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TAROT: Targeted Data Selection via Optimal Transport | Accept (poster) | Summary: This paper proposes to address the problem of task-specific sample selection from the perspective of distribution matching that can be solved by optimal trasnport. Experiments on influence estimation, semantic segmentation, motion prediction, and instruction tuning show the effectivenss of the proposed method.... | Rebuttal 1:
Rebuttal: > ### Q3: Clarifying Modality
By **modality**, we refer to **distributional modality**, rather than input modality. We elaborate on why other tasks exhibit greater distributional multi-modality compared to **instruction tuning**, as summarized in the table below:
| | Motion Predict... | Summary: The paper introduces a framework for targeted data selection by minimizing the distance between selected data and target data distribution. The method addresses the limitations of existing influence-based greedy heuristics, which does not perform well on multimodal data distributions. The framework is evaluate... | Rebuttal 1:
Rebuttal: **Difference Between Data Distillation and Valuation**
Thank you for this insightful comment. We appreciate the opportunity to clarify the relationship between TAROT and other data selection paradigms, particularly **Data Valuation via Reinforcement Learning (DVRL)** [Yoon et al., ICML 2020] ... | Summary: The paper proposes a data selection method for a specific target domain from a candidate set by posing it as a distribution matching problem. The paper proposes to use whitened gradient features as the base distance to compute the optimal transport between the two sets. Effectiveness of this proposed method i... | Rebuttal 1:
Rebuttal: **Q1. Discussion and Comparison with TSDS and DSIR**
Thank you for the suggestion. We have revised the related work section to better highlight how **TAROT** differs from related methods, particularly **TSDS** and **DSIR**, which also address data selection from a distribution-matching perspectiv... | Summary: The authors formulate targeted data selection as a distribution matching problem and propose a new framework to efficiently select the most suitable training datasets. Massive experiments were conducted to support the effectiveness of the proposed method.
Claims And Evidence: The authors conducted massive exp... | Rebuttal 1:
Rebuttal: **Q1: No supporting evidence for this claim: the linear additive assumptions inherent in greedy selection strategies are restrictive.**
Thank you for highlighting this. The limitations of linear additive assumptions in influence estimation have been thoroughly examined in the paper _Most Influen... | null | null | null | null | null | null |
DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence | Accept (poster) | Summary: The paper introduces DTZO (Distributed Trilevel Zeroth Order Learning), a novel framework for solving distributed trilevel learning problems with missing gradient information. This is achieved by constructing a cascaded polynomial approximation without relying on gradients or sub-gradients, leveraging zeroth-o... | Rebuttal 1:
Rebuttal: **We truly appreciate your insightful suggestions. We have provided a point-by-point reply to all your questions and hope we have successfully addressed all your concerns.**
**(Q1)** In general the experimental designs and analysis are sound, which is partially discussed in Methods And Evaluation... | Summary: This paper proposes a zeroth-order constrainted trilevel learning optimizer which is versatile and can be adapted to a wide range of TLL problems. The authors provide both convergence analysis and experiment validation for the proposed DTZO framework. The improvement in performance from the experiments is very... | Rebuttal 1:
Rebuttal: **We sincerely appreciate your insightful and valuable suggestions. We have provided a detailed point-by-point response to your questions below.**
**(Q1)** A table that compares the latest progress with the related analysis.
**(R1)** This work is the **first** to explore solving the distributed... | Summary: This paper introduces DTZO, a framework for solving trilevel learning problems where gradient information is unavailable at all levels like black-box or with partial zeroth order constraints with analysis of the convergence rate and communication complexity.
Claims And Evidence: The paper provides an extensiv... | Rebuttal 1:
Rebuttal: **We truly appreciate your insightful and valuable suggestions. We have provided a detailed response addressing each of the questions you raised.**
**(Q1)** Experiments on another domain and ablation study.
**(R1)** This work is the **first** to investigate solving the distributed TLL problem wi... | null | null | null | null | null | null | null | null |
The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products | Accept (poster) | Summary: This paper analyzes various tensor product operations in equivariant neural networks for 3D modeling. It introduces measures of expressivity and interactability and improves the Gaunt tensor product (GTP) with a spherical grid, achieving a 30% speedup. The paper also presents microbenchmarks, showing discrepan... | Rebuttal 1:
Rebuttal: We thank the reviewer for your reading of our work and feedback. We appreciate that the reader finds our explanation of differences between TPOs clear.
Regarding the weaknesses:
## Weaknesses
1. We would like to clarify that the main point of the paper is understanding how alternatives to CGTP ar... | Summary: The paper presents a comprehensive analysis of tensor products and tensor product operations used in $E(3)$-equivariant models based on spherical tensors, including the Clebsch-Gordan tensor product (CGTP), Gaunt tensor product (GTP), and Matrix tensor product (MTP). The authors introduce expressivity and inte... | Rebuttal 1:
Rebuttal: We thank the reviewer for your careful reading of our work and positive feedback. We appreciate that the reviewer finds our evaluation criteria and experiments well aligned with the problem, the theoretical claims well supported, and discussion on expressivity a clear explanation for why certain T... | Summary: This paper aims to advance the fundamental understanding of equivariant neural networks by studying different mappings from the product of vector spaces into tensor product spaces (including the well-known CG tensor product), which serve as the building blocks of expressive equivariant architectures. In partic... | Rebuttal 1:
Rebuttal: We thank the reviewer for your careful reading of our work and helpful feedback. We appreciate that the reviewer finds our measure of expressivity reasonable and our runtime evaluation rigorous and comprehensive.
Regarding the weaknesses
1. > Contribution of the new GTP implementation appears lim... | Summary: This paper investigates tensor product operations in E(3)-equivariant neural networks, an
important class of models for 3D modeling tasks, which have been recently proposed as a
faster alternative to the standard Clebsch-Gordan tensor product. In particular, the authors
introduce measures of expressivity an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough feedback on our work. We appreciate that the reviewer finds our work well-organized and presented, the experiments thorough, and provides important insights to practitioners.
Regarding the weaknesses
## Weaknesses
1. This is a great question. The motivation ... | null | null | null | null | null | null |
DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra | Accept (poster) | Summary: The paper introduces DiffMS, a diffusion-based framework for generating molecular structures from mass spectra. DiffMS combines existing approaches in discrete graph diffusion (DiGress), with a pretraining framework in encoder-decoder transformer architecture. The authors conducted experiments and evaluations ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful feedback and for highlighting the novelty of our discrete diffusion method and pretraining strategies. Below, we have addressed the concerns regarding exact accuracy of annotation:
> Reliance on external tools for formula determination: The method relies on... | Summary: The paper introduces DiffMS, a novel diffusion-based generative model for de novo molecular structure prediction from mass spectra. This work addresses the inverse mass spectrometry (MS) problem, which involves reconstructing molecular structures based on experimental mass spectra data.
Claims And Evidence: T... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful feedback and suggestions, and respond accordingly below:
>While the DiffMS encoder leverages transformers for mass spectrum embeddings, no ablation is performed on the impact of different conditioning strategies. Does spectral conditioning significantly im... | Summary: The paper introduces DiffMS, a diffusion-based model for generating molecular structures from mass spectra, addressing the "inverse" MS problem. It uses a pretraining-finetuning framework with large-scale fingerprint-structure datasets and achieves state-of-the-art performance on benchmarks like CANOPUS and Ma... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful feedback and for highlighting the novelty of our discrete diffusion method and pretraining strategies. Below, we have addressed the reviewer’s concerns about the applicability of DiffMS and shown DiffMS performs comparatively well without formula annotations... | null | null | null | null | null | null | null | null |
$\texttt{I$^2$MoE}$: Interpretable Multimodal Interaction-aware Mixture-of-Experts | Accept (poster) | Summary: The paper introduces a new mixture-of-experts framework designed to explicitly model diverse modality interactions and multi-modality fusion, through specialized parameters and weakly-supervised interaction losses. The proposed method is validated on five multimodal datasets, across different modalities, showi... | Rebuttal 1:
Rebuttal: Thanks for your encouraging feedback. Point-to-point responses below.
>Q1. For using triplet margin loss to model uniqueness interactions, what is the margin used in the work, why is the margin loss used here?
We use triplet margin loss to uniqueness interactions, as it naturally aligns with the... | Summary: This paper addresses multimodal learning using mixture-of-experts, where dedicated experts learn distinct information from input modalities. The authors introduce a reweighting model to interpretably assign weights to the experts, facilitating understanding of their individual importance. The proposed approach... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful feedback. Point-to-point responses below. **All supplementary on [GitHub](https://anonymous.4open.science/r/I2MoE-rebuttal-8308/README.md)**.
>Q1. I2MoE lower scores on MM-IMDB?
This is primarily due to **differences in experimental setups**:
1. Evaluation Setup: Our e... | Summary: In this paper, the authors introduced I2MOE, a novel multimodal model that trains a different set of experts to model each type of multimodal interaction between modalities. Each expert is trained with a different interaction loss specifically designed for the type of interaction it has to deal with, in additi... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback. Point-to-point responses below.
> Q1.The support for "local interpretation" is only backed by qualitative samples from one task
Thanks for suggesting to strengthen the evidence for local interpretability. We conducted a human evaluation with 15 participants on ... | Summary: The paper introduces $I^2MoE$, an end-to-end mixture-of-experts framework that explicitly models heterogeneous interactions between input modalities. By deploying specialized interaction experts (e.g., uniqueness, synergy, redundancy) and a reweighting module, $I^2MoE$ not only improves task performance—demons... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback. Point-to-point responses below.
>Q1. The connection between interaction loss and PID
We would appreciate any insights from the reviewer on this point. Below, we attempt to connect each expert trained on the perturbed input views to a distinct PID component... | null | null | null | null | null | null |
Emotional Face-to-Speech | Accept (poster) | Summary: This paper describes an approach for mapping silent video of a talking face to the synthesized voice. The approach is based on a discrete diffusion transformer that is conditioned on the (visual) speaker identity and a learned representation of the facial expression of emotion. Together these help to preserv... | Rebuttal 1:
Rebuttal: We are grateful for your kind words and appreciating the significance of our contributions, and we try our best to address your questions as follows.
**Q1: Impact of expressiveness variation**
Thank you for the insightful suggestion. In this paper, we use one-hot emotion labels to learn identi... | Summary: This paper argues that extracting and applying emotional expressions as well as identities when generating speech based on face prompt input is effective in resolving face-speech mismatch.
To this end, we propose an Emotional Face-to-Speech (eF2S) method that goes beyond the existing Face-to-Speech (F2S) meth... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments and suggestions, motivating us to rethink a more comprehensive experiment evaluation. We try our best to address your questions as follows.
**Q1: Emotion ambiguity between text and face**
Thank you for your insightful comment. Determining the do... | Summary: The paper introduces a task named Emotional Face-to-Speech (eF2S), which aims to synthesize emotional speech directly from expressive facial cues. The proposed DEmoFace leverages a discrete diffusion transformer with curriculum learning to achieve the SOTA eF2S performance.
Claims And Evidence: The claims mad... | Rebuttal 1:
Rebuttal: Thank you very much for your positive comments and efforts in reviewing our manuscript. We try our best to address your questions as follows.
**Q1: Limited novelty**
Thank you for the opportunity to clarify the distinctions from previous methods. Although DEmoFace builds on existing discrete dif... | Summary: The paper introduces Emotional Face-to-Speech (eF2S), a novel task that synthesizes emotional speech solely from expressive facial cues. The authors propose DEmoFace, a generative framework leveraging a discrete diffusion transformer (DiT) with curriculum learning, integrated with a multi-level neural audio co... | Rebuttal 1:
Rebuttal: We are grateful for your positive feedback and constructive suggestions, and try our best to address your concerns as follows.
**Q1: Facial expression ambiguity**
Thank you for your insightful comment. In this paper, we leverage a pre-trained facial expression recognition model to generate on... | null | null | null | null | null | null |
Reinforcement Learning for Quantum Control under Physical Constraints | Accept (poster) | Summary: The authors address the problem of optimal quantum control using reinforcement learning (RL). Specifically, they define an RL framework that incorporates several real-world physical constraints to enhance performance.
First, they limit the agent’s possible actions to those that require a small number of simula... | Rebuttal 1:
Rebuttal: ## Reviewer K5by
Thank you for recognising our incorporation of physical constraints, which significantly advance the state of the art, as a valuable contribution.
### Weaknesses
> "contributions are somewhat limited, as the approach builds on a standard RL framework for quantum optimal control... | Summary: This paper introduces a RL approach for quantum control under physical constraints, aiming to improve the fidelity and robustness of quantum control tasks in real-world scenarios.
Main Findings and Results:
1). The proposed physics-constrained RL algorithm achieves high-fidelity quantum control solutions acr... | Rebuttal 1:
Rebuttal: ## Reviewer 8yQf
We thank the reviewer for recognising our integration of physical constraints into RL, which achieves high-fidelity, noise-resilient solutions.
### Weaknesses
> "The term "physics-constrained" is used throughout the paper but is not adequately defined or justified."
A3.1: Thank... | Summary: This paper explores the application of reinforcement learning (RL) for quantum control, introducing constraints aimed at improving learning efficiency. The authors present a rigorous approach to adapting RL for quantum applications and provide detailed reasoning behind the necessary modifications. The study fo... | Rebuttal 1:
Rebuttal: ## Reviewer vWkD
Thank you for recognising our rigorous adaptation of RL for quantum control and well reasoned introduction of constraints.
### Weaknesses
> "Missing discussion of prior RL-based quantum computing research". [...] "... Prior work such as [1-4] should be discussed..."
A2.1: We t... | Summary: The paper proposes a physics-constrained reinforcement learning algorithm to explore physically realizable pulses for quantum control tasks. The constraint on the pulses ensures smooth transitions and low energies, which result in noise robust pulses that may achieve higher fidelities.
Comprehensive experimen... | Rebuttal 1:
Rebuttal: ## Reviewer sD3o
We appreciate your positive feedback on our work and are glad that our research in AI for quantum science was well received.
### Weaknesses
> "The proposed method requires precise calibration of the target system, and after each calibration the RL algorithm needs to be rerun to o... | null | null | null | null | null | null |
Online Clustering of Dueling Bandits | Accept (poster) | Summary: The work studies the integrated setting of clustering and dueling bandits. The clustering bandit setting assumes the arms are clustered where each cluster shares the same reward function. The dueling bandit setting assumes that the learner chooses two arms in each iteration and obtains the preference feedback ... | Rebuttal 1:
Rebuttal: Thanks for the valuable advice. Our responses are as follows. We will add the discussions.
**Q1:** Technical challenges:
**A1:**
**Novel Algorithm Design:**
- Cluster Estimation under Dueling Feedback: Our setting does not allow direct use of prior cluster vector estimation methods in classical... | Summary: This paper primarily investigates dueling bandits in an online clustering setting, where clusters of arms share a reward function. It examines cases where the reward function is linear or modeled by a deep neural network. The authors propose algorithms with sub-linear regret bounds and demonstrate their effect... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback and valuable suggestions, our responses are as follows.
**Q1:** Lower bounds:
**A1:** Thanks for the helpful suggestion. Based on prior techniques (Wang et al., 2024a; Liu et al., 2022) and the single-user dueling bandit lower bound (Saha, 2021), we can derive ... | Summary: The paper introduces the first algorithms for clustering users in dueling bandit settings where feedback is based on preferences between pairs of items rather than absolute numerical rewards. The authors propose two novel approaches:
- Clustering of Linear Dueling Bandits (COLDB) for linear reward functions ... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions. Our responses are as follows. We will incorporate them after revision.
**Q1**: Lemma B.2, C.5, and more robust deletion:
**A1**: We will emphasize these lemmas in the main text. Our edge deletion mechanism and analysis follow prior clustering of bandits (CB... | null | null | null | null | null | null | null | null |
Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent | Accept (poster) | Summary: This paper views model merging from a multi-task learning angle. It designs an adaptive projective gradient descent method that tries to minimize the gap between the merged model and individual models, subject to the constraint of retaining shared knowledge. Specifically, the method only uses gradients in the ... | Rebuttal 1:
Rebuttal: Thanks for your review and detailed comments. We hope the following discussion can address your concerns!
___
> Q1: Based on Tables 1, 2, and 3, the model merging proposal does not maintain the same level of performance as the individually trained ones, and is even worse than multi-task learning i... | Summary: The authors introduced an approach to merging tasks for a multi-task learning purpose while maintaining performance comparable to task-specific models. They formulated the problem as a constrained optimization task, solved using adaptive projected gradient descent. To facilitate task merging, they introduced a... | Rebuttal 1:
Rebuttal: > Q1: The omission appears intentional, since the prior work demonstrates superior performance.
A1: Our approach differs fundamentally from [1,2] in both setting and methodology. Our objective is to close the performance gap between model merging and multi-task learning **without introducing addi... | Summary: This paper addresses the challenge of merging multiple task-specific models into a unified model without accessing their original training data. The authors identify critical limitations in existing methods, such as discarding task-specific information during conflict resolution and over-enforcing orthogonalit... | Rebuttal 1:
Rebuttal: > Q1: Taylor expansion may need to point this out and justify.
A1: During fine-tuning, parameter evolution in pre-trained models is frequently minimal, indicating that training remains within the tangent space where Taylor expansion closely approximates network behavior. This aligns with MAP, whi... | Summary: This paper proposes a new perspective on model merging—treating it as a multi-task learning problem rather than merely a parameter-level combination of multiple expert models. The main idea is to preserve each task’s strong performance while reconciling the potential conflicts that arise when unifying several ... | Rebuttal 1:
Rebuttal: Thanks for your detailed comments. We hope the following discussion can address your concerns!
___
>Q1: Some relevant work that tackle model merging on subspace need to be discussed.
A1: Thanks for suggesting additional relevant work. We will discuss them in related work: *TSV [1] aggregates task... | null | null | null | null | null | null |
Learnable Spatial-Temporal Positional Encoding for Link Prediction | Accept (poster) | Summary: This paper introduces a framework L-STEP for learning representations on discrete temporal dynamic graphs, which comprises two key components: 1) LPE (Learnable Positional Encoding): a learnable spatial-temporal positional encoding designed to capture evolving graph topology from a spectral viewpoint. Concrete... | Rebuttal 1:
Rebuttal: Thanks very much for your review!
We are excited to learn your appreciation of our model's design, effectiveness and efficiency, and paper's writing.
Your suggestions are actionable and helpful.
First of all, we correct the typo in Eq (11) and promise to update it in our camera-ready version.
... | Summary: This work explores the problem of temporal link prediction, and proposes a semi-non-graph model that shows quite good performance across a variety of datasets. Their model, LSTEP, works by introducing a learnable time-dependent positional encoding, where the time dependence is parameterized by a learnable four... | Rebuttal 1:
Rebuttal: Thanks for your appreciation of our method’s soundness and experimental design! Addressing your concerns improved the quality of our paper, and we prepared the answer below.
> How learnable positional encodings work in dynamic environment.
Due to the length limit of response, we sincerely invite... | Summary: The paper introduces a new method for predicting connections in networks that change over time. Instead of using fixed rules or complex models that require a lot of computing power, L-STEP learns how positions in the network change over time using the Fourier Transform. The authors show that their approach kee... | Rebuttal 1:
Rebuttal: Thanks very much for your review! We are excited to learn your appreciation of our extensive experiments and corresponding outperformance.
Your suggestions are quite actionable, and we seriously prepared the answer in Q&A format below.
> W1: The low changing aspect needs to be tested.
First, t... | null | null | null | null | null | null | null | null |
On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists | Accept (poster) | Summary: The paper introduces a method for training language models collaboratively between multiple devices/clients and personalizing them to their on-device data at the same time. This is done by introducing a mixture of generalist experts and specialist experts, where the generalists are trained collaboratively (e.g... | Rebuttal 1:
Rebuttal: Thank you very much for your time and expertise in reviewing our work, as well as for your encouraging and positive feedback. Below, we address all your questions and concerns. If any issues remain, please let us know, and we’ll be happy to provide further clarification.
**Novelty of the algorith... | Summary: The authors focus on the problem of on-device collaborative fine-tuning of LLMs to address both computational resource heterogeneity and data heterogeneity among users. The authors try to develop a framework that can balance general and personalized knowledge for each token generation while being robust agains... | Rebuttal 1:
Rebuttal: Thank you very much for your time and expertise in reviewing our work, as well as for your encouraging and positive feedback. Below, we address all your questions and concerns. If any issues remain, please let us know, and we’ll be happy to provide further clarification.
Regarding your question a... | Summary: This paper introduces CoMiGS, a modular federated learning framework for adapting LLMs using a mixture of generalist and specialist LoRA experts. CoMiGS employs a bi-level optimization strategy, alternating between routing and expert parameter updates. Experimental results on GPT-125M and Llama-3.2-1B demonstr... | Rebuttal 1:
Rebuttal: Thank you very much for your time and expertise in reviewing our work, as well as for your encouraging and positive feedback. Below, we address all your questions and concerns. If any issues remain, please let us know, and we’ll be happy to provide further clarification.
**Methods And Evaluation ... | null | null | null | null | null | null | null | null |
Predicting mutational effects on protein binding from folding energy | Accept (poster) | Summary: The authors propose a deep learning method for predicting binding energy differences (i.e. ΔΔGs) for closely related pairs of protein-protein complexes. To do so the authors rely on a well-known identity that relates binding energy differences to folding free energies. The authors then proceed to fine-tune a p... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate your thoughtful suggestions and have conducted additional experiments that provide additional insights to our method.
Fine-grained evaluation: We examine StaB-ddG’s performance on different subsets of SKEMPI.
Complex size: total number of residues of a P... | Summary: This work introduces Stab-DDG, a deep-learning method for DDG prediction that leverages both folding energy and binding energy data during pre-training. The authors relate folding energy to binding energy in a principled manner, which leads to a loss function for pre-training on folding energy data specificall... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate your recognition that our use of folding energy data to improve binding prediction is novel and well-motivated. We found your suggestions helpful and address them below.
Baselines: We agree with the reviewer’s comment that the lack of deep learning baselin... | Summary: This paper proposes a novel approach to modeling binding energy by leveraging folding energy and fine-tuning a protein inverse folding model. The proposed STAB-DDG model demonstrates improved performance in predicting binding energy, an area that has often been lacking in experimental results. This method effe... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate you pointing out our novelty of using folding energy data for binding ddG prediction. We address your comments/questions below.
Including multiple mutation sites: We have now evaluated folding ddG performance on multi-mutants in the megascale test set and ... | Summary: This paper presents StaB-DDG, a finetuning method for predicting mutational effects on protein binding. Specifically, it uses proteinMPNN, an inverse folding model, to calculate folding energy for a protein and binding energy a protein complex. It then finetunes proteinMPNN on experimental folding and binding ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and appreciate that they find it interesting that StaB-ddG allows folding ddG data to improve binding ddG prediction. We hope addressing the comments has helped strengthen our submission.
Baselines: We agree with your comment that the paper wi... | null | null | null | null | null | null |
UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models | Accept (poster) | Summary: The paper proposes a new benchmark for reasoning in physics by LLMs, they used 31 LLMs to evaluate the performance on the proposed benchmark, then they have introduced a new method MARJ to evaluate the outputs on these 31 LLM on the benchmark. Overall they show that OpenAI-01-mini gives the best performance on... | Rebuttal 1:
Rebuttal: Dear wqeU,
Thank you for your time and effort to review our work! We will reply to your questions one by one as follows:
> the human evaluation of the proposed method was only checked with 100 examples, which I feel is a very small sample, apart from this the model used for evaluation is Open A... | Summary: The paper introduces a comprehensive bilingual benchmark UGPhysics for evaluating undergraduate physics reasoning, featuring 5520 questions across 13 subjects. The benchmark also comes with a proposed evaluation pipeline that combines rule-based and model-based methods for improved accuracy. Notably, the study... | Rebuttal 1:
Rebuttal: Dear Reviewer SPQT,
Thank you for your valuable suggestions! We will reply to your questions one by one as follows:
> However, one concern I have is that in 5.2 Reliability of Evaluation, only 100 random test examples are being examined and it's not clear what are the answer types of those quest... | Summary: This paper proposes a new benchmark that targets underground-level physics prompts. The prompts are mined from physics textbooks via a rigorous processing pipeline. The two stage eval protocol is designed for this benchmark, in which a rule based metric used followed by using llm (gpt4o) to double check those ... | Rebuttal 1:
Rebuttal: Dear Reviewer 3YX8,
Thank you for your helpful comments! We will reply to your questions one by one as follows:
> It would be great if the authors can provide an analysis how robust the MARJ eval method is. e.g., how often it makes wrong judgement? In what scenarios LLMs can't correctly compare ... | Summary: This paper introduces UGPhysics, a large‐scale, bilingual benchmark specifically designed for evaluating undergraduate-level physics reasoning with large language models. UGPhysics comprises 5,520 distinct physics problems (11,040 when including both English and Chinese versions) spanning 13 subjects and 59 to... | Rebuttal 1:
Rebuttal: Dear Reviewer zXs9,
Thank you for your constructive feedback! We will reply to your questions one by one as follows:
> Prior works on mathematical reasoning (such as [1]) evaluation of LLMs use evaluation techniques similar to the MARJ evaluation (i.e. using a combination of rule based + LLM-as-... | null | null | null | null | null | null |
HashAttention: Semantic Sparsity for Faster Inference | Accept (poster) | Summary: This paper proposes a simple, effective and plug-and-play method for accelerating inference in autoregressive transformers via top-k attention. The authors propose to accelerate top-k operation by learning mappings to encode queries and keys in Hamming space in a way that the ranking induced using negative Ham... | Rebuttal 1:
Rebuttal: We thank the reviewer for supporting our paper. Please find responses to the questions and comments below. Kindly let us know if you have any additional follow-up questions.
1. **Hyperparameters for HashAttention Training / Frozen backbone LLM**
Yes the LLM weights are kept frozen during train... | Summary: Dynamic sparse attention has been widely explored in long-context scenarios. This paper proposes a learned hash function-based token-level dynamic sparse loading method. Specifically, it formulates the sparse attention top-K problem as a recommendation task, utilizing a learnable hash function to predict top-K... | Rebuttal 1:
Rebuttal: We thank the reviewer for supporting our paper. Please find responses to the questions and comments below. Kindly let us know if you have any additional follow up questions.
1. **training-free hashing**
To compare HashAttention to training free methods, we measure quality of sparse attention while... | Summary: The authors propose a method to identify relevant tokens in the attention computation by framing it as a MIPS search, using the relationship between MIPs and cosine similarity plus the approximation of cosine similarity in terms of the hamming distance of the corresponding hamming embeddings. The authors propo... | Rebuttal 1:
Rebuttal: We thank the reviewer for supporting our paper. Please find responses to the questions and comments below. Kindly let us know if you have any additional follow up questions.
1. **Discussion on RAG** – We will remove this from related work.
2. **Dimension of hamming codes:**
We can answer this q... | Summary: This paper introduces HashAttention, framing pivotal token identification as a recommendation problem. Given a query, HashAttention encodes keys and queries in Hamming space, capturing the required semantic similarity, using learned mapping functions. HashAttention efficiently identifies pivotal tokens for a g... | Rebuttal 1:
Rebuttal: We thank the reviewer for supporting our paper. We are working on extending Hash Attention to scenarios involving KV cache offloading and reasoning tasks, and we will present these in our future work. Please let us know if you have any other questions; we would be happy to clarify. | null | null | null | null | null | null |
Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn | Accept (poster) | Summary: A recent line of research has highlighted a problem where standard deep-learning methods gradually lose plasticity (i.e., the ability to learn new things) in continual learning settings (Lyle et al., 2022; Dohare et al., 2024). This paper examines plasticity loss in deep continual reinforcement learning (RL) t... | Rebuttal 1:
Rebuttal: > On other possible baseline methods, e.g., L2 regularization, LayerNorm (Lyle et al., 2024) continual backprop (Dohare et al., 2024) and ReDo (Sokar et al. 2023)
In addition to existing empirical evidence for LayerNorm, L2 Regularization and ReDo, we additionally implemented and ran LayerNorm, R... | Summary: The paper investigates the loss of plasticity issue in the continual deep reinforcement learning setting from the lens of churn ("undesirable generalization", empirical excess out-of-training-batch variation).
They claim that
1) loss of plasticity and high churn are connected: the decrease of the NTK matrix r... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s valuable comments and recognition. Our response aims to add more discussion and clarification on the points mentioned in the inspiring comments.
In addition, we provide additional experimental results for AdamRel [1], ReDo and LayerNorm, along with Reliable... | Summary: This paper studies the loss of plasticity in the continual reinforcement learning problem. The authors present a method that is based on reducing the churn to help prevent the collapse of the NTK rank. Through a series of experiments, the paper shows the effectiveness of the proposed method against other basel... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s valuable comments and the recognition of the importance of the topic studied in our paper.
> On the reviewer’s comments including “the empirical evaluation doesn't show conclusive results”, “the improvement is not consistent”, and “little insight is provide... | Summary: The manuscript investigates the loss of plasticity in continual reinforcement learning (CRL) from the perspective of churn. The authors establish a connection between plasticity loss and churn through the Neural Tangent Kernel (NTK) framework, demonstrating that churn exacerbation correlates with the rank decr... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s valuable comments and the recognition of our method and experiments. Our response aims to address these aspects in detail.
> On the experimental setups, task difference, and additional CRL setups
**[Clarification on environment choice and task difference]*... | null | null | null | null | null | null |
Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models | Accept (poster) | Summary: This paper proposes a novel algorithm for zero-shot inverse problem solving using diffusion models. The method build off the recent DMPlug model, which optimizes the input to conform with partial methods. The authors highlight a key insight, the optimization through the diffusion sampling process can be done m... | Rebuttal 1:
Rebuttal: Thanks for your recognition of this paper and the valuable comments and suggestions. Our responses to the main concerns are given as follows.
(**The metrics shown do not include image perceptual quality metrics, like FID or KID. & Evaluation datasets are limited in scope. Can the method operate... | Summary: This paper introduces two novel methods, DMILO and DMILO-PGD, to address computational and convergence challenges in solving inverse problems (IPs) using diffusion models (DMs).
Claims And Evidence: The core claims, such as memory efficiency, and improved convergence, are not supported by experiments. Thus, t... | Rebuttal 1:
Rebuttal: Thanks for your useful comments and questions. Our responses to the main concerns are given as follows.
(**The analysis does not explicitly address whether the bound generalizes to N > 2, leaving open questions about scalability.**)
We follow the work for ILO (Daras et al., 2021) to set $N = ... | Summary: The paper proposes DMILO and DMILO-PGD, two novel methods for solving inverse problems using diffusion models. DMILO introduces Intermediate Layer Optimization (ILO) to reduce memory burden while improving reconstruction by allowing model variations. DMILO-PGD further integrates Projected Gradient Descent (PGD... | Rebuttal 1:
Rebuttal: Thanks for your positive assessment of this paper and the valuable comments and suggestions. Our responses to the main concerns are given as follows.
(**Table 2 should be completed with linear deblurring (Gaussian or Motion), and baselines such as DPIR and/or DiffPIR. Would the authors be able ... | Summary: This paper proposes a novel approach for solving inverse problems using diffusion models through an iterative intermediate layer optimization strategy (DMILO). The optimization process is enhanced by introducing sparse deviations off the manifold of the diffusion trajectory, which allows the model to generaliz... | Rebuttal 1:
Rebuttal: Thanks for your recognition of this paper and the valuable feedback and suggestions. Our responses to the main concerns are given as follows.
(**My main concern is the computational cost of the proposed method. If the total number of function evaluations (NFEs) is fixed, does the proposed metho... | null | null | null | null | null | null |
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options | Accept (poster) | Summary: The paper introduces the Flow-of-Options (FoO) framework, a structured reasoning approach for large language models (LLMs) that systematically generates and evaluates multiple decision options at each step. Instead of following a single reasoning path, FoO constructs a directed acyclic graph (DAG) where each n... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We have consolidated the reviewer concerns into the following topics and will include them in our updated paper.
# 1. Computational cost compared to fine-tuning
Fine-tuning introduces two additional implicit costs that do not impact our approach:
- Fine-t... | Summary: The paper introduces Flow-of-Options (FOO), an agentic system designed for auto-ML. The core contribution is a framework based on fully-connected network structures of step-by-step solution paths generated by LLMs. The framework is evaluated comprehensively on multiple domains including standard data science ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. We will incorporate the suggestions into our paper.
# Q1
We measure the reduction in execution time when consistency checker is added to identify invalid paths (as opposed to w/o it), and planner when the adapter is added (as opposed to w/o i... | Summary: This paper proposes FoO (flow of options) approach to diverse the LLM's reasoning paths. An FoO-based agentic system is developed for solving traditional machine learning tasks including regression, classification, reinforcement learning, and image generation tasks. The authors show that their framework outper... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. We will incorporate the suggestions into our updated paper.
# Experimental Designs or Analyses
## Point 1
The documentation for DS-Agent, AutoGluon, and SELA note that they are indeed suited to tabular tasks similar to the ones explored in ou... | Summary: This paper proposes Flow-of-Options, a planning method for LLM agents, that can effectively track an optimal path over the combinations of possible options. More formally, Flow-of-Options can be represented as a directed-acyclic graph (DAG) of depth n, where a node is an option and an edge is a path between op... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and will incorporate these suggestions into our updated paper.
# W1
While our paper is on *Application-Driven Machine Learning*, **FoO does not explicitly specify steps such as model selection or feature engineering, but rather adapts to the t... | null | null | null | null | null | null |
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models | Accept (poster) | Summary: The paper proposes introducing multiple conditions into the protein sequence design process based on DPLM using a method similar to ControlNet. It achieves the integration of various conditions through the designed RCFE and AGFM modules. The performance on protein design tasks with multiple conditions shows a ... | Rebuttal 1:
Rebuttal: ### **Reviewer s6DK**
We appreciate your recognition of the novelty and strong performance of our method. Your questions raise important points, and we provide detailed clarifications and new quantitative results below. We would be happy to receive any additional constructive feedback.
---
**Q1... | Summary: This paper presents CFP-GEN, a large-scale diffusion language model developed for Combinatorial Functional Protein Generation under multiple constraints from diverse modalities. CFP-GEN facilitates de novo protein design by jointly incorporating functional, sequence, and structural constraints. It employs an i... | Rebuttal 1:
Rebuttal: ### **Reviewer p2fD**
We sincerely thank the reviewer for the positive feedback. We have carefully addressed the concerns below with new analyses and additional experiments, which will be incorporated into the final version. We welcome any further suggestions.
---
**Q1. In-depth analysis of per... | Summary: This paper introduces CFP-GEN, a diffusion-based language model for combinatorial functional protein generation that integrates multimodal constraints. The proposed Annotation-Guided Feature Modulation and Residue-Controlled Functional Encoding modules enable flexible conditioning across diverse modalities. Th... | Rebuttal 1:
Rebuttal: ### **Reviewer JkEt**
We appreciate your thoughtful comments and have addressed your concerns in detail below. The corresponding clarifications and expanded discussion will be reflected in the final version. We welcome any valuable suggestions you may have.
---
**Q1.** **Generalizability on rar... | Summary: This paper proposes a novel protein language model, CFP-GEN, which leverages discrete diffusion generation to design functional proteins. The key innovation lies in incorporating annotated protein labels, such as Gene Ontology (GO) terms, InterPro (IPR) domains, and Enzyme Commission (EC) numbers, during diffu... | Rebuttal 1:
Rebuttal: ### **Reviewer 1Xgk**
Thanks so much for acknowledging the novelty of our work and for providing thoughtful and constructive comments. We provide clarifications to your concerns below, which we will incorporate into the final version. Please let us know if you have any further valuable comments o... | null | null | null | null | null | null |
MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-text Decoding | Accept (poster) | Summary: A large body of research in visual image processing has focused on either brain encoding or decoding, aiming to understand how the brain processes natural scenes or reconstructs images from human brain activity. Recent fMRI brain decoding studies have specifically targeted advanced brain-computer interfaces, w... | Rebuttal 1:
Rebuttal: **C1** It is unclear how the fMRI encoder handles different numbers of voxels.
**R1** Each voxel is treated as a token in our model, and the attention layer learns to map sequences of varying lengths into a fixed-dimensional representation. This is similar to using multiple [CLS] tokens in a BERT... | Summary: This works a versatile and subject-agnostic model for fMRI-to-text decoding. It demonstrated a brain-instruction tuning approach, inspired from visual instruction tuning framework. This model has specifically designed with application across subjects with varied number of recording voxels, which is a common ch... | Rebuttal 1:
Rebuttal: **C1**
The manuscript lacks comparisons or references on brain-machine alignment or image/video decoding [1][2][3].
**R1**
We thank the reviewer for pointing out these relevant references. [1] [2] deal with fMRI time series, while our method focuses on static fMRI (i.e., fMRI signals at a momen... | Summary: The paper proposes a subject-agnostic encoding from fMRI recordings into an LLM space to enable text decoding from brain data. The paper claims this approach generalizes across subjects with different numbers of voxel measurements, and that it outperforms existing baselines.
Claims And Evidence: As written in... | Rebuttal 1:
Rebuttal: **C1**
The text claims UniBrain is subject-agnostic, but Table 1 lists MindLLM as the only subject-agnostic model—this inconsistency needs clarification.
**R1**
We apologize for the mistake and thank the reviewer for bringing this to our attention.
> with only a few exceptions (Mai & Zhang, 20... | Summary: This paper proposes MindLLM for subject-agnostic and versatile fMRI-to-text decoding. MindLLM consists of an fMRI encoder and an off-the-shelf LLM. The paper evaluates MindLLM on several fMRI-to-text benchmarks.
Claims And Evidence: The paper claims "a voxel’s position alone can theoretically serve as effecti... | Rebuttal 1:
Rebuttal: **C1**
The paper claims "a voxel’s position alone can theoretically serve as effective keys for attention weight computation", but no evidence is provided.
**R1**
We would like to point out politely that it is not a guess—it is strongly supported by the ablation study (note the blue line) in se... | null | null | null | null | null | null |
Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices | Accept (poster) | Summary: This paper studies the non-isotropic wrapped Gaussian distribution on the manifold of positive definite (PD) matrices. Specifically, the authors derive theoretical properties of the non-isotropic wrapped Gaussian distribution and propose maximum likelihood estimators for its parameters. They also define an equ... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their work, their valuable comments and interesting questions.
## Regarding the "Claims And Evidence":
We agree that the He-WDA should be a special case of the Ho-WDA when the covariance matrices for each classe are the same. In order to evaluate t... | Summary: The authors propose a new version of Gaussian on the SPD manifold (with the affine metric) by wrapping a Gaussian from a tangent space onto the manifold. Their method has two main differences than previously proposed methods 1. the distribution need not be isotropic and 2. the footpoint of the distribution on ... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their work, their valuable comments and interesting questions.
## Regarding the Claims and Evidence:
You say that you have doubts about the claims on the MLE made in section 6.1. The only claim on the MLE made in this section is that the LDA uses an... | Summary: The authors proposed a wrapped Gaussian formalism and give ML estimator for mean and covariance. The authors showed usefulness of proposed formulation in LDA and QDA on several datasets.
Claims And Evidence: 1. My biggest concern regarding the intrinsic nature of the formalism as claimed, the formalism is ess... | Rebuttal 1:
Rebuttal: ## Regarding the “Essential References Not Discussed”
The reference [1] you mention is actually the first reference given in Section 2 devoted to related works, on the first page of our paper. It was a key reference for the development of our theory as the authors proposed an __isotropic__ Gaussia... | null | null | null | null | null | null | null | null |
Universal Approximation Theorem of Deep Q-Networks | Accept (poster) | Summary: The paper is proports some approximation guarantees for Deep Q-networks (Theorem 3.1) for the optimal action-value of certain control problems.
The paper is imprecise at various parts. For instance, in the statement of Theorem 3.1, L is lower-bounded by $C(\epsilon,L)/\epsilon$. What is the point of the d... | Rebuttal 1:
Rebuttal: We certainly acknowledge the reviewer poured time into this. That being said, honesty compels the expression of profound disappointment regarding both the fundamental rigor and the overall professional conduct reflected in the assessment provided. Frankly, the feedback gives off a strong vibe of h... | Summary: 1. This paper studies universal approximation theorem for deep Q-network using residual blocks. The paper first connects SDE representation of viscosity solution of HJB, and this can be approximated by residual net.
2. The authors consider a continuous-time Markov decision process. The state-space is a open ... | Rebuttal 1:
Rebuttal: Here's how we'll handle the points you raised, including the specific manuscript changes. We'll add clarification on this in the revision.
1, Boundedness assumptions are frequent in stochastic approximation theory. They are vital for ODE-based convergence proofs (see Kushner \& Yin, 2003). Such a... | Summary: This paper introduces a connection between deep Q networks and SDEs. By viewing the forward pass as a continuous time process and using tools from stochastic control theory, the paper provides results on approximation theorems for deep Q networks.
Claims And Evidence: Claim (i) on page 1: Evidence is given in... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 5CGE for their constructive feedback and positive assessment. We address the comments below.
Lack of Numerical Experiments: We thank the reviewer. While the paper's focus is theoretical, we agree an illustrative example is valuable. We have implemented a discrete-time... | Summary: This paper develops a theoretical framework for Deep Q Networks (DQNs) in continuous time, by establishing connections among DQN, residual neural networks, stochastic control, and forward-backward SDEs. It links the neural network output at each layer to the Euler discretization of an SDE that models the state... | Rebuttal 1:
Rebuttal: Thanks for the detailed comments regarding clarity, refs, terms, etc. We address each below and will revise the paper accordingly.
Writing Clarity / Organization (Forward Referencing): We'll revise so all assumptions, definitions, equations, and lemmas are defined \textit{before} use. The specifi... | null | null | null | null | null | null |
Automatically Interpreting Millions of Features in Large Language Models | Accept (poster) | Summary: The paper presents an automated pipeline for generating and evaluating natural language interpretations of SAE latents in LLMs. The authors introduce five scoring techniques—Detection, Fuzzing, Surprisal, Embedding, and Intervention Scoring—to assess interpretation quality, with Intervention Scoring evaluating... | Rebuttal 1:
Rebuttal: We agree with this comment by the reviewer. Our generated interpretations only interpret the activations of individual latents, and are far from full explanations of their behaviour and downstream impact. Intervention based methods, like the one we proposed, are the ideal candidates to probe the c... | Summary: This paper introduces an automated pipeline for interpreting the latent features of sparse autoencoders (SAEs), which decompose large language model (LLM) representations. The authors propose five scoring methods to assess interpretation quality, including detection, fuzzing, surprisal, embedding, and interven... | Rebuttal 1:
Rebuttal: We thank the reviewer for this feedback.
> Lack of direct numerical comparison with existing methods
We compare our scoring techniques with the standard scoring technique at the time of writing, simulation scoring. In Table 1, we compare the costs of both scoring techniques, and we perform human... | Summary: The paper develops an automated pipeline for interpreting latent features identified by sparse autoencoders (SAEs) in LLMs. The authors implement a three-stage approach that first collects latent SAE activations, then generates natural language interpretations using external LLMs, and finally evaluates interpr... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments.
> While being a useful tool for a broad community (both interpretability community and people who want to use the interpretability toolsets), the overall insightful findings and novelty in the method are limited. The automated interpretability pipeline is not ... | Summary: This paper proposes an automatic concept explanation method based on LLM to address the problem of poor human comprehension of sparse autoencoders. Specifically, the author collects highly responsive sentences and corresponding concepts in SAE and carefully designs LLM prompts to prompt LLM. Then LLM automatic... | Rebuttal 1:
Rebuttal: We thank the reviewer for your helpful comments.
>The authors only used one LLM to interpret SAE in this article. It would be more convincing if the authors could consider more LLMs and perform prompt sensitivity analysis.
In the current version of the article, we evaluate the explanation given... | null | null | null | null | null | null |
Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise | Accept (oral) | Summary: This paper studies the problem of learning multiclass linear classifiers of form $
f_w(x) := \arg\max_{i} \\{\langle w_i, x \rangle\\},$
under random classification noises with known noise channel. The paper shows that, unlike the binary classification case where efficient SQ algorithm is possible, the case w... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our theoretical results and for providing useful feedback. We next respond to the comments from the reviewer as follows.
>Classification v.s. Regression:
We thank the reviewer for drawing our attention to the multi-class regression setting. In that respect,... | Summary: This paper is concerned with the task of multiclass linear classification (MLC) with $k$ labels under random classification noise (RCN). The problem parameters are a $k \times k$ row-stochastic noise matrix $H$, and a target linear classifier $f^\star$ that maps $\mathbb{R}^d$ to $[k]$ as $f^\star(x)=argmax_{i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation and useful feedback.
>Technical Contributions:
We point out that our proof cannot be viewed as a simple modification of a previously developed SQ lower bound. As explained in the submission, the generic framework follows the moment-matching approach f... | Summary: The paper studies linear multiclass classification under random classification noise within the statistical query (SQ) model. It considers a setting with positive noise separation, meaning that for a given labeled pair $(x, f^{\star}(x))$ under the ground truth labeling hypothesis, the probability of correctly... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for appreciating our theoretical results and for pointing out typos in the manuscript. We next respond to the comments from the reviewer as follows.
>Definition of $H$ in the proof of Theorem 6.2:
We want to thank the reviewer for pointing out this typo in th... | null | null | null | null | null | null | null | null |
Discrete and Continuous Difference of Submodular Minimization | Accept (poster) | Summary: This paper explores the minimization of the difference of submodular (DS) functions over both discrete and continuous domains, extending prior work that was restricted to set functions. The authors introduce a new variant of the DC Algorithm (DCA) to minimize DS functions, providing theoretical guarantees comp... | Rebuttal 1:
Rebuttal: Thank you for your positive review and helpful feedback. We address below your comments and questions.
---
**1- Results on the proposed algorithm's running time**
We report the average running time of the compared methods on the integer least squares experiment (Section 5.1) in [Figure 4](http... | Summary: Submodular functions are commonly studied as set functions, which can be viewed as functions defined on the vertices of the hypercube $ \\{ 0,1 \\}^n$. This paper, however, similar to some prior literature, considers an extension of submodularity, where functions are defined over cartesian products of compac... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address below your comments and questions.
---
**1- Explain the claim "The results can be easily extended to unequal $k_i$'s"**
The extension to unequal k_i’s follows directly, though the notation becomes more cumbersome. The key modifications are:\
- T... | Summary: This paper considers the minimization of a difference of submodular functions (DS) in both the continuous and discrete domains. Unlike the submodular minimization problem, which can be solved in polynomial time, this problem cannot even be approximated efficiently. This paper accomplishes two main things: (i) ... | Rebuttal 1:
Rebuttal: Thank you for your positive review. We will improve the writing of the introduction. | Summary: The minimization of a difference of submodular functions is studied, in a discrete and continuous setting. The discrete setting is more like a lattice that generalizes set optimization. A variant of the DC algorithm is developed that uses local search ideas. Experiments are performed to validate the algorithm.... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address below your questions.
---
**1- Distinguishing novel contributions from background**
Our main contributions are outlined in the introduction (lines 61-74, 1st col). The DS minimization problem over general discrete and continuous domains $\mathcal... | Summary: The paper investigates the minimization of difference-of-submodular (DS) functions over both discrete (products of finite sets) and continuous (products of intervals) domains. The authors establish that every function on a discrete domain and every smooth function on a continuous domain admits a DS decompositi... | Rebuttal 1:
Rebuttal: Thank you for your positive review and helpful feedback. We address below your comments and questions.
---
**1 - Extension of theoretical guarantees to continuous domains**
Theorem 4.5 extends to continuous domains as follows:\
Let $F'$ be defined as in Section 4.1, i.e., $F'(x) = F(x/(k-1))$ w... | null | null | null | null |
Learning-Order Autoregressive Models with Application to Molecular Graph Generation | Accept (poster) | Summary: This paper introduces a method for learning the order in which discrete elements from a masked set should be generated. The authors assume the generation process begins with a set of masked elements and, in addition to predicted the unmasked element, the model learns a policy which dictates the order in which ... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to the reviewer for their expert insights and valuable suggestions regarding the incorporation of chemistry-specific metrics to enhance our evaluation. We address their points below.
## 1. Report results against chemistry-specific metrics
We evaluated our best-perf... | Summary: This paper addresses a fundamental limitation in Autoregressive Models (ARMs)—the assumption of a fixed generation order, which may not be optimal for complex data types like graphs. The authors introduce Learning-Order Autoregressive Models (LO-ARMs), a novel approach where the model learns a context-dependen... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on the quality of our work. We now address each of your concerns as below.
## 1. Preliminary results on larger bioactive molecule dataset (ChEMBL)
To address your concern regarding the scalability of LO-ARM to larger datasets, we include a prelimina... | Summary: This paper proposes a method to learn an optimal generation order for autoregressive models in data domains that do not possess a natural canonical ordering (e.g., graphs or images). By framing the ordering itself as a latent variable with a dynamic, learnable distribution (the “order-policy”), the authors uni... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback! We are glad that you find our theoretical results sound, our improvement consistent and our ablations thorough. We address your concerns as below, especially your concern on training stability with REINFORCE.
### 1. Performance gains on QM9 and ZINC250k
We bel... | Summary: This paper propose a new generative modeling framwork named Learning-Order Autoregressive Models. The core of this framework is to extend the traditional ARs to learning a dynamic order of sampling. Speicifically train a order polocy to determine the order. To train such model, the authers use a variational ... | Rebuttal 1:
Rebuttal: Thank you for the time you’ve taken to review our work and for the positive and constructive feedback! We are glad that you found the problem we dealt with important and our approach novel and well-grounded. In response to the weaknesses and questions:
### 1. Generalization to other high-dimensio... | null | null | null | null | null | null |
Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization | Accept (poster) | Summary: ## Summary
* The authors propose a two-stage approach for black box optimization using diffusion model. The first stage is training stage. The authors propose to train a weighted unconditional model for density estimation, and an ensemble of proxy models to capture the value and uncertainty of target. This dif... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our paper's extensive experiment results. We've attempted to answer your questions below.
>**Claims And Evidence)** Therefore, whether simple reweighted training achieve weighted likelihood such as Eq. 11 remains questionable.
Thank you for pointing out ... | Summary: This paper proposes a novel high-dimensional black-box optimization method, where the authors train a diffusion model based on the weighted data as the prior and performs posterior sampling when combined with a uncertainty-aware function proxy. The authors also use local search and filtering strategies to furt... | Rebuttal 1:
Rebuttal: Thank you for your positive comment and for considering our key idea, incorporating the diffusion model as prior and casting sampling as posterior inference for solving high-dimensional black-box optimization, as novel. We answer your questions below.
>**Other Comments Or Suggestions)** Given the... | Summary: This paper utilizes the diffusion model for high-dimensional black-box optimization. At each iteration, they sample the candidates from the posterior distribution. The empirical results show that the proposed method outperforms other baselines.
Claims And Evidence: The authors claim that by sampling candidate... | Rebuttal 1:
Rebuttal: Thank you for your concrete review. Below we answer the questions and concerns you raised.
> **Claims and Evidence)** There is no measurement regarding the uncertainty of the generative model or discussion about the relationships between two terms of uncertainty.
While utilizing the uncertainty ... | null | null | null | null | null | null | null | null |
Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes | Accept (spotlight poster) | Summary: The paper presents a geometrical analysis of memorization in generative diffusion models based on the Hessian of their energy function around generated points. The idea follows naturally from recent results on the geometry of generative diffusion, which relate memorization and generalization to the spectrum of... | Rebuttal 1:
Rebuttal: > Relation between the trace of the Jacobian and the norm of the score function in the non-Gaussian case
Thank you for the insightful comment regarding the relation between the trace of the Jacobian and the score norm in the non-Gaussian case.
We found that Lemma 4.1 is indeed generalizable beyo... | Summary: This paper proposes to understand and mitigate the memorization of diffusion models from the perspective of the sharpness of probability landscapes. More specifically, it first shows that the large negative eigenvalues of the Hessian matrix, which reflects the sharpness, can indicate the risk of memorization. ... | Rebuttal 1:
Rebuttal: > Evaluate methods on more datasets.
Thank you for the suggestion. For our Stable Diffusion experiments, we adopted the established benchmark of known memorized prompts introduced by [1], which has become a standard dataset in the current literature [2-4]. In line with prior work, we made an effo... | Summary: To alleviate the memory effect of the diffusion model, this paper proposes a sharpness-based detection metric and develops an effective mitigation strategy based on this metric. The strengths of this paper lie in its clarity and the progressive experiments and theoretical analysis that illustrate the rationale... | Rebuttal 1:
Rebuttal: > Connection to [1].
We thank the reviewer for highlighting the connection to [1], which we will properly acknowledge. While both works study memorization through geometric properties of the density, there are key differences:
- [1] focuses on first-order smoothness via comparisons between train... | Summary: This paper studies the memorization phenomenon in diffusion models, which is a crucial task that is well-motivated by its practical significance in privacy preservation in the era of GenAI. This paper discovers a new pattern that can differentiate memorized and non-memorized generations of diffusion models tha... | Rebuttal 1:
Rebuttal: Thank you for your positive review and for recognizing our contributions to this topic. We sincerely appreciate the time and effort you dedicated to reviewing our work. Please do not hesitate to reach out with any further questions or suggestions. | null | null | null | null | null | null |
SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs | Accept (oral) | Summary: The paper proposes a dataset for visual question answering with external knowledge or for evaluating MLLM + RAG systems.
The dataset is constructed by using images from multiple datasets as seed images, and then writing context for those images using GPT-4o or using paired Wikipedia context when available, a... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review and your recognition of the dataset’s quality, thoughtful design, large scale, and strong transfer performance. We have carefully addressed your comments and concerns as follows:
> **...did not see any analysis done on how many model errors are the r... | Summary: This paper provides and analyzes a new dataset called SK-VQA, which is a large-scale dataset designed to train multimodal language models for knowledge-based visual question answering with context augmentation.
The authors’ motivation is that existing datasets for this specific task do not cover large and div... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed review and your recognition of the dataset’s scale, diversity, and its potential to benefit context-aware multimodal research. We have addressed your concerns as follows:
> **it is not clear why the results of training and testing with SK-VQA are missing in ... | Summary: This paper presents a large-scale synthetic dataset containing over 2 million visual questions with answers that require information from associated context. The images used in this dataset are from a hybrid of synthetic images from COCO-CFs and real images from Wikipedia and LAION, while the context and answe... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive review. We're grateful for your recognition of the strengths of our approach — particularly the scale and diversity of the dataset, our use of varied image and context sources, and the overall soundness of our methodology. We have addressed your conce... | Summary: This paper introduces SK-VQA, a dataset with over 2 million question-answer pairs associated with context documents for training multimodal language models in knowledge-based visual question answering. Using GPT-4, the authors generated context documents and diverse QA pairs for images from varied sources, cre... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of the strengths of our work, including the dataset’s scale and diversity, the quality-controlled generation process, and the robustness of our experimental design. We have carefully addressed all your concerns as follows:
> **However, there is one major l... | null | null | null | null | null | null |
COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning | Accept (poster) | Summary: The paper proposes a framework that trains a cost model for performance prediction on general-purpose hardware and then performs few-shot fine-tuning on emerging hardware accelerators. It focuses on optimizing sparse tensor programs on hardware accelerators. The proposed method achieves better hardware perform... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing the significance of the problem we address and the contributions of our work. We are especially grateful for the time and effort you dedicated to providing such a detailed and thoughtful review. We hope that our following response addresses your sugg... | Summary: This paper introduces COGNATE, a framework designed to optimize sparse tensor programs (e.g., SpMM and SDDMM) for emerging hardware accelerators using transfer learning. The key innovation lies in leveraging inexpensive data from general-purpose hardware (e.g., CPUs) to pre-train cost models and then fine-tuni... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing the significance of the problem we address and the contributions of our work. We are especially grateful for the time and effort you dedicated to providing such a detailed and thoughtful review. We hope that our following response addresses your sugg... | Summary: The submission introduces COGNATE, a novel framework designed to optimize sparse tensor programs on emerging hardware accelerators using machine learning-based cost models. It addresses the challenges of optimizing sparse tensor programs, such as Sparse Matrix-Matrix Multiplication (SpMM) and Sampled Dense-Den... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing the significance of our contributions. We are especially grateful for the time and effort you dedicated to providing such a detailed and thoughtful review. We also appreciate the additional references you shared, which we'll include in the final vers... | Summary: The paper introduces COGNATE, a novel framework for developing learned cost models to optimize sparse tensor programs on emerging hardware platforms. COGNATE leverages transfer learning to adapt cost models from general-purpose hardware (e.g., CPUs) to specialized accelerators with minimal fine-tuning data.
M... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing the significance of the problem we address and for acknowledging the contributions of our work. We are especially grateful for the time and effort you invested in providing a detailed and thoughtful summary of the paper’s strengths and areas for impr... | null | null | null | null | null | null |
am-ELO: A Stable Framework for Arena-based LLM Evaluation | Accept (spotlight poster) | Summary: The paper addresses challenges in ranking consistency and the variety of annotator abilities in arena-based evaluation of LLMs. They develop an enhanced ELO framework that replaces iterative updates with Maximum Likelihood Estimation (m-ELO). They prove theoretically that this MLE approach provides consistent ... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your high appreciation of the contribution and novelty presented in our paper. Your positive feedback means a lot to us. We also appreciate your valuable suggestions and questions for the computational complexity and experiments aspect of the pape... | Summary: The paper focuses on Arena-based LLM evaluation. The main algorithmic ideas include enhancing the ELO Rating System. It replaces the iterative update method with a MLE approach (m-ELO), which is more stable as it is insensitive to sample order. The am-ELO is also proposed, which modifies the ELO probability fu... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! Regarding the questions you raised, we have carefully considered each point and have made the following responses:
> **Q1**: The annotator modeling in the paper is somewhat simplistic. It mainly focuses on the annotator's discriminatory ability and consistenc... | Summary: This paper introduces am-ELO, an evaluation framework designed to enhance the ELO rating system for evaluating LLMs through arena-based comparisons. Traditional ELO systems exhibit instability mainly due to their sensitivity to data ordering and their failure to account for variations in annotator expertise, r... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our manuscript. We sincerely appreciate your time and effort in evaluating our work, and we will explain your questions and suggestions one by one below:
> **Q1**: The evaluation criteria still lack a detailed sensitivity analysis.
We've conducted sensitivity analy... | Summary: The paper introduces a novel stable arena framework, am-ELO, for evaluating LLMs using an enhanced ELO rating system. The authors address the instability issues in the traditional ELO method by replacing the iterative update approach with a MLE method, termed m-ELO. They further propose am-ELO, which incorpora... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. Regarding the questions you raised, we have carefully considered each point and have made following responses:
> **Q1**: One potential weakness is the simplicity of the annotator ability modeling, which primarily focuses on discriminatory ability and consiste... | null | null | null | null | null | null |
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos | Accept (poster) | Summary: This paper introduces Uncertainty-Based Extensible-Codebook Federated Learning (UEFL), a novel framework designed to address data heterogeneity in federated learning (FL). The key innovation lies in dynamically mapping latent features to trainable discrete vectors (codewords) and extending the codebook for sil... | Rebuttal 1:
Rebuttal: We appreciate the recognition of the novelty of our method, and the robustness of our experimental design. Additionally, we value the insightful critique regarding the limitations of our work. In response, we address these issues below:
> However, the introduction of an extensible codebook raises... | Summary: The paper addresses the challenge of data heterogeneity in federated learning (FL) by proposing Uncertainty-Based Extensible-Codebook Federated Learning (UEFL). The method dynamically extends a codebook of latent vectors using uncertainty estimates (via Monte Carlo Dropout) to adapt to diverse data distributio... | Rebuttal 1:
Rebuttal: We sincerely thank you for the insightful and constructive feedback, as well as the recognition of our contributions and experiments. Below, we address the specific questions raised:
> What is the extra information, except for the model parameters, that the clients send to the central server? Is ... | Summary: The paper introduces Uncertainty-Based Extensible-Codebook Federated Learning to address data heterogeneity in federated learning (FL) by dynamically expanding a discrete codebook based on model uncertainty. UEFL improves generalization by mapping latent features to trainable codewords and selectively extendin... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments and suggestions. In response, we address these issues below:
> Scalability to thousands of clients is not tested. Estimate overhead w.r.t. the number of clients
While we did not test UEFL with thousands of clients, we evaluated it with 50 & 100 clients, show... | Summary: This paper introduces Uncertainty-Based Extensible-Codebook Federated Learning (UEFL), a novel framework addressing data heterogeneity in federated learning. The key idea is to dynamically extend a codebook of discrete latent vectors based on model uncertainty, which is evaluated via Monte Carlo Dropout. UEFL ... | Rebuttal 1:
Rebuttal: Thanks for the insightful comments and suggestions. We address your concerns as follows:
> While the theorems ... connection to UEFL’s empirical success is not explicitly discussed
We provide a detailed analysis in response to the later theoretical questions. Please refer to that.
> The paper m... | null | null | null | null | null | null |
AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization | Accept (poster) | Summary: This paper proposes AlphaDPO which is a direct preference optimization method with a data-dependent margin. The authors first observe that the DPO objective can be looked at as making the likelihood of the chosen response to be greater than the losing response, with a margin set as the difference between the l... | Rebuttal 1:
Rebuttal: **Q1: Justification of Loss Function**
While space constraints limited introductory intuition, we provide multi-faceted justification through:
1) **Weak-to-Strong Generalization** (Lines 197-200): Similar to weak-to-strong alignment, our adaptive reference models enable policy specialization whil... | Summary: This paper introduces AlphaDPO, an adaptive preference optimization framework that improves alignment in large language models (LLMs) by dynamically adjusting the reward margin in preference learning. The key contribution is the introduction of an implicit reference model that interpolates between policy-drive... | Rebuttal 1:
Rebuttal: We thank the reviewer for raising this important concern. Below, we provide a rigorous theoretical justification for the approximation in Lemma 4.1 and clarify its empirical validity.
### **Theoretical Justification**
**1. Problem Formulation with Robustness Constraints**
Our objective is to... | Summary: This paper propose AlphaDPO, a new preference optimization framework. The core novelty of this framework is to modify the reference model distribution as the product of a uniform distribution and the ratio between policy model and original reference model, with power factor of alpha. This is effectively equiva... | Rebuttal 1:
Rebuttal: **Q1: Clarification on DPO's Reference Model Limitation**
We appreciate this insightful question. The necessity for $\pi\_{\text{ref}}$ to distinguish between $y\_w$ and $y\_l$ stems from two fundamental aspects of KL-regularized policy optimization:
1. **Theoretical Foundation of KL-Regularized... | Summary: This paper proposes a novel strategy for LLM alignment designed to address the limitations of SimPO and DPO. The proposed AlphaDPO adaptively sets the reward margin based on the ratio between the preference model and the policy model. The relations to SimPO and TDPO loss have been studied. Extensive experiment... | Rebuttal 1:
Rebuttal: **Q1: What is the practical utility of the theoretical connection between AlphaDPO and online methods given that online methods themselves lack strong theoretical guarantees?**
We appreciate the reviewer raising this important point. Although we did not explicitly emphasize the theoretical connec... | Summary: This paper proposes a new training algorithm for LLM alignment. First, the authors unify the training objective of the two representative alignment training algorithms, DPO and SimPO, into a single one with a fixed margin. Next, they propose a new training algorithm to mitigate the limitation of each algorithm... | Rebuttal 1:
Rebuttal: **Q1: Similar idea in previous works.**
We sincerely thank the reviewer for pointing out relevant prior works. While both our method and previous approaches involve model interpolation, we highlight three key distinctions:
- **Adaptive reward margin**: Unlike existing works that focus on regul... | null | null | null | null |
Unbiased Evaluation of Large Language Models from a Causal Perspective | Accept (poster) | Summary: The paper explores bias in agents as evaluators (LLMs generating new tasks for evaluating another agent), and detects different kinds of biases. They introduce an unbiased evaluator using causal inference.
"## update after rebuttal"
I thank the authors for answering my questions. After seeing the other review... | Rebuttal 1:
Rebuttal: ## Response to reviewer zS8E
> **Q1**: Reference AI-MO 2024 should be AIME 2024
**A1:** AI-MO (refer to https://aimoprize.com), the AI Mathematical Olympiad, adapts data from AIME 2024 as its competition benchmark. This widely-used version is publicly available as [aimo-validation-aime](https://h... | Summary: This paper studies potential biases in LLM-based evaluators (“Agents-as-an-Evaluator”) and proposes a new protocol, called the “Unbiased Evaluator,” which systematically introduces small interventions (“Bags Of Atomic Interventions”) into evaluation tasks to mitigate data and model biases. The authors present ... | Rebuttal 1:
Rebuttal: ## Response to reviewer A1m7
> **Q1**: Bias mitigation approaches in the LLM-as-a-Judge are related to the paper, such as Length-controlled AlpacaEval and Arena-Hard Style Control.
**A1:** Thank you for your suggestion. Unlike previous bias mitigation approaches in the LLM-as-a-Judge, which prima... | Summary: The paper introduces ‘Agent as an evaluator’ paradigm with the goal to increase the robustness of LLM-as-a-Judge based evaluations. The evaluation protocol introduces the ability to test model and data bias by taking an active/intervening (agentic) process of evaluating the benchmarks. The query breakdown focu... | Rebuttal 1:
Rebuttal: ## Response to reviewer 3rKe
> **Q1**: The paper writing and organization can be improved a lot. The paper starts and shows the cryptic Fig. 1 and non-standard Fig 2 (and talks about Fig 2 much later) - the definitions are vague (still confused how strength parameter is varies during evaluations)
... | Summary: This paper presents Bags of atomic interventions (BOAT) to address the data contamination problem in LLM evaluation. It first develops a theoretical formulation of evaluation bias, and identity the data and model bias in agents-as-an-evaluator paradigm. It then proposes the unbiased evaluator to help evaluate ... | Rebuttal 1:
Rebuttal: ## Response to reviewer D15D
> **Q1**: The unbiased evaluator heavily depends on the BOAT, which is hand-designed, and how these principles are hand-designed to fully follow the theoretical framework.
**A1**: **The design of the Unbiased Evaluator is grounded in our theoretical findings** (see th... | null | null | null | null | null | null |
Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling | Accept (poster) | Summary: The paper introduces CPSDE, a new model for designing cyclic peptides using harmonic SDE and explicit atom-bond modeling conditioned on a 3D structure of a protein target. CPSDE comprises two key components: a generative structure prediction model and a residue type predictor. Alternating between these two mod... | Rebuttal 1:
Rebuttal: **Q1: "The main weakness I see for this paper in an ML venue is that it is highly specialized to tackle the problem of cyclic peptide design; arguably this is an important application, but the authors seem to propose a rather complex model to handle the cyclic peptide generation task."**
A1: Than... | Summary: This work tackles the task of cyclic peptide design. Cyclic peptides can have unique advantages in terms of stability and affinity when producing binders compared to other types of peptides or ligands. While there is much work in small molecule as well as protein and peptide generation, there is no prior work ... | Rebuttal 1:
Rebuttal: **Q1: "It would be great if the authors would release their curated training dataset as well as models and code for the broader community."**
A1: We would like to open source our work to contribute to the community.
**Q2: "I have some minor wording comments."**
A2: Thanks for pointing this out.... | Summary: This paper describes a generative method to design cyclic peptides given a protein target. The method uses two diffusion models utilized in a coupled fashion. One to generate the structure, the other to predict the sequence.
## Update After Rebuttal
I thank the authors for addressing my review. I have decided... | Rebuttal 1:
Rebuttal: **Q1: "Comparing the generated ligands to known cyclic peptides."**
A1: We conducted a comparison of our method with known cyclic peptides.
Vasopressin, a natural cyclic peptide featuring intramolecular disulfide (S-S) bond cyclization, is utilized in the treatment of antidiuretic hormone defic... | Summary: The paper proposes an approach for the design of cyclic peptides using score-based generative models and diffusion. It is termed harmonic SDE, mainly because of conditioning on chemical graph that gives rise to a slightly non-standard forward process. The approach has been evaluated in peptide design against f... | Rebuttal 1:
Rebuttal: **Q1: "I find the idea of conditioning generative models using chemical graphs interesting, and also the problem of designing cyclic peptides highly relevant for therapeutics purposes. However, the empirical evaluation of both ideas is inadequate."**
A1: Our work focuses on designing cyclic pepti... | null | null | null | null | null | null |
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? | Accept (poster) | Summary: The paper investigates whether diffusion models can learn hidden inter-feature rules in images by designing synthetic tasks that simulate real-world relationships (e.g., the connection between the sun’s height and the length of its shadow). The study finds that while these models can capture coarse-grained rul... | Rebuttal 1:
Rebuttal: We thank the reviewer's efforts on reviewing this paper. We now address the questions raised as follows.
---
>Q1: Broader datasets / Real-world data.
Thanks for your good question. To supprot that DMs can learn coarse rules but hard to learn fine-grained rules, we conduct additional experiment... | Summary: This paper investigates whether diffusion models can learn hidden inter-feature rules in images, focusing on the distinction between coarse-grained and fine-grained relationships. Through carefully designed synthetic tasks inspired by real-world phenomena—such as the spatial relationship between the sun and it... | Rebuttal 1:
Rebuttal: Thanks for your time and efforts reviewing our paper. We now address raised questions as follows.
---
>Q1: Further ablation studies across different architectures and training configurations.
``Section D.3 (Lines 964-1032)`` considers different architectures (U-Net, SiT, DiT) and training conf... | Summary: This paper evaluates diffusion models from both experimental and theoretical perspectives on inter-feature rule learning, indicating that while they can capture coarse rules, they struggle with fine-grained ones. The authors also provide a preliminary method to mitigate this shortcoming in learning fine-graine... | Rebuttal 1:
Rebuttal: We thank the reviewer's efforts on reviewing this paper. We now address the questions raised as follows.
---
>Q1:The proposed approach to facilitating fine-grained rule learning appears to have no direct connection with the theoretical analysis. /The proposed method is unrelated to the main anal... | Summary: This paper is motivated by some prevalent real world failure case of diffusion model learning rules between spatial parts and features. They developed a few synthetic tasks to test the learning of diffusion model on inter object rules (spatial or non spatial). Though the overall layout is correct and rough sce... | Rebuttal 1:
Rebuttal: Thanks for your time reviewing this paper. We now address your questions as follows.
---
> Q1: Theory
Thanks for your good points. First, Gaussian setup in [W2025] is a **special case** of our theory. Particularly, as ``Theorem 4.2``, the score can be written as
$$\nabla \log p_t(x_t^{(1)}, x_... | null | null | null | null | null | null |
MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible Cost | Accept (poster) | Summary: This paper proposes MuLan, a text-encoder adapter that equips T2I models that are pre-trained with data dominant in English now with multilingual capabilities. With MuLan, T2I modes may take in prompts in purely non-English terms and
generate images in quality on par with those from English prompts.
Claims ... | Rebuttal 1:
Rebuttal: Dear reviewer fvJg,
Thanks so much for your constructive comments and support for acceptance. We hope our responses can address your concerns.
**Q1: Lack of as-is baseline performances from English-only T2I backbone models.**
**A1**: We thank the reviewer for emphasizing the importance of “as-i... | Summary: This paper proposes a simple yet effective way to handle multilingual text input in text-to-image generation. By utilizing a pre-trained multilingual text encoder and introducing a light-weighted adapter, the resulting model is shown to handle multilingual input well.
Claims And Evidence: The paper claims to ... | Rebuttal 1:
Rebuttal: Dear reviewer RQVZ,
Thanks very much for your valuable comments. We hope our responses can address your concerns and clarify our contribution.
**Q1. Comparison with translation baseline.**
**A1:** In fact, previous multilingual text-to-image generation works (e.g., AltDiffusion) typically compa... | Summary: This paper introduces MuLan, a lightweight and plug and play language adapter that enables multilingual text to image generation for diffusion models with minimal computational cost. The central idea is that multilingual text encoders can be used to enable multilingual image generation without the need for ext... | Rebuttal 1:
Rebuttal: Dear reviewer BvZv,
Thanks a lot for your insightful reviews and support for our work! We hope our responses can address your questions.
**Q1. Lack of a cross lingual consistency evaluation**
**A1:** We appreciate the reviewer’s suggestion to evaluate cross-lingual conceptual consistency. Our w... | null | null | null | null | null | null | null | null |
Learning Multiple Initial Solutions to Optimization Problems | Reject | Summary: This paper addresses the setting where a parametric optimization problem must be solved repeatedly, such as in online settings. For these settings, the authors argue that a key concern is the provision of a good initial guess for a local optimization solver. The paper proposes the MISO framework, which uses a ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your feedback. We appreciate your recognition of our clear writing, motivation, and coherent algorithm.
In the following, we clarify misconceptions regarding our claims, address concerns about benchmark tasks, solver comparisons, and imple... | Summary: This paper proposes a method called MISO (Learning Multiple Initial Solutions Optimization) to improve the performance of local optimization algorithms by predicting multiple high-quality initial solutions. The framework leverages a transformer-based architecture and introduces three loss (PD, WTA, and MIX) fu... | Rebuttal 1:
Rebuttal: We thank you for your constructive feedback and for recognizing the key contributions of our work. We are pleased that you found the idea of learning multiple diverse initial solutions both valuable and well-motivated and that you appreciated the design of our methodology and the clarity of our pr... | Summary: In this work, the authors aim to improve the performance of solving sequential optimization problems, and the key is to improve the quality of initial solutions. To address this issue, the paper introduces MISO that uses a Transformer-based predictor to predict multiple diverse initial solutions conditioned on... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and insightful comments. We appreciate your recognition of our extensive experiments, clarity in presenting methods and results, comprehensive baseline comparisons, and our unique contributions relative to existing literature.
In the following, we clarify ... | Summary: The paper introduces MISO, a novel framework designed to improve the performance of local optimization algorithms by predicting multiple diverse initial solutions to warm start optimization problems. The motivation is to make these solutions diverse so they cover different regions of the solution space. The pa... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful and constructive feedback. We greatly appreciate your positive comments regarding the extensive experimentation, soundness of the method, and comprehensive evaluation across multiple optimizers and benchmark tasks. We are particularly pleased you found the ablation... | null | null | null | null | null | null |
Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning | Accept (poster) | Summary: The paper takes a look at harmful fine-tuning after a policy for alignment has been incorporated using a subset of curated examples at the provider's. The idea is that certain subsets of alignment examples are more vulnerable to being forgotten during HFT. The work, VAA, looks to identify these groups, and use... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback on our work. We appreciate your recognition that this is a forward-looking work in a new domain that will gain importance. We are also grateful for your acknowledgment that our experiments are well-designed and comprehensive, and that our design of VAA based ... | Summary: The paper introduces Vulnerability-Aware Alignment (VAA), a method aimed at enhancing the safety of large language models by focusing on data subsets that are vulnerable to harmful fine-tuning. VAA employs group-based robust optimization to boost model robustness, lowering harmful scores while preserving perfo... | Rebuttal 1:
Rebuttal: Thank you for your insightful and positive feedback. We appreciate your recognition of our adaptation of the Group DRO framework into a two-player game between a hard sampler and the LLM as both interesting and technically sound. We also value your acknowledgment that our extensive experimental re... | Summary: This paper studies the Harmful Fine-Tuning (HFT) problem from data perspective. The authors find that there are specific subsets (vulnerable samples) in the aligned data that are more likely to be forgotten in HFT. To address this problem, this paper proposes a new method called Vulnerability-Aware Alignment (... | Rebuttal 1:
Rebuttal: Thank you for your professional and encouraging feedback. We are pleased that you recognize our work as the first to reveal uneven vulnerability under HFT. We also appreciate your recognition of VAA as a novel method that significantly reduces harmful scores while preserving performance, and your ... | Summary: The paper observes that certain subsets of alignment data are more likely to be forgotten during harmful fine-tuning (HFT) of large language models (LLMs). To mitigate this issue, the paper proposes a new alignment-stage method called Vulnerability-Aware Alignment (VAA). VAA first divides data into “vulnerable... | Rebuttal 1:
Rebuttal: Thank you for the professional and constructive feedback! We are delighted that you recognized our adaptation of GDRO to address a new and important problem, and appreciated the various interesting observations in our work. We're glad you found our experimental setup largely acceptable and our res... | null | null | null | null | null | null |
Explaining the role of Intrinsic Dimensionality in Adversarial Training | Accept (poster) | Summary: This paper investigates the intrinsic dimensionality in the layerwise fashion for adversarially trained models. This paper provides a new perspective for adversarial training in different model architectures from manifold conjecture. The off-manifold adversarial examples (AEs) enhance robustness, and the on-ma... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments and suggestions. In response, we conducted additional experiments to address the raised concerns and outlined the further revisions planned for the paper.
**Evaluation on vision models:**
> Given that SMAAT is presented as a generalized framework,... | Summary: This paper reveals the fundamental reasons behind the varying effectiveness of adversarial training across different types of neural networks and proposes a novel and efficient training method, SMAAT. The study finds that early layers of vision models (e.g., CNNs) and generative language models (e.g., LLaMA) e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments and suggestions aimed at improving the clarity of the paper. Below are our responses to the reviewer’s points.
**Theoretical proofs:**
> While the theoretical claims are strongly supported by systematic experimental design—spanning different mo... | Summary: The authors investigate how the relationship between perturbations and the data manifold influences whether adversarial training leads to improved generalization or robustness. Based on this insight, they propose SMAAT, a method that generates perturbations at specific layers to target different manifolds—leve... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and suggestions. In response, we have conducted additional experiments to address the critiques and provide a summary of further revisions that will be made to the paper.
**Details on Fig. 1 and Fig. 2:**
> Figure 1 and Figure 2 lack sufficient ex... | null | null | null | null | null | null | null | null |
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret | Accept (poster) | Summary: This paper studies the problem of differentially private stochastic bandit. It proposes a new algorithm which is roughly Thompson sampling, with an option to re-use previous samples. Their algorithm offers a trade-off between regret and privacy, a strict improvement over previous works on the privacy when main... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive comments. We address each of your questions as follows.
(1) Regarding the **similarity between our proposed algorithm and UCB**,
as theoretically justified in Lemma 4.1 and the content just above it (Lines 266 to 271), the reason why we call it UCB is tha... | Summary: This paper examines the regret-privacy trade-off for the Gaussian TS algorithm under Gaussian Differential Privacy. By drawing the connection between Gaussian TS and UCB, authors propose the DP-TS-UCB algorithm, which does not need to sample a Gaussian model at each round, the paper achieves a new privacy-regr... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive comments. We address each of your questions as follows.
(1) Thank you very much for referring us to this interesting paper [1]. Under the notion of Bayesian regret and using Gaussian priors, we think it is possible to achieve an improved trade-off between... | Summary: This paper describes a stochatic MAB algorithm that preserves DP. It uses Thompson sampling with a limited budget of samples per epoch. Once the samples are exhausted within a round it uses the maximum of those samples. This is akin to an upper confidence bound. It also has a parameter $\alpha$ that can tune ... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive comments. Regarding the results of GDP guarantees, we would like to clarify that when choosing $\alpha = 0$, the privacy guarantee depends on $T$. In Theorem 4.4, the GDP guarantee is in the order of $ \sqrt{ T^{0.5(1-\alpha)} \ln^{1.5(1-\alpha)}(T)}$. So, ... | null | null | null | null | null | null | null | null |
Elucidating the design space of language models for image generation | Accept (poster) | Summary: The paper investigates the application of large language models (LLMs) to image generation, demonstrating that LLMs can achieve near state-of-the-art performance without relying on domain-specific designs. The study also analyzes the learning and scaling behavior of autoregressive models, showing that larger m... | Rebuttal 1:
Rebuttal: Thanks a lot for your valuable comments. Below, we will address your concern in detail.
``` Discuss with LlamaGen and MAR```
We agree that both [1]LlamaGen(Sun et al.) and [2]MAR(Li et al.) have made important contributions to advancing autoregressive image generation. We would like to clarify t... | Summary: This paper systematically explores how to utilize LLMs for image generation, providing detailed comparisons and analyses across tokenization methods, modeling approaches, scan patterns, vocabulary design, and sampling strategies, offering some interesting conclusions. Based on these integrated experiments and ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comment! Below, we will address your concern in detail.
```W1. methodological innovation```
We acknowledge that our work does not center around proposing a new model architecture, but rather focuses on **systematically analyzing and understanding** how existing design co... | Summary: This paper systematically explores the design space of large language models for image generation, evaluating factors such as tokenization, model architecture, scanning patterns, vocabulary decomposition, and sampling strategies. The proposed ELM achieves state-of-the-art FID scores, demonstrating the potentia... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments! We will address your concerns in detail.
```Limit 1. no results on text-to-image generation```
Our main goal is to assess the effectiveness of LLMs as a unified generation paradigm, especially when applied to images. To isolate modeling factors like tokenizers,... | Summary: In this work, authors focus on the research topic of AR image generation, and conduct extensive studies focusing on 1) tokenizer, 2) AR model design (AR or Mask) 3) image scan direction. With large number of stduies, authors proposed ELM, which is able to achieve sota performance in ImageNet256 generation task... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments! Below, we will answer your concern in detail.
```W1: Difference performance of VQGAN from LlamaGen```
To ensure a **controlled comparison between structurally different quantization methods**, we use the **original VQGAN** from Taming Transformers [Esser et al.... | null | null | null | null | null | null |
Core Knowledge Deficits in Multi-Modal Language Models | Accept (poster) | Summary: This paper introduces the CoreCognition dataset focusing on a systematicity evaluation of multimodal models. Tasks in the benchmark are designed based on the well-established core-knowledge theory in developmental psychology. These tasks cover multiple aspects of human multimodal cognition, spanning from low-l... | Rebuttal 1:
Rebuttal: ```>>> Q1``` The tasks are much harder than children cannot easily solve compared to developmental CogSci
```>>> A1``` Thanks for bringing up this nuance. We address the concern in two aspects. First, while all our tasks are derived from standard developmental cognitive science prototypes, e.g.,... | Summary: The paper investigates the hypothesis that the limitations of MLLMs in performing intuitive tasks, which are simple for humans, stem from the absence of "core knowledge"—innate cognitive abilities present from early childhood in humans. To explore this, the authors develop a novel benchmark called the CoreCogn... | Rebuttal 1:
Rebuttal: ```>>> Q1``` typo: double dots.
```>>> A1``` Thanks. We will revise and remove all typos.
---
```>>> Q2``` Assume that replicating human-like core knowledge is essential for the effective functioning of AI systems is controversial and may not necessarily hold?
```>>> A2``` Thank you for the ... | Summary: The paper investigates core cognitive abilities in multimodal large language models. The authors find that models underperform in abilities that develop early in humans, while they perform comparable to humans on higher level abilities. They show that multimodal language models often rely on shortcut learning.... | Rebuttal 1:
Rebuttal: ```>>> Q1``` "high-level abilities do not correlate with the corresponding low-level abilities" is too strong
```>>> A1``` Thanks! In Sec 4.3, the correlations between lower- and higher-level abilities are generally below 0.4. This is considerably lower than what's commonly observed in humans, t... | Summary: This paper presents an evaluation framework for assessing the image understanding capabilities of multimodal language models (MLLMs) from a lens of cognitive taxonomy of concepts of learning. Inspired by the cognitive science literature on visual concept learning, the authors present a “CoreCognition” benchmar... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. However, due to limited space, we could only answer the most significant questions here. We look forward to discussing the rest (i.e., illusion vs. shortcut and 4 quadrants of Fig 8, suggested citations, etc) in the discussion phase!
```>>> Q1``` W... | null | null | null | null | null | null |
QUTE: Quantifying Uncertainty in TinyML models with Early-exit-assisted ensembles for model-monitoring | Accept (poster) | Summary: This paper proposes QUTE, a new uncertainty quantification (UQ) method for tinyML models on low-power devices. It uses a lightweight early-exit ensemble to reduce size and computation while maintaining accuracy. QUTE is 59% smaller than previous models which can reduce latency by 31%, and as a result, it impro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and thoughtful feedback.
Given that your comments are highly encouraging and did not raise significant concerns, we would appreciate any insights into the current score to help improve our work. We look forward to having a fruitful discussion and ... | Summary: The paper introduces QUTE, a novel uncertainty quantification (UQ) framework optimized for TinyML models, addressing the challenge of efficient model monitoring in resource-constrained environments. QUTE leverages early-exit-assisted ensembles, where lightweight classification heads at the final network exit r... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. We appreciate the recognition of our work’s originality and the insightful suggestions.
> an ablation study isolating the impact of early-exit (EE) knowledge transfer and stronger justification for diversity benefits of EV-assistance
Appendix B... | Summary: **Problem**
- This paper focuses on uncertainty quantification (UQ) for TinyML models that are specifically designed to operate on microcontrollers with extremely limited memory and computational resources.
**Method**
- The authors propose QUTE, which combines ideas from early-exit (EE) ensembles and multi-h... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We’re delighted that you found our contribution both novel and clear.
> on temperature scaling results
We apply temperature scaling to BASE and QUTE (following [1] ). The results are included in Appendix B.1. (Tables 4 and 5).
> on absence of statistical ... | Summary: In this paper, the authors propose a novel method for uncertainty estimation in low-resource configurations. Specifically, the method builds on the well-known early-exit approach—where the model produces multiple predictions as the prediction depth increases and then combines them in an ensemble-like manner—bu... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful comments. We are glad to hear that you appreciate the novelty of our approach and recognize the thorough evaluation of the proposed method.
> experiments on larger datasets would be also appreciated
To demonstrate the scalability of our proposed method,... | null | null | null | null | null | null |
Active Evaluation Acquisition for Efficient LLM Benchmarking | Accept (poster) | Summary: The paper deals with efficient evaluation and offers a new way to dynamically choose which examples to use, per model. It shows great results, the improvements are very clear, the novelties are too, the writing is also mostly easy to follow.
Claims And Evidence: Well supported
Methods And Evaluation Criteria... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed feedback and strong endorsement of our work. We address each point below:
## Terminology: "Prompts" vs "Examples"
We thank the reviewer for highlighting the potential confusion in our use of the term "prompt." We use "prompt" to mean benchmark ex... | Summary: The paper presents an approach to improve the efficiency of evaluating large language models (LLMs) by selecting a subset of evaluation prompts through a learned policy. The authors claim that their RL-based approach significantly reduces computational cost while maintaining accuracy.
Claims And Evidence: 1. ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback and concerns. Below we address the specific points raised:
## Evidence for Prompt Correlations
The reviewer questions our assumption that evaluation scores across prompts are correlated. This correlation is well-documented in prior literature on LLM evaluati... | Summary: This paper introduces a novel RL-based method to LLM's benchmarking evaluation. From the aspects of efficiency and accuracy, they improve the accuracy in evaluation and also lower the computation overhead in the process of evaluation. They are inspired by active learning and propose their approach by modeling ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough evaluation of our work and the positive recommendation. Below, we address the specific concerns and questions raised:
## Evaluation Metrics and Result Interpretation
The reviewer raised questions about our use of absolute error as an evaluation ... | Summary: The paper focuses on LLM efficient evaluation , that is, estimating overall performance based on a subset of data. The authors first model dependencies across evaluation prompts using neural processes, then analyze various selection methods and propose a RL-based method. Additionally, for the cold start probl... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough evaluation of our work and the positive recommendation. Below, we address the points raised:
## Practical Applications of Efficient Evaluation
We appreciate the suggestion to highlight practical applications of efficient evaluation in the introd... | Summary: This paper proposes a large language model (LLM) evaluation method, which considers dependency modeling and subset selection to improve efficiency. The authors develop a model that captures dependencies across evaluation prompts and propose subset selection policies based on these dependencies. Extensive exper... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful assessment of our work. Below, we address the key points raised:
## Concern about Subset Selection and Fairness Across Models
The reviewer raises a valid concern about whether variations in selected subsets for different groups of LLMs might a... | null | null | null | null |
Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning | Accept (poster) | Summary: The authors introduce formally and investigate mathematically the manifold deviation issue due to approximate guidance in the context of trajectory-planning (for reward maximization) via diffusion models. They provide a lower bound on this error, and to address this issue they introduce LoMAP, a training-free ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback on our work. We especially appreciate the reviewer pointing out the clarity issue regarding our claims. Please find our detailed response below.
- **“The claim "The current sample is then projected onto this subspace, thereby remaining o... | Summary: The authors tackle the problem of approximate energy guidance with diffusion policies in the context of offline RL. Partial energies naively trained through MSE loss such as in Diffuser are only lower bounds to the true energy, and so using their gradients for conditional sampling can push trajectories off the... | Rebuttal 1:
Rebuttal: Thank you for the positive review and constructive feedback. Please find our detailed response below.
- **“With the IVF method for faster nearest neighbors as described in Appendix D, what was the wall clock time to generate a single trajectory for a task like AntMaze? How expensive is the proced... | Summary: Classifier guidance can introduce distribution shift during diffusion sampling. This paper proposes a training-free method to constrain guided diffusion within a learned manifold by projecting noisy samples onto a local low-dimensional manifold, approximated using nearest neighbors from the training set at eac... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback on our work. Please find our detailed responses below.
- **Not a strong improvement over chosen baselines**
We appreciate the valuable feedback provided by the reviewer. However, we respectfully disagree with the subjective judgment tha... | Summary: This paper addresses the limitation in diffusion-based trajectory planning for RL tasks. Previous works in diffusion models often produce infeasible trajectories due to "manifold deviation" during the sampling process, so the authors proposed a novel method LoMAP, which is a training-free framework. It project... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback on this work. We especially appreciate the insightful questions regarding the accurate manifold approximation. Please find our detailed answers below.
- **"I do not fully understand why LoMAP can help with the issue of diffusion sampling... | null | null | null | null | null | null |
An Architecture Search Framework for Inference-Time Techniques | Accept (poster) | Summary: The paper introduces an inference-time "architecture search" framework (not to be confused with NAS) designed to optimize language model performance on specific benchmarks. The framework systematically selects and integrates multiple inference-time techniques (ensembling, fusion, ranking, critiquing, verificat... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our paper! We appreciate your feedback and comments.
We’d like to address each of your concerns individually:
- *Weaknesses: -The biggest weakness of the method, in my opinion, is its requirement to get specific benchmarks to optimize the architecture for. Wh... | Summary: This work focuses on how to combine inference-time techniques of LLMs to achieve better performance. It first proposes a framework termed Archon, which is able to incorporate different inference-time techniques rather flexibly. Then, a search method based on Bayesian optimization is designed, which takes as in... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our paper! We appreciate your feedback and comments.
*However, I find that the major analyses/conclusions are not presented in Section 3.:*
- We agree that understanding the utilities of inference-time techniques is central to our contributions. We've enhance... | Summary: The paper introduces a framework for optimizing inference-time techniques in large language models (LLMs). The contributions, as stated in the paper, are an algorithm that identifies optimal ways to combine inference-time techniques (such as ensembling, ranking, fusion, verification, and critique), as well as ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our paper! We appreciate your feedback and comments.
*However, compute budgets are not always controlled for... controlling only for token budget as in Table 1 and Figure 5 doesn't give a clear picture:*
- We appreciate your concerns regarding compute matchin... | Summary: This paper proposes a framework called ARCHON for optimizing combinations of inference time techniques to improve the performance of large language models. Their approach combines multiple inference techniques such as ensembles, rankings, etc. and uses automatic architecture fusion search to find the optimal c... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our paper! We appreciate your feedback and comments.
*The claim that Utilities of Inference-Time Techniques is not well studied:*
- We agree that understanding the utilities of inference-time techniques is central to our contributions. We've enhanced the expl... | null | null | null | null | null | null |
Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection | Accept (poster) | Summary: This paper proposes a causality-aware contrastive learning method for time-series anomaly detection. Experiments on five real-world and two synthetic datasets validate that the integration of causal relationships improve the anomaly detection capabilities.
Claims And Evidence: Most the claims in the paper ar... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer J2ZH’s insightful and constructive comments. This response presents additional experiments and discussion to address the reviewer’s concerns, all of which will surely be integrated into the main paper.
>**Time series anomalies can arise from various sources… discu... | Summary: This paper proposes a new anomaly detection method called CAROTS, tailored for multivariate time-series data. Its central idea is to leverage stable causal relationships among variables discovered through a forecasting-based causal model. These discovered relationships guide two specialized data-augmentation “... | Rebuttal 1:
Rebuttal: We are grateful for Reviewer Xmpv’s detailed yet positive comments. Overall, the reviewer believes that “the paper’s claims are are supported by results on multiple datasets,” which “makes it suitable for real-world scenarios.” This response includes additional experiments and discussion to consol... | Summary: This paper firstly addresses the problem of Multi-variate Time-Series Anomaly Detection (MTSAD) by incorporating causality relationships. The authors propose novel data augmentation methods, CPA and CDA, which generate samples by leveraging causality learned from previous causality learning approaches. Further... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer aXkV for helpful comments, which we believe will enrich the depth of our work. We are delighted that the reviewer commends that the proposed method is intuitively convincing, outperforms existing approaches, and exhibits robustness across diverse datasets. We tried ... | Summary: The paper proposes a way to detect anomalies from multivariate time-series data using causality. The proposed method employs two data augmentors to obtain causality-preserving and causality-disturbing samples, respectively. Afterwards, regarding those samples as positive and negative samples, contrastive learn... | Rebuttal 1:
Rebuttal: We thank Reviewer vFwM for constructive comments. We are encouraged that the reviewer acknowledges our work's potential to impact various domain that involves multivariate time-series. We hope our response addresses your concerns. Should the reviewer have more follow-up questions, we would be happ... | null | null | null | null | null | null |
Bifurcate then Alienate: Incomplete Multi-view Clustering via Coupled Distribution Learning with Linear Overhead | Accept (poster) | Summary: In the paper, the authors propose a dual-determinant incomplete multi-view clustering algorithm BACDL. They partition feature clusters through a bifurcation scheme, and alienate bifurcations to differentiate determinants. With coupled distribution learning, it alleviates the dimension inconsistency by introduc... | Rebuttal 1:
Rebuttal: **Q1:** More details regarding Remark 1.
**A1:** Thanks. Due to $\mathbf{X} _r\in\mathbb{R}^{d_r\times n}$ and $\mathbf{G} _r\in\mathbb{R}^{n\times n_r}$, computing $q=\|\mathbf{X} _r\mathbf{G} _r-\widehat{\mathbf{A}} _r\mathbf{E} _r^{\top}\mathbf{G} _r\| _F^2$ will take at least $\mathcal{O... | Summary: This work aims to alleviate the issue of single-determinant paradigm in incomplete multi-view clustering. It introduces distribution learning and associates each type of determinants to bifurcated feature clusters. Through mutual exclusion learning and view guidance learning, it eliminates the dimension incons... | Rebuttal 1:
Rebuttal: **Q1:** More descriptions on the sample cluster distribution in Eq.(3).
**A1:** Thanks. Each row of the sample cluster $\mathbf{E}_r$ represents a probability distribution. So, for each row of $\mathbf{E}_r$, its sum needs to be 1. For handling the incompleteness, we introduce the index matrix $\... | Summary: A BACDL algorithm with dual-determinant learning is specially designed for incomplete multi-view clustering (IMC) in this paper. It bifurcates feature clusters and further alienates them through mutual exclusion learning to strengthen the discrimination. It alleviates the dimension inconsistency, and bridges ... | Rebuttal 1:
Rebuttal: **Q1:** View guidance may impair the efficiency goal.
**A1:** Thanks. The computing cost of view guidance is linear, and thus hardly affects the efficiency goal. It requires constructing $\mathbf{X} _r\mathbf{G} _r\mathbf{G} _r^{\top}\mathbf{E} _r\mathbf{D} _{\gamma}^{\top}\mathbf{C}^{\top}$, $\m... | Summary: This paper introduces a new incomplete multi-view clustering (IMC) algorithm named BACDL. It simultaneously explores both perspective-shared and perspective-specific determinants through coupled distribution learning, with linear overhead. The approach bifurcates feature clusters and enhances discrimination vi... | Rebuttal 1:
Rebuttal: Sincerely thank Reviewer 7KrE for the very constructive comments.
**Q1:** Providing iteration level convergence and approximation guarantees would enhance the theoretical rigor.
**A1:** Many thanks! This is a challenging task for authors during the rebuttal period. At this moment, authors have n... | null | null | null | null | null | null |
Differentially Private Federated $k$-Means Clustering with Server-Side Data | Accept (poster) | Summary: This paper proposes a novel fully federated and differentially private k-means clustering algorithm (FedDP-KMeans). This method overcomes the problem that existing differentially private (DP) clustering methods require good clustering initialization by utilizing the data on the server side. Experiments have be... | Rebuttal 1:
Rebuttal: Thank you for your review and helpful comments. We have run additional experiments which can be found here https://anonymous.4open.science/r/FedDP-KMeans-Rebuttal-Figures-5B34/Rebuttal_Figures.pdf. We will reference this pdf when we address your specific concerns below.
> lack of experiments for ... | Summary: This submission proposes an $(\\epsilon, \\delta)$-differentially private algorithm for aligning a server-side k-means clustering with client-side data by private, federated computation. In particular, the authors propose an initialization procedure FedDP-Init, where clients compute an SVD on their data in a f... | Rebuttal 1:
Rebuttal: Thank you for your time and your review. We address your questions and comments below.
> Could you confirm that the privacy is not proven if the input is not from a Gaussian mixture?
This appears to be a misunderstanding that we find important to correct. Our algorithms (1 and 2) are always priv... | Summary: To adress the need of conducting clustering on distributed and private data, the authors proposed a private and disttubuted clustering framework. In detail, considering the performance of clustering highly relies on the initialization of the clustering center, the authors proposed ‘FedDP-Init’ that leverages a... | Rebuttal 1:
Rebuttal: Thank you for your time and for your review. We discuss your comments below.
> I would say the writing of this paper needs to be improved. Generally, I cannot grab a clear structure of Background-Motivation-Method-Contribution from the intrroduction. (‘background’ here doesn’t refers to the Secti... | Summary: This paper proposes a k-means clustering algorithm in the federated learning model under differential privacy. The chief difficulty with such a setup is the seeding algorithm, since many non-private algorithms would be too slow in the federated learning model, and possibly not robust to the noise added by priv... | Rebuttal 1:
Rebuttal: Thank you for your time and for your review. We discuss your feedback below.
> One potential drawback is in the requirement that the server have a representative datapoint for each cluster on hand. This assumption may be too strong, since often the purpose of running k-means clustering is to ide... | null | null | null | null | null | null |
Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN | Accept (poster) | Summary: The paper presents a new model FAMPNN for fixed-backbone protein sequence design that models both sequence and sidechains. FAMPNN addresses the limitations of existing methods that rely solely on backbone and sequence identity. The authors demonstrate that FAMPNN improves sequence recovery, achieves state-of-t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and questions. Below, we address these questions in detail:
> FAMPNN sampling time comparisons
Please refer to the last section of the response to reviewer GPV6
> Standard deviation in the predictions
Please refer to the first section of the ... | Summary: This paper introduces FAMPNN (Full-Atom MPNN), a model that explicitly incorporates sidechain conformation modeling for fixed-backbone protein sequence design. While existing deep learning methods implicitly reason about sidechain interactions based solely on backbone geometry and amino acid sequence, FAMPNN j... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s comprehensive evaluation and suggestions to improve clarity and benchmark comparisons in our paper. Below, we provide detailed responses and clarifications:
> Inclusion of perplexity in addition to sequence recovery for evaluation
This is a great suggestion. With 1 s... | Summary: This paper presents FAMPNN, an iterative inverse folding algorithm capable of co-generating sidechain conformations and sequences.
Such design allows the model to condition on the currently known sidechain atoms in addition to the fixed backbone and sequence.
FAMPNN models the per-residue sequence type and si... | Rebuttal 1:
Rebuttal: We'd like to thank the review for their positive evaluation and interest in the methodological choices behind our model. Below, we clarify these points:
> Ghost atoms for masked tokens
To encode the ghost atom for masked tokens, we set them at the position of the central CA atom by default. Beca... | Summary: The paper introduces FAMPNN for protein sequence design that explicitly models both the sequence identity and sidechain conformation of each residue. Unlike existing methods that rely solely on backbone geometry, FAMPNN uses a combined categorical cross-entropy and diffusion loss objective to jointly learn the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their suggestions to strengthen our baselines and evaluations. Below, we address the raised concerns:
> Inclusion of more recent sequence recovery baselines
We thank the reviewer for pointing out this omission. To this end, we report Frame2Seq (Dec. 2023), a structure-c... | null | null | null | null | null | null |
Statistical Hypothesis Testing for Auditing Robustness in Language Models | Accept (poster) | Summary: This work presents a statistical framework based on hypothesis testing for assessing the sensitivity of language models to perturbations of their inputs or even the model parameters. They describe their framework in detail including various design choices and then explore empirical validations of how their fra... | Rebuttal 1:
Rebuttal: Dear reviewer k56Q,
Thank you for engaging with our work. A lot to respond to with little space, forgive us for being brief.
---
# A. Expanding on embedding evaluation
Your critique that the embedding function choise is not adequately addressed is well taken.
First, we agree that not *all* em... | Summary: The paper presents a framework for measuring how input perturbations affect large language model (LLM) outputs. DBPA uses Monte Carlo sampling to construct empirical output distributions and evaluates perturbations in a low-dimensional semantic space, enabling robust, interpretable hypothesis testing. It is mo... | Rebuttal 1:
Rebuttal: Dear reviewer 62GP,
Thank you for your thoughtful feedback on our work. We appreciate your recognition that our work presents a framework for measuring how input perturbations affect LLM outputs, that our case studies are clear and backed up with convincing evidence, and that our evaluation is we... | Summary: The paper introduces Distribution-Based Perturbation Analysis (DBPA), a novel framework for assessing how input perturbations affect the outputs of LLMs by reformulating the perturbation analysis as a frequentist hypothesis testing problem. This model-agnostic approach constructs empirical null and alternative... | Rebuttal 1:
Rebuttal: Dear Reviewer grUt,
Thank you for your thoughtful feedback on our work. We appreciate your recognition that our work presents a novel framework for assessing how input perturbations affect the outputs of LLMs, that our case studies effectively demonstrates the efficacy of our proposed methods, an... | Summary: The paper proposes a Distribution-Based Perturbation Analysis (DBPA) framework to evaluate the sensitivity of LLM outputs to input perturbations. Addressing the limitations of traditional methods, which struggle to distinguish between semantic changes and the inherent randomness of models, this study reformula... | Rebuttal 1:
Rebuttal: Dear Reviewer jcUj,
Thank you for your thoughtful feedback on our work. We appreciate your recognition that our work effectively addresses issues related to sensitivity-based measures, that our case studies effectively verify our method, that our approach is insightful, and that our evaluation is... | null | null | null | null | null | null |
Hierarchical Refinement: Optimal Transport to Infinity and Beyond | Accept (oral) | Summary: This paper proposes a new large-scale hierarchical optimal transport algorithm, between distributions with the same number of samples. The authors algorithm is based on multi-scale partitions of the source and target datasets, as well as recent development in low-rank optimal transport. Through a series of exp... | Rebuttal 1:
Rebuttal: We thank reviewer _9vCU_ for their feedback and careful reading.
> As I mentioned ... authors could have cited a few papers ...
Thank you for these suggestions, we will include the following sentences in our Background:
_"Hierarchical OT (Schmitzer and Schnorr '13) is a variant of OT modeling d... | Summary: This paper proposes a hierarchical framework to obtain Kantarovich plans in Optimal Transport. The authors conceptually build on many prior works of low-rank OT (notably Scetbon 2021, Halmos 2024) and propose a rank annealing schedule to obtain the full rank Kantarovich plan using many low-rank solutions of se... | Rebuttal 1:
Rebuttal: We thank reviewer _j2d6_ for their feedback and careful reading.
> At a high level, the main message of the paper is to propose the use of low rank OT solvers to sequentially obtain a full rank solution. This submission does NOT propose a new low rank OT solver. If we credit the authors for focus... | Summary: This paper focuses on the solving of Optimal Transformer (OT) problems. To this end, it derives an algorithm, HR-OT that leverages the invariant under Monge map and dynamically constructs a multi-scale partition of each dataset using low-rank OT subproblems. By doing that, it could use linear space and achiev... | Rebuttal 1:
Rebuttal: We thank reviewer _nCzR_ for their feedback and careful reading.
> Yes. The experimental designs are acceptable. However, it is better to include large scale real data, e.g., 3DMatch (https://3dmatch.cs.princeton.edu/}{https://3dmatch.cs.princeton.edu/) or KITTI (https://www.cvlibs.net/datasets/k... | Summary: This work concerns the use of hierarchical refined version of low-rank optimal transport (HR-LOT). In previous works, low-rank transportation plans have been explore to reduce (high) computational costs for solving the optimal transport problem, and hierarchically refined version of this problem was also studi... | Rebuttal 1:
Rebuttal: We thank reviewer _dsLw_ for their feedback and careful reading.
> The theoretical result Proposition 3.1 seems to have strong assumptions about the transportation plan, although a more general result might be simply very challenging to formulate and prove.
Yes, two important generalizations of ... | null | null | null | null | null | null |
Algorithms and Hardness for Active Learning on Graphs | Accept (poster) | Summary: The authors study a "Graph Label Selection" (GLS) problem of Blum and Chawla and Guillory and Bilmes, which, given a graph G=(V,E) and parameter k, asks the learner to find a subset |L|=k of vertices maximizing the unnormalized min-cut outside of $L$:
$\Psi(L)=\min_{C \subset V \setminus L} \frac{e(C,V\setmin... | Rebuttal 1:
Rebuttal: Dear reviewer gCbm,
We thank you for your work reviewing our paper, we will address your points in the final version. Here we will focus on discussing the points you raised.
1) Motivation of GLS: Thanks for the suggestion. We will include a discussion of the motivation behind the GLS objective... | Summary: The authors propose an approximation algorithm (or, more specifically, a resource augmented algorithm) for the graph label selection problem (GLS). GLS is an abstraction of the active learning task of selecting a small set of data points to label out of a pool of unlabeled data.
The main contribution of the ... | Rebuttal 1:
Rebuttal: Dear reviewer 3ZDj,
We thank you for your work reviewing our paper. Here we focus on responding to your question and the points you raised.
1) ``In Figure 4, the heuristic of Guilroy and Bilmes has better performance than your proposed method for $k \geq 30$. This is not acknowledged or mention... | Summary: This paper tackles the problem of *active learning on graphs* under a label smoothness assumption. The authors study how to select a set of $k$ labeled vertices in a graph such that the labels can best predict all other vertices’ labels. The core contributions are two-fold: **(1)** a new **approximation algori... | Rebuttal 1:
Rebuttal: Dear reviewer qHp3,
Thank you for your thoughtful review, we will address your comments in the final version. Here we will focus on responding to the points you raised.
1) Scalability: We agree that the scalability, together with removing the resource augmentation requirement, is the main resea... | null | null | null | null | null | null | null | null |
GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs | Accept (poster) | Summary: This paper is concerned with the problem of large language model unlearning, which is the process of removing a specific piece of information from a pre-trained language model. The authors note that unlearning usually comes with the cost of harming the performance of the model on other tasks. To mitigate this,... | Rebuttal 1:
Rebuttal: Thank you sincerely for your constructive comments and for helping us identify typos. We hope that the feedback provided below will address your concerns.
> Q1. A major concern about the empirical results is the choice of baseline methods. Given that the authors are already using the WMDP benchma... | Summary: This paper addresses the problem of Machine Unlearning in Large Language Models (LLMs). To balance performance between the retain set and the forget set, the unlearning update is adjusted to avoid harming the performance on the retain set during the unlearning process. Starting from a fundamental optimization ... | Rebuttal 1:
Rebuttal: Sincere thanks for your constructive comments, and we hope the following feedbacks can address your concerns.
> Q1. Specifically, I question whether the current way of handling the retain set is sufficient. In my view, unless the utility performance on the retain set (e.g., MU or similar metrics... | Summary: The paper introduces Gradient Rectified Unlearning, an unlearning framework that constrains the unlearning gradient by projecting it onto the half-space where retention is preserved, using the gradient of the loss computed on mini-batch retain samples. This framework can be applied orthogonally to common objec... | Rebuttal 1:
Rebuttal: Due to strict space limits, we try our best to address the most critical questions as briefly as possible. We sincerely welcome any further concerns and will try our best to respond to them.
> Q1. The improvement in removal on WMDP appears not as evident or consistent as on TOFU.
**A1**. WMDP use... | Summary: The authors introduce GRU as a flexible framework designed to be integrated with existing unlearning methods to balance the trade-off between knowledge removal and retention in LLM unlearning. GRU constrains unlearning gradients to minimize their negative impact on retention. Theoretical analysis and empirical... | Rebuttal 1:
Rebuttal: Many thanks for your great support and constructive comments! Please see our responses below.
> Q1. It is unclear how the constraints on gradients from different samples and mini-batches are enforced in Eq. (7).
**A1**. As shown in Algorithm 1, **mini-batches $B_{\rm r}$, $B_{\rm u}$ replace $D... | null | null | null | null | null | null |
Constrained Online Convex Optimization with Polyak Feasibility Steps | Accept (poster) | Summary: The paper investigates online convex optimization subject to fixed convex constraint by incorporating Polyak feasibility steps. This leads to a sublinear regret and different constraint violation guarantees: no violation if initial start satisfies constraint, no violation after $O(\log T)$ rounds or cumulative... | Rebuttal 1:
Rebuttal: We appreciate your detailed review and positive appraisal of the work. We discuss each of your points below.
**Q.1. typo: line 316 : $y_{t}$ instead of $y_{t+1}$**
You are correct. Thank you for pointing this out.
**Q.2. section 3.4 begins with using eqn (9), and quoted Fact 1 which was with re... | Summary: This paper addresses the problem of Online Convex Optimization (OCO) with constraints, where the goal is to minimize regret while ensuring that the constraints are satisfied anytime. The authors propose an algorithm that combines gradient descent steps with Polyak feasibility steps to achieve $O(\sqrt{T})$ reg... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review. We have responded to each concern below.
**Q1. The contribution is marginal compared to the previous work.**
We respectfully disagree. The contribution of our paper is an algorithm for constrained OCO that *for the first time* attains anytime cons... | Summary: In this work, the authors study online convex optimization with a fixed constraint function. Their method employs Polyak feasibility steps to guarantee constraint satisfaction without compromising regret. Specifically, they introduce an algorithm for constrained OCO that applies Polyak feasibility steps to ens... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review. We have responded to each concern below.
**Q1. The algorithm in the paper closely resembles the one in Liang (2024), which studies constrained variational inequalities.**
We were not previously aware of the recent work (Liang et al, 2024), which s... | null | null | null | null | null | null | null | null |
Understanding Mode Connectivity via Parameter Space Symmetry | Accept (poster) | Summary: The paper investigates (linear) mode connectivity of neural networks via symmetries in parameter space. Besides quantifying the number of connected components of invertible linear networks with and without skip connections, it sheds light on when modes can be connected linearly by investigating the difference ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback. We appreciate that they have taken the time to read our proofs. We also appreciate the valuable questions and the many relevant pointers to related work, which we discuss below.
**Relation to broader scientific literature**
> It would be inte... | Summary: This paper provides a group theory framework to study the connectivity and the number of components of the zero-loss set in a (non-convex) loss landscape. A precise characterization of the number of components and connectivity of the zero-loss set is given for deep linear neural networks. Some results on the e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback and insights on our work’s relation to broader literature. We will incorporate their suggestions into the final version of the paper.
> Some well-known simple things are presented as Propositions (for ex. Propositions 3.4 and 3.5). These are n... | Summary: This work provides an interesting perspective on mode connectivity by linking topology of symmetry groups to the topology of minima. The key technique used in the paper is based on deriving the number of connected components of minima in linear networks (showing $2^{l-1}$ components for a network with $l$ laye... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and insightful questions. We are encouraged by the recognition of our paper’s novelty, rigor, and intuition. Below we expand on the practical takeaways and respond to the questions.
**Practical takeaways**
Our work shows that parameter space ... | Summary: The paper studies the topology of the minima in neural networks through their symmetries. Results of the paper consists of different simplified models of neural networks, for example using single units and single data point, or assuming linear networks. The paper the studies the effect of different types of sy... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Below, we address concerns about the scope and generalizability of our results, and clarify how our approach can be extended.
**Scope and generalizability of results**
> Sometimes the scope of the findings is inaccurate when a high-level picture is given... | Summary: This paper look at the topology of the loss level sets in order to understand their connectedness, i.e. mode connectivity. They are specifically investigating the topology of symmetry groups of the weights under the loss. They deduce the number of connected components of full-rank multi-layer linear networks (... | null | null | null | null | |
The Logical Implication Steering Method for Conditional Interventions on Transformer Generation | Accept (poster) | Summary: This paper proposes a method named LIMS to integrate logical implications into transformer models. Specifically, it builds upon the linear representation hypothesis and activation steering technique, which are extensively studied in recent years. When a certain “concept vector” is detected from the input promp... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and valuable insights. We agree with the relevancy of the interpretability papers you reference and have added them to Section 2. Below, we address the your comments and describe the corresponding updates we will make to the paper:
> The current Figure 1 does ... | Summary: This paper considers the problem of conditionally steering generative LLMs. Conditional steering refers to controlling the behavior of LLMs such that whenever the input prompt satisfies some condition, the model output should follow a specified behavior. The paper also refers to this as Logical Implication Mod... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you dedicated reviewing our work in detail, and have made the following revisions and clarifications in response:
The reference [A] you include is relevant, and we will add it to Section 2.
## Questions
> 1. Fig 1 appears on …
We will revise figure ... | Summary: The paper introduces an interpretable model steering method -- Logical Implication Model Steering (LIMS), to steer a LLM to behave according to a $P \rightarrow Q$ rule. The method first extracts concept vectors for $P$ and $Q$, then performs necessary post-processing, and uses the vectors in an activation-pa... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and review.
> Table 2 discussed an interesting finding that LIMS improved reasoning with not causing over-verbosity on non-math prompts, which demonstrated the robustness of the method. However, it is still possible that concepts would be coupled and... | Summary: This paper proposes Logical Implication Model Steering (LIMS), a novel method to embed logical implication circuits into pre-trained transformer models. LIMS leverages the linear representation hypothesis, which posits that high-level concepts are represented as directions in activation space. By identifying c... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review and encouraging feedback. We also appreciate you pointing out the typos, which we have corrected, and hope our clarifications and additions address your points. We will add a section in the appendix for each of the following:
# 1. Clarification on lay... | null | null | null | null | null | null |
One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation | Accept (poster) | Summary: The authors introduce One-Step Diffusion Policy (OneDP), which distills pre-trained diffusion policies into single-step generators for robotic control. OneDP achieves 42× faster inference (62.5Hz vs 1.5Hz). Evaluation is performed on six simulation and four real-world tasks.
Claims And Evidence: The results a... | Rebuttal 1:
Rebuttal: We find it disheartening that Reviewer Bfs2 rated our paper a 1, accompanied by a very brief review and a complete dismissal of our contributions, despite our focus on a critical problem in learning fast visuomotor policies for robotic control and our efforts to advance the state of the art in thi... | Summary: This paper adapts diffusion distillation techniques from text-to-3D generation to achieve one-shot diffusion policies for robotics. The authors compare two types of distillation: 1) distilling to a stochastic one-step diffusion policy, and 2) distilling to a deterministic one-step policy. They show that the st... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer kXET's recognition of our work's value and your insightful suggestion on extending our distillation technique to facilitate online RL fine-tuning. We agree this is a promising future direction. Below, we address your remaining concerns and questions.
### **Refere... | Summary: The paper introduces the One-Step Diffusion Policy, a novel approach that distills a pre-trained multi-step diffusion-based visuomotor policy into a single-step action generator for robot control. This one-step policy greatly accelerates inference (boosting action output frequency from ~1.5 Hz to 62 Hz) while... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer 5rTc's positive feedback and address your remaining concerns below:
### **Weaknesses:**
1. **Mode-Seeking Behavior in Reverse KL Minimization**
While reverse KL minimization encourages mode-seeking, this does not necessarily lead to mode collapse. In our rea... | null | null | null | null | null | null | null | null |
Generative Social Choice: The Next Generation | Accept (oral) | Summary: The paper investigates the scope of generative social choice, aiming to generate a slate of statements (usually from a large set such as textual information) representing the voters. Queries (usually implemented by LLMs) can be made on agent utilities and on generating a good statement among a group of agents.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We will improve the clarity of the proof of Theorem 3.1 and expand the discussion on its relation to Fish et al.; see also our response to Reviewer dWvn.
> How do you justify your implementation of GEN queries in PROSE? Does your implementation still foll... | Summary: The authors consider the problem of generating a set of statements that is representative of a collection of agent opinions on some topic, motivated by participatory budgeting. Extending an earlier definition of proportionality for such a setting, balanced justified representation (BJR), the authors introduce ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments! We will revise the paper to include a detailed discussion of the limitations of our experimental setup and LLM-based query implementations. We will also add a dedicated section elaborating on the relationship to Fish et al. (2024).
> for the drug review d... | Summary: The paper addresses the task of producing a slat of statements representative of users'opinions. The framework is based on social choice and supported by large language models (LLMs). Theoretical guarantees are provided about the accuracy of the LLM output. The case studies revolve around city improvement meas... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review.
> How do you quantify the tolerance and error made by Chat GPT 4o output? How do you measure the accuracy from the language?
The general approach taken in this paper is to design a mechanism (Algorithm 1) that is agnostic to the specific implementation of... | Summary: The paper proposes a method for AI assisted democratization, aka using a model to select and aggregate representative candidate statements from social participants.
Main contribution of the work:
- Adding control for summary length instead of number of representative responses, allowing for direct control on... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review.
> Regarding the evaluation method: Using total user utility as the main metric in evaluation does not immediately come across as the best way to quantify democracy, but neither was I sure how to make it better –underrepresented groups are still going to be ... | null | null | null | null | null | null |
Perceptually Constrained Precipitation Nowcasting Model | Accept (poster) | Summary: This paper proposes a model called PercpCast for precipitation nowcasting, aiming to predict future rainfall patterns more accurately while also improving how realistic those predictions appear. The authors use a two-stage approach in a single end-to-end framework: first, they generate a "posteriori mean" seq... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed feedback. We will address their concerns and eager to engage in a more detailed discussion with the reviewer.
### **Q1.**
Thank you for the comment. In precipitation nowcasting, the high uncertainty in short-term evolution means a single historical observa... | Summary: This work proposes PercpCast, integrating both Precipitation Estimator (Video prediction model) and the Rectified Flow module. Rectified Flow module learns the transmission from the distribution of the posterior mean predicted by Precipitation Estimator to the distribution of ground truth. Further, LPIPS regul... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's acknowledgment of our work and their detailed feedback, which will help us refine our research.
### **Theoretical Claims.**
Equation (2) can be solved through either Equation (20) or our proposed method, which has different error bounds. Freirich et al. establi... | Summary: This article proposes a new precipitation forecasting model PercpCast, which introduces perceptual constraints into precipitation forecasting tasks. This method first uses ConvLSTM as a precipitation estimator to obtain the posterior mean sequence of future frames. Then, a module based on "rectified flow" is u... | Rebuttal 1:
Rebuttal: Thanks for the reviewer's valuable suggestions. We will try to address the reviewer's concerns and are eager to engage in a more detailed discussion with the reviewer.
### **Theoretical Claims**.
Thank you for pointing out this issue. We perform normalization (not binarization) to rescale images... | Summary: This paper proposes a precipitation forecast model based on perceptual constraints. Its main contributions include: proposing a new perspective on the precipitation forecast problem, that is, converting it into a posterior mean square error problem under specific constraints; designing a model architecture bas... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our ideas and theory.
### **W1**
Thank you for your question. Due to the introduction of perceptual constraint, our model has the advantage of accurately preserving high-value part in prediction image, which indicates extreme weather storms. As shown in Fig... | null | null | null | null | null | null |
Flopping for FLOPs: Leveraging Equivariance for Computational Efficiency | Accept (spotlight poster) | Summary: The paper introduces three new equivariant neural networks (adapted from three non-equivariant models: ResMLP, ViT and ConvNeXt) for the purpose of showing that the equivariant models maintain a comparable number of floating-point operations (FLOPs) per parameter versus their non-equivariant counterparts. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review. We address their concerns here, which will sharpen the paper.
> [...] the version of Schur's Lemma (A.2) provided by the authors was a bit overkill [...]
We are unaware of simpler versions of Schur’s Lemma for real irreps of finite groups than the o... | Summary: The paper suggests a clever implementation of flop-equivariant linear layers - together with some strategies to adapt other layers too - which allows to achieve flop-equivariance in most popular vision architecture while halving the computation cost with respect to their non-equivariant counterparts.
The pape... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and for highlighting Section 3.5. We are pleased to read that the reviewer finds the paper valuable.
> The block diagonal decomposition of the linear layers in Sec 3.1 doesn't directly work for convolution layers, unless one only considers flop-invariant filte... | Summary: In this paper, the authors present a flopping equivariant variant of known vision models like ConvNext, ViTs, and ResMLP. The key idea is to parameterize feature space in terms of mirror symmetry and mirror anti-symmetry features. This approach reduces FLOPs and wall-clock time to give an efficient scalable an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful review and relevant questions.
> The claim in section 5: equivariant networks can be designed to have the same number of FLOPs... seems to lack evidence [...].
We thank the reviewer for the pointer and will refine it to “flopping equivariant networks”.
> Es... | Summary: The paper's main goal is to develop new equivariant vision models that scale effectively. It presents equivariant networks that preserve mirror symmetry while keeping FLOPs comparable to non-equivariant models. The focus is on simple image symmetries for more efficient computation. The proposed network divides... | Rebuttal 1:
Rebuttal: We thank the reviewer for the clear review, which accurately summarises our main contributions.
> I think references are discussed adequately. However, I would like to suggest the authors also take a look at the following paper: "Symmetry-Based Structured Matrices for Efficient Approximately Equi... | null | null | null | null | null | null |
The Complexity of Learning Sparse Superposed Features with Feedback | Accept (poster) | Summary: In this work, the authors study how well the feature learned by a neural network can be retrieved by the means of some agents, e.g., an LLM, in the form of *relative triplet comparisons*. Formally, leveraging the linear feature decomposition that encodes features in a dictionary, the authors investigate how we... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful questions and suggestions. Below, we provide detailed responses to the main concerns.
---
**1. Can the authors compare theoretical vs empirical bounds?**
We perform experiments based on RFM (cf Section 6, p.8) on a monomial regression task to establish... | Summary: This paper proposes a new problem, inspired by the recent works in ML interpretability, specifically in the literature that posits that activations *linearly* encode concepts. (This is known as the linear representation hypothesis).
The authors suggest that we can use an agent (potentially an LLM) to generat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful questions and suggestions. Below, we provide detailed responses to the main concerns.
---
**1. Potential lack of practicality of the first three theorems.**
As discussed in our response to Reviewer 2 (gr2L), our framework considers general feature lear... | Summary: The paper demonstrates that complex features encoded in sparse superposed representations can be effectively learned through a surprisingly minimal and indirect form of feedback. More specifically, the authors show that there is low feedback complexity required to learn sparse superposed features using relativ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful questions and suggestions. Below, we provide detailed responses to the main concerns.
---
**1. Evaluation of the paper.**
We emphasize the broad relevance of our theoretical work
- They apply to feature extraction in LLMs (see Section 6 and Appendi... | Summary: This paper investigates theoretical bounds on feedback complexity for learning feature matrices through triplet comparisons. The authors analyze both constructive settings (where agents select activations) and distributional settings (with sampled activations). In particular, for a rank-r feature matrix in p-d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful questions and suggestions. Below, we provide detailed responses to the main concerns.
---
**1. Potential lower bound to complement the sparse sampling upper bound (Theorem 4)**
We conjecture that the dependence on $p^2$ and $\log \tfrac{1}{\delta}$ in ... | null | null | null | null | null | null |
Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions | Accept (poster) | Summary: This work provides a foundation model for wearable devices on the behaviour data instead of the raw sensor signals. The model was trained on a large-scale wearable dataset totalling over 2.5B hours of wearable data from 162K individuals. This paper has performed extensive experimentations on the choice of toke... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and helpful comments towards improving our work. We focus on responding to the major themes of your comments:
**Measuring Difficulty of Downstream Tasks**
We agree that it is important to contextualize the difficulty of the tasks. To overcome this, we included... | Summary: This paper develops a foundation model for behavioural data from wearables to improve health predictions. The authors process 2.5 billion hours of wearable data and comparing different tokenization strategies and model architectures. They find a Mamba-2 architecture with TST tokenization performs best. The mod... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and suggestions aimed towards improving our work!
**Contextualizing Comparisons between WBM and Baseline/PPG**
We appreciate your feedback on tempering our claims.
First, we clarify the WBM vs baseline comparison. The subject-level tasks in Figure 3 are int... | Summary: This manuscript considers the problem of health condition tracking using pretrained foundation models trained on the Apple health movement dataset. In contrast to past work that used raw sensor signals from PPG and ECG, they leverage higher-level ‘behavioral’ metrics that are extracted from IMU (eg steps), use... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and useful comments for enhancing our work. We respond to specific suggestions below:
**Interpretability of WBM**
Interpretability of foundation models is an active area of research that remains extremely important. Unfortunately, it remains non-trivial to u... | Summary: This paper proposes WBM, a foundation model trained on wearables dataset to improve health predictions. The paper states that behavioral signals including physical activity and mobility metrics align better with physiologically relevant timescales than raw sensor data. The proposed model is trained on over 2.5... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and constructive feedback to help us improve this work! We focus on responding to the major themes of your comments:
**Choice of Mamba-2 and comparison to SOTA deep learning models:**
This is an important point to clarify. As stated in Sections 4.2 and 4.3 and ... | null | null | null | null | null | null |
FIC-TSC: Learning Time Series Classification with Fisher Information Constraint | Accept (poster) | Summary: The paper introduces a novel framework for time series classification (TSC) that addresses domain shift issues by leveraging Fisher information as a constraint.
Main Contributions:
Domain Shift Problem in TSC: The paper highlights the challenge of domain shifts in time series classification, where the test s... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's effort in carefully reviewing our paper and giving very constructive suggestions. We also thank the reviewer for recognizing our novelty, significance, and theoretical and empirical analysis.
---
### **Explicit Domain Shift Scenario.**
We consider the follo... | Summary: FIC-TSC introduces a novel training framework for time series classification by enforcing a Fisher information constraint to guide the optimizer toward flatter minima, aiming to improve robustness against domain shift. The method leverages two key approximations—a diagonalized Fisher information matrix and a g... | Rebuttal 1:
Rebuttal: Thanks for these insightful suggestions, and we are very glad to hear that you enjoyed the reading.
---
### **Unjustified motivation/Questionable assumption/Sharpness in data domain.**
- Our primary motivation is that time series data often suffers from domain shift between train and test set,... | Summary: This paper addresses the failure of RevIN in out-of-distribution (OOD) scenarios for time series classification and proposes a constraint method based on the Fisher Information Matrix to enable smoother model optimization, thereby improving the generalization ability of the classification model. Furthermore, c... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and insightful comments, especially for recognizing our contributions and empirical validation. We addressed the main concerns as follows.
---
### **Q1. Data and Sharpness.**
Time series datasets are often small in size (e.g., UW has 120 sampl... | null | null | null | null | null | null | null | null |
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series | Accept (poster) | Summary: This paper formulates the abnormal prediction problem in time series, aiming to forecast specific future time points where anomalies will occur. Accordingly, the authors propose Anomaly-Aware Forecasting and Synthetic Anomaly Prompting to address the problem.
## update after rebuttal
The authors adequately ad... | Rebuttal 1:
Rebuttal: Thank you for giving us meaningful feedback!
**More Datasets and Baselines**
- TimeSeAD
|Model|Exathlon-Avg.F1|SMD-Avg.F1|
|:-:|:-:|:-:|
|P-TST+AT|14.76|30.58|
|**A2P(Ours)**|**15.28**|**39.72**|
- TranAD, BeatGAN, TimeMixer, DiffusionAD
|F model|AD model|Avg.F1|
|:-:|:-:|:-:|
|P-TST|TranAD|42.... | Summary: This paper proposes a novel framework, Anomaly to Prompt (A2P), to address the Anomaly Prediction (AP) task in time series analysis, which aims to predict future anomalies. The framework integrates two key components: Anomaly-Aware Forecasting (AAF) that learns relationships between anomalies and future signal... | Rebuttal 1:
Rebuttal: We are sincerely grateful for giving us positive comments and acknowledging the contribution of our proposed method A2P for tackling the challenges of AP.
**Initialization and Optimization Details**
All additional parameters introduced for APP are initialized using a standard uniform initializat... | Summary: The paper introduces a novel framework called A2P designed to forecast future anomalies in time series data. Unlike traditional forecasting models—typically trained on standard signals and consequently fail to accurately predict abnormal events—the proposed method integrates anomaly-aware components into the f... | Rebuttal 1:
Rebuttal: We appreciate your meaningful feedback, and your insights are invaluable as we continue to improve our work!
**Computational Complexity**
Please refer to the Computational Complexity section in Reviewer ADJk.
**AP Task**
The only prior work on Anomaly Prediction in time series is [1], which on... | null | null | null | null | null | null | null | null |
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping | Accept (poster) | Summary: This paper attempts to optimize the dynamic chute mapping problem to optimize throughput/reduce recirculation rates by formulating the problem as a multi-agent RL problem. The authors then extend the vanilla MARL framework by introducing concepts from group distributionally robust optimization into the framewo... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, thoughtful questions, and encouraging feedback. Your suggestions greatly improve the paper. Below are our responses, following the order of the reviewer comments, with references to tables and papers prefixed by “R-” for clarity.
We address all readability ... | Summary: The paper addresses the “dynamic chute mapping” task in robotic warehouses, where packages must be assigned to chutes in the face of uncertain and shifting arrival (induction) patterns. It proposes a distributionally robust multi-agent reinforcement learning (DRMARL) framework, combining group distributionally... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and thoughtful questions. Your suggestions greatly improve the paper. Below are our responses, following the order of the reviewer comments, with references to tables prefixed by “R-” for clarity. If any referred content is missing here, please find it in th... | Summary: This paper proposes the Distributionally Robust Multi-Agent Reinforcement Learning (DRMARL) framework for dynamic chute mapping in robotic warehouses. The integration of group Distributionally Robust Optimization (DRO) with a contextual bandit-based predictor to handle induction rate variations is the main con... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, thoughtful questions, and encouraging feedback. Your suggestions greatly improve the paper. Below are our responses, following the order of the comments, with references to tables and papers prefixed by “R-” for clarity. If any referred content is missing he... | null | null | null | null | null | null | null | null |
Inverse Flow and Consistency Models | Accept (poster) | Summary: This work addresses the challenge of denoising corrupted data in the absence of ground truth observations with 2 proposed methods: Inverse Flow Matching and Inverse Consistency Model.
IFM leverages a reverse flow process modeled by an ODE to transform noisy data towards a cleaner state, while ICM offers a mo... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's detailed feedback. We address these questions one by one below:
1.**Regarding recovering the distribution of $x_0$**, our theoretical analysis (see Theorem 1 and Appendix A.2.1) establishes that under appropriate assumptions our ODE-based inverse flow does indeed reco... | Summary: The authors present two novel methods, inverse flow matching (IFM) and inverse consistency models (ICM), for unsupervised denoising based on flow matching and consistency models.
For training, both methods require only noisy data as well as a statistical model for the measurement noise.
IFMs learn a vector fi... | Rebuttal 1:
Rebuttal: Thank you for your encouraging feedback on our approach. We address each of your points in detail below.
1.**For the RNA-seq experiment**, we performed denoising in a linear latent space transformed by PCA. We expect the noise in the latent space to be very close to Gaussian distribution due to c... | Summary: The authors provide a method to do inverse sampling of clean data p(x0) with having access to a distribution (data) of corrupted data p(x1) and assuming a knowledge of p(x1 | x0). They do not assume access to p(x0) at training time.
They propose an approach similar to the consistency model to do the trick.
T... | Rebuttal 1:
Rebuttal: We thank you for raising these important points. Below we address each point in the same order and numbering as in the review.
1.**Regarding “Since the solution of ODE is unique…”**: In our framework we assume that the learned ODE (typically a neural ODE) satisfies standard conditions (e.g. Lipsc... | null | null | null | null | null | null | null | null |
Towards an Explainable Comparison and Alignment of Feature Embeddings | Accept (poster) | Summary: The authors propose the Spectral Pairwise Embedding Comparison (SPEC) framework for comparing feature embeddings in an explainable manner. The goal is to identify differences in how two embeddings cluster data points, rather than relying solely on downstream performance metrics. The main contributions are: 1) ... | Rebuttal 1:
Rebuttal: We thank Reviewer KUAe for the thoughtful and constructive feedback on our work. Below is our response to the comments and questions in the review: ([Our Numerical results are shown in this link](https://github.com/ICML6204/ICML6204/blob/main/ICML_Rebuttal.pdf))
**1- Comparison with existing embe... | Summary: The work is proposed to allow an interpretable comparison of feature embeddings from different methods through a Spectral Pairwise Embedding Comparison (SPEC) on clustering and also proposes an approach to align one embedding with another. The work is relevant for a wide audience where interpretability and fle... | Rebuttal 1:
Rebuttal: We thank Reviewer 7vC9 for the thoughtful and constructive feedback on our work. Below is our response to the comments and questions in the review: ([Our Numerical results are shown in this link](https://github.com/ICML6204/ICML6204/blob/main/ICML_Rebuttal.pdf))
**1- API endpoints for the embeddi... | Summary: This paper proposes a method for comparing the embedding spaces for pairs of models by constructing PSD kernel matrices of each embedding space and studying the differences of these PSD matrices. The theoretical section discusses how, under some assumptions, the eigendecomposition of this difference between th... | Rebuttal 1:
Rebuttal: We thank Reviewer iKx5 for the thoughtful and constructive feedback on our work. Below is our response to the comments and questions in the review: ([Our Numerical results are shown in this link](https://github.com/ICML6204/ICML6204/blob/main/ICML_Rebuttal.pdf))
**1- Quantitative evaluation of th... | Summary: The authors derive a method for identifying clusters which are strongly clustered by one encoder and weakly clustered by another encoder. The run time of the method scales linearly with the number of samples in the dataset. This is deployed on a few image datasets for some established image foundation models, ... | Rebuttal 1:
Rebuttal: We thank Reviewer hTAA for the thoughtful and constructive feedback on our work. Below is our response to the comments and questions in the review: ([Our Numerical results are shown in this link](https://github.com/ICML6204/ICML6204/blob/main/ICML_Rebuttal.pdf))
**1- Experiments on GPT-4o generat... | null | null | null | null | null | null |
Provable Maximum Entropy Manifold Exploration via Diffusion Models | Accept (poster) | Summary: The authors consider the problem of exploration in planning and decision-making problems. This problem has many applications including to the exploration-exploitation paradigm in reinfocement learning. While in most applications the exploration step is performed by sampling from a Gaussian process, the autho... | Rebuttal 1:
Rebuttal: We thank the Reviewer for appreciating our work and asking interesting questions. In the following, we address several important points mentioned within the review that can hopefully let the Reviewer appreciate more the content of this work.
**Asymptotic convergence guarantees**
We thank the Revi... | Summary: This paper introduces a maximum entropy manifold exploration problem. They proposes a modification to the pretrained diffusion model to maximize an entropy objective function. They also proposed an algorithm to solve this optimization problem. They supported their results with numerical experiments.
Claims An... | Rebuttal 1:
Rebuttal: We thank the Reviewer for reading our work. In the following, we address several fundamental points and questions mentioned within the review that can hopefully let the Reviewer appreciate more the content of this work.
**Motivation of the work**
In the following, we aim to make clear the main mo... | Summary: The paper presents a framework to perform optimal exploration of the data manifold defined by a pre-trained diffusion model. This can be useful whenever one wants to sample using diffusion models and explore the full data region within the learned data manifold. The approach that the paper proposes is based on... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the interesting questions. In the following, we address several points that can hopefully let the Reviewer appreciate more this paper, and which will be included in a revised version.
**Uncertainty quantification**
We agree, our method does not employ *explicit* uncertai... | Summary: This paper considers the problem of exploring the underlying data manifold learned with a diffusion model.
It formulates the problem as maximizing the entropy of the probability distribution, and proposes to solve it with a KL-regularized optimization of the first variation of the entropy. It proves that the... | Rebuttal 1:
Rebuttal: We thank the Reviewer for recognizing our work as very well written, theoretically solid, and practical. In the following, we address several points and questions mentioned within the review.
**Inception Score (IS) and Vendi Score (VS).**
IS: To the best of our understanding, the Fréchet Incepti... | null | null | null | null | null | null |
Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation | Accept (spotlight poster) | Summary: This paper presents a novel training framework for VAEs for count data (i.e., negative binomial likelihood), where it enforces straight lines in latent space to map to geodesic paths on the data manifold, an assumption often made in VAE models for scRNA data but not explicitly enforced (or checked). In doing s... | Rebuttal 1:
Rebuttal: We sincerely thank zkLA for their thorough review and positive assessment of our work, and we welcome the opportunity to address their remaining questions.
> A1
$\alpha$ relaxes the strictness of Euclidean regularisation. When the metric tensor $\mathrm{M}(\mathbf{z})$ is identity matrix $\mathb... | Summary: The manuscript focuses on learning a specific representation intended to understanding dynamics, specifically arising from single-cell RNA sequencing (scRNA-seq). The authors regularize the latent space of a Variational Autoencoder (VAE) using the geodesics of a statistical manifold. This approach ensures that... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer 5Fan for taking the time to review our work and for highlighting the aspects they found positive. We also appreciate their constructive suggestions regarding structural improvements and areas that could benefit from further clarification.
> Interpretability of Fig... | Summary: The paper proposes FlatVI, a VAE training strategy that enforces local Euclidean geometry in the encoder’s latent space by regularizing the pullback metric of the decoder to approximate local Euclidean geometry in latent space. It validated the proposed method on simulated data and the single-cell trajectory i... | Rebuttal 1:
Rebuttal: We sincerely thank oZ7a for their elaborate review. Their informed criticism offers us an opportunity to improve our submission. We will refer to two additional rebuttal figures stored at https://figshare.com/s/74ca822781c60b2a85f2.
> Global regularisation
Similar to GAE, we propose to:
- Sampl... | Summary: The paper proposes FlatVI, a novel training framework for variational autoencoders (VAEs) applied to single-cell RNA-seq (scRNA-seq) data. Its central goal is to enforce Euclidean geometry in the latent space of discrete-likelihood VAEs—specifically, negative binomial VAEs commonly used for modeling gene expre... | Rebuttal 1:
Rebuttal: We thank NJwg for thoroughly reviewing our paper and providing constructive criticism to improve our submission. We kindly point the reviewer to the anonymous link https://figshare.com/s/74ca822781c60b2a85f2 containing the figures we refer to in some of our answers.
> A1. Limited use/comparison w... | null | null | null | null | null | null |
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design | Accept (poster) | Summary: This article discusses the limitations of population-based LLM-based automatic heuristic design, which makes it difficult to fully explore the heuristic space. This paper introduces MCTS for heuristic exploration and planning. Overall, in experiments, MCTS-AHD achieves significant performance advantages in var... | Rebuttal 1:
Rebuttal: Thank you so much for your time and effort in reviewing our work. We are glad to know that you find the proposed MCTS-AHD achieves significant performance advantages in various typical applications, using MCTS in LLM-based AHD is reasonable and novel and this paper is well-written.
We address you... | Summary: This paper introduces MCTS-AHD, a novel method that leverages MCTS to enhance the evolution of heuristic functions generated by LLMs for solving complex optimization tasks. The key contributions include the use of MCTS to organize and evolve heuristics in a tree structure, allowing for comprehensive exploratio... | Rebuttal 1:
Rebuttal: Thank you so much for your time and effort in reviewing our work. We are glad to know that you find the proposed method demonstrates significant improvements and the paper includes comprehensive experiments and ablation studies.
We address your concerns as follows.
>**Weakness 1. Convergence spe... | Summary: This paper introduces MCTS-AHD, a Monte Carlo Tree Search (MCTS)-based method for automatic heuristic design (AHD) using Large Language Models (LLMs). MCTS-AHD organizes heuristics in a tree structure to enable more comprehensive exploration and refinement. Ultimately, the goal is to generate more efficient, r... | Rebuttal 1:
Rebuttal: Thank you so much for your time and effort in reviewing our work. We are glad to know that you find MCTS-AHD addressing specific shortcomings of existing approaches and the paper is well-written.
We address your concerns as follows.
>**Weakness 1. Computational complexity analysis:** There is no... | Summary: This paper introduces MCTS-AHD, which integrates MCTS into LLM-based AHD to improve heuristic search exploration. MCTS-AHD organizes heuristics in a tree structure, allowing for deeper refinement of temporarily weaker candidates. Key techniques include progressive widening, exploration decay, and tree-path rea... | Rebuttal 1:
Rebuttal: Thank you so much for your time and effort in reviewing our work. We are glad to know that you find the proposed MCTS-AHD is an effective method for overcoming local optima, the paper is very well written, and the experiments are detailed.
We address your concerns as follows.
> **Weakness. Robus... | null | null | null | null | null | null |
System-Aware Unlearning Algorithms: Use Lesser, Forget Faster | Accept (poster) | Summary: This paper introduces a system-aware unlearning framework, which is a new definition of machine unlearning that relaxes the unlearning definition by assuming a weaker attacker that has no access to the full training data, i.e., the definition only requires the unlearned model to be indistinguishable from a mod... | Rebuttal 1:
Rebuttal: > CE 1 - *The claims in the paper are not convincing to me. The paper claims that the new definition ...*
The authors disagree with the claim that data-free machine unlearning methods would provide more privacy protection than the proposed method. Data-free machine unlearning methods still attemp... | Summary: The authors propose system-aware machine unlearning, which constitutes unlearning against an attacker who can observe the entire state of the system (including whatever the learning system uses internally). If the system does store the entire remaining dataset, then system-aware unlearning definition becomes a... | Rebuttal 1:
Rebuttal: > CE 2 - *To fully verify the claims being made (as unlearning is a task with practical real-world ...*
> MAEC 1 - *There is very limited (and almost non-existent) evaluation...*
We emphasize that our primary contribution is theoretical. We focus on unlearning algorithms with provable unlearning... | Summary: The authors propose a new definition for unlearning that they refer to as “system-aware unlearning” where the aim for the unlearned model is to be indistinguishable from a model that was trained on *any* subset of the training data excluding the forget set (rather than a model specifically trained on exactly t... | Rebuttal 1:
Rebuttal: > CE 1
We acknowledge that there are empirical unlearning algorithms that have demonstrated good performance on nonconvex models; however, recent work [1] has demonstrated that many such empirical methods fail to properly unlearn; thus, we focus on algorithms that meet theoretical guarantees of c... | Summary: The paper introduces a new system-aware unlearning setting, where attackers have access to only a partial dataset stored in the system rather than the entire dataset. The authors argue that this relaxation enables a more efficient and practical unlearning framework for attackers. To address this setting, they ... | Rebuttal 1:
Rebuttal: > TC 1 - *Lemma 4.7: Let the deletion distribution μ be the uniform distribution. Is it always valid to ...*
We agree that assuming the deletion distribution to be uniform is not always valid. Our main theorem (Theorem 4.6) applies to general data-dependent deletion distributions. Lemma 4.7 is an... | Summary: This paper introduces system-aware unlearning, a novel framework that generalizes traditional machine unlearning by relaxing privacy guarantees to account for realistic attacker access to the system’s internal data. It proposes a general approach using sample compression or core sets, where reduced algorithmic... | null | null | null | null | |
Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence | Accept (poster) | Summary: This paper proposes a discrete-time stochastic control framework with linear dynamics and KL regularization for fine-tuning diffusion models. It establishes well-posedness, proves the regularity of the optimal value function, and develops a policy iteration algorithm (PI-FT) with guaranteed regularity and stab... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We are glad that you found our theoretical results strong and rigorous. Below please find our response to your questions.
```
I have to say that the theory in this paper is very solid. However, the lack of empirical results is also the biggest drawback of this paper.
`... | Summary: This paper proposes a stochastic control framework for fine-tuning diffusion models. The key contribution is establishing theoretical properties such as well-posedness, regularity, and linear convergence of a proposed policy iteration algorithm.
Claims And Evidence: The paper makes strong theoretical claims a... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We are delighted that you found our theoretical results rigorous and our presentation clear. Below is our point-to-point response to your comments:
```
Proper evaluation is missing: Algorithm 1 is defined and justified clearly but is purely theoretical; ho... | Summary: The authors propose a discrete-time stochastic optimal control framework with linear dynamics and KL regularization to model the problem of fine-tuning of diffusion models. They analyze well-posedness and regularity of the control formulation, propose a novel algorithmic scheme based on policy iteration, and t... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We are glad to receive your positive feedback on our theoretical contributions and that you found our placement of the work clear and honest. Below please find our response to your questions.
```
It is not clear to me if the policy iteration algorithm present... | null | null | null | null | null | null | null | null |
Spectral-Aware Reservoir Computing for Fast and Accurate Time Series Classification | Accept (poster) | Summary: This paper introduces a novel method for time series classification. The approach begins by decomposing the time series into multiple prominent frequencies, each representing different cyclic patterns in the data. For each extracted frequency, a reservoir computing model—called FreqRes—is applied to generate c... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for your time and recognition. Detailed responses to the raised concerns are listed below. We agree with the suggestions mostly and will clarify those details.
**1. Impact of Spectral Insights across Different Models**
The table below provides a compariso... | Summary: Typical reservoir computing (RC) considers recursive updates from adjacent states and has difficulty handling long-term dependencies. For this issue, this paper proposes a Spectrum-Aware Reservoir Computing framework (SARC) that incorporates spectral insights to enhance long-term dependency modeling. Prominent... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s efforts and recognition. Our responses to the raised concerns are listed below:
**1. Mathematical Explanation of FreqRes**
We formally define a general FreqRes, and then give some theoretical claims for ESN-based FreqRes, as described in Eq. (6).
**(1) Def... | Summary: The paper enhances reservoir computing (RC) for TSC by incorporating spectral insights. It extracts prominent frequencies to identify cyclical patterns and refines RC module to capture cyclical dynamics. Features from next-step prediction tasks are used for classification. Extensive experiments on the UCR 128 ... | Rebuttal 1:
Rebuttal: We are thankful for the reviewer’s recognition of the work. The processing of datasets with variable lengths or NaN values is clarified as follows:
- For middle NaN values (i.e., real values exist on both sides of the NaN), we employ the interpolation for imputation.
- Then, we shift real values ... | Summary: The paper proposes SARC, a novel framework that combines spectral analysis with RNN-based models for time series classification. SARC identifies prominent frequencies and captures corresponding temporal dynamics. The framework analyzes time series on multiple scales, achieving state-of-the-art accuracy on the ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s efforts and acknowledgement. Below, we provide detailed responses to your concerns:
**1. Comparison by Sequence Length**
In the table below, we group 128 datasets by sequence length and report the average rank of different methods across each group. The results show ... | null | null | null | null | null | null |
CollabLLM: From Passive Responders to Active Collaborators | Accept (oral) | Summary: While state-of-the-art Large Language Models (LLMs) trained with RLHF are good at following the instructions from users, this paper argues that they are often ``passive responders'' where they only passively respond to ambiguous or open-ended user requests. To address this limitation, this paper proposes to tr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their approval and rigorous comments! Here we address the remaining concerns:
---
### **[Experimental Designs] "It would be nice to have additional discussions about using a stronger model as the user simulator.”**
Great catch! A user simulator should follow the langua... | Summary: COLLABLLM is a new training framework designed to improve multi-turn human–LLM collaboration. Its core idea is to simulate a collaborative conversation setup where a Multiturn-aware Reward (MR) function estimates the long-term impact of model’s responses, rather than focusing solely on immediate single-turn ou... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' extensive and thoughtful outputs! We'll address each comment below:
---
### **[Other Weaknesses] "The improvements (over prompt engineering) on simulated experiments of Tab 1 are small… What would be the topline obtained w/ gpt4-o + prompt engineering?”**
Thanks fo... | Summary: This paper studies how to enhance human-AI collaboration by improving multi-turn conversations. Concretely, authors propose a learning framework CollabLLM that uses a reward function aware of multi-turn setup in reinforcement finetuning. This multiturn-aware reward takes account of both task performance and us... | Rebuttal 1:
Rebuttal: We sincerely appreciate your approval and insightful comments! We further address your comments:
---
## **Comment 1: "A comparison of the potential divergence of simulated user and human user during training would further strengthen this work"**
Good point! We agree that prompt-defined user si... | Summary: Existing fine-tuning techniques for LLMs, such as Reinforcement Learning from Human Feedback (RLHF), primarily maximize the reward for immediate and single-turn responses. However, real-world users often reveal their intents or preferences until later interactions; thus, to streamline their interaction with us... | Rebuttal 1:
Rebuttal: We appreciate your approval and useful feedback! Here we address your concerns:
---
## **[Claims And Evidence] "How does the multiturn reward effectively encourage collaboration?"**
- At the **methodology** level, Multiturn-aware Reward (MR) encourages collaboration by accounting the long-term ... | Summary: This paper introduces CollabLLM, a training framework designed to enhance the capability of large language models (LLMs) to collaborate with humans in multi-turn interactions.
The basic idea is to introduce forward-looking behaviors in LLMs to maximize long-term collaborative outcomes. This is achieved throug... | Rebuttal 1:
Rebuttal: Thank you for your constructive and thoughtful suggestions touching on the practicalness of our work! We address your remaining concerns:
---
## **[Experimental Analyses | Weaknesses] "(What is) the computational costs associated with larger window sizes”**
Thanks for this suggestion! In onlin... | null | null | null | null |
ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning | Accept (poster) | Summary: The paper proposes a guardrail/shielding scaffold for LLM-based agents. The core idea is to parse safety documentation such as policies into atomic rules, which are then encoded as probabilistic circuits. The use of probabilistic circuits thus allows for efficient marginal inference of the likelihood that a ru... | Rebuttal 1:
Rebuttal: We thank the reviewer for recommending our paper for acceptance and we really appreciate their valuable suggestions! Below we have addressed the questions one by one.
> more discussion of the impact of the hyperparameters
Following the reviewer's suggestions, we have provided a detailed ablation... | Summary: Main findings:
- The paper introduces SHIELDAGENT, a novel guardrail agent designed for LLM agents to explicitly ensure compliance with safety policies during sequential decision-making through auto-mated probabilistic policy reasoning. It addresses significant vulnerabilities of LLMs to malicious instructions... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's recognition of our paper's novel contribution and thoughful suggestions!
> how to deal with the case when there is not such document.
Thank you for this insightful question! Instead of strictly requiring a document for policy extraction, we mainly aim to ac... | Summary: This paper presents ShieldAgent, a new technique for determining whether LLM
outputs conform to a given policy. ShieldAgent starts by using an LLM to
formalize a policy document and produce a set of rules expressed in LTL. These
LTL formulae are embedded into probabilistic circuits which can be used to
efficie... | Rebuttal 1:
Rebuttal: We really appreciate the reviewer's valuable suggestions and we have accordingly updated the paper to include additional results and examples in [this link](https://anonymous.4open.science/r/shieldagent-icml-rebuttal-30B0/rebuttal.pdf).
> inclusion of some ablation studies
We provide a detailed ... | Summary: This paper proposes SHIELDAGENT, an LLM-based guardrail agent, to enforce explicit safety policy compliance of the action sequences of other LLM agents via automated probabilistic reasoning. SHIELDAGENT constructs an action-based probabilistic safety model (APSM) by extracting verifiable rules from policy docu... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We have followed your suggestions and improved our paper to incorporate more examples and additional experiment results.
> lack examples
We provide a list of more comprehensive examples in [this link](https://anonymous.4open.science/r/shieldagent-icml-rebut... | null | null | null | null | null | null |
Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions | Accept (oral) | Summary: The authors first show masked diffusion models (MDMs) indeed train on computationally intractable subproblems compared to their autoregressive counterparts. Then an adaptive Top-K probability margin inference strategy is proposed to sidestep hard subproblems that are not properly learned in the training time. ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable questions and comments. Below, we address the main concerns.
## (1) Further experiments on text data
In response to the reviewer’s comments, we ran additional experiments and found that **Top-k margin indeed outperforms Top-k on challenging code and math task... | Summary: The paper takes a close look at training and inference of masked diffusion models (MDMs), which are a type of discrete diffusion models where the noising process consists of randomly “masking” tokens until all tokens are masked, and training a model to reverse this degradation process.
The paper claims that th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review and address the comments below.
## Soundness of theoretical claims
There are several misunderstandings, so we'd like to clarify them. The statement "There exist distributions where for some noise levels (namely, when all tokens are masked) solving... | Summary: The main contribution of the paper is the use of theoretical arguments and carefully designed experiments to show the following:
1. The complexity of training Masked Diffusion Models (MDMs) is higher than Auto-regressive Models (ARMs).
2. The flexibility of any-order decoding offered by MDMs helps it to per... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's overall positive evaluation and comments. We will make sure to include a paragraph of related work in the main body and fix the typo mentioned. Below, we respond to the reviewer’s main concerns:
## (1) Scope of our contributions
The reviewer stated “The contri... | Summary: This work presents two contributions to a emerging discrete diffusion model called masked diffusion models.
* The first contribution is a theoretic construction showing the hardness of prediction subtasks within masked diffusion, motivating an inference time solution to sidestep these challenging subtasks.
*... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's positive evaluation and the insightful comments and questions. Below, we respond to the reviewer’s suggestions and questions.
## (1) Further experiments on text data--Top-k margin outperforms Top-k on challenging code and math tasks
To examine the effect of d... | null | null | null | null | null | null |
Knowledge Retention in Continual Model-Based Reinforcement Learning | Accept (poster) | Summary: The authors propose a method for continual model-based reinforcement learning, where, ideally, an agent retains previously learned skills while learning new skills mitigating the catastrophic forgetting problem. The main problem addressed is the bounded storage problem, where the agent is not assumed to have i... | Rebuttal 1:
Rebuttal: Thank you for the detailed and constructive feedback. We are encouraged that you find our ideas valuable and appreciate your suggestions on clarity and comparisons. We address each concern below:
Q: Replay-based Baseline
A: Thank you for pointing this out – we are adding a **fixed-size replay bu... | Summary: This paper presents a new approach to model-based continuous reinforcement learning (DRAGO) aimed at improving the incremental development of world models across a range of tasks. DRAGO consists of two key components: Synthetic Experience Rehearsal and Regaining Memories Through Exploration. Empirical evaluati... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and thoughtful questions.We address your questions below.
Q: “how the synthetic experiences generated by the generative model differ from actual past experiences in terms of their representational accuracy?”
A: Synthetic experiences in DRAGO are ... | Summary: The work aims to develop a new model-based reinforcement learning method that trained on a set of tasks with consistent changes and different reward functions. The researchers assume that the environment's dynamics remain the same for all tasks. They use TD-MPC as the basic approach, and train a separate gener... | Rebuttal 1:
Rebuttal: Thank you for your very positive evaluation and accept recommendation. We believe we can resolve your concerns.
Q: TDMPC2’s Multitask Training
A:We clarify that TDMPC2 is evaluated in a multitask regime: it is trained on all tasks jointly, with access to the full replay buffers from every task s... | Summary: The authors introduce a new method (DRAGO) aimed at mitigating catastrophic forgetting in model-based RL in situations where previous experience cannot be stored. The authors propose to learn world model that compresses experience of previous tasks, and propose a novel intrinsic reward which encourages the pol... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and for acknowledging the strong empirical results of DRAGO, as well as the clarity of our writing. We address your concerns below:
Q: bounded memory and On-device MBRL
A: We agree that **bounded-memory continual learning** is most critical in constrained ... | null | null | null | null | null | null |
Angle Domain Guidance: Latent Diffusion Requires Rotation Rather Than Extrapolation | Accept (poster) | Summary: This paper introduces Angle Domain Guidance (ADG), a simple and effective sampling algorithm designed to improve the performance of text-to-image latent diffusion models, particularly under high guidance weights. The authors focus on the shortcomings of Classifier-Free Guidance (CFG), specifically its tendency... | Rebuttal 1:
Rebuttal: Thank you for your positive comments. We provide our responses below.
### 1. **Clarification on Computational Costs of ADG**
**Reviewer Concern:**
The reviewer mentions that computational costs associated with ADG, particularly in high-dimensional spaces, are not discussed in detail.
**Response... | Summary: This paper focuses on the problem of color distortions in the generated images when classifier-free guidance is set to a high value. This paper identifies that these distortions come from the amplification of sample norms in the latent space. To address this problem, this paper proposes Angle Domain Guidance (... | Rebuttal 1:
Rebuttal: Thank you for your positive comments. We provide our responses below.
### **1. Performance of Higher CFG Values**
**Reviewer Comment:**
> I am curious about the performance of a higher CFG, e.g., larger than 20. This would further show the effectiveness of ADG.
**Response:**
We appreciate t... | Summary: The paper presents angle domain guidance (ADG), an alternative to classifier-free guidance (CFG) for conditional diffusion models. The key observation is that CFG leads to excessively large sample norms, causing oversaturated colors in the generated images. The paper claims that this is a result of CFG's linea... | Rebuttal 1:
Rebuttal: Thank you for your positive comments. We provide our responses below.
### **1. Plot norm against guidance weight for ADG**
**Reviewer Comment:**
> To further strengthen this claim, I encourage the authors to plot norm against guidance weight (similar to Figure 2a) to find out whether ADG can e... | Summary: This paper attempts to analyze the distributions of conditional generation vs. unconditional generation, and claims that in some occasions the direction of classifier-free guidance may be "abnormal", i.e., leads to low-probability density areas, and proposes "Angle-Domain Guidance Sampling" (ADG) as a remedy.
... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful comments. While the overall evaluation was critical, your constructive feedback is highly valuable and will help us improve both the clarity and impact of our work. Below, we provide detailed responses to your key concerns.
### 1. Comparison wi... | null | null | null | null | null | null |
Continuous machine learning on Euclidean graphs with unordered vertices | Reject | Summary: The authors introduce a new invariant for Euclidean graphs called Nested Centered Distribution that capture many-body unordered relative distance in a hierarchical way, and show that this graph invariant is complete (can distinguish non-isomorphic Euclidean graphs), robust (Lipschitz continuous to coordinate p... | Rebuttal 1:
Rebuttal: Dear Reviewer 6FoT,
Thank you for the highly supportive review.
>a complete, Lipschitz continuous invariant is proposed for Euclidean graphs with formal proof,shown by Theorem 4.6.
Thank you for correctly summarizing the main theoretical result.
>The complete invariant of Euclidean graphs coul... | Summary: The paper proposes a new graph invariant descriptor Nested Centered Distribution (NCD), which satisfies completeness, Lipschitz continuity, invertibility, and computability for all Eulicdean graphs embedded in $\mathbb{R}^n$.
Claims And Evidence: The paper provides detailed mathematical constructions and proo... | Rebuttal 1:
Rebuttal: Dear Reviewer biqw, thank you for the detailed review.
>The paper provides detailed mathematical constructions and proofs (Theorem 4.6) for establishing that the NCD is a complete invariant under rigid motion, that it is Lipschitz continuous, and that it is invertible. These proofs offer clear su... | Summary: This paper proposes a framework for graph isomorphic testing on Euclidean graphs by defining certain invariants. A sweep of invariants and corresponding metrics are introduced, with different time complexity. Experiments on performing central atom prediction on QM9 and Geom-Drugs have been conducted to verify ... | Rebuttal 1:
Rebuttal: Dear Reviewer Sxz9, thank you for the detailed review.
>The summary of different invariants and corresponding metrics with different complexity is highly valuable.
Thank you for highlighting this strength.
>the task of central atom prediction is quite synthetic
This task was studied in many pa... | null | null | null | null | null | null | null | null |
FlexTok: Resampling Images into 1D Token Sequences of Flexible Length | Accept (poster) | Summary: This paper presents a novel method for tokenizing an image into a one-dimensional token sequence, which allows for flexible image representation and processing. Most of the existing VAE/VQVAE methods employ quantization on 2D grids, thus the token size is proportional to the image size. This paper proposes a n... | Rebuttal 1:
Rebuttal: We thank reviewer jx6L for the thoughtful and constructive feedback. Below we address the main points raised:
**1. Relation between image size, complexity, and token count**
This is a good point, and we haven't explored this explicitly yet. We performed all our experiments at 256x256 resolution ... | Summary: This paper introduces FlexTok, a novel 1D tokenizer that can encode images with variable token lengths. It combines casual masking and nested dropout in training to force the tokenizer to learn to reconstruct an image with a varying number of tokens. This strategy further promotes the tokenizer to encode image... | Rebuttal 1:
Rebuttal: We thank reviewer ekCv for the thoughtful and constructive feedback. Below we address the main points raised:
**1. Variable-length generation limitation (fixed token length in generator)**
To clarify: in our current setup, we train a single autoregressive (AR) model capable of generating a full ... | Summary: In this paper, the authors introduce a tokenizer that maps 2D images into variable-length, ordered 1D token sequences. This tokenizer allows images to be represented with a flexible number of tokens based on their content. In addition, an autoregressive model leverages this approach to achieve high-quality gen... | Rebuttal 1:
Rebuttal: We thank reviewer 6zZ5 for the thoughtful and constructive feedback. Below we address the main points raised:
**1. Higher computational cost of rectified flow decoder and token-count limitations**
Please see our response to reviewer MMdz. Our preliminary experiments suggested that higher token c... | Summary: The paper proposes FlexTok, a method for improving the tokenizer (VAE compression) used in image generation frameworks. Like previous approaches (TiTok, ALIT), FlexTok compresses 2D images into 1D tokens initialized as learnable registers. These tokens interact with encoded image patch tokens via attention mec... | Rebuttal 1:
Rebuttal: We thank reviewer MMdz for the thoughtful and constructive feedback. Below we address the main points raised:
**1. Single-token generation claim**
Image tokenization is commonly performed with lossy autoencoders that abstract away imperceptible information, meaning all tokenizer decoders (whethe... | null | null | null | null | null | null |
EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents | Accept (oral) | Summary: The paper proposes a powerful and comprehensive benchmark, EmbodiedBench, for both high-level and low-level actions in embodied intelligence. It consists of four distinct subdatasets, ranging from high-level semantic tasks to low-level metric tasks, with each subdataset having its own focus. To build the entir... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work and providing valuable feedback. We have carefully addressed your concerns below. Please let us know if you have any further questions.
The anounymous link for figures is https://anonymous.4open.science/r/rebuttal-3568/rebuttal_file.pdf. We use "4o" to refer to G... | Summary: This paper introduces EmbodiedBench, a comprehensive benchmark for evaluating vision-driven embodied agents based on multi-modal large language models (MLLMs). The benchmark features 1,128 testing instances across four environments, covering both high-level semantic tasks and low-level atomic actions, with six... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work and providing valuable feedback. We have carefully addressed your concerns below. Please let us know if you have any further questions.
**Q1: The absence of evaluation in real-world physical environments ... Embodied intelligence is largely meant to operate in the... | Summary: This paper proposes embodiedbench, a benchmark for evaluating MLLMs' capability in a diverse set of embodied tasks. Specifically, the tasks range from high-level semantic tasks to low-level tasks with atomic actions. Furthermore, the tasks under different simulators are classified into different subsets, to ev... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work and providing valuable feedback. We have carefully addressed your concerns below. Please let us know if you have any further questions.
**Q1: MLLMs performance might relate to the prompt being used, and one single prompt is used for all the models. Exploring how ... | Summary: The authors present EmbodiedBench - a set of diverse tasks and environment to evaluate MLLMs for embodied agents. They present high-level benchmark environments - EB-Habitat, EB-ALFRED and low-level benchmark environments - EB-Navigation and EB-Manipulation. The tasks are also divided into basic task solving,... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work and providing valuable feedback. We have carefully addressed your concerns below. Please let us know if you have any further questions.
**Q1: No real-world evaluations. It is hard to say how aligned with the real-world performance**
**A1:** We agree with the revi... | null | null | null | null | null | null |
Auto-reconfiguration for Latency Minimization in CPU-based DNN Serving | Accept (poster) | Summary: This manuscript investigates methods for accelerating neural network model tasks on CPU-based servers to minimize latency. Specifically, the authors found that current frameworks such as TorchServe, although effective in reducing inference latency through intra-operator parallelism across multiple threads, exh... | Rebuttal 1:
Rebuttal: Thank you for your reviews and valuable feedback.
- For NLP tasks, where token counts vary across sentences, the optimal configuration can be determined by profiling the effective batch size in terms of tokens rather than just the number of samples. However, if the variability in token counts mak... | Summary: This paper proposes an automated optimization framework for CPU-based serving of DNNs (Packrat) aimed at minimizing inference latency. It addresses a known limitation in intra-operator parallelism—diminishing returns as thread count increases—by introducing an approach to run multiple instances of models concu... | Rebuttal 1:
Rebuttal: Thank you for your reviews and valuable feedback.
- **Target Audience for CPU-Based DNN Serving:** Packrat is aimed at users and organizations that rely on existing CPU infrastructure, such as large data centers or cloud providers with extensive CPU fleets, where GPUs might be too costly, underut... | Summary: The main message the paper wants to convey seems to be "running multiple instances each with smaller batch sizes and fewer threads for intra-op parallelism can provide lower inference latency." Based on this insight, the paper introduces Packrat that optimizes the Batch, Threads, Instances, to get optimal perf... | Rebuttal 1:
Rebuttal: Thank you for your reviews and valuable feedback. Below, we provide our responses to the questions in the same order as they were asked:
- Memory can become a bottleneck; however, our optimizer already accounts for this during the profiling phase. Suppose a model is highly constrained by memory b... | null | null | null | null | null | null | null | null |
Metadata Conditioning Accelerates Language Model Pre-training | Accept (poster) | Summary: The paper proposes a metadata-enhanced training strategy for LLMs across various model sizes, ranging from 600M to 8B parameters. Specifically, during the first 90% of training, metadata is prepended to the training documents, enabling comparable performance while reducing data usage by 33%. The authors conduc... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We address your concerns below:
**Q1: Randomness of the experiment results**
A1: Thank you for raising this point. We acknowledge that a limitation of our study is the lack of multiple runs with different random seeds for most experiments, primarily due to t... | Summary: This paper proposes to include metadata (source links) in the pre-training of language models to boost learning efficiency. The proposed method, MeCo, pre-trains language models with text augmented with metadata in the first 90% of data and the metadata are removed in the last 10% of data for “cooldown”. MeCo ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We address your concerns here:
**W1: Evaluation only includes commonsense reasoning and only reflects a few sources such as Wikipedia.**
A1: **We evaluate our models by using OLMES ([Gu et al., 2024](https://arxiv.org/abs/2406.08446v1)), the industry-standar... | Summary: The paper presents a novel method named Metadata Conditioning then Cooldown (MeCo), which appends metadata (primarily URLs) to pretraining documents and significantly accelerates pre-training. The authors also show how MeCo can be used for model steering by conditioning prompts on metadata, enhancing both down... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and questions! We appreciate that you recognize the paper’s contributions and strengths. To address your concerns:
**W1: Lack of theoretical explanation—how does MeCo change the training dynamics?**
A1: Thank you for raising this point! We acknowledge that this pap... | Summary: This paper proposes Metadata Conditioning then Cooldown (MeCo) to accelerate LM pre-training. MeCo starts with pre-training LMs with metadata (URL's absolute domain) prepended in front of the text in the first 90% of pre-training and uses text only (no metadata) for pre-training in the last 10% of pre-training... | Rebuttal 1:
Rebuttal: Thanks for your positive review! We are glad that you found our method interesting and our experiment design sound. To answer your questions:
**Q1: Chiang and Lee, 2024 show that providing different source information often does not affect RAG results. How to interpret this?**
A1: Thanks for pro... | null | null | null | null | null | null |
Multilayer Matrix Factorization via Dimension-Reducing Diffusion Variational Inference | Accept (poster) | Summary: This work presents a diffusion variational inference algorithm for multilayer matrix factorization. The authors treat each layer as a diffusion step. Another difference is that the dimension of the latent variable reduces with the layer depth, termed as dimension reduction diffusion VI. This is the nature of l... | Rebuttal 1:
Rebuttal: We are much obliged to you for your careful review and constructive comments. We will carefully take into account your comments in our revision, and we would like to discuss various aspects as follows.
**Regarding “Other Strengths and Weaknesses”**
Point 1: Thank you for expressing your view in ... | Summary: The paper introduces a novel diffusion-model based variational inference method for multilayer matrix factorization (MMF), using a dimension-reducing Markov chain as the noise. The method is evaluated on hyperspectral image unmixing, where it outperforms state-of-the-art MMF and deep learning methods in abunda... | Rebuttal 1:
Rebuttal: We appreciate your time and effort in reviewing our work, and we are grateful for your generally positive feedback. We will take your advice to improve the paper. We also want to discuss some of the main points you raised.
**Regarding “Essential References not Cited”**
We agree, and thank you fo... | Summary: The paper presents presents a diffusion model (DM) based variational inference (VI) method for multilayer matrix factorization (MMF). They derive a variational process which is computationally efficient and lighter weight than other methods such as VAEs. Their method DRD-VI also reduces latent dimensionality a... | Rebuttal 1:
Rebuttal: We are very thankful for your thoughtful comments. We will do our best to revise and improve the paper. Here we would like to give a reflection on the main points you raised.
**Regarding “Essential References not Cited”**
Thank you for pointing out some references related to dimension-varying di... | null | null | null | null | null | null | null | null |
Provable In-Context Vector Arithmetic via Retrieving Task Concepts | Accept (poster) | Summary: This work analyzes the optimization dynamics of transformer networks trained on in-context learning tasks via Gradient-descent on an L2-regularized cross-entropy loss. The analysis relies on a simple specific data-generating process which provides a way to formalize the notion of task concept vector. The resul... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our theoretical contribution and recognizing our analysis non-trivial.
**Q**:Confusion over Theorem 1
- We'd make it in the theorem statement explicitly by referring Algorithm 1.
- As we claim for any ε>0, the varying item here is ε, with other parameters satisfying ... | Summary: The authors study task vectors in the context of single-token factual-recall ICL tasks. In this context, they show that training on QA data enables learning a task vector which can effectively solve ICL problems for Word-Label and QA tasks. They additionally analyze this phenomenon theoretically.
Claims And E... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our theory as empirically-supported and sound. We appreciate your professional review and address your concerns below.
**Q**:Are QA and Word-Label tasks natural language or simplifications? The example in QA Sentence Distribution is confusing. The paper would benefit f... | Summary: To study retrieval of task vectors in ICL, the authors perform a careful gradient descent analysis on residual self-attention modules (with nonlinearities and normalization) under a synthetic (but empirically-motivated) data distribution. They find that when pre-training on QA distribution (and testing on word... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review!
**Q**:Chicken-and-Egg Dilemma & real-world impact
We'd like to emphasize that theories often relies on abstract, empirical-motivated models to enable tractable analysis and explore a model’s potential—an approach we adopt. However, we appreciate the opportun... | null | null | null | null | null | null | null | null |
From Feature Interaction to Feature Generation: A Generative Paradigm of CTR Prediction Models | Accept (poster) | Summary: This paper highlights issues in discriminative CTR-based recommendation models, such as information redundancy and information collapse. To address these challenges, it proposes a feature generation framework that reformulates CTR prediction as a generative problem using a customized decoder network. The decod... | Rebuttal 1:
Rebuttal: We are grateful for your kind remarks. We hope the following responses can address your remaining concerns.
## Concerns about Theoretical Analysis
> Response to "Theoretical Claims", and Point 3 of "Other Strengths And Weaknesses"
Following DirectCLR[1], we have explored theoretical justificati... | Summary: The paper “From Feature Interaction to Feature Generation: A Generative Paradigm of CTR Prediction Models” proposes a novel Supervised Feature Generation framework for Click-Through Rate (CTR) prediction models. The main algorithmic idea is to shift from the discriminative “feature interaction” paradigm to a g... | Rebuttal 1:
Rebuttal: We are truly thankful for your review efforts. We apologize for the missing information on datasets and notations, and would clarify them as follows.
## Dataset Details
> Response to "Methods And Evaluation Criteria", "Other Strengths And Weaknesses", and Q1 of "Questions For Authors"
Thank you... | Summary: This paper introduces a feature generation framework that reformulates conventional CTR models through a generative paradigm, effectively addressing dimensional collapse and information redundancy issues in feature embeddings. The claims are substantiated by rigorous empirical evidence spanning widely adopted ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments. We hope the following responses can address your concerns.
## Concerns about Batch-wise Analysis
> Response to Weaknesses of "Experimental Designs Or Analyses" & Q1 of "Questions For Authors".
Thank you for your suggestion. The suggested analys... | Summary: This paper proposes a Supervised Feature Generation (SFG) framework that reformulates the conventional discriminative CTR prediction paradigm into a generative paradigm. Rather than modeling direct interactions among raw ID embeddings, the proposed method generates each feature embedding based on the concatena... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We have addressed the comments in the rebuttal below.
## Clarification on the Motivation.
> Response to Point 1 of "Claims And Evidence" & "Methods And Evaluation Criteria".
We'll elaborate more on our claim. Our work is mainly inspired by the Interact... | null | null | null | null | null | null |
RobustZero: Enhancing MuZero Reinforcement Learning Robustness to State Perturbations | Accept (poster) | Summary: The paper adapts the MuZero algorithm (RobustZero) with a state-robustness loss and two adaptive hyper-parameter adjustment methods. The method is designed to deal with both worst-case and random perturbations of the state, if those are available during training. The authors evaluate their approach against the... | Rebuttal 1:
Rebuttal: We appreciate your positive and valuable comments.
$\textbf{Response to Methods and Evaluation Criteria:}$ 1) Regarding the projector and predictor networks, please refer to the response to Q1; 2) Regarding the bold entries, we have revised all tables (see https://anonymous.4open.science/r/Robus... | Summary: This work proposed a robust version of Muzero framework called RobustZero to gain robustness when facing state perturbations. Muzero features a self-supervised representation network to generate a consistent initial hidden state and a unique loss function to gain robustness. In the experiment setting, Muzero s... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and recognition of our contributions.
$\textbf{Response to Experimental Designs or Analyses:}$ Please refer to the responses to Q1-Q3.
$\textbf{Response to Essential References Not Discussed:}$ We will add the two references as follows. The recent studies... | Summary: The authors propose RobustZero, the first MuZero-class method designed to ensure robustness against state perturbations, including both worst-case and random-case scenarios. The proposed method introduces a training framework that includes a self-supervised representation network, which facilitates the generat... | Rebuttal 1:
Rebuttal: We appreciate your positive and valuable comments.
$\textbf{Response to Claims and Evidence:}$ Following your comments, we have analyzed the relationship between the number of environment samples and the natural, worst-case, and random-case rewards for RobustZero and the two robust model-free bas... | Summary: The paper introduces RobustZero, an enhanced MuZero framework designed to be robust against both random-case and worst-case adversarial perturbations. RobustZero dynamically balances data generation between these perturbations and incorporates them directly into online training.
Claims And Evidence: The claim... | Rebuttal 1:
Rebuttal: We appreciate your comments and our responses are detailed below.
$\textbf{Response to Method and Evaluation Criteria and Q1}$: We would like to address your comments as follows:
1) We have studied RobustZero and all baselines on five Mujoco environments, including Hopper, Walker2d, HalfCheetah,... | null | null | null | null | null | null |
The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training | Accept (poster) | Summary: This paper uncovers a sharpness disparity across different blocks in Transformers, which persists throughout the training process. The authors propose a novel Blockwise Learning Rate strategy to accelerate LLM (e.g., GPT and LLaMA) pre-training. Furthermore, the proposed method consistently achieves lower loss... | Rebuttal 1:
Rebuttal: Thank you for your great efforts on the review of this paper and your appreciation. We will try our best to address your questions.
**Q1: Concerns about the gap between estimated digonal Hessian and the true Hessian.** "The approximation is only performed for the diagonal Hessian matrix, and the ... | Summary: The authors demonstrate that there is a sharpness disparity between the different transformer blocks, which appears early in training and persists throughout. Based on their observation, the authors introduce a novel approach called Blockwise Learning Rate, which adjusts the learning rate of each transformer b... | Rebuttal 1:
Rebuttal: Thank you for your great efforts on the review of this paper and your appreciation. We will try our best to address your questions.
**Q1: Concerns about the downstream performance.** "However, the authors only report the loss and do not evaluate the downstream performance, which would strengthen ... | Summary: The paper proposed a blockwise learning rate method to accelerate training. The blockwise learning rate is designed based on blockwise sharpness estimation. The writing is clear. The principle is reasonable and makes sense to me. The experiments are mostly convincing. Overally speaking, this is a good paper a... | Rebuttal 1:
Rebuttal: Thank you for your great efforts on the review of this paper and your appreciation. We will try our best to address your questions.
**Q1: Questions of the meaning of "(50k)" and "(100k)".** "In Figure 4 and 5, what do "(50k)" and "(100k)" mean? I cannot find the description anywhere around the fi... | Summary: This paper presents the Sharpness Disparity Principle, which identifies a systematic difference in sharpness across different transformer components. Specifically, the authors find that normalization layers exhibit the highest sharpness, while embedding layers have the lowest, with other blocks lying in betwee... | Rebuttal 1:
Rebuttal: Thank you for your great efforts on the review of this paper and your appreciation. We'll try our best to address your questions.
**Q1: Suggestion for larger-scale experiments.**
**A1**: Thanks for this question.
- We clarify that our largest model (1.1B) is large enough for current dataset scal... | null | null | null | null | null | null |
Transfer Q-Learning with Composite MDP Structures | Accept (poster) | Summary: The paper addresses transfer reinforcement learning (RL) under a new framework model called composite MDP, where transition dynamics consist of a low-rank “shared” component plus a sparse “task-specific” component. This setup reflects how different tasks can share a core set of dynamics while still varying in ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments!
**Low-Rank:** We agree that clarifying this terminology is important for improving readability and avoiding confusion. In classical low-rank MDPs literature, "low-rank" refers to embedding-based models where the effective feature dimension $d$ is small com... | Summary: This paper introduces a composite MDP framework combining low-rank shared dynamics and sparse task-specific variations, along with the UCB-TQL algorithm for transfer Q-learning. Theoretically, it establishes a dimension-free regret bound for the target task by leveraging structural similarities between tasks. ... | Rebuttal 1:
Rebuttal: **Response to Reviewer fH8W**
We sincerely thank the reviewer for the thoughtful feedback and recognition of our theoretical contributions. We address the two concerns raised below.
---
**Q1: Lack of empirical validation of UCB-TQL**
We appreciate the reviewer’s point. While this submission do... | Summary: The paper introduces a Upper Confidence Bound method for Transfer Q-Learning for transfer RL settings where it is assumed that transition dynamics are assumed to decompose to a low-rank shared matrix and a sparse matrix that captures task specific dynamics. A key feature is that the method allows for high-dime... | Rebuttal 1:
Rebuttal: **We thank the reviewer for their positive assessment and constructive suggestions.** We are especially grateful for the recognition of our theoretical contribution, which we believe offers a timely and foundational advancement in transfer reinforcement learning.
Our work provides the *first prov... | null | null | null | null | null | null | null | null |
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization | Accept (poster) | Summary: For Bayesian Optimization, the paper proposes to use tree-based functions for the basis for the Gaussian Process prior. The paper outlines the mechanics of this, including:
* Kernel definition
* Sampling MCMC method
* Acquisition definition
The paper then shows that the regression capabilities are reaso... | Rebuttal 1:
Rebuttal: Thank you for your careful review, and for recognizing our contribution to mixed-space Bayesian optimization.
> what exactly motivated the authors to consider that using trees would be good for BO
We agree with the reviewer that we should better motivate BARK to ensure practitioners use BARK app... | Summary: The paper introduces a new tree-based surrogate model for use in Bayesian optimization. It extends of prior work in tree-based regression, particularly BART, to be more suitable for acquisition function optimization and Bayesian optimization. The tree model is fully Bayesian, including MCMC over tree structure... | Rebuttal 1:
Rebuttal: We kindly thank the reviewer for their thoughtful review, and for recognizing the potential of our method.
> lack of discussion of wall-time for BO
As mentioned in Appendix G.1, we limit each MIP optimization to 100 seconds. However, we agree that a more complete comparison is highly relevant. P... | Summary: The paper proposes a combination of forest kernel GPs and Bayesian tree models (BART) specifically tailored for the use in Bayesian Optimisation (BO). The main idea is to directly optimise the expected acquisition function values over the posterior distribution of the kernel parameters. This is done through po... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review, and for identifying the strengths of our work.
> The choice of UCI datasets
Section 7.1 demonstrates that the BARK model performs similarly in regression to the BART model. BART is typically used in regression settings with tabular datasets, so th... | null | null | null | null | null | null | null | null |
Contract Design Under Approximate Best Responses | Accept (poster) | Summary: This paper studies a repeated contracting game between a principal and an agent. The principal offers contracts to the agents who has the choice to accept the contract or not. The paper assumes a hidden action on which the outcome (to which are related the principal's and the agent's utilities) depends. Instea... | Rebuttal 1:
Rebuttal: We believe there is misunderstanding on the main contributions of our paper and the challenges in obtaining efficient optimization algorithms.
- **Computing robust equilibria is much harder than standard one**. The Reviewer claims: “The principal knows the agent's deviation, which makes it way ea... | Summary: This paper studies optimal contract design under approximate best response agents in hidden-action principal-agent games. First, they propose an efficient algorithm to compute an optimal contract. They also show that the principal's utility is $\delta$ close to the optimal contract under best response agents... | Rebuttal 1:
Rebuttal: **Re**:*"Can the simple discretization apply to non-robust settings? "*
Yes, the simple discretization can be employed in the non-robust version of the problem. Intuitively, it is sufficient to follow the same steps as in the proof of Theorem 2, with $\delta$ going to zero.
**Re**: *"Can algori... | Summary: The paper studies hidden-action principal‐agent problems where a principal designs a contract (an outcome‐dependent payment scheme) to incentivize an agent to take actions that are in favor of the principal. Unlike traditional models assuming the agent always plays an exact best response, here the agent may ch... | Rebuttal 1:
Rebuttal: **Re**: *"The online learning setting assumes the follower's action to be invisible, rendering the follower's response model a "black box" and thus an exponential dependency is unavoidable. My question is, If the follower consistently takes the worst -suboptimal action (from the leader's perspecti... | Summary: This paper explores hidden-action principal-agent problems where the agent follows approximate best responses rather than exact optimal strategies. The authors propose a polynomial-time algorithm for computing optimal contracts under these conditions and introduce a no-regret learning algorithm for scenarios w... | Rebuttal 1:
Rebuttal: **Re**: *"Challenges in Our Setting and Technical Novelty.”*
We believe that the computational results presented in Section 4 are rather “surprising”. Indeed, such positive results are unexpected, as very similar problems are computationally intractable. In particular, computing an optimal $\delt... | null | null | null | null | null | null |
Logits are All We Need to Adapt Closed Models | Accept (poster) | Summary: This paper studies the problem of adapting a black-box LLM to a downstream task, assuming access to the logits of output tokens. The authors propose a token-level probability reweighting algorithm that modifies token logits during inference. The core idea is to frame the adaptation problem as label noise corre... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, which has helped us strengthen our paper.
**Regarding Logit access assumption**
The central goal of this paper is to encourage closed-source LLM providers to offer logit-level access as a practical middle ground when releasing full model weights is not fe... | Summary: The paper proposes logit reweighting to adapt closed-source LLMs for task-specific generation without accessing model weights. By learning an autoregressive transition matrix from task data, it adjusts token probabilities during inference to align outputs with target domains. Experiments show improved style/ke... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s encouraging words and thoughtful feedback.
**Regarding human evaluation and limited generalization tests**
We already conducted a human evaluation (line 366, details in Appendix C.7) where three evaluators compared Plugin and ICL-3 on 100 Adidas samples, with Plugin p... | Summary: The paper tackles the issue of having to rely on prompt engineering while adapting closed-source LLMs. The proposed work formulates the problem as a supervised learning, where few task specific dataset is used to train the model. The closed source model is assumed to learn noisy labels of specific application,... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s positive remarks and valuable insights.
**Regarding Inclusion of Closed Source Models**
We acknowledge that including more closed-source models could further strengthen the generality of our findings. However, most proprietary models currently do not expose thei... | Summary: The key idea of the paper is to treat next-token prediction as a label noise correction problem, where discrepancies between the LLM’s broad training distribution and task-specific data are modeled through a transition matrix that reweights token probabilities during inference. The proposed Plugin model consis... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and insightful review.
**Regarding Comparison of Plugin with Parameter Efficient Fine-tuning (PEFT) methods like LoRA, Adapters:**
>"... The Plugin model is positioned as an alternative to fine-tuning, but it is not directly compared to parameter-efficient ... | null | null | null | null | null | null |
A Closer Look at Backdoor Attacks on CLIP | Accept (poster) | Summary: The paper presents a detailed analysis of which type/location of layers are effected by backdoor attacks in transformer based VLMs by the help of representation decomposing. The findings indicate: global trigger based attacks mostly affect MLPs whereas localized trigger patches influence Attention heads. Based... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful comments. We are encouraged by your recognition of the novelty and the solid experimental analysis of our work. Below are your concerns and our corresponding responses:
**Q1: The experiments of backdoor learning from scratch.**
**A:** Thank you for your... | Summary: This paper presents a comprehensive empirical study to analyze the effects of backdoor attacks on CLIP. They found three empirical findings about how different types of backdoor attacks have various effects on CLIP. The authors conducted extensive experiments and showed visualized results, which validates thei... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments. We are encouraged by your recognition of the novelty and significance of our work. Below are your concerns and our corresponding responses:
**Q1: More experimental results should be further analyzed.**
**A:** Thank you for your valuable comment.... | Summary: This paper investigates how backdoor attacks infect different components of a ViT-based CLIP model (notably attention heads vs. MLP layers) and proposes a “repair” mechanism that selectively ablates or replaces infected representations in the last few layers. The authors conduct detailed experiments to show th... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments. We appreciate your recognition of the novelty of our work and the empirical analysis in the paper. Below are your concerns and our corresponding responses:
**Q1: Necessity of “Partial Repair” vs. Simple Replacement**
**A:** Thanks for your insi... | null | null | null | null | null | null | null | null |
Text-to-CAD Generation Through Infusing Visual Feedback in Large Language Models | Accept (poster) | Summary: The authors use DPO/Rinforcement Learning to fine-tune a LLM to produce CAD instructions from a prompt, so tht the rendered objects are ranked VLM. They fined tune Llamma and use prewarming to make sure it can generate CAD sewuences froma prompt before alternating betwen DPO and direct sequential learning step... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for the valuable comments. We are pleased to know that you are happy with our evaluation details including the qualitative results and user study. We hope the following clarifications address your concerns.
## More References
Thanks for pointing out these relevant work... | Summary: This paper introduces CADFusion, a framework for Text-to-CAD generation that leverages LLMs and incorporates visual feedback to improve the quality and accuracy of generated CAD models. The core contribution is a two-stage training procedure that alternates between Sequential Learning (SL) and Visual Feedback ... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your thoughtful comments and for recognizing our contributions, particularly in the design approach and experimental setup. We appreciate the opportunity to clarify the points you raised.
## Claim Could be a Bit More Precise
We realize this problem and apologize for... | Summary: This paper proposes a text-to-CAD model that leverages LLMs to generate CAD commend sequences as sequential signals. The authors use a pre-trained LLM as the backbone and perform SFT on CAD parametric sequences. They further use DPO to perform RL-based fine-tuning and introduce an LVM-based scoring pipeline to... | Rebuttal 1:
Rebuttal: Dear reviewer,
We sincerely appreciate your thoughtful comments and the opportunity to clarify the concerns raised.
## How Well Captions Describe CAD Models and Model Accomodates to Real Users
During development, we considered similar concerns and would like to share our findings:
* VLM-genera... | Summary: Authors build upon existing CAD data representation and introduce a novel visual feedback into text2cad. A dpo algorithm is used together with LVM-based scoring to improve the text2cad pipeline. Two new datasets are also proposed (text-cad pair dataset and preference dataset).
Claims And Evidence: From table ... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your feedback. We appreciate the opportunity to clarify your concerns.
## SL(no VF) & SL-VF(pro)
We are unsure what SL-VF(pro) refers to. We presume you are mentioning SF-VF(rpo). If so, we would like to point out that this is **not our main method**, but **an ablati... | null | null | null | null | null | null |
Robust Multi-bit Text Watermark with LLM-based Paraphrasers | Accept (poster) | Summary: The paper introduces methodologies for embedding imperceptible multi-bit text watermarks. The proposed algorithm aims to fine-tune a pair of LLM paraphrasers that are designed to behave differently so that their paraphrasing difference reflected in the text semantics can be identified by a trained decoder. The... | Rebuttal 1:
Rebuttal: **Ablation study of similarity reward** (Lack of ablation experiments to show the effectiveness of the similarity reward rs in Equation 4. ... If a larger model is used, such as Llama-2-7b, is the similarity reward rs in Equation 4 still needed?)
**Response**: We show the effect of similarity rew... | Summary: The paper proposes a method for injecting watermarks to text. The key idea proposed is to use two LLM paraphrasers (one for the '0' bit
and one for the '1' bit). The decoder (classifier) and the paraphrasers are trained together in a co-training setup. The results achieved are impressive, achieving 99.9% AUC ... | Rebuttal 1:
Rebuttal: **Different Segmentation** (what is the impact of using a different segmentation method?)
**Response**: To compare the performance of different segmentation strategies, we conduct an extra experiment in which we design a "segment-by-token" strategy, where we segment the text every 20 tokens an... | Summary: The paper presents a robust multi-bit text watermarking method that leverages LLM-based paraphrasers to embed imperceptible watermark signals into text while maintaining semantic fidelity. The approach involves fine-tuning a pair of paraphrasers designed to generate text variations that encode a predefined bin... | Rebuttal 1:
Rebuttal: **Ablation Study** (The method's performance is highly reliant on model initialization and carefully chosen hyperparameters (e.g., λw, λs, λk). To what extent do these hyperparameters influence the effectiveness of the approach?)
**Response**: We agree with the reviewer that the hyperparameter ch... | Summary: The authors proposed a multi-bit text watermark by paraphrasing a piece of text to inject watermark signals. The watermark consists of an encoder-decoder pair. The encoder is fine-tuned to generate text that is classified by the decoder. The decoder is trained with a classification loss to better classify betw... | Rebuttal 1:
Rebuttal: **Different Segmentor** (The text segmentor simply consider each sentence in the text as a segment. Are there other better segment strategies?)
**Response**: We agree with the reviewer that we use a simple segment strategy which splits text by sentences. Nevertheless, we argue that our current st... | null | null | null | null | null | null |
Diffusion Instruction Tuning | Accept (poster) | Summary: This paper introduces Lavender, the first framework designed to directly align the attention layers of vision-language models (VLMs) with Stable Diffusion. Notably, Lavender is model-agnostic, and the authors evaluate it across multiple pretrained VLMs, demonstrating its strong generalization on both in-distri... | Rebuttal 1:
Rebuttal: ### **Methods And Evaluation Criteria**:
> "… the choice of diffusion architecture matters … Does the analysis hold for DiT-based models?"
>
Thank you for highlighting this important point, previously discussed with Reviewers Xyv6 and xQnL. Briefly, Lavender's effectiveness indeed depends on th... | Summary: The paper introduces Lavender, a supervised fine-tuning (SFT) method for enhancing vision-language models (VLMs). It aligns the core transformer attention in VLMs with the attention maps of Stable Diffusion during SFT. This approach enriches the model's visual understanding, improves text generation quality, a... | Rebuttal 1:
Rebuttal: ### Weaknesses and Limitations:
> "Only experiment on Stable Diffusion v1.4 …"
>
Thank you for highlighting this important point. We acknowledge that Lavender’s performance indeed depends on the chosen diffusion model. While our current results with Stable Diffusion v1.4 demonstrate strong atte... | Summary: The paper introduces Lavender, a novel framework that enhances image-to-text generation in vision-language models by aligning their attention mechanisms with text-to-image diffusion models, specifically Stable Diffusion. The key motivation is that diffusion models, which reconstruct images at the pixel level,... | Rebuttal 1:
Rebuttal: ### Weaknesses:
> "The experimental evaluation dataset ... scalability with larger data sizes."
>
We thank the reviewer for highlighting this important point. Indeed, as explicitly noted in the Limitations and Future Works (lines 405-409) and Appendix D (lines 1140-1141): *"Lavender was evaluat... | Summary: Diffusion Instruction Tuning introduces Lavender, a fine-tuning framework that aligns a vision-language model’s (VLM) image-to-text attention with a text-to-image diffusion model’s attention maps. The key idea is to leverage the precise cross-attention of a pretrained Stable Diffusion model as a training sign... | Rebuttal 1:
Rebuttal: ### Weaknesses:
> "Dependence on Diffusion Model Quality ..."
>
Thank you for highlighting this important point. We acknowledge that Lavender’s effectiveness indeed depends on the quality and training domain of the chosen diffusion model. While our qualitative and quantitative results confirm t... | null | null | null | null | null | null |
Monte-Carlo Tree Search with Uncertainty Propagation via Optimal Transport | Accept (spotlight poster) | Summary: The paper introduces Wasserstein-MCTS, an uncertainty-aware version of MCTS based on Wasserstein barycenter updates for the optimal value estimates and their variance. Due to the power-mean like updates the version is particularly suitable for highly stochastic environments. A theoretical analysis is included,... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for their thoughtful and constructive feedback and comments, and appreciate the reviewer's acknowledgment of the strengths of our paper. We have provided detailed responses to address each of their concerns.
## On reference suggestions.
We thank the Reviewer for ... | Summary: This paper introduces Wasserstein Monte-Carlo Tree Search (W-MCTS), a new MCTS variant designed for highly stochastic and partially observable environments. The key innovation lies in propagating uncertainty through the search tree using L1-Wasserstein barycenters combined with alpha-divergences, enabling robu... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for the thorough review of our paper on Wasserstein Monte-Carlo Tree Search (W-MCTS).
## On Key Innovations
We are pleased that the Reviewer recognizes our main contribution in propagating uncertainty through the search tree using L1-Wasserstein barycenters combin... | Summary: This paper takes a distributional approach to Monte Carlo tree search. The
authors propose a framework for planning in environments with uncertainty and/or
partial observability. The proposed framework models state and state-action
values as distributions. They also introduce a backup operator that propagates
... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for your positive review and for recommending acceptance of our paper. We appreciate their recognition of our work's novelty and clarity.
## On Our Claims and Evidence
We thank the Reviewer for acknowledging that our experimental results support our claims about r... | Summary: This paper introduces Wasserstein Monte-Carlo Tree Search (W-MCTS), a novel approach that represents value nodes as Gaussian distributions (mean and variance), allowing explicit uncertainty propagation throughout the search tree. The method employs a backup operator based on the Wasserstein barycenter and α-di... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for their thoughtful review and insightful questions that help us improve our paper. Below are our responses to your specific concerns:
## Key Performance Factors
Thank you for this insightful question. We would like to point out that the superior performance of o... | null | null | null | null | null | null |
An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability | Accept (poster) | Summary: The paper explores how to enhance the performance of Multimodal Large Language Models (MLLMs) in Multimodal Sentiment Analysis (MSA) by optimizing the configuration of In-Context Learning (ICL) demonstrations. The main findings include:
1. Enhancing MSA Performance: The authors show that by carefully configu... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and in-depth comments! Below, we present detailed responses to the weaknesses (**W**) and other concerns (**O**).
>**W4(1). Metric definitions in Table 2**
In the caption of Table 2, we specify that "R strategy" represents random retrieval, and we rep... | Summary: This paper conducts an empirical study to unleash the power of MLLMs using in-context learning for multimodal sentiment analysis. The authors study three key factors influencing in-context learning performance: similarity measurement, modality presentation, and sentiment distribution. Experiments are performed... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and valuable advice! Below, we present detailed responses to the weaknesses (**W**), comments (**C**), questions (**Q**) and other concerns (**O**).
>**C1&W1. Unclear figures**
In the revised manuscript, we will replace redundant components in Figur... | Summary: This paper explores using In-Context Learning (ICL) to enhance MLLMs for Multimodal Sentiment Analysis (MSA). The authors identify that MLLMs under the zero-shot paradigm exhibit weak performance on MSA tasks. They propose a systematic study of three key factors in ICL demonstration configuration: similarity m... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and constructive suggestions! Below, we present detailed responses to the weaknesses (**W**), comments (**C**) and questions (**Q**).
>**W1. Computational feasibility of optimal strategies**
Please refer to the response to **Q2** of Reviewer nCbm.
... | Summary: The paper addresses Multimodal Sentiment Analysis using MLLMs by enhancing In-Context Learning through optimized demonstration retrieval, presentation, and distribution. It achieves significant accuracy gains over zero-shot and random ICL baselines and mitigates inherent sentiment bias.
Claims And Evidence: Y... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and helpful comments! Below, we present detailed responses to the questions (**Q**) and weaknesses (**W**).
>**Q1. Generalization to other tasks or datasets**
Our strategies cover three factors: similarity measurement, modality presentation, and sen... | null | null | null | null | null | null |
Hypo3D: Exploring Hypothetical Reasoning in 3D | Accept (poster) | Summary: This paper presents the construction of a new dataset, Hypo3D, for evaluating the hypothetical reasoning performance on 3D scene. Then, the authors conduct evaluation of several benchmark methods on this dataset and show huge performance gaps between human's performance and model's performance.
Claims And Evi... | Rebuttal 1:
Rebuttal: Interesting.
**Q1: About Novelty.**
Our Hypo3D benchmark is a novel, methodological contribution in itself, as noted by other reviewers. It focuses on structured evaluation and it is the first 3D reasoning benchmark with explicit question-type annotations, enabling fine-grained evaluation. Many... | Summary: The paper introduces Hypo3D, a benchmark task evaluating foundation models' ability to use hypothetical reasoning to "imagine" missing perceptual information in dynamic 3D scenes. It provides a dataset with various context changes and questions, showing that current models significantly underperform humans, fr... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We have added further explanations and additional experimental results to address your concern regarding the input data type issue.
**Q1: 2D VLM Input**
We adopted top-view images for 2D VLMs to maintain consistency with established 3D reasoning benchmarks l... | Summary: The paper introduces a 3D reasoning benchmark called Hypothetical 3D Reasoning (Hypo3D). Specifically, the components of the benchmark can be summarized as follows. Consider a 3D scene representation (S) and a world frame from the scene (F) that contains an anchor object for specifying the direction to the mod... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful feedback. We’re pleased to hear that you found our dataset to be a great contribution. All 2D VLM results reported below use the semantic map by default.
**Q1: Answer Annotation**
Similar to the answer types in Fig. 12 of SQA3D, our annotations include obj... | Summary: This paper introduces a novel 3D-VQA benchmark called Hypo3D. Given a 3D scene representation (e.g., point clouds, BEV images) and a description of how the scene has changed, the model must infer the updated scene and answer a question based on it. The authors benchmark a range of open-source and closed-source... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback. We’re glad to hear that you found our work to be novel and interesting.
**Q1: Scene Orientation**
For 3D VLMs using point clouds (e.g., LEO), inputs have been explicitly aligned to a top-view perspective with the floor on the XY-plane and vertical structu... | null | null | null | null | null | null |
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