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Backdoor Attacks in Token Selection of Attention Mechanism | Accept (poster) | Summary: Motivated by the need for theoretical foundations underpinning backdoor attacks on self-attention transformers/LLMs (good), this paper: (1) investigates LLM backdoor attacks targeting the token selection mechanism of attention, (2) proves that "single-head attention transformers can interpolate poisoned traini... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's interest and recognition of our contributions and the novelty of our work. We will correct the typo in the final version.
Regarding the connection with practical settings, we have some conjectures about possible defense mechanisms. Suppose that the learner has knowle... | Summary: This paper discusses the vulnerability of the attention module to backdoor attacks from an interesting perspective, and this work provides theoretical analysis and simulation verification. This paper proves that a layer of attention module does remember poisoned samples after some assumptions are met.
Claims ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's interest and recognition of our contributions and the novelty of our work.
W1: The lower bound on the number of iterations $\tau_0$ depends on the proportion of relevant tokens $\zeta_R$, the proportion of irrelevant tokens $\zeta_P$, the number of tokens $T$, and th... | Summary: This paper presents a theoretical analysis of backdoor attacks targeting the token selection process in single-head self-attention transformers. The authors demonstrate that gradient descent can interpolate poisoned training data and establish conditions under which backdoor triggers dominate model predictions... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's interest and recognition of our contributions and the novelty of our work.
Q1: Regarding the orthogonality assumption, such assumption can be relaxed to the setting where the relevant signals $\mu_{\pm 1}$ and the poisoned signals $\tilde\mu_{\pm 1}$ are correlated, ... | Summary: This paper uses extensive mathematical proofs to reveal how backdoor triggers affect model optimization. If the signal from the backdoor trigger is strong enough but not overly dominant, an attacker can successfully manipulate the model predictions.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's interest and recognition of our contributions and the novelty of our work. We acknowledge that our current results rely on restrictive assumptions and that our experiments serve primarily as a proof-of-concept for our theoretical findings. We have discussed these limit... | null | null | null | null | null | null |
ERICT: Enhancing Robustness by Identifying Concept Tokens in Zero-Shot Vision Language Models | Accept (poster) | Summary: This paper introduces ERICT, a novel method to enhance model robustness by identifying concept tokens and mitigating spurious correlations at the inference stage. ERICT operates in two key steps: (1) identifying invariant concept tokens using auxiliary prompts to generate a token-level mask and (2) applying th... | Rebuttal 1:
Rebuttal: > **Q1: The paper observes that “tokens whose semantics align more closely with the prompt tend to have lower similarity scores,” but provides only limited examples to support this claim. Given that this observation appears counterintuitive, stronger empirical validation is necessary.**
**A1**: T... | Summary: This paper presents ERICT, a novel method to enhance the robustness of vision-language models (VLMs) by mitigating spurious correlations at the inference stage. The approach identifies concept tokens to create a token-level mask, which is then applied to the vision encoder’s attention mechanism. Experimental r... | Rebuttal 1:
Rebuttal: > **Q1: Section 5 lacks a detailed discussion on disentangling invariant and spurious factors, requiring further exploration.**
**A1**: Consistent with previous works[1-2], our theoretical analysis adopts the classic data assumption, which disentangles spurious datasets into invariant and spuriou... | Summary: The paper introduces ERICT and ERICT-C, mitigate spurious correlations in vision-language models (VLMs) during zero-shot inference. These approaches aim to enhance model robustness by identifying invariant features within image tokens and focusing the model's attention on relevant regions through token masking... | Rebuttal 1:
Rebuttal: > **Q1: However, the distinction between ERICT and ERICT-C needs clarification**
**A1**: The key difference between ERICT and ERICT-C lies in the way they obtain the auxiliary embeddings. As shown in Step 1 of Figure 2, ERICT uses auxiliary prompts (e.g., "bird in photo") as input to the text enc... | Summary: This paper proposes ERICT, a zero-shot method to improve robustness in vision-language models by identifying “concept tokens” that represent invariant image features. ERICT uses auxiliary prompts to generate masks applied to attention weights, aiming to reduce spurious correlations. The authors evaluate ERICT ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our work. We **sincerely appreciate your recognition of our contributions and the significance of the research problems we address**. Your support further strengthens our confidence in the proposed approach. If you have any additional questions or suggestion... | null | null | null | null | null | null |
SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models | Accept (poster) | Summary: This paper introduces SENSEI (SEmaNtically Sensible ExploratIon), a LM-based framework for guiding the exploration phase of RL agents towards "interesting" states. It trains a reward model based on VLM-generated rankings of interestingness on prior exploration data. The final exploration reward is the sum of t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive review. We appreciate that you found our paper comprehensive, the introduced concepts interesting, and our experimental design sound.
### VLM-based exploration baselines
Existing VLM-based exploration methods (Omni, Motif, ELLM) rely on assumptions that... | Summary: The paper introduces a novel framework for intrinsic motivation in reinforcement learning (RL) agents, enabling them to explore environments meaningfully without relying on task-specific rewards. The authors propose SEmaNtically Sensible ExploratIon (SENSEI), which leverages Vision Language Models (VLMs) to gu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and greatly appreciate that they found our paper well-organized, our experiments thorough, and our evidence clear and convincing. We aim to address the remaining concerns below.
> The paper lacks comparisons to recent exploration methods beyond P2X like Cur... | Summary: The paper proposes SENSEI, a framework designed to enhance exploration in model-based RL by integrating semantic guidance from VLMs. SENSEI distills a reward signal of interestingness from VLM-generated annotations of observations, guiding agents toward semantically meaningful interactions. This intrinsic rewa... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback and recognition of SENSEI’s novelty, thorough evaluations, and strong empirical evidence. Below, we address your key questions and minor comments.
### SENSEI in newly unlocked areas
> What happens if exploring a behavior in one area unlocks uncert... | Summary: The paper proposes incorporating human priors into RL exploration to encourage policies to internalize a model of *interestingness*. This is done by first annotating pairs of frames for interestingness using VLMs (which, owing to training on internet-scale human data, has incorporated these priors). This is th... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and highlighting that our research direction is important and acknowledging that our experimental results are interesting.
### New Environment: Pokémon Red
As per your suggestion, we now apply SENSEI to another environment: the classic Game Boy game Pokémon Red.... | null | null | null | null | null | null |
Prediction via Shapley Value Regression | Accept (poster) | Summary: This paper introduces a framework for estimating Shapley values in explainable AI. Typically, a single observation $\mathbf{x}$ is considered for attribution and gives rise to a set function $v: 2^{[n]} \to \mathbb{R}$. Then we compute the Shapley values for just the set function $v$ i.e., $\phi_i = \frac1{n} ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and feedback. Please find our responses below.
> A) It's unclear to me what it means to run KernelSHAP until convergence...
> B) KernelSHAP produces estimates which are slightly biased...
We agree with the reviewer that KernelSHAP can be biased, therefore, we em... | Summary: The paper presents a method called ViaSHAP which aims to learn a function to compute the Shapley Values as the model trains. This function predicts the Shapley Values from inputs, directly uses those values to form the model output and bypasses the need for post-hoc computation (i.e. to fit a KernelSHAP to the... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and feedback on our paper. Below, we provide our responses and clarifications.
> I'm a bit concerned with how the comparisons between ViaSHAP and the ground truth are made...
We appreciate the reviewer’s concern and would like to clarify our experimental setup. ... | Summary: This paper proposes a method that simultaneously computes both the Shapley values and the predicted output, where the predicted output is equal to the sum of the Shapley values. To achieve this, the authors train the network to learn and approximate Shapley values by minimizing the weighted least squares in Eq... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback. We provide answers in the following part.
> My major concern is its positioning...
The proposed method does not fall strictly into the categories of model-specific or model-agnostic approaches. Instead, we introduce a framework for training by-design explai... | Summary: The paper proposes a new method, ViaSHAP, which learns a function that computes Shapley values alongside the model’s prediction. This method works by training a machine learning model that minimizes a weighed least squares lost similar to that of KernelSHAP and FastSHAP. The authors present many experiments sh... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable feedback on our manuscript. We provide answers and clarifications below.
> The authors argue that one major drawback of existing methods is that Shapley values are computed post-hoc....
We do not intend to suggest that computing Shapley values in... | null | null | null | null | null | null |
A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization | Accept (poster) | Summary: The authors study a convex compositional problem with a particular structure and propose a new algorithm called Alexr.
## update after rebuttal
I think the paper deserves to be accepted and I am confident that the authors will make the recommended changes to make the paper even better.
Claims And Evidence: T... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our paper and greatly appreciate your valuable feedback.
> **Q1:** Can you tell me if the following papers are relevant? (1) Alacaoglu et al. "Forward‑reflected‑backward method with variance reduction", 2021; (2) Alacaoglu and Malitsky "Stochastic... | Summary: This paper introduces ALEXR, a single-loop, primal-dual block-coordinate algorithm aimed at convex finite-sum coupled compositional problems (both strongly convex and merely convex, even if nonsmooth). By carefully interleaving primal updates with an extrapolated dual variable, the authors achieve improved or ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our paper and greatly appreciate your valuable feedback. | Summary: The paper presents ALEXR, a novel stochastic algorithm for convex Finite-sum Coupled Compositional Optimization (cFCCO). It reformulates cFCCO as a convex-concave min-max problem and introduces a single-loop primal-dual block-coordinate stochastic approach. ALEXR applies mirror ascent for the dual variable and... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our paper and greatly appreciate your valuable feedback.
> **Q1:** The paper provides strong theoretical results, but there is limited discussion on the sensitivity of ALEXR to hyperparameter choices. How sensitive is ALEXR’s performance to those ... | Summary: This paper studies a regularized convex finite-sum coupled compositional optimization (cFCCO) problems: $\min F(x), \text{ where } F(x) := \frac{1}{n}\sum_{i = 1}^n f_i(g_i(x)) + r(x)$. Here, all functions ($f_i, g_i, r, F$) are convex, and each inner function $g_i(x) = E_{\xi_i \sim P_i} [g_i(x; \xi_i)]$ f... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our paper and greatly appreciate your valuable feedback.
> **Q1:** Assumption 4 looks strong to me: the authors assume the bounded variance for both zeroth- and first-order oracles. What are the assumption (cf Assumption 4 here) the prior work on ... | Summary: This work studied convex FCCO problems, by reformulating into a convex-concave min-max problem, this work proposed ALEXR algorithm by incorporating coordinate descent and SAPD, the convergence guarantees are provided, also the lower bound analysis show that the complexity of proposed algorithm is near-optimal.... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our paper and greatly appreciate your valuable feedback.
> **Q1:** For the lower bound part, there seems to be no clear definition of the oracle, regarding the layer-wise structure, you need to access $\nabla f_i$ and $\nabla g_i$ separately, also... | null | null | null | null |
Hadamard Representations: Augmenting Hyperbolic Tangents in RL | Reject | Summary: This paper addresses the issue of "dying neurons" in reinforcement learning, focusing particularly on continuously differentiable activation functions like hyperbolic tangent (tanh). The authors demonstrate that the dying neuron phenomenon is not exclusive to ReLU activations but also affects tanh, where satur... | Rebuttal 1:
Rebuttal: We appreciate Reviewer KgES’s detailed review and appreciation of our work’s experiments and theoretical claims. Your suggestions are valuable, and we hope to address them here:
1. **Layer Normalization (LN):** We now see that we could have emphasized the effect of LN with respect to dying neur... | Summary: The paper demonstrated through experiments that activation functions such as tanh, sigmoid suffer from dying neurons in a comparable scale to that of relus in RL settings. A hadamard product with a carry gate is used to mitigate the dying neuron issue. It was shown that the hadamard representation method effic... | Rebuttal 1:
Rebuttal: We thank Reviewer BWA7 for the review, and for acknowledging our experimental efforts and discussion around the dying neuron effect.
We are happy to clarify your question about Srivastava et al. [1] . While both approaches use products of hidden layers, they slightly differ in purpose and design... | Summary: The paper is about developing new strategies to mitigate dead neurons that are prominent in typical reinforcement learning settings. The authors proposes Haamard representations, which uses two hidden layers and an activation function. Experiments were done using Atari games using DQN, PPO and PQN (which is a ... | Rebuttal 1:
Rebuttal: We’re grateful for Reviewer X4xE’s positive comments on our paper!
- You’re correct about the typo on Line 143. It should be "layer j," or it could then also be called "the hidden layer z^j" and we will fix it in the revision. We thank the Reviewer for pointing it out!
- We have now ran prelimi... | null | null | null | null | null | null | null | null |
Scaling Laws for Upcycling Mixture-of-Experts Language Models | Accept (poster) | Summary: This paper studies the computationally efficient training of large language models (LLMs) through upcycling where smaller pretrained dense models are utilized as initial checkpoints to train larger Mixture-of-Experts (MoE) models. Given that training large-scale language models from scratch demands considerabl... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and positive evaluation of our work. We especially appreciate the **recognition of its novelty and relevance, as well as the clarity of our presentation**.
We also appreciate the constructive suggestions.
Let us clarify some of the questions and ... | Summary: This work investigates the scaling behavior of upcycling dense LLMs into mixture-of-experts architectures. Through extensive experiments, the authors design and fit scaling laws that describe how language modeling performance depends on dataset size and MoE configuration, including sparsity and number of exper... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and positive evaluation of our work. We are especially grateful for the **recognition of our experimental design, the quality of the fitted scaling laws, and the clarity of our empirical methodology**.
We also appreciate the constructive suggesti... | Summary: This paper investigates the scaling laws for upcycling pretrained dense language models (LLMs) into sparse Mixture-of-Experts (MoE) architectures. By conducting extensive experiments with models up to 1B dense and 7B MoE, the authors identified scaling laws which describe the relationship between the cross-ent... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and positive evaluation of our work.
We especially appreciate the **recognition of our empirical approach, the soundness of our experimental design, and the validation of our scaling law derivation in Section 4.1**.
We also thank the reviewer for... | null | null | null | null | null | null | null | null |
Equivalence is All: A Unified View for Self-supervised Graph Learning | Accept (oral) | Summary: This paper proposes a novel self-supervised graph learning framework grounded in the principle of node equivalence, which unifies structural (automorphic) and attribute-based equivalence classes to learn robust node representations. The work is well-motivated, offering a principled unification of structural an... | Rebuttal 1:
Rebuttal: **Q1. The paper makes valuable contributions but omits some reference, such as [1] formalizes equivalence (e.g., group-equivariant networks) in convolution neural networks. Citing these would strengthen the paper’s positioning. Refs: [1] Kondor, R., & Trivedi, S. On the generalization of equivaria... | Summary: This paper introduces a self-supervised graph learning framework that unifies automorphic equivalence (structural symmetry) and attribute equivalence (node feature similarity) into a cohesive representation learning paradigm. The work bridges structural and feature-based node similarities, offering a principle... | Rebuttal 1:
Rebuttal: **Q1. I have reviewed most of the experimental design and analysis, which appear comprehensive and reasonable. For the equivalence class matching evaluation, I recommend adding other classic metrics, such as the Rand Index.**
>R1: Thank you for the valuable feedback. We have added a table below t... | Summary: This paper presents a novel framework for self-supervised graph representation learning that emphasizes the importance of node equivalence. The authors propose GALE, which unifies automorphic equivalence (based on graph structure) and attribute equivalence (based on node attributes) into a single equivalence c... | Rebuttal 1:
Rebuttal: **Q1. The paper mentions connections to other fields such as social network analysis. However, it does not cite recent work that could provide additional context. For example, the paper by Santo Fortunato titled "Community detection in graphs" provides a comprehensive review of community detection... | Summary: The paper introduces GALE, a self-supervised graph learning framework that unifies automorphic and attribute equivalence into a single node equivalence concept, enforcing intra-class similarity and inter-class dissimilarity through a novel loss function. The paper claims that by explicitly modeling and enforci... | Rebuttal 1:
Rebuttal: **Q1. Using PageRank to approximate automorphic equivalence is an efficient approach, but nodes with similar PageRank scores are not necessarily structurally symmetric. Could this have impact on the model's performance?**
>R1: We acknowledge that similar PageRank scores do not strictly guarantee ... | null | null | null | null | null | null |
BounDr.E: Predicting Drug-likeness via Biomedical Knowledge Alignment and EM-like One-Class Boundary Optimization | Accept (poster) | Summary: This paper presents a novel approach for estimating the drug-likeness of compounds. Drug-likeness refers to a compound's potential to become a successful drug, factoring in its synthesizability, bioavailability, and safety. Many existing drug-likeness estimators rely on basic physicochemical properties or stru... | Rebuttal 1:
Rebuttal: Thank you, reviewer `CLET`, for your time and thoughtful comments. We sincerely appreciate the depth of your understanding and the effort you invested in reviewing our work. Below, we provide point-by-point responses, with all referenced Tables and Figures available in attached pdf at blind repo: ... | Summary: This paper tackles the challenge of identifying drug-like molecules amid huge chemical libraries. Unlike previous methods that either rely on hard negative sets or purely structural filters, the authors propose BOUNDRE, an iterative one-class approach that defines a “drug-likeness” boundary around approved dru... | Rebuttal 1:
Rebuttal: ### Overall Response
We sincerely thank the reviewer `KY6P` for the insightful and constructive comments. We're especially grateful for your thoughtful summary and understanding of our iterative one-class framework, as well as the recognition of our method's contribution to early-stage drug-likene... | Summary: This paper proposes a method which predicts drug-like molecules by defining a hypersphere in a latent embedding space. They show promising results at predicting which drugs are clinically approved.
### update after rebuttal
The authors performed additional experiments and answered my questions. I raised my s... | Rebuttal 1:
Rebuttal: We would like to thank reviewer `Ha6n` for their time invested for such deep understanding of our work and also insightful comments. By reading the comments we were able to see how much time and effort you have taken to understand the intentions of our study. Below, we provide point-by-point respo... | null | null | null | null | null | null | null | null |
ETTA: Elucidating the Design Space of Text-to-Audio Models | Accept (poster) | Summary: This paper presents a high-quality audio-caption dataset containing 1.35M pairs of data, named as AF-Synthetic. The dataset is established through the state-of-the-art (SoTA) audio-language model, Audio Flamingo. In addition, the paper introduces a text-to-audio system based on the Diffusion Transformer (DiT) ... | Rebuttal 1:
Rebuttal: Thank you for your supportive review. We address your concerns as follows:
**Q: The paper lacks sufficient experiments directly comparing the dataset's contribution, such as showing how other SoTA models, such like Tango and AudioLDM, perform when trained with AF-Synthetic. What is the performanc... | Summary: This paper proposes ETTA, a state-of-the-art text-to-audio model trained on public data. Its innovations include:
- A new dataset called AF-Synthetic that follows the audio captioning pipeline from AF-AudioSet but scales up to million-scale. This is done by captioning or re-captioning audio in AudioCaps, Audio... | Rebuttal 1:
Rebuttal: Thank you for your supportive review. We address your questions as follows:
**Q: according to Table 6, the 2.08B 36-layer model seems to produce better results. So why is the balance achieved by the 1.44B model optimal?**
A: We observed improvements for the deeper model (2.08B), but the gap narr... | Summary: This paper explores the design space affecting text-to-audio generation models. Specifically, the authors analyze the effects of dataset quality and scale, architectural and training/inference design choices, and sampling methods during inference. For this purpose, a new large-scale synthetic dataset, AF-Synth... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your concerns as follows:
**Q: Table 5 indicates that performance with AF-AudioSet is comparable or even superior… It remains challenging to conclude that AF-Synthetic is crucial for model improvement… This observation questions the necessity of AF-Synthetic ... | Summary: The paper provide an extensive analysis on the design choices of TTA models, achieving much superior quantitative performance to baselines across most metrics. The authors provided extensive results showing the superiority and generalization of their method as well as extensive ablations justifying their choic... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and appreciating our large-scale extensive study on the design choices of TTA to reach state-of-the-art results.
**Q: AutoCap has open-sourced a dataset of size 40+ millions.**
A: Thank you for mentioning the status of this concurrent work. We will discuss th... | null | null | null | null | null | null |
Demystifying MPNNs: Message Passing as Merely Efficient Matrix Multiplication | Reject | Summary: This paper investigates the role of different aggregation and graph types on the performance of GNNs. They state several connections for different connectivity patterns on the density of the adjacency matrix with increased power iterations. They argue that gradient decay is a key issue for GNNs because the per... | Rebuttal 1:
Rebuttal: Dear Reviewer 5Agy,
Thank you for your thoughtful feedback. We address your concerns below to clarify potential misunderstandings and reaffirm the validity of our work.
1. On UAT and Nested Non-linearities (Lemmas 2.7, 2.8, and 2.9)
You asked how the Universal Approximation Theorem (UAT) justif... | Summary: This paper studies the message passing mechanism commonly used in GNN. It investigates how k-layer GNN can be empirically approximated by a k-order adjacency matrix with a single-layer GNN. It further studies the influence of loop structures in the graph. It then examines if node features are necessary to perf... | Rebuttal 1:
Rebuttal: Dear Reviewer LMqE,
Thank you for your time and detailed feedback. We appreciate your comments and would like to address your concerns as follows:
Rebuttal 1. Definition of k-hop Neighbors
You noted that "Line 130 is not accurate." In our paper (Definition 2.1), we define k-hop neighbors as node... | Summary: This paper presents a comprehensive analysis of GNN behavior through several fundamental aspects.
- (Contribution 1) The authors establish that k-layer Message Passing Neural Networks efficiently aggregate k-hop neighborhood information through iterative computation
- (Contribution 2) The authors analyze how ... | Rebuttal 1:
Rebuttal: Dear Reviewer unW8,
We sincerely appreciate your time and effort in reviewing our manuscript and providing valuable feedback. Below, we provide a point-by-point response addressing your comments and concerns.
1. Novelty Relative to Prior Work about Contribution 1
You stated that "Contribution 1... | Summary: The ideas in this paper have merit and are interesting. A multi-layer MPNN with 𝑘 with adjacency A is roughly equivalent to a single-layer MPNN utilizing the adjacency matrix with adjacency A^k, which essentially means that intermediate information is disregarded. The authors also present some analysis relate... | Rebuttal 1:
Rebuttal: Dear Reviewer tXTD,
Thank you for your thoughtful review of our paper and for recognizing the correctness and importance of each of our individual claims and the validity of our experimental results. We appreciate the effort you’ve invested and are grateful for the opportunity to address your con... | null | null | null | null | null | null |
CLOVER: Cross-Layer Orthogonal Vectors Pruning | Accept (poster) | Summary: This paper proposes a method dubbed CLOVER to address the memory-bound in large language models during inference. Specifically, CLOVER performs singular value decomposition (SVD) on the Query-Key and Value-Output parameter matrices in the attention layer, thereby orthogonalizing the vectors within attention he... | Rebuttal 1:
Rebuttal: **Q1: Benchmark the effectiveness of the proposed method.**
**A1:** For the pruning experiments, we evaluated the effectiveness of CLOVER using the Wikitext2 eval-dataset with the following settings: batch size of 32, max sequence length of 1600, and tested on an A100-PCIE-40GB GPU with different... | Summary: The paper addresses the memory increase in large language models due to kv caching and applies SVD to the pairs of KV and QO matrices. After an SVD decomposition, the new representation can be used for efficient pruning or for fine-tuning. The authors include experiments on both these fronts, showing that the ... | Rebuttal 1:
Rebuttal: **Q1: Is the comparison in Section 4.5 fair, considering that LoRA is designed for low-rank?**
**A1:** As noted in **A2 for Reviewer DDfo**, CLOVER and LoRA have an identical number of trainable parameters. CLOVER offers slight improvements in both training time and GPU memory consumption when co... | Summary: The manuscript introduces CLOVER which orthogonalizes the Query, Key, Value, and Output vectors in the attention layers, aiming to reduce the computational overhead and thus guiding pruning and serving for effective fine-tuning. Specifically, it is based on treating pairs of attention layers as low-rank decomp... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful and constructive feedback. We have carefully addressed each of your concerns and will incorporate your valuable suggestions into the camera-ready version of the paper.
**Q1: Clarify how the proposed method reduces memory load during inference.**
**A1:** Au... | Summary: Decoder - only models face memory issues during inference as the key/value cache grows. This paper introduces CLOVER to address this by treating attention layers as low - rank decompositions. CLOVER applies SVD to the Q−K and V−O pairs in each attention head. The resulting singular values can guide pruning or ... | Rebuttal 1:
Rebuttal: **Q1. Clarification of Domain**
**Q1.1: Whether CLOVER is a PEFT method.**
**A1.1:** CLOVER is a straightforward yet effective re-initialization method that benefits **both pruning and PEFT**. Pruning and PEFT are closely interconnected, as both aim to achieve efficient training and inference un... | null | null | null | null | null | null |
Flexible Tails for Normalizing Flows | Accept (poster) | Summary: This paper addresses learning heavy tailed distributions with generative models and normalizing flows in particular. Similar to the previous approach COMET, it first transforms the tails of the input data to be light tailed and then applies a classical normalizing flow.
Claims And Evidence: Let's span this by... | Rebuttal 1:
Rebuttal: Thanks for the detailed review which has provided some helpful new insights into our results.
## Success rate
Failure rates for normal, m_normal, g_normal in Table 1 were 100% for $\nu=0.5$, and in the range 10-20% for $\nu=1$.
For $\nu=1$, even in runs which did converge for these 3 methods, t... | Summary: The paper introduces an invertible transformation to overcome difficulties that exist when fitting normalising flow models to heavy-tailed data distributions. The transformation is to be used at the tip of a normalising flow "chain", handling the heavy tails of the data and enabling normalising flows to be fit... | Rebuttal 1:
Rebuttal: Thanks for the positive review of our paper. We appreciate you taking the time to read it! | Summary: The paper proposes a way to enhance normalising flows models by incorporating ideas from Extreme Value Theory literature for representing distributions with heavy tails. The authors propose adding a new invertible layer after the traditional flow layers that does not have a Lipschitz transformation and hence p... | Rebuttal 1:
Rebuttal: Thanks for the helpful review. We respond to each of your questions below, and invite you to increase your score if you think we have addressed the points fully.
## Flow-Matching
Thanks for raising this interesting question: does it remain challenging to fit heavy tailed observations in Flow-Mat... | Summary: Normalizing Flows have been shown to be difficult to make work when data/density to be represented has heavy tails, something common to see to atleast some degree in tasks such as density estimation and Variational inference. Neural networks are known to have difficulty in converging during training where heav... | Rebuttal 1:
Rebuttal: Thanks for the positive review of our paper. We appreciate you taking the time to read it!
## Left/right tails
This is a great suggestion. We will update the paper to include terminology around left/right tails.
## Additional experiments
Unfortunately each experiment on real world data took se... | null | null | null | null | null | null |
LightGTS: A Lightweight General Time Series Forecasting Model | Accept (poster) | Summary: This paper presents LightGTS, a lightweight base model for time-series forecasting, along with Periodical Patch Embedding to adapt to intrinsic periodic differences across datasets and Periodical Parallel Decoding to prevent error accumulation. LightGTS achieves 10 to 100 times size reduction based only on 4 m... | Rebuttal 1:
Rebuttal: **Q1: Discussions and experimental comparisons with Tiny Time Mixers (TTMs).**
A1: Thank you for mentioning TTMs, which are also lightweight TSFMs like LightGTS. We will differentiate LightGTS from TTMs in the following two aspects:
- **Flexibility**: TTMs have fixed input and output formats, wh... | Summary: The paper introduces LightGTS, a lightweight time series forecasting model leveraging consistent periodical modeling. It proposes a periodical tokenization, which adaptively splits time series into patches aligned with intrinsic periods to handle varying scales, and periodical parallel decoding, which leverage... | Rebuttal 1:
Rebuttal: **Q1: The zero-shot evaluation requires further validation for generalizability to unseen scales and periods, such as an ablation study on cycle-length estimation.**
A1: To further evaluate the generalizability of LightGTS, we add experiments on Chronos Benchmark II which contains 27 evaluation d... | Summary: In this paper, the authors proposed a lightweight pretrained TSF model with a new tokenization technique. With the proposed periodical tokenization method, the authors claimed that one can naturally deal with time series with different granularity and periodicity. In addition, it significantly reduced the numb... | Rebuttal 1:
Rebuttal: **Q1: Larger benchmark datasets should be used for evaluating the pretrained TSF model.**
A1: We use Chronos Benchmark II to further evaluate the effectiveness of LightGTS. As shown in the table below, LightGTS shows outstanding performance, second only to Moirai-large whose training corpus overl... | Summary: The paper proposes a general purpose pretrained time series forecasting model called LightGTS. The model is an encoder-decoder transformer operating on patches of time series observations. However, unlike existing works which operate on fixed patch lengths, LightGTS uses a dynamically adjusted patch length whi... | Rebuttal 1:
Rebuttal: **Q1: Improve the quality of evaluation**
A1: We use Chronos Benchmark II you mentioned to further evaluate the generalizability of LightGTS. As shown in the table below, LightGTS shows outstanding performance, second only to Moirai-large whose training corpus overlap is much higher. In addition,... | null | null | null | null | null | null |
Settling the Maximin Share Fairness for Scheduling among Groups of Machines | Accept (poster) | Summary: The paper studies a variant of the fair scheduling problem. There are groups of machines and tasks which need to be scheduled on these machines. The paper focuses on the fairness objective of (group) maximin share (GMMS) and shows that a 2 approximation to GMMS can be computed in polynomial time. The paper add... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation of the paper and the detailed and constructive comments. We will carefully revise the paper following these suggestions.
*Comment*: On the theoretical proofs.
*Response*: We thank the reviewer for the suggestion of restructuring the proofs.
We will foc... | Summary: This paper addresses the maximin share (MMS) fairness problem in the context of job scheduling among groups of machines. . The study paper builds upon the work of Li et al. (NeurIPS 2023), which considered MMS fairness for groups of identical or related machines but left open the case where machines within a g... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and the constructive comments. In the following, we answer your questions. We will carefully address these questions in the revised paper.
*Question*: On line 118, all $k$-partitions of $S$.
*Answer*: Yes, it is a typo, which should be all $k$-part... | Summary: The paper discusses the problem of group maximin share job allocation, where groups need to be assigned sets of jobs which are distributed to machines within each group to ensure the minimum largest makespan within that group. In the heterogenous setting, where machines within a group can have different cost f... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation of the results and the insightful comments. We will carefully review the paper to refine the presentation, including the introduction and the technical proofs. Below, we address your specific comments.
*Comment*: On the term of Maximin Share.
*Response... | Summary: This paper considers a fair resource allocation problem called Group Maximin Share (GMMS). There are two layers of allocation: at the first layer, there are $m$ items to be allocated to $n$ groups. Then, once items are allocated to each group $G_i$, they are further allocated to the $g_i$ agents in $G_i$. Each... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's supportive review and constructive suggestions. We will carefully address the typos and polish the presentation.
*Comment*: On the terminologies in related work and the model.
*Response*: We thank the reviewer for pointing out this issue.
We will thoroughly examine t... | null | null | null | null | null | null |
Calibrated Physics-Informed Uncertainty Quantification | Accept (poster) | Summary: The paper focuses on uncertainty quantification in physics-informed models via conformal prediction. It uses a neural network based surrogate (specifically FNO) as the base model and provides uncertainty via marginal and joint CP. The main innovation when compared to previous methods (Gopakumar et al 2024a) is... | Rebuttal 1:
Rebuttal: # Evaluation times for CP-AER and CP-PRE:
Thank you for highlighting the need for clarity regarding computational costs. We've updated the table to separately report calibration times for both methods:
| PDE | UQ | L2 \(ID\) | Coverage \(ID\) | L2... | Summary: This paper presents a method for estimating uncertainties in neural PDE solvers without requiring labeled data. The authors propose PRE-CP, which combines PDE residuals and conformal prediction. By using the PDE’s own equations as the reference, the method calibrates each model’s physical errors directly. They... | Rebuttal 1:
Rebuttal: # README for Code:
Thank you for pointing out this error that happened during anonymisation and giving us a chance to rectify it. For the purpose of the review, we are providing an abridged README below.
## Installation
```bash
pip install -r requirements.txt
```
## Quick Start
Run standalon... | Summary: This paper proposes a model-agnostic, physics-informed conformal prediction network that provides guaranteed uncertainty estimates independent of input data.
Claims And Evidence: 1. The proposed approach is model-agnostic and physics-informed. The physics-informed aspect is evidenced in Section 4, but whether... | Rebuttal 1:
Rebuttal: # Quantitative evaluation to baselines
Thank you for raising this important point. We'd like to clarify that our framework indeed demonstrates superior performance in guaranteed coverage compared to baseline methods. In Appendix C (Tables 3-5), we comprehensively compare our method (CP-PRE) again... | Summary: Papers consider uncertainty quantification of PDEs. They claim that by utilising a physics based approach they can quantify and calibrate the model’s inconsistencies with the PDE.
Claims And Evidence: (see Other Strengths And Weaknesses))
Methods And Evaluation Criteria: (see Other Strengths And Weaknesses))... | Rebuttal 1:
Rebuttal: # The big picture:
We appreciate this opportunity to clarify our motivation. Our work stems from the need to make neural PDE solvers more practical for scientific modelling. Numerical PDE solvers have been essential to scientific modelling since the 1950s, enabling cost-effective simulation of co... | null | null | null | null | null | null |
Counterfactual Graphical Models: Constraints and Inference | Accept (spotlight poster) | Summary: The paper presents a novel framework for counterfactual reasoning using graphical models. The paper introduces two key contributions: Ancestral Multi-World Networks (AMWN) – a new graphical representation for counterfactuals, and Counterfactual Calculus (ctf-calculus) – a set of rules for transforming counterf... | Rebuttal 1:
Rebuttal: Thank you for reading our work, providing feedback, and asking questions.
We refer next to the research mentioned in the review. Also, thank you for sharing the references. We describe the work as we understand it, but we would be happy to hear more about it from the reviewer. Regarding Ma et al ... | Summary: The paper studies the identification of counterfactual queries. It studies the constraints induced by the casual graph: consistency, exclusion, and independence. The paper proposes a sound and complete method for testing independencies among counterfactual variables based on constructing a simplified graph (AM... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work, providing feedback, and giving suggestions.
To answer your question about the intuition for counterfactual ancestors (Definition 2.4), they are the counterfactual variables that are causally relevant to the variable in question. This extends the idea that, in gra... | Summary: The paper is focused on the graphical modelling and the (symbolic) calculus of counterfactual inferences within the framework of Pearlian structural causal models. There are two major contributions: (i) a novel graphical representation called Ancestral Multi-World Networks (AMWN), which efficiently encodes cou... | Rebuttal 1:
Rebuttal: Thank you for reading our work, pointing out typos, and providing suggestions, which we will incorporate into the manuscript.
In particular, we will clarify in the paper that the unnesting corresponds to a transformation that starts with a nested counterfactual and ends with an expression involvin... | Summary: The paper introduces an efficient graphical construction called Ancestral Multi-world Network, which is sound and complete for interpreting counterfactual independencies from a causal diagram through d-separation. Furthermore, the authors propose the counterfactual (ctf-) calculus, which provides three transfo... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper.
To address your question about a scenario where our method succeeds but the other approaches mentioned in Table 1 fail, let us consider the question of whether the causal graph in Figure 4(b) implies that $(Y_{xw}, W_{x'} \perp X | {Z_x}')$. Figure 5(a) shows a... | null | null | null | null | null | null |
CursorCore: Assist Programming through Aligning Anything | Accept (poster) | Summary: The core problem this paper addresses is that existing coding benchmarks are incongruent with human development processes. The paper argues that an effective coding assistant should be able to use various types of information available to humans to make edits, rather than simply respond to constrained prompts.... | Rebuttal 1:
Rebuttal: Thanks for your review. We sincerely appreciate your recognition of our work. | Summary: Current code LLMs typically use only the code context and, optionally, user instruction as input, without considering the code’s development history. In this paper, the authors propose training a model to integrate various types of the information - particularly editing history - along with the current context... | Rebuttal 1:
Rebuttal: Thanks for your review. Please see our detailed response below:
> Claims and Evidence & Q1
Thanks for the suggestion. We can provide the results of removing H from the samples in APEval that contain H, and compare them with the results obtained without removal. This ensures a fair comparison und... | Summary: The paper introduces a new family of models called CursorCore, which enables handling of historical context while making code generation or assistant response predictions. The authors also propose Programming-Instruct which is a framework designed to collect data to train CursorCore with the historical code ed... | Rebuttal 1:
Rebuttal: Thanks for your review. Please see our response below:
> Baseline of prompt-engineering to incorporate H is missing
The reviewer may have misunderstood our experimental setup. Our prompt engineering baseline does include H, as shown in Tables 19 and 20.
> No empirical justification for incorpor... | null | null | null | null | null | null | null | null |
Isolated Causal Effects of Natural Language | Accept (poster) | Summary: Effects of the attribute of a text on an outcome can be influenced by the surrounding linguistic context around this attribute. This motivates defining an "isolated causal effect of this attribute (the "focal" text), where the surrounding context is marginalized (the "non-focal" text). A framework for estimati... | Rebuttal 1:
Rebuttal: Thank you for your positive remarks describing our work as interesting, theoretically correct, and largely well-justified. We address questions and concerns below.
**[Why isolated effects vs. natural effects?]** To illustrate why isolated causal effects are important for language, consider the ef... | Summary: The paper introduces a framework for estimating isolated causal effects of language, which focuses on how specific linguistic attributes influence external outcomes while controlling for non-focal language to mitigate OVB. It uses doubly robust estimators to ensure unbiased estimations. The authors define thre... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We appreciate your positive remarks describing our work as theoretically and empirically well-supported, experimentally sound, and enhancing the rigor of text-based causal inference. We address further comments and questions below.
**[Experiments with additional LM em... | Summary: This paper, based on the principle of omitted variable bias, proposes a framework to estimate the sensitivity of bias in evaluating the non-focal language outside of the intervention and the quality of isolated effect estimation along the two key dimensions of fidelity and overlap.
Claims And Evidence: The w... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful questions and feedback! We address your comments and clarify points below.
**[Technical language]** We will revise the paper to more clearly introduce and contextualize technical terms.
**[Motivation for isolated effects]** Due to character limits, please see our re... | null | null | null | null | null | null | null | null |
Variational Control for Guidance in Diffusion Models | Accept (poster) | Summary: This paper introduces DTM - Diffusion Trajectory Matchning - a novel and general guidance approach for generic diffusion models. The idea is to add a guidance vector at each time point, u_t, such that it serves the given measurements while also maikg sure that the original diffusion trajectory is not deviated ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback about experiment design and readability. We address specific questions below:
> Of these three, #2 complicates things and makes the method non-linear and more complicated for optimization. Therefore, an ablation that shows the effect of excludi... | Summary: The paper introduces a framework called Diffusion Trajectory Matching (DTM) for guiding diffusion models without requiring retraining. This approach is rooted in variational inference and optimal control, allowing for guidance by optimizing control signals based on terminal costs. The proposed method, Non-line... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Please find our responses below.
> The evaluation tasks make sense but the comparisons [...] lack [...] more recent methods. . Please refer to Essential References Not Discussed.[...] authors did not discuss or compare, such as [1-8].
This was also a com... | Summary: In this paper, the authors formulate the diffusion posterior guidance problem as a variational control problem and propose a novel training-free framework for this problem. They introduce a new algorithm for diffusion guidance and their framework also unifies many existing training-free diffusion guidance algo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and helpful feedback. Please see our response below.
> I do find the statement [...] the authors claim in the introduction [...] that prior works which are based on optimal control [...] (all of which are widely studied [1,2,3,4])
Our framework indeed gen... | Summary: The paper proposes to optimize the guidance signal with three losses $C_{\text{score}}, C_{\text{control}} and C_{\text{terminal}$. After that, the guidance signal is utilized in the sampling process as normal guidance. The guidance signal is updated through the greedy scheme.
Claims And Evidence: no problem
... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We address specific concerns as follows in the order of sections:
> The method might be too expensive. The running time cost should be provided
> Please compare the running time of the algorithm with other CFG methods.
Runtime is often faster than the c... | null | null | null | null | null | null |
Parrot: Multilingual Visual Instruction Tuning | Accept (poster) | Summary: This paper proposed an MOE architecture to handle multilingual multimodal tasks in vision-language models. And created a new multimodal understanding benchmark including 6 languages translated by GPT-4 with human post-edit.
Note: this paper uses an incorrect template, which might have risk of getting rejected... | Rebuttal 1:
Rebuttal: Thank you for your kind comments and constructive feedback on our paper.
> **Q1: Motivation of data efficiency.**
A1: While large-scale translated multilingual data may seem abundant, its quality (especially for low-resource languages) is often critically compromised due to translation errors, c... | Summary: This paper introduces PARROT, a novel approach to enhance the multilingual capabilities of MLLM, using language specific embeddings to fuse to visual embeddings and multilingual MoE. It addresses the issue of multilingual erosion, where MLLM loses proficiency in non-English languages after multimodal alignmen... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough and constructive feedback, as well as their endorsement of our work.
> **Q1: MoE vs. Simpler Methods (LoRRA).**
A1: We explored the LoRRA-based (abbreviated as L-based) adaptation shown in the table below but found it insufficient for two reason... | Summary: The paper proposes Parrot, an MLLM targeting to handle multilingual tasks. Parrot based on the LLava architecture, and employ an MoE module to enhance multilingual VQA ability. The paper employ a new alignment method that aligns a english-biased clip encoder to various languages modality. Moreover, it proposes... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful and candid feedback.
> **Q1: Prior work and drawback.**
A1: **In section B in Appendix**, we have discussed prior multilingual MLLM methods like mCLIP, VisCPM, and M3IT. Most prior work relies on large-scale multilingual multimodal data (e.g., M3... | Summary: The paper is addressing what authors call as "multi lingual erosion" in multimodal large language models (MLLMs) - a phenomenon where post multi modal alignment the model loses ability to respond in or process non-English inputs. The authors identify that existing vision-language alignment methods (LlaVa) use ... | Rebuttal 1:
Rebuttal: We are deeply grateful for the reviewer’s thorough and thoughtful assessment of our work, as well as their recognition of Parrot’s contributions.
> **Q1: Scalability to many languages.**
A1: Parrot’s MoE framework is inherently designed to support seamless integration of new languages. The addit... | null | null | null | null | null | null |
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning | Accept (poster) | Summary: In this paper, the authors investigated to extend Value Iteration Network (VIN) to address longer-term and larger-scaled planning tasks. It is not feasible just by applying VIN, and they found the reasons from invariant transition in the network and inefficient loss design for long-term planning tasks. To addr... | Rebuttal 1:
Rebuttal: We appreciate the insightful and helpful comments from Reviewer vY56.
Please find our responses to your concerns and questions below.
---
### Suggestion 1: The tasks are related to diffusion models for long-term planning.
### Answer:
Thank you for noting the connection to diffusion-based plan... | Summary: Value Iteration Networks (VIN) struggle to scale with large scale planning problems, typically in problems involving a higher number of steps to reach to goal. The paper provides two observations that explain the poor performance (1) low representation capacity (2) lack of depth in VINs - due to vanishing grad... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer TBA2 for the valuable comments.
---
### Concern 1: Clarification on state/observation-dynamic in Sec 3.1.
### Answer:
To clarify: the *observation* refers to the maze image input, which is mapped by the model to a latent MDP. Each *latent state* corresponds to a spe... | Summary: This paper tackles the problem of extending value iteration networks (VINs) to handle very long-horizon planning. To achieve this, the authors propose two main modifications:
- A dynamic transition kernel that relaxes the standard weight sharing of convolution layers (i.e., removing strict translation equivar... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer HD2P's valuable time and constructive comments.
**To improve clarity and conciseness, we have reorganized the reviewers’ comments by grouping similar points.**
---
### Q1: Experiments are limited to 2D path planning. Evaluating more realistic tasks, integrating ... | null | null | null | null | null | null | null | null |
PASS: Private Attributes Protection with Stochastic Data Substitution | Accept (spotlight poster) | Summary: This paper introduces PASS (Private Attributes protection with Stochastic data Substitution), a novel approach to protect private attributes in user data while preserving utility. Unlike existing adversarial training-based methods, PASS employs stochastic data substitution where each original sample is replace... | Rebuttal 1:
Rebuttal: **Q1**. The paper overlooks memory requirements for storing substitute datasets and embeddings, particularly problematic for high-dimensional data applications.
**A1**. Thanks for your question! Importantly, the substitution dataset itself does not need to be loaded into memory during inference. ... | Summary: PASS (Private Attributes Protection with Stochastic Data Substitution) introduces a novel method to protect private attributes in machine learning datasets by replacing original data samples with others from a substitution dataset using a stochastic algorithm trained with an information-theoretic loss. Unlike ... | Rebuttal 1:
Rebuttal: **Q1**: "...apply the "unfinetuned" classifier metric to other datasets..."
**A1**: Thanks for the suggestion! We applied the "NAG-unfinetuned" metric to the useful attributes in the AudioMNIST and CelebA datasets for consistency. The results are shown in **Tables A** and **B**, respectively. For... | Summary: This paper addresses the challenge of protecting private attributes in machine learning (ML) services while preserving the utility of the data for downstream tasks. Existing methods primarily rely on adversarial training to remove private attributes, but the authors identify a fundamental vulnerability in thes... | Rebuttal 1:
Rebuttal: **Q1**: "The experiments consider only a single private attribute per dataset, despite multiple useful attributes being present. Evaluating the method with multiple private attributes would strengthen the analysis."
**A1**: Thanks for your question. This paper included experiments with multiple p... | Summary: This paper proposes a feature substitution method based on an information-theoretic objective to preserve privacy for certain data attributes. The method does not depend on any specific adversarial strategy, making it more robustness than existing adversarial-based approaches.
#update after rebuttal: the reb... | Rebuttal 1:
Rebuttal: **Q1**: "Embedding function $g(x')$."
**A1**: Thanks for pointing it out! Our embedding function $g(x')$ is implemented as an embedding layer with trainable parameters (e.g., like the embedding layer in language models). We will update it as $g_\psi(x')$ to indicate its associated parameters clea... | null | null | null | null | null | null |
On the Emergence of Position Bias in Transformers | Accept (poster) | Summary: This paper studies the "position bias" in transformers, that is, the bias of the model to focus on certain regions of the input. The authors investigate how causal mask and positional encoding impact this position bias. To that end, they leverage a graph-theoretic formalization of the attention module to study... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and positive assessment of our work. Below, we provide individual responses to the comments and questions you raised.
> I wonder how MLPs would impact the current analysis.
Thank you for the question. Our analysis focuses on the self-attention mechanism, a... | Summary: This paper presents a graph-theoretic framework to analyze how position bias emerges in transformer architectures. The authors mathematically model attention masks as directed graphs to understand how tokens interact based on their sequential positions across multiple layers of attention.
The authors support t... | Rebuttal 1:
Rebuttal: We appreciate your positive assessment and constructive comments, which have helped strengthen our work. Below, we provide responses to your comments.
> The paper primarily analyzes the attention mechanism without deeply exploring how other transformer components might interact with position bias... | Summary: This paper analyses position bias in transformers, both theoretically and experimentally.
The paper first proposes to analyse a transformer as a graph, with attention weights representing weighted-edges between two tokens’ representations in adjacent transformer layers; an *attention flow* can then be computed... | Rebuttal 1:
Rebuttal: We appreciate your positive assessment and constructive feedback, which have helped strengthen our work. Below, we provide individual responses to the comments you raised.
> The paper misses some critical literature in interpretability and analysis of language models.
Thank you for the pointer... | Summary: This paper aims to analyze the effect of attention masks, such as the causal mask, of the observed attention patterns. In particular, the authors suggest modeling the possible paths that the information can flow using attention as edges of a graph. By looking at this graph, they obtain certain bounds which the... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing thoughtful feedback. Below, we provide detailed responses to the comments.
> Theorem 4.1 only yields an upper bound on the weight given to a token.
Thank you for the comment. We agree that Thm 4.1 provides an upper bound rather than a strict inequality, and w... | null | null | null | null | null | null |
Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks | Accept (spotlight poster) | Summary: This paper studies the relation between functional similarity and representational similarity, finding that there is a disassociation: i.e., functional equivalent networks may have very different representations. To be concrete, they mainly study a two-layer linear networks, concerning their weights $W_1, W_2$... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive and detailed feedback on the manuscript.
First, we address the reviewer's question regarding the optimization reasons behind an artificial network seeking robust solutions. Our analysis is general—we study the entire solution manifold and derive broad ... | Summary: The paper presents a mathematical analysis of the nature of solutions (for a given problem) in an overparameterised two layer linear neural network. This is done through a theoretical study of the manifold of generic solutions, i.e. different choices of weights values which give the same fit of the training d... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewer for their time and detailed feedback. Below, we address the key points raised in the review.
The reviewer asks whether our analysis provides insights beyond the conclusion that internal representations cannot be interpreted in isolation. First, we em... | Summary: The authors study a two-layer linear network. They characterize the space of solutions for such networks, with emphasis on several normalization schemes. The result is that there are many zero-loss solutions that differ in how minimal they are. Specifically, whether the transformation from input to hidden or f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough evaluation of our work, and for providing explicit feedback!
First, we address the reviewer’s concern about the novelty of our analysis. To clarify this, we have added a dedicated "Contributions" section to the revised manuscript and reproduce it here in d... | Summary: This paper analytically studies the hidden representations of two-layer feedforward networks trained to minimize differentiable, convex loss functions. The only sets of weights in the networks studied are read-in and read-out weights, leading to simple expressions for both in terms of the input data. The paper... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough evaluation of the manuscript and supplementary material, and sincerely appreciate their effort and positive feedback! We would like to kindly ask the reviewer, if time permits, to elaborate on the specific strengths and significance of the work to help the ... | null | null | null | null | null | null |
Incorporating Arbitrary Matrix Group Equivariance into KANs | Accept (poster) | Summary: This paper introduces Equivariant Kolmogorov-Arnold Networks (EKANs), an extension of Kolmogorov-Arnold Networks (KANs) that incorporates matrix group equivariance. The authors follow a similar approach as Equivariant MLPs (EMLP) by Marc Finzi et al. (2021) to enforce equivariance constraints on KANs. Basicall... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and valuable feedback! Below we will address each of your concerns point by point.
**Claims And Evidence**
> However, the key methodological innovation—incorporating equivariance via equivariant linear layers—is a direct adaptation of the approach used in EMLP ... | Summary: This paper introduces Equivariant Kolmogorov-Arnold Networks, an extension of KANs that incorporates equivariance to arbitrary matrix groups, addressing a key limitation of KANs: their inability to respect symmetries in data. The authors achieve this by constructing gated spline basis functions and equivariant... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and valuable feedback! Below we will address each of your concerns point by point.
**Claims And Evidence**
For all trained models $f_\theta$ in the paper, we use $L_{equi}=E_{x,g}\\|\rho_o(g)f_\theta(x)-f_\theta(\rho_i(g)x)\\|^2$ to evaluate their equivariant l... | Summary: This paper introduces Equivariant Kolmogorov-Arnold Networks (EKANs), a framework to construct group equivariant architectures, with respect to arbitrary matrix groups, as an extension of the previously proposed Kolmogorov-Arnold networks, akin to the way Equivariant MLPs (EMLPs) extend conventional MLPs. Cont... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and valuable feedback! Below we will address each of your concerns point by point.
**Other Strengths And Weaknesses**
Weaknesses
(1) Indeed, the use of gating mechanisms can reduce the expressive power of the network. However, due to the inherently complex str... | Summary: The work introduces an equivariant version of the KAN by incorporating two principal components: 1) introducing an additional scaler that controls the gating mechanism and 2) using equivariant MLP for different non-scaler features.
The proposed model, EKAN, is evaluated on particle scattering, top quark taggi... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and valuable feedback! Below we will address each of your concerns point by point.
**Claims And Evidence**
Our claim is: We propose a method to incorporate symmetry into KANs. As mentioned in Section 1 (Lines 25-29, Column 2), KANs struggle to respect symmetry,... | null | null | null | null | null | null |
BaWA: Automatic Optimizing Pruning Metric for Large Language Models with Balanced Weight and Activation | Accept (poster) | Summary: This paper focuses on unstructured pruning of LLMs and introduces a new pruning metric. Unlike previous methods that estimate parameter importance based solely on magnitude, activations, or gradients, the proposed approach also considers the impact of outliers in model parameters.
The authors first demonstrat... | Rebuttal 1:
Rebuttal: Dear zn1Z:
We sincerely appreciate the valuable suggestions provided by the reviewer. We note the two main concerns you raised, which we address below.
Firstly, we thank the reviewer for emphasizing the importance of comparing with structured pruning methods. We would like to compare structured ... | Summary: This work proposes a weight pruning method based on Wanda by performing normalization through input and output channels and scaling normalization factors. Wanda is a simple weight pruning method which uses scores measured by L1 of weight and L2 of input, but it suffers issues of imbalance in weight magnitude a... | Rebuttal 1:
Rebuttal: Dear xLm6:
We greatly appreciate your insightful comments. Below we provide a point-by-point response to your concerns.
### **Scaling Factor Analysis**：
We agree that analyzing scaling factors is critical. In the revised manuscript, we will add:
+ A new comparative table (below) demonstrating ... | Summary: Existing pruning metrics are limited by their reliance on simple symbolic combinations of weights and activations, failing to account for imbalanced weight magnitudes and the disproportionate impact of activation outliers. To address these shortcomings, this paper introduces BaWA, a pruning metric that balance... | Rebuttal 1:
Rebuttal: Dear mDNk:
We sincerely appreciate your thoughtful feedback regarding BaWA’s novelty and computational overhead. Below, we address your concerns in detail.
### **Novelty of BaWA**:
We respectfully disagree with the provided novelty concern for three key reasons:
(a) Problem Characterization in... | null | null | null | null | null | null | null | null |
Catching Two Birds with One Stone: Reward Shaping with Dual Random Networks for Balancing Exploration and Exploitation | Accept (poster) | Summary: This work develops a new reward shaping approach “DuRND” specialized for sparse reward environments, which uses two random networks (RNs): one RN guides agents to goal states while the other RN prevents the agent from getting stuck in distracting or harmful states. The method is tested against other reward sha... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thanks for the valuable comments. We respond as follows.
---
Regarding the claim on PBRS, thanks for pointing this out, our statement may cause confusion, many PBRS methods don't require a dynamic model, instead compute potentials directly by collected data. What we intended to h... | Summary: This paper proposes DuRND, a simple variation on top of RND that uses two random networks in order to compute two reward bonus terms for sparse reward tasks: 1) a modified novelty bonus and 2) an exploitative reward shaping bonus. The two random networks are trained on different data, with the positive network... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your valuable feedback. We respond to your comments as follows:
---
Regarding the reviewer’s insightful comment on the interaction between novelty estimation and data distribution under off-policy settings, as noted, since DuRND is built on an on-policy algorithm (P... | Summary: The authors propose Dual Random Networks Distillation (DuRND), a reward shaping framework for sparse-reward reinforcement learning. DuRND consists of two random networks as its primary components, which simultaneously generate complementary rewards: one encouraging novelty-driven exploration, and the other mea... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thanks for the comments. Our responses include new experiments and detailed elaboration below.
Regarding the claims of optimal, convergence and efficient, we highlight that they're grounded in comprehensive empirical evidence:
1. **Optimal** refers to final evaluation performance.... | Summary: The paper proposes Dual Random Networks Distillation (DuRND), a novel reward shaping framework designed for efficient exploration and stable (extrinsic reward) convergence in sparse-reward reinforcement learning tasks. DuRND utilizes two lightweight random network modules, namely positive and negative Random N... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thanks for the comments and below we provide detailed responses.
Regarding the coefficient sensitivity and the nature of contribution reward:
1. **Coefficient sensitivity**: we conducted [additional experiments to evaluate the reward coefficients in Atari games (Fig A.3) [link]](... | null | null | null | null | null | null |
Perception in Reflection | Accept (poster) | Summary: This paper introduces Reflective Perception (RePer), a system for improving VLMs through iterative self-reflection. It adopts a policy–critic framework, where a policy model generates outputs, and a critic model provides feedback to refine responses over multiple turns. The paper also proposes Reflective Perce... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and for recognizing the value of our method and experiments. We carefully address the concerns and clarify potential misunderstandings as follows:
## Q1: Alignment with Human Visual Focus
We address this concern by clearly defining “ground-truth hu... | Summary: This paper proposes RePer, which teaches the VLM to iteratively revise and provide gradually better responses given a strong pre-built critic model.
The algorithm works by first collecting responses of varying quality, using these to construct an iteratively revised dataset, and then employing a “Reflective U... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback, particularly for recognizing our work as a “well-motivated method” with solid experiments. Below, we carefully address each of your concerns and clarify potential misunderstandings.
## Q1: LLaVA-1.5 BoN vs RePer
We appreciate the suggestion and clar... | Summary: The paper proposes a reflective perception framework, named Reflective Perception (RePer), aimed at enhancing the capabilities of large vision-language models (LVLMs). By introducing dual-model interaction between policy and critic models, RePer seeks to enable LVLMs to iteratively refine their visual percepti... | Rebuttal 1:
Rebuttal: Thank you very much for constructive feedbacks and for recognizing our presentation quality and experimental design.
We carefully address each of your concerns below.
## Q1: Evaluation Reliability
To address concerns about potential bias from using the same LLM for both data construction and evalu... | null | null | null | null | null | null | null | null |
Principled Algorithms for Optimizing Generalized Metrics in Binary Classification | Accept (poster) | Summary: This paper studies the problem of optimizing a broad class of metrics used in class imbalance or class asymmetric scenarios. Previous approaches rely on threshold-based methods that approximates Bayes-optimal classifiers with guarantees of consistency which is asymptotic. This paper first shows that optimizing... | Rebuttal 1:
Rebuttal: We thank the reviewer for their strong support of our work. Below please find responses to specific questions.
**1. Questions: The authors propose to leverage Rademacher bounds, which can be approximated via classical learning process such as ERM. I am curious on the inherent difficulty in the me... | Summary: This paper proposes METRO for generalized metric optimization in binary classification. The authors reformulate metric optimization as a generalized cost-sensitive learning problem, and introduce a new family of surrogate loss functions. They theoretically prove the $\mathcal{H}$-consistency guarantees for the... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**1. Weaknesses: The experiments were limited to the classic image classification task. Algorithms were not applied to scenarios s... | Summary: This article introduces a novel optimization approach for generalized metrics in binary classification. The primary method involves converting the fractional form into a summation form. However, the method introduces a parameter, $\lambda$, whose optimal value is unknown and requires estimation, indicating tha... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**1. Method:** Thank you for the insightful comments. The quantities $\hat{\mathcal{E}} _{\ell^{\lambda}}$ and $\hat{h}$ are... | null | null | null | null | null | null | null | null |
ProSec: Fortifying Code LLMs with Proactive Security Alignment | Accept (poster) | Summary: The paper introduces PROSEC, a method for proactively identifying weaknesses in code-generating AI models by creating specific coding scenarios that are likely to introduce vulnerabilities. PROSEC creates a significantly larger dataset of vulnerability-inducing situations compared to previous methods. Experime... | Rebuttal 1:
Rebuttal: We appreciate your feedback and respectfully clarify key differences between CodeLMSec and ProSec to illustrate our unique contributions:
## Different goals
CodeLMSec and ProSec serve fundamentally different purposes. CodeLMSec is a codeLM security **benchmark** that evaluates codeLMs with vulner... | Summary: This work enhances the security of code LLMs by proposing the PROSEC framework. PROSEC is an automated pipeline designed to synthesize code security-related preference data. It consists of three stages: 1) Construct instructions that induce insecure code based on Common Weakness Enumerations (CWEs) and ensure... | Rebuttal 1:
Rebuttal: Thank you for your detailed and supportive review.
## ProSec’s relevance to ICML
We respectfully contend that our work aligns well with previous contributions recognized at ICML. For example, prior works such as data selection for language model training [Qurating, ICML’24], data synthesis for c... | Summary: The paper proposes ProSec (Proactive Security Alignment), an approach to align code LLMs with secure coding practices.
* It exposes the vulnerabilities by synthesizing error-inducing scenarios from Common Weakness Enumerations (CWEs) and generates fixes to vulnerable code.
* Models are then trained with pref... | Rebuttal 1:
Rebuttal: Thank you for the supportive review.
>Is the improvement only affected by the fact that the generated dataset is 7x larger than SafeCoder? Can you do a control experiment where both datasets have the same size (and same secure / vulnerable mixture ratio), and test if the data quality of ProSec i... | Summary: This work proposes ProSec, a LLM-based framework to generate synthetic preference/alignment data containing security vulnerabilities using CWEs (Common Weakness Enumerations) data. Authors demonstrate that models (Phi3-mini-Inst and CodeLlama-7B-Inst) trained (SimPO) with ProSec alignment data produce code tha... | Rebuttal 1:
Rebuttal: Thank you for the supportive and detailed review. We will include the discussions below to the paper.
## Q1: Explain static analyzers
We use the static analyzers in PurpleLlama. It consists of three tools: regular expressions, semgrep, and weggli.
All tools work on the generated source code. Reg... | null | null | null | null | null | null |
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales | Accept (poster) | Summary: This paper proposes a “Symmetric Reinforcement Learning Loss” (SRL) to improve the robustness of policy-gradient algorithms—namely A2C and PPO—when facing noisy or inconsistent advantage estimates.
The core idea is to adapt the concept of “symmetric cross-entropy,” to the RL setting, originally developed for ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and constructive feedback on our paper. To address your questions and concerns, we have provided detailed responses below.
**Question 1**
> PPO practitioners have found much larger batch sizes of 100k samples to work much better than smaller batch sizes
=> Than... | Summary: The manuscript introduces a symmetric reinforcement learning (RL) loss function designed to improve the robustness of RL algorithms like A2C and PPO. The proposed symmetric RL loss is inspired by reverse cross-entropy (RCE) used in noisy classification tasks, and the authors apply it to both discrete (Atari) a... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback on our paper. We hope the response below addresses your concerns.
**Weakness 1**
> Insufficient Comparison with Other Robustness Methods: While the paper focuses on the symmetric RL loss, it lacks detailed comparisons with other established... | Summary: Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) introduce additional challenges. For instance, diverse preferences complicate the a... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback on our paper. We try to handle your questions and provide additional details below.
**Please let us know if our responses resolve your questions and concern. If so, we would greatly appreciate your consideration in updating your score. We’r... | Summary: The paper proposes a new family of loss, symmetric A2C and symmetric PPO loss for RL tasks.
Claims And Evidence: Claims: The paper asserts that its policy gradient formulation with the newly proposed loss achieves superior or at least competitive performance in Atari benchmarks, and that using GPT-J with RLHF... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback on our paper. We hope the response below addresses your concerns.
**Weakness 1**
> The approach enumerates all possible actions in the loss, which can be computationally feasible for small/medium discrete action spaces (e.g., Atari), but be... | null | null | null | null | null | null |
Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries | Accept (poster) | Summary: The paper introduces Lexico, a KV cache compression method using sparse coding over universal dictionaries. By leveraging Orthogonal Matching Pursuit for sparse approximation, Lexico provides flexible compression while maintaining high performance.
Claims And Evidence: yes
Methods And Evaluation Criteria: ye... | Rebuttal 1:
Rebuttal: We thank you for recognizing the novelty of applying sparse dictionary learning for KV cache compression and our extensive experimental evaluation—a strength also highlighted by reviewers Vzmg and EtKf. We address each concern below.
***Q1: Task-specific dictionaries***
Yes, using task-specific ... | Summary: LLMs that process long contexts need to store token embeddings in a KV-cache: two matrixes K and V.
The KV cache can grow arbitrarily large, and thus should be compressed.
Lexico compresses the KV-cache using sparse coding where K is decomposed as D * S where D is a dictionary matrix of fixed size and S a sp... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad that you appreciated the novelty of Lexico, our effort in optimizations, and the overall readability of the paper. We address the remaining weaknesses and questions below.
***W1: Paper organization***
We greatly appreciate the reviewer’s feedback... | Summary: This paper proposes a novel KV cache compression method based on sparse coding over a learned “universal” dictionary. The key idea is to represent KV cache vectors with a small number of dictionary “atoms,” reducing memory costs while preserving model performance across different tasks and domains. The paper r... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback. We are pleased that you appreciated Lexico’s flexible design, near-lossless performance on benchmarks, and the advantage of using a dictionary for KV cache compression. It is also encouraging that reviewers Vzmg and EhUD similarly... | Summary: Lexico compresses the KV cache by finding a sparse representation of each key and value vector using a pre-trained dictionary. Instead of storing the full high-dimensional vectors, Lexico approximates them as a sparse linear combination of a small number of “atoms” (basis vectors) from this dictionary. It uses... | Rebuttal 1:
Rebuttal: We are pleased that you appreciated the clarity of our experiments, Lexico's effectiveness, and our use of sparse coding. EtKf and EhUD similarly recognized the soundness of our experimental design. We address your concerns below, and would be grateful if you reevaluate our work in light of them.
... | null | null | null | null | null | null |
Large Language Models are Demonstration Pre-Selectors for Themselves | Accept (poster) | Summary: This paper introduces FEEDER, a demonstration pre-selection framework designed to improve the efficiency and effectiveness of large language models (LLMs) in in-context learning (ICL) and fine-tuning tasks. FEEDER identifies a representative subset of training data using two new metrics: "sufficiency" and "nec... | Rebuttal 1:
Rebuttal: We thank Reviewer mKZf for recognizing our novelty, acknowledging its efficiency and performance improvement, appreciating our comprehensive evaluations on multiple LLMs and tasks, and noting its compatibility with existing demonstration selection strategies.
**Q1. More comparison beyound basic s... | Summary: The paper introduces FEEDER, a pre-selection framework designed to improve in-context learning (ICL) in large language models by identifying a representative subset of training data demonstrations. FEEDER uses "sufficiency" and "necessity" metrics to balance representativeness with redundancy, and employs a tr... | Rebuttal 1:
Rebuttal: We thank Reviewer S3Tb for recognizing our novelty, solid theoretical contributions and good writing. Below, we respond to each of your questions in detail.
**Q1. The paper is not contextualized well within the active learning literature.**
**R1.** We clarify the relationship between our FEEDER ... | Summary: This submission presents a pre-selection framework for in-context learning designed to identify a representative subset of examples from the training set. The proposed framework FEEDER evaluates demonstration examples based on their sufficiency and necessity. Aside from benefiting in-context learning, the fram... | Rebuttal 1:
Rebuttal: We thank Reviewer s2sv for recognizing our extensive experiments across multiple benchmarks and LLMs, showing consistent improvements of our framework.
Below, we respond to each of your questions in detail.
**Q1.1 The differences discussion between this submission and related work [1]. Some ove... | Summary: This paper introduces FEEDER (FEw yet Essential Demonstration prE-selectoR), a novel pre-selection framework designed to improve In-Context Learning (ICL) and fine-tuning in large language models (LLMs). The key contribution of FEEDER is a pre-selection stage, where a representative subset of training data is ... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer oHZY for recognizing our method is efficient and practical for real-world deployment, as well as we proposing an interpretable way to explain the demonstration.
Below, we respond to each of your questions in detail.
**Q1. Should consider larger LLM.**
**R1.** We... | null | null | null | null | null | null |
TUMTraf VideoQA: Dataset and Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes | Accept (poster) | Summary: The paper introduces TraffiX-VideoQA, a benchmark for evaluating spatio-temporal video understanding in traffic scenes. It provides 1,000 videos, 85,000 QA pairs, 2,300 object descriptions, and 5,700 grounding annotations, covering diverse traffic conditions. The authors propose TraffiX-Qwen, a baseline model ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We appreciate your valuable feedbacks and address your concerns as follows.
Q1: Clarification on whether TraffiX-Qwen’s performance gain stems from more input frames.
A1: As open-source VideoQA models often adopt model-specific frame sampling strategies, which are tightly coupled... | Summary: The paper presents a comprehensive video-language dataset designed for complex traffic video understanding, named TrafficX-VideoQA. Meanwhile, a benchmark is provided, including multiple-choice video question answering, referred object captioning, and spatiotemporal object grounding tasks. Experimental results... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your positive feedback and valuable reviews. Our responses to your comments are detailed below:
Q1. There is a need to incorporate the recent work [1] for a more comprehensive analysis.
A1. Thank you for pointing out this important related work. We have di... | Summary: This paper proposes a new traffic VQA dataset captured from the roadside. The paper proposes three tasks based on the dataset, including Multi-Choice Question Answering, Video Referred Object Captioning, and Spatio-Temporal Object Grounding. The author further proposes a new, unified method to tackle all three... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your positive feedback and valuable comments. Below, we provide point-by-point responses to the concerns raised.
Q1: What are the differences between the proposed multi-choice QA and existing VQA work, aside from the camera view?
A1: Thank you for the tho... | Summary: This paper provides a comprehensive dataset tailored for multiple tasks in traffic scenarios. It includes QA such as predicting the weather, counting objects, providing motion status, spatio-temporal grounding, and more. It consists of 1,000 videos, with 85,000 QA pairs, 2,300 object captioning, and 5,700 obje... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your positive feedback and insightful questions. We provide detailed responses to your concerns below.
Q1: Would the authors provide a brief discussion on how this dataset, especially the QA part, will remain challenging for upcoming new multimodal LLMs giv... | null | null | null | null | null | null |
Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts | Accept (poster) | Summary: This paper presents VIVACE for learning invariant subgraphs under distribution shifts using variance contrastive estimation. The authors propose a three-module framework to disentangle invariant and variant subgraphs, estimate the impact of spurious correlations, and employ inverse propensity weighting for pre... | Rebuttal 1:
Rebuttal: - **Q1. Theoretical analysis on the rationale of the approach.**
We would like to clarify that the rationale of our method is to achieve OOD generalization by **accurately disentangling invariant and variant subgraphs**. We have added the following theorem.
**Theorem 1.** Denote the optimal inva... | Summary: The submission explores the challenge of out-of-distribution generalization in GNNs. The authors propose a novel model that enhances out-of-distribution generalization by explicitly identifying invariant subgraphs and leveraging contrastive learning on variant subgraphs. Their approach consists of three key co... | Rebuttal 1:
Rebuttal: - **Q1. Clarification on the reliance on accurate variant subgraph identification.**
We would like to clarify that our method can provably learn accurate variant subgraphs with theoretical guarantee.
**Theorem 1.** Denote the optimal invariant subgraph generator $\Phi^*$ that disentangles the g... | Summary: This study addresses a critical problem in GNNs regarding their limited generalization capabilities under distribution shifts. Current approaches mainly use correlations in graph patterns rather than discovering fundamental causal substructures for predictions. To overcome this limitation, the paper jointly co... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We addressed all the comments. Please kindly find the detailed responses to the comments below.
- **Q1. Figure 1 is not clear to show the method’s training procedure.**
Thank you for this comment. We would like to follow your suggestion to improve... | Summary: This manuscript studies out-of-distribution generalization issue in graph neural networks. The authors propose learning invariant subgraphs via variant subgraph contrastive estimation, which can handle graph distribution shifts with severe bias. The key innovation is leveraging contrastive learning on variant ... | Rebuttal 1:
Rebuttal: - **Q1. Theoretical analyses.**
We have added **Theorem 1** to show our method can accurately identify the invariant and variant subgraphs for OOD generalization.
**Theorem 1.** Denote the optimal invariant subgraph generator $\Phi^*$ that disentangles the ground-truth invariant subgraph $G_I^*... | null | null | null | null | null | null |
Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing | Accept (poster) | Summary: This paper introduces a diffusion-based method for video generation via causal transformers. The main idea is to apply kv-caching (technique widely used for AR transformers in NLP) to a causal diffusion transformer. This leads to faster video generation and potentially enables streaming scenarios. The method i... | Rebuttal 1:
Rebuttal: ## Anonymous link for additional experiment results
https://anonymous.4open.science/r/additional-exp-results-for-anonymous-github-F6EB/readme.md This includes: Table_R1, Table_R2, Figure_R1, and Figure_R2
$~$
## Q1: Evaluation on VBench
VBench is primarily designed for text-to-video evaluatio... | Summary: This work propose an optimized ar video diffusion model, Ca2-VDM, which aims to enhance the efficient long-term, and real-world video generation. In Ca2-VDM, **causal generation** is proposed to reduce the redundant computations of previous conditional frames, and **cache sharing** is proposed to reduce the st... | Rebuttal 1:
Rebuttal: ## Anonymous link for additional experiment results
https://anonymous.4open.science/r/additional-exp-results-for-anonymous-github-F6EB/readme.md This includes: Table_R1, Table_R2, Figure_R1, and Figure_R2
$~$
## Q1: Essential References Not Discussed
> Yin, Tianwei, et al. "From slow bidirecti... | Summary: Ca2-VDM is an autoregressive video diffusion model designed to generate long videos more efficiently. The paper identifies that existing autoregressive video diffusion models (VDMs) suffer heavy redundant computation when generating videos in chunks: overlapping frames between successive clips are repeatedly p... | Rebuttal 1:
Rebuttal: ## Anonymous link for additional experiment results
https://anonymous.4open.science/r/additional-exp-results-for-anonymous-github-F6EB/readme.md This includes: Table_R1, Table_R2, Figure_R1, and Figure_R2
$~$
## Q1: Additional metrics for per-frame perceptual evaluation
We conducted additiona... | Summary: This paper introduces Ca2-VDM, an autoregressive video diffusion model tailored for generating long videos efficiently. The core idea is to eliminate redundant computation of conditional (overlapped) frames when chaining multiple short clips. To achieve this, the model applies causal generation that replaces s... | Rebuttal 1:
Rebuttal: ## Anonymous link for additional experiment results
https://anonymous.4open.science/r/additional-exp-results-for-anonymous-github-F6EB/readme.md This includes: Table_R1, Table_R2, Figure_R1, and Figure_R2
$~$
## Q1: Unsatisfactory supplementary video quality
We acknowledge that the current qua... | null | null | null | null | null | null |
Test-Time Immunization: A Universal Defense Framework Against Jailbreaks for (Multimodal) Large Language Models | Reject | Summary: This paper focuses on the task of jailbreak detection, which is based on the concern of large language models's vulnerability against jailbreaking attack. The paper proposed a method that is universal against different types of attacks. The assumption held by this paper is that detection is easier to implement... | Rebuttal 1:
Rebuttal: Thanks for your reviews.
> Desipte the performance reported by the authors, the current contribution is incremental because jailbreaking attacks have been heatedly studied by experts in this domain. The proposed method mainly improves the defense method in the original setting.
We first build a ... | Summary: This paper proposes Test-Time Immunization, a defense framework against jailbreak attacks for LLMs and multimodal LLMs. Specifically, this method actively collects jailbreak instructions during model deployment, then continues to improve the defense performance during deployment. Extensive experiments demonstr... | Rebuttal 1:
Rebuttal: Thanks for your kind reviews. We provide additional experimental results in the link https://anonymous.4open.science/r/ICML109sda-E0E4/tab_and_fig.pdf. We will address your concerns one by one.
> If the proposed method cannot detect a sophisticated jailbreak attack, then it might never be able t... | Summary: The authors propose a novel defense framework for mono- and multi-modal generative models. in response to test-time LLM classifier defenses. The core novel contribution is their development of a detector for harmful outputs which uses an optimised gist token inserted at the end of a input + output pair to summ... | Rebuttal 1:
Rebuttal: We are grateful for your valuable suggestions. The figures and tables of the additional results are provided in the link https://anonymous.4open.science/r/ICML109sda-E0E4/tab_and_fig.pdf. We will do our best to address your concerns.
> The concern about the efficiency and effectiveness of our de... | Summary: In a nutshell, this paper introduces Test-Time Immunization (TIM) as a universal defense against jailbreak attacks on large language models. Specifically, the authors insert a special "gist token" which is used for binary classification of spotting harmful outputs, i.e., question, answer, gist token to predict... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive feedback. Below we provide point-by-point responses with methodological clarifications and supplementary experimental evidence. All referenced figures/tables are available in our link https://anonymous.4open.science/r/ICML109sda-E0E4/tab_and_fig.pdf.
> Reg... | null | null | null | null | null | null |
Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks | Accept (poster) | Summary: This paper considers the thickness of the mesh when predicting the deformation. T-EMNN is proposed to preserve E(3)-equivariance and invariance. Experiments show that the proposed method outperforms baselines and the introduction of thickness benefits the baselines as well.
## Update after rebuttal
I apprecia... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. For a comprehensive response, please refer to the attached [link](https://shorturl.at/gOsz6). All materials within the link are indexed starting with the letter ‘L’ (e.g., Fig. L1).
---
**A1. Novelty of Our Work.**
We would like to organize our contribution... | Summary: This paper presents the Thickness-aware E(3)-Equivariant Mesh Neural Network (T-EMNN), a novel graph neural network designed to efficiently integrate the thickness of 3D objects into mesh-based static analysis. The authors introduce an innovative thickness-aware framework that explicitly considers interactions... | Rebuttal 1:
Rebuttal: We are grateful for your thorough review. For a comprehensive response, please refer to the attached [link](https://shorturl.at/gOsz6). All materials within the link are indexed starting with the letter ‘L’ (e.g., Fig. L1).
---
**A1. Practical Utility of the Method (Response to Weakness and Q1).... | Summary: This paper presents a novel graph neural network that incorporates thickness edges—connections between opposite sides of a surface mesh—to enable thickness-aware processing. To maintain E(3)-equivariance, it introduces a data-driven coordinate transformation. The model is evaluated on an injection molding data... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. For a comprehensive response, please refer to the attached [link](https://shorturl.at/gOsz6). All materials within the link are indexed starting with the letter ‘L’ (e.g., Fig. L1).
---
**A1. Problem Definition and Dynamic Analysis**
The task is to predic... | Summary: The paper introduces **Thickness-aware E(3)-Equivariant Mesh Neural Networks (T-EMNN)**, a framework designed to address the limitations of existing mesh-based 3D analysis methods, which often overlook the inherent thickness of real-world 3D objects. The authors argue that thickness plays a critical role in ph... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. For a comprehensive response, please refer to the attached [link](https://shorturl.at/gOsz6). All materials within the link are indexed starting with the letter ‘L’ (e.g., Fig. L1).
---
**A1. Additional Datasets for Challenging Meshes (Response... | null | null | null | null | null | null |
MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency | Accept (poster) | Summary: This paper introduces MMECoT, a benchmark designed to evaluate the Chain-of-Thought (CoT) reasoning performance of Large Multimodal Models (LMMs) across six domains: math, science, OCR, logic, space-time, and general scenes. It proposes a comprehensive evaluation suite with three novel metrics to assess reason... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We find them extremely helpful and will incorporate them in the final version. We address each comment in detail, hoping to address your concerns.
> **Q1: Accuracy concern of questions of multiple correct solutions**
**We believe this concern is ad... | Summary: This paper introduces MME-CoT, a novel benchmark for evaluating chain-of-thought (CoT) reasoning capabilities in Large Multimodal Models (LMMs). The authors present a comprehensive evaluation framework that assesses three critical aspects of multimodal reasoning: quality, robustness, and efficiency. The insigh... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We find them extremely helpful and will incorporate them in the final version. We address each comment in detail, hoping to address your concerns.
> **Q1: Potential GPT-4o bias in evaluation**
Thank you for your valuable advice. We want to address ... | Summary: This paper introduces MME-CoT, a new benchmark for evaluating Chain-of-Thought (CoT) reasoning in Large Multimodal Models (LMMs). The work addresses a timely and important gap in the evaluation of multimodal reasoning. The authors have identified key limitations in existing benchmarks and propose a more compre... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We find them extremely helpful and will incorporate them in the final version. We address each comment in detail, hoping to address your concerns.
> **Q1: Need studies on human agreement**
Thank you for your advice. As you suggested, we conduct add... | Summary: The paper introduces MME-CoT, a specialized benchmark evaluating the CoT reasoning performance of LMMs.
It is the first comprehensive study in LMM CoT evaluation: it spans six domains: math, science, OCR, logic, space-time, and general scenes; and it proposes a thorough evaluation suite incorporating novel m... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We find them extremely helpful and will incorporate them in the final version. We address each comment in detail, hoping to address your concerns.
> **Q1: Difficulty in understanding Sec 2.2 and difference with existing metrics**
Thanks for your v... | null | null | null | null | null | null |
Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning | Accept (poster) | Summary: The paper proposes a novel shortcut approach—termed ASI (Activation Subspace Iteration)—that aims to improve the efficiency of on-device learning by addressing the activation memory bottleneck during backpropagation. The key idea is to perform a single subspace iteration with a “warm start” for low-rank decomp... | Rebuttal 1:
Rebuttal: **Question 1: How our rank selection strategy might scale to transformer-based models or LLMs?**
Our rank selection strategy is fully applicable to transformer-based models and LLMs with billions of parameters, and it incurs only a **one-time cost**.
The main idea of our strategy involves perfor... | Summary: The authors focus on the problem of reducing activation memory usage and computational complexity during on-device learning. The authors try to deploy learning tasks on resource-constrained edge devices while maintaining acceptable performance. The evaluation based on the MCUNet model shows the performance on ... | Rebuttal 1:
Rebuttal: We appreciate your review, below is the answer to your only question.
**Question: How do we calculate training FLOPs?**
Currently, we measure training FLOPs based on theoretical calculations, which consist of the sum of the FLOPs required for both the forward and backward passes. The necessary f... | Summary: This paper proposes Activation Subspace Iteration (ASI), a novel technique to address memory bottlenecks in on-device learning. The method compresses activation maps in neural networks using low-rank decomposition strategies. The key innovations include: (1) a perplexity-based rank selection strategy that iden... | Rebuttal 1:
Rebuttal: **Weakness 1: Use of \citet.**
Thank you for this note. We will revise it in the camera-ready version.
**Weakness 2: Other rank search algorithms besides brute-force?**
Yes, there are certainly alternative methods—for example, using dynamic programming, we might reduce the computational complex... | null | null | null | null | null | null | null | null |
Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching | Accept (poster) | Summary: This work addresses the challenge of modeling long-range interactions in deep graph networks, which are often hindered by oversmoothing, oversquashing, and underreaching in message passing. The authors propose a variational inference framework that adaptively adjusts message depth and filters information to mi... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing that the paper is well written and the findings are convincing. We will comment below on the questions raised.
**Other Strengths And Weaknesses:**
1. While oversmoothing, oversquashing, and underreaching are topics of great interest in the community, which ... | Summary: This paper introduces Adaptive Message Passing (AMP) a novel approach to enrich GNNs with learnable depth and message filtering distributions. A variational inference framework is adopted to jointly train netwoks representing these distributions with both mechanisms being aplied to a range of existing GNN base... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed review and the valuable suggestions. We will do our best to clarify doubts and address the points of the reviewer.
**Claims And Evidence:**
*On Figure 3:* We will improve the coloring scheme as suggested, thank you. Also, the reason why we do not ... | Summary: Graph Neural Networks (GNNs) often struggle to capture long-range dependencies in graphs due to challenges such as oversmoothing, oversquashing, and underreaching. In this work, the authors introduce a variational inference framework that allows GNNs to dynamically adapt their depth and selectively filter mess... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. Below, we clarify some of the points raised.
**Claims And Evidence:** We apologize for the incorrect statement in the abstract about “surpassing” the state of the art. That statement was true in the past, before we revised Table 2. Indeed, we had fixed our ... | Summary: The authors propose a general framework to tackle certain long-range interaction problems in GNNs, namely (1) oversmoothing, (2) oversquashing, and (3) underreaching. Their Adaptive Message Passing (AMP) framework extends the work of Nazaret and Blei on unbounded depth networks to the GNN setting. The idea is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the merits of our contribution and for providing constructive criticism. Below we comment on some of the points raised by the reviewer.
**Methods And Evaluation Criteria:** Following Reviewer ZB4Z’s suggestion, we will include node classification tasks relate... | null | null | null | null | null | null |
Overtrained Language Models Are Harder to Fine-Tune | Accept (poster) | Summary: This paper investigates the phenomenon of "catastrophic overtraining" in language models, where models trained on significantly more tokens than compute-optimal regimes exhibit degraded performance after fine-tuning, despite showing continued improvement in pre-training loss. The authors present both empirica... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback! We are happy to hear your positive comments such as that our methodology is “appropriate, comprehensive, and designed [...] rigorously”, that our paper addresses a problem that is “highly relevant and timely”, and that our results are “novel and counter-intuit... | Summary: This work challenges the widely held belief in the field that scaling pre-training robustly improves LM performance. The authors find that increasing token budgets during pre-training can actually lead to suboptimal performance on downstream fine-tuned tasks. They leverage popular open-source models and datase... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback! We are happy to hear your positive comments that there is “compelling empirical evidence”, that “the studies involving intermediate OLMo checkpoints and newly trained models from scratch are especially persuasive”.
> During pre-training, it may be difficult ... | Summary: The authors study a phenomenon they observe where the more overtrained a pretrained language model is (as a function of pretraining tokens per parameter, w.r.t. training-compute optimal amounts), the more difficult it is to fine-tune the model. The study is motivated by an initial example for OLMo-1B, where th... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback! We are happy to hear your positive comments that there is “significant insight in this paper”, that it “would be of interest to the community”, and that we include a “wealth of empirical results”.
We were also glad to read:
> My issues with this work mostly ... | Summary: The paper demonstrates how overtraining language models (training on more than the compute-optimal number of tokens) affects their ability to be fine-tuned on new data. For example, the authors perform experiments on OLMo-1B models and show that models pretrained on 3T tokens performed 3% worse on AlpacaEval a... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback! We are happy to hear your positive comments that our experiments “support the main finding” and that our theory "helps explain the observed phenomenon”.
Summary of changes: To address your main concerns: (1) we clarify that our fine-tuning setups do lead to c... | null | null | null | null | null | null |
Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation | Accept (poster) | Summary: The paper studied counterfactual outcomes with spatial-tempora attributes using transformers and proved consistency and aymptotic normality of the estimator. The authors also conducted synthetic experiments and studied forest loss in Colombia. This paper is generally well-written.
*Edit after rebuttal: I chan... | Rebuttal 1:
Rebuttal: ## Response to reviewer Uw2a
We are glad the reviewer found our paper well-written. We would like to respond to each detailed point individually.
>1. I failed to be convinced that there is significnat novelty on the theoretical front -- the proof in Appendix B2 is thorough, but Proposition 3 see... | Summary: This paper introduces a Transformer-based framework for counterfactual outcome estimation in spatial-temporal data. It aims to improve causal inference in settings where treatments and outcomes are structured across both space and time. The authors propose a novel deep-learning-based estimator with a CNN-Based... | Rebuttal 1:
Rebuttal: ## Response to reviewer pyND
We are glad the reviewer found the theoretical framework rigorous. We would like to respond to each detailed point individually.
>1. The theoretical evidence is adundant, whereas the empirical results lack sufficient ablation to support the claims such as the improve... | Summary: The paper introduces an approach to estimate counterfactual outcomes in a spatial-temporal setting, where both treatment and outcome may be represented in a high-dimensional space. The proposed method adapts IPW to the spatial-temporal setting, leveraging propensity score. The approach is implemented in practi... | Rebuttal 1:
Rebuttal: ## Response to reviewer p6Zj
We greatly appreciate the reviewer's comments and suggestions. We would like to respond to each detailed point individually.
> 1. The estimator estimates the expected number of outcomes in a specific region at a specific time. This estimator (Eq. 3) seems to be the ... | null | null | null | null | null | null | null | null |
Imitation Learning from a Single Temporally Misaligned Video | Accept (poster) | Summary: The paper tackles the problem of imitation learning from a single video demonstration that may be temporally misaligned with the learner’s execution (e.g., inconsistent execution speed, pauses, etc.). The authors show that severe misalignments such as those that were created in their experiments (long pauses o... | Rebuttal 1:
Rebuttal: We are excited that the reviewer finds our claims well-supported and that our experiments highlight the strength of ORCA. Below, we address the reviewer's concern.
# Concerns
## Clarification on applications of ORCA
We clarify that our focus is on learning from temporally misaligned data. In robo... | Summary: Reinforcement learning and imitation learning from a single visual demonstration is a challenging problem because the demonstration may be temporally misaligned: the demonstration and online trajectories may differ in timing. Frame-level matching is not adequate because it does not enforce the correct ordering... | Rebuttal 1:
Rebuttal: We are grateful that the reviewer engages closely with our ideas. We thank the reviewer for the detailed suggestions to make our paper better, and we address the concerns below.
# Concerns
## Clarification about paper title
We agree that a portion of imitation learning literature assumes access to... | Summary: This paper tries to address the challenge of learning sequential tasks from a single visual demonstration, particularly when the demonstration is temporally misaligned with the learner's execution. This misalignment can arise from variations in timing, differences in embodiment, or inconsistencies in task exec... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work clear, intuitive, and engaging. Thank you for the feedback that helps further strengthen it. We will update the paper to fix minor formatting issues, and we address the reviewer's questions and concerns below.
# Questions
## Q1: "Could the authors clarif... | Summary: This paper studies how to provide a policy with rewards using a single video. The paper argues that temporal misalignment may occur due to pauses in the video. The authors design an algorithm that calculates the probability that the policy's frame at time t corresponds to the video's frame at time j, thereby p... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback on how we can strengthen our work. We answer the questions and address the concerns below.
# Questions
## Q: “Can ORCA improve with multiple videos?”
ORCA's performance improves with more demonstration videos. We kindly refer the reviewer to our reply... | Summary: This paper focuses on learning sequential tasks from a single temporally misaligned video, which belongs to the imitation learning paradigm. They propose a novel reward function - ORCA, which measures the matching at the sequence level to ensure that the agent covers all subgoals in the correct order. Experime... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s deep engagement with our paper, and we thank them for feedback that strengthens its clarity. We will update the paper to unify our references to tables. Please find below our responses to questions and concerns.
# Questions
## Q: "When should pre-training be perf... | null | null | null | null |
EcoMapper: Generative Modeling for Climate-Aware Satellite Imagery | Accept (poster) | Summary: This paper introduces two generative models for the generation of controllable satellite images. The models, based upon Stable Diffusion 3, enable the controlled generation of satellite images with several control types, including: Image-conditioned generation, spatiotemporal conditioning (location, date, land... | Rebuttal 1:
Rebuttal: **Reviewer comment:**
I am missing a reference to SatCLIP, which I find to be closely related to the contents of this paper.
**Response:**
Thank you for this helpful suggestion. We agree that SatCLIP (Klemmer et al., 2023) is highly relevant and will include a citation and brief discussion i... | Summary: This paper extends previous work on satellite image generation by introducing a larger dataset and considering two generation scenarios - text2img and ControlNet. Quantitative comparison shows superior performance only in FID but not other metrics (i.e., CLIP, SSIM, PSNR, LPIPS).
Claims And Evidence: This pap... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive feedback. In the following, we address the key points you raised.
**Reviewer comment:**
While the dataset's size is notable, the paper's primary contribution and the novelty of its proposed prompting setups remain unclear.
**Response:**
The prompting... | Summary: This paper introduces EcoMapper, which combines climate data with satellite imagery based on Sentinel-2 images. Satellite images often face observational challenges, such as areas affected by cloud cover and inherent resolution limitations that can hinder accurate analysis. To overcome these issues, the author... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We would like to address the main critics:
**Reviewer:**
The paper does not provide detailed ablation studies on the selection and impact of different control images.
**Response:**
While it's not fully clear what is meant by "selection and impact" of c... | Summary: The paper introduces EcoMapper, a generative modeling framework designed to synthesize climate-aware satellite imagery. It provides two primary contributions:
- EcoMapper Dataset: A comprehensive dataset comprising 2.7 million Sentinel-2 RGB satellite images from 104,424 global locations, annotated with climat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and thoughtful evaluation of our work. Below we respond to the key points raised.
**Reviewer comment:**
The paper lacks explicit evidence that the synthetic images are directly useful for downstream tasks like environmental monitoring or sce... | null | null | null | null | null | null |
Controlled Generation with Equivariant Variational Flow Matching | Accept (poster) | Summary: In this paper, the authors present a controlled generation objective in the framework of Variational Flow Matching (VFM), as well as an equivariant formulation of VFM which has applications in 3D molecule generation. For controlled generation, the authors demonstrate that both end-to-end constrained training a... | Rebuttal 1:
Rebuttal: Dear reviewer uXdp,
Thank you for the detailed and thoughtful review. We appreciate your close reading and thank you for your constructive suggestions. Below we address the main points raised in your review and clarify several aspects that were not sufficiently emphasized in the original submissi... | Summary: The paper proposes two novel methods within the variational flow matching (VFM) framework for generative modeling. The first is controlled generation, which enables conditional generation using unconditional generative models without requiring retraining (though it is not necessarily limited to this scenario).... | Rebuttal 1:
Rebuttal: Dear reviewer 6iCA,
We sincerely thank the reviewer for their comprehensive assessment of our paper.
We appreciate your recognition that these contributions are "conceptually insightful and practically impactful" and that our work addresses an important gap in flow matching frameworks. We are g... | Summary: The paper focuses on extending the recently proposed Variational Flow Matching (NeurIPS 2024) approach for conditional generation and for incorporating inductive biases as symmetries. They derive two different ways for controlled generation, the first one is similar to conditional diffusion models, with the di... | Rebuttal 1:
Rebuttal: Dear reviewer TqUv,
Thank you for the detailed and thoughtful review and for engaging deeply with both the theoretical and empirical aspects of our work. Below we respond to the key points and how they will be addressed in the revised manuscript.
**1. Unified Objective and Variational Distributi... | Summary: The core contributions of the paper are twofold.
**[1.Inference time control of VFM]**
The authors show that a conditional VFM distribution can be factorized into unconditional VFM part and the classifier part. Based on this factorization, the authors propose an iterative approximate solution to $\underset{x_... | Rebuttal 1:
Rebuttal: We thank the reviewer for the clear summary and thoughtful comments. We appreciate the recognition of our core contributions, as well as the constructive suggestions that helped us clarify and strengthen the presentation. Below, we address the reviewer’s main points.
**1. Inference-Time Control: ... | null | null | null | null | null | null |
Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function | Accept (poster) | Summary: This paper introduces a simple, parameter-free modification to the loss function, separating decorrelation loss from spectral amplitude errors. Fine-tuning the GraphCast model with this new loss function yields sharper deterministic forecasts, increases effective resolution from 1,250 km to 160 km, improves en... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. While we disagree with some of the reviewer's conclusions (see below), the fact that they raise these issues demonstrates that some sections of the paper should be rephrased in a camera-ready version. We also think that we can add further extreme-weather... | Summary: A spherical loss variation of the MSE is introduced that breaks MSE's tendency to push ML models to converge to the mean via its double penalty. The prestented AMSE effectively conserves amplitudes in weather forecasts, as demonstrated with a carefully fine tuned GraphCast model, leading to sharper forecasts t... | Rebuttal 1:
Rebuttal: First, we thank the reviewer for their comments. They accurately note limitations of this work and suggest extensions that will improve the robustness of these results in a camera-ready version of the paper.
Several of the suggested ablation studies should be possible, but they will take long e... | Summary: This paper addresses a significant issue in state-of-the-art data-driven weather forecasting models: the tendency for forecasts to be overly smooth, particularly at finer scales. This smoothing is attributed to the commonly used mean squared error (MSE) loss function, which penalizes models for misplacing feat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. This review brings up some interesting points regarding the limitations, applications, and extensions of AMSE, and our detailed response follows.
> Briefly looked at the code. It's somewhat complicated the way it's structured and would suggest cleaning t... | null | null | null | null | null | null | null | null |
Reward Translation via Reward Machine in Semi-Alignable MDPs | Accept (poster) | Summary: This paper considers a setup where we want to transfer the reward from source domain to the target domain so that we can train RL agents in target domain where the reward design is tedious or difficult. But this will be difficult for many source-target domains because many domains don't share the same structur... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our paper. We appreciate your feedback and the opportunity to address your concerns regarding our approach and experimental results. We have updated the additional experiment in:
https://drive.google.com/file/d/1_U1d13bM4kG1reHUdx2wkLNb9zfn4otS/view?usp=sha... | Summary: This paper proposes a way to derive reward functions for cross domain transfer learning. This is achieved via reward machines for obtaining a transferable reward in semi-align MDPs. Experiments conducted on 3D visual navigation and a few Mujoco tasks demonstrate the benefits when agents are learned with PPO.
... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our paper. We appreciate your careful reading and questions, which help us improve the clarity and rigor of our work. We have updated the additional experiment in: https://drive.google.com/file/d/1_U1d13bM4kG1reHUdx2wkLNb9zfn4otS/view?usp=sharing
(Due to tim... | Summary: The paper introduces the Neural Reward Translation (NRT) framework, a novel methodology designed to transfer knowledge from completing a task in one environment to quickly learning to solve a (sufficiently similar) task in another environment. For example, NRT can transfer knowledge gained from completing a ta... | Rebuttal 1:
Rebuttal: We deeply appreciate your thoughtful review and constructive feedback on our NRT paper. We have updated the additional experiment in:
https://drive.google.com/file/d/1_U1d13bM4kG1reHUdx2wkLNb9zfn4otS/view?usp=sharing
(Due to time constraints, many supplementary experiments could not be run with mu... | null | null | null | null | null | null | null | null |
When can in-context learning generalize out of task distribution? | Accept (poster) | Summary: This paper examines the generalization properties of in-context learning (ICL) in transformers. Specifically, it explores the conditions necessary for ICL to emerge and extend beyond the pretraining distribution. To investigate this, the authors conduct a series of experiments across various tasks and summariz... | Rebuttal 1:
Rebuttal: Thank you for highlighting the clarity and importance of our work! We appreciate your comments and suggestions, which help us improve the paper.
> The experiments are relatively simple…
We agree that our experimental setups are relatively simple. However, this simplicity is precisely wha... | Summary: The paper explores the effect of pretraining task diversity, i.e., instead of the number of pretraining tasks, the paper considers the diversity of the fixed number of pretraining tasks.
The same number $N$ of tasks could be more diverse than the other $N$ tasks.
Specifically, the paper draws samples from a su... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions! We are pleased that you find our work interesting and well supported.
> The paper could do better via diversifying the experimental setting on the tasks.
> …consider more either proposing another task, possibly on classification rather than reg... | Summary: The authors study a new notion of task diversity--task similarity--and investigate the condition on task diversity for out-of-distribution generalization to emerge. The authors find that there is a transition from specialized models to generalizable models with increasing task diversity. They also show that th... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments and suggestions!
> Specifically, the authors have not discussed how they determine the transition point. For example, in Fig. 8 and 15, it appears from the plot that the threshold slightly increases with number of layers.
Thank you for the opportunity t... | Summary: This paper studies empirically the task diversity to the generalizability, focusing on the transformer trained to learn a linear regression problem. It proposes the new axis of task diversity, namely the task similarity, independent of the unique task numbers seen during pretraining.
Claims And Evidence: See ... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and suggestions!
> why [do we] mention the "loss plateaus"?
We mention the loss plateaus because they are characteristic of ICL behavior (Fu et al, ICML 2024; Reddy, ICLR 2023), confirming that our training is consistent with ICL phenomenology.
We wi... | null | null | null | null | null | null |
Quantifying Memory Utilization with Effective State-Size | Accept (poster) | Summary: The paper proposes to study the memory stored in a wide range of sequential neural network architecture through the notion of effective state-size, which is motivated by minimal realization theory and applicable out of the box to many architecture. The authors empirically validate it as a sound measure by corr... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments! The paper will be revised to better explain each of the following points:
## Why is ESS a function of time? Why does ESS decrease before the EOS token?
ESS at each time step $i$ captures the lower bound for the minimal state-size required at that specific step... | Summary: This work introduces the Effective State-Size (ESS) metric to quantify memory utilization in sequence models while previous approaches focus on memory capacity (such as cache size/total memory available). ESS aims to measure how effectively a model uses its available memory. Using this metric the authors analy... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback! Below, we respond to the points raised.
## Claims and Evidence
1. Yes, this is a good point, we will extend the regularization experiments to include parameters beyond the range in the plot and update the plot in the paper. We anticipate that at... | Summary: The paper introduces Effective State-Size (ESS) as a novel measure for quantifying memory utilization in causal sequence modeling architectures. ESS provides interpretable and actionable metrics that can enhance initialization strategies, regularizers, and model distillation. The paper develops a unified frame... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and positive outlook on the work!
## Presentation and Accessibility
Thank you for pointing out that some theoretical sections may be inaccessible to many due to technical jargon. We will simplify those parts and add working examples of computing ESS, as Rev... | Summary: This paper claims to propose a new metric "effective state-size (ESS)", which can not only
evaluate the memory-utilization of different models, but also brings instruction to the selection
of initialization/regularization, and distillation strategies. Several empirical results are presents to
supports the effe... | Rebuttal 1:
Rebuttal: We appreciate your detailed feedback. Below, we address the main concerns.
## Mischaracterization of Our Claims
Your review states that our work involves measuring the rank of parameter matrices or hidden states using SVD—a practice that has existed for years. However, we would like to clarify t... | null | null | null | null | null | null |
Exact Upper and Lower Bounds for the Output Distribution of Neural Networks with Random Inputs | Accept (poster) | Summary: This paper provides upper and lower bounds on the cdf of a ReLU neural network which converge to the exact cdf as the granularity increases. Other monotonic piecewise differentiable activation functions can in turn be approximated using ReLU activation functions, extending the results. Experiments validate the... | Rebuttal 1:
Rebuttal: *Relation To Broader Scientific Literature and Questions For Authors:*
Our approach aims to approximate as accurately as possible the **cdf** of the output in a probabilistic NN. Krapf et al (2024) estimate the **pdf** of a NN output. Our Theorem 3.8 derives the exact cdf (not pdf) of a ReLU NN w... | Summary: The paper addresses the challenge of uncertainty quantification in neural network (NN) outputs by deriving exact upper and lower bounds for the cumulative distribution function (CDF) of NN outputs under noisy (stochastic) inputs. The method is designed to apply to feedforward NNs and convolutional NNs (CNNs) u... | Rebuttal 1:
Rebuttal: *Claims And Evidence and Theoretical Claims:*
The proofs are not detailed as most of the steps are based on well known calculus facts and are easy to obtain. The challenge is that they are numerous and this is why we provided only the sketch of the proof. We appreciate that for the sake of clarit... | Summary: This paper proposes a novel method to compute exact upper and lower bounds for the cdf of a neural network’s output, assuming stochastic inputs. Key contributions include:
- A method to compute the exact cdf of the output of ReLU networks under inputs with piecewise polynomial pdf over compact hyperrectangle.
... | Rebuttal 1:
Rebuttal: *Theoretical Claims:*
You are right that Theorem 3.11 follows from the universal approximation theorem. But it also **constructs** the sequences whereas the UAT simply shows their existence.
Indeed the significance of Theorem 3.13 is that the bounds are built using ReLU functions, which is our ... | null | null | null | null | null | null | null | null |
Adaptive Flow Matching for Resolving Small-Scale Physics | Accept (poster) | Summary: Applying conditional diffusion (CDM) and flow matching (FM) to natural images is very effective for super-resolving small-scale details, like the image semantic- or geometric information. However, CDM and FM will have difficulties with physical sciences, particularly for weather, mainly due to 1) spatially mis... | Rebuttal 1:
Rebuttal: We thank the reviewer for their remarks and for recognizing the extensive experimental validation of our work.
*1. When we compare the visually generated images between SFM and CorrDiff, it seems CorrDiff is better. Can the authors provide more convincing explanations about the advantages of Corr... | Summary: The paper introduces stochastic flow matching (SFM) for super-resolving small-scale physics in weather data, tackling challenges such as data misalignment, multiscale dynamics, and limited data availability. The approach employs an encoder to project coarse-resolution inputs into a latent space, followed by fl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. Below are our responses to your remarks.
*1. In my opinion, VAE is a more natural choice ...* (we shorten the questions due to the character limit)
**Response.** Thank you for the insightful question. As clarified in our Remark in Sec 4.2, w... | Summary: The paper focuses on tackling small-scale physical science problems (e.g., weather super-resolution). It proposes the joint encoder and flow-matching training objective over the prior two-stage methodologies to improve the overfitting. Specifically, this work introduces Adaptive Flow Matching (AFM) with the he... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and for finding our paper interesting.
*1. In the abstract itself, it is claimed that AFM achieves SOTA on regional downscaling. However, this is not the case. According to Table 2 and Figure 2, improvements over CFM are arguable. However, on Kolmogorov-Fl... | Summary: The paper addresses image 'super-resolution' in the context of atmospheric physics. The super-resolution aims at generating small stochastic scales into the input data while preserving and aligning large scale physics. The authors propose to adapt and apply flow matching for this purpose. To this end, the auth... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and constructive feedback, and for recognizing our clear motivation and rigorous methodology.
*1. In figure 2, 4: there is a confusion between the acronyms: SFM -> AFM*
**Response.**
Thank you for noticing the typo. In the revised manuscript, we have us... | null | null | null | null | null | null |
LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models | Accept (poster) | Summary: This paper introduces LlavaGuard, a suite of vision safeguards. They decribe a systematic framework including safety taxonomy, data preprocessing, augmentation, and training setup. Then they build a multimodal safety dataset and train LlavaGuard models on this. Through extensive experiments, they demonstrate t... | Rebuttal 1:
Rebuttal: Thanks for your detailed response and the constructive feedback! Below we address your concerns.
---
### **1. Generalization Beyond the Held-out Test Set**
While achieving strong performance on the held-out test set is not a small step, we agree that exploring additional datasets provides valuab... | Summary: The paper introduces a vision-language-based framework specifically designed for safety compliance verification in visual content. It first establishes a context-aware assessment covering nine safety taxonomies and uses it to curate a human-labeled dataset. This dataset includes ground truth safety ratings, vi... | Rebuttal 1:
Rebuttal: Thanks for your detailed response and the constructive feedback! Below we address your concerns.
---
### **W1 Additional VLMs**
According to your suggestion, we included additional prominent VLMs (e.g., Qwen-VL) and baseline image-centric models (SigLip2), please refer to Response3 for LJnY.
#... | Summary: - Key contribution: This paper presents a safety guard suite, LLavaGuard, with a dataset consisting of ~5K images annotated with safety labels and rationales, and two models trained using the dataset.
- Motivation: The key motivation behind this is that safeguard models and datasets are rare in the visual dom... | Rebuttal 1:
Rebuttal: Thanks for your detailed response and the constructive feedback! Below we address your concerns.
---
### **1. Annotators and Dataset Information**
We reaffirm our commitment to ethical and regulatory standards. To ensure annotators' well-being, dataset annotation was directly performed by the aut... | null | null | null | null | null | null | null | null |
RobustLight: Improving Robustness via Diffusion Reinforcement Learning for Traffic Signal Control | Accept (poster) | Summary: The paper introduces RobustLight, a novel framework designed to enhance the robustness of Traffic Signal Control (TSC) systems against adversarial attacks and missing data. The authors propose a plug-and-play diffusion model that integrates with existing TSC platforms to recover from noise attacks and resto... | Rebuttal 1:
Rebuttal: First and foremost, we sincerely thank you for pointing out the issues, as your suggestions are invaluable in enhancing the quality of this paper.
1. Scalability: The paper mentions that the computational cost increases with the number of intersections. Could the authors elaborate on potential st... | Summary: This paper point out the current challenge in the TSC systems, which include significant performance degration, limitation of existing defense methods and lack of online ability. To address these issues, authors propose RobustLight, a framework to enhance the robustness of online TSC systems, consisting of a T... | Rebuttal 1:
Rebuttal: First and foremost, we sincerely thank you for pointing out the issues, as your suggestions are invaluable in enhancing the quality of this paper.
1. For the experiment part, my concern is whether this method is still effective under some potential adaptive attacks, such as attacks effective on D... | Summary: The paper introduces RobustLight, a novel framework designed to enhance the robustness of Traffic Signal Control (TSC) systems against adversarial attacks and missing data. The key contribution of RobustLight is the integration of an improved diffusion model into TSC, which enables real-time recovery of noisy ... | Rebuttal 1:
Rebuttal: First and foremost, we sincerely thank you for pointing out the issues, as your suggestions are invaluable in enhancing the quality of this paper.
W1: https://anonymous.4open.science/r/RobustLight-72B2/README.md
W2：Traffic movements and TSP are defined in Figure 1 and Section 2.1, with cyan col... | Summary: This paper focuses on a very interesting problem. For the data missing problem faced in traffic signal control, the authors use the diffusion model to complete and clean the data. The experimental results show that this method effectively improves the control performance of the reinforcement learning model in ... | Rebuttal 1:
Rebuttal: First and foremost, we sincerely thank you for pointing out the issues. Your suggestions are invaluable in enhancing the quality of this paper. Below is our answer to your questions.
1. Experimental results on some datasets are not reported.
Due to space constraints, data noise results for JN2 ... | null | null | null | null | null | null |
Robust and Conjugate Spatio-Temporal Gaussian Processes | Accept (poster) | Summary: The authors combine ideas from recent work on robust Gaussian processes with filtering ideas used in (spatio)temporal Gaussian process regression. They use temporal structure of the problem to set parameteres in the robust Gaussian process framework proposed by Altimirano et al 2024 (RCGP) in a sensible and au... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful consideration of our paper and helpful feedback. We address below the valuable suggestions made to further improve our work:
**Scaling is shown to be linear in the number of time steps (but not in the number of spatial locations).**
We agree with this con... | Summary: This paper introduces Spatio-Temporal Robust and Conjugate Gaussian Processes (ST-RCGPs), which are an extension of robust and conjugate Gaussian processes (RCGPs) [1]. ST-RCGPs leverage the state-space formulation of spatio-temporal GPs (STGPs) to achieve computational efficiency while maintaining the robustn... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and positive remarks on our empirical evaluation and the ST-RCGP’s novel combination of robustness and efficiency. We comment on the feedback below:
**Since the data is synthetic, I have some concerns about how meaningful the results really are.**... | Summary: This paper introduces a methodology for spatio-temporal Gaussian Processes based on a state-space model and generalized Bayesian inference. Building on the robust and conjugate Gaussian processes (RCGPs) framework, it addresses and overcomes its key limitations, enhancing both robustness and computational effi... | Rebuttal 1:
Rebuttal: We thank the reviewer’s feedback and are glad our theoretical results and experimentation on our proposed method were appreciated. It has been mentioned that:
**“The baseline models used for comparison vary across experiments due to data characteristics. It would be beneficial for the authors to ... | Summary: This paper expands the robust and conjugate GP framework to what it refers to as spatiotemporal GPs (sometimes referred to in other places as Markovian GPs, linear time GPs, or state space GPs). This is achieved by a generalized Bayes filtering solution, somewhat similar to other recent works on sequential gen... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful feedback and the expertise on the matter. We are glad you found the paper clearly written and easy to follow. We comment on the feedback below:
**1) “provides inferences that are comparable to state-of-the-art non-Gaussian STGPs in the presence of outliers, but at a f... | null | null | null | null | null | null |
GaussMark: A Practical Approach for Structural Watermarking of Language Models | Accept (poster) | Summary: This paper proposes a watermarking for large language models. The watermarking injection process has an almost negligible overhead and applies to LLMs of any structure. The authors observed that a small perturbation to the parameters of LLMs will not significantly affect the model's performance. Based on this,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful attention to our work.
## Model Quality: Further benchmarks and safety
We agree that more benchmarks, such as translation or code, would be better and we acknowledge in lines 407-408 2nd column that such checks are necessary before deployment. We also agr... | Summary: This paper introduces GaussMark, a novel watermarking scheme for language models. The approach involves adding small Gaussian perturbations to a single MLP layer during generation and using statistical tests based on Gaussian independence to detect watermarked text. The watermarking scheme comes with formal st... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful attention to our work.
## Choice in Perturbation Parameters
We agree with the reviewer that a more thorough understanding of which parameters to perturb would be beneficial and included Figures 7-12 as some empirical guide as to the effect of layer, parame... | Summary: This paper proposes a watermarking scheme for large language model output (LLMs) using gradient-based test statistics. The properties of the proposed method, GaussMark, are analyzed theoretically to derive signficance levels and power of the test. The method and its properties (efficiency, quality-preservation... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful attention to our work.
## Comparison to other watermarking schemes
Due to space, we deferred an extensive comparison with Kirchenbauer et al to appendix H, alluded to in the paragraph beginning in line 406. Please see that for results and explanation of w... | Summary: The paper proposes GaussMark, a structural watermarking method for LLMs that perturbs model weights with Gaussian noise during generation. The authors claim this approach addresses limitations of token-level watermarking by embedding watermarks directly into model parameters. Detection leverages hypothesis tes... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful attention to our work. We wish to clarify a few points.
## The linear approximation
We wish to emphasize that while the motivation and power bounds do rely on some simplifying assumptions, as we demonstrate in Proposition 3.1, *the statistical validity of... | Summary: The paper introduces GaussMark, a watermarking scheme that embeds a subtle signal into a language model by additively perturbing its weights with a small Gaussian noise. Instead of operating at the token level, GaussMark leverages the inherent structure of text by modifying a single MLP layer within the model.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful attention to our work. One point on the theoretical claims that we wish to clarify is that the statistical validity of our test does not depend on the linear approximations being sound and so the test’s statistical significance holds under virtually no assu... | null | null | null | null |
Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport | Accept (poster) | Summary: The paper studies the problem of Partial Domain Adaptation (PDA), where the target label space is a subset of the source label space. The authors propose a theoretically grounded approach based on Partial Optimal Transport (POT) to tackle PDA, deriving generalization bounds that justify the use of the Partial ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comprehensive evaluation and helpful suggestions.
> _Questions for authors_:
**Given that solving the partial optimal transport problem is computationally expensive, how does WARMPOT compare in training time to previous approaches?**
Indeed, solving the optima... | Summary: This paper deals with Partial Domain Adaptation (PDA), a setting where source and target domain distributions differ, and where the target domain label space is a subspace of the source domain label space. The authors propose to tackle this important problem through Optimal Transport (OT), an established field... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and helpful comments.
> _Methods_ and _Weaknesses_:
**While the authors provide a comprehensive comparison with the state-of-the-art, they do so in a single benchmark, i.e., the Office-Home benchmark. In my view the authors should complete their exp... | Summary: This submission studies the generalization bound and empirical model for the partial domain adaptation problem, where the label spaces across domains are different. The key idea of this paper is to use the weight deduced from the partial transportation mass, which is claimed to be able to filter the outliers (... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of the paper, relevant references, and constructive comments.
>_C1 and Essential References_:
We thank the reviewer for pointing out these relevant references. While all of them are
relevant, consider similar settings and techniques, and deserve ... | Summary: The paper presents (PAC) bounds and on the (expected) empirical loss using partial Wasserstein distance in either the marginal (features only) or joint (features and labels). The first two terms of the loss are the loss weighted provided by the marginal of the partial transport plan and the partial Wasserstein... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful and constructive comments.
> Q1. **For the ground distance in the label space the loss should be a metric, but this is not specified. Is a metric actually required for $\ell$? For instance, cross-entropy/log loss/KL divergence doesn't satisfy the requiremen... | null | null | null | null | null | null |
msf-CNN: Multi-Stage Fusion with Convolutional Neural Networks for TinyML | Reject | Summary: The paper presents a multi-stage fusion technique for optimizing CNN inference on memory-constrained microcontrollers (MCUs), called msf-CNN. The main objective of this work is to efficiently execute deep neural networks on resource-limited IoT devices by reducing RAM usage through layer fusion while balancing... | Rebuttal 1:
Rebuttal: Thank you very much for the insightful feedback on our manuscript. Below, we address each of your comments and questions in detail.
**[C1. The current formulation only applies to CNNs and does not extend to other architectures like transformers, RNNs, or hybrid models.]**
Thanks for the suggesti... | Summary: In this paper, the authors introduce msf-CNN, a multi-stage fusion (msf) approach that identifies optimal fusion settings for CNNs by navigating the fusion solution space represented as a directed acyclic graph (DAG). The goal is to reduce RAM usage without introducing significant computational overhead. The m... | Rebuttal 1:
Rebuttal: Thank you very much for the insightful feedback on our manuscript. Below, we address each of your comments and questions in detail.
**[Q1. Why is model accuracy not reported in the experimental results?]**
We appreciate the reviewer raising this, as it allows us to clarify a crucial aspect of ou... | Summary: msf-CNN is a framework for reducing CNN memory usage on very small devices (e.g., MCUs) through multi-stage layer fusion. The authors represent CNN layers as edges in a directed acyclic graph, then systematically search for fusion “blocks” using graph-based algorithms to minimize peak RAM or computation cost. ... | Rebuttal 1:
Rebuttal: Thank you very much for the insightful comments and constructive suggestions on our work. Below, we address each of your comments and questions in detail.
**[C1. Essential References Not Discussed]**
Sorry for the omitting the mention of TVM's "AutoScheduler". We will definitely mention it in th... | Summary: This work proposes a multi-stage fusion method, called MSF-CNN, to optimize RAM usage for tiny CNNs on microcontrollers. By modeling fusion as a graph optimization problem, it minimizes memory and computation costs. Experiments on various MCUs demonstrate that MSF-CNN significantly reduces peak RAM usage, prov... | Rebuttal 1:
Rebuttal: Thank you very much for the insightful remarks on our manuscript. We address below your questions/comments:
**[C1. Can msf-CNN practically improve latency under a given peak memory constraint compared to previous heuristic solutions? ]**
We agree that this is a key consideration.
Compared to p... | Summary: The paper presents msf-CNN, a novel approach with open-source code to optimize convolutional neural network (CNN) inference on microcontrollers (MCUs) by employing multi-stage fusion techniques. Motivated by TinyML’s stringent memory and computational constraints, it reformulates the fusion configuration searc... | Rebuttal 1:
Rebuttal: Thank you so much for the supportive comments and for your valuable suggestions for future work in various directions. We are incidentally working on exploring hardware-specific tuning, leveraging more advanced caching techniques and covering architectures other than CNN. | Summary: This paper present msf-CNN, by leveraging traditional DAG and kernel fusion techniques, msf-cnn achieves 50% less peak memory usage while achieving similar inference performance. The paper logic flow is clear, but lack of novelty, and some experiment results baseline are not strong.
## update after rebuttal
... | Rebuttal 1:
Rebuttal: Thank you very much for your comments pointing out that a reader might potentially confuse **multi-stage fusion** (msf-CNN, our approach) on the one hand, and on the other hand traditional **kernel fusion** techniques. These two approaches are orthogonal and can be applied concurrently for maximum... | null | null |
Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits | Accept (poster) | Summary: This paper presents a new exact satisfiability-modulo-counting solver: `Koco-SMC`, and demonstrates its performance on benchmarks from a UAI benchmark set, compared to existing state-of-the-art exact and approximate SMC solvers.
The paper attempts to address a weakness in existing exact solvers: the need for ... | Rebuttal 1:
Rebuttal: Thank you very much for the detailed review. We will address your concerns and questions in the following reply.
---
### **Q1: Related works**
We deeply appreciate your effort in pointing out the lines of work we missed. We have carefully reviewed the related literature and summarized the main c... | Summary: This paper investigates the Satisfiability Model Counting problem, an extension of SAT that incorporates constraints involving probabilistic inference. The authors propose, KOCO-SMC, an efficient exact SMC solver that leverages probabilistic circuits through knowledge compilation to accelerate repeated probabi... | Rebuttal 1:
Rebuttal: Thank you very much for your review and suggestions.
---
### **Q1: Beyond conflict detection, is KOCO-SMC able to derive propagations from probabilistic inference constraints?**
This is a very good question. In our context, propagation specifically refers to unit propagation, which derives new v... | Summary: This paper aims to provide an efficient solution to the satisfiability modulo counting problem. It proposes to use probabilistic circuits to encode the propositional formula which is further combined with a conflict-driven clause learning framework to compute bounds for the marginal distributions. Empirical ev... | Rebuttal 1:
Rebuttal: Thank you for your careful review and for raising valuable questions.
---
### **Q1: Why is Koco-SMC More Efficient?**
In general, KOCO-SMC saves time by detecting conflicts early using partial variable assignments, whereas baseline solvers require full variable assignments.
- **Baseline Solver... | Summary: Satisfiability modulo counting (SMC) is a generalisation of SAT that consists of a propositional formula phi(x,b) and a collection of statements of the forms
1) the marginalisation of a discrete probability function f(x,y) (marginalising the variables in y) is at least some constant q,
2) the marginalisatio... | Rebuttal 1:
Rebuttal: Thank you very much for your review and your positive assessment of our paper. We appreciate your concise summary of our contributions and your acknowledgment of the practical value demonstrated by our experimental results.
---
### **Q1. Main Contribution**
We propose an integrated **exact SMC ... | null | null | null | null | null | null |
Rethink the Role of Deep Learning towards Large-scale Quantum Systems | Accept (poster) | Summary: The authors conduct a thorough investigations of Deep Learning (DL) vs Machine Learning (ML) methods and all of the design choices surrounding them for the tasks of quantum system learning (QSL).
They consider the tasks of quantum phase classification (QPC) and ground state property estimation (GSPE) and stud... | Rebuttal 1:
Rebuttal: We thank Reviewer 3fBm for the positive recognition of our work. Below, we address the remaining concerns. For clarity, questions in `Comments Or Suggestions` and `Questions For Authors` are referred to as `COS` and `QA`. All newly added simulations are attached to [*LINK*].
[*LINK*] https://anon... | Summary: The paper examines the necessity and effectiveness of deep learning in quantum system learning (QSL), particularly in estimating ground state properties (GSPE) and quantum phase classification (QPC).
The paper systematically benchmarks deep learning models against traditional machine learning approaches while... | Rebuttal 1:
Rebuttal: We appreciate the Reviewer m6FX's positive affirmation of our work. Below, we provide detailed responses to the remaining concerns. For clarity, questions in `Strengths and Weaknesses` and `Questions for Authors` are referred to as `S&W` and `QA`, respectively.
> **Q1 [`S&W`, `QA`] The paper sho... | Summary: This paper considers the state properties of quantum systems problems. In order to deal with the issue of unfair comparison, this paper benchmarks DL models against traditional ML approaches across the Hamiltonian.
Claims And Evidence: yes
Methods And Evaluation Criteria: yes
Theoretical Claims: yes
Experi... | Rebuttal 1:
Rebuttal: We appreciate Reviewer Wxrf's thoughtful review. For clarity, questions in `Strengths And Weaknesses` are abbreviated as `S&W`. All newly added simulations are attached to [*LINK*].
[*LINK*] https://anonymous.4open.science/r/ml4quantum-C80F/Rebuttal_icml_25.pdf
> **Q1 [`S&W`] The motivation is n... | Summary: The authors study supervised machine learning in the framework of quantum tasks in particular the Ground State Properties identification and Quantum Phase Classification. The authors evaluate a set of shallow and deep classifiers in both tasks and evaluate the computational cost as well as accuracy. The author... | Rebuttal 1:
Rebuttal: We thank Reviewer HcSt for insightful comments. We have addressed all your concerns in the detailed responses below. For clarity, questions in `Claims and Evidence`, `Theoretical Claims`, `Strengths and Weaknesses`, and `Questions for Authors` are referred to as `C&E`, `TC`, `S&W`, and `QA`, respe... | null | null | null | null | null | null |
Unified K-Means Clustering with Label-Guided Manifold Learning | Accept (poster) | Summary: This paper introduces a framework that integrates K-means clustering, Kernel K-means clustering, and Fuzzy K-means clustering with manifold learning. This framework aims to tackle the challenges of initial centroid sensitivity, handling nonlinear datasets, and achieving balanced clustering in traditional K-mea... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition and valuable comments. We provide the following responses according to your questions:
**1. Explain the formula (20) to (24)**
**A**: We compute the $\ell_{2,1}$-norm of the cluster indicator matrix $\mathbf{F}^\top$ and then maximize this term to achieve... | Summary: In this paper, a novel K-Means clustering was proposed, named unified K-Means clustering with label-guided manifold learning, to solve the problems of traditional K-Means algorithm, such as the sensitivity of initial centroid selection, the limited recognition ability of intrinsic manifold structure of nonline... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition and valuable comments. Here are our responses:
**1. Connection to manifold learning and its advantages**
**A**: We have shown the equivalence between K-means and manifold learning by transforming K-means into the form of manifold learning using the cluste... | Summary: The manuscript presents a new balanced k-means clustering framework based on manifold learning for k-means clustering problem. The framework formulates balanced k-means as an optimization problem about the clustering label matrix and realizes clustering in one step by minimizing the objective function. In this... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition and valuable comments. Here are our responses:
**1. Necessity of balanced clustering**
**A**: Many clustering algorithms like K-Means are based on distance tend to assign more data samples to larger clusters, which may lead to a decrease in algorithm perf... | Summary: This work introduces an innovative centerless K-means clustering framework combined with manifold learning to improve clustering robustness and accuracy. By eliminating centroid initialization and utilizing a label matrix for similarity computation, the proposed method aligns manifold structures with class lab... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition and valuable comments. Here are our responses:
**1. The method relies on selection of $\lambda$**
**A**: In the model proposed in this paper, the parameter $\lambda$ is a hyperparameter associated with the $\ell_{2,1}$-norm of the matrix $\mathbf{F}$. It ... | null | null | null | null | null | null |
Improving Compositional Generation with Diffusion Models Using Lift Scores | Accept (poster) | Summary: This paper aims to improve compositional generation at inference time via rejection sampling using Lift scores on each condition to be composed.
**Update after rebuttal**
I appreciate the additional data (which I found convincing) and clarifications provided by the authors during the rebuttal. I was previousl... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We address your concerns as follows:
> Chamfer Distance
We chose Chamfer Distance because it (1) applies to uniform distributions and (2) is sensitive to out-of-distribution samples. KL is inapplicable for out-of-distribution samples with undefined density r... | Summary: - The paper introduces a novel criterion CompLift for rejecting samples of conditional diffusion models based on lift scores.
- For compositional generation, i.e., cases in which the condition for sampling, e.g, a text prompt can be described as a composition of conditions (like desired individual objects in t... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address your concerns below.
> Overstatement of minimal
We will modify the abstract to make the contribution accurate as "significantly improved the condition alignment for compositional generation".
> Limited comparisons: no T=50 for vanilla CompLift; m... | Summary: This work proposes CompLift, a resampling criterion based on the concept of lift scores used to improve the compositional generation capabilities of pretrained diffusion models. CompLift approximates the lift scores with the diffusion modules noise estimation, without requiring any external reward modules to m... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We address your concerns as follows.
> Relationship to CAS [1]
Thank you for pointing out this important related work, which we previously overlooked. We will add reference to this valuable work, and incorporate a discussion of CAS in the... | Summary: This paper proposes a training-free post-processing approach, CompLift, to select images with specified concepts from diffusion model-generated image candidates. The main idea is to use the lift score, which is equivalent to point-wise mutual information, to evaluate if conditioning $c$ reduces uncertainty of ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and questions. We address your concerns below:
> On CompLift's dependence on underlying generative model
We agree and will add this theoretical limitation to our Conclusion. While theoretically CompLift cannot improve if the base method produces no accurate i... | null | null | null | null | null | null |
OV-MER: Towards Open-Vocabulary Multimodal Emotion Recognition | Accept (poster) | Summary: This paper proposes a new platform for emotion recognition studies. It extends the previously released MER2023 dataset, where GPT-3.5 is heavily utilized to group emotions meaningfully. For comparison, the evaluation benchmark includes many existing LMMs.
Claims And Evidence: This research aims to overcome th... | Rebuttal 1:
Rebuttal: **Q1:** This research aims to overcome the limitations of previous studies that rely on predefined taxonomies to capture more complex, subtle emotions. However, the grouping and emotion recognition performance results are not so different from those of the existing studies. The methods and evaluat... | Summary: This paper extends traditional MER and introduces a novel task called open-vocabulary MER (OV-MER). The primary motivation behind this is to expand the scope of emotion recognition to encompass more fine-grained emotion labels. Since OV-MER is a newly proposed task lacking datasets, metrics, and baselines, the... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and recognition of our contributions to advancing MER research. Your comments on our work are truly valuable to us.
**Q1:** In Table 2, some baseline models include both 7B and 13B versions. Please specify which version is used in the leaderboard.
*... | Summary: The paper presents a novel paradigm for Open-Vocabulary Multimodal Emotion Recognition (OV-MER), addressing the limitations of existing MER systems that rely on predefined emotion taxonomies. The key contributions include:
1. A new MER paradigm (OV-MER): Unlike traditional MER, which limits emotions to a fixed... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our work. We greatly appreciate your recognition that OV-MER represents a significant advancement in MER systems, enhancing their generalizability and enabling more human-like emotional understanding in AI applications.
**Q1:** While human reviewers refine ... | Summary: This paper proposes a novel paradigm by integrating the open-vocabulary concept into Multimodal Emotion Recognition (MER), which facilitates emotion prediction without relying on predefined categories. Specifically, the authors introduce a new dataset generated via their proposed CLUE-Multi Generation method, ... | Rebuttal 1:
Rebuttal: **Q1:** CLUE-Multi achieves higher scores than GPT-4V. This performance gap suggests potential methodological issues and undermines the credibility of the results.
**A1:** We believe the reviewer may have misunderstood the results in Table 2. As explained in Section 4.1 and illustrated in Figure ... | null | null | null | null | null | null |
Gandalf the Red: Adaptive Security for LLMs | Accept (poster) | Summary: The paper introduces a crowdsourced platform (RedCrowd) to test model security defences against prompt attacks and quantify their usability impact. Towards that they propose D-SEC a model for assessing the security-utlilty trade off. Using 279,000 attacks they demonstrate the strengths and weaknesses of variou... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper.
We group your comments and questions below and respond to each separately.
**Generalizability of the results**
- The D-SEC framework is not specific to any one application and generalizes beyond the experimental “password extraction” setup prese... | Summary: This paper points out that the current defenses against jailbreaking could not block adaptive attacks but impose useability penalties on common users. They propose D-SEC, a threat model which models the attackers and the common users in a session view. They then build a platform called RedCrowd to collect prom... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper.
We appreciate the reviewer’s interest in the safety policy used in RedCrowd. We have now clarified our ethics, privacy and safety policy in the manuscript by adding the following to the introduction: “RedCrowd is a white-hat red-teaming system de... | Summary: - This paper tackles a really important issue in LLM security – how do we stop prompt attacks without making the user experience terrible? I really liked how the authors separate attackers from regular users in their D-SEC model. A lot of past work just looks at how well a defense blocks attacks, but this pape... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper.
We have addressed both of your suggested improvements in the manuscript and respond explicitly to the comments below.
**Additional citation and discussion on real-world applications**
- We will make the implications of RedCrowd for real world de... | Summary: - the paper studies LLM prompting attacks (crafting prompts to (adversarially) manipulate model behavior
- the paper provides the following:
- "D-SEC", a threat model for prompting attacks that:
- encompasses an attacker, a model user (who wishes to use the system for benign purposes), and the model deve... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper.
Below, we respond to all of the concerns and questions in your review. Please let us know if we missed anything or should expand on our responses.
**Comments regarding the significance and novelty of the D-SEC framework**
- We appreciate the re... | null | null | null | null | null | null |
Maintaining Proportional Committees with Dynamic Candidate Sets | Accept (poster) | Summary: This paper consider a series of problems about modifying the winner set of a multi-winner election when there are changes on candidates. The paper investigate three types of voter preferences: rankings, metric-space, and approval (0-1). Both positive (algorithms satisfying certain fairness axioms) and negative... | Rebuttal 1:
Rebuttal: - Explain your contradiction for Theorem 3.1
Answer: We are given a profile with $n$ voters and a committee $f(t-1)$ that previously satisfied PSC. At time $t$, a new candidate $c_t$ becomes feasible, and $f(t-1)$ may now fail to satisfy PSC -- we would like to fix that with a single swap involvi... | Summary: This paper introduces a temporal element to multi-winner voting by studying a model wherein the set of candidates changes over time. This is separated into three settings: incremental (candidates are added over time), decremental (candidates are removed over time), and fully dynamic (candidates can be added or... | Rebuttal 1:
Rebuttal: Thank you very much for the kind review!
- I would be interested to hear your perspective on the informativeness of any experiments that might be done on this setting.
Answer:
We have thought about this for a bit. There are a few possible experiments we could see. Firstly, we already did a bit... | Summary: The paper considers multiwinner voting rules when the candidate sets are dynamic in one of 3 ways - candidates arrive one at a time, leave one at a time, or a mix of both. They consider 3 different types of preference classes - ordinal, distance-based, and approval based. They show that in some settings, there... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions. We implemented the changes (for the next version). | null | null | null | null | null | null | null | null |
Latent Mamba Operator for Partial Differential Equations | Accept (poster) | Summary: This paper introduces LaMO, which is an SSM-based neural operator designed to overcome the computational limitations of traditional neural operators for solving PDEs. It establishes a kernel integral interpretation of the SSM framework, proving its equivalence to integral kernel neural operators. It achieves a... | Rebuttal 1:
Rebuttal: Thank you for the positive comments. Please see the responses to your questions below.
>Computational complexity analysis: While LaMO is claimed to be more efficient, a more precise breakdown of runtime (e.g., FLOPs, GPU memory usage per operator) would improve the claims.
**A:** The above respo... | Summary: - The Latent Mamba Operator (LaMO) is a scalable state-space model integrated with a kernel integral formulation.
- The authors also provide a theoretical foundation for their approach.
- LaMO demonstrates state-of-the-art performance across various problems.
Claims And Evidence: The authors present a nov... | Rebuttal 1:
Rebuttal: Thank you for the positive comments. Please see the responses to your questions below.
>They also conduct scaling experiments, particularly focusing on the amount of training data. While Figure 2 implicitly provides scaling behavior with model size, a more detailed analysis of how the model scale... | Summary: This paper introduces a new approach to solving PDEs by introducing the SSM-based Neural Operator on the latent space. The proposed method achieves a good balance on performance and efficiency. The authors provide theoretical analysis that reveals the equivalence of LaMO with kernel integration. With extensive... | Rebuttal 1:
Rebuttal: Thank you for the positive comments and for supporting the work. Please see the responses to your questions below.
>Results in Supplementary E.3 show that the efficiency of your model is only comparable to TRANSOLVER, but the main text presents it as if your model is superior in all aspects/datas... | Summary: This paper introduces a methodology to use Mamba based architecture to model the spatial dynamics of a PDE, in an operator based setting. The authors take inspiration from the Perceive model and use a latent space, in addition to directly modeling the input physical properties, however instead of a Transformer... | Rebuttal 1:
Rebuttal: Thank you for the positive comments and for supporting the work. Please see the responses to your questions below.
>Sample Complexity: However, the theorem does not...
**A:** A Monte Carlo integral approximation typically has $O\left(\frac{1}{\sqrt{n}}\right)$ convergence rate which is empirica... | null | null | null | null | null | null |
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics | Accept (poster) | Summary: This paper presents the first comprehensive benchmark study on defense mechanisms for image quality assessment (IQA) metrics, systematically evaluating the performance of 30 defense strategies against 14 adversarial attacks on 9 IQA models.
Claims And Evidence: All the content submitted has corresponding evid... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their thorough evaluation and constructive feedback, and will apply their recommendations to refine the revised paper. We answer your questions below:
1. For adversarial training, there is no inference overhead since the fine-tuning is done during the training ... | Summary: This paper addresses the vulnerability of neural-network-based Image Quality Assessment (IQA) metrics to adversarial attacks. The authors present the first comprehensive benchmark for evaluating defense mechanisms against such attacks. The study systematically evaluates 30 defense strategies—including purifica... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions and thoughtful feedback. We will use suggestions to enhance the revised version of the paper. Your questions are answered below:
1. The discrepancy between subjective and objective metrics is not uncommon in IQA tasks. While Real-ESRGAN may not achieve the b... | Summary: The manuscript presents a comprehensive benchmark on defenses against adversarial attacks targeting neural network-based Image Quality Assessment (IQA) metrics. It evaluates 30 defense strategies across three categories (purification, adversarial training, and certified methods) against 14 adversarial attacks.... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable comments and questions. We appreciate the recognition of our study and analysis and will address the questions below:
1. We employed three metrics for attack strength estimation depending on the attack type. For $L_{\infty}$, we chose $\frac{2}{... | Summary: The paper proposes a benchmark for defending neural-network based image quality assesment (IQA) against adversarial attacks. The paper makes an extensive study with numerous datasets, IQA models and adversarial attacks of different types and discusses the evaluation results.
Claims And Evidence: I think, the ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and detailed feedback. We truly value your questions and your time reviewing our paper. We address your concerns below:
1. Our contributions include a novel methodology for comparing defenses for the IQA tasks, addressing a critical gap in the field, an extensive subj... | Summary: Image Quality Assessment (IQA) mostly uses DNNs to calculate the score, leaving space for attackers to perturb the image to manipulate the score for commercial advantage in ranking. Compared to (Antsiferova et al., 2024) that benchmark attacks to IQA, this paper benchmarks defenses in this task. It considers 1... | null | null | null | null | |
Learning Mean Field Control on Sparse Graphs | Accept (poster) | Summary: The paper studies mean field multi-agent methods on sparse graphs.
Claims And Evidence: The paper is clearly written, and the reviewer is not aware of any misleading claims. However, some definitions could be improved.
For instance, in Definition 2.1, it is unclear what the expectation on the right-hand si... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and constructive evaluation of our work.
The reviewer raised the concern that “[…] in Definition 2.1, it is unclear what the expectation on the right-hand side is defined over. […] the source of randomness needs to be explicitly stated.” Thank you fo... | Summary: The main focus of this paper is a variation of mean field control (MFC) problems in which the agents' interactions are encoded by a graph-like structure which is not necessarily uniform, contrary to standard MFC. After studying the foundation of the problems (well-posedness and connection with finite-agent pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and the constructive feedback.
The reviewer noted that: “While the theoretical analysis of the control problem is interesting, I am not sure if this conference is the best fit. It would be better to develop further the learning aspects.”
We agree ... | Summary: The paper proposes a Local Weak Mean Field Control (LWMFC) model to address cooperative control in sparse networks, leveraging a local weak convergence framework to overcome limitations of traditional graph-theoretic methods in scenarios with finite average degree but diverging variance. Experiments demonstrat... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our work and the detailed positive comments.
The answer with respect to the reviewer’s questions on $k^*$ is as follows. In all computational examples we set $k^* = 10$ for the standard LWMFC approximation, and $k^* = 4$ for the extensive LWMFC* approxi... | null | null | null | null | null | null | null | null |
Efficient Time Series Processing for Transformers and State-Space Models through Token Merging | Accept (poster) | Summary: This paper introduces local token merging, a novel algorithm for accelerating time series processing in transformers and state-space models (SSMs). A domain-specific token merging method that computes token similarities within a constrained neighborhood (size k), reducing complexity from quadratic to linear (... | Rebuttal 1:
Rebuttal: Dear Reviewer 6Amp,
Thank you for taking the time to read our paper and for your valuable questions. We are happy to answer them in the following. To this end, we conducted 5 new experiments. Please find anonymous results here: https://figshare.com/s/679d2c1d825228385b2d
**Q:** Gap between token... | Summary: This paper introduces a novel local token merging algorithm for time series models aimed at reducing the computational burden of processing long sequences in transformers and state-space models. By merging tokens within local neighborhoods, the method scales the complexity from quadratic to linear while preser... | Rebuttal 1:
Rebuttal: Dear Reviewer mSjb,
Thank you for your valuable feedback and effort.
The "Rough Transformers" paper is very interesting and we will further discuss it in our related work section. Thank you for pointing us to that. We would like to emphasize that we see local merging as a method to accelerate a... | Summary: This paper proposes to apply Token Merging (ToMe), which was originally developed for vision transformers, to time series models.The main difference between the author’s work and standard token merging is the use of local neighborhoods, ie local merging. The original ToMe formulation allowed tokens from differ... | Rebuttal 1:
Rebuttal: Dear Reviewer eLzT,
Thank you for taking the time to read our paper and for your valuable comments. We are happy to answer them in the following.
**Q:** Local merging is a minimal contribution. \
**A:** We see our main contribution in investigating token merging for time series in great detail.... | Summary: The paper proposes local token merging to improve transformer efficiency. Building up on Bolya 2023, the proposed method appear to compute similar tokens within a local neighborhood (as opposed to all to all) and merge them. There are other techniques mentioned in the text, but the writeup is not organized eno... | Rebuttal 1:
Rebuttal: Dear Reviewer HVPc,
Thank you for taking the time to read our paper. We would like to address your concerns in the following:
**Q:** Write-up of the paper \
**A:** We are sorry, that you were confused by the write-up. We will rework the writeup of our Methods and Introduction section to point ou... | null | null | null | null | null | null |
Weakly-Supervised Contrastive Learning for Imprecise Class Labels | Accept (spotlight poster) | Summary: This paper proposes a graph-theoretic framework for contrastive learning with weakly-supervised information. This framework is recognized as effective according to the superior results in noisy label learning and partial label learning by introducing the continuous semantics similarity to define positives and ... | Rebuttal 1:
Rebuttal: Dear reviewer LHXf:
Thanks for your valuable suggestions, we will try to address your concerns and we are eager to engage in a more detailed discussion with you.
> **W1: Lack of in-depth analyses of experiment results...**
- We analyze the effectiveness of the proposed method in detail in the t... | Summary: The paper introduces a graph-theoretic framework for weakly supervised contrastive learning, leveraging continuous semantic similarity to better utilize ambiguous supervisory signals from imprecise class labels. This approach enhances model performance on multiple benchmark datasets in noisy and partially labe... | Rebuttal 1:
Rebuttal: Dear reviewer kufB:
Thank you for your valuable suggestions. We truly appreciate your review and are committed to addressing your concerns with care and attention. We sincerely look forward to engaging in a more in-depth discussion with you, as your insights are essential in helping us improve an... | Summary: This paper tackles a key challenge in contrastive learning: handling real-world datasets with messy or ambiguous labels. The authors propose replacing traditional binary positive/negative pairs with "continuous semantic similarity," modeled via a graph where edge weights reflect how likely two examples belong ... | Rebuttal 1:
Rebuttal: Dear reviewer x5zp:
Thank you for your valuable suggestions. We truly appreciate your review and are committed to addressing your concerns with care and attention. We sincerely look forward to engaging in a more in-depth discussion with you, as your insights are essential in helping us improve an... | Summary: This work rethinks contrastive learning for noisy real-world settings by replacing rigid class-based positive/negative sampling with adaptive, graph-driven “semantic similarity”. By blending self-supervised augmentations with weak supervision signals (e.g., noisy/partial labels), the method achieves state-of-t... | Rebuttal 1:
Rebuttal: Dear reviewer jhEr:
Thank you for your valuable suggestions. We truly appreciate your review and are committed to addressing your concerns with care and attention. We sincerely look forward to engaging in a more in-depth discussion with you, as your insights are essential in helping us improve an... | null | null | null | null | null | null |
LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation | Accept (poster) | Summary: This paper introduces LaMAGIC2, a circuit formulation approach analog topology generation. The authors identify limitations in previous methods, particularly LaMAGIC, which used inefficient circuit representations with quadratic token length complexity and showed low sensitivity to numeric input precision. Exp... | Rebuttal 1:
Rebuttal: ### Addressing limitations
Thanks for your advice on discussing the weakness of our methods. This is a helpful idea to contribute more to the community.
The circuit space will grow exponentially with number of nodes increases. In addition, the simulation time for larger circuits will also incre... | Summary: This paper introduces LaMAGIC2, an improved approach for language model-based analog topology generation. It proposes SFCI, which enhances component recognition, reduces token complexity from $O(|V|^2)$ to $O(|V| + |E|)$, and improves numerical precision sensitivity. The method achieves a 34% higher success ra... | Rebuttal 1:
Rebuttal: ## Question for computational efficiency
Based on the question, we further record the training steps that required for matrix formulation (SFM) and the succinct canonical formulation with identifier (SFCI). Specifically, SFM saturates at 8943 steps, and SFCI converges at 6886 steps. This shows th... | Summary: This paper addresses the automation of analog topology design, which aims to determine the optimal connections between given nodes while satisfying various constraints. Existing methods, including search-based and reinforcement learning-based approaches, are often inefficient. This paper analyzes the structure... | Rebuttal 1:
Rebuttal: ## Clarification on previous work and our methodology
We thank the reviewer for this important comment. Our paper focuses on developing supervised fine-tuning (SFT) methods for language models in analog topology generation task. Our SFCI formulation contributes these key innovations:
1. It propose... | Summary: This paper proposes LaMAGIC2, introducing succinct formulations (SFM and SFCI) for language-model-based analog circuit topology generation. Compared to the previous LaMAGIC approach, these formulations effectively reduce output sequence length and improve component-type recognition. Experiments demonstrate tha... | Rebuttal 1:
Rebuttal: ### Q1: Choice of Model Architecture
We appreciate the suggestion to evaluate our proposed formulations using other model architectures. In this work, we adopted T5 to maintain architectural consistency with previous work LaMAGIC, enabling a fair comparison focused on the impact of new formulation... | null | null | null | null | null | null |
BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference | Accept (poster) | Summary: This paper introduces BlockDialect, a block-wise fine-grained mixed format quantization technique designed to enhance the energy efficiency of large language model (LLM) inference. Unlike traditional quantization methods that focus on scaling values, BlockDialect assigns a number format to each block using a p... | Rebuttal 1:
Rebuttal: **Performance comparison of BlockDialect (BDFP4), NVFP4, and MXFP4 across various model sizes and architectures**
* We compare accuracy and perplexity for both `Linear` layer quantization and `Full`-path (including activation-activation multiplication) quantization. Perplexity (PL) and common reas... | Summary: This work presents BlockDialect, a block-wise mixed format quantization method for energy-efficient LLM inference. It assigns each block an optimal number format from a predefined formatbook to better capture data distributions. The proposed DialectFP4, a set of FP4 variants, enhances flexibility while maintai... | Rebuttal 1:
Rebuttal: **Evaluating the impact of BlockDialect on inference latency and energy consumption**
* That’s a valid point. Given the clear energy and latency benefits from low-precision MACs and reduced data movement due to 4-bit weight/activation quantization (including KV cache), we assess the overhead from ... | Summary: They proposed BlockDialect, a block-wise finegrained mixed format technique that assigns a per-block optimal number format from a formatbook for better data representation. DialectFP4 ensures energy efficiency by selecting representable values as scaled integers compatible with low-precision integer arithmetic... | Rebuttal 1:
Rebuttal: **Resource overhead of real-time MSE calculation**
* Qualitatively, MSE-based method requires 16 rounds (per dialect) of quantization, each involving FP16 square mean error accumulations for every block element, whereas our 2-stage selection efficiently operates in a single pass using 5-bit fixed-... | Summary: The paper introduces a block-wise finegrained mixed format technique (called BlockDialect) that assigns an optimal number format to each block and FP4 variants data format (called DialectFP4) that is built on shared exponent among a group of numbers. They also propose the method of efficient online quantizatio... | Rebuttal 1:
Rebuttal: **Reason for Subtracting 2 from the Shared Exponent**
* When normalizing by the maximum exponent, normalized values fall within [0, 2), whereas FP4 variants span [0, 7.5]. Subtracting 2 from the shared exponent extends the range to [0, 8), enabling FP4 variants to represent normalized values witho... | null | null | null | null | null | null |
Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss | Accept (poster) | Summary: This paper presents a new method for addressing loss of plasticity. The authors investigate why Dropout doesn’t help with loss of plasticity, although it helps with generalization. Through empirical work, the authors pinpoint the causes and introduce AID, an improvement over Dropout that maintains plasticity a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and their positive assessment of the paper. We appreciate the recognition of our motivation, empirical scope, and clarity, and we respond below to the concerns raised.
---
> It’s not clear how AID is sensitive to its hyperparameter. The authors ... | Summary: The paper proposes AID, a novel activation function that generalizes Dropout to intervals, where it can be applied with different probabilities to different intervals of the activations. In its simplified version, it has 2 intervals (positive and negative) and can be interpreted as an interpolation between ReL... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and thoughtful questions. We appreciate your recognition of the motivation, experimental setup, and analysis, and we address your concerns below.
---
> 2. b) Reinforcement learning [...] I would expect to see the vanilla method collapse [...]
>
... | Summary: The proposed method in this paper is motivated by the characteristic of dropout, poor trainability and good generalizability. By enhancing the trainability of dropout in AID, the model can have both good trainability and generalizability. The main point of AID is separating the neurons into positive and negati... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. First, we clarify a potential misconception: **Dropout does not significantly improve generalizability.**
> “The proposed method in this paper is motivated by the characteristic of dropout, poor trainability and **good ... | null | null | null | null | null | null | null | null |
From Jack of All Trades to Master of One: Specializing LLM-based Autoraters to a Test Set | Accept (poster) | Summary: While traditional LLM evaluators (Autoraters) are generally trained for generalization to a broad set of tasks, this paper studies specializing existing models into autoraters for particular known test sets. In particular, the proposed new prompting strategy leverages in-context learning examples obtained from... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you very much for your careful and comprehensive review of our paper. We appreciate that you recognize our sound experimental setup and clear presentation, as well as convincing results and parameter studies. We would like to address your concerns about the practicality of the... | Summary: This paper presents a novel approach to enhancing automatic evaluation metrics based on LLMs, focusing on Machine Translation (MT) evaluation. The authors propose a Specialist method that leverages historical human-generated ratings on a fixed test set to construct ICL demonstrations. This specialization allow... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you very much for your feedback, which has helped us to improve our paper. In response to your primary concern, namely that the Specialist method's "effectiveness on tasks with fundamentally different characteristics (e.g., open-ended generation, [...]) remains uncertain", we ... | Summary: This paper introduces an LLM-based automatic evaluation method, called "Specialist". At a high-level, Specialist closely imitates a human rater's behavior by making ICL examples from the ratings (1) of the same rater (2) on the same example (3) on different model outputs. In other words, the only place where e... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you very much for your positive and thorough review of our paper. We will address your outstanding concerns below, starting with the question which you stated as most important.
> Can the authors explain to me what they are trying to achieve in 5.4.1 and what the results mean... | Summary: This paper introduces the "Specialist" method, a novel approach to specialize LLM-based Autoraters to specific test sets by leveraging historical ratings through in-context learning (ICL) examples. The method is applied to fine-grained machine translation (MT) evaluation (AutoMQM), achieving significant perfor... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you very much for your thoughtful and detailed review of our paper. We will address your concerns below.
>*Q1*: Could you clarify how their method relates to, differs from, or improves upon BatchEval: Towards Human-like Text Evaluation? (same as *Weakness 3*)
BatchEval is onl... | null | null | null | null | null | null |
LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding | Accept (poster) | Summary: This paper introduces LongVU, a video-LLM designed for Long Video-Language Understanding. LongVU is built on three main components: (1) DINOv2 Features, which is used to remove redundant frames that exhibit high similarity, (2)Text-Guided Cross-Modal Query, which can selective frame feature reduction and (3)S... | Rebuttal 1:
Rebuttal: **1. The paper claims that STC helps compress video tokens, but it does not provide quantitative details on how many tokens are actually reduced.**
Please refer to Figure 4(b), where we illustrate the reduction in the number of tokens after applying STC across different video durations. On avera... | Summary: This paper proposes to reduce long video context by: 1) temporal frame reduction based on DINO feature similarity to extract keyframes (DINO module), 2) cross-modal query (Query module) to capture important tokens, 3) Spatial Token Compression (STC module) to further reduce tokens for excessively long videos.
... | Rebuttal 1:
Rebuttal: **1. The core idea of Query and STC lacks substantial novelty. Cross-modal query are known to reduce sequence length long time ago[1], and sparsity in MLLM has already been explored by FastV [2]. This paper differs from FastV in that it compresses tokens before feeding into LLM, but the authors di... | Summary: This paper introduces a novel spatiotemporal token compression method named LongVU, designed for long video understanding. Specifically, LongVU divides the video token compression process into three stages: Temporal Reduction, Selective Feature Reduction, and Spatial Token Compression.
In the Temporal Reducti... | Rebuttal 1:
Rebuttal: **1. The drawbacks of uniform sample and dense sampling.**
Numerous studies, including LLaVA-OneVision, LongVA, and SlowFast-LLaVA, have explored the trade-offs between these approaches. Below, we present results of the baseline LongVA using either uniform sampling or dense sampling (1fps, with t... | Summary: The paper proposes LongVU, a method that addresses the challenge of processing long videos within multimodal language models' limited context by implementing a three compression approaches: reducing temporal redundancy through inter-frame similarity, leveraging cross-modal dependencies, and eliminating spatial... | Rebuttal 1:
Rebuttal: **1. LLama-VID OOM issue.**
Thanks for your concern. We encountered an OOM issue while running on an 80GB A100 GPU for long videos, using the same settings as other comparison models. To address this, we need to precompute the video features in advance and then load the entire model for inference... | null | null | null | null | null | null |
The Choice of Normalization Influences Shrinkage in Regularized Regression | Reject | Summary: The paper proposed to study the nature and impact of feature normalization schemes with respect to linear models under the L1, L2, and Elastic-Net penalties. This is done only for regression, and is focused particularly on binary features. The results are primarily theoretical in nature, with a limited number ... | Rebuttal 1:
Rebuttal: Thank you for your extensive review of our paper. We appreciate the time and
effort you have put into providing feedback and hope that our responses
will address your concerns. We will start by addressing your comments
regarding the experimental design and request for additional experiments.
## E... | Summary: This paper investigates how different normalization strategies affect the shrinkage in regularized regression models such as Lasso, Ridge, and Elastic Net. The authors analyze the impact of normalization on binary and continuous features, noting that class balance directly affects regression coefficients and t... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper. We appreciate the time and effort you
have put into providing feedback and hope that our responses to your comments
will address your concerns.
> ## Claims And Evidence
>
> (1)The evaluation is based on synthetic and small-scale data, which is far from
> th... | Summary: This paper studies the effects of input normalization for LASSO, Ridge, and ElasticNet regression, focusing on normal and binary features. See below for more detailed discussions on the settings and contributions of this paper.
Claims And Evidence: yes.
Methods And Evaluation Criteria: yes.
Theoretical Clai... | Rebuttal 1:
Rebuttal: Thank you for your detailed review of our paper. We appreciate the time and
effort you have put into providing feedback. We will start by addressing
your comments regarding the assumption of orthogonality.
## Assumption of Orthogonality
We agree that the assumption is strong and unrealistic. Nev... | null | null | null | null | null | null | null | null |
Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence | Accept (poster) | Summary: This paper extends the toy model and task setup of [Reddy 2023] to the multi-task case, which requires models to infer tasks from in-context learning to label the last token.
Their main findings show that in this setup there are three phases in learning, where each phase is associated with the transition of a... | Rebuttal 1:
Rebuttal: Thank you for your detailed review.
We added experiments (notably Figures 21, 23, 24, 25, 26 and 27), in the following website: https://sites.google.com/view/in-context-meta-learning.
**> W1**
> Experiment training with data of varying context lengths and next token prediction
We conducted ad... | Summary: The paper proposes a problem setting named In-Context Meta-Learning (ICML) with multiple tasks.
For the same query, the answer would be different from task to task, so the model needs to infer the task to make a prediction.
Trained on this setting, the paper found that the model training has multiple phases: (... | Rebuttal 1:
Rebuttal: Thank you for your detailed review.
We revised the figures and added experiments (notably Figures 17 and 22) in the following website: https://sites.google.com/view/in-context-meta-learning.
**> W1**
> The red block potentially make it even more hard to observe
We replaced the red squares wit... | Summary: The main contributions of this paper are the following.
1. A novel synthetic in-context sequence modelling data set, based on identifying which of a number of classification rules are active and using it to predict the label of the query.
2. Demonstrating through analysis of the loss and attention mechanisms ... | Rebuttal 1:
Rebuttal: We appreciate the reviewers’ insights and address each point below.
**> W1**
> It seems to me that the RLA should be higher during SCC than during FCC.
Random Label Accuracy (RLA) can increase whenever label information is incorporated into the final token’s prediction. Even if the model uses ... | Summary: The paper investigates how transformers acquire in-context learning (ICL) abilities by extending a simple copy task into an In-Context Meta Learning (ICML) setting that requires task inference rather than simply copying from the context. The authors train a two‐layer, attention-only transformer on a synthetic ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review.
We revised the figures and added experiments (notably Figures 17, 18, 19, 20 and 21) in the following website: https://sites.google.com/view/in-context-meta-learning.
**> W1**
> attention maps in Figure 2, it is not simple to identify the circuits.
Please s... | null | null | null | null | null | null |
Test-time Adapted Reinforcement Learning with Action Entropy Regularization | Accept (poster) | Summary: This submission proposes Test-time Adapted Reinforcement Learning (TARL) to address the transfer gap between offline learning and online testing. TARL has two main components: minimizing the entropy of action probabilities by filtering actions with high entropy and efficiently updating only the layer normaliza... | Rebuttal 1:
Rebuttal: >Q1. The improvements reported on most tasks are relatively marginal. This raises questions about the practical significance and robustness under different conditions and task complexities.
A1. Thanks for your valuable feedback. The marginal improvements should be interpreted through four aspects... | Summary: This paper presents Test-time Adapted Reinforcement Learning (TARL) for the distribution shift issue of offline RL. TARL creates unsupervised test-time objectives for different control tasks. Moreover, it uses a KL divergence constraint to avoid bias. TARL only updates layer normalization parameters during tes... | Rebuttal 1:
Rebuttal: >Q1. In Line 70, the authors claim that they propose a novel offline reinforcement learning paradigm. However, the method belongs to a test-time adaptation method. Is there a conflict between offline reinforcement learning and test-time adaptation? I suggest further clarification on this.
A1. Tha... | Summary: This paper introduces Test-Time Adapted Reinforcement Learning (TARL), a method designed to help offline RL policies adapt to distribution shift during deployment by leveraging test data—without needing additional reward signals. The core idea involves (1) learning objectives that minimize policy entropy for n... | Rebuttal 1:
Rebuttal: >Q1. Your paper seems to rest on the hypothesis that selective training on low-entropy states enables beneficial knowledge transfer to other similar states. The paper should provide ablation studies comparing selective entropy minimization against global entropy minimization.
A1. Thank you for yo... | Summary: This paper proposes TARL, a framework that minimizes action uncertainty at test time to mitigate distribution shift issues.
Claims And Evidence: The authors conduct experiments on the D4RL and Atari benchmarks to validate the effectiveness of their framework.
Methods And Evaluation Criteria: Yes, the evaluat... | Rebuttal 1:
Rebuttal: > Q1. Implementing the TARL framework on top of other offline RL algorithms, such as IQL.
A1. Thank you for your valuable feedback. The state-action out-of-distribution (OOD) issues stem from data distribution shifts between training and testing phases, rather than being specific to particular of... | null | null | null | null | null | null |
A Memory Efficient Randomized Subspace Optimization Method for Training Large Language Models | Accept (poster) | Summary: The paper highlights a critical limitation in existing methods: while prior approaches have focused on reducing the memory burden of optimizer states, they have largely overlooked the substantial memory consumption imposed by activations—especially in scenarios with long context sequences or large mini-batches... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful feedback on our manuscript. Below are our detailed responses to the weaknesses and questions you raised:
- Questions:
1. We would like to clarify that, as stated in our manuscript, $\eta_k$ is required to satisfy $\eta_k \le 1/\hat{L}$ to guarantee theoreti... | Summary: The paper introduces a Randomized Subspace Optimization (RSO) framework for LLM training, breaking the problem into lower-dimensional subproblems to reduce memory and communication overhead. It also offers comprehensive convergence guarantees for various optimizers, with refined results for Adam.
Claims And E... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and valuable feedback on our manuscript. Below, we provide our detailed responses to the questions you raised.
- Questions:
In our convergence analysis, we allow the use of different optimizers to solve each subproblem. In our experiments, we employ the Adam... | Summary: The paper introduces a new optimization method designed to reduce communication costs in distributed training of large language models (LLMs). The method proposed by the authors is based on breaking up the problem into smaller low-rank subproblems that we can optimize using arbitrary optimizers, by introducing... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our manuscript. We greatly appreciate your valuable feedback. Below, we provide our responses to your comments:
- Weaknesses:
- In the RSO method, a series of subproblems need to be solved. However, this does not introduce additional complexity in tunin... | Summary: The paper introduces a memory-efficient optimization method named RSO, which optimizes subspace variables rather than the original weight. Different from the conventional subspace method such as Galore, which projects the original weight's gradient into subspace, they propose to directly optimizes the subspace... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our manuscript. We greatly appreciate your valuable feedback. Below, we provide our responses to your comments:
- Experimental Designs Or Analyses:
In Table 5, we set the rank to one-fourth of the embedding dimension for both the LLaMA-1B and LLaMA-7B mo... | null | null | null | null | null | null |
Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks | Accept (poster) | Summary: The paper proposes using a taylored prior-fitted network to extrapolate neural scaling laws. A new prior over the set of potential curves is designed, capturing both simple power law behavior and more complex double-descent curves. The method is evaluated extensively on several data sets and shows improved per... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful comments. We address each of your comments below. If you have further questions, we’d be happy to discuss them during the author-reviewer interaction phase.
---
> **[Q1]** The MCMC models appear to be specified without noise... This makes the Bayesian infer... | Summary: The authors propose a method to infer scaling laws using a Bayesian approach which allows them to model predictive uncertainty.
To accomplish this, they rely on prior-data fitted networks (PFNs) introduced by Müller et al., (2022) and their ability to meta-learn from large amounts of synthetic data.
The met... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful comments. We address each of your comments below. If you have any further questions or comments, we would be happy to discuss them during the author-reviewer interaction phase.
---
>**[Q1]** Missing related work - Neural Processes.
- Thank you for the sugge... | Summary: This paper proposes the use of Bayesian Neural Networks (BNNs) to model deep learning scaling laws.
Scaling laws in deep learning have gained a considerable amount of interest recently, as they provide quantitative ways to estimate, for example, the amount of computational resources, model size, or dataset siz... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful comments. We address each of your comments below. If you have any further questions or comments, we would be happy to discuss them during the author-reviewer interaction phase.
---
>**[Q1]** Additional experiments - calibration of the predictive uncertaint... | Summary: In this paper, the authors investigate the use of Prior-Data Fitted Networks (PFNs), a Bayesian framework for estimating neural scaling law curves. Existing methods typically produce point estimates, which fail to capture the true scaling law dynamics across different scenarios (e.g., double descent, flat cut-... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful comments. We address each of your comments below. If you have any further questions or comments, we would be happy to discuss them during the author-reviewer interaction phase.
---
>**[Q1]** Main novelty of this work compared to LC-PFN at both high- and lo... | null | null | null | null | null | null |
Monte Carlo Tree Diffusion for System 2 Planning | Accept (spotlight poster) | Summary: The paper introduces Monte Carlo Tree Diffusion (MCTD) for long-horizon planning problems. MCTD combines the benefits of generative Diffusion models with tree-based planning in Monte Carlo Tree Search, without the requirement of a forward dynamics model. The primary aim is to utilise Diffusion models for impro... | Rebuttal 1:
Rebuttal: **Essential References Not Discussed**
We thank you for recommending traditional long-horizon planning literatures. We will incorporate these valuable references in our revised version.
> ... the lack of details provided in the Experimental Setup. ... lacking in the main manuscript.
>
Thank yo... | Summary: The paper "Monte Carlo Tree Diffusion for System 2 Planning" introduces Monte Carlo Tree Diffusion (MCTD), a novel algorithm that merges diffusion models with Monte Carlo Tree Search (MCTS) to enhance test-time compute scalability in planning with diffusion models. It proposes three key innovations: restructur... | Rebuttal 1:
Rebuttal: **Relation To Broader Scientific Literatures**
We thank you for acknowledging our work's connection to the broader scientific literatures. We will ensure these connections are clearly articulated in the revised version.
> Lack of ablations investigating the parameters of MCTS search. In particul... | Summary: Standard diffusion-based planners don't improve with additional test-time computation. This limits their effectiveness on complex long-horizon tasks.The authors introduce Monte Carlo Tree Diffusion (MCTD) which formulates the diffusion process in such a way that each denoising process can effectively "branch o... | Rebuttal 1:
Rebuttal: > The claim of "System 2 planning" is bit of a stretch as the system does not implictly have a world model it can search all variations upon.
>
We acknowledge that there may be differing perspectives on what constitutes System 2 planning in the context of ML. Drawing on Kahneman [1], we define S... | Summary: This paper proposes a new algorithm, MCTD, which involves MCTS in the diffusion model. MCTD uses whole trajectories as its states and introduces a meta-action to generate child nodes based on whether a guidance function is used. By leveraging the MCTS process, MCTD achieves better success rates and runtime per... | Rebuttal 1:
Rebuttal: > I am not sure why we need a **no-guidance action**.
>
Diffusion planners often rely on strong guidance, but under unseen goals or complex long-horizon tasks, this can be detrimental. Allowing “no-guidance” (or weak guidance) provides essential exploration. We demonstrate this by **showing the ... | null | null | null | null | null | null |
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation | Accept (poster) | Summary: The authors outline a generative modeling technique to produce functional solutions to multi-function problems, with an emphasis on emulating multi-function physical systems. This is accomplished by casting the typical DDPM framework into the functional space, where the noise is framed as a Gaussian process. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and valuable suggestions.
> It would be interesting to see how uncertainty intervals or estimates obtained from this method compare to those obtained from other capable methods, such as simformer.
R1: Thank you for the great suggestion — we com... | Summary: The paper presents Arbitrarily-Conditioned Multi-Functional Diffusion (ACM-FD), a novel probabilistic surrogate model for multi-physics emulation. The key contributions include:
- A multi-functional diffusion framework based on DDPM that models noise as multiple Gaussian processes, enabling generation of mult... | Rebuttal 1:
Rebuttal: We thank the reviewer for the many constructive comments.
> Consider adding ablation studies to demonstrate importance of each component (random masks, Kronecker structure)
R1: Great suggestion. Actually, we have already conducted studies on **the effect of random masks**. In Section 5.3, we com... | Summary: The paper introduces Arbitrarily-Conditioned Multi-Functional Diffusion (ACM-FD), a novel probabilistic surrogate model designed for multi-physics emulation. It aims to address limitations of traditional machine learning-based surrogate models, which are typically task-specific and lack uncertainty quantificat... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive comments.
> no analysis of UQ performance.
R1: Great suggestion. Here we add UQ performance evaluation & analysis results.
First, to evaluate UQ quality, we computed the **emprical coverage probability** [1]: $CP = \frac{1}{N} ... | null | null | null | null | null | null | null | null |
Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling | Accept (poster) | Summary: The authors propose a new approximation for Gaussian Process Variational Autoencoders to improve computational efficiency. A GP-VAE is a VAE for the structured data case (e.g. where we have both images Y, and auxiliary information X, such as time, or location) that replaces the independent Gaussian prior over ... | Rebuttal 1:
Rebuttal: We genuinely appreciate your insightful and encouraging feedback and highlighting aspects of our paper that might have gone unnoticed. Below, we hope to address the points in the review.
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### 1. Using auxiliary information $X$
We acknowledge that our non-GP baselines, VAE and HI-VAE, do *not*... | Summary: This paper successfully reduces the computational cost of Gaussian Process Variational Autoencoder (GPVAE) by incorporating the Nearest Neighbour Gaussian Process (NNGP). The experimental results demonstrate that the proposed method achieves high performance while reducing computational costs, compared to exis... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback. We hope to address your concerns below.
---
### 1. Selecting neighbours in the data space vs. the latent space
**A natural extension from NNGPs with local correlation**
Our approach follows the principle of NNGPs, which typically rely on adjacen... | Summary: Variational Autoencoders are deep generative models the learn low dimensional latent representations or high dimensional data, e.g. images in pixel space. When we have extra auxiliarly information, such as images from video and we also know the timestamps of the frames, instead of inferring independant latent ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable feedback. We provide a detailed response below.
---
### 1. Mathematical notations and clarification
Conceptually, our approach closely aligns with the NNGPs proposed by [1], where a hierarchical mechanism enforces local sparsity on inducing variables. To ... | null | null | null | null | null | null | null | null |
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets | Accept (poster) | Summary: This paper proposes TeaRGIB to enhance the robustness of dynamic graph representation learning methods by using the Information Bottleneck (IB) principle to reduce the redundancy and noise in dynamic graphs. TeaRGIB first employs von Neumann entropy (VNGE) to model the evolution of dynamic graphs. And it decom... | Rebuttal 1:
Rebuttal: Thank you for your review. But it seems that you may have mistakenly pasted review comments intended for another paper here. So, we will wait for your correction. | Summary: This paper examines the phenomenon of spurious correlations induced by the cooperative rationalization approach, where a generator selects informative subsets of data as rationales for predictors. It reveals that even clean datasets can develop unintended spurious correlations due to the cooperative game betwe... | Rebuttal 1:
Rebuttal: Thank you for taking the time to carefully review our work and provide constructive feedback.
**Claims&Wekaness1**. The claim seems to broad.
**A1**. Thanks a lot for the valuable suggestion. We will narrow down the scope to the text and graph domains to ensure rigor.
We'd like to kindly clarif... | Summary: This paper addresses a crucial issue in rationalization frameworks. They find that even if the original dataset does not have spurious correlations, the cooperative generator-predictor setup can cause spurious correlations that the predictor can exploit. This paper identifies the cause of this issue and propos... | Rebuttal 1:
Rebuttal: We sincerely thank you for dedicating your time and expertise to review our paper. Your insightful comments and suggestions are highly valued and appreciated.
**Q1**. Experiment of Figure 3: I am not fully convinced that this experiment provides the evidence that the generator-predictor interacti... | Summary: The paper examines the unintended biases in self-rationalizing models, where a generator selects key input segments for a predictor. It reveals that cooperative training can introduce spurious correlations even in clean datasets. An adversarial inspection method is proposed to detect and mitigate these biases,... | Rebuttal 1:
Rebuttal: Thank you deeply for taking the time to thoroughly review our paper.
If we understand correctly, the only weakness you mentioned is about the lack of ablation study.
**Q1**. There is no ablation study on how each of the component of the proposed algorithm contribute to the success the effectiven... | null | null | null | null | null | null |
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch | Accept (poster) | Summary: This paper introduces FunBO, Building on the FunSearch framework to discover novel acquisition functions for BO. FunBO iteratively modifies an initial acquisition function to improve the BO performance. The discovered AFs are provided as explicit code snippets, enhancing interpretability and facilitating direc... | Rebuttal 1:
Rebuttal: Thanks for your in-depth review of our work and valuable feedback.
**Missing Baselines** Our work focuses (footnote 2) on AFs that can be evaluated in closed form given GP posterior parameters thus being fast-to-evaluate and easily deployable while avoiding complexities like Monte-Carlo sampling... | Summary: The authors propose FunBO, a novel method for designing novel acquisition functions (AFs) for BO using LLMs. FunBO aims to design new AFs that perform well across a variety of objective functions. FunBO takes a target set of objective functions and some initial standard AF. Then, FunBO iteratively modifies the... | Rebuttal 1:
Rebuttal: Thanks for your feedback and for highlighting the strengths of our work. We appreciate the constructive comments and recommendation for acceptance. Regarding the specific questions:
**Application to Other Real-World Problems** This is an excellent point. Our primary goal in this initial work was ... | Summary:
The authors present FunBO, an algorithm that leverages an LLM to propose novel acquisition functions (AFs) for Bayesian optimization. The authors evaluate FunBO under three settings, in-distribution (ID), out-of-distribution (OOD), and few-shot. FunBO relies on the availability of a large set of relate... | Rebuttal 1:
Rebuttal: Thanks for your in-depth review of our work and valuable feedback.
**Transformer Cost** We agree that re-training large transformer architectures is expensive. Our statement was intended to mean that a pre-trained transformer surrogate model could benefit from using a cheap-to-evaluate, FunBO-di... | Summary: The paper proposes FunBO, a method to learn novel acquisition functions to increase efficiency of the Bayesian optimisation. In particular, acquisition function is represented as a Python program and FunSearch is adopted to search over programs' space. Authors evaluate their approach on both in-distribution se... | Rebuttal 1:
Rebuttal: Thanks for your feedback and for highlighting the strengths of our work. We hope the responses below clarify the motivation and practical considerations for FunBO.
**Benefits of Representing AFs as Programs** Representing AFs as programs offers the following advantages:
- *Interpretability*: As ... | null | null | null | null | null | null |
SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting | Accept (poster) | Summary: This paper introduces SKOLR, a structured Koopman operator-based approach to time-series forecasting that connects Koopman operator approximations with linear recurrent neural networks (RNNs). By representing dynamic states using time-delayed observations, the method approximates the Koopman operator in a stru... | Rebuttal 1:
Rebuttal: ## Claim 1
We would kindly ask the reviewer for more supporting arguments for the assertions.
> "connection is somewhat superficial"
We establish a clear connection between an EDMD-style approximation of the Koopman operator for a history-augmented state and a linear RNN. This motivates a hig... | Summary: The authors develop an approach to forecast time-series, via Koopman operator theory, through the use of linear RNNs. The authors demonstrate that their approach delivers state-of-the-art performance on long and short term forecasting benchmarks and is significantly less computationally expensive.
Claims And ... | Rebuttal 1:
Rebuttal: ## Strengthen Claims with more Convincing Evidence
### Branch Decomposition - frequency band specialization
We appreciate the reviewer’s insightful feedback and agree that "specialization" is too strong a claim. We consider that Fig. 3 provides evidence that different branches place more emphasis... | Summary: This paper introduces a novel approach that connects Koopman operator approximations with RNNs for efficient time-series modeling. Koopman operator theory provides a linear representation of nonlinear dynamical systems but is typically infinite-dimensional, making it challenging to apply directly. To address t... | Rebuttal 1:
Rebuttal: ## 1. High Efficiency
### 1.1 Computational efficiency:
We provide additional results for all datasets. Please see [Tab.4](https://anonymous.4open.science/r/SKOLR-1D6F/Tab4_Computation.pdf). SKOLR achieves a compelling trade-off between memory, computation time, and accuracy.
### 1.2 Theoretical ... | Summary: This paper proposes a new linear RNN for time-series forecasting inspired by Koopman operator theory. The problem setup is, for an autonomous dynamical system $\mathbf{x}\_{k+1}=F(\mathbf{x}\_k)$ with observable $\mathbf{y}\_k=h(\mathbf{x}\_k)$ for an unknown $h(\cdot)$, to condition on a partial trajectory $\... | Rebuttal 1:
Rebuttal: ## Longer test-time forecast horizon
We have now conducted experiments for increased test-time horizon. Please see [Tab. 1](https://anonymous.4open.science/r/SKOLR-1D6F/Tab1_ScaleUp.pdf). SKOLR has a recursive structure. Even if we train over a given horizon, we can recursively predict for a longe... | null | null | null | null | null | null |
Quadruple Attention in Many-body Systems for Accurate Molecular Property Predictions | Accept (poster) | Summary: The paper introduces MABNet, a machine-learning model designed to improve molecular property predictions by explicitly modeling four-body interactions. The model is designed to be computationally efficient and maintains E(3)-equivariance, ensuring consistency with physical symmetries. Experiments on MD22 and S... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed review and insightful comments. Below, we provide point-by-point responses.
> Limited discussion of computational cost.
R: We have added a detailed discussion of computational cost in the revised manuscript, along with timing comparisons against b... | Summary: This paper introduces MABNet, an attention-based framework for molecular property prediction that explicitly models four-body interactions. The authors argue that current Graph Neural Networks (GNNs) and Transformers, while promising, struggle to directly capture complex many-body interactions, often relying o... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed feedback and constructive suggestions to improve our manuscript. Briefly, the reviewer has two main concerns: (1) the contribution of MABNet compared to VisNet, and (2) missing details for baselines, computational efficiency, and visualization of at... | Summary: This paper introduces MABNet (MAny-Body interaction Network), a novel geometric attention framework designed to explicitly model four-body interactions for accurate molecular property predictions. MABNet aims to address the limitations of existing models that often implicitly approximate these complex interact... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s careful review of our paper, positive feedback, and recognition of our work, particularly the novelty of our approach and the meaningfulness of our experiments. Below, we address the concerns point by point:
> Comparison with Geometric Transformers
R: We have include... | null | null | null | null | null | null | null | null |
Understanding Sharpness Dynamics in NN Training with a Minimalist Example: The Effects of Dataset Difficulty, Depth, Stochasticity, and More | Accept (poster) | Summary: The paper investigates sharpness dynamics in neural network training using a minimalist deep linear network with one neuron per layer. It theoretically and empirically shows that this model captures progressive sharpening and edge-of-stability behaviors observed in practice. Key contributions include:
1. Ide... | Rebuttal 1:
Rebuttal: We deeply appreciate your constructive feedback. Below are our responses to your questions and suggestions.
## Extending our work to more realistic scenarios
Thank you for the insightful suggestion. To explore the applicability of our findings in more realistic settings, we conducted additional ... | Summary: This paper analyzes sharpness dynamics and characterizes the effect of dataset complexity, depth, batch size (Phenomena 1). Furthermore, they show a simplified model, a deep linear network with unit width trained on multiple examples, captures the Phenomena 1 and analyzes it in detail. By analyzing the sharpne... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback. Below, we address your comments.
## Balanced initializations for D>2 & GD/SGD analysis only on D=2
While it is true that the assumption of balanced initialization for $D > 2$ may appear restrictive, we believe it is justified for the following reasons:
- In... | Summary: This paper first shows that a "minimalist model"--one that has a single unit per layer with linear activations--can effectively captures a recently observed phenomenon called "progressive sharpening", where the sharpness of the loss increases as training progresses. This sharpness then stabilizes around $2/\et... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback. We have provided figures for additional experiments in the supplementary PDF file [[Link]](https://anonymous.4open.science/r/understand_progressive_sharpening-E2F5/Experimental_Results_2e3Z.pdf). Below, we address your comments.
## Applicability of results ... | Summary: This paper employs a minimalist model to investigate the progressive sharpening phenomenon in the training of deep neural networks. Progressive sharpening is a widely observed phenomenon characterized by the enhancement of sharpness during training using gradient descent or stochastic gradient descent, before ... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful comments. Below, we address your recommendations and questions.
## Comprehensive Literature Review & Implications
Thank you for highlighting relevant literature that we initially omitted. We agree that incorporating a more comprehensive review will strengthen our wo... | null | null | null | null | null | null |
Speeding up Policy Simulation in Supply Chain RL | Accept (poster) | Summary: This paper provides an iterative algorithm that is easy to parallelize with GPUs to speed up policy evaluation in RL. The theoretical analysis is specific to supply chain optimization, where the authors leverage the assumption that demand is significantly higher than supply so that actions at different time st... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! Please see our responses below.
## Comments on Exposition
Thank you for these helpful comments! We will be sure to clarify these points in the updated manuscript. Please see specific clarifications below.
### Re: Intro and scope
You are correct that, while P... | Summary: The paper studies the acceleration of policy simulation for supply chain problems often solved via RL. The objective is to accelerate sequential evaluations using caching mechanisms to be able to parallelize/batch simulations. Numerical experiments are performed for supply chain problems and beyond.
Claims An... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! Please see our responses below.
*Re: Do you have a supporting reference for the claim?*
Our claim is based on direct experience from industry deployments and discussions with practitioners at major e-commerce companies. These conversations consistently confirm... | Summary: This paper introduces the Picard Iteration algorithm to accelerate policy simulation in reinforcement learning for supply chain optimization (SCO) problems, where simulating a single trajectory can take hours due to the serial nature of policy evaluations. The key innovation allows for the batched evaluation o... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! Please see our responses below.
## Implementation Details
*Re: "...it provides limited detail on implementation challenges..."*.
Thank you for raising this point. Due to space constraints, we provide detailed implementation specifics in Appendix A.3. As you n... | null | null | null | null | null | null | null | null |
Non-Stationary Predictions May Be More Informative: Exploring Pseudo-Labels with a Two-Phase Pattern of Training Dynamics | Accept (poster) | Summary: This paper investigates the potential of a novel type of pseudo-labels—two-phase labels—in semi-supervised learning. Unlike conventional methods that rely on high-confidence and stable predictions, two-phase labels exhibit relatively low correctness and demonstrate a unique two-phase training dynamic: they ini... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough and constructive feedback! Below, we present our responses to each of your concerns and questions.
**Weakness 1**: *In the Booster test experiments, the authors adopted a three-stage protocol. This design is not commonly seen in general pseudo-labeling approa... | Summary: The paper proposes a novel and interesting 2-phasic metric for two-phase pseudo-label learning. The 2-phasic metric characterizes the two-phase pattern through both spatial and temporal measures. Extensive experimental results show the effectiveness of the proposed 2-phasic metric, especially when the number o... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive comments! We have carefully studied them and revised the paper accordingly.
**Weakness 1**: *More theoretical analysis behind the observations should be presented, which could make the paper more convincing.*
**Response:**
We appreciate your insightful f... | Summary: This paper discovers a new type of predicted labels suitable for pseudo-labeling, termed two-phase labels, which exhibit a two-phase pattern during training and are informative for decision boundaries. This finding is different from existing methods which typically select predicted labels with high confidence... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough and constructive feedback! Below, we present our responses to each of your concerns and questions.
**Weaknesses**: *The method introduces hyperparameters that must be carefully selected to ensure the effectiveness of the proposed approach.*
**Response:**
We... | Summary: This paper introduces a novel type of pseudo-labels that hold significant potential for enhancing pseudo-labeling strategies and complementing existing methods. The authors further propose a metric to efficiently identify these two-phase labels. Extensive experiments on eight datasets demonstrate that the 2-ph... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed and constructive comments! We have carefully studied them and revised the paper accordingly.
**Q1**: *The paper identifies limitations, including parameter sensitivity and computational overhead. Could you discuss any ongoing or planned future work to address ... | null | null | null | null | null | null |
No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets | Accept (poster) | Summary: This paper is about evaluating graph datasets with the goal of finding good datasets. They argue that for good datasets, graph structure and node features should have two properties: (P1) they should be task-relevant; (P2) they should contain complementary information. To test this, they propose creating data... | Rebuttal 1:
Rebuttal: Thank you for your perceptive comments and your support of our work. In the following, we address the points raised under “Weaknesses” (W1–W4) and “Other Comments Or Suggestions” (Q1–Q4). The point raised under “Methods and Evaluation Criteria” is addressed with W4.
- **W1 (Accounting for edge fea... | Summary: This paper focuses on the problem of evaluating benchmark datasets for the task of graph classification. In particular, they consider studying the importance that both structure and node features have on performance. They consider measuring this by perturbing the original graph, both in terms of the structures... | Rebuttal 1:
Rebuttal: Thank you for your encouraging feedback. We address your points by the section in which they were raised.
- **Methods And Evaluation Criteria: Measuring performance separability.**
We designed performance separability mirroring model-centric evaluation practices to facilitate adoption but agree ... | Summary: This paper introduces a novel framework, Rings, for evaluating the quality of graph-learning datasets by quantifying differences between the original dataset and its perturbed representations. The authors propose two key metrics: performance separability and mode complementarity, which assess the relevance and... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and your support of our work. In the following, we address C1 and C2 as well as your additional question (“Q1”).
- **C1 (restriction to graph-level tasks).**
As noted in “Discussion→Future Work” (ll. 431–435r), we see extending our framework to node-level ... | null | null | null | null | null | null | null | null |
RelGNN: Composite Message Passing for Relational Deep Learning | Accept (poster) | Summary: The paper proposes a graph neural network with attention mechanism, called RelGNN, for predictive tasks on relational tables. The paper introduces atomic routes based on primary-foreign key connections and design a composite message passing using atomic Routes. RelGNN achieves good results on RELBENCH that is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging RelGNN’s efficiency, strong performance, and the clarity of our writing. We also appreciate the constructive feedback and address each point below:
> **Regarding Discussion on the Limitations (W1)**
The reviewer mentions that the limitations of RelGNN are ... | Summary: The paper introduces RelGNN, a graph neural network (GNN) framework specifically designed for Relational Deep Learning (RDL), the task of doing end-to-end predictive modeling directly on relational databases (multiple tables linked by primary/foreign keys). RelGNN leverages “atomic routes,” which reflect shor... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We appreciate the recognition of our method’s novelty, strong results, and the clarity of our experimental setup. We address each point below:
> **Regarding Handling Self-Loop Tables (W1, C2, Q1)**
The reviewer suggests discussing solutions fo... | Summary: The manuscript proposes RelGNN, a graph neural network framework tailored for relational deep learning (RDL), enabling predictive modeling on relational databases. RelGNN introduces atomic routes, which capture high-order tripartite structures to facilitate direct single-hop interactions between heterogeneous ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and address each concern as follows:
> Regarding Justification of Claims
The message passing process through an intermediate node introduces an imbalance: the source node’s signal is aggregated twice, while signals from intermediate nodes are only aggregate... | null | null | null | null | null | null | null | null |
Learning the RoPEs: Better 2D and 3D Position Encodings with STRING | Accept (spotlight poster) | Summary: This submission proposes STRING, a novel method that generalizes the popular Rotary Position Encodings (RoPE) used in Transformers. Unlike RoPE, which is naturally suited for 1D inputs and then extended to higher dimensions, STRING offers a theoretical framework that accommodates multi-dimensional (2D/3D) inpu... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the Reviewer for the comments. We provide detailed answers below.
$\textbf{Additional tests, e.g. 3D-based imitation learning methods from [1,2]}$:
Thank you very much for the comment. The main goal of the 3D bi-arm KUKA experiments was to combine the best techni... | Summary: This paper proposes STRING, which generalizes the 2D rotation matrix in position encoding in RoPEs to a more general form of rotation, which is parameterized by the linear combinations of skew-symmetric generator matrices. This allows the framework to obtain exact translational invariant or rotational invarian... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the Reviewer for the comments. We provide detailed answers below.
$\textbf{Discussion on trade-offs regarding using Cayley-STRING and Circulant-STRING}$:
Thank you very much for an excellent comment. With token dimensionality per head $d_{\mathrm{head}}$, Cayley-... | Summary: This paper proposes a class of positional encoding for high-dimensional tokens with both separability and translation invariance. The proposed positional encoding is a generalization to the rotary positional encodings (RoPE) based on Lie groups. The authors also provide a computationally efficient implementati... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the Reviewer for the comments. We provide detailed answers below.
$\textbf{Theorem 3.5}$:
The proof relies on the fact that the computation of $exp(\mathbf{M})$ for a given matrix $\mathbf{M}$ can be re-written as: $\exp(\mathbf{M}) = \Sigma \exp(\mathbf{D}) \Sig... | Summary: This paper introduces STRING (Separable Translationally Invariant Position Encodings), a new family of position encodings. STRING extends RoPE using a theoretical framework based on Lie groups, maintaining key properties like separability and translational invariance, while supporting 2D/3D token representatio... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the Reviewer for the comments. We provide detailed answers below.
$\textbf{Increasing the number of trials in testing (currently 10 trials / task for Aloha-sim and 50 trials for bi-arm KUKA)}:$
We sincerely thank the Reviewer for the comment. We would like to not... | null | null | null | null | null | null |
SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics | Accept (poster) | Summary: This paper proposed a method for jointly denoising and improving resolution for spatial transcriptomics data.
## update after rebuttal
I raised score.
Claims And Evidence: Yes, their claim is supported.
Methods And Evaluation Criteria: I have a question about problem definition, which is discussed in the q... | Rebuttal 1:
Rebuttal: We want to thank Reviewer RB2n for the valuable comments and suggestions, which have greatly helped us improve the quality of our work.
1. **Definition of imputation**: Thank you for pointing out this issue. In the manuscript, we use "spatial imputation" to refer to predict gene expressions at 2D... | Summary: The paper proposes SUICA, a method for continuous modeling of spatial transcriptomics data by combining a graph-augmented autoencoder (GAE) with implicit neural representations (INRs). The key idea involves compressing high-dimensional, sparse gene expression data into a low-dimensional latent space using a GA... | Rebuttal 1:
Rebuttal: We appreciate the efforts of Reviewer Q3xk by offering comments and suggestions, which would definitely help us to improve the manuscript for its potential readers. We are also afraid that there is some critical misunderstanding towards SUICA, so we hope this rebuttal could lead to a better alignm... | Summary: The authors introduce SUICA, an implicit neural representation-based ST prediction method, which demonstrates another way of gene/spatial imputation for ST. This provides a different take on ST inference frameworks, which predominantly were based on image-based or neighborhood-cell based without relying on nea... | Rebuttal 1:
Rebuttal: We are sincerely thankful for Reviewer C5Qu's comments and suggestions, and we hope the following responses could help to resolve the concerns.
1. **Pipeline of SUICA**: Thank you for raising the issue about the confusion of data flow. We will modify the manuscript to help readers better understa... | null | null | null | null | null | null | null | null |
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization | Accept (poster) | Summary: This paper propose a quite interesting and promising way to combine strength of RL with effective BO optimizer. The authors first pose the limitations of current BO optimizer and hence locate the "myoptic" issue wihtin these BO methods. To address that, the authors propose modelling the BO optimization iterati... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed assessment of our work and for raising important questions that help strengthen the paper. We appreciate the time taken to thoroughly review our submission and provide constructive feedback.
### **Regarding the claim about "efficiently solving SD... | Summary: The paper proposes a new framework for combining RL (via meta-learning) with BO to provide effective optimisation. They propose a NN architecture that deals well with the sequential nature of data-collecting. For each BO step, off-line policy learning attempts to learn how to mimic TURBO, and then model-based ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful comments and positive feedback on our work. We appreciate the time taken to understand our contribution and the valuable suggestions to strengthen the paper.
### **Regarding the "planning delusion" concept:**
We will conduct further experiment... | Summary: The paper introduces EARL-BO, a novel reinforcement learning (RL)-based framework for multi-step lookahead Bayesian Optimization (BO) in high-dimensional black-box optimization problems.
Claims And Evidence: The paper makes several key claims:
- EARL-BO improves multi-step lookahead BO performance in high-dim... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful review and valuable suggestions for improving our work. Your feedback is extremely helpful, and we are grateful for your careful consideration of our work.
### **Planning Delusion and Uncertainty-Aware Mechanisms**
Regarding 'planning delusion,' or the effect of ove... | null | null | null | null | null | null | null | null |
When Dynamic Data Selection Meets Data Augmentation: Achieving Enhanced Training Acceleration | Accept (poster) | Summary: Paper centers around the idea of combining dynamic data selection and augmentation in order to increase both quality and diversity of data. The proposed method is applicable specifically to multimodal data. The proposed methods selects augmentation candidates that are both low density and do not represent nois... | Rebuttal 1:
Rebuttal: Dear reviewer 3S6N,
Thank you for your careful review and constructive suggestions on our work. We appreciate your recognition of our work's strengths, e.g, novelty, well-structured experiments.
We provide responses to address the comments as follows:
- **Q1: More results in Table 1.**
- **A1... | Summary: The authors propose a unified framework combining dynamic data selection and data augmentation to accelerate model training. An online nearest neighbour search is used to find low-density samples along with a semantic consistency score from a pre-trained CLIP model to filter out noisy data. The targeted augmen... | Rebuttal 1:
Rebuttal: Dear reviewer GA1s,
Thank you for your meticulous review and valuable suggestions on our work. We appreciate your recognition of our work's strengths, e.g., novelty, sound and valid experiments.
We provide responses to address the comments as follows:
- **Q1: Universal applicability and relia... | Summary: Data selection---eliminating unhelpful samples---plays a crucial role in machine learning. While selecting high-value samples improves training efficiency without degrading performance, it can reduce data diversity and harm model generalization. To address this, this paper proposes a unified framework that int... | Rebuttal 1:
Rebuttal: Dear Reviewer bQXA,
We sincerely thank you for the careful review and insightful comments/questions. We appreciate your recognition of our work's strengths, e.g., novelty and empirical effectiveness.
For the comments and questions, we provide our responses here:
- **Q1: The impact of data selec... | Summary: This paper combines data augmentation and dynamic data selection. The main idea is to augment examples that the model is uncertain about, while filtering noisy examples using semantic consistency. The experimental results show that with only 50% training compute, equal performance can be achieved to training o... | Rebuttal 1:
Rebuttal: Dear Reviewer JbtQ,
We sincerely thank you for the comments and constructive suggestions. We appreciate your recognition of our work's strengths, e.g., reasonable experimental designs and well-supported claims.
For the comments, we provide our response as follows.
- **Q1: Comparison with Data-... | null | null | null | null | null | null |
Large Continual Instruction Assistant | Accept (poster) | Summary: To address catastrophic forgetting in Large Foundation Models (LFMs), this paper proposes a general continual instruction tuning framework. It introduces a dynamic exponential moving average update method to preserve prior knowledge while assimilating new information. For realizing the balance of stability and... | Rebuttal 1:
Rebuttal: Dear reviewer 4GQq, thanks for your valuable suggestions. Here are our responses:
**Response 1 (QWen Baselines)**: We newly implemented another two strong baselines: EProj and EWC on the QWen-VL architecture. The results are presented as:
|Method|Venue|ScienceQA|TextVQA|ImageNet|GQA|VizWiz|Groun... | Summary: This paper proposes a novel framework to address the catastrophic forgetting in Continual Instruction Tuning (CIT). Starting from the trade-off between the plasticity and stability, the paper introduces an optimal balance weight of Exponential Moving Average (EMA), determined automatically by gradients and lea... | Rebuttal 1:
Rebuttal: Dear reviewer 9qib, thanks for your valuable suggestions. Here are our responses:
**Response 1 (Fixed EMA)**: To address your concern, we have conducted ablation studies with more fixed EMA weights, *i.e.* 0.993, 0.996 0.999 (as the EWA weight is usually set in [0.990, 0.999]) [1]. The results ar... | Summary: This paper addresses an important catastrophic forgetting challenge and proposes a solution in the continual instruction tuning. Based on the ideal conditions of balancing plasticity and stability, meanwhile combined with the Exponential Moving Average (EMA) update, the authors adopt the optimization method to... | Rebuttal 1:
Rebuttal: Dear reviewer pe71, thanks for your valuable suggestions. Here are our responses:
**Response 1 (Resource Consumption)**: We compare the time consumption between the dynamic EMA update and the fixed EMA method under same experimental settings (adopting LLaVA-7B). We measure the time consumption fo... | Summary: This paper presents a novel approach to Continual Instruction Tuning for vision-language models, addressing the problem of catastrophic forgetting. The proposed method is built on Exponential Moving Average (EMA)-based updates, dynamically adjusting weight balance based on Taylor expansion and Lagrange multipl... | Rebuttal 1:
Rebuttal: Dear reviewer cghr, thanks for your valuable suggestions. Here are our responses:
**Response 1 (Additional Experiments)**: To address your concerns, we set two diverse task settings: Conventional **Image Continual Classification** and **NLP Continual Classification** to further validate the effec... | null | null | null | null | null | null |
Consensus Is All You Get: The Role of Attention in Transformers | Accept (poster) | Summary: In this paper, the authors the asymptotic properties of the attention mechanism of transformers. In order to do so, the authors introduce a continuous differential equation emulating the evolution of tokens across an increasing number of layers, and they show that asymptotically -- i.e., as the number of layer... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time devoted to reading our paper and providing feedback.
**Why are the results important for the community? Is there any implication for real-world applications?**
The paper answers the following question, what is the role of attention? The role is to bring the to... | Summary: This paper theoretically demonstrates that a large language model using Transformers may collapse. The model collapse is analyzed through a mathematical analysis of the asymptotic properties of attention in Transformers. The authors claim that all tokens asymptotically converge to each other. This claim is sup... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper and providing feedback.
While there is no section titled "related work", after each theorem there is a remark titled "closest result available in the literature" where we provide a detailed comparison with the most relevant work. We would be happy to c... | Summary: This paper try to theoretically analyze the phenomenon that using auto-regressive attention model, all tokens will asymptotically converge to the "consensus set". Authors construct discrete/continues-time attention model and show that under full/auto-regressive attention cases, tokens will converge to some spe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time devoted to reading our paper and providing feedback.
We were not aware of the referenced papers. All but the second paper discuss training dynamics whereas we study already trained transformers. Hence, they do not seem relevant. The second paper shows that the ... | Summary: This paper theoretically investigates the phenomenon of token representation collapse in transformers as the number of layers blow up. The authors analyze a continuous-time differential equation of the attention model and show that all tokens converge asymptotically. They present results under different assump... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time devoted to reading our paper and providing feedback.
We were not aware of the paper Wu et al. (2024). Upon reading it we found that it confirms the results in our paper since it provides several scenarios of rank degeneration, i.e., consensus. It also provides ... | null | null | null | null | null | null |
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels | Accept (poster) | Summary: The paper proposes a model-based RL (Dreamer-like) algorithm that utilizes pre-trained (and then fine-tuned) slot-attention based object-centric representations as the underlying state representation, in contrast to the standard single-vector representation (holistic) typically employed when learning from pixe... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for appreciating the potential of our idea and the clarity of the paper. We address your questions below.
***"I’m worried about the generalizability of this method to more pixel-wise challenging tasks where SAVi fails to decompose the scenes."***
Please see our resp... | Summary: The manuscript introduces the slot object-centric latent dynamics (SOLD) model, a reinforcement learning (RL) algorithm that leverages an object-centric latent world model, which is learned directly from pixels, for behavior learning. The world model is an extension of object-centric video prediction (OCVP), a... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for acknowledging the clarity of our paper and methodology and for the valuable references, which we will discuss in the final version. In the following, we aim to address your questions.
***"The method’s differences to other OC world models should be discussed in (m... | Summary: The paper proposes SOLD, a method for model-based reinforcement learning (MBRL) with object-centric world models. The paper is mainly a combination of ideas from OCVP object-centric video prediction and DreamerV3-style MBRL with latent dynamics model being shaped to be object-centric. The main motivation is to... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for the detailed feedback. We are encouraged that they found our idea strong and clear, and that they appreciate the improvement in interpretability and sample efficiency. We aim to answer remaining questions and concerns below, but will incorporate all feedback for t... | Summary: The work focuses on the development of a novel model-based reinforcement learning (RL) algorithm that utilizes an object-centered representation of visual scenes. The authors extend the standard model-based approach of Dreamer by incorporating a slot representation generated using the SAVi transformer model. I... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for acknowledging that our approach, and the combination of object-centric (OC) representations and RL generally, is interesting and a promising research area. Further, we want to thank them for acknowledging the SAT architecture as one of the key novel contributions ... | null | null | null | null | null | null |
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