating Memorization during LLM Training | Accept (poster) | Summary: The paper studies memorization vs general capabilities in models by introducing their Sequence-Tied Dropout (SeqTD). This method a pool of shared neurons and a set of memorization neurons. They use the sequence ID to determine which memorization neurons to use. Note that there is some overlap between the seque... | Rebuttal 1:
Rebuttal: Thank you for the sharp and honest feedback. We're happy that you found:
- (i) our ablations **insightful and well-done**,
- (ii) the method **interesting for isolating memorization**, and
- (iii) the paper a **valuable starting point** for deeper investigation.
---
### **Theme 1: Scale & Trai... | Summary: The paper presents an investigation into sequence memorization in large language models (LLMs) and introduces Sequence-Tied Dropout (SeqTD) as a novel method to isolate memorization while maintaining generalization capabilities. The key argument is that typical memorization is not confined to specific neurons ... | Rebuttal 1:
Rebuttal: Thank you for the detailed and insightful feedback. We really appreciate that you found:
- (i) SeqTD to be a **simple yet novel idea**,
- (ii) the **methodology rigorous**, and
- (iii) our **ablations helpful and revealing**.
We've made specific additions to respond to your concerns.
---
###... | Summary: This work tackles privacy risks in LLMs from memorizing repeated sequences. Current post-hoc neuron isolation methods fail for data entangled with general capabilities. The authors propose SeqTD, a training method splitting neurons into shared (generalization) and memorization groups. By activating fixed memor... | Rebuttal 1:
Rebuttal: Thank you for your constructive and balanced review. We're glad you found:
- (i) the **formulation of SeqTD clear and valuable**,
- (ii) our **experiments well-designed within the scope**, and
- (iii) the **motivation around privacy and unlearning promising**.
We've carefully addressed your m... | Summary: The paper introduces a training strategy called Sequence-Tied Dropout (SeqTD) for large language models that aims to isolate memorized sequences into a specific subset of neurons while still allowing the model to learn general language patterns. The authors argue that standard training causes memorization to b... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive review. We're glad that:
- (i) you found our **theoretical analysis sound**,
- (ii) appreciated the **clarity and motivation of our experiments**, and
- (iii) recognized the novelty of using dropout for disentangling memorization from generalizati... | null | null | null | null | null | null |
From Language Models over Tokens to Language Models over Characters | Accept (spotlight poster) | Summary: The paper presents algorithms to convert token-level language models to character-level ones, addressing the prompt boundary problem. It introduces the concept of covering and proposes both exact and approximate algorithms. The main findings include that the method can accurately approximate the character-leve... | Rebuttal 1:
Rebuttal: ### General response
Many reviewers suggested that our evaluation methodology, which uses a large beam as a proxy for ground truth character-level probabilities, may have some systematic bias. We will add discussion to the paper about the challenge of designing a faithful evaluation as well as th... | Summary: The paper highlights the fact that language models over tokens are _not_ language models over characters, at least the way they are normally used. To be specific, the standard procedure of taking a prompt, tokenizing it, and then sampling from the model conditioned on the token sequences is _not_ the same thin... | Rebuttal 1:
Rebuttal: ### General response
Many reviewers suggested that our evaluation methodology, which uses a large beam as a proxy for ground truth character-level probabilities, may have some systematic bias. We will add discussion to the paper about the challenge of designing a faithful evaluation as well as th... | Summary: This paper presents an algorithm for converting token-level language models for character level language models. The authors present compelling analysis as well as detailed explanation. This work also includes practical experimental evaluation results.
Claims And Evidence: The claims are backed by convincing ... | Rebuttal 1:
Rebuttal: ### General response
Many reviewers suggested that our evaluation methodology, which uses a large beam as a proxy for ground truth character-level probabilities, may have some systematic bias. We will add discussion to the paper about the challenge of designing a faithful evaluation as well as th... | Summary: This paper is motivated by addressing the "prompt boundary problem" in token-level language models. In models using tokenizers like BPE, even small changes at the prompt boundary, such as adding a whitespace, can dramatically alter the next token distribution in unintuitive ways, which is undesired behavior.
T... | Rebuttal 1:
Rebuttal: ### General response
Many reviewers suggested that our evaluation methodology, which uses a large beam as a proxy for ground truth character-level probabilities, may have some systematic bias. We will add discussion to the paper about the challenge of designing a faithful evaluation as well as th... | null | null | null | null | null | null |
Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization | Accept (poster) | Summary: CBMs aim to improve explainability of models by making decisions based on human-interpretable concepts but often suffer from mislabeled concept data, leading to significant performance drops. To address this, the Concept Preference Optimization (CPO) objective is introduced, leveraging Direct Preference Optimi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and helping us improve our work.
`Consider visualizing gradient behavior over time...`
We agree with the reviewer that this figure can aid in illustrating our claim. A version of it is already in App D, where we empirically confirm our theoretical result... | Summary: One limitation of concept bottleneck models (CBMs) is that their training requires the set of correct concept annotations for all samples. However, concept mislabelling is inevitable due to labeling noise or subjective annotations. To this end, this paper proposes a CBM that is robust to concept-label noise. S... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and encouragements. Here we provide some detailed responses to some of the questions you have raised.
`In Eq (6), should $D$ be $\mu$ ?`
Yes, that is a notational mistake on our end. We will fix it.
`1. In Eq. (5), are c and c′ scalars or k-dimensional... | Summary: The paper proposes training CBMs (and its variants) using Concept Preference Optimization (CPO) - a method directly borrowing from the Preference Optimization (PO) literature
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: Yes.
Experimental Designs Or Analyses: Yes, all... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their time and effort in helping us improve our work.
### W 1
We agree. We will address these concerns in the camera-ready version. We discuss the details in comment 2 of our response to reviewer **2knp**.
### W 2
We also agree (thank you). As you and o... | Summary: The paper proposes a Preference Optimization (PO) based training Paradigm - CPO for training CBMs. The paper gives a detailed analysis of the proposed method and has experiments on intervention and random label flips. The preference set is taken as observed empirical evidence while the negative sampling of con... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their time and effort in helping us improve our work.
## Weaknesses
### 1 Diverse Experiments to Strengthen Conclusions
1.1 Thank you for this valuable feedback. We agree that incorporating experiments with more structured noise would strengthen our conclusio... | null | null | null | null | null | null |
SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval | Accept (poster) | Summary: SCENIR is a novel unsupervised scene graph-based retrieval framework that prioritizes semantic content over low-level visual features. SCENIR uses a Graph Autoencoder to eliminate the need for labeled data. It outperforms vision-based, multimodal, and supervised GNN approaches in both accuracy and efficiency. ... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer PwzX's thoughtful feedback and their recognition of the validity of our method and the soundness of our evaluation. We now provide clarifications for each of the concerns raised.
- W1: We appreciate the reviewer’s suggestion regarding Figure 1 and would like to cl... | Summary: The paper introduces an unsupervised framework for scene graph retrieval using GNN to prioritize semantic content over low-level visual biases. It employs a graph autoencoder to learn scene graph embeddings without labeled data and advocates for Graph Edit Distance as a deterministic evaluation metric.
Claims... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer tG8k for their thoughtful comments and for acknowledging the strengths of our approach, methodology, and evaluation process. We now address your concerns systematically
- W1: To address the reviewer’s valuable concern regarding the comprehensiveness of the datasets use... | Summary: This paper presents SCENIR, an unsupervised framework for scene graph retrieval that aims to improve semantic understanding in image-to-image retrieval tasks. It introduces a Graph Autoencoder-based architecture, eliminating the dependence on supervised ground truth labels like captions, which suffer from vari... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer kfau for their thorough feedback and for recognizing the validity of our evaluation methods, the soundness of our experiments, and the clarity of our presentation. We address their reported limitations and respond to their questions below.
- Other comments and suggesti... | Summary: This paper tackles the problem of image-to-image retrieval, focusing on improving the retrieval performance by emphasizing semantic content over low-level visual features. The authors argue that current models often rely on visual biases (e.g., colors, lighting conditions) rather than semantic contents/underst... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer s1fN for their thoughtful feedback and for the care they took in understanding our claims and evaluation methods. We greatly appreciate their recognition of the validity of our work and their positive assessment of its presentation. We would like to address their main c... | null | null | null | null | null | null |
Are High-Quality AI-Generated Images More Difficult for Models to Detect? | Accept (poster) | Summary: This paper investigates whether high-quality AI-generated images (AIGIs), as preferred by human perception models, are more difficult for detection models to distinguish from real images. Contrary to intuition, the authors find that images with higher human preference scores tend to be easier to detect by exis... | Rebuttal 1:
Rebuttal: **Q1: How do negative prompts and modifiers influence image characteristics and quality?**
Firstly, we provide an ablation study, comparing the average quality scores of SDXL images with and without negative prompts and positive modifiers. The table below suggests that **prompt engineering improv... | Summary: This paper reveals an interesting yet counterintuitive phenomenon, where a higher-quality AI-generated image (AIGI) preferred by humans can be more easier for existing AIGI detectors to detect. The authors then investigate this effect and find that (1) images generated from short prompts and (2) certain image ... | Rebuttal 1:
Rebuttal: **Q1: It is recommended to discuss similar research focusing on resolution, such as [1,2].**
Thank you for your valuable suggestion. While high resolution is usually an important aspect of "high-quality" images in a broad sense, this paper considers a narrower sense of image quality, which is eva... | Summary: This work considers the relationship between AI-generated images and real images, noting a counterintuitive phenomenon: generated images with higher quality scores, as assessed by human preference models, tend to be more easily detected by existing AIGI detectors. Additionally, it is observed that images gener... | Rebuttal 1:
Rebuttal: **Q1: The relationship between the curve and the histogram in the plots is not clearly explained.**
Thank you for pointing out the potential difficulty for readers to understand the figures. In Figure 1-3, the red curve illustrates how variable $y$ (e.g., the accuracy in Fig. 1) changes with resp... | Summary: The paper investigates the detectability of AI-generated images (AIGIs), revealing a counterintuitive finding: higher-quality AI-generated images, preferred by humans, are actually easier for detection models to identify. It shows that prompt complexity (with shorter prompts producing higher quality, more dete... | Rebuttal 1:
Rebuttal: **Q1: Discrepancy between existing datasets and real-world applications.**
The discrepancy between existing datasets and real-world applications lies in many aspects, such as semantics, quality, and image compression. This paper focus on the quality (as explained in lines 110-117, left column), w... | Summary: This paper studied the correlation between the quality score of AI-generated images and the detection accuracy of AI-generated images. They found that AI-generated images with higher quality scores are easier to be detected by models. Then, they analyzed the influence of the length of text prompts and image qu... | Rebuttal 1:
Rebuttal: **Q1: It is questionable that the quality scores given by the pre-trained preference models can reflect actual human preference/image quality.**
We agree that the preference models could not replace humans in assessing image quality, and it is difficult to predict the human preference on image qu... | null | null | null | null |
Update Your Transformer to the Latest Release: Re-Basin of Task Vectors | Accept (poster) | Summary: This paper introduces TransFusion, a method that re-basins task vectors by aligning model weights to base models in the parameter space, aiming at adapting task vectors to a later version of the model. In particular, TransFusion employs a two-step permutation process: inter-head alignment using spectral distan... | Rebuttal 1:
Rebuttal: ## Functional equivalence
The central criticism of the reviewer revolves around the preservation of functional equivalence in self-attention layers. To address this justifiable concern, we provide a proof showing that our two-stage method **ensures** functional equivalence.
**Theorem** Let $P\_{i... | Summary: This paper addresses a critical challenge for foundation models, i.e., fine-tuned models becoming obsolete when their base models are updated. The authors introduce TransFusion, a data-free method to transfer task vectors from an old base model to a new one. By leveraging a structured permutation strategy tail... | Rebuttal 1:
Rebuttal: ## Complexity analysis
To assess how computational complexity scale with model size, we define:
- $|L|$: number of layers, evenly divided into MLP ($\frac{|L|}{2}$) and self-attention ($\frac{|L|}{2}$).
- $|H|$: number of attention heads.
- Each MLP layer contains two linear projections with dime... | Summary: The introduces a new rebasin method for models for keeping models up-to-date as their underlying pretrained backbones evolve, focusing on transformers in particular. The author's method involves a “transport” the fine-tuning modifications—captured as a task vector—from the older base model to a new checkpoint... | Rebuttal 1:
Rebuttal: ## Clarification
We clarify that our paper does not assume similarity between $\theta\_A$ and $\theta\_B$. As stated in Sec. 1 (line 67) and 3, these checkpoints may result from training on distinct data distributions or techniques. The re-basin mechanism is indeed designed to align models with si... | Summary: This paper explores how to update a fine-tuned downstream model from an older version of a pre-trained model to a newer pre-trained model without requiring re-finetuning. The paper proposes the TransFusion method, which incorporates attention head matching, alignment, and residual connection handling. Experime... | Rebuttal 1:
Rebuttal: ## Backbones used in NLP experiments
> ... For example, which pre-trained models were used in the NLP experiments?
We sincerely apologize for the omission. In our experiments, we used two variants of OpenCLIP's ViT-B-16 text encoder: "ViT-B-16/commonpool-l-s1b-b8k" for $\theta\_A$ and "ViT-B-16/... | null | null | null | null | null | null |
Multiaccuracy and Multicalibration via Proxy Groups | Accept (poster) | Summary: The authors propose way to measure multicalibration/multiaccuracy by leveraging proxies (that way is to necessary to have access to the data) in order to compute worst case scenarios (formalized as an upper bound on their proposed metrics). They show that postprocessing model to satisfy multicalibration and mu... | Rebuttal 1:
Rebuttal: Thank you for the review!
**Concern 1: Motivation and Method Mismatch**
We believe there might be a misunderstanding of the motivation behind our work - we apologize if it was not sufficiently clear.
Our goal is to develop a predictor $f$: $\mathcal{X} \rightarrow [0,1]$ that is multiaccurate/... | Summary: The paper "Multiaccuracy and Multicalibration via Proxy Groups" addresses the challenge of ensuring fairness in predictive machine learning models when sensitive group data is missing or incomplete. The authors focus on two fairness notions—multiaccuracy and multicalibration—which aim to ensure that model pred... | Rebuttal 1:
Rebuttal: Thank you for the review! We are glad you enjoyed the paper. Our responses to your questions and concerns are below.
**Concern 1: Validity of some claims**
We apologize if some claims seem unsupported; let us clarify:
*Claim 1: "even when sensitive information is incomplete or inaccessible, pr... | Summary: In this paper, the authors study the problem of fairness in ML. To be specific, they focus on a scenario where different groups are evaluated independently with respect to their accuracies and calibration errors (coined as Multiaccuracy and Multicalibration fairness in the literature). The literature has studi... | Rebuttal 1:
Rebuttal: Thank you for your careful review! We are glad you enjoyed the paper. Our responses to your questions and concerns are below.
**Concern 1: Use of proxies**
We agree with the reviewer: proxy-sensitive attributes can be problematic as they introduce another source of error into an already sensitiv... | Summary: This paper explores the problem of evaluating and achieving multiaccuracy (MA) and multicalibration (MC) when sensitive group information is missing. The authors address this challenge by learning proxy functions to predict group membership without direct access to sensitive attributes.
1. Theoretical results:... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review! Our responses to your questions and concerns are below.
**Concern 1: General Concerns around Worst-Case Violations**
As you correctly state, we establish upper bounds on the MA/MC violations across the true groups in terms of the violations on the proxies, th... | null | null | null | null | null | null |
Contrastive Localized Language-Image Pre-Training | Accept (poster) | Summary: This paper explores a data-driven approach to enhance the regional representation capabilities of CLIP. The authors designed a data annotation pipeline to expand regional-level annotations and developed a training architecture featuring a Prompter. This architecture enables more effective utilization of the an... | Rebuttal 1:
Rebuttal: We thank you for the positive review and constructive comments.
> UMG-CLIP
We thank you for pointing out the reference that we briefly compared in L363 left. We agree that there is some technical similarity between our CLOC and UMG-CLIP, but the goals and positioning of our work and theirs a... | Summary: This work introduces a dynamic attention mechanism, inspired by SAM, to aggregate regional image features and perform contrastive learning at both the image-text and region-text levels. The approach is novel in the context of visual pretraining.
Claims And Evidence: The experiments (Table 2, 3) demonstrate st... | Rebuttal 1:
Rebuttal: We thank you for the positive review and constructive comments.
> Unclear whether the performance gains stem from the use of more or cleaner data or from the pretraining schema itself.
In Table 2, we provide detailed ablations of our proposed ingredients on top of the CLIP we trained by ourselv... | Summary: The submission introduces a new pre-training method called Contrastive Localized Language-Image Pre-training (CLOC). The pre-training method extends CLIP pre-training with additional losses based on the outputs of a new "Prompter" module. This new module consists of a light-weight transformer layer that enhanc... | Rebuttal 1:
Rebuttal: We thank you for the positive review and constructive comments.
> The main weakness of the work is the superficial ablation of the VESL captioning pipeline introduced.
Thank you for your constructive comments, and we will consider better examples in our figures. We agree with the reviewer tha... | Summary: This paper proposes Contrastive Localized Language-Image Pre-training (CLOC), an approach extending CLIP-style image-text contrastive learning to also incorporate region-level alignment. The authors introduce a lightweight “Prompter” module that can transform global image embeddings into region-aware represent... | Rebuttal 1:
Rebuttal: We thank you for the positive review and constructive comments.
> The quality and diversity of region-text pairs depend heavily on the open-vocabulary detector and captioning pipeline.
Thank you for pointing out this important aspect. We agree that the quality of the open-vocabulary detector an... | null | null | null | null | null | null |
Provably Efficient RL for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces | Accept (poster) | Summary: This paper establishes a regret bound applicable to both star-convex and non-star-convex cases. Moreover, the violation of safety constraints is zero with high probability throughout the learning process. A key technical challenge in these settings is bounding the covering number of the value-function class, w... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful feedback. We respond to each of your comments below.
**Q1** My main concern is on the unique challenges and technical novelty compared to prior works on safe RL, especially [Amani et al. 2021].
**A1** Below we highlight our unique contributions and technical challe... | Summary: This paper investigates safe reinforcement learning (RL) with instantaneous safety constraints and linear function approximation, where the objective is to ensure zero violations at each step. The authors first identify a technical error in previous work (Amani et al., 2021) and introduce a novel approach, OCD... | Rebuttal 1:
Rebuttal: We appreciate your comments and have addressed your points individually below.
**Q1** Can the covering number be bounded by the union of the covering number of Q_h^k at each step? It will only induce an acceptable logK factor in the final term.
**A1** We thank the reviewer for this insightful... | Summary: This paper studies the theoretical problem of online reinforcement learning with instantaneous hard constraint in the context of *non-star-convex* decision space, which better characterizes some critic domains requiring safety than the existing star-convex counterpart. The authors propose the Non-Convex Safe L... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing the constructive review. Please see our responses to your questions below.
**Q1** How should we interpret the regret bound as the constraint threshold $\tau$ decreases, especially as $\tau \to 0$, where always choosing the known safe action yields linear regre... | Summary: The paper addresses the challenge of safe reinforcement learning (RL) under instantaneous hard constraints in an episodic Linear MDP setting. The focus is on scenarios where the set of safe actions can be non-convex (or non-star-convex) – for example, safe actions might form disjoint or irregular regions due t... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful feedback. We respond to each of your comments below.
**Q1** Is the regret bound $\tilde{O}(d^3 H^4 K + \text{const})$ optimal, or can a bound of $\tilde{O}(\sqrt{K})$ be achieved?
**A1** Please note that the regret is already $\tilde{O}(\sqrt{K})$. The actual the re... | null | null | null | null | null | null |
PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling | Accept (spotlight poster) | Summary: The paper introduces PANDAS (Positive Affirmation, Negative Demonstration, and Adaptive Sampling), which is a method for improving many shot jailbreaking (MSJ) made up of three smaller techniques. The first two (positive affirmation and negative demonstration) entail inserting fake statements as if from the ta... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and detailed response. We are glad that the reviewer finds our claims well-substantiated and our experimental design sound and valid. In the revised paper, we will include a discussion on the manual inspection of model responses during evaluation, pr... | Summary: This paper proposes PANDAS, an improvement on the many-shot jailbreaking (MSJ) by adding positive affirmations (PA), negative demonstrations (ND), and adaptive sampling (AS) demonstrations using Bayesian optimization. Positive affirmations acknowledge the desired behavior in the fabricated model output, and ne... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and detailed response. We are glad that the reviewer finds our claims well-supported by empirical evidence and our experiment designs sound and valid. We will revise the paper by clarifying the quality of the malicious demonstrations, improving the o... | Summary: The paper describes a method ("PANDAS") to make many-shot jailbreaks more effective by:
- inserting positive affirmations (encouraging phrases that reinforce the instruction-following behavior)
- inserting negative demonstrations (examples of recovery from refusal)
- using adaptive sampling (instead of uniform... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback. We are glad that the reviewer finds our claims well-supported, our choice of datasets and baselines reasonable, and our experiment designs sound and valid. We will revise the paper by including additional attention analysis on other models, highligh... | Summary: The paper presents a novel method to strengthen the many-shot-jailbreaking attack (MSJ), which uses many question-answer pairs as malicious demonstrations in context to jailbreak LLMs on safety queries. The authors introduce PANDAS, a hybrid technique designed to improve MSJ by incorporating three strategies: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback. We are glad that the reviewer finds our method novel and our experiments are extensive. We will revise the paper by clarifying the improvements introduced by PANDAS, expanding the discussion on model selection and the challenges of evaluating propri... | null | null | null | null | null | null |
FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields | Accept (spotlight poster) | Summary: This paper proposes a novel method for drag-based image editing. Compared with the previous work, the proposed method takes the 3D geometric information into consideration through mesh construction, ensuring a stable and 3D-plausible editing. The method is claimed to achieve state-of-the-art performance.
Clai... | Rebuttal 1:
Rebuttal: **[Q1] The paper lacks comparison with DragGAN, which achieves better performance in certain cases.**
**[A1]** We provide additional qualitative comparisons between FlowDrag and DragGAN in Fig. 14(a) (please refer to the link below). To ensure fair comparisons, we reproduced DragGAN using the off... | Summary: This paper proposes FlowDrag, a method that leverages pre-trained stable diffusion models for drag-based image editing. This method improves the drag-based image editing by building a field of 3D-aware dragging instruction from the user's input. Specifically, FlowDrag first leverages an image-to-depth or image... | Rebuttal 1:
Rebuttal: **[Q1] What is the value of $\lambda$ in Equation 8?**
**[A1]** The parameter $\lambda$ in Eq. (8) represents the incremental step size at each iteration, indicating the fraction of the displacement between the handle vertex ($v_h$) and the target vertex ($v_t$). However, we discovered a minor ty... | Summary: This paper proposes a novel drag-based editing framework called FlowDrag. Its key feature is the introduction of control points generated through the deformation of a 3D mesh, which helps to mitigate the geometric discontinuities commonly present in existing drag-based editing methods. Judging from the results... | Rebuttal 1:
Rebuttal: **[Q1] The authors do not discuss the impact of 3D mesh construction. Could failures or severe artifacts in mesh reconstruction cause image editing to fail?**
**[A1]** Yes, severe artifacts or failures in 3D mesh reconstruction could hinder the accurate generation of an accurate 2D vector flow, p... | Summary: This paper proposes FlowDrag, which focuses on improving geometry consistency of drag-based image editing. It reconstructs a 3D mesh from the image, and uses an energy function to guide mesh deformation. The deformed mesh is then projected into 2D and used to guide the image editing denoising process. This pap... | Rebuttal 1:
Rebuttal: **[Q1] The method facilitates overall geometric consistency, but edited images still show some fine-grained inconsistencies, e.g., the hat shape in the first sample of Fig.6.**
**[A1]** Yes, we agree with the reviewer’s observation. While FlowDrag significantly improves overall geometric consiste... | null | null | null | null | null | null |
Multi-Objective Causal Bayesian Optimization | Accept (poster) | Summary: The paper introduces a novel framework, MO-CBO, which integrates causal inference with multi-objective Bayesian optimization, addressing an underexplored research area. The theoretical characterization of Pareto-optimal intervention sets via causal graph topology is a notable contribution. However, several asp... | Rebuttal 1:
Rebuttal: Dear Reviewer 8ruG,
Thank you for your insightful suggestions to improve our work with a more extensive literature review, better defined limitations, and improved experimental evaluation! We hope that our response below will further reinforce your confidence in our work:
# Extensions to MO-CBO... | Summary: The paper considers the problem of multi-objective optimisation with knowledge of the causal graph of the underlying system, where the objectives are the interventional means of a set of target variables. The authors propose a bayesian optimisation solution and prove theoretically that knowledge of the causal ... | Rebuttal 1:
Rebuttal: Dear Reviewer easG,
Thank you for recognizing our contributions and for your insightful feedback pointing out the need to better discuss the applicability of our approach and adding further evaluations.
# Applicability of MO-CBO
Requiring prior knowledge about the causal graph is indeed a notab... | Summary: - Decision-making outcomes depend on causal relationships and evaluating them is costly.
- Causal Bayesian optimization uses these relationships to find optimal interventions efficiently.
- Multi-objective causal Bayesian optimization (MO-CBO) extends causal Bayesian optimization to identify Pareto-optimal int... | Rebuttal 1:
Rebuttal: Dear Reviewer 7sGg,
Thank you for your thoughtful feedback and for recognizing our contributions! We have incorporated all of your suggestions to enhance readability, include additional baselines, and provide a clearer formulation of the causal Pareto front for the community.
# Baselines
Thank y... | Summary: This paper presents a unification of Multi-objective Bayesian Optimisation and Causal Bayesian Optimisation to causal settings in which dependence between variables is mediated by a causal graph, maintaining the same assumptions of a known causal graph but not the structural equations or exogenous distribution... | Rebuttal 1:
Rebuttal: Dear Reviewer fTDx,
We greatly appreciate your detailed feedback and your recognition of our contributions! We are happy to address your remaining questions below:
# Proposition 4.2
Proposition 4.2 is based on Proposition 1 of Lee & Bareinboim (2018), which formalizes the concept of minimal inte... | null | null | null | null | null | null |
Agent Reviewers: Domain-specific Multimodal Agents with Shared Memory for Paper Review | Accept (poster) | Summary: This paper proposes a multi-agent system to simulate the review process of research papers, called Agent Reviewers. It is equipped with multi-agent interaction, shared memory pool, and multimodal agent. It empowers agent reviewers with observations not only on textual content but also on visual content. The ex... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments.
**Q1**: Suggestion of put the explanation of metrics in the main text of this paper.
**A1**: Thank you for your suggestion. We agree that a clear explanation of metrics is crucial for understanding the paper. However, due to the page limit of the main text in... | Summary: This paper introduces a multi-agent system that enhances automated peer review. It mimics human review processes by employing domain-specific agents, a multimodal reviewer for visual analysis, and a shared memory pool (SMP) that retrieves past paper reviews for informed evaluation. The system also introduces *... | Rebuttal 1:
Rebuttal: Thanks for your detailed and insightful comments.
**Q1**:What kind of LLMs are used in the baseline methods in Table 2?
**A1**:For the main experiment, all baselines and our Agent Reviewers use GPT-4o-mini for fair comparison (see Table 2 header). Other baseline settings like hyperparameters fo... | Summary: This paper introduces Agent Reviewers, a multi-agent system designed to enhance peer review processes using Large Language Models (LLMs). The system comprises domain-specific agents with a shared memory pool (SMP) that enables them to incorporate historical knowledge and multimodal reviewers that assess visual... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments.
**Q1**: How is the NeurIPS dataset obtained, given that only the reviews for accepted papers are released?
**A1**: All our data(papers and reviews) stems from the public data on OpenReview, including the NeurIPS dataset. As you noted, 96% of the NeurIPS paper... | null | null | null | null | null | null | null | null |
TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state | Accept (poster) | Summary: This paper introduces TimePro, a model designed for multivariate long-term time series forecasting, but it is marred by significant writing issues. The main problem lies in the overly complex and unclear explanations. The terminology used, such as "variable- and time-aware hyper-states," is vague and confusing... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and efforts in reviewing our work and providing thoughtful feedback that can further strengthen our manuscript. We have added some experiments following your suggestions and they are available at https://anonymous.4open.science/r/Anonymous_figure-2319/figure_r... | Summary: This paper introduces TimePro, a novel Mamba-based framework designed for multivariate long-term time series forecasting. The core contribution lies in its variable- and time-aware hyper-state mechanism, which dynamically refines hidden states by adaptively selecting critical temporal intervals to address the ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and efforts in reviewing our work and providing thoughtful feedback that can further strengthen our manuscript. We have added some experiments following your suggestions and they are available at https://anonymous.4open.science/r/Anonymous_figure-2319/figure_r... | Summary: This paper proposes TimePro, a Mamba-based model for multivariate long-term time series forecasting. By introducing a hyper-state mechanism that adaptively selects critical temporal intervals, TimePro aims to address the multi-delay problem, where variables influence targets over heterogeneous time spans. Empi... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and efforts in reviewing our work and providing thoughtful feedback that can further strengthen our manuscript. We have added some experiments following your suggestions and they are available at https://anonymous.4open.science/r/Anonymous_figure-2319/figure_r... | Summary: This paper proposes TimePro, a Mamba-based model for multivariate long-term time series forecasting. TimePro adaptively selects critical time points to refine variable states, preserving temporal granularity and capturing dynamic variable relationships. Experiments on various benchmarks show competitive perfor... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and efforts in reviewing our work and providing valuable feedback that can further strengthen our manuscript. We have added figures with more experiments following your suggestions and they are available at https://anonymous.4open.science/r/Anonymous_figure-23... | null | null | null | null | null | null |
Robust Conformal Outlier Detection under Contaminated Reference Data | Accept (poster) | Summary: The authors analyze the impact of contamination on the validity of conformal methods. They show that under realistic, non-adversarial
settings, calibration on contaminated data yields conservative type-I error control. This conservativeness, however, typically results
in a loss of power. To alleviate this limi... | Rebuttal 1:
Rebuttal: Thank you for the careful review and encouraging feedback. We answer your question below.
> **R1:** How does the concept of contaminated reference set relate to the ambiguous ground truth case?
To the best of our understanding, the papers you referenced study ambiguity in the labeling process wi... | Summary: This manuscript, titled "Robust Conformal Outlier Detection under Contaminated Reference Data," focuses on the problem of conformal outlier detection in the presence of contaminated reference data. It discovers that in non-adversarial scenarios, data contamination makes conformal prediction methods conservativ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging feedback. To fully address your comments, we have conducted additional experiments, available at https://tinyurl.com/rcod-exps. In our responses below, we refer to these as Supp-Figure X and Supp-Table Y.
> **R1:** The effectiveness of the Label - Tri... | Summary: This paper studies conformal outlier detection with contaminated reference sets. It theoretically shows that non-adversarial contamination induces conservative type-I error control, explaining empirical performance gaps. To address power loss, the authors propose Label-Trim: an active data-cleaning framework l... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We appreciate the opportunity to provide some clarifications. To address your questions and strengthen the empirical foundation of our claims, we have also conducted a range of new experiments available at https://tinyurl.com/rcod-exps, whic... | Summary: This paper analyzes the impact of such contamination on the validity of conformal methods. The paper proves that under realistic, non-adversarial settings, calibration on contaminated data yields conservative type-I error control.
Claims And Evidence: This paper focuses on detecting outliers with conformal pr... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We’ve conducted additional experiments to answer your questions and further support and clarify our contributions. These results are available at https://tinyurl.com/rcod-exps, and we refer to them as *Supp-Figure X* and *Supp-Table Y* in our responses below.
... | null | null | null | null | null | null |
Variational Phylogenetic Inference with Products over Bipartitions | Accept (poster) | Summary: This paper targets variational inference of ultrametric phylogenetic trees and proposed a method called VIPR.
Although many efforts have been paid in the field of machine learning based variational phylogenetic infernece, very few researchers consider this on ultrametric trees.
VIPR sample a ultrametric phylog... | Rebuttal 1:
Rebuttal: Thank you for your insightful review: see our responses below.
*For inference accuracy, VIPR's trees' likelihoods lagged behind VBPI. For inference speed, no results report computation time.*
The aspect where VIPR shines is in time (or number of parameter updates) to attain an approximation erro... | Summary: This paper proposes a variational Bayesian phylogenetic tree analysis method using a matrix representation of tree structures. Phylogenetic tree analysis is one of the important analytical techniques used to estimate the developmental process and diffusion pathways of a target, and is more and more in demand i... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful suggestions below, and hope that we have addressed your comments sufficiently.
*One minor concern is that a similar tree-structured matrix representation has been studied independently in another paper [Bouckaert2024] very recently.*
As we discuss in our literature r... | Summary: This paper introduced a new method, VIPR, for phylogenetic inference. This new method greatly improves the computational efficiency without sacrificing accuracy compared with the traditional MCMC based method. The new method derives a closed-form density of the distribution over the entire tree space based on ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review: please find detailed responses below.
*How do parameter numbers influence the time complexity of VBPI?*
In [Zhang and Matsen, JMLR2024], as the number of taxa grows, the number of parameters grows with the number of trees in the SBN. There is no closed form ... | null | null | null | null | null | null | null | null |
Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM | Accept (poster) | Summary: The paper proposed Freeze-omni, which can enable speech input and output capabilities for any LLM backbone without tuning its parameters. In such a case, the model can achieve end-to-end chat experience without losing the intelligence behind the LLM backbone. The framework consists of multiple speech encoder a... | Summary: The paper introduces Freeze-Omni, a novel speech-text multimodal large language model (LLM) designed for speech-to-speech interaction while keeping the backbone LLM’s parameters frozen throughout the training process. This architecture enables low-latency, end-to-end spoken response while preserving the intell... | Summary: This paper proposes a framework to provide a frozen text LLM with spoken dialogue abilities, by integrating it with a speech encoder and a speech generation system. When orchestrated by an auxiliary turn prediction module, this allows for the model to interact along multi-turn conversations with a latency lowe... | null | null | null | null | null | null | null | null | |||
Learning Adversarial MDPs with Stochastic Hard Constraints | Accept (poster) | Summary: This paper considers adversarial MDP problems with stochastic constraints. The paper seeks to bound both the regret and the violations (hard). The paper shows that $O(\sqrt{T})$ regret and $O(\sqrt{T})$ violations bound only when the Slater's condition is satisfied. The paper shows that $O(\sqrt{T})$ regret an... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the effort in evaluating our work.
> On the comparison with existing works and [W1]
[A1] focuses on CMDPs with stochastic rewards and constraints, showing how it is possible to achieve $\sqrt T$ regret and violation. The only technique proposed by [A1] which is of inte... | Summary: This paper studies online learning in constrained Markov Decision Processes with adversarial losses and stochastic hard constraints under bandit feedback. The authors introduce novel algorithms for three distinct scenarios of CMDPs, ensuring sublinear regret while managing constraint violations in different wa... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the effort in evaluating our work.
> W1
We agree with the Reviewer that experiments are always beneficial; nevertheless, we underline that both in the online CMDPs literature and the adversarial MDPs one, many works do not have experimental results (see e.g., Rosenberg... | Summary: This paper studies episodic Constrained Markov Decision Processes (CMDPs) with adversarial losses and stochastic hard constraints under bandit feedback. It is the first to address a setting that combines both adversarial losses and strict hard constraints, whereas prior work has either considered adversarial l... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive evaluation and for the interesting question. Indeed, this is an interesting future direction, nonetheless, we believe that it would be highly non-trivial to employ a primal-dual approach to solve any of the scenarios we study, due to the following reasons. Fo... | Summary: This paper introduces algorithms for constrained Markov Decision Processes (MDPs) with stochastic hard constraints, considering different assumptions and objectives for constraint violations. Specifically, it examines three key cases: (1) when constraints are feasible, (2) when constraints are strictly feasibl... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive evaluation of the paper.
> Question 1.
We thank the Reviewer for the opportunity to clarify this aspect. Indeed, there is no contradiction between [1] and our lower bound, since our lower bound holds for our second and third scenario only, namely, when we a... | null | null | null | null | null | null |
Efficient Diffusion Models for Symmetric Manifolds | Accept (poster) | Summary: The paper introduces a new framework for designing efficient diffusion models on symmetric manifolds, including torus, sphere, special orthogonal group and unitary group. The paper incorporates a spatially varying covariance structure that allows efficient training without computing the manifold heat kernel. I... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We are glad that you appreciate that the motivation and findings of this work is significant, and the benefits of our methods in terms of efficiency and sample quality. We answer your specific question below.
> Could the authors comment on h... | Summary: To improve the efficiency and accuracy of diffusion model on manifold, this work first defined a novel diffusion process on a so-called symmetric manifold by applying the projection map with mild smoothness condition. For the reverse process, instead of considering the manifold's head kernel that has no closed... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We are glad you appreciate the novel approach to diffusion on manifolds, and our significant improvement in computational efficiency. We answer your specific questions below.
>…motivation of the symmetry property of $\mathcal{M}$?
Thank you f... | Summary: This paper introduces a new method for producing scalable diffusion models on Riemannian manifolds with certain symmetries. The method is constructed by placing a diffusion process on a Euclidean space that can be projected entirely onto the manifold, with a partial inverse.
The training speed of this algorit... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We are glad that you appreciate the significant runtime improvement, our contribution of several pieces of new theory, and excellent experimental results in high dimension scaling. We answer your specific questions below.
> One missing exper... | Summary: This work proposes a new efficient algorithm to generate symmetric manifold data, which enjoys $O(1)$ gradient evaluation and nearly $d$ arithmetic operations (exactly $d$ for sphere and torus data) and significantly improves previous results. The main intuition is to take advantage of Ito Lemma and the projec... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We are glad that you appreciate that our method significantly improves previous results, the intuition behind the algorithm design, and the technique novelty. We answer your specific questions below.
> It would be better to add a notation par... | null | null | null | null | null | null |
Efficient Long Context Fine-tuning with Chunk Flow | Accept (poster) | Summary: This paper introduces ChunkFlow, an LLM training (fine-tuning) method that aims to improve the computational as well as memory efficiency of long-context training / fine-tuning. The authors start from three empirical observations in long-context fine-tuning, point out existing efficiency bottlenecks, and desig... | Rebuttal 1:
Rebuttal: We are sincerely grateful to Reviewer **dcDa** for devoting time to review our work and providing invaluable feedback. Regarding the clarity of figures and the impact statement, we will, in strict compliance with Reviewer dcDa’s suggestions, **incorporate an impact statement and refine all figures... | Summary: This paper propose Chunk Flow, a novel chunking and scheduling method for pipeline parallel long sequence training. It first discussed the long-tail phenomena of long-context LLMs training, and the potential issues result from it, such as underutilize of GPU memory and pipeline bubbles. Then the authors propos... | Rebuttal 1:
Rebuttal: We thank **xBtd** for their very positive review of our work, noting that our paper is "a solid study and have provide well explaination for the majority of the paper." and emphasizing strongly the importance of the research problem. In accordance with reviewer xBtd’s suggestions, we will redo all... | Summary: This paper introduces ChunkFlow, a novel method for efficient long-context fine-tuning of large language models (LLMs). ChunkFlow addresses the challenges of variable sequence lengths in training datasets by reorganizing input sequences into uniformly sized chunks, merging short sequences and splitting long on... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer **HVAi**'s insightful feedback. Below we clarify the concerns regarding related work discussion and broader evaluations, with references to our methodology and results in the paper.
***(Q1): The paper does not discuss other efficient training methods like LoR... | null | null | null | null | null | null | null | null |
GRAM: A Generative Foundation Reward Model for Reward Generalization | Accept (poster) | Summary: The paper proposes a method for training a generative reward model that generalizes better across domains with minimal fine-tuning. The approach involves a two-step training process: large-scale unsupervised learning followed by supervised fine-tuning on labeled data. The paper also demonstrates that applying ... | Rebuttal 1:
Rebuttal: Dear Reviewer Jdyw,
We appreciate that you find "the superior generalization of our GRAM compared to both discriminative and prior generative reward models" and our pre-training approach is "make sense".
We will provide explanations for the main points that you are concerned about.
---
>*W1: Th... | Summary: This paper proposes an interesting reward model training method using both unlabeled and labeled data. Building on the generative models in LLMs, the authors develop a generative reward model that is first trained via large-scale unsupervised learning and then fine-tuned via supervised learning. This method pr... | Rebuttal 1:
Rebuttal: Dear Reviewer pUcN,
We sincerely thank the reviewer for your positive and insightful feedback.
We greatly appreciate your recognition of our paper's proposed training paradigm for the reward model as "interesting and sound", and are pleased that the method is considered to be "extensively evalu... | Summary: Authors propose improvements on the training of generative reward models (GenRMs). First, they pre-train GenRMs on pairs of responses. Second, they apply label smoothing. This approach is called GRAM. Authors also make an observation that label smoothing shall be understood as the regularization of Bradley-Ter... | Rebuttal 1:
Rebuttal: Dear Reviewer WL65,
We appreciate the reviewer’s constructive and thoughtful feedback.
We appreciate your recognition that the main claim of our paper is "empirically well supported", and that our experiments are "comprehensive, meticulously studying each of the design choices".
We provide exp... | Summary: This work introduces GRAM, a generative foundation reward model for aligning LLMs with human preferences. Unlike conventional reward models that rely only on labeled human preference data, GRAM incorporates both labeled and unlabeled data through a two-stage training process: unsupervised “pre-training” on inp... | Rebuttal 1:
Rebuttal: Dear Reviewer 3tRc,
We would like to thank the reviewer for the positive feedback regarding the novelty of pre-training for reward models and the strong results.
Below, we explain the main points you are concerned about.
---
>*One component that does not make sense for this problem is the pre... | null | null | null | null | null | null |
Generalized Interpolating Discrete Diffusion | Accept (poster) | Summary: The method overcomes existing limitations in autoregressive models and discrete diffusion approaches by introducing a generalized interpolating discrete diffusion. This innovation offers enhanced flexibility in the noising process design by combining masking and uniform noise, enabling the revision of previous... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and insightful questions. We especially feel that 2) deserves careful discussion since we consider the theoretical contributions a key part of our work. We would like to encourage the reviewer to share some additional detail on their concerns.
1. We agree ... | Summary: As a class of models currently attracting significant attention, masked diffusion models suffer from a fundamental limitation: once a token is generated, it cannot be modified. To address this issue, this paper introduces General Interpolating Discrete Diffusion (GIDD), which allows for a more flexible noise f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review and constructive feedback. In the following, we would like to respond to the reviewer’s comments and suggestions and provide additional experimental results to further bolster the claim of self-correction in the presented models.
1. We mostly agre... | Summary: This paper generalizes discrete diffusion models with masked or uniform transition kernels to a larger design space. Specifically, the authors introduce a Generalized Interpolating Discrete Diffusion process (GIDD), which transfers data not to the [mask] state but to an arbitrary predefined distribution. They ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and detailed review and for acknowledging the theoretical contributions of GIDD.
In the following, we would like to shed some additional light on the empirical evaluation of the proposed model and, hopefully, address the reviewer’s concerns.
Broadly spea... | null | null | null | null | null | null | null | null |
Reward-Augmented Data Enhances Direct Preference Alignment of LLMs | Accept (poster) | Summary: This paper addresses a common limitation in preference-based alignment methods, where only relative preferences are considered, while qualitative aspects of responses are overlooked. It introduces reward-conditioned LLM policies that are trained to generate responses conditioned on rewards. Leveraging a simple... | Rebuttal 1:
Rebuttal: Your valuable comments have greatly helped us improve our manuscript. Below are our specific responses to the raised questions:
**Weakness 1: Analysis of possible optimization outcomes.**
- In Section 3, we analyzed categorical LLM policies, i.e., tabular stochastic policies without function app... | Summary: The paper presents a data augmentation approach for learning on pairwise preference data that doubles the amount of data by modifying the prompt to include a description of the quality (a reward score) of the preferred response and treating each response as the chosen response. DPO is then used to update the p... | Rebuttal 1:
Rebuttal: **The only baselines are DPO and SPPO.**
In addition to DPO and SPPO, we compared with 15 baselines in Figure 4 and Table 12.
**Gains across LLMs are inconsistent. Marginal improvements on NLP benchmarks.**
- The effectiveness of our method is demonstrated on 5 LLMs. It consistently offers impr... | Summary: This paper studies the preference alignment problem in Large Language Models (LLMs) and proposes a Reward-Augmented Data Relabeling method to improve Direct Preference Optimization (DPO). Traditional preference learning focuses only on relative preferences while ignoring the absolute quality scores of response... | Rebuttal 1:
Rebuttal: We thank the reviewer for identifying our work's soundness and technical contributions. Your valuable comments have greatly helped us improve our manuscript. Below are our specific responses to the raised questions:
**Weakness 1 and Question 1: The authors directly apply DPO on the re-labeled dat... | Summary: Direct preferential optimization (DPO) has shown great potential for finetuning language models with user preferences. However, DPO highly depends on positive vs negative samples, and therefore, if some of the relatively good samples are rejected by evaluator model, that can significantly worsen the performanc... | Rebuttal 1:
Rebuttal: We thank the reviewer for identifying our work's soundness and technical contributions. The valuable comments have greatly helped us improve our manuscript. Below are our specific responses to the raised questions:
**Weakness 1 and Question 2: SOTA works cited in the related work but not compared... | null | null | null | null | null | null |
Enhancing Visual Localization with Cross-Domain Image Generation | Accept (poster) | Summary: Summary
This paper proposes a novel cross-domain data generation framework to enhance visual localization in scenarios with significant domain variations. The main results look solid and impressive. The contributions include 1) A modified 3D Gaussian Splatting framework that models photometric variations via l... | Rebuttal 1:
Rebuttal: **1 Limited discussion of failure cases.**
Thank you for your valuable suggestions.
We observe two main failure cases in our current method. First, in scenes like Piatrium_Night that contain extremely dark regions, the fine-tuned image editing model sometimes fails to produce realistic textures... | Summary: The paper focuses on improving visual localization accuracy with cross-domain image generation by three contributions. First, a crossdomain is developed 3DGS to to generate realdomain consistent images. Second, a text-guided image editing model is presented to enhance data diversity for addressing the long-ta... | Rebuttal 1:
Rebuttal: **1. Limitation results on 360Loc.**
Thank you for your valuable suggestion. Our work targets the challenging task of cross-domain visual localization, which requires datasets containing query images captured by **various types of cameras**. To the best of our knowledge, 360Loc is the only exist... | Summary: This paper addresses cross-domain visual localization challenges by proposing a novel data generation framework based on 3D Gaussian Splatting (3DGS). The key contributions include: (1) a cross-domain 3DGS that models photometric variations and mitigates dynamic object interference, (2) a text-guided image edi... | Rebuttal 1:
Rebuttal: **1. Limited discussion of related work**
Thank you for your valuable suggestion. We have expanded the related work section in the revised manuscript by incorporating the recommended references to better highlight the novelty of our proposed method. The revised portion of the related work is show... | Summary: This paper proposes a novel cross-domain data generation framework to enhance visual localization in scenarios with significant appearance variations (e.g., lighting conditions, camera types). The key contributions include a cross-domain 3D Gaussian Splatting framework, a text-guided image editing model, an an... | Rebuttal 1:
Rebuttal: **1. What is the APR method used in the proposed approach—PN, MS-T, or other methods?**
Sorry for the unclear details. Our proposed approach employs the MS-T method for absolute pose regression (APR).
**2. The ablations are mainly evaluated based on rendering quality. It would be beneficial to ... | null | null | null | null | null | null |
Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks | Accept (poster) | Summary: This paper studies the implicit bias of gradient descent for separable classification task and non-homogeneous model. The class of non-homogeneous models studied in this paper is quite general and covers many of the common deep learning models. The results of this paper shows that, if the model's difference to... | Rebuttal 1:
Rebuttal: Thank you for your feedback. For the technical novelty, please refer to the section **Technical novelty** in our response to the reviewer R5kD. For insights of our results, please refer to the section **Insights of our results** in our response to the reviewer NQfK. Below, we address your other qu... | Summary: The main contribution of this work is a generalization of previous theoretical results on the implicit bias of gradient descent for homogeneous networks to the case including non-homogeneous networks that satisfy a mild near-homogeneous condition, such as linear layers with an additional bias term (i.e., $Ax +... | Rebuttal 1:
Rebuttal: Thank you for supporting our paper! We address your questions first and then discuss our technical novelty at the end of this response.
---
**Q1.** “...my question is whether the formulation of exponential loss is necessary. Can the results still be valid for multi-classification with cross-ent... | Summary: The paper characterizes the implicit bias of non-homogeneous deep models trained with gradient-flow or gradient-descent to minimize an exponential loss, under some seperability and near-homogeneity conditions. The results are extensions of previous works that found similar implicit bias in strictly homogeneous... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and will fix all grammar issues and typos in the revision. For the technical novelty, please refer to the section **Technical novelty** in our response to the reviewer R5kD. For insights of our results, please refer to the end of this response. Bel... | Summary: This paper establishes the asymptotic implicit bias of gradient descent for generic non-homogeneous deep networks under exponential loss. Specifically, the authors show that (1) the normalized margin increases nearly monotonically, (2) the direction of the parameters converges, and (3) the directional limit sa... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and suggestions on the writing style. We will include more discussions on intuitions and technical innovations for our theorems in the revision. For the technical novelty, please refer to the section **Technical novelty** in our response to the reviewer R5kD.... | null | null | null | null | null | null |
Vision Graph Prompting via Semantic Low-Rank Decomposition | Accept (poster) | Summary: The paper introduces Vision Graph Prompting (VGP), a novel parameter-efficient fine-tuning method tailored for Vision Graph Neural Networks (ViG). The authors propose that semantic information in vision graphs resides primarily in low-rank components of the latent feature space, a key insight derived from PCA-... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
### Q1,W1. Generalizability to Graph Tasks
From the efficacy of our method on chemistry/biology graph datasets, we hypothesize **similar latent semantic low-rank patterns** also exist in these graph data. In particular, **chemical bonds** and **protein inter... | Summary: This paper introduces a novel parameter-efficient method called **V**ision **G**raph **P**rompting (**VGP**) with semantic low-rank decomposition for Vision GNNs. Empirical results demonstrate that the proposed approach achieves impressive performance on both image classification and traditional graph classifi... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
### Q1. Cluster Located in Bottom-Right Corner of Figure 1
We further check the correspondence between the t-SNE clusters and images patches, finding that the bottom-right cluster corresponds to **the bird's reflection on the water**. This observation aligns... | Summary: This paper proposes a novel approach called Vision Graph Prompting (VGP), which enables parameter-efficient fine-tuning of the Vision GNN (ViG) model. Additionally, the paper observes that essential semantic information in Vision Graph structures is concentrated in low-rank components and leverages this insigh... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
### Q1. Experiments on Other Graph-based Vision Models
We supplement additional experiments on other graph-based vision models, including **MobileViG and GreedyViG**, across six vision datasets with ImageNet-1k pre-trained backbones. As table shown below, our... | Summary: In this work, the authors present Vision Graph Prompting (VGP), a parameter-efficient fine-tuning method for Vision Graph Neural Networks. The core insight is that semantic information in vision graphs primarily resides in the low-rank components of the latent feature space. The authors propose three semantic ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
### Q1. Adding LoRA as a Prompt Alternative
We supplement additional experiments comparing with LoRA across ten vision datasets. As table shown below, LoRA surpasses traditional visual prompting method (VPT) due to its **low-rank adaptation property** and mat... | null | null | null | null | null | null |
Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability | Accept (poster) | Summary: The paper establishes improved convergence rates for local gradient descent in the context of distributed logistic regression with separable data. This improvement is attained by employing significantly larger step sizes than those typically used for general smooth loss functions.
# Update after rebuttal
In ... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our submission. Below we have responded to the comments in your review.
1. **Tightness in terms of $M$ and $\gamma$.** This is an interesting question. For $M$, the current dependence may be tight, but of course we cannot know for sure without a lower bound. Here we... | Summary: This paper studies local gradient descent (GD) for logistic regression with separable data in a distributed setting. Building on prior work by [Wu et al., 2024], which showed that a large stepsize improves optimization efficiency in a single-machine setting, this work extends the analysis to multiple machines ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. Below we have responded to the points in your review.
1. **Comparison to the single-machine case.** The issue of $\gamma$ dependence stems from the gradient bias $b_r$ in Lemma A.5. Notice that other conditions for entering the stable phase (Lemma A.3, Lemm... | Summary: The authors demonstrate that Local GD for distributed logistic regression converges for any step size $\eta$ > 0 and any communication interval K ≥ 1. Experimental results on both synthetic and real-world data support the theoretical finding that acceleration is possible by permitting nonmonotonic decreases in... | Rebuttal 1:
Rebuttal: Thank you for your efforts in the review process. Below we have responded to your comments about additional experimental setups.
1. **Additional experimental setups**. In the review, you asked "Have you tested more datasets to verify your method?". First, we would like to clarify that the goal of... | null | null | null | null | null | null | null | null |
Memory Efficient Block Coordinate Descent Method for Forward-Only Second-Order Finetuning of LLM Models | Reject | Summary: This paper proposes a memory-efficient optimization method for fine-tuning large language models by integrating a block coordinate descent scheme with Hessian-informed zeroth-order optimization. The authors claim that their method achieves reduced memory overhead while maintaining comparable accuracy to existi... | Rebuttal 1:
Rebuttal: **Dear Reviewer Nphh,**
We sincerely appreciate your feedback. In addressing the comments from all reviewers, we have made efforts to clarify and improve each point raised. Below, we provide a concise summary of the key changes made in response to your concerns and recommendations.
### **Weaknes... | Summary: The authors propose a new zero-order optimizer for fine-tuning the pre-trained model to the downstream task that incorporates second-order information. The main issue addressed in the study is the infeasible memory consumption of classical optimizers for the fine-tuning process. The main idea is to use a block... | Rebuttal 1:
Rebuttal: **Dear Reviewer CqZr,**
Thank you for your thoughtful questions and insights.
We appreciate your feedback and will address it as follows:
### **Claims and Evidences:**
Training and test loss for figure 3, train loss are smoothed as in the figure:
| Method | Training Loss | Test Loss |
|-------... | Summary: This paper proposes B-PDF, a memory efficient bcd-newton optimization method for LLM fine-tuning, especially for low-end devices, which integrates block coordinate descent with a zeroth-order Newton-method optimizer. This approach reduces memory overhead by updating parameters and diagonal Hessian information ... | Rebuttal 1:
Rebuttal: **Dear Reviewer h68x,**
Thanks for your feedback and valuable suggestions.
Below, we address each of your concerns to improve our manuscript.
### **Experimental Designs and Question 1:**
Baseline Comparisons with GaLore and Other Methods:
We have carefully considered your comments and those fro... | Summary: The paper proposes a zero-order method for fine-tuning large language models (LLMs), utilizing a block coordinate descent approach to reduce memory costs.
In this approach, blocks are defined as layers of the LLM, which are updated individually while the remaining layers are frozen.
To improve convergence spee... | Rebuttal 1:
Rebuttal: **Dear Reviewer K578,**
We sincerely appreciate your thoughtful review and apologize for the confusion caused by the typos and unclear claims. We are grateful for your careful reading and will address each point thoroughly.
### **Theoretical Claims:**
For Equation 2 and the EMA, the first term ... | null | null | null | null | null | null |
Analytical Lyapunov Function Discovery: An RL-based Generative Approach | Accept (poster) | Summary: This paper proposes to use transformers and reinforcement learning (RL) to construct local analytical Lyapunov functions for high-dimensional non-polynomial systems. The proposed framework consists of three components: 1) a symbolic transformer to generate candidate Lyapunov functions; 2) a numerical verifier ... | Rebuttal 1:
Rebuttal: We thank reviewer s9Wv for the valuable time and constructive feedback. We provide point-by-point responses below.
**Q1: It would be great if more baselines for analytical Lyapunov functions were introduced, e.g., sum-of-squares methods (at least for the polynomial systems).**
Thanks for the val... | Summary: 1. Similarly to other lines of work, authors train a new model for a single nonlinear dynamical system.
2. Key innovation lies on the symbolic formulation of the problem: instead of training a fixed model M (which would serve as the Lyapunov candidate) as per previous line of work, they work with symbolic tra... | Rebuttal 1:
Rebuttal: We thank reviewer ve2p for the valuable time and constructive feedback. We provide point-by-point responses below.
**Q1: Given the symbolic answer provided by the model, can this help to enlarge the stability area given you are not overfitting on a particular locality domain?**
Thanks for the in... | Summary: The paper presents an RL framework for discovering analytical Lyapunov functions for nonlinear dynamical systems. The proposed approach trains a symbolic transformer from scratch. The transformer generates candidate Lyapunov functions in a symbolic form, which is then refined and verified via a combination of ... | Rebuttal 1:
Rebuttal: **Q1 and Q2: Justify the claimed advantage over SOS techniques. Sensitivity issues of SOS. Completeness of their approach.**
Thanks for this valuable advice. We tested SOS using SOSTOOLS on polynomial (poly) and non-polynomial (non-poly) systems. Below is a summary of the setup and results.
Poly... | Summary: This paper presents a novel and promising approach to discovering analytical Lyapunov functions for nonlinear dynamical systems using reinforcement learning and transformer-based generative models. The work addresses a fundamental challenge in control theory with significant practical implications. The framewo... | Rebuttal 1:
Rebuttal: We thank reviewer SwEU for the valuable time and constructive feedback. We provide point-by-point responses below.
**Q1: Alfarano et al. (2024) was excluded from direct comparison**
To complete the comparison, we contacted the authors of Alfarano et al. (2024), who conducted the evaluation of th... | null | null | null | null | null | null |
HiRemate: Hierarchical Approach for Efficient Re-materialization of Neural Networks | Accept (poster) | Summary: The paper introduces HiRemate, a hierarchical framework for neural network re-materialization to reduce memory usage during training. The core idea involves recursively partitioning the computation graph into manageable subgraphs, solving each with optimized strategies, and merging solutions hierarchically.
C... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and detailed feedback. Below we address each of point of the review in turn.
**On Lack of Theoretical Guarantees and Suboptimality Gap**
We agree that HiRemate does not provide formal guarantees on optimality. This is also true of Rockmate and TW-Rema... | Summary: The paper presents a novel hierarchical framework to optimize memory usage during neural network training. They recursively partitions large computation graphs and apply optimized solvers at multiple levels to significantly reduces memory consumption while maintaining efficient training performance.
Claims An... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and detailed feedback. Below we address each of point of the review in turn.
**On Solver Overhead and Scalability**
Indeed, H-ILP introduces a solving time overhead. However, this can be mitigated in the following ways:
* Solving separate subgraphs ca... | Summary: This paper develops a procedure for hierarchically partitioning a data flow graph in order to find a schedule for selectively recomputing neural network activations so that memory usage during backpropagation fits within a specified budget.
Claims And Evidence: The proposed method is a modular framework that ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and detailed feedback. Below we address a question regarding gradient accumulation comparison.
Gradient accumulation reduces memory usage by splitting a large batch into smaller sub-batches and accumulating gradients across them. This strategy avoids... | null | null | null | null | null | null | null | null |
Understanding Input Selectivity in Mamba: Impact on Approximation Power, Memorization, and Associative Recall Capacity | Accept (poster) | Summary: This paper provide theoretical justifications for selective SSM layer (S6) in Mamba architecture. They show
1. S6 has better expressiveness than S4D layer
2. S6 suffers from exponential memory decay
3. 1-layer Mamba (with S6) solves MQAR tasks with SSM mixer. MQAR is an information retrieval tasks that normal... | Rebuttal 1:
Rebuttal: ## Common Response
We refer the reviewer to the Common Response in the Rebuttal to WpKK.
## Individual Response
***SD4 is mentioned without definition in abstract (and the 1st page).*** \
We thank the reviewer for pointing out the mistake: this has been corrected in the text. We further thank ... | Summary: This paper analyzes the flexibility of MAMBA showing that the S6 layer can i) it can project the input into Haar wavelet basis, ii) counteract memory decay, iii) solve multi-query associative recall (MQAR) problem proposed by Arora et al. tasks. While this is mostly a theoretical paper, the authors demonstrate... | Rebuttal 1:
Rebuttal: ## Common Response
We refer the reviewer to the Common Response in the Rebuttal to WpKK.
## Individual Response
We thank the reviewer once again for their positive feedback, and remain open to include any suggestion for improvement, or answer any question they might have.
---
Rebuttal Commen... | Summary: This paper aims to understand the effect of gating in Mamba models in terms of function approximation power, long-term memory, and associative recall capabilities. Both theoretical derivations and empirical results are provided.
Claims And Evidence: Three major claims as outlined in the paper:
* S6 layer is ... | Rebuttal 1:
Rebuttal: ## Common Response
We are grateful for the positive comments from reviewers on our paper, particularly regarding its **clarity**
(kLHf: *“this paper is of very high quality”*; R4Me: *“quite clearly written”*),
**soundness** (kLHf: *“very refreshing yet solid”*; R4Me: *“These results are proven ri... | null | null | null | null | null | null | null | null |
Learning Attribute-Aware Hash Codes for Fine-Grained Image Retrieval via Query Optimization | Accept (poster) | Summary: This paper presents a novel learn-to-hash method for large-scale fine-grained image retrieval. It introduces a query learning mechanism that can capture nuanced attribute-level information, making each bit of the hash code interpretable. The paper also introduces auxiliary branches during the training process ... | Rebuttal 1:
Rebuttal: **Comment1:** *Figure 3 illustrates the curves at different values of c, and the authors further support their analysis with the visual results in Figure 4. However, Figure 4 only provides visualization for the specific case of C=200. Does the trend of the loss landscape at different values of c a... | Summary: In this paper, the authors propose a query optimization-based fine-grained image hashing method, which enables the generated hash bits to exhibit attribute-aware characteristics. From the perspective of cosine similarity, the challenges in generating effective low-bit hash codes are analyzed. Based on this ana... | Rebuttal 1:
Rebuttal: **Comment1:** *The paper lacks a comparison with the recent method ConceptHash*
**Reply1:**
| Method | bits | CUB-200 | Aircraft | Stanford Cars |
|--------------|------|---------|----------|---------------|
| ConceptHash | 16 | 83.45 | 82.76 | 91.70 |
| | 32 ... | Summary: This work treats the hash learning process as a set prediction problem, using a cross-attention-based decoder to decouple attribute-specific features and further compress them into hash codes. From the perspective of cosine similarity, it argues that large class numbers with low feature dimensions lead to poor... | Rebuttal 1:
Rebuttal: **Comment1:** *The subtle feature extractor employs a multi-scale framework in conjunction with a multi-head self-attention (MHSA) mechanism. This method is widely adopted in deep learning, particularly for image feature extraction. For instance, architectures like Mobile-Former and ConVit exempli... | Summary: This paper presents a query optimization-based attribute-aware hash code generation method. First, a hybrid convolution and attention structure is utilized to obtain rich representations. Second, unlike other works that simply use fully connected layers to generate hash codes, this paper leverages a decoder an... | Rebuttal 1:
Rebuttal: **Comment1:** *One of the main contributions of the paper is the introduction of an additional auxiliary branch. However, the description of this operation is too brief and does not sufficiently clarify its specific implementation. It is recommended that the authors provide a more detailed explana... | null | null | null | null | null | null |
Optimizing Temperature for Language Models with Multi-Sample Inference | Accept (poster) | Summary: The authors introduce a data-free way to automatically find the best sampling temperature for multi-sample generation in LLMs. By detecting a sharp “turning point” in the model’s token-level entropy, they find a good balance between quality and diversity.-.
Claims And Evidence: Yes.
Methods And Evaluation Cr... | Rebuttal 1:
Rebuttal: Dear Reviewer tFFz,
Thank you for your thoughtful feedback and for recognizing the significance of our work. We appreciate your positive remarks on our approach and presentation. Our detailed responses are as follows:
> *It is not entirely clear why the authors do not include a comparison to a b... | Summary: Authors explores how to automatically determine the optimal temperature for large language models (LLMs) in multi-sample inference settings, without relying on labeled validation data. The authors analyze temperature’s role in balancing diversity and quality in generated samples and propose TURN (Turning Point... | Rebuttal 1:
Rebuttal: Dear Reviewer JAaC,
Thank you for acknowledging our efforts. Our detailed responses are as follows:
> ***Essential References Not Discussed:** Not strictly related but also a multi-sample or more specifically multi-domain scaling approach: Robust Calibration with Multi-domain Temperature Scaling... | Summary: This paper introduces TURN, an entropy-based approach for automatically determining optimal sampling temperatures for large language models using multi-sample aggregation strategies. The authors identify that different models require different temperature settings based on their training and observe an "entrop... | Rebuttal 1:
Rebuttal: Dear Reviewer 3FXt,
We appreciate your insightful recommendations. Our point-by-point responses are detailed below:
> *Minimal discussion of computational overhead for entropy calculation*
>
Table 3 in our paper presents the computational overhead associated with sampling. The results indicate... | Summary: The paper tries to find the optimal temperature on various question answering tasks.
They notice that the optimal temperature is higher for models that are fine-tuned on the task, and lower for more general models.
Motivated by that, they set out to find a way to set the temperature that doesn't require a va... | Rebuttal 1:
Rebuttal: Dear Reviewer TgFe,
Thank you for your supportive review and thoughtful suggestions. Please find our detailed responses below:
> *It would be good to see performance before/after rather than performance drops (as in Table 1). These performance drops are quite difficult to interpret.*
>
We appr... | Summary: This paper proposes a method to estimate the right temperature LLM. The method relies on "multi-sample aggregation strategies" which has the advantage to spare the costly and task-specific validation data. The idea is based on an extensive analysis of the impact of temperature temperature while varying model... | Rebuttal 1:
Rebuttal: Dear Reviewer NxtV,
Thank you for your valuable and positive feedback. We are pleased to hear that you find our research problem compelling and appreciate the novelty of our proposed algorithm. Please find our detailed responses below:
> *For instance, Llama models commonly use a temperature of ... | Summary: The paper investigates the role of temperature when using best-of-n and majority-voting inference strategies. The authors discover an intriguing phenomenon coined "The Entropy Turning Point" that can accurately predict the change-point between generating diverse high-quality samples and generating low-quality ... | Rebuttal 1:
Rebuttal: Dear Reviewer 3z2D,
Thank you for the insightful suggestions. We are glad you find our research problem important and provide positive reviews. Please find our detailed responses below:
> *it'd be interesting to see both code and math models on each of the Figure 3 plots. A good distance measure... | null | null |
Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation | Accept (poster) | Summary: The paper proposes DIRECT, an algorithm for deep active learning under the dual challenges of class imbalance and label noise. DIRECT reduces the multi-class problem to a set of one-dimensional agnostic active learning subproblems by identifying an “optimal separation threshold” for each class. Annotating exam... | Rebuttal 1:
Rebuttal: **Experimental Settings Compared to SIMILAR**
Our settings actually closely mirror the settings in SIMILAR. In SIMILAR, the rare class setup is very close to the long tail distribution setups in our paper. SIMILAR’s setting reduces the number of examples in some of the classes to form rare classe... | Summary: ## update after rebuttal
After read the rebuttal and other reviews, the reviewer maintains the initial recommendation.
The paper introduces DIRECT, a new algorithm for deep active learning under class imbalance and label noise. The main contribution is a reduction of the imbalanced classification problem into... | Rebuttal 1:
Rebuttal: Thank you for the insightful and detailed review. We make the following clarifications to address your concerns.
**Handling Label Noise by Agnostic Active Learning Algorithm**
The large body of classic literature of agnostic active learning studies exactly the active learning under label noise sc... | Summary: This paper studies active learning under both class imbalance and label noise. An improved algorithm for agnostic active learning is proposed, referred to as DIRECT, which is considered an advanced version of GALAXY. Various experiments are done to validate the property of DIRECT, showing its superiority in th... | Rebuttal 1:
Rebuttal: Thank you for providing the insightful review. We address your concerns below.
**Using a different scoring in Eqn. 2**
As you suggested, we could indeed subtract the mean instead of the max of the per-class softmax scores. The mean will simply be a constant for all examples, which makes the scori... | null | null | null | null | null | null | null | null |
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression | Accept (poster) | Summary: The ACBench framework proposed is designed to systematically evaluate the effects of compression on both agent capabilities and large language models (LLMs). It tests agent capabilities across key areas such as action execution, workflow generation, long-context understanding, and real-world application perfor... | Rebuttal 1:
Rebuttal: **Q1**: About Theory.
> Theoretical claims are poor for this article. What is the relationship between the degradation of the compression has on LLMs and the degradation of the compression has on the Agentic Behaviors?
>
***Ans for Q1***: This paper is primarily an empirical study, as indicate... | Summary: This is a very interesting paper that studies agentic capabilities in LLM compression. The authors have carefully selected a series of evaluation benchmarks that cover practical scenarios of agent manipulation to assess the performance drop after compression.
Claims And Evidence: Yes, the claims are well supp... | Rebuttal 1:
Rebuttal: Dear Reviewer Sovg,
Thank you for your thorough and constructive review of our work. We sincerely appreciate your recognition of the experimental rigor and practical relevance of our evaluation benchmarks, as well as your encouraging feedback. Your insights strongly support our goal of providing ... | Summary: Large language models (LLMs) have significantly advanced areas such as code synthesis and multi-agent collaboration; however, their practical deployment remains constrained due to substantial computational and memory requirements. Compression techniques, including pruning and quantization, effectively reduce m... | Rebuttal 1:
Rebuttal: **Q1**: About the practical utility of the proposed metrics:
> the proposed metrics significantly enhance understanding of compression impacts is less convincing, as their practical utility remains unclear.
**Ans for Q1**: . We would like to address each metric:
- **ERank**: Diff-eRank[1] is a... | Summary: The authors introduce ACBench (Agent Compression Benchmark), a benchmark designed to evaluate how compression techniques (quantization and pruning) affect the agentic capabilities of large language models (LLMs), such as multi-step planning, workflow generation, tool use, and long-context understanding. They a... | Rebuttal 1:
Rebuttal: Thank you for the thorough comments and recognition of our work. We appreciate that you acknowledge our experiments are “comprehensive and sound”. Please see our responses to your questions and concerns below.
**Q1**: About the metrics.
> Can you clarify if (and how) the novel metrics (ERank, T... | null | null | null | null | null | null |
Prediction-Powered E-Values | Accept (poster) | Summary: The authors propose to combine e-values to prediction-powered inference. The result is an e-value that combines in its definition both observed data as well as prediction of an auxiliary model. This approach is shown to improve the power of the testing procedure as long as the model is good enough. The author ... | Rebuttal 1:
Rebuttal: Thank you for pointing out the paper of [Xu et al.]. It is definitely a work we need to cite, and we will do so. That said, our contribution differs from theirs.
1. Our construction is significantly more general than theirs. In equation (8) they construct what would be our prediction-powered e-pr... | Summary: This paper introduces prediction-powered e-values. Its primary contribution is extending prediction-powered inference (PPI) beyond Z-estimation problems (e.g. inference of means) to the broader class of inference problems solvable via e-values. The authors show that their method retains key benefits of classic... | Rebuttal 1:
Rebuttal: We'd like to thank the reviewer for their review. Please refer to our responses below.
**Labeling budgets:** We agree. The choices of 1% and 0.5% are indeed a bit arbitrary, and were made mainly so as to be a reasonably large reduction in the number of collected labels, while keeping the underlyi... | Summary: This paper proposes an extension of "prediction powered inference" to e-values, a general class of statistics that are used in many methods outside of the estimation. The authors show theoretical results justifying the validity of their prediction powered e-value, and its validity under active sampling, as wel... | Rebuttal 1:
Rebuttal: We'd like to thank the reviewer for their insightful comments.
**Growth rate as a function of the MAE of the predictor:** Yes, we can; in particular, we can show that the Wasserstein distance in Theorem 2.2 is upper bounded by the MAE of the predictor:
$$\begin{aligned}
W(\mu\_i(X\_i) || Y\_i... | Summary: The paper proposes a methodology for converting any e-value based inference procedure into a prediction-powered counterpart. More concretely, they consider the setting where we have a data stream $(X_i, Y_i, \pi_i, \xi_i)_{i=1}^\infty$ where $X_i$ is cheap data, $Y_i$ is expensive labelled data, and $\xi_i \si... | Rebuttal 1:
Rebuttal: We'd like to thank the reviewer for their comments.
**Regarding modelling missingness through $\pi_i(X_i)$:** Our construction is focused on an active/experimental setting in which we can actually sample $\xi_i \sim Bern(\pi_i(X_i))$ and collect $Y_i$ when $\xi_i=1$. Extending it to a "semi-super... | Summary: This paper extends the ideas of prediction-powered inference to e-values. The contribution is focused but nice, since as the authors note, prediction-powered e-values allow for a broader set of possible inference techniques and guarantees (anytime validity in particular). The core methodology involves leveragi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive remarks, addressed as follows:
**Sequential updating and [Zrnic & Candès, 2024]:** The reviewer raised a good point. We will add comparisons in Section 3.1 to the method of [Zrnic & Candès, 2024]. We've run a preliminary experiment on this and indeed, ... | null | null | null | null |
Compositional Generalization via Forced Rendering of Disentangled Latents | Accept (poster) | Summary: The paper develops theoretical and empirical results as to why a disentangled representation in
input does not necessarily lead to OOD compositional generalization.
First, the paper demonstrates the failure of common generative architectures (decoder-only CNN / MLP) to
perform compositional generalization, eve... | Rebuttal 1:
Rebuttal: **Architecture Choice (Size & Type)**
- **Data–Model Size and Memorization:** In our experiments, the network does not memorize when we alter the data (e.g., 1D stripes with few conjunctions), even with significantly fewer samples. Thus, we do not see evidence that a “small dataset vs. large mode... | Summary: The authors investigate why disentanglement is not sufficient for compositional generalization. First they observe that models are unable to reconstruct simple bumps in unseen locations in visual space from fully factorized latents. They use this as evidence that disentanglement alone is not enough for composi... | Rebuttal 1:
Rebuttal: **Novelty and Positioning Relative to Prior Work**
We fully agree that claiming the insufficiency of disentanglement is not novel (also see response to Reviewer 1, vKC8). Our primary contribution lies instead in providing a detailed mechanistic explanation of why disentanglement fails—specificall... | Summary: The paper investigates conditions under which a neural network learns to generalize "compositionally". The setting involves learning to generate a 2D "bump function". A key result is that a "disentangled" representation is not sufficient to ensure compositional generalization. The authors then describe data cu... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and encouraging feedback on our work. We greatly appreciate the insightful comments and constructive questions raised, and we respond to each point concisely below.
**Definition of Compositionality and OOD Generalization**
We thank the reviewe... | Summary: The authors investigate the role of disentangled representations in compositional generalization, which remains unclear in the literature. The authors observe that while inputs may be disentangled, this disentanglement can "erode" through subsequent layers, such that the model overall is not able to generalize... | Rebuttal 1:
Rebuttal: **Input Encoding Types**
We appreciate the reviewer's suggestion regarding the comprehensiveness of input encoding formats. Indeed, we have included both rate-based (scalar) and population-based encodings (e.g., 1-hot, Gaussian bumps, and positional encoding), as depicted in Fig. 1. These variati... | null | null | null | null | null | null |
Observation Interference in Partially Observable Assistance Games | Accept (poster) | Summary: The paper studies POAG where human and AI assistant only have partial observations. It shows that an optimal assistant who aims to maximize human reward needs to take observation-interfering actions, defined as an action showing a subset of information to human, for 3 purposes:
1. Communicate AI’s private inf... | Rebuttal 1:
Rebuttal: # Thanks + response
Thank you for your review. We are glad you think our work “provides a useful contribution to the literature” with technical details that are “sound,” “effective,” and “intuitive and easy to follow.” We appreciate the questions you ask about some of our examples and experiments... | Summary: This work studies a two player decentralised POMDP called Partially Observable Assistance Games. In this game, the authors study cases where it might be beneficial to one of the player (called assistant) to "interfere" with the observations of the other player (called human). They also identify situations, whe... | Rebuttal 1:
Rebuttal: # Thanks + response
Thank you for your review. We are glad you find our setting “of strong interest,” and we appreciate your help with the clarity and simplicity of our paper.
## 1.
> real difference between Definition 3.2 and Definition 4.6 is unclear… I did not understand the example after Th... | Summary: Paper studies the conditions under which an agent has an incentive to perform observation interference (take an action which returns partial state observation to the human) even when the goals are aligned. The thought of taking such an action, at surface level seems adversarial and counter intuitive. However, ... | Rebuttal 1:
Rebuttal: # Thanks + response
Thank you for your review. We are glad you consider our work “very relevant” to the broader scientific literature and that “the general reasoning discussed in the paper is helpful to build intuition for the theory.” We appreciate the questions you raise about the practical app... | Summary: This paper investigates observation interference by AI assistants in partially observable assistance games (POAGs), where both the AI and the human have limited information. The authors demonstrate that an optimal assistant may have incentives to interfere with observations to communicate private information, ... | Rebuttal 1:
Rebuttal: # Thanks + response
Thank you for your review. We are glad you consider our work to "address a crucial aspect of the human-AI value alignment problem" and "have important implications for building trustworthy AI systems." We appreciate the discussion points you raised.
## 1.
> The analysis… rel... | null | null | null | null | null | null |
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation | Accept (poster) | Summary: The paper introduces FluxSR, a novel one-step diffusion model for real-world image super-resolution (ISR), leveraging flow trajectory distillation (FTD) to distill a multi-step diffusion model into a one-step model. The authors propose several innovations, including TV-LPIPS as a perceptual loss and attention ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer mAgS (denoted as R5)
**Q5-1:** Could the authors provide more details on the scalability of FluxSR? For instance, how does the method perform on larger or more diverse datasets, and what are the implications for real-time applications?
**A5-1:** We generate 10k noise-imag... | Summary: The paper introduces FluxSR, a novel one-step diffusion model for Real-ISR (Real-World Image Super-Resolution). The primary goal is to reduce the high computational cost associated with multi-step diffusion models while preserving high-quality image generation. The key innovation is Flow Trajectory Distillatio... | Rebuttal 1:
Rebuttal: ## Response to Reviewer jQYF (denoted as R4)
**Q4-1:** How does the noise-to-image mapping in T2I models fundamentally differ from LR-to-SR degradations in practice? Could you provide a more detailed analysis to support the claim in Figure 2?
**A4-1:** Although $x_t$ in the diffusion process *ap... | Summary: This paper improves on the one-step diffusion-based super-resolution methods that target the real-world image super-resolution (Real-ISR) task by distilling on a larger and more advanced baseline image generation model (FLUX) compared to existing works that leverage Stable Diffusion as a backbone. It introduce... | Rebuttal 1:
Rebuttal: ## Response to Reviewer ozHQ (denoted as R3)
**Q3-1:** Does not include the DrealSR that is widely used in various baselines.
**A3-1:** Thank you for pointing out this issue. The table below shows the quantitative comparison on the DRealSR dataset. From the table below, our FluxSR obtains signif... | Summary: The authors claim that most existing one-step diffusion methods are constrained by the performance of the teacher model, where poor teacher performance results in image artifacts. To this end, the authors proposed a one-step diffusion Real-ISR technique, namely FluxSR, based on FLUX.1-dev and flow matching mod... | Rebuttal 1:
Rebuttal: ## Response to Reviewer uJUz (denoted as R2)
**Q2-1:** The paper lacks innovation. The TV-LPIPS and ADL proposed in the paper are both existing works.
**A2-1:** The main contribution of this paper is the introduction of FTD, and our method is the first work to distill a large-scale flow matching... | Summary: This paper proposes FluxSR, a one-step diffusion model for real-world image super-resolution (Real-ISR). The author introduces Flow Trajectory Distillation (FTD) to distill multi-step diffusion models into a single step. FluxSR addresses distribution shifts by aligning noise-to-image and low-to-high-resolution... | Rebuttal 1:
Rebuttal: ## Response to Reviewer m3JT (denoted as R1)
We sincerely thank the reviewer for the constructive comments. We provide the detailed responses to all the concerns below.
**Q1-1:** Incomplete Ablation on FTD.
**A1-1:** We add the relevant experiments. The results are shown in the table below. In p... | null | null | null | null |
Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation | Accept (poster) | Summary: This paper is the first to produce a 10m resolution time-series forest height map of Europe from 2019 to 2022. The 2020 results were compared with multiple tree height studies, revealing that its accuracy is also the most reliable. The data used include GEDI, Sentinel-1, and Sentinel-2, with the model being 3D... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for acknowledging our work. Let us address your remarks and questions in detail below.
> However, the analysis section needs further improvement. [...]
We agree that this is an important aspect. Our primary goal was to develop an openly available model that... | Summary: The paper presents an approach for creating large-scale temporal tree canopy height maps using satellite imagery. The main contributions include a deep learning model (3D U-Net architecture) that can track forest height changes across Europe from 2019-2022 at 10m spatial resolution; a canopy height map of Euro... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. In our manuscript, we proposed seasonal variation and geolocation shifts as possible factors influencing the model's performance. We agree that some of our statements have been to explicit from a environmental perspective (e.g., to make use of the geolocation sh... | Summary: The article describes a method for calculating canopy height using satellite data and reference values from GEDI LiDAR. The authors propose the use of a UNet network for regression. Using multispectral Sentinel images, they provide canopy height estimates for Europe between 2019 and 2022. Their R² is 0.819. Wh... | Rebuttal 1:
Rebuttal: Thank you for your positive and thorough review. Let us address your concerns and questions in detail.
> How do you know that you are looking at trees (as opposed to buildings, for instance)?
That is a very good and true observation, indeed, given that GEDI measures the height of all objects, we... | Summary: This paper introduces a novel deep learning approach for generating high-resolution, large-scale temporal canopy height maps across Europe using satellite imagery, specifically leveraging Sentinel-2 time series data and GEDI LiDAR measurements as training data. The proposed method significantly improves accura... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for acknowledging the contributions of our work. Let us address your concerns and questions one by one.
> More references to remote sensing timeseries understanding literature such as [1] can be discussed.
Thank you for the suggestion. We have added additio... | null | null | null | null | null | null |
ReferSplat: Referring Segmentation in 3D Gaussian Splatting | Accept (oral) | Summary: This paper introduces a new task—Referring 3D Gaussian Splatting Segmentation (R3DGS), which aims to segment target objects in 3D Gaussian Splatting scenes based on natural language descriptions. The authors construct the dataset specifically for this task, named Ref-LERF, and propose a framework called ReferS... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on our work: clever, reasonable, effective PCMI and GTCL design, and comprehensive ablation studies.
>**Q1&Q2: Generalization ability**
**A1:** The experiments in our main paper follow a per-scene optimization setup, which naturally limits direct ge... | Summary: This paper formulates the Referring 3D Gaussian Splatting Segmentation (R3DGS) task, which focuses on segmenting 3D entities that correspond to a given referring-expression in the form of a language-based query. The R3DGS differs by the currently employed task formulation of open-vocabulary 3D segmentation by ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on our work: meaningful, reasonable, and thorough.
>**Q1: Claim 1**
**A1:** In established 2D/3D referring expression segmentation (RES) tasks, referring segmentation involves segmenting target objects based on free-form natural language expressions... | Summary: The paper introduces ReferSplat, a framework for Referring 3D Gaussian Splatting Segmentation (R3DGS), aiming to segment 3D objects based on **natural language descriptions**, even when occluded or not directly visible. Key contributions include:
1. **R3DGS Task**: A new task requiring 3D multi-modal understa... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on our work: novel, well-motivated, effective PCMI and GTCL design, and well-written.
>**Q1: Diversity comparison to existing datasets**
**A1:** The comparision to ScanRefer and Multi3DRefer is shown in the table below. Due to the limited availabili... | Summary: - This paper introduces Referring 3D Gaussian Splatting Segmentation (R3DGS), a task aimed at segmenting target objects in a 3D Gaussian scene based on natural language descriptions.
- The proposed method addresses key challenges, including identifying occluded objects in novel views.
- The authors present Ref... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on our work: dataset contribution, SOTA result, relationship modeling, and exhaustive ablations.
>**Q1&Q7: Detailed evidence like video**
**A1:** We have provided additional qualitative video results at **[ReferSplat.mp4](https://anonymous.4open.sci... | null | null | null | null | null | null |
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs? | Accept (poster) | Summary: The paper investigates modality imbalance issues in existing VLMs by designing multiple tasks with diverse settings. Comprehensive experiments reveal interesting findings, and gradient analysis further illustrates how different settings impact the final results.
Claims And Evidence: Mostly, but with some cave... | Rebuttal 1:
Rebuttal: # Authors' Response to Reviewer NF8o
We thank the reviewer for their time and effort to review our paper. Please find responses to your comments and questions below.
# Questions
> Experiments are restricted to Synthetic Tasks
We use synthetic tasks to enable controlled studies of modality imbal... | Summary: This paper studies visual reasoning using vision-language models (VLMs). The authors focus on three tasks: Table Readout, Grid Navigation, and Visual Analogy. They run experiments under simple to hard settings to test each task's generalization.
They propose distilling knowledge from large language models (LLM... | Rebuttal 1:
Rebuttal: # Authors' Response to Reviewer d6eT
We thank the reviewer for their comments and suggestions. Please find our responses to your comments below.
> Is the paper related to knowledge distillation or prompt engineering?
We would like to clarify that **we do not employ any knowledge distillation**.... | Summary: This work investigates the modality imbalance in simple-to-hard generalization of VLMs. The main findings are: Explicit image-to-text conversion is important in improving S2H generalization on images, and the conversion can be internalized at test time.
## update after rebuttal
The rebuttal partly solves my c... | Rebuttal 1:
Rebuttal: # Authors' Response to Reviewer pko5
We thank the reviewer for their thoughtful comments and suggestions regarding the paper. Please find our responses to your comments below.
# Weaknesses
> Experiments are limited to one base model
Please see our reply to Reviewer fXux. We observe the same con... | Summary: This paper investigates the "modality imbalance" problem in Vision Language Models (VLMs), where models perform worse on visual reasoning tasks compared to equivalent text-based tasks. The authors introduce a framework for studying simple-to-hard (S2H) generalization in VLMs using three synthetic tasks: Table ... | Rebuttal 1:
Rebuttal: # Authors' Response to Reviewer fXux
We thank the reviewer for their careful review of our paper. Please find responses to your concerns and questions below.
# Weaknesses
> Experiments are limited to one architecture
We used EAGLE because it was the best performing open-source model which also ... | null | null | null | null | null | null |
MoRAgent: Parameter Efficient Agent Tuning with Mixture-of-Roles | Accept (poster) | Summary: This work presents a novel framework to fine-tune LLMs to solve agent-specific task in a parameter-efficient manner. Specifically, the capabilities of agent are firstly decomposed into three roles. A relative fine-tuning framework MoR and a multi-role data generation pipeline are subsequently proposed to ensur... | Rebuttal 1:
Rebuttal: Dear Reviewer Mktg:
Grateful for your support and helpful review. All concerns and questions are meticulously responded.
**1. Experiments with a single role is supposed to be conducted.**
Thanks for the helpful suggestion. We supplement the experiment of a single lora without roles on BFCL lead... | Summary: The paper introduces a novel parameter-efficient fine-tuning method to enhance LLMs for agent tasks, such as function-calling and mathematical reasoning. The authors propose three main strategies: (1) decomposing agent capabilities into three roles—reasoner, executor, and summarizer—based on the Reason+Action ... | Rebuttal 1:
Rebuttal: Dear Reviewer rpMC:
We sincerely thanks for your support and meticulous review. The concerns and questions are answered as follows.
**1. How is the rule-aware gate implemented during training? Are there labels in the training data indicating which role should be active for each token?**
Yes! Yo... | Summary: This paper proposes multiple strategies to improve the efficiency of applying PEFT to agent. First, the capabilities necessary for the agent tasks are decomposed into three distinct roles: reasoner, executor, and summarizer. The Mixture-of-Roles (MoR) framework, which comprises three specialized LoRA groups, e... | Rebuttal 1:
Rebuttal: Dear Reviewer MDgP:
We deeply appreciate your support and insightful feedback, detailed rebuttals to all queries are provided.
**1. The difference between ours and α-UMi.**
α-UMi decomposes the agent ability into planner,executor and summarizer. However, each role is implemented by a separate L... | Summary: This paper explores parameter-efficient fine-tuning (PEFT) methodologies for large language model (LLM)-based agent tasks, an area that remains largely unexplored. The authors propose three key strategies:
1. Role Decomposition: Inspired by the Reason+Action paradigm, the authors decompose agent capabilities ... | Rebuttal 1:
Rebuttal: Dear Reviewer QZb4:
Sincerely thank you for your constructive review, the concerns and questions are answered in detail.
**1. What's the gain by SFT on the same datasets, w/o lora? and other PEFT methods?**
Thanks for the helpful suggestion. Based on the same multi-roles dataset, we supplement ... | null | null | null | null | null | null |
Tight and Fast Bounds for Multi-Label Learning | Accept (poster) | Summary: This paper provide general theoretical guarantees for the generalization of multi-label learning. By developing and leveraging a novel vector-contraction inequalities for smooth base losses, the author induces tight generalization bounds for multi-label learning that have no dependency on the number of labels.... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
The following are our responses to the Questions:
**1. Response to Weakness.**
We will add concrete examples of multi-label methods after the definition of the function class to improve... | Summary: The paper focuses on the theoretical analysis of multi-label learning, particularly in the context of smooth base loss functions. The authors introduce novel vector-contraction inequalities and derive tighter generalization bounds for multi-label learning with smooth base loss functions. These bounds exhibit i... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
The following are our responses to the Questions:
**1. Response to Weakness 1.**
As the reviewer pointed out, when there is some type of label correlation between the labels of the datas... | Summary: This paper investigates the generalization bound of multi-label loss functions. Specifically, for smooth base loss functions, the authors improve the generalization bounds by removing the dependency on the number of labels $c$. By exploiting local Rademacher complexity, the authors further improve the bound fr... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
The following are our responses to the Questions:
**1. Response to Weakness 1.**
As the reviewer commented, the theoretical results for unbounded base losses need to be further explored ... | Summary: By incorporating smoothness assumption, author provides generalization guarantee achieving a tighter bound - independent of c, the number of labels up to log factors, a faster bound - 1/n, and a similar tighter bound for Macro-averaged AUC.
Claims And Evidence: Mostly seems sound, but I have a question.
Zhang... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
The following are our responses to the Questions:
**1. Response to the Question in Claims And Evidence.**
Here we mention that the bounds with a square-root dependency on $c$ in literatu... | Summary: This paper focuses on the problem of multi-label classification, where each instance can be associated with multiple labels simultaneously. The authors derive several generalization bounds for this setting, assuming smooth loss functions. Their analysis relies on standard techniques for characterizing the comp... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
**1. Response to C1**
We add concrete examples of multi-label learning (MLL) methods after the function class definition to improve readability and practical interpretation, please refer ... | null | null | null | null |
Highly Compressed Tokenizer Can Generate Without Training | Accept (poster) | Summary: This paper proposes an optimization-based method to tweak the latent space of tokenizer for image editing tasks.
Claims And Evidence: - I think this paper highlights the generation ability of the 1D tokenizer, specifically TiTok. However, I find using the word of "image editing" or "image variation" is better... | Rebuttal 1:
Rebuttal: Thank you for your review\! We hope to address some of your concerns in this response.
* **Image Editing vs. Generation**
We decided to choose the "image generation" nomenclature in line with previous work on so-called retrieval-augmented generative models, such as RDM \[1\].
**Furthermore, as ... | Summary: The paper finds that tokens in the latent space of 1D tokenizers are strongly correlated with specific attributes of images (e.g. captured subject, lighting, background).
The authors build on this finding and propose gradient-based text-guided image editing and inpainting algorithms that optimize these 1D toke... | Rebuttal 1:
Rebuttal: We thank you for your thorough and detailed comments and suggestions, as well as the thoughtful questions. We hope the following answers can address your concerns.
* **mec.q.1.** Optimizer Iterations
The assessment that the number of optimizer iterations will result in the input image deviating ... | Summary: This paper introduces a generative pipeline leveraging a 1D image tokenizer (e.g., TiTok) to enable image editing and generation without training a dedicated generative model. By compressing images into highly compact 1D token sequences (e.g., 32 tokens), the authors demonstrate that simple token manipulations... | Rebuttal 1:
Rebuttal: We thank you for your review! As you point out, the idea of latent space optimization is indeed not new. However, we do believe that its application in the case of highly compressed latent spaces is noteworthy for a few reasons:
1. **Previous attempts to use test time latent space optimization fo... | Summary: This work shows that a highly compressed 1D token set can learn different attributes in tokens, and perform generaion tasks such as inpainting and text-guided image editing with only a tokenizer, without any extra generative model training.
Claims And Evidence: The claims are supported by experiments. The aut... | Rebuttal 1:
Rebuttal: Thank you for your review and helpful comments\!
* **Per-Token Attributes as Emergent Properties**
The direct token editing examples do indeed rely on emergent properties that are not controllable using the standard autoencoder-style/VQGAN training scheme used by TiTok.
As such, we agree that p... | Summary: The submission explores training-free image generation on TiTok's 1D tokenizer. It builds upon the observation that a heavily compressed tokenizer, like TiTok-L-32, is somewhat amenable to interpretable manipulation and editing of latents. The authors first demonstrate that by varying individual tokens, and by... | Rebuttal 1:
Rebuttal: We are happy to hear you enjoyed our paper, and would like to thank you for the great questions, which we hope to answer below.
* **Q1.** 2D Tokenizer Results
We have produced visualizations of the optimization process using MaskGIT’s VQGAN, alongside the 32-token TiTok tokenizer, which we will ... | null | null | null | null |
Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs | Accept (poster) | Summary: In this work the authors study quadratic optimization problems with linear constraints and answer the question if the objective value, feasibility and optimal solution of this problem class can be approximated by a class of graph neural networks. More precisely they study the standard graph representation of L... | Rebuttal 1:
Rebuttal: __Reply to "Methods And Evaluation Criteria":__ Thank you for the encouraging comments, and we really appreciate it. We agree that generalization analysis is an important direction, especially for structured problems like LCQPs and MI-LCQPs, and we will highlight it as a key avenue for future work... | Summary: This work proves that message-passing GNNs can universally represent fundamental properties of quadratic programs, including feasibility, optimal objective values, and optimal solutions. They also prove that GNNs are not universal for mixed-integer problems.
Claims And Evidence: Yes.
Methods And Evaluation C... | Rebuttal 1:
Rebuttal: __To reviewer:__ Thank you for your insightful comments. Due to the 5000-character limit, our responses must be brief, but we’d be happy to elaborate on any specific points in the next stage of rebuttal.
__Reply to "Essential References Not Discussed":__ Thanks for highlighting this relevant work... | Summary: This paper provides a theoretical analysis to investigate the expressive power of standard Message-Passing GNNs (MPGNNs) in solving the Linearly Constrained Quadratic Program (LCQP) and Mixed-Integer (MI) LCQP tasks. Specifically, the paper focuses on three mappings
with MPGNNs: feasibility mapping, optimal ob... | Rebuttal 1:
Rebuttal: __To reviewer:__ Thank you for your valuable comments. Due to the 5000-character limit, our responses must be brief, but we’d be happy to elaborate on any specific points in the next stage of rebuttal.
__Reply to "Experimental Designs Or Analyses":__ We agree that the training error on the Maros-... | Summary: The paper establishes that message-passing GNNs can express the feasibility, optimal value and optimal solution of convex linearly constraint quadratic programs as well as of mixed-integer linearly constraint quadratic programs, if they adhere to certain conditions often true in practice. Negative results are ... | Rebuttal 1:
Rebuttal: __To reviewer:__ Thank you for your detailed comments! Due to the 5000-character limit, our responses must be brief, but we’d be happy to elaborate further in the next rebuttal stage.
__Reply to "Claims And Evidence":__ We will clearly state the convex assumption in the revision:
1. **Page 2 (Con... | null | null | null | null | null | null |
Symmetry-Robust 3D Orientation Estimation | Accept (poster) | Summary: This work presents a full orientation estimation method for generic shapes. Concretely, a two-stage framework is proposed for this task. The method first uses a quotient orienter to recover the shape's orientation up to octahedral symmetries by continuous regression. Then a flipper is employed to predict one o... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and your helpful comments. In this rebuttal, we will address many of the questions you have asked in your review.
*In the experiments, the ShapeNet dataset is divided into a 90% training split and a 10% testing split. Then, where does the validation set come f... | Summary: This paper explores the challenges faced by current baselines that aim at predicting 3D-shape orientation. It shows that trying to minimize an L2 distance cannot recover the ground-truth orientation in the presence of intrinsic symmetries as the solution to the L2 distance will not be unique. To address this c... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and your helpful comments. In this rebuttal, we will answer the two questions you have posed to us in your review.
*We are wondering in lines 296-306 of the experimental setup. It seems the proposed method is trained on 10K points per shape and 2K points are s... | Summary: The paper presents a two-stage deep learning method to orient shapes, dubbed Orient Anything. It proves why naive L2 regression of the orientation matrix fails for shapes with symmetries, and presents a theoretical framework to overcome the problem. The method consists in selecting a finite group $\hat{R}$ tha... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and your helpful comments. We will gladly fix the typos you have pointed out and make the changes to the figures that you have requested in the camera-ready. **Please see our response to Reviewer 3giS for links to new versions of Tables 1 and 2 and Figure 6, wh... | Summary: The paper introduces a two-stage method for estimating the pose of an object (3D point cloud). In the first stage, the pose is regressed modulo octahedral symmetries, which prevents prediction collapse for symmetric objects. In the second stage, the remaining octahedral ambiguity is resolved through classifica... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and your helpful comments. We address your questions below.
**Proposition 3.2:**
*If I'm not mistaken, the proof shows that there exists an $f^\*$ of the specified form, not that all minimizers are of the specified form (as claimed in the prop.).*
This is c... | null | null | null | null | null | null |
DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs | Accept (oral) | Summary: The paper introduces DistiLLM-2, a novel approach to distilling knowledge from large language models (LLMs) into smaller, more efficient student models. DistiLLM-2 employs a contrastive approach that leverages the synergy between loss formulations and data types, simultaneously increasing the likelihood of te... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments. We have rephrased the question for ease of reference and provide our corresponding responses below. Look forward to any further discussions.
***Q1. Inclusion of acceptance rates in speculative decoding analysis for completeness***
**A1.** Thank you for t... | Summary: This paper addresses the critical challenge of compressing large language models (LLMs) for practical deployment by focusing on knowledge distillation (KD). The authors highlight the limitations of existing KD approaches, which primarily focus on either optimizing loss functions (like Kullback-Leibler divergen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. Below we address each concern in detail. Look forward to any further discussions.
***Q1. Lines 152–156 might be confusing – rephrasing suggested***
A1. Thank you for the suggestion. We will revise Lines 152–156 to improve clarity. This parag... | Summary: This paper introduces DistiLLM-2, a contrastive approach for LLM distillation, optimizing student models by increasing teacher response likelihood (SKL loss) and decreasing student response likelihood (SRKL loss). It improves data curation, adaptive learning, and curriculum-based loss weighting, outperforming ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. Below we address each concern in detail. Look forward to any further discussions.
***Q1. Empirical validation of the approximation in Equation (7) for sequence-level probabilities***
**A1.** Thank you for pointing out the potential mismatch ... | Summary: The paper introduces DISTILLM-2, a novel approach for LLM knowledge distillation. Unlike prior work that applies identical loss functions to both teacher- and student-generated data, DISTILLM-2 leverages a contrastive loss function to explicitly increase the likelihood of teacher responses while decreasing tha... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have rephrased your comments for simpler reference and have included our respective responses. Look forward to any further discussions.
***Q1. Use of models up to 9B; interest in evaluating scalability to larger models***
Thank you for the insightful co... | null | null | null | null | null | null |
Focus On This, Not That! Steering LLMs with Adaptive Feature Specification | Accept (poster) | Summary: The paper proposes a modification to the typical instruction-tuning process by including a "focus prompt" in the context, to guide the model to focus on certain aspects of the user input. Experiments on two synthetic settings demonstrated the effectiveness of the proposed method over vanilla SFT and few-shot b... | Rebuttal 1:
Rebuttal: ## P1: Spurious Feature Knowledge and Baselines
**Simpler Baselines suggested by the reviewer:**
- **Test-Time Prompting using focus instructions without FIT training:** Already tested in our paper via:
- **Zero-Shot** (lines 318–321, Figures 6–8)
- **SFT(y)** (line 1053, Figures 6–8)
*... | Summary: The main aim of this work is to finetune language models to be robust to known spurious correlations and features. To this end, Focus Instruction Tuning (FIT) is introduced, a method for instruction tuning language models with prioritization towards certain features, and “ignoring” others. the approach is ba... | Rebuttal 1:
Rebuttal: ## P1: SS Dataset, Keyword Feature Choices, and Theoretical Assumptions
The SS dataset provides a controlled setting to verify FIT’s effectiveness by comparing focus accuracies against theoretical predictions without confounding artifacts present in more complex datasets (e.g., BBQ, SMNLI). These ... | Summary: This work presents a training method to improve model steerability w.r.t specific features. The main idea is to add instructions during training about what to focus on and what to ignore. The trained model is evaluated on a modified SST dataset, a modified MNLI dataset, and BBQ, and it demonstrates significant... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments regarding our introduction, specifically noting the “significantly better steerability” and overall “effectiveness” of our method. We hope that this rebuttal response addresses your additional comments regarding our paper.
## P1: Showing Further Generalisa... | null | null | null | null | null | null | null | null |
Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries | Accept (poster) | Summary: This work revisits the problem of robust cardinality sketches for adaptive queries and provides improved results. In the classic cardinality sketch, the queries are independent of the sampled sketch, and the sketches can answer an exponential number of queries (in the sketch size $k$). In the adaptive queries ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and comments. | Summary: Cardinality sketches support computing a small sketch/summary of a set of keys S from a universe U. The sketch supports estimating |S|. This problem is trivial without further requirements as one can merely store |S| in log(|U|) bits. A cardinality sketch thus further requires that given sketches of two sets A... | Rebuttal 1:
Rebuttal: Thank you very much for your time and comments.
**> The biggest weakness is the section with the simulated data and the plots, which I think would benefit from a better explanation.**
We will make sure to add a more clear explanation of the empirical evaluation for the full version of the paper.... | Summary: The paper addresses the problem of sketching under the additional constraint that each key appears in at most $r$ queries. This setting generalizes prior work by introducing a finer-grained robustness notion based on per-key participation rather than the total number of queries. The main result establishes tha... | Rebuttal 1:
Rebuttal: Thank you very much for your time and comments.
**> The paper lacks a rigorous, self-contained problem formulation, making it difficult for those unfamiliar with sketching literature to follow the argument effectively. The definition of "query" is ambiguous in the introduction and should be clari... | null | null | null | null | null | null | null | null |
Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift | Accept (poster) | Summary: This paper explores the integration of automated computational phenotypes (ACPs) in semi-supervised learning settings. ACPs are used to derive phenotype data from electronic health records (EHRs) using machine learning models, reducing the labor-intensive nature of manual phenotype extraction. However, direct ... | Rebuttal 1:
Rebuttal: *Q1: [Claims and Evidence]*
**Response:**
- Thank you for this insightful comment! To understand the benefits of incorporating ACP $\hat Y$, we introduce an assumption with a format similar to covariate shift, as presented in Lines 181-192. In real world scenarios, this assumption can be tested w... | Summary: This paper introduces a semiparametric framework for efficient inference under covariate shift by leveraging automated computational phenotypes (ACPs). The authors propose a doubly robust, semiparametrically efficient estimator for a target parameter $\beta$ by integrating ACPs, density ratios, and conditional... | Rebuttal 1:
Rebuttal: *Q1: [Methods and Evaluation Criteria]*
**Response:**
- Thank you. We provided a more comprehensive response in our answer to Q1 of reviewer mNmv. Please kindly refer to that section for details.
*Q2: The simulations...[Experimental Designs or Analyses]*
**Response:**
- Yes. We conducted compar... | Summary: This paper proposes an approach that leverages both labeled and unlabeled data to estimate target parameters. The approach first uses the pre-trained model to estimate $Y$ for the unlabeled data and then uses the estimated $\hat{Y}$ to estimate the target parameters. The proposed approach is applied to both si... | Rebuttal 1:
Rebuttal: *Q1: However, it is a bit unclear...[from Methods and Evaluation Criteria]*
**Response**:
- Thanks for raising this question. In general, the outcome $Y$ and covariate $X$ are variables of scientific interest. The variable $Z$ represents additional information that may not be of direct scientifi... | null | null | null | null | null | null | null | null |
TeDS: Joint Learning of Diachronic and Synchronic Perspectives in Quaternion Space for Temporal Knowledge Graph Completion | Accept (poster) | Summary: This paper proposes TeDS, a temporal knowledge graph completion method cosidering both diachronicic and synchronic flows within and between temporalized facts. Extensive experiments demonstrate that TeDs is capable of achieving state-of-the-art performance across multiple benchmarks.
Claims And Evidence: The ... | Rebuttal 1:
Rebuttal: Thanks for all your valuable comments. Note: The pictures and tables used in response are available at https://anonymous.4open.science/r/TEDS-033A/To_Re_rpDA.pdf See: To_Re_rpDA.pdf
Q1: The performance improment compared with baselines is more marginal on first three datasets (Table2), could the ... | Summary: This paper introduces TeDS, which is a unified framework to simultaneously consider both diachronic timestamp and synchronic timestamp for TKGC. TeDS achieves significant improvements over the existing SOTA on six datasets.
Claims And Evidence: The effectiveness of modeling TKGs from the perspectives of synch... | Rebuttal 1:
Rebuttal: Thanks for all your valuable comments. The pictures and tables used in response are available at https://anonymous.4open.science/r/TEDS-033A/To_Re_WGeZ.pdf See: To_Re_WGeZ.pdf
Q1: The concepts of diachronic timestamp and synchronic timestamp appear to correspond to the temporal and structural dep... | Summary: The paper introduces a quaternion-based model for temporal knowledge graph completion that integrates diachronic and synchronic perspectives. The model demonstrates significant improvements over state-of-the-art methods on six benchmark datasets, showcasing its effectiveness in handling both short and long tem... | Rebuttal 1:
Rebuttal: Thanks for all your valuable comments. Note: The pictures and tables used in response are available at https://anonymous.4open.science/r/TEDS-033A/To_Re_fTHA.pdf See: To_Re_fTHA.pdf
Q1: A more detailed discussion on model's computational complexity and scalability would strengthen the claims. The... | Summary: The paper introduces TeDS, a framework designed for temporal knowledge graph completion using quaternion representations to merge time and relational data. Key findings show that TeDS significantly outperforms existing models on various benchmarks, effectively managing issues like data sparsity and incompleten... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review and evaluate our manuscript. Your comments have not only helped us improve the manuscript but also given us confidence to further enhance quality of our work. Note: The pictures and tables used in response are available at https://anonymous.4open.science/r/T... | Summary: The paper proposes TeDS, a novel temporal knowledge graph completion (TKGC) model that jointly learns diachronic (temporal evolution) and synchronic (cross-relation interactions) perspectives in quaternion space. The key innovations include: 1) Dual temporal perception through synchronic (time-relation composi... | Rebuttal 1:
Rebuttal: Thanks for all your valuable comments. Note: The pictures and tables used in response are available at https://anonymous.4open.science/r/TEDS-033A/To_Re_cu6z.pdf See: To_Re_cu6z.pdf
Q1: The claim in Section 3.1 about handling various temporal constraints lacks explicit evaluation on datasets with... | null | null | null | null |
Sample-efficient diffusion-based control of complex nonlinear systems | Reject | Summary: The paper presents SEDC, a new approach to improving how we control complex systems using limited data. Traditional methods struggle with high-dimensional spaces, nonlinear behaviors, and the challenge of learning from imperfect training data. SEDC tackles these problems with three key ideas: Decoupled State D... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's feedback. Our responses are as follows.
Tip: Please visit the link(https://drive.google.com/file/d/1VWaCyEv0NPMPPqCdVfgJDPXoTiuN76MV/view?usp=sharing) for new Tables and Figures.
**1. New optimal control baseline**
We additionally compare SEDC with learnin... | Summary: This paper introduces SEDC (Sample-Efficient Diffusion-based Control), a novel diffusion-based framework designed for controlling complex nonlinear systems while addressing key challenges in sample efficiency, high-dimensional state-action spaces, and non-optimal training data. The proposed approach incorporat... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer vifZ for thorough review and strong recommendation. We greatly appreciate your positive assessment of our work, particularly your recognition of our experimental design, ablation studies, and the potential impact of our work.
**Response to extending SEDC to stochastic ... | Summary: 1. The paper proposes a diffusion-based controller for high-dimensional nonlinear systems.
2. A diffusion model is used to generate a sequence of states y, and an additional autoregressive MLP is used for learning the control inputs through inverse dynamics.
3. Gradient-guidance during the reverse process an... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's feedback. Our responses are as follows.
Tip: Please visit https://drive.google.com/file/d/1JmK5ZuMIg0CJCf1L2fQqobK6gtueOgts/view?usp=sharing for new tables and figures.
**1.The core innovations**
The gradient guidance and in-painting for goal conditioning ... | Summary: The paper presents SEDC, a novel diffusion-based control framework designed to achieve sample-efficient and robust control of complex nonlinear systems. SEDC is developed to overcome challenges associated with high-dimensional state–action spaces, strongly nonlinear dynamics, and the scarcity of optimal traini... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer qYwS for positive assessment of our work and thorough review.
**Response to extending SEDC to stochastic control problems:**
We appreciate the reviewer's insightful suggestion regarding potential extensions to Schrödinger bridge problems and stochastic optimal control... | null | null | null | null | null | null |
LAuReL: Learned Augmented Residual Layer | Accept (poster) | Summary: Authors propose extensions to ResNet blocks that can improve performance with minimal addition of parameters. The extensions modifies the residual connection by adding a learnable transformation to it and/or utilises the output of previous layers.
Claims And Evidence: OK, see below.
Methods And Evaluation Cr... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions.
#### __Ablation study with practical footprint metrics__
Thank you for suggesting this experiment. This section demonstrates improvements of LAuReL variants individually, as well as when they are combined.
For the purpose of comparing different LAuRe... | Summary: The paper introduces Learned Augmented Residual Layer (LAUREL), a novel enhancement to residual connections in CNNs and Transformers. LAUREL enriches the residual stream by incorporating learned scalar parameters and low-rank transformations, improving efficiency and expressivity.
Key Contributions:
1. Thre... | Rebuttal 1:
Rebuttal: Thank you for your kind comments and valuable feedback!
#### __Justification into why specific variants perform better under certain conditions__
Deep networks generally do better with reasoning, math, coding etc. tasks. However, residual connections are crucial for such networks, and LAuReL help... | Summary: The paper introduces a new method for residual connections with an additional layer called Laurel. Their method involves introducing learnable parameters into the residual stream, which the authors argue might be too restrictive in its original form. The learnable parameters allow the authors to decide how muc... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions.
#### __Tuning $r$__
Since is $r$ the rank of $A$, $B^T$, we expect $r \ll D$. Indeed, if $D = 512, 768, 1024, …$, this leaves a small range of discrete values for $r$ (unlike hyperparameters such as learning rate, weight decay, that can take continuo... | Summary: The paper introduces LAUREL (Learned Augmented Residual Layer), a new generalization of residual connections that can replace standard skip connections in neural networks. LAUREL outperforms traditional residual connections in both model quality and efficiency across vision and language tasks. When tested on I... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions.
#### __Ablation study with practical footprint metrics__
Thank you for suggesting this experiment. For the purpose of comparing different LAuReL variants on the LLM pre-training task, we set up the following baseline. We pre-trained on the C4 corpu... | null | null | null | null | null | null |
N2GON: Neural Networks for Graph-of-Net with Position Awareness | Accept (poster) | Summary: This paper introduces Graph-of-Net (GON), a novel graph structure where each node is itself a graph, enabling multi-level modeling of complex systems that involve hierarchical relationships. Examples include biological networks (e.g., protein-protein interactions, where individual proteins are represented as g... | Rebuttal 1:
Rebuttal: **Q1. The Introduction's definition of Graph-of-Net (GON) is vague and needs a precise mathematical formulation of each node-as-graph (size, structure, features) and clarification of how it differs from traditional graphs in modeling hierarchical, multi-dimensional relationships.**
>R1: Thank you... | Summary: The paper introduces a novel framework called Graph-of-Net (GON), which extends traditional graph structures by modeling each node as a graph itself, creating a multi-level perspective on relationships between objects. This approach enables the capture of both the internal structure of individual nodes and the... | Rebuttal 1:
Rebuttal: **Q1. The term "Position Awareness" is not strictly defined in the paper. A formal definition could be added for clarity.**
>R1: Thank you for highlighting the need for a more formal definition of the term "Position Awareness." In our work, we use "Position Awareness" to refer to the model’s abi... | Summary: The paper N2GON presents a new approach to graph learning, with a focus on the Graph-of-Net structure and a position-aware neural network model. The comprehensive experimental evaluation and detailed methodology are significant strengths. However, the paper could be further improved by including runtime compar... | Rebuttal 1:
Rebuttal: **Q1. It is recommended to add runtime comparisons.**
>R1: Thank you for your comment. We conducted the runtime experiments and the results (the average elapsed time per epoch), summarized in the table below, indicate that on benchmark graphs, our runtime is comparable with that of SOTA baselines... | Summary: This paper introduces Graph-of-Net (GON), a novel graph structure where each node itself is a graph, enabling multi-level representation and analysis of complex real-world systems. To effectively learn representations within GONs, the authors propose N2GON, a position-aware neural network that captures both in... | Rebuttal 1:
Rebuttal: **Q1. The partitioning of GON —particularly in datasets like CiteSeer—appears questionable, where the partitioning is performed directly using KNN, resulting in computed outcomes that merely replicate the information propagation process of GNN.**
>R1: Thank you for your feedback. Below, we provid... | null | null | null | null | null | null |
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions | Accept (poster) | Summary: The paper studies the setting of cross-learning contextual bandit problem and provides a new analysis to the algorithm in Schneider & Zimmert (2023), showing the algorithm can achieve near optimal bound in high probability. They utilizes the weak dependency structure between different epochs and this approach ... | Rebuttal 1:
Rebuttal: Dear review McaC:
Thank you for your positive feedback. We answer your question below.
---
**Question: The line 319 seems overfull**
Thanks for pointing this out. We will fix it in the new version.
---
**Question: whether this analysis approach can be applied to other bandit algorithms**
T... | Summary: This paper considers contextual bandits with cross learning, where the learner observes the loss associated with the action across all possible contexts. The losses are adversarial, but the contexts are stochastic.
The authors improve the analysis of Schneider & Zimmert (2023) to prove high probability regret ... | Rebuttal 1:
Rebuttal: Dear Reviewer m6dx,
Thank you for your positive feedback. We answer your question below.
---
**Question: the novelty and the broader impact**
The reviewer questioned the novelty of our work, particularly noting that our contribution appears to be “only the analysis.” We would like to clarify t... | Summary: This paper provides a high probability bound for the cross-learning problem in Schneider & Zimmert, 2023, where a regret bound is provided in expectation.
Claims And Evidence: N/A
Methods And Evaluation Criteria: N/A
Theoretical Claims: N/A
Experimental Designs Or Analyses: N/A
Supplementary Material: No ... | Rebuttal 1:
Rebuttal: Dear Reviewer JF8C,
Thank you for your positive feedback. We answer your question below.
---
**Question: the significance of this term $\sum \sum Pr(c) <\pi_c, \tilde{l} _{t, c} - l_{t, c} >$**
To achieve a high-probability bound, saving this term is essential. In Schneider & Zimmert (2023),... | Summary: The submission studies contextual bandits with cross-learning, where the feedback information is the loss of the chosen action in all contexts. The main result is a regret bound that holds in high probability, which improves on a previous bound that holds only in expectation (Schneider & Zimmert 2023).
## Up... | Rebuttal 1:
Rebuttal: Dear Reviewer Kzo3,
We sincerely appreciate your valuable suggestions and thoughtful feedback. Below, we address each of your concerns in detail.
---
**Question: The impact of our results on the theoretical community**
The reviewer noted that “the result seems to be a specific treatment to a s... | null | null | null | null | null | null |
ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy | Accept (poster) | Summary: High content screening (HCS) involves subjecting cells to thousands of perturbations in parallel, and capturing subsequent morphological changes via fluorescent imaging. The scale of data generated my modern experimental workflows has since necessitated automated analysis. In light of the success of foundation... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and encouraging review.
**On training ViT-G/8 on RPI-93M**: We agree this would have been valuable. Due to compute constraints, and the insight gained from training ViT-L/8 on both RPI-93M and PP-16M, we prioritized training ViT-G/8 on the curated PP-16M dataset only.... | Summary: This paper proposes a three-stage framework for pretraining foundation models on large-scale microscopy datasets to address measurement errors and enhance biological signal extraction. The framework involves (1) curating diverse and self-consistent training samples, (2) scaling a vision transformer architectur... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and supportive comments.
**On the cost of whole-genome benchmarking**: This was a key motivation for developing lightweight classification proxy tasks. Selecting a small subset of perturbations for genome-wide benchmarks is challenging due to the random distrib... | Summary: The authors present a framework for training large-scale computer vision models for microscopy imaging data.
Claims And Evidence: The authors claim to train a large-scale ViT model that should work better than a previous model by Kraus et al, 2024.
The evidence for that is quite unclear and scarce: The tabl... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful comments.
**On tables and metrics clarity**: We apologize for not clearly marking the Kraus et al. 2024 model in the tables. As noted in the “Models/prior work” section, MAE-ViT-L/8+ trained on RPI-93M is from Kraus et al. In Table 1 and 3, we repo... | Summary: This paper provides a framework to improve biological representation learning on large-scale microscopy datasets. Three steps are introduced: (1) Curating the training dataset to have a better distribution of samples over the phenotypic spectrum, (2) scaling training to larger models, and (3) evaluating interm... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback.
**On the significance of data curation**: Filtering is critical even in large language models. Penedo et al [2023] highlight the value of de-duplication in RefinedWeb. While such techniques (e.g., MinHash) don't directly apply to images, ... | null | null | null | null | null | null |
Bayesian Weight Enhancement with Steady-State Adaptation for Test-time Adaptation in Dynamic Environments | Accept (poster) | Summary: This paper proposes steady-state adaptation (SSA), a novel test-time adaptation (TTA) method that can be combined with existing ones.
SSA aims to reduce noise accumulation in gradients caused by the unsupervised nature of a TTA loss (e.g., entropy).
SSA models the distribution of the model weights and estimate... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable review and insightful comments. We have carefully examined your feedback, fully understood the concerns and questions you raised, and have made every effort to reflect them thoroughly and faithfully in our manuscript.
**Methods And Evaluation Criteria:**
> A... | Summary: This manuscript proposes a novel Bayesian-based framework to enhance existing weight-based TTA methods. They investigate the distribution shifts issue and reflect the reason behind gradient noise. A tailored steady-state adaptation algorithm shows SOTA performance on several benchmarks.
Claims And Evidence: T... | Rebuttal 1:
Rebuttal: We deeply appreciate your valuable review and insightful comments. We have carefully considered your feedback, fully understood the concerns and questions raised, and endeavored to address them thoroughly and sincerely.
**Relation To Broader Scientific Literature:**
> However, the paper's exclus... | Summary: This paper proposes using a stochastic differential equation (SDE) to handle temporal distribution shifts in test-time adaptation scenarios. The SDE is applied to handle the temporal dynamics of stochastic gradient descent, balancing the current updates of the model weight with that of the pre-trained model. T... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable review and insightful comments. We have carefully examined your feedback, fully understood the concerns and questions raised, and have earnestly endeavored to address them.
**Claims And Evidence:**
> However, the approach appears to primarily apply an SDE to... | Summary: Test-time adaptation assumes that only the inputs of the test dataset are given for adaptation, where the model parameters are updated using an unsupervised loss without labels. Consequently, the model parameters are inevitably updated by a noisy gradient, which differs from the gradient obtained using true la... | Rebuttal 1:
Rebuttal: We deeply appreciate your valuable review and insightful comments. In line with your suggestions, we are extending our research to address a general problems of DNNs. Once again, thank you very much for your thoughtful feedback. | null | null | null | null | null | null |
A Sample Efficient Conditional Independence Test in the Presence of Discretization | Accept (poster) | Summary: This paper addresses the critical challenge of conducting conditional independence tests on discretized data, where traditional methods often fail due to information loss from binning. The authors propose DCT-GMM, a new method leveraging the Generalized Method of Moments to infer latent continuous variable rel... | Rebuttal 1:
Rebuttal: > Multivariate Gaussian distribution.
Thank you for raising the concern. We fully agree that the assumption of Gaussianity will limit the generality of the proposed test. At the same time, please allow us to share a few points regarding its reasonableness:
1. **Challenges in Conditional Independe... | Summary: This paper introduces a Conditional Independence (CI) test designed for scenarios where continuous data is represented at a discretized level due to measurement limitations. In such settings, applying standard CI tests directly can lead to incorrect conclusions. Assuming the continuous data follows a multivari... | Rebuttal 1:
Rebuttal: > The multivariate normal distribution assumption.
Thank you for raising the insightful concern. Please kindly refer to our response~1 to Reviewer 9WLL for a detailed explanation. Due to the 5000-character limit, we were unable to include the full response here. We apologize for the inconvenience... | Summary: This work tackles the problem of detecting conditional independence among hidden continuous variables, while the observed variables are discrete. Specifically, the authors rely on a recent work, the Discretization-Aware CI Test (DCT), which establishes a workflow to estimate covariances between continuous vari... | Rebuttal 1:
Rebuttal: > Most of the claims presented are clear, except for Theorem 3.5.
Thank you for this construction comments. We followed your comment and carefully revised Theorem 3.5 to make it more intutive and clear. Note that the formal theorem demonstrating the superiority of DCT-GMM over DCT is provided in... | Summary: The paper proposes a conditional independence (CI) test for testing CI relations in discretized data. It does not rely on binarizing the data to infer the CI relations between latent variables. The paper argues that it does not need to rely on binarization like the previous work to establish correct CI relatio... | Rebuttal 1:
Rebuttal: > Some practical scenarios
Thank you for your valuable question. We appreciate the chance to highlight the **common and often unavoidable discretization** due to practical measurement constraints. In principle, **any variables measuring "degree" or "intensity"**(e.g., happiness, severity) are i... | null | null | null | null | null | null |
Are Sparse Autoencoders Useful? A Case Study in Sparse Probing | Accept (poster) | Summary: This paper studies the benefits and limits of Sparse Autoencoders (SAEs). The topic is quite relevant given they are attracting more and more research in Large Language models, specifically in the context of Mechanistic Interpretability (MI). The authors propose a set of benchmarks to study both probing and in... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work! We are grateful for your time and help, especially related to missing discussion of related work and questions about the automated interpretability techniques we use. We were especially glad to hear that you appreciated the depth to which we studie... | Summary: The paper comprises of several experiments evaluating the efficacy of sparse autoencoder (SAE) approaches to probing.
The paper first focuses on the accuracy of probes under various settings (such as imbalanced data), and finds that SAEs do not improve upon baselines. The paper introduces a "quiver of arrows... | Rebuttal 1:
Rebuttal: We are thankful for your time and help, especially related to your points about the quiver of arrows and the clarity of our interpretability experiments. We were glad to hear that you appreciated the breadth of our experiments and found the problem we are investigating important.
---
> However, ... | Summary: In this work, the authors propose a fair evaluation of SAEs by modelling them as a tool in a practitioner's toolkit, or “quiver of arrows” with the overall question of asking “When is it useful for a practitioner to incorporate SAE probes into their downstream application?”
Claims And Evidence: The author’s c... | Rebuttal 1:
Rebuttal: Thank you for your time and help! We were very glad to hear that you appreciated our use of the quiver of arrows technique and found our evidence and claims clear.
---
> While I do observe evidence that SAE probes contribute less to downstream use cases than other probes, the gap is not incred... | Summary: This paper deals with the problem of evaluating the downstream utility of sparse autoencoders (SAEs). SAEs have recently gained popularity as a means to disentangle concepts learnt by layers of a model, paritcularly LLMs, in order to gain a better mechanistic understanding of their workings. However, evaluatin... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions and comments! We are especially grateful for your suggestions on better latent selection methods, and we are glad you feel our paper is a valuable contribution.
---
> However, since SAE latents (e.g. in TopK SAEs, Gao et al. 2024, L086 right) are not on th... | null | null | null | null | null | null |
Large Displacement Motion Transfer with Unsupervised Anytime Interpolation | Accept (poster) | Summary: This paper presents an anytime interpolation framework for flexible and accurate motion-driven frame generation. Specifically, during training, the model searches for an optimal intermediate time step that produces the highest-quality interpolated frame for training. To ensure valid motion transfer, the author... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments.
Q1: The authors provide several experimental details to validate the algorithm, but some ablation analyses are missing.
A1: Thank you for your comments. The Reviewer 3 LLC asked the same question in Q3.
In the ablation experiment, we mainly validate three m... | Summary: The proposed method advances unsupervised motion transfer by addressing the challenge of large displacement motions through interpolation and strategic training. While it excels in pose accuracy, it faces minor challenges in maintaining appearance details, particularly in complex scenarios. This work provides ... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions you gave. We have incorporated your feedback into the paper.
Q1: The method is reasonable but assumes linear motion, which may not hold for complex cases.
A1: In the interpolation method, we assume that the local keypoints' motion is linear to obtain k... | Summary: This paper propose a unsupervised motion transfer algorithm that can transfer pose in the driving video to the object of the source image so that the source image can copy the movement of the driving video. To be exact, the method decompose complex large displacement motion into many small displacement motions... | Rebuttal 1:
Rebuttal: Thank you for your valuabe comments. We have incorporated your feedback into the maniscript. We believe it will help strengthen the work and present it better.
Q1: The experimental results are not convincing, such as when comparing with state-of-the-art qualitatively, it only shows one state-of-t... | Summary: This paper proposes a novel method for transferring large motion from a driving to a source image. The proposed method is to find a middle step which essentially adds non linearity to the motion transfer. The method generates a set of interpolated in between images based on key point transfers and selects the ... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions.
Q1: The experiments are missing results on TedTalks and VoxCeleb.
A1: The unsupervised optimal interpolation method proposed in this paper aims to address the large motion problem in motion migration. To validate the effectiveness of this method, we se... | null | null | null | null | null | null |
Features are fate: a theory of transfer learning in high-dimensional regression | Accept (poster) | Summary: The manuscript theoretically analyzes the transfer learning from a feature-centric viewpoint. Specifically, the authors consider the deep linear model and two transfer schemes, i.e. linear transfer and fine-tuning. Multiple theoretical results, such as phase diagrams, are established to uncover when transfer l... | Rebuttal 1:
Rebuttal: Dear Reviewer jfTK,
Thank you for your thoughtful review of our work. We are grateful you found the manuscript easy to follow and we appreciate your feedback on areas that can be improved. We respond to your smaller comments below and leave our response to finite source datasets to the end, as th... | Summary: This paper theoretically analyzes transfer learning under a multi-layer neural network model. The exact setting considered is a label shift setting with Gaussian noise and linear targets. The paper analyzes the features learned by the penultimate layer of the linear network and studies how the learned features... | Rebuttal 1:
Rebuttal: Dear Reviewer AyJw,
Thank you for your review of our work. We are glad you found the paper well written and we thank you for your feedback on how to improve the manuscript. First, we appreciate your pointing out the additional references. We will discuss their relevance to our work in the Relate... | Summary: The paper analyzes the transferability capability of deep linear networks. Specifically, it theoretically analyzes the generalization error of deep linear networks when they are trained from scratch versus linear transfer and fine-tuning in a regression problem. The paper also extends this study to the use of ... | Rebuttal 1:
Rebuttal: Dear Reviewer UEjP,
Thank you for taking the time to review our paper. We are happy to hear that you found our results interesting and the presentation clear. We agree that we could be more detailed in our presentation of the simulations. In the final draft of the manuscript we plan to make the c... | Summary: The authors develop a a feature-centric theory of transfer learning, based on their insight that transferability is a property of the learned
feature space and not only of the source and target datasets.
The theory is developed for a deep linear networks and analytically characterizes the transferability phase... | Rebuttal 1:
Rebuttal: Dear Reviewer 65EM,
Thank you for your thoughtful review and feedback. You are correct that the rigorous proofs are limited to deep linear networks. This architecture exhibits certain symmetries which generate a conserved quantity in gradient flow that we exploit heavily to prove convergence. How... | null | null | null | null | null | null |
RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals | Accept (poster) | Summary: **Post-rebuttal edit: In light of the discussion with the authors, in which they were very engaged and forthcoming, I spent a lot of time debating whether I should increase my score from from 2 to 3. I continue to share the concerns of Reviewer pJdZ that the two key assumptions motivating the method are unlike... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review!
A critical point to clarify is that Assumptions 1 and 2 in Section 4 are merely *sufficient* conditions. There’s no reason a priori to expect that imperfect rewrites can be used in causal estimation, so the role of Theorem 4.1 is simply to show that the appr... | Summary: The manuscript presents Rewrite-based Attribute Treatment Estimator (RATE) as a novel approach to estimate the causal effect of response attributes on reward models. It addresses the challenge of reward model opacity by leveraging LLM-generated counterfactual rewrites. While the work is theoretically grounded ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and your support!
We will address some of your higher-level comments and questions first.
- We appreciate your kind words about relevance and novelty. We agree that practical implications are important, which is why we demonstrate the utility of our method on re... | Summary: The paper proposes to evaluate the responsiveness of reward models used for LLM training on certain attributes of interest via average treatment effect and average treatment effect on the (un)treated. To simulate interventions on the interested attributes accurately, the paper proposes to rewrite the response ... | Rebuttal 1:
Rebuttal: Thank you for your review and your detailed comments.
We agree that the assumptions in Theorem 4.1 are somewhat strong, albeit less onerous than they may first appear: the cancellations only need to occur *in expectation*, which is a weaker condition than cancellation for each unit.
However, we ... | Summary: The paper introduces RATE, a framework for understanding reward models in the context of LLMs. The core idea is to do rewrites of rewrites, so that confounding factors (such as typos and sentence length etc.) can be filtered out. Some experiments are done showing the method appears better than the alternative ... | Rebuttal 1:
Rebuttal: Thanks for your review. We agree that the behavior of reward models is an important and understudied area of alignment research.
First, we will respond to some higher level points:
- Regarding your concern about the causal claims, we think there may be a confusion here. We are not defining our c... | null | null | null | null | null | null |
SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models | Accept (poster) | Summary: This submission proposes SelfCite, an approach for LLMs to improve their citation of sentences from the input context to support their responses. SelfCite evaluates the quality of a citation in terms of the LLM's probability of generating the same response without the cited sentences and with only the cited se... | Rebuttal 1:
Rebuttal: We thank Reviewer PWPX for the constructive comments!
> …different notions of "citation" targeted by ContextCite on the one hand and evaluated by LongBench-Cite (and similar benchmarks) on the other hand.
Thanks for pointing out the mismatch between the objectives of corroborative (sources that ... | Summary: This paper proposes an attributable response generation strategy, SelfCite, which cites relevant sentences in the context supportive of the generated response. SelfCite can both operate during inference or during training. For inference, SelfCite pick the Best-of-N using a newly designed reward/score composed ... | Rebuttal 1:
Rebuttal: We thank Reviewer Cy66 for the constructive comments!
## Ours vs ContextCite
> The proposed method makes sense, but not entirely novel, ablation technique has been proposed by ContextCite
While inspired by ContextCite (CC)’s context ablation (L029, right column), our key contribution differs nota... | Summary: This paper proposes a method ("Self-cite") to automatically evaluate cited text using context ablation -- i.e. changing the context and compare probability of generating a given sentence. It then proposes to use this signal to enhance citation quality as the reward model for two approaches: (1) Best-of-N sampl... | Rebuttal 1:
Rebuttal: We thank Reviewer WRpv for the constructive comments!
## A Better Baseline: SimPO with NLI Rewards (Also for **Reviewer Cy66**)
> …ContextCite baseline is not appropriate. … [1] has been proposed to leverage NLI models to measure citation precision / recall, which seems to bit a more appropriate b... | Summary: The paper presents SelfCite, a self-supervised method for improving citation accuracy in Large Language Models (LLMs). The key innovation lies in using context ablation to compute a self-rewarding signal based on necessity and sufficiency scores, which are then used to enhance citation quality through best-of-... | Rebuttal 1:
Rebuttal: We thank Reviewer ci8i for the constructive comments!
## A Comparison Table (Also for **Reviewer Cy66**)
> The paper should more explicitly differentiate SelfCite from previous work… A comparison table summarizing key differences could be helpful.
We follow your suggestion to make a table and poi... | null | null | null | null | null | null |
Toward Efficient Kernel-Based Solvers for Nonlinear PDEs | Accept (poster) | Summary: This paper presents a fair contribution to kernel-based PDE solvers for nonlinear PDEs, improving upon prior methods by eliminating the need for differential operator-embedded kernels. The proposed algorithm enhances computational efficiency by leveraging Kronecker product properties and avoiding complex Gram ... | Rebuttal 1:
Rebuttal: Thanks for your valuable and constructive comments.
>The method assumes a structured grid, which limits its generalization to unstructured meshes and other complex discretization routines.
R1: We appreciate your insightful comments. Indeed, we agree that our method relies on a structured grid an... | Summary: This paper introduces a novel kernel learning framework for efficiently solving nonlinear partial differential equations (PDEs). Unlike existing methods that embed differential operators within kernels, this approach eliminates these operators from the kernel, using standard kernel interpolation to model the s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable and constructive comments.
>C1: Can you provide more details on the virtual grid on an irregular domain, which is important for the application of your method?
R1: Great question. Our virtual grid is chose as the smallest rectangular grid that fully covers ... | Summary: The paper proposes a new twist to a kernel-based solver for PDEs. It builds on previous work by relaxing the constraints associated to the PDE to facilitate optimization. By placing the collocation points on a grid and using a decomposable kernel, the inversion of a large matrix is broken down into many invers... | Rebuttal 1:
Rebuttal: Thanks for your valuable and insightful comments.
>The introduction lacks references. Only the direct relevant work is cited (2 papers)
R1: Thank you for the helpful suggestion. We will include additional references in the introduction to provide broader context and better situate our work withi... | Summary: The authors propose an assymetric RBF collocation method for solving general PDEs from a Gaussian process/RKHS point of view. They parametrize the solution $u(x;\eta)$ as a (Gaussian RBF) kernel interpolant given function values on collocation points, then differentiate this representation to obtain derivative... | null | null | null | null | null | null | |
Lightspeed Geometric Dataset Distance via Sliced Optimal Transport | Accept (poster) | Summary: The paper proposes a sliced optimal transport-based dataset distance measure that uses moment transformation projection. The method improves upon a previously proposed optimal transport based dataset distance measure and makes the dataset distance computation more efficient. The paper presents a theoretical an... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time and constructive feedback.
**Q11**. Please provide large-scale experiments where OTDD is too expensive, and s-OTDD excels.
**A11.** In the paper, we showed that OTDD cannot be used when the dataset size reaches more than 30,000 samples on MNIST ... | Summary: The authors propose a similarity measure for supervised tasks based on sliced optimal transport. The data with labels are mapped to 1D slices via a feature projection map for features and the induced moment transform projection for label distributions. Then the dataset distance is defined using the 1D optimal ... | Rebuttal 1:
Rebuttal: We appreciate the time and constructive feedback of the reviewer. We would like to extend the discussion with the reviewer as follows:
**Q6**. Proposition 1 gives two conditions for the MTP to be injective, and the metric properties of s-OTDD are based on the injectivity of MTP. Does the MTP use... | Summary: This paper is a straightforward application of sliced OT on OT-based dataset distances. The main novelty is Moment Transform Projection by which the authors could project dataset labels to scalars, enabling the use of Radon transformation and hence sliced Wasserstein distances. Authors then follow standard pro... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for the review and feedback. We would like to answer questions from the reviewer as follows:
**Q3.** A weakness of the proposed sliced OTDD is the extra hyper-parameter - the number of moment $\lambda$ as in (7). In the code, it’s set to 5. I didn’t find... | Summary: This paper tackles the Dataset Distance problem with a proposed sliced optimal transport dataset distance (s-OTDD) method.
The core module is called Moment Transform Projection (MTP), mapping a label (represented as a distribution over features) to a real number. Then, s-OTDD is defined as the exptected Was... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for the time and the feedback. We extend the discussion as follows:
**Q1.** The line below Eq. (8), empirical distribution is missing the variable.
**A1.** Thank you for pointing out the typo. We have fixed the typo in the revision.
**Q2.** In Eq. (9),... | null | null | null | null | null | null |
Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games | Accept (poster) | Summary: This submission studies multiplayer (n-player where n >= 3) symmetric zero-sum games (such as Mahjong and Poker). Unlike two-player zero-sum games, equilibria in multiplayer games are neither unique nor non-exploitable, which poses a challenge when competing against opponents who play strategies from different... | Rebuttal 1:
Rebuttal: Thank you for your inspiring comments. We address your concerns as follows.
**Q1: Novelty and contributions**
**A1:**
Please refer to **A1** to **Reviewer Ada2** for clarification on the novelty and technical contribution of our work.
**Q2: Extension to asymmetric games**
**A2:** As mentioned... | Summary: The paper proposes a new solution concept for constant-sum, multi-player, symmetric games, which is referred to as equal share. What this means is that each player secures the same utility. The paper observes that usual equilibrium concepts do not necessarily satisfy equal share, and it then proceeds by identi... | Rebuttal 1:
Rebuttal: Thanks a lot for reading our paper and for your insightful comments.
**Q1: The theoretical results are not surprising**
**A1: Novelty.** We remark that, **compared to developing complex algorithmic techniques, identifying and formulating the right question to solve is equally—if not more—importa... | Summary: The authors present an algorithms for learning in symmetric constant-sum multiplayer games, where the solution concept is equal allocation of social welfare among the players. They show that standard no-regret learning algorithms in a self-play setup cannot achieve "equal share". They demonstrate necessary con... | Rebuttal 1:
Rebuttal: Thanks a lot for reading our paper and for your insightful comments.
**Q1: Clarification of the experiment**
**A1:** Our experiment aims to demonstrate that although self-play-based meta-algorithms have been effective in many real-world multiplayer games, they can still fail to secure an equal ... | Summary: This paper proposes a novel Monte Carlo Tree Search–inspired algorithm for multi-agent, simultaneous-move games under imperfect information.
Claims And Evidence: The proposed NN-CCE algorithm achieves performance that is superior or competitive with some multi-agent reinforcement learning algorithms (e.g., MA... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our submission. Upon reviewing your comments, we believe there may have been a misunderstanding, as the points raised do not seem to be relevant to our paper.
We appreciate the time and effort you have put into your review and are happy to provide further clarificat... | null | null | null | null | null | null |
Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions | Accept (poster) | Summary: Coresets serve as a compact summary of training data, offering an efficient method for reducing data processing and storage complexity during training. In the context of vertical federated learning (VFL), where different clients hold distinct data features, coresets help reduce communication complexity.
This ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking time to read our submission and providing useful remarks. Below we address your concerns
> However, certain statements, while technically correct, may lack sufficient clarity for a non-expert audience. For example, the statement: "This is the most computationally ... | Summary: The paper introduces a coreset construction algorithms for Vertical Federated Learning (VFL), focusing on regularized logistic and linear regression (ridge regression). The authors present algorithms to efficiently construct coresets that significantly reduce communication complexity in VFL, essential due to c... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer for taking the time to read our submission and providng a detailed review.
> Limited experimental validation on diverse or large-scale real-world datasets.
In the literature, it is standard to perform experiments on publicly available datasets to support the theo... | Summary: This paper studies regularized linear regression and regularized logistic regressions in the vertical federated learning (VFL) setting, where clients store different data features. The goal is to reduce communication complexity. The paper introduces coreset algorithms for these two problems and achieves improv... | Rebuttal 1:
Rebuttal: We thank reviewer Br26 for the time taken to read our submission and the provided feedback. We address the concerns.
> The introduction section introduces several dense math notations but does not provide the motivations for the problem.
Regularized linear regression and regularized logistic reg... | null | null | null | null | null | null | null | null |
Adaptive Partitioning Schemes for Optimistic Optimization | Accept (poster) | Summary: This paper proposes an adaptive partitioning schemes which divides the search domain into a hierarchical subspace tree to reduce search space and enhance optimization performance. The subspace matrix is learned and updated as a hidden layer of neural network surrogate model. Experimental results on several syn... | Rebuttal 1:
Rebuttal: We respond to the reviewer's questions below.
> For each problem instance, the surrogate network should be trained from scratch to obtain the partitioning matrix
We trained the neural network using the Ray package for hyperparameter tuning. The search space included hidden layer sizes (500, 1000... | Summary: The paper proposes an adaptive partitioning scheme for optimistic optimization that extends existing gradient-free algorithms such as SequOOL. The authors consider both a two-stage and an interleaved algorithm. In the context of multi-index functions (defined on a n-dimensional subspace within m dimensions), t... | Rebuttal 1:
Rebuttal: We respond to the reviewer's questions below.
> I would have also liked to see SAASBO and TuRBO.
Thank you for sharing these two relevant baselines. Previously, we tried to ran the TuRBO method and after 100 evaluations the computation time is very large, hence we did not include it in our resul... | Summary: The authors propose two different versions of learning partitioning ideas for Optimistic Optimization algorithms for black-box optimization. The first, uses a two step approach that first learns the partitioning and then optimizes. The second updates the partitioning while optimizing. The authors support their... | Rebuttal 1:
Rebuttal: We respond to the reviewer's questions below.
> Claim 1: We demonstrate the benefit of using a learned partitioning scheme & Claim 4: We pose the quantization of Large Language Model (LLM) as a high-dimensional black-box optimization problem
We thank the reviewers for recognizing our claims. Our... | null | null | null | null | null | null | null | null |
Approximate Forest Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees | Accept (poster) | Summary: The paper considers Metric Forest Completion (MFC) problem.
Let $G$ be an edge-weighted complete graph whose weights satisfy the triangle inequality (it induces a metric space).
Given a forest $F \subseteq G$, the MFC asks for the set of edges with a minimum sum of weights that connects the components of $F$.... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our manuscript!
Thanks for your question about the parameter t, which we agree can be better clarified. As you noticed, the apparent discrepancy comes from whether or not one incorporates the time taken to form the initial forest. The theoretical analysis in lines 3... | Summary: The paper presents a new framework for computing approximate minimum spanning trees (MSTs) in arbitrary metric spaces, improving the time complexity compared to traditional exact MST algorithms. The exact MST algorithms require $\tilde{O}(m)$ time complexity, where $m$ is the number of edges. For a complete gr... | Rebuttal 1:
Rebuttal: Thank you for the feedback on our manuscript! We appreciate the open question you posed. In response to those questions:
1. This is an interesting question. As potential follow-up work, we have indeed wondered whether we could use assumptions or parameters relating to the structure of the initial... | Summary: The paper considers the problem of building Spanning tree of a set of points in a metric space. The goal is to have a tree that is a good approximation of the MST without querying all the Theta(n^2) pairwise distances. The approach proposed is to have an algorithm that starts from an initial forest and then me... | Rebuttal 1:
Rebuttal: Thank you for the review and helpful feedback on our manuscript! | Summary: This paper considers the problem of finding subquadratic algorithms for metric MST (minimum spanning tree in a metric space) by leveraging learning-augmented algorithms. The goal is to break through strong lower bounds on runtime: the authors motivate the present work with the result that in an arbitrary metri... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our manuscript!
Regarding the section in the appendix on MSTs with predictions, we agree that this related work provides good context for our work and would be nice to include in the main text. Ultimately we moved this to the appendix because of space constraints. H... | null | null | null | null | null | null |
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets | Accept (spotlight poster) | Summary: The authors introduce a novel formulation for distributionally robust optimization based on Bayesian posterior updates. Their model leverages KL-divergence, and they propose two ambiguity set designs with efficient sampling methods. These methods are computationally better than Bayesian DRO under mild conditio... | Rebuttal 1:
Rebuttal: Thank you for providing feedback that greatly improves the paper. Please see this anonymous link for Tables and Figures: https://github.com/ICML-anon-2025/paper-11717
1. **Table 1**: We have changed the table and caption to distinguish between variables and the input size of the dual (see link a... | Summary: This paper introduces KL-based DRO formulations with two kinds of Bayesian ambiguity sets, the posterior expectation and the posterior predictive. The authors show that both formulations can be recast into a direct minimization problem, with more efficient closed form worst-case risk solution for exponential f... | Rebuttal 1:
Rebuttal: Thank you for carefully considering our work and providing feedback that greatly improves the paper:
1. **Upper bound in Eq. 13**: There is potentially a misunderstanding here: our work does not rely on this bound and goes beyond this inequality to achieve the strong duality result (with equality... | Summary: In this paper, the authors focus on Distributionally Robust Optimization and introduce two new ambiguity sets with the aim of informing the construction of ambiguity sets using Bayesian Statistics. More specifically, they use the posterior distribution to construct these ambiguity sets. In the first set, we co... | Rebuttal 1:
Rebuttal: Thank you for carefully considering our work and providing feedback and suggestions that greatly improve the paper:
1. **Additional comparisons to other DRO ambiguity sets**: Firstly, the suggested empirical-based ambiguity sets, based on the KL-divergence and Wasserstein distance, are fundamenta... | Summary: The paper provides two new ways to define the distributional robust counterparts for the Distributionally Robust Optimization (DRO) taking into account posterior information, called Bayesian Ambiguity Sets (BAS) . In particular, the authors address the problem of the worst-case risk optimization where the wo... | Rebuttal 1:
Rebuttal: Thank you for carefully considering our work and providing feedback that greatly improve the paper:
1. **Clarification of line 248 right side**: $\epsilon^{\star}\_{\text{PE}}(n)$ in Eq. 20 is defined as the expected KL divergence between the data-generating process $\mathbb{P}^\star$ and model $... | null | null | null | null | null | null |
Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups | Accept (poster) | Summary: This paper proposes a new approach to producing self-explainable (attributing) NNs. It does so after identifying limitations of previous approaches. The authors thereupon introduce a group-based SANN approach. They evaluate on several datasets and investigate multiple aspects of the approach.
Claims And Evide... | Rebuttal 1:
Rebuttal: Thank you for the feedback! In the following response, we clarify the posed questions and commit to adjustments as needed.
## Experimental Setup Clarifications
- *What features are groups based on?* Groups are simply subsets of the input features (the $x_{G_i}$ from Definition 1). A SANN takes s... | Summary: This paper proposes a method for extending models to serve as self attributing neural networks (SANNs). The novelty is that it uses a group-based SANN where the grouping itself is learned end-to-end. SANNs are useful in that they provide an added explainable layer on top of black-box models. The paper's theore... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We respond to your questions as follows:
## Backing up the Claim that SOP’s Groups are Superior
We would like to clarify a misunderstanding of the experiments: In referring to semantic utility, we believe the reviewer is confusing the claims on group quality with the ... | Summary: The paper introduces Sum-of-Parts (SOP), a framework for transforming any differentiable model into a group-based Self-Attributing Neural Network (SANN). The key innovation is the end-to-end learning of feature groups without requiring explicit group supervision. SOP addresses the limitations of per-feature SA... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We provide an additional PDF at https://github.com/icml2025-3311/icml2025-3311/blob/main/ICML2025_3311_rebuttal.pdf for additional figures and will respond to your questions below.
## Comparison with Additional Shapley-Based Baselines
We first would like to clarify a ... | Summary: The paper addresses the limitations of existing self-attributing neural networks (SANNs) in high-dimensional data. The authors theoretically prove a lower bound on the error of per-feature SANNs and demonstrate that group-based SANNs can overcome this limitation. The main algorithmic contribution is the Sum-of... | Rebuttal 1:
Rebuttal: Thank you so much for the valuable and encouraging review! We provide an additional PDF at https://github.com/icml2025-3311/icml2025-3311/blob/main/ICML2025_3311_rebuttal.pdf for additional figures.
## Explaining the Deletion Performance of SOP.
To explain how SOPs usage of multiple groups can af... | null | null | null | null | null | null |
Learning to Plan with Personalized Preferences | Reject | Summary: This paper proposes a framework/benchmark, named Preference-based Planning (PbP), for learning preferences from human behavior and subsequently planning actions guided by these learned preferences. The PbP is an embodied benchmark built upon NVIDIA Omniverse and OmniGibson, and provides provides a large-scale,... | Rebuttal 1:
Rebuttal: Dear Reviewer kiee:
Thank you for your thoughtful review and constructive feedback.
> Some potential limitations include these results/claims are mainly validated via simulations, the real-world applicability of PbP is not quite clear.
Most of the claims in this paper are mainly validated via s... | Summary: This work attempted to develop agents capable of learning preferences from few-shot demonstrations and generalize across diverse household task-planning scenarios. in that pursuit, the work also presents the 'Preference-based Planning' (PBP) benchmark featuring a set of demonstrations rendered within a simulat... | Rebuttal 1:
Rebuttal: Dear Reviewer rU32:
Thank you for your thoughtful review and constructive feedback.
> the motivations for formulating preference learning as a few-shot learning from demonstrations are technically naive. Relevant literature in embodied AI and preference learning needs to be reviewed.
Our formul... | Summary: The paper introduces Preference-based Planning (PBP), a benchmark for learning human preferences and integrating them into AI planning. The framework enables AI agents to infer user-specific preferences from a few demonstrations and apply them in task planning.
Claims And Evidence: Following claims are made w... | Rebuttal 1:
Rebuttal: Dear Reviewer mzDn:
Thank you for your thoughtful review and constructive feedback.
> No direct comparison with baseline retrieval-based or reinforcement learning based approaches.
> Other Strengths And Weaknesses: a detailed comparison with RL based methods is missing.
Retrieval-based and... | Summary: The paper introduces a framework to enhance embodied AI planning by incorporating personalized preferences learned from limited human demonstrations. It proposes the Preference-based Planning (PBP) benchmark and shows that learned preferences serve as effective abstractions, improving personalized plan generat... | Rebuttal 1:
Rebuttal: Dear Reviewer eCTV:
Thank you for your thoughtful review and constructive feedback. We appreciate your recognition of the benchmark’s novelty and the clarity of our primary findings. We address your concerns point by point:
> The proposed benchmark is novel within embodied AI. However, its si... | null | null | null | null | null | null |
Models of Heavy-Tailed Mechanistic Universality | Accept (poster) | Summary: Recent advancements in deep learning, including neural scaling laws, have highlighted the prevalence of heavy-tailed or power law behaviors in key network components such as the Jacobian, Hessian, and weight matrices. This phenomenon, termed heavy-tailed mechanistic universality (HT-MU), has been empirically l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work and for taking the time to verify the correctness of our proofs. We appreciate that the reviewer finds the experiments to be sound and suitably validate our model class. We agree about the high writing density; there are many individu... | Summary: This paper argues that many phenomena observed in neural scaling laws arises from universal random matrix theory effects which the authors term heavy tailed universality. The paper introduces a theory which breaks up the deep network optimization into an optimization over features and optimization over the las... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work, and for providing several references whose inclusion greatly enhances the working document! In particular:
- Thamm et al. do an excellent job of highlighting the phenomenon we are interested in and provides further evidence for our ... | Summary: This paper explores heavy-tailed mechanistic universality (HT-MU) in deep learning by proposing a new family of random feature matrix models based on the "high-temperature inverse-Wishart" ensemble. The paper reviews two mechanisms and presents a third one for the emergence of power laws in different matrices ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful examination of our work and for the thoughtful feedback. We appreciate that they find our framework to be ambitious, and understand the concern about empirical verification of individual claims within our theory. One of our fundamental assumptions---that the... | null | null | null | null | null | null | null | null |
Bridging Protein Sequences and Microscopy Images with Unified Diffusion Models | Accept (poster) | Summary: This paper introduces CELL-Diff, a diffusion-based model capable of bidirectionally generating microscopy images from protein sequences and protein sequences from microscopy images. Using conditional morphology reference images (nucleus, ER, microtubules), the model combines continuous diffusion for images and... | Rebuttal 1:
Rebuttal: **Comment 1:** The claim of accurate image-to-sequence transformation lacks rigorous quantitative evidence. Evidence provided for sequence generation is limited to qualitative motif analyses.
**Response:** To quantitatively evaluate the accuracy of the generated sequences, we used DeepLoc 2.1[1] ... | Summary: The paper introduces CELL-Diff, a unified diffusion model designed for bidirectional transformations between protein sequences and their corresponding microscopy images. Given cell morphology images and a protein sequence, CELL-Diff generates corresponding protein images. Conversely, it can also output prote... | Rebuttal 1:
Rebuttal: Due to the limitation on the number of characters, we provide a general response to the reviewer's comment.
### 1. The dataset size and model generalizability.
The generalizability of the sequence-to-image task depends on the downstream application.
One application is **virtual staining**. Trad... | Summary: This paper introduces CELL-Diff, a unified diffusion model that enables bidirectional generation between protein sequences and fluorescence microscopy images. It combines continuous diffusion for image synthesis and discrete diffusion for sequence prediction, integrating transformer-based cross-attention to fu... | Rebuttal 1:
Rebuttal: Due to character limitation, we provide a general response to the comments.
We understand the reviewer’s concerns about the concept of sequence-to-image mapping, especially with limited data from HPA and OpenCell. While a universal sequence-to-image model requires massive data, CELL-Diff, as a s... | Summary: The paper, Bridging Protein Sequences and Microscopy Images with Unified Diffusion Models, presents CELL-Diff, a novel generative model that enables bidirectional transformations between protein sequences and fluorescence microscopy images. By leveraging a transformer-based U-Net architecture and integrating b... | Rebuttal 1:
Rebuttal: **Comment 1:** The potential for dataset biases or domain shifts between HPA and OpenCell is not explicitly explored.
**Response:**
The main difference between HPA and OpenCell is that HPA is larger, but OpenCell has more consistent labeling and higher image quality. The effect of domain shift ... | null | null | null | null | null | null |
"Why Is There a Tumor?": Tell Me the Reason, Show Me the Evidence | Accept (poster) | Summary: Medical AI models effectively detect and segment tumors but often fail to provide explicit clinical reasoning, making their outputs less trustworthy in practice. Existing methods either localize abnormalities without justification or generate textual explanations without spatial grounding. To bridge this gap, ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your encouraging feedback. Please find our point-by-point responses to the comments below.
> **Q1:** Do the Dice loss and InfoNCE loss have the same weight in Eq. 2?
**A1:** The Dice loss and InfoNCE loss are balanced using a hyperparameter rather than having equal weigh... | Summary: This work addresses a highly interesting topic by formulating the task of tumor localization, which involves explaining and identifying tumor regions in medical data. The authors construct a dedicated dataset for this task and establish methods to quantify both the performance and explainability of their appro... | Rebuttal 1:
Rebuttal: We sincerely appreciate your encouraging feedback. The following are our responses to the comments.
> **Q:** Since all experiments are conducted on Prostate MRI scans, it raises concerns about the generalizability of the proposed method. Can this framework be extended to other modalities (e.g., C... | Summary: This paper addresses the challenge of enhancing interpretability in medical AI models for tumor detection and segmentation. The authors propose a novel framework that generates predictions supported by both clinical concepts and visual evidence. To achieve this, they curate a “first-of-its-kind” dataset (will ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your encouraging feedback. The following are our point-to-point responses to the comments.
> **Q1:** Verification on other types of tumors.
**A1:** We fully agree that our method will be further strengthened with validation on additional tumor types (e.g., breast, lung, l... | null | null | null | null | null | null | null | null |
Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers | Accept (poster) | Summary: This paper proposes Unisolver, a universal neural PDE solver designed as a “foundation model” for solving a broad range of PDEs. Unisolver leverages Transformer architectures pre-trained on a diverse set of PDEs. The model make use of many PDE components, incorporating information such as coefficients, boundar... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer WyYo for providing a detailed review and insightful questions.
> **Q1:** "The proposed techniques may be limited in its applicability. Though it's a good combination of existing techniques."
Thank you for the feedback. Regarding the applicability, we would like to cla... | Summary: The paper proposes a method for solving various types of PDEs by leveraging a pretrained LLM alongside known parameterizations in the form of equations and values. The symbolic equations are embedded using the pretrained LLM, while numerical values and boundary/initial conditions are incorporated separately th... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer GsAC for providing the insightful review and valuable suggestions.
> **Q1:** "The claim of universality may be overstated. The equations considered remain a relatively small subset of possible PDEs." "The domain remains discretized on a grid, even in CFDBench, which fu... | Summary: The paper introduces Unisolver, a universal neural PDE solver that can handle a wide range of PDEs, unlike traditional neural solvers that are limited to specific equations and coefficients. Instead of merely scaling up data and parameters, Unisolver leverages theoretical insights into PDE structures, embeddin... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 8zJe for providing valuable feedback and insightful questions.
> **Q1:** "'Universal' is too big to say."
Thanks for this rigorous review, which is very instructive for us.
**(1) We adopt "Universal" in the context of deep learning to highlight model's flexibility an... | Summary: This paper introduces Unisolver, a framework that conditions a transformer model on various physical parameters relevant to PDEs. The framework distinguishes between domain-wise components (such as equation symbols and coefficients) and point-wise components (such as external forcing). These are incorporated v... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer Z6hm for providing valuable feedback and suggestions.
> **Q1:** About the incomplete scenario setup and incomplete ratio.
**(1) Clarify our setting.**
Sorry for the confusion. We clarify the incomplete component scenario setup:
- **During training**, each PDE compone... | null | null | null | null | null | null |
Inverse problems with experiment-guided AlphaFold | Accept (poster) | Summary: In this paper, the author introduce a method to guide diffusion-based structure prediction models (e.g., AlphaFold 3) with experimental data to sample conformational ensembles.
Claims And Evidence: The claims are well supported by the results shown in the paper.
Methods And Evaluation Criteria: The baseline ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review.
**Underlying noise model.**
*Eq. 1.* We assume a Laplace noise model. Here, the difference between $F_{o}$ and $F_{c}$ is drawn from a Laplace distribution centered at zero with unit scaling. This model, along with the Gaussian model, is used in electron d... | Summary: This paper introduces Experiment-Guided AlphaFold3, a framework that integrates experimental data with deep learning priors to generate structural ensembles aligned with experimental observables. Standard protein structure predictors like AlphaFold3 produce single static structures, failing to capture conforma... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
> tested on a few case studies (e.g., ubiquitin, selected crystal structures)
This paper is as a proof of concept that AlphaFold3 (AF3) can be guided by experimental observations to generate heterogeneous ensembles.
We note that there was extensive quan... | Summary: This paper proposes to use AlphaFold3 as a structure prior for protein crystal structure determination from cryo-EM or NMR experiments. Specifically, it uses AlphaFold3 to predict the initial structure and then use the gradient from the electron densities to guide the diffusion module of AlphaFold3 to refine i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review.
**More baselines**
Following the reviewer’s suggestion, we extended the evaluation for both the X-ray crystallography and the NMR experimental observations.
We include the following additional baselines:
- AlphaFlow (Jing et al., 2024)
- ESMFlow (Jing et ... | Summary: This paper examines the inverse problem of resolving protein structure from experimental data and capturing the heterogeneity arising from the dynamic nature of proteins as an ensemble. To do so, it guides the diffusion module of AF3 with experimental data to satisfy NOE constraints and substructure likelihood... | Rebuttal 1:
Rebuttal: We hope our answers explain the purpose and novelty of the proposed framework and invite the reviewer to ask further questions.
**Clarification 1: The problem is not trivial**
Raw experimental observations do not contain the atomic model. **In crystallography**, the raw experimental data are int... | null | null | null | null | null | null |
Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples | Accept (poster) | Summary: This paper introduces "Flow of Reasoning" (FOR), a novel, data-efficient method for finetuning large language models (LLMs) to achieve divergent reasoning, i.e., generating multiple, diverse, and valid solutions to multi-step reasoning problems. FOR formulates LLM reasoning as a Markovian flow on a directed ac... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments and suggestions.
- Q1. How representative are the benchmarks? Are there reasoning types where it fails?
A1: Some tasks like GSM8K and Game24, are popular LLM benchmarks, while others, like BlocksWorld and 1D-ARC, are known to be challenging for LLMs. FoR remain... | Summary: The paper introduces a novel fine-tuning method called Flow of Reasoning (FOR) that trains large language models (LLMs) to generate diverse, high-quality multi-step reasoning paths using minimal training examples. The key idea is to formulate the reasoning process as a Markovian flow on a directed acyclic grap... | Rebuttal 1:
Rebuttal: Thanks for recognizing our strong performance across 6 benchmarks, ablation studies, data efficiency, and case studies.
- Q1: Hyperparameters selection and analysis? Guidelines for designing rewards? Handcrafted rewards?
A1: Please refer to Section 4.7 and Figure 3 for the hyperparameter analysi... | Summary: The paper introduces Flow of Reasoning (FOR), a method for training Large Language Models (LLMs) to generate diverse, high-quality reasoning paths with minimal training examples. The authors formulate multi-step LLM reasoning as a Markovian flow on a DAG-structured reasoning graph, adapting Generative Flow Net... | Rebuttal 1:
Rebuttal: Thank you for recognizing our innovative Markovian flow approach to multi-step LLM reasoning with GFlowNets, our strong performance across six benchmarks, and our method’s data efficiency.
- Q1: More complex real-world tasks?
A1: We evaluate FoR on six benchmarks that pose significant challenges... | null | null | null | null | null | null | null | null |
Manipulation Inversion by Adversarial Learning on Latent Statistical Manifold | Reject | Summary: This paper aims at improving the gan inversion method to achieve both good reconstruction and realism of editing. Several findings from the paper indicates that to really invert an image back to the latents space, it better to prevent from finding only the local minimum (which harms the realism of editing), bu... | Rebuttal 1:
Rebuttal: We wish to sincerely thank the reviewer for the valuable comments and insightful suggestions.
**[Q1 Weakness 1: Editing Performance]**
Indeed, as pointed out by Lemma 4.2 of our manuscript, the performance of editing is in accordance with the accuracy of manipulation inversion. Our best performa... | Summary: This paper aims to enhance the editing ability of current GAN inversion methods. First, this paper investigates the properties of the latent space of StyleGAN and obtains three interesting findings (Sec. 3). Based on these findings, this paper proposes adversarial learning for latent manipulation inversion and... | Rebuttal 1:
Rebuttal: Many thanks for the insightful suggestions.
**[Q1 Weakness 1: Quality and Accuracy of the Editing Results]**
Indeed, the realism and accuracy of image editing are tightly related to the accuracy of manipulation inversion, which has been proved in our manuscript. We further conducted comprehensiv... | Summary: This article introduces a manipulation inversion method for GAN models. It constructs the generative manifold using different editing vectors to create a more stable and reliable inversion space.
Claims And Evidence: This article conducts extensive experiments to demonstrate that their method achieves state-o... | Rebuttal 1:
Rebuttal: Many thanks for the insightful comments.
**[Q1 Theoretical Claims: Multiple Directions]**
**Questions for Authors 1: Handling Multiple Directions in Manifold Construction:** Indeed, our manifold is established based on the semantic direction $\mathbf{S}$ and Jacobian matrix $\mathbf{J}$. The Jac... | Summary: The paper proposes Manipulation Inversion, a novel GAN inversion method that addresses the inherent trade-off between accurate image reconstruction and realistic editing by focusing on manipulating latent spaces rather than direct image reconstruction. Motivated by a systematic analysis of the latent space in ... | Rebuttal 1:
Rebuttal: Many thanks for the valuable and insightful comments!
**[Q1 Claims and Evidence: More Visual Examples on Analysis]**
Yes. We have conducted further analysis on the generating latent space of StyleGAN, revealing the possible limitations for GAN inversion. More specifically, Figure 2-(a) of our ma... | null | null | null | null | null | null |
On Teacher Hacking in Language Model Distillation | Accept (poster) | Summary: The paper introduces the phenomenon of "teacher hacking," where using a fixed offline dataset for distillation degrades performance, and proposes solutions like online data generation and increased data diversity to mitigate this issue.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoret... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer jZCq for the valuable feedback. In the following, we address the questions raised in the review.
> **The paper lacks significant theoretical claims or proofs, focusing primarily on empirical observations. A more theoretical understanding of the teacher hacking phen... | Summary: This paper identifies and formally defines phenomenon of teacher hacking, which describes a trend for student LM to "overfit" to the teacher model instead of the ground-truth, golden oracle distribution we want it to learn. Authors identify the use of fixed offline dataset to be a key reason for teacher hackin... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer KCu2 for the helpful feedback! In the following, we answer the questions raised in the review.
> **Q1: You designed two stages for the experiment, and in the first stage, oracle LM generates oracle dataset for SFT on both teacher and student models to provide an in... | Summary: This work investigates a novel phenomenon termed "teacher hacking," where student language models (LMs) over-optimize to imperfections in the teacher model during knowledge distillation, leading to degraded performance on the true objective. The authors propose a controlled experimental setup involving an orac... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer z8Ux for the detailed and valuable feedback! In the following, we address the issues raised in the review.
## Dataset diversity limitations.
> **The analysis doesn't quantify the trade-off between prompt diversity and generation redundancy**
> **The experiments d... | null | null | null | null | null | null | null | null |
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples | Accept (poster) | Summary: This paper investigates the effect of “difficult” examples in preference optimization (particularly, in the context of DPO).
It finds that these examples harm performance, and propose a data selection algorithm to filter these examples to be applied before DPO.
Claims And Evidence: The claims are mostly convi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and insightful comments. We address the concerns below:
**Q1) Claims about hallucinations**
We revised the statement “reduces undesired hallucinations” to “generates policies that have lower NLLs”.
---
**Q2) Application to other DPO variants**
O... | Summary: The paper starts w/ an observation that preference samples have different difficulty level (i.e., how easy/hard it is to learn for abgiven model w/ a different capacity). The paper posits that harder examples deteriorates preference alignment due to the examples being too hard for a model to learn. The way to ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful feedback and for pointing us to highly relevant related works. Your kind comments have helped us better position our contribution. Below we address the concerns in detail:
**Q1) Missing related work.**
Thank you for highlighting `arxiv 2410.088... | Summary: This paper investigates the impact of difficult samples in DPO settings and finds that overly difficult examples can be detrimental to LLM alignment. Following the curriculum learning (CL) pattern, which organizes examples from easy to difficult, the authors propose Selective DPO. This method utilizes the orig... | Rebuttal 1:
Rebuttal: Thank you for the insightful suggestions regarding comparisons and related work. We address each comment in detail below:
**Q1) Comparison with other difficulty metrics.**
Prior work [0] has examined *prompt length* and *attention scores*, finding limited benefits for alignment. Building on this... | Summary: This paper focuses on the alignment performance w.r.t. data difficulties. The central claim is that the difficult data points exceeds model capabilities, and therefore harm the alignment results.
To start with, it is crucial to define the difficulty measure, authors use "learned step" as a metric to quantify ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback, particularly regarding the label flipping experiment and the perplexity gap baseline. These comments help clarify and reinforce our central contribution: that alignment performance is critically influenced by the mismatch between ... | null | null | null | null | null | null |
Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models | Accept (spotlight poster) | Summary: The authors of this paper study two separate but related problems: sampling from an isoperimetric distribution and also sampling from a score-based diffusion model. Under
some assumptions on the target distribution $\pi$---e.g. it satisfies a
log-Sobolev inequality (isoperimetric) and is $\beta$-log smooth---... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and constructive feedback. We are encouraged by your recognition of the core contribution—**the diagonal reordering of Picard iterations to achieve improved parallel iteration complexity, the soundness of the theoretical analysis,
and the potential applica... | Summary: The proposed paper introduces a novel parallel sampling technique that significantly enhances the time complexity of both sampling under isoperimetry and score-based diffusion model inference problems from $O(\log^2 d)$ to $O(\log d)$. The primary algorithmic innovation lies in the parallel sampling across tim... | Rebuttal 1:
Rebuttal: Thank you for your detailed and positive feedback. We greatly appreciate your recognition of **our novel parallel sampling technique, the clarity of the paper’s structure, the soundness of the theoretical analysis, and the potential of our approach to substantially improve time complexity and cont... | Summary: The paper obtains novel rates for the parallel complexity of sampling, both in the gradient oracle (MCMC) setting and the score-based denoising setting. The rates are O(log d/eps), which is sharp (in epsilon). The proof is based on a refined Picard iteration scheme, with careful analysis in order to obtain the... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and positive evaluation of our work, particularly your recognition of **the sharp iteration complexity in terms of $\varepsilon$ and the rigor of our analysis**. We also appreciate your conclusion that the work merits acceptance. Below, we address your questi... | Summary: Parallel sampling methods propose to speed up sampling by more efficiently simulating diffusions. Prior work splits up the simulation interval into $\log(d)$ chunks and performs $\log(d)$ iterations on each chunk sequentially. This yields an overall complexity of $\log(d)^2$. This paper proposes to remove the... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions and appreciation of our work, particularly recognizing the clarity of the writing and **the effectiveness of the diagonal communication scheme for parallel sampling**. Below, we address your questions and comments:
**"...gradient mapping contraction...missi... | null | null | null | null | null | null |
How Effective Can Dropout Be in Multiple Instance Learning ? | Accept (poster) | Summary: The authors investigate the effectiveness of dropout in Multiple Instance Learning (MIL), asserting that removing the top-k most important instances within a bag enhances performance and generalization, even under noise attacks. Additionally, they introduce a dropout method, termed MIL-Dropout, which integrate... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our work and for providing many valuable comments. Please find our detailed responses to the reviewer’s concerns below:
---
1. **Unclear Contributions:**
Our investigation reveals two key findings:
- **Finding 1:** DropInstance produces... | Summary: This paper investigates the effectiveness of Dropout in Multiple Instance Learning (MIL), particularly for histopathological whole-slide image classification. To address the overfitting issue in conventional two-stage MIL training (feature extraction + aggregation) caused by noisy embeddings and weak supervisi... | Rebuttal 1:
Rebuttal: **Thank you very much for taking the time to review our work and for providing such valuable feedback. In response to the reviewer's concerns, we address the following point:**
1. **Related Literature:**
Regarding the sampling-related papers mentioned by the reviewer, our method differs, and... | Summary: The authors study how dropout can be used specifically for multiple instance learning (MIL), with a focus on its use for WSI-level prediction tasks within computational pathology.
They propose a MIL-specific dropout method named MIL-Dropout. It entails dropping the top K instances, along with G instances whic... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for the valuable feedback, and for taking the time to read and engage with our paper. Below we address the reviewer’s concerns point by point.**
---
### 1.**Questions/Suggestions**:
- **Experimental setup:**
Figure 5(c) reports ablation results on two data... | Summary: This paper mainly studies the Dropout scheme for Multi-Instance Learning (MIL). First, this work reveals the superiority of DropInstance over traditional Dropout scheme through empirical experiments on a WSI dataset. Then, this work investigates the suitable scheme for DropInstance, Top-K instance dropping. Fi... | Rebuttal 1:
Rebuttal: **We greatly appreciate the reviewer’s valuable feedback. Below is our response:**
1. **Sub‑optimal** refers to using a feature extractor that isn’t trained jointly with the MIL module, resulting in non‑optimal embeddings. Even strong feature extractors (self‑supervised training foundation models... | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.