Weekly TMLR digest for Jun 22, 2025
TMLR
New certifications
==================
Reproducibility Certification: Reproducibility Study of "Improving Interpretation Faithfulness For Vision Transformers"
Meher Changlani, Benjamin Hucko, Ioannis Kechagias, Aswin Krishna Mahadevan
https://openreview.net/forum?id=a0rytDAGUD
---
Survey Certification: Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules
Yiqing Liang, Mikhail Okunev, Mikaela Angelina Uy, Runfeng Li, Leonidas Guibas, James Tompkin, Adam W Harley
https://openreview.net/forum?id=fzmw8Joug4
---
Reproducibility Certification: Revisiting XRec: How Collaborative Signals Influence LLM-Based Recommendation Explanations
Cătălin-Emanuel Brița, Hieu Nguyen, Lubov Chalakova, Nikola Petrov
https://openreview.net/forum?id=cPtqOkxQqH
---
Accepted papers
===============
Title: Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale
Authors: Yangyang Shu, Yuhang Liu, Xiaofeng Cao, Qi Chen, Bowen Zhang, Ziqin Zhou, Anton van den Hengel, Lingqiao Liu
Abstract: Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain data. The primary difficulty in this task is that the model's predictions may be inaccurate, and using these inaccurate predictions for model adaptation can lead to misleading results. To address this issue, this paper proposes a novel approach that considers multiple prediction hypotheses for each sample and investigates the rationale behind each hypothesis. By consolidating these hypothesis rationales, we identify the most likely correct hypotheses, which we then use as a pseudo-labeled set to support a semi-supervised learning procedure for model adaptation. This approach distinguishes itself from conventional semi-supervised learning by relying solely on pseudo-labels rather than ground-truth annotations. To achieve the optimal performance, we propose a three-step adaptation process: model pre-adaptation, hypothesis consolidation, and semi-supervised learning. Extensive experimental results demonstrate that our approach achieves state-of-the-art performance in the SFUDA task and can be easily integrated into existing approaches to improve their performance. The codes are available at \url{https://github.com/GANPerf/HCPR}.
URL: https://openreview.net/forum?id=fywo0eRzAu
---
Title: Gradient Inversion Attack on Graph Neural Networks
Authors: Divya Anand Sinha, Yezi Liu, Ruijie Du, Athina Markopoulou, Yanning Shen
Abstract: Graph federated learning is of essential importance for training over large graph datasets while protecting data privacy, where each client stores a subset of local graph data, while the server collects the local gradients and broadcasts only the aggregated gradients. Recent studies reveal that a malicious attacker can steal private image data from the gradient exchange of neural networks during federated learning. However, the vulnerability of graph data and graph neural networks under such attacks, i.e., reconstructing both node features and graph structure from gradients, remains largely underexplored. To answer this question, this paper studies the problem of whether private data can be reconstructed from leaked gradients in both node classification and graph classification tasks and proposes a novel attack named Graph Leakage from Gradients (GLG). Two widely used GNN frameworks are analyzed, namely GCN and GraphSAGE. The effects of different model settings on reconstruction are extensively discussed. Theoretical analysis and empirical validation demonstrate that, by leveraging the unique properties of graph data and GNNs, GLG achieves more accurate reconstruction of both nodal features and graph structure from gradients.
URL: https://openreview.net/forum?id=a0mLrqkWyx
---
Title: Theoretical Learning Performance of Graph Networks: the Impact of Jumping Connections and Layer-wise Sparsification
Authors: Jiawei Sun, Hongkang Li, Meng Wang
Abstract: Jumping connections enable Graph Convolutional Networks (GCNs) to overcome over-smoothing, while graph sparsification reduces computational demands by selecting a submatrix of the graph adjacency matrix during neighborhood aggregation. Learning GCNs with graph sparsification has shown empirical success across various applications, but a theoretical understanding of the generalization guarantees remains limited, with existing analyses ignoring either graph sparsification or jumping connections. This paper presents the first learning dynamics and generalization analysis of GCNs with jumping connections using graph sparsification.
Our analysis demonstrates that the generalization accuracy of the learned model closely approximates the highest achievable accuracy within a broad class of target functions dependent on the proposed sparse effective adjacency matrix $A^*$. Thus, graph sparsification maintains generalization performance when $A^*$ accurately models data correlations. We reveal that jumping connections lead to different sparsification requirements across layers. In a two-hidden-layer GCN, the generalization is more affected by the sparsified matrix deviations from $A^*$ of the first layer than the second layer. To the best of our knowledge, this marks the first theoretical characterization of jumping connections' role in sparsification requirements. We validate our theoretical results on benchmark datasets in deep GCNs.
URL: https://openreview.net/forum?id=Q9AkJpfJks
---
Title: Reproducibility Study of "Improving Interpretation Faithfulness For Vision Transformers"
Authors: Meher Changlani, Benjamin Hucko, Ioannis Kechagias, Aswin Krishna Mahadevan
Abstract: This paper attempts to reproduce the findings of the study "Improving Interpretation Faith-fulness For Vision Transformers" Hu et al. (2024). The authors focus on making visual transformers (ViTs) more robust to adversarial attacks, and calling these robust ViTs faithful ViTs (FViTs). In their paper they propose a universal method to transform ViTs to FViTs called denoised diffusion smoothing (DDS). The reproduction of the authors study suffers from certain challenges, but the main claims still hold. Furthermore, this study extends the original paper by trying different diffusion models for DDS and tries to generalize the increased robustness of FViTs.
URL: https://openreview.net/forum?id=a0rytDAGUD
---
Title: On the Generalizability of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals"
Authors: Asen Dotsinski, Udit Thakur, Marko Ivanov, Mohammad Hafeez Khan, Maria Heuss
Abstract: We present a reproduction study of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals" (Ortu et al., 2024), which investigates competition of mechanisms in language models between factual recall and counterfactual in-context repetition. Our study successfully reproduces their primary findings regarding the localization of factual and counterfactual information, the dominance of attention blocks in mechanism competition, and the specialization of attention heads in handling competing information. We reproduce their results on both GPT-2 (Radford et al., 2019) and Pythia 6.9B (Biderman et al., 2023). We extend their work in three significant directions. First, we explore the generalizability of these findings to even larger models by replicating the experiments on Llama 3.1 8B (Grattafiori et al., 2024), discovering greatly reduced attention head specialization. Second, we investigate the impact of prompt structure by introducing variations where we avoid repeating the counterfactual statement verbatim or we change the premise word, observing a marked decrease in the logit for the counterfactual token. Finally, we test the validity of the authors’ claims for prompts of specific domains, discovering that certain categories of prompts skew the results by providing the factual prediction token
as part of the subject of the sentence. Overall, we find that the attention head ablation proposed in Ortu et al. (2024) is ineffective for domains that are underrepresented in their dataset, and that the effectiveness varies based on model architecture, prompt structure,
domain and task.
URL: https://openreview.net/forum?id=15keyzQj9h
---
Title: Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules
Authors: Yiqing Liang, Mikhail Okunev, Mikaela Angelina Uy, Runfeng Li, Leonidas Guibas, James Tompkin, Adam W Harley
Abstract: Gaussian splatting methods are emerging as a popular approach for converting multi-view image data into scene representations that allow view synthesis. In particular, there is interest in enabling view synthesis for dynamic scenes using only monocular input data---an ill-posed and challenging problem. The fast pace of work in this area has produced multiple simultaneous papers that claim to work best, which cannot all be true. In this work, we organize, benchmark, and analyze many Gaussian-splatting-based methods, providing apples-to-apples comparisons that prior works have lacked. We use multiple existing datasets and a new instructive synthetic dataset designed to isolate factors that affect reconstruction quality.
We systematically categorize Gaussian splatting methods into specific motion representation types and quantify how their differences impact performance. Empirically, we find that their rank order is well-defined in synthetic data, but the complexity of real-world data currently overwhelms the differences. Furthermore, the fast rendering speed of all Gaussian-based methods comes at the cost of brittleness in optimization. We summarize our experiments into a list of findings that can help to further progress in this lively problem setting.
URL: https://openreview.net/forum?id=fzmw8Joug4
---
Title: Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models
Authors: Atharv Mittal, Agam Pandey, Amritanshu Tiwari, Sukrit Jindal, Swadesh Swain
Abstract: Large Vision-Language Models (VLMs) have revolutionized computer vision, enabling tasks such as image classification, captioning, and visual question answering. However, they re- main highly vulnerable to adversarial attacks, particularly in scenarios where both visual and textual modalities can be manipulated. In this study, we conduct a comprehensive reproducibility study of "An Image is Worth 1000 Lies: Adversarial Transferability Across Prompts on Vision-Language Models" validating the Cross-Prompt Attack (CroPA) and confirming its superior cross-prompt transferability compared to existing baselines. Be- yond replication we propose several key improvements: (1) A novel initialization strategy that significantly improves Attack Success Rate (ASR). (2) Investigate cross-image trans- ferability by learning universal perturbations. (3) A novel loss function targeting vision encoder attention mechanisms to improve generalization. Our evaluation across prominent VLMs—including Flamingo, BLIP-2, and InstructBLIP as well as extended experiments on LLaVA validates the original results and demonstrates that our improvements consistently boost adversarial effectiveness. Our work reinforces the importance of studying adversarial vulnerabilities in VLMs and provides a more robust framework for generating transferable adversarial examples, with significant implications for understanding the security of VLMs in real-world applications.
URL: https://openreview.net/forum?id=5L90cl0xtf
---
Title: Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers
Authors: Furkan Mumcu, Yasin Yilmaz
Abstract: Deep Neural Networks (DNNs) are notoriously vulnerable to adversarial input designs with limited noise budgets. While numerous successful attacks with subtle modifications to original input have been proposed, defense techniques against these attacks are relatively understudied. Existing defense approaches either focus on improving DNN robustness by negating the effects of perturbations or use a secondary model to detect adversarial data. Although equally important, the attack detection approach, which is studied in this work, provides a more practical defense compared to the robustness approach. We show that the existing detection methods are either ineffective against the state-of-the-art attack techniques or computationally inefficient for real-time processing. We propose a novel universal and efficient method to detect adversarial examples by analyzing the varying degrees of impact of attacks on different DNN layers. Our method trains a lightweight regression model that predicts deeper-layer features from early-layer features, and uses the prediction error to detect adversarial samples. Through theoretical arguments and extensive experiments, we demonstrate that our detection method is highly effective, computationally efficient for real-time processing, compatible with any DNN architecture, and applicable across different domains, such as image, video, and audio.
URL: https://openreview.net/forum?id=0CY5APFnFI
---
Title: Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
Authors: Matteo Tucat, Anirbit Mukherjee, Procheta Sen, Mingfei Sun, Omar Rivasplata
Abstract: We present and analyze a novel regularized form of the gradient clipping algorithm, proving that it converges to global minima of the loss surface of deep neural networks under the squared loss, provided that the layers are of sufficient width. The algorithm presented here, dubbed $\delta-$GClip, introduces a modification to gradient clipping that leads to a first-of-its-kind example of a step size scheduling for gradient descent that provably minimizes training losses of deep neural nets. We also present empirical evidence that our theoretically founded $\delta-$GClip algorithm is competitive with the state-of-the-art deep learning heuristics on various neural architectures including modern transformer based architectures. The modification we do to standard gradient clipping is designed to leverage the PL* condition, a variant of the Polyak-Łojasiewicz inequality which was recently proven to be true for sufficiently wide neural networks at any depth within a neighbourhood of the initialization.
URL: https://openreview.net/forum?id=ABT1XQLbOx
---
Title: Enhancing Molecular Conformer Generation via Fragment- Augmented Diffusion Pretraining
Authors: Xiaozhuang Song, YUZHAO TU, Tianshu Yu
Abstract: Recent advances in diffusion-based methods have shown promising results for molecular conformer generation, yet their performance remains constrained by training data scarcity---particularly for structurally complex molecules. In this work, we present Fragment-Augmented Diffusion (FragDiff), a data-centric augmentation strategy that incorporates chemical fragmentation techniques into the pre-training phase of modern diffusion-based generative models. Our key innovation lies in decomposing molecules into chemically meaningful fragments that serve as building blocks for systematic data augmentation, enabling the diffusion model to learn enhanced local geometry while maintaining global molecular topology. Unlike existing approaches that focus on complex architectural modifications, FragDiff adopts a data-centric paradigm orthogonal to model design. Comprehensive benchmarks show FragDiff's superior performance, especially in data-scarce scenarios. Notably, it achieves 12.2--13.4% performance improvement on molecules 3$\times$ beyond training scale through pretraining on fragments. Overall, we establish a new paradigm integrating chemical fragmentations with diffusion models, advancing computational chemistry workflows. The code is available at https://github.com/ShawnKS/fragdiff.
URL: https://openreview.net/forum?id=t5WzHOniAF
---
Title: Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts
Authors: Youngseog Chung, Dhruv Malik, Jeff Schneider, Yuanzhi Li, Aarti Singh
Abstract: The traditional viewpoint on Sparse Mixture of Experts (MoE) models is that instead of training a single _large_ expert, which is computationally expensive, we can train many _small_ experts. The hope is that if the total parameter count of the small experts equals that of the singular large expert, then we retain the representation power of the large expert while gaining computational tractability and promoting expert specialization. The recently introduced Soft MoE replaces the Sparse MoE's discrete routing mechanism with a differentiable gating function that smoothly mixes tokens. While this smooth gating function successfully mitigates the various training instabilities associated with Sparse MoE, it is unclear whether it induces implicit biases that affect Soft MoE's representation power or potential for expert specialization. We prove that Soft MoE with a single arbitrarily powerful expert cannot represent simple convex functions. This justifies that Soft MoE's success cannot be explained by the traditional viewpoint of many small experts collectively mimicking the representation power of a single large expert, and that multiple experts are actually _necessary_ to achieve good representation power (even for a fixed total parameter count). Continuing along this line of investigation, we introduce a notion of expert specialization for Soft MoE, and while varying the number of experts yet fixing the total parameter count, we consider the following (computationally intractable) task. Given any input, how can we discover the expert subset that is specialized to predict this input's label? We empirically show that when there are many small experts, the architecture is implicitly biased in a fashion that allows us to efficiently approximate the specialized expert subset. Our method can be easily implemented to potentially reduce computation during inference. For example, using our method on ImageNet, one can perform inference using only $1/8$ of the experts and still retain $99$% of the test accuracy of using all experts.
URL: https://openreview.net/forum?id=II9agMKTb1
---
Title: AQA-Bench: An Interactive Benchmark for Evaluating LLMs’ Sequential Reasoning Ability in Algorithmic Environments
Authors: Siwei Yang, Bingchen Zhao, Cihang Xie
Abstract: This paper introduces AQA-Bench, a novel benchmark to assess the sequential reasoning capabilities of large language models (LLMs) in algorithmic contexts, such as depth-first search (DFS). The key feature of our evaluation benchmark lies in its interactive evaluation protocol — for example, in DFS, the availability of each node’s connected edge is contingent upon the model’s traversal to that node, thereby necessitating the LLM’s ability to effectively remember visited nodes and strategize subsequent moves considering the possible environmental feedback in the future steps. We comprehensively build AQA-Bench with three different algorithms, namely binary search, depth-first search, and breadth-first search, and to evaluate the sequential reasoning ability of 14 different LLMs. Our investigations reveal several interesting findings: (1) Closed-source models like GPT-4 and Gemini generally show much stronger sequential reasoning ability, significantly outperforming open-source LLMs. (2) Naively providing in-context examples may inadvertently hurt few-shot performance in an interactive environment due to over-fitting to examples. (3) Instead of using optimal steps from another test case as the in-context example, a very limited number of predecessor steps in the current test case following the optimal policy can substantially boost small models’ performance. (4) The performance gap between weak models and strong models is greatly due to the incapability of weak models to start well. (5) The scaling correlation between performance and model size is not always significant, sometimes even showcasing an inverse trend. We hope our study can catalyze future work on advancing the understanding and enhancement of LLMs’ capabilities in sequential reasoning.
URL: https://openreview.net/forum?id=W22g6Ksmbi
---
Title: ModernTCN Revisited: A Critical Look at the Experimental Setup in General Time Series Analysis
Authors: Önder Akacik, Mark Hoogendoorn
Abstract: While numerous time series models claim state-of-the-art performance, their evaluation often relies on flawed experimental setups, leading to questionable conclusions. This study provides a critical re-evaluation of this landscape, using ModernTCN as a case study. We conduct a rigorous and extended benchmark, correcting methodological issues related to data loading, validation, and evaluation methods, and show that performance claims are sensitive to these details. Additionally, we find that ModernTCN overlooks a line of research in global convolutional models, and our comparison reveals that despite claims of an enlarged effective receptive field (ERF), it falls short of these methods. More than a critique, we introduce an architectural innovation: by embedding irregularly sampled data with a continuous kernel convolution and processing it with the ModernTCN backbone, we achieve new state-of-the-art performance on the challenging PhysioNet 2019 dataset. This work not only provides a robust reassessment of ModernTCN but also serves as an audit of the commonly used general time series analysis experimental setup, which includes tasks such as forecasting, imputation, classification, and anomaly detection.
URL: https://openreview.net/forum?id=R20kKdWmVZ
---
Title: Synthesizing Minority Samples for Long-tailed Classification via Distribution Matching
Authors: Zhuo Li, He Zhao, Jinke Ren, Anningzhe Gao, Dandan Guo, Xiang Wan, Hongyuan Zha
Abstract: In many real-world applications, deep neural networks (DNNs) often perform poorly on datasets with long-tailed distributions. To address this issue, a promising approach is to propose an optimization objective to transform real majority samples into synthetic minority samples. However, this objective is designed only from the classification perspective. To this end, we propose a novel framework that synthesizes minority samples from the majority by considering both classification and distribution matching. Specifically, our method adjusts the distribution of synthetic minority samples to closely align with that of the true minority class, while enforcing the synthetic samples to learn more generalizable and discriminative features of the minority class. Experimental results on several standard benchmark datasets demonstrate the effectiveness of our method in both long-tailed classification and synthesizing high-quality synthetic minority samples.
URL: https://openreview.net/forum?id=VqLe8tPbZn
---
Title: Cross-lingual Transfer in Programming Languages: An Extensive Empirical Study
Authors: Razan Baltaji, Saurabh Pujar, Martin Hirzel, Louis Mandel, Luca Buratti, Lav R. Varshney
Abstract: Large language models (LLMs) have achieved state-of-the-art performance in various software engineering tasks, including error detection, clone detection, and code translation, primarily leveraging high-resource programming languages like Python and Java. However, many critical languages, such as COBOL, as well as emerging languages, such as Rust and Swift, remain low-resource due to limited openly available code. This scarcity hampers the training and effectiveness of LLMs for these languages, increasing software maintenance costs and stifling innovation. Addressing this gap, we investigate the potential of transfer learning to enhance LLM performance on low-resource programming languages by leveraging data from high-resource counterparts. Our extensive empirical study evaluates transferability across 10 to 41 programming languages and five key tasks: code generation, clone detection, code repair, solution domain classification, and error detection. Additionally, we develop a performance prediction model to guess the best source languages for a given target and task, and analyze the features that influence transfer performance. We further replicate a representative subset of experiments with a larger model to test the generalizability of our conclusions to contemporary large‑scale LLMs. Our findings demonstrate that cross-lingual transfer significantly outperforms zero-shot learning, with effectiveness varying based on both source and target languages. Languages such as Java and Go emerge as the best targets, while Kotlin and JavaScript are excellent sources. Furthermore, our model reliably predicts successful transfer sources by considering linguistic and dataset-specific features, offering practical guidance for data acquisition and model training. This work contributes to the development of LLM-driven tools for low-resource programming languages and provides insights into the characteristics that facilitate transfer across language pairs.
URL: https://openreview.net/forum?id=1PRBHKgQVM
---
Title: Neural varifolds: an aggregate representation for quantifying the geometry of point clouds
Authors: Juheon Lee, Xiaohao Cai, Carola-Bibiane Schönlieb, Simon Masnou
Abstract: Point clouds are popular 3D representations for real-life objects (such as in LiDAR and Kinect) due to their detailed and compact representation of surface-based geometry. Recent approaches characterise the geometry of point clouds by bringing deep learning based techniques together with geometric fidelity metrics such as optimal transportation costs (e.g., Chamfer and Wasserstein metrics). In this paper, we propose a new surface geometry characterisation within this realm, namely a neural varifold representation of point clouds. Here, the surface is represented as a measure/distribution over both point positions and tangent spaces of point clouds. The varifold representation quantifies not only the surface geometry of point clouds through the manifold-based representation, but also subtle geometric consistencies on the surface due to the combined product space. This study proposes neural varifold algorithms to compute the varifold norm between two point clouds using neural networks on point clouds and their neural tangent kernel representations. The proposed neural varifold is evaluated on three different sought-after tasks -- shape matching, few-shot shape classification, and shape reconstruction. Detailed evaluation and comparison to the state-of-the-art methods demonstrate that the proposed versatile neural varifold is superior in shape matching and few-shot shape classification, and is competitive for shape reconstruction.
URL: https://openreview.net/forum?id=P02hoA7vln
---
Title: Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Authors: Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming, Yueqian Lin, Qing Yu, Go Irie, Shafiq Joty, Yixuan Li, Hai Helen Li, Ziwei Liu, Toshihiko Yamasaki, Kiyoharu Aizawa
Abstract: Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine learning systems and has shaped the field of OOD detection. Meanwhile, several other problems are closely related to OOD detection, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD). To unify these problems, a generalized OOD detection framework was proposed, taxonomically categorizing these five problems. However, Vision Language Models (VLMs) such as CLIP have significantly changed the paradigm and blurred the boundaries between these fields, again confusing researchers. In this survey, we first present a generalized OOD detection v2, encapsulating the evolution of these fields in the VLM era. Our framework reveals that, with some field inactivity and integration, the demanding challenges have become OOD detection and AD. Then, we highlight the significant shift in the definition, problem settings, and benchmarks; we thus feature a comprehensive review of the methodology for OOD detection and related tasks to clarify their relationship to OOD detection. Finally, we explore the advancements in the emerging Large Vision Language Model (LVLM) era, such as GPT-4V. We conclude with open challenges and future directions. The resource is available at https://github.com/AtsuMiyai/Awesome-OOD-VLM.
URL: https://openreview.net/forum?id=FO3IA4lUEY
---
Title: GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation
Authors: Quankai Gao, Qiangeng Xu, Zhe Cao, Ben Mildenhall, Wenchao Ma, Le Chen, Danhang Tang, Ulrich Neumann
Abstract: Creating 4D fields of Gaussian Splatting from images or videos is a challenging task due to its under-constrained nature. While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly supervising Gaussian motions remains underexplored. In this paper, we introduce a novel concept, Gaussian flow, which connects the dynamics of 3D Gaussians and pixel velocities between consecutive frames. The Gaussian flow can be obtained efficiently by splatting Gaussian dynamics into the image space. This differentiable process enables direct dynamic supervision from optical flow. Our method significantly benefits 4D dynamic content generation and 4D novel view synthesis with Gaussian Splatting, especially for contents with rich motions that are hard to handle by existing methods. The common color drifting issue that occurs in 4D generation is also resolved with improved Guassian dynamics. Superior visual quality in extensive experiments demonstrates the effectiveness of our method. As shown in our evaluation, GaussianFlow can drastically improve both quantitative and qualitative results for 4D generation and 4D novel view synthesis.
URL: https://openreview.net/forum?id=XBL7xi5rt0
---
Title: Reassessing Fairness: A Reproducibility Study of NIFA’s Impact on GNN Models
Authors: Ruben Figge, Sjoerd Gunneweg, Aaron Kuin, Mees Lindeman
Abstract: Graph Neural Networks (GNNs) have shown strong performance on graph-structured data but raise fairness concerns by amplifying existing biases. The Node Injection-based Fairness Attack (NIFA) (Luo et al., 2024) is a recently proposed gray-box attack that degrades group fairness while preserving predictive utility. In this study, we reproduce and evaluate NIFA across multiple datasets and GNN architectures. Our findings confirm that NIFA consistently degrades fairness—measured via Statistical Parity and Equal Opportunity—while
maintaining utility on classical GNNs. However, claims of NIFA’s superiority over existing fairness and utility attacks are only partially supported due to limitations in baseline reproducibility. We further extend NIFA to accommodate multi-class sensitive attributes and evaluate its behavior under varying levels of graph homophily. While NIFA remains effective in multi-class contexts, its impact is more sensitive in mixed and highly homophilic graphs. Although this is not a comprehensive validation of all NIFA claims, our work provides targeted insights into its reproducibility and generalizability across fairness-sensitive scenarios. The codebase is publicly available at: https://github.com/sjoerdgunneweg/Reassessing-NIFA.
URL: https://openreview.net/forum?id=l5fXUKi8GO
---
Title: A Survey on Verifiable Cross-Silo Federated Learning
Authors: Aleksei Korneev, Jan Ramon
Abstract: Federated Learning (FL) is a widespread approach that allows training machine learning (ML) models with data distributed across multiple storage units. In cross-silo FL, which often appears in domains like healthcare or finance, the number of participants is moderate, and each party typically represents a well-known organization. For instance, in medicine data owners are often hospitals or data hubs which are well-established entities. However, malicious parties may still attempt to disturb the training procedure in order to obtain certain benefits, for example, a biased result or a reduction in computational load. While one can easily detect a malicious agent when data used for training is public, the problem becomes much more acute when it is necessary to maintain the privacy of the training dataset. To address this issue, there is recently growing interest in developing verifiable protocols, where one can check that parties do not deviate from the training procedure and perform computations correctly. In this paper, we present a survey on verifiable cross-silo FL. We analyze various protocols, fit them in a taxonomy, and compare their efficiency and threat models. We also analyze Zero-Knowledge Proof (ZKP) schemes and discuss how their overall cost in a FL context can be minimized. Lastly, we identify research gaps and discuss potential directions for future scientific work.
URL: https://openreview.net/forum?id=uMir8UIHST
---
Title: [Re] Benchmarking LLM Capabilities in Negotiation through Scoreable Games
Authors: Jorge Carrasco Pollo, Ioannis Kapetangeorgis, Joshua Rosenthal, John Hua Yao
Abstract: Large Language Models (LLMs) demonstrate significant potential in multi-agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust and generalizable benchmarks. Abdelnabi et al. (2024) introduce a negotiation benchmark based on Scoreable Games, with the aim of developing a highly complex and realistic evaluation framework for LLMs. Our work investigates the reproducibility of claims in their benchmark, and provides a deeper understanding of its usability and generalizability. We replicate the original experiments on additional models, and introduce additional metrics to verify negotiation quality and evenness of evaluation. Our findings reveal that while the benchmark is indeed complex, model comparison is ambiguous, raising questions about its objectivity. Furthermore, we identify limitations in the experimental setup, particularly in information leakage detection and thoroughness of the ablation study. By examining and analyzing the behavior of a wider range of models on an extended version of the benchmark, we reveal insights that provide additional context to potential users. Our results highlight the importance of context in model-comparative evaluations.
URL: https://openreview.net/forum?id=BVH81SAAh2
---
Title: Revisiting XRec: How Collaborative Signals Influence LLM-Based Recommendation Explanations
Authors: Cătălin-Emanuel Brița, Hieu Nguyen, Lubov Chalakova, Nikola Petrov
Abstract: Recommender systems help users navigate large volumes of online content by offering personalized recommendations. However, the increasing reliance on deep learning-based techniques has made these systems opaque and difficult to interpret. To address this, XRec (Ma et al., 2024) was introduced as a novel framework that integrates collaborative signals and textual descriptions of past interactions into Large Language Models (LLMs) to generate natural language explanations for recommendations. In this work, we reproduce and expand upon the findings of Ma et al. (2024). While our results validate most of the original authors’ claims, we were unable to fully replicate the reported performance improvements from injecting collaborative information into every LLM attention layer, nor the claimed effects of data sparsity. Beyond replication, our contributions provide evidence that the Graph Neural Network (GNN) component does not enhance explainability. Instead, the observed performance improvement is attributed to the Collaborative Information Adapter, which can act as a form of soft prompting, efficiently encoding task-specific information. This finding aligns with prior research suggesting that lightweight adaptation mechanisms can condition frozen LLMs for specific downstream tasks. Our implementation is open-source.
URL: https://openreview.net/forum?id=cPtqOkxQqH
---
Title: MagicPose4D: Crafting Articulated Models with Appearance and Motion Control
Authors: Hao Zhang, Di Chang, Fang Li, Mohammad Soleymani, Narendra Ahuja
Abstract: With the success of 2D and 3D visual generative models, there is growing interest in generating 4D content. Existing methods primarily rely on text prompts to produce 4D content, but they often fall short of accurately defining complex or rare motions. To address this limitation, we propose MagicPose4D, a novel framework for refined control over both appearance and motion in 4D generation. Unlike current 4D generation methods, MagicPose4D accepts monocular videos or mesh sequences as motion prompts, enabling precise and customizable motion control. MagicPose4D comprises two key modules:
(i) Dual-Phase 4D Reconstruction Module which operates in two phases. The first phase focuses on capturing the model's shape using accurate 2D supervision and less accurate but geometrically informative 3D pseudo-supervision without imposing skeleton constraints. The second phase extracts the 3D motion (skeleton poses) using more accurate pseudo-3D supervision, obtained in the first phase, and introduces kinematic chain-based skeleton constraints to ensure physical plausibility. Additionally, we propose a Global-local Chamfer loss that aligns the overall distribution of predicted mesh vertices with the supervision while maintaining part-level alignment without extra annotations. (ii) Cross-category Motion Transfer Module leverages the extracted motion from the 4D reconstruction module and uses a kinematic-chain-based skeleton to achieve cross-category motion transfer. It ensures smooth transitions between frames through dynamic rigidity, facilitating robust generalization without additional training. Through extensive experiments, we demonstrate that MagicPose4D significantly improves the accuracy and consistency of 4D content generation, outperforming existing methods in various benchmarks.
URL: https://openreview.net/forum?id=qgHq1NFUJk
---
Title: A reproducibility study of “User-item fairness tradeoffs in recommendations”
Authors: Sander Honig, Elyanne Oey, Lisanne Wallaard, Sharanda Suttorp, Clara Rus
Abstract: Recommendation systems are necessary to filter the abundance of information presented in our everyday lives. A recommendation system could exclusively recommend items that users prefer the most, potentially resulting in certain items never getting recommended. Conversely, an exclusive focus on including all items could hurt overall recommendation quality. This gives rise to the challenge of balancing user and item fairness. The paper “User-item fairness tradeoffs in recommendations” by Greenwood et al. (2024) explores this tradeoff by developing a theoretical framework that optimizes for user-item fairness constraints. Their theoretical framework suggests that the cost of item fairness is low when users have varying preferences compared to each other, and may be high for users whose preferences are misestimated. They empirically measured these phenomena by creating their own recommendation system on arXiv preprints, and confirmed that the cost of item fairness is low when users have preferences that differ from one another. However, contrary to their theoretical expectations, misestimated users do not encounter a higher cost of item fairness. This study investigates the reproducibility of their research by replicating the empirical study. Additionally, we extend their research in two ways: (i) verifying the generalizability of their findings on a different dataset (Amazon books reviews), and (ii) analyzing the tradeoffs when recommending multiple items to a user instead of a single item. Our results further validate the claims made in the original paper. We concluded the claims hold true when recommending multiple items, with the cost of item fairness decreasing as more items are recommended.
URL: https://openreview.net/forum?id=vltzxxhzLU
---
Title: Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
Authors: Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann
Abstract: Generalization of machine learning models can be severely compromised by data poisoning, where adversarial changes are applied to the training data. This vulnerability has led to interest in certifying (i.e., proving) that such changes up to a certain magnitude do not affect test predictions. We, for the first time, certify Graph Neural Networks (GNNs) against poisoning attacks, including backdoors, targeting the node features of a given graph. Our certificates are white-box and based upon $(i)$ the neural tangent kernel, which characterizes the training dynamics of sufficiently wide networks; and $(ii)$ a novel reformulation of the bilevel optimization problem describing poisoning as a mixed-integer linear program. Consequently, we leverage our framework to provide fundamental insights into the role of graph structure and its connectivity on the worst-case robustness behavior of convolution-based and PageRank-based GNNs. We note that our framework is more general and constitutes the first approach to derive white-box poisoning certificates for NNs, which can be of independent interest beyond graph-related tasks.
URL: https://openreview.net/forum?id=jIAPLDdGVx
---
Title: SynCode: LLM Generation with Grammar Augmentation
Authors: Shubham Ugare, Tarun Suresh, Hangoo Kang, Sasa Misailovic, Gagandeep Singh
Abstract: LLMs are widely used in complex AI applications. These applications underscore the need for LLM outputs to adhere to a specific format, for their integration with other components in the systems. Typically the format rules – e.g., data serialization formats such as JSON, YAML, or Code in Programming Language – are expressed as context-free grammar (CFG). Due to the hallucinations and unreliability of LLMs, instructing LLMs to adhere to specified syntax becomes an increasingly important challenge.
We present SynCode, a novel framework for efficient and general syntactical decoding with LLMs, to address this challenge. SynCode ensures soundness and completeness with respect to the CFG of a formal language, effectively retaining valid tokens while filtering out invalid ones. SynCode uses an offline-constructed, efficient lookup table, the DFA mask store, created from the DFA (Deterministic Finite Automaton) of the language’s grammar for efficient generation. SynCode seamlessly integrates with any language defined by CFG, as evidenced by experiments focusing on generating JSON, SQL, Python, and Go outputs. Our experiments evaluating the effectiveness of SynCode for JSON generation demonstrate that SynCode eliminates all syntax errors and significantly outperforms state-of-the-art baselines. Furthermore, our results underscore how SynCode significantly reduces 96.07% of syntax errors in generated Python and Go code, showcasing its substantial impact on enhancing syntactical precision in LLM generation.
URL: https://openreview.net/forum?id=HiUZtgAPoH
---
New submissions
===============
Title: LBMamba: Locally Bi-directional Mamba
Abstract: Mamba, a State Space Model (SSM) that accelerates training by recasting recurrence as a parallel selective scan, has recently emerged as a linearly-scaling, efficient alternative to self-attention. Because of its unidirectional nature, each state in Mamba only has information of its previous states and is blind to states after. Current Mamba-based computer-vision methods typically overcome this limitation by augmenting Mamba's global forward scan with a global backward scan, forming a bi-directional scan that restores a full receptive field. However, this operation doubles the computational load, eroding much of the efficiency advantage that originally Mamba have. To eliminate this extra scans, we introduce LBMamba, a locally bi-directional SSM block that embeds a lightweight locally backward scan inside the forward selective scan and executes it entirely in per-thread registers. Building on LBMamba, we present LBVim, a scalable vision backbone that alternates scan directions every two layers to recover a global receptive field without extra backward sweeps. We validate the versatility of our approach on both natural images and whole slide images (WSIs). We show that our LBVim constantly offers a superior performance–throughput trade-off. That is under the same throughput, LBVim achieves 0.8% to 1.6% higher top-1 accuracy on the ImageNet-1K classification dataset, 0.6% to 2.7% higher mIoU on the ADE20K semantic segmentation dataset, 0.9% higher AP$^b$ and 1.1% higher AP$^m$ on the COCO detection dataset. We also integrate LBMamba into the SOTA pathology multiple instance learning (MIL) approach, MambaMIL, which uses single directional scan. Experiments on 3 public WSI classification datasets for show that our method achieves a relative improvement of up to 3.06% better AUC, 3.39% better F1, 1.67% better accuracy.
URL: https://openreview.net/forum?id=e1aXaIXblQ
---
Title: Uncertainty Quantification in Retrieval Augmented Question Answering
Abstract: Retrieval augmented Question Answering (QA) helps QA models overcome knowledge gaps by incorporating retrieved evidence, typically a set of passages, alongside the question at test time. Previous studies show that this approach improves QA performance and reduces hallucinations, without, however, assessing whether the retrieved passages are indeed useful at answering correctly. In this work, we propose to quantify the uncertainty of a QA model via estimating the utility of the passages it is provided with. We train a lightweight neural model to predict passage utility for a target QA model and show that while simple information theoretic metrics can predict answer correctness up to a certain extent, our approach efficiently approximates or outperforms more expensive sampling-based methods. Code and data are available at XXXX.
URL: https://openreview.net/forum?id=JLkgI0h7wy
---
Title: Active Learning with a Noisy Annotator
Abstract: Active Learning (AL) aims to reduce annotation costs by strategically selecting the most informative samples for labeling. However, most active learning methods struggle in the low-budget regime where only a few labeled examples are available. This issue becomes even more pronounced when annotators provide noisy labels. A common AL approach for the low- and mid-budget regimes focuses on maximizing the coverage of the labeled set across the entire dataset. We propose a novel framework called Noise-Aware Active
Sampling (NAS) that extends existing greedy, coverage-based active learning strategies to handle noisy annotations. NAS identifies regions that remain uncovered due to the selection of noisy representatives and enables resampling from these areas. We introduce a simple yet effective noise filtering approach suitable for the low-budget regime, which leverages the inner mechanism of NAS and can be applied for noise filtering before model training. On multiple computer vision benchmarks, including CIFAR100 and ImageNet subsets, NAS significantly
improves performance for standard active learning methods across different noise types and rates.
URL: https://openreview.net/forum?id=i3FHtvbCKc
---
Title: Finding Landmarks of Covariate Shift with Max-Sliced Kernel Wasserstein Distance
Abstract: To detect and understand covariate shifts, especially those caused by localized changes in the distribution, we propose a more interpretable divergence through a kernel-based sliced Wasserstein divergence, which is computationally efficient for two-sample testing. The proposed landmark-based slicing seeks a single data point, defining a slice in the reproducing kernel Hilbert space, that maximizes the kernel max-sliced Wasserstein distance. This point and points that surround it from the two samples provide an interpretation of localized divergences. We investigate this new divergence on various shift scenarios and the effect of the choice of learning representations, compared to maximum mean discrepancy (MMD). Results on MNIST and CIFAR-10 dataset demonstrate superior statistical power of the divergence, and analysis of the landmark and its neighborhood are revealing about the discrepancy between the distributions.
URL: https://openreview.net/forum?id=SxKAl2K8N9
---
Title: Multi-Modal Language Models as Text-to-Image Model Evaluators
Abstract: The steady improvements of text-to-image (T2I) generative models lead to slow deprecation of automatic evaluation benchmarks that rely on static datasets, motivating researchers to seek alternative ways to evaluate the T2I progress. In this paper, we explore the potential of multi-modal large language models (MLLMs) as evaluator agents that interact with a T2I model, with the objective of assessing prompt-generation consistency and image aesthetics. We present Multimodal Text-to-Image Eval (MT2IE), an evaluation framework that iteratively generates prompts for evaluation, scores generated images and matches T2I evaluation of existing benchmarks with a fraction of the prompts used in existing static benchmarks. We show that MT2IE’s prompt-generation consistency scores have higher correlation with human judgment than prompt consistency metrics previously introduced
in the literature. MT2IE generates prompts that are efficient at probing T2I model performance, producing the same relative T2I model rankings as existing benchmarks while evaluating on 80× less prompts. We hope that these results will unlock the development of dynamic and interactive evaluation frameworks, and mitigate the deprecation of automatic evaluation benchmarks.
URL: https://openreview.net/forum?id=uGwNXxuLu0
---
Title: Using Platt’s scaling for calibration after undersampling — limitations and how to address them
Abstract: When modelling data where the response is dichotomous and highly imbalanced, response based sampling where a subset of the majority class is retained (i.e., undersampling) is often used to create more balanced training datasets prior to modelling. However, the models fit to this undersampled data, which we refer to as base models, generate predictions that are severely biased. There are several calibration methods that can be used to combat this bias, one of which is Platt’s scaling. Here, a logistic regression model is used to model the relationship between the base model’s original predictions and the response. Despite its popularity for calibrating models after undersampling, Platt’s scaling was not designed for this purpose. Our work presents what we believe is the first detailed study focused on the validity of using Platt’s scaling to calibrate models after undersampling. We show analytically, as well as via a simulation study, that Platt’s scaling should not be used for calibration after undersampling without critical thought. If Platt’s scaling would have been able to successfully calibrate the base model had it been trained on the entire dataset (i.e., without undersampling), then Platt’s scaling might be appropriate for calibration after undersampling. If this is not the case, we recommend either beta calibration or a modified version of Platt’s scaling that fits a logistic generalized additive model to the logit of the base model’s predictions, as they are both theoretically motivated and performed relatively well across the settings considered in our study.
URL: https://openreview.net/forum?id=80b2zaeTUe
---
Title: Schauder Bases for $C[0, 1]$ Using ReLU, Softplus and Two Sigmoidal Functions
Abstract: We construct four Schauder bases for the space $C[0,1]$, one using ReLU functions, another using Softplus functions, and two more using sigmoidal versions of the ReLU and Softplus functions. This establishes the existence of a basis using these functions for the first time, and improves on the universal approximation property associated with them.
URL: https://openreview.net/forum?id=YT79Qu1bOi
---
Title: Symmetry in Neural Network Parameter Spaces
Abstract: Modern deep learning models are highly overparameterized, resulting in large sets of parameter configurations that yield the same outputs. A significant portion of this redundancy is explained by symmetries in the parameter space—transformations that leave the network function unchanged. These symmetries shape the loss landscape and constrain learning dynamics, offering a new lens for understanding optimization, generalization, and model complexity that complements existing theory of deep learning. This survey provides an overview of parameter space symmetry. We summarize existing literature, uncover connections between symmetry and learning theory, and identify gaps and opportunities in this emerging field.
URL: https://openreview.net/forum?id=jLpWq5QY6I
---
Title: Behaviour Discovery and Attribution for Explainable Reinforcement Learning
Abstract: Building trust in reinforcement learning (RL) agents requires understanding why they make certain decisions, especially in high-stakes applications like robotics, healthcare, and finance. Existing explainability methods often focus on single states or entire trajectories, either providing only local, step-wise insights or attributing decisions to coarse, episodelevel summaries. Both approaches miss the recurring strategies and temporally extended patterns that actually drive agent behavior across multiple decisions. We address this gap by proposing a fully offline, reward-free framework for behavior discovery and segmentation, enabling the attribution of actions to meaningful and interpretable behavior segments that capture recurring patterns appearing across multiple trajectories. Our method identifies coherent behavior clusters from state-action sequences and attributes individual actions to these clusters for fine-grained, behavior-centric explanations. Evaluations on four diverse offline RL environments show that our approach discovers meaningful behaviors and outperforms trajectory-level baselines in fidelity, human preference, and cluster coherence. Our code is publicly available.
URL: https://openreview.net/forum?id=JbHtpOIH9l
---
Title: Towards stable and sparse saliency maps via feature map smoothing
Abstract: Input-gradient-based attribution methods, such as Vanilla Gradient, Integrated Gradients, and SmoothGrad, are widely used to explain image classifiers via saliency maps. However, these methods often produce explanations that are noisy, or unstable. While prior work primarily focuses on refining the explanation techniques themselves, we explore a complementary model-centered perspective grounded in explainability-by-design. Specifically, we examine how adversarial training affects saliency map quality and propose a lightweight feature-map smoothing mechanism that can be integrated during training. Evaluating across FMNIST, CIFAR-10, and ImageNette, we find that local smoothing (e.g., mean, median filters) improves stability and perceived clarity of explanations while preserving sparsity gains from adversarial training. However, gains in faithfulness are method and dataset dependent, highlighting that interpretability improvements may not generalize uniformly. A user study with 65 participants further confirms that explanations from smoothed adversarial models are perceived as more comprehensible and trustworthy. Our work highlights the value of model-level interventions for improving post-hoc explanations. Our code is available at \url{https://anonymous.4open.science/r/ImprovingVG-2BFA/README.md}.
URL: https://openreview.net/forum?id=HgkZucjJv3
---
Title: RouteFinder: Towards Foundation Models for Vehicle Routing Problems
Abstract: This paper introduces RouteFinder, a comprehensive foundation model framework to tackle different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model for VRPs should be able to represent variants by treating each as a subset of a generalized problem equipped with different attributes. We propose a unified VRP environment capable of efficiently handling any combination of these attributes. The RouteFinder model leverages a modern transformer-based encoder and global attribute embeddings to improve task representation. Additionally, we introduce two reinforcement learning techniques to enhance multi-task performance: mixed batch training, which enables training on different variants at once, and multi-variant reward normalization to balance different reward scales. Finally, we propose efficient adapter layers that enable fine-tuning for new variants with unseen attributes. Extensive experiments on 48 VRP variants show RouteFinder outperforms recent state-of-the-art learning methods. Our code is publicly available at https://anonymous.4open.science/r/routefinder.
URL: https://openreview.net/forum?id=QzGLoaOPiY
---
Title: Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning
Abstract: Pruning of deep neural networks has been an effective technique for reducing model size while preserving most of the performance of dense networks, crucial for deploying models on memory and power-constrained devices. While recent sparse learning methods have shown promising performance up to moderate sparsity levels such as 95% and 98%, accuracy quickly deteriorates when pushing sparsities to extreme levels due to unique challenges such as fragile gradient flow. In this work, we explore network performance beyond the commonly studied sparsities, and develop techniques that encourage stable training without accuracy collapse even at extreme sparsities, including 99.90%, 99.95\% and 99.99% on ResNet architectures. We propose three complementary techniques that enhance sparse training through different mechanisms: 1) Dynamic ReLU phasing, where DyReLU initially allows for richer parameter exploration before being gradually replaced by standard ReLU, 2) weight sharing which reuses parameters within a residual layer while maintaining the same number of learnable parameters, and 3) cyclic sparsity, where both sparsity levels and sparsity patterns evolve dynamically throughout training to better encourage parameter exploration. We evaluate our method, which we term Extreme Adaptive Sparse Training (EAST) at extreme sparsities using ResNet-34 and ResNet-50 on CIFAR-10, CIFAR-100, and ImageNet,achieving competitive or improved performance compared to existing methods, with notable gains at extreme sparsity levels. Code is available at redacted.
URL: https://openreview.net/forum?id=XX9JdOJD8R
---
Title: Exponential Scaling of Factual Inconsistency in Data-to-Text Generation with Fine-Tuned LLMs
Abstract: Data-to-text (D2T) generation is a core task in text generation that involves converting semi-structured data (e.g., tables, graphs) into text. Recent advances in large language models (LLMs) have led to significant improvements in D2T. Despite these gains, factual inconsistency remains a persistent issue in LLMs for D2T. Understanding how such inconsistencies scale with factors like model size, compute (FLOPs), and data size is crucial for building trustworthy systems. While prior scaling studies focus on generalization error via power law scaling, the impact of these factors on factual inconsistency in D2T remains unexplored. This paper addresses the gap by empirically investigating how factual inconsistency scales with various scaling factors. Unlike prior studies that focus solely on power law scaling, we also examine exponential scaling. To rigorously compare these models, we introduce \textit{VaCScal}, a three-stage statistical validation framework: (1) predictive performance estimation, (2) goodness-of-fit assessment, and (3) comparative analysis. Experiments are conducted across five diverse LLM families and five D2T datasets. Factual inconsistency is inversely measured using four state-of-the-art consistency metrics, including human evaluation. QLoRA and Prefix-Tuning are employed for fine-tuning the LLMs. Our analysis, validated through the \textit{VaCScal} framework, consistently shows that factual inconsistency in D2T generation follows exponential scaling with respect to model (LLM) size, compute (FLOPs), and fine-tuning data size---challenging the prevailing assumption of power law scaling. To support this finding, a mathematical rationale is also provided, demonstrating why exponential scaling behavior is expected in factual inconsistency under typical D2T conditions.
URL: https://openreview.net/forum?id=xPaPd6g5WG
---
Title: Evaluating Selective Encryption Against Gradient Inversion Attacks
Abstract: Gradient inversion attacks pose significant privacy threats to distributed training frameworks such as federated learning, enabling malicious parties to reconstruct sensitive local training data from gradient communications between clients and an aggregation server during the aggregation process. While traditional encryption-based defenses, such as homomorphic encryption, offer strong privacy guarantees without compromising model utility, they often incur prohibitive computational overheads. To mitigate this, selective encryption has emerged as a promising approach, encrypting only a subset of gradient data based on the data's significance under a certain metric. However, there have been few systematic studies on how to specify this metric in practice. This paper systematically evaluates selective encryption methods with different significance metrics against state-of-the-art attacks. Our findings demonstrate the feasibility of selective encryption in reducing computational overhead while maintaining resilience against attacks. We propose a distance-based significance analysis framework that provides theoretical foundations for selecting critical gradient elements for encryption. Through extensive experiments on different model architectures (LeNet, CNN, BERT, GPT-2) and attack types, we identify gradient magnitude as a generally effective metric for protection against optimization-based gradient inversions. However, we also observe that no single selective encryption strategy is universally optimal across all attack scenarios, and we provide guidelines for choosing appropriate strategies for different model architectures and privacy requirements.
URL: https://openreview.net/forum?id=PQWI9fa7Lq
---
Title: A second-order-like optimizer with adaptive gradient scaling for deep learning
Abstract: In this empirical article, we introduce INNAprop, an optimization algorithm that combines the INNA method with the RMSprop adaptive gradient scaling. It leverages second-order information and rescaling while keeping the memory and compute requirements of standard DL methods as AdamW or SGD. INNAprop is evaluated on CIFAR-10, Food101, and ImageNet with ResNets, VGG, DenseNet, and ViT. We also train GPT-2 (OpenWebText) from scratch and with LoRA fine-tuning (E2E). INNAprop consistently matches or outperforms AdamW both in training speed and accuracy, with minimal hyperparameter tuning in large-scale settings.
URL: https://openreview.net/forum?id=3khtiJDXQW
---
Title: Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation
Abstract: Recent advances in 2D image generation have achieved remarkable quality, largely driven by the capacity of diffusion models and the availability of large-scale datasets. However, direct 3D generation is still constrained by the scarcity and lower fidelity of 3D datasets. In this paper, we introduce Zero-1-to-G, a novel approach that addresses this problem by enabling direct single-view generation on Gaussian splats using pretrained 2D diffusion models. Our key insight is that Gaussian splats, a 3D representation, can be decomposed into multi-view images encoding different attributes. This reframes the challenging task of direct 3D generation within a 2D diffusion framework, allowing us to leverage the rich priors of pretrained 2D diffusion models. To incorporate 3D awareness, we introduce cross-view and cross-attribute attention layers, which capture complex correlations and enforce 3D consistency across generated splats. This makes Zero-1-to-G the first direct image-to-3D generative model to effectively utilize pretrained 2D diffusion priors, enabling efficient training and improved generalization to unseen objects. Extensive experiments on both synthetic and in-the-wild datasets demonstrate superior performance in 3D object generation, offering a new approach to high-quality 3D generation.
URL: https://openreview.net/forum?id=GVizav9Zf8
---
Title: Early Classification of Time Series: A survey and Benchmark
Abstract: In many situations, the measurements of a studied phenomenon are provided sequentially, and the prediction of its class needs to be made as early as possible so as not to incur too high a time penalty, but not too early and risk paying the cost of misclassification. This problem has been particularly studied in the case of time series, and is known as Early Classification of Time Series (ECTS). Although it has been the subject of a growing body of literature, there is still a lack of a systematic, shared evaluation protocol to compare the relative merits of the various existing methods. This document begins by situating these methods within a principle-based taxonomy. It defines dimensions for organizing their evaluation and then reports the results of a very extensive set of experiments along these dimensions involving nine state-of-the-art ECTS algorithms. In addition, these and other experiments can be carried out using an open-source library in which most of the existing ECTS algorithms have been implemented (github available upon release, see attached zip file).
URL: https://openreview.net/forum?id=bcNDYmBicK
---
Title: Cross-Domain Graph Anomaly Detection via Test-Time Training with Homophily-Guided Self-Supervision
Abstract: Graph Anomaly Detection (GAD) has demonstrated great effectiveness in identifying unusual patterns within graph-structured data. However, while labeled anomalies are often scarce in emerging applications, existing supervised GAD approaches are either ineffective or not applicable when moved across graph domains due to distribution shifts and heterogeneous feature spaces. To address these challenges, we present GADT3, a novel test-time training framework for cross-domain GAD. GADT3 combines supervised and self-supervised learning during training while adapting to a new domain during test time using only self-supervised learning by leveraging a homophily-based affinity score that captures domain-invariant properties of anomalies. Our framework introduces four key innovations to cross-domain GAD: an effective self-supervision scheme, an attention-based mechanism that dynamically learns edge importance weights during message passing, domain-specific encoders for handling heterogeneous features, and class-aware regularization to address imbalance. Experiments across multiple cross-domain settings demonstrate that GADT3 significantly outperforms existing approaches, achieving average improvements of over 8.2\% in AUROC and AUPRC compared to the best competing model.
URL: https://openreview.net/forum?id=sB3LqdOlNb
---
Title: ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment
Abstract: We present ReHub, a novel graph transformer architecture that achieves linear complexity through an efficient reassignment technique between nodes and virtual nodes. Graph transformers have become increasingly important in graph learning for their ability to utilize long-range node communication explicitly, addressing limitations such as oversmoothing and oversquashing found in message-passing graph networks. However, their dense attention mechanism scales quadratically with the number of nodes, limiting their applicability to large-scale graphs. ReHub draws inspiration from the airline industry's hub-and-spoke model, where flights are assigned to optimize operational efficiency. In our approach, graph nodes (spokes) are dynamically reassigned to a fixed number of virtual nodes (hubs) at each model layer. Recent work, Neural Atoms (Li et al., 2024), has demonstrated impressive and consistent improvements over GNN baselines by utilizing such virtual nodes; their findings suggest that the number of hubs strongly influences performance. However, increasing the number of hubs typically raises complexity, requiring a trade-off to maintain linear complexity. Our key insight is that each node only needs to interact with a small subset of hubs to achieve linear complexity, even when the total number of hubs is large. To leverage all hubs without incurring additional computational costs, we propose a simple yet effective adaptive reassignment technique based on hub-hub similarity scores, eliminating the need for expensive node-hub computations. Our experiments on long-range graph benchmarks indicate a consistent improvement in results over the base method, Neural Atoms, while maintaining a linear complexity instead of $O(n^{3/2})$. Remarkably, our sparse model achieves performance on par with its non-sparse counterpart. Furthermore, ReHub outperforms competitive baselines and consistently ranks among the top performers across various benchmarks.
URL: https://openreview.net/forum?id=L4S54TUOQR
---
Title: MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
Abstract: Referring Image Segmentation (RIS) is an advanced vision-language task that involves identifying and segmenting objects within an image as described by free-form text descriptions. While previous studies focused on aligning visual and language features, exploring training techniques, such as data augmentation, remains underexplored. In this work, we explore effective data augmentation for RIS and propose a novel training framework called Masked Referring Image Segmentation (MaskRIS). We observe that the conventional image augmentations fall short of RIS, leading to performance degradation, while simple random masking significantly enhances the performance of RIS. MaskRIS uses both image and text masking, followed by Distortion-aware Contextual Learning (DCL) to fully exploit the benefits of the masking strategy. This approach can improve the model's robustness to occlusions, incomplete information, and various linguistic complexities, resulting in a significant performance improvement. Experiments demonstrate that MaskRIS can easily be applied to various RIS models, outperforming existing methods in both fully supervised and weakly supervised settings. Finally, MaskRIS achieves new state-of-the-art performance on RefCOCO, RefCOCO+, and RefCOCOg datasets.
URL: https://openreview.net/forum?id=EtK4madHmc
---
Title: Temporal horizons in forecasting: a performance-learnability trade-off
Abstract: When training autoregressive models to forecast dynamical systems, a critical question arises: how far into the future should the model be trained to predict? Too short a horizon may miss long-term trends, while too long a horizon can impede convergence due to accumulating prediction errors. In this work, we formalize this trade-off by analyzing how the geometry of the loss landscape depends on the training horizon. We prove that for chaotic systems, the loss landscape's roughness grows exponentially with the training horizon, while for limit cycles, it grows linearly, making long-horizon training inherently challenging. However, we also show that models trained on long horizons generalize well to short-term forecasts, whereas those trained on short horizons suffer exponentially (resp. linearly) worse long-term predictions in chaotic (resp. periodic) systems. We validate our theory through numerical experiments and discuss practical implications for selecting training horizons. Our results provide a principled foundation for hyperparameter optimization in autoregressive forecasting models.
URL: https://openreview.net/forum?id=BeudQIxT1R
---
Title: Beyond Instance Consistency: Investigating View Diversity in Self-supervised Learning
Abstract: Self-supervised learning (SSL) conventionally relies on the instance consistency paradigm, assuming that different views of the same image can be treated as positive pairs. However, this assumption breaks down for non-iconic data, where different views may contain distinct objects or semantic information. In this paper, we investigate the effectiveness of SSL when instance consistency is not guaranteed. Through extensive ablation studies, we demonstrate that SSL can still learn meaningful representations even when positive pairs lack strict instance consistency. Furthermore, our analysis further reveals that increasing view diversity, by enforcing zero overlapping or using smaller crop scales, can enhance downstream performance on classification and dense prediction tasks. However, excessive diversity is found to reduce effectiveness, suggesting an optimal range for view diversity. To quantify this, we adopt the Earth Mover’s Distance (EMD) as an estimator to measure mutual information between views, finding that moderate EMD values correlate with improved SSL learning, providing insights for future SSL framework design. We validate our findings across a range of settings, highlighting their robustness and applicability on diverse data sources.
URL: https://openreview.net/forum?id=urWCU3YMA0
---
Title: Byzantine-Robust and Hessian-Free Federated Bilevel Optimization
Abstract: In the last few years, Byzantine robust algorithms to solve a minimization problem in the Federated setup have received significant attention. Most of the existing works consider the problem of byzantine-robustness for single-level optimization or consider the federated bilevel optimization without Byzantine nodes. However, problem formulation such as federated bilevel optimization in the presence of byzantine nodes is unexplored. Recognizing the gap, for the first time, we propose a computationally efficient and robust algorithm for solving Federated Bilevel Optimization with Byzantine (FedBOB) nodes that: \One Work under the assumption that the data across nodes are heterogeneous (non-iid), \2 Consider the lower-level objective is non-convex and satisfies the Polyak-\L ojasiewicz (PL)-inequality, and \3 Is fully first-order and does not rely on second order information. We achieve this by reformulating the federated bilevel problem into a single penalty problem. We provide the theoretical performance of the proposed algorithm and experimentally corroborate our theoretical findings.
URL: https://openreview.net/forum?id=5trmyvtkeo
---
Title: Recurrent Natural Policy Gradient for POMDPs
Abstract: Solving partially observable Markov decision processes (POMDPs) is a long-standing challenge in reinforcement learning (RL) due to the inherent curse of dimensionality arising from the non-stationarity of optimal policies. In this paper, we address this by integrating recurrent neural network (RNN) architectures into a natural policy gradient (NPG) method and a multi-step temporal difference (TD) method within a natural actor-critic (NAC) framework for computational efficiency. We establish non-asymptotic theoretical guarantees for this method, which demonstrate its effectiveness for solving POMDPs and identify the pathological cases that stem from long-term dependencies. By integrating RNNs into the NAC framework with theoretical guarantees, this work advances the theoretical foundation of RL for POMDPs and provides a scalable framework for solving complex decision-making problems.
URL: https://openreview.net/forum?id=6G01e0vgIf
---
Title: Tunable Domain Adaptation Using Unfolding
Abstract: Machine learning models often struggle to generalize across domains with varying data distributions, such as differing noise levels, leading to degraded performance. Traditional strategies like personalized training, which trains separate models per domain, and joint training, which uses a single model for all domains, have significant limitations in flexibility and effectiveness. To address this, we propose two novel domain adaptation methods for regression tasks based on interpretable unrolled networks—deep architectures inspired by iterative optimization algorithms. These models leverage the functional dependence of select tunable parameters on domain variables, enabling controlled adaptation during inference. Our methods include Parametric Tunable-Domain Adaptation (P-TDA), which uses known domain parameters for dynamic tuning, and Data-Driven Tunable-Domain Adaptation (DD-TDA), which infers domain adaptation directly from input data. We validate our approach on compressed sensing problems involving noise-adaptive sparse signal recovery and domain-adaptive gain calibration, demonstrating improved or comparable performance to domain-specific models while surpassing joint training baselines. This work highlights the potential of unrolled networks for effective, interpretable domain adaptation in regression settings.
URL: https://openreview.net/forum?id=Aj6XP597dn
---
Title: DRDT3: Diffusion-Refined Decision Test-Time Training Model
Abstract: Decision Transformer (DT), a trajectory modelling method, has shown competitive performance compared to traditional offline reinforcement learning (RL) approaches on various classic control tasks. However, it struggles to learn optimal policies from suboptimal, reward-labelled trajectories. In this study, we explore the use of conditional generative modelling to facilitate trajectory stitching given its high-quality data generation ability. Additionally, recent advancements in Recurrent Neural Networks (RNNs) have shown their linear complexity and competitive sequence modelling performance over Transformers. We leverage the Test-Time Training (TTT) layer, an RNN that updates hidden states during testing, to model trajectories in the form of DT. We introduce a unified framework, called Diffusion-Refined Decision TTT (DRDT3), to achieve performance beyond DT models. Specifically, we propose the Decision TTT (DT3) module, which harnesses the sequence modelling strengths of both self-attention and the TTT layer to capture recent contextual information and make coarse action predictions. DRDT3 iteratively refines the coarse action predictions through the generative diffusion model, progressively moving closer to the optimal actions. We further integrate DT3 with the diffusion model using a unified optimization objective. With experiments on multiple tasks of Gym and AntMaze in the D4RL benchmark, our DT3 model without diffusion refinement demonstrates improved performance over standard DT, while DRDT3 further achieves superior results compared to state-of-the-art conventional offline RL and DT-based methods.
URL: https://openreview.net/forum?id=I6zjLhIzgh
---
Title: LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models
Abstract: Some of the simplest, yet most frequently used predictors in statistics and machine learning use weighted linear combinations of features. Such linear predictors can model non-linear relationships between features by adding interaction terms corresponding to the products of all pairs of features. We consider the problem of accurately estimating coefficients for interaction terms in linear predictors. We hypothesize that the coefficients for different interaction terms have an approximate low-dimensional structure and represent each feature by a latent vector in a low-dimensional space. This low-dimensional representation can be viewed as a structured regularization approach that further mitigates overfitting in high-dimensional settings beyond standard regularizers such as the lasso and elastic net. We demonstrate that our approach, called LIT-LVM, achieves superior prediction accuracy compared to elastic net and factorization machines on a wide variety of simulated and real data, particularly when the number of interaction terms is high compared to the number of samples. LIT-LVM also provides low-dimensional latent representations for features that are useful for visualizing and analyzing their relationships.
URL: https://openreview.net/forum?id=3uW5nxESu1
---
Title: Simple and Nearly-Optimal Sampling for Rank-1 Tensor Completion via Gauss-Jordan
Abstract: We revisit the sample and computational complexity of the rank-1 tensor completion problem in $\otimes_{i=1}^{N} \mathbb{R}^{d}$, given a uniformly sampled subset of entries. We present a characterization of the problem which reduces to solving a pair of random linear systems. For example, when $N$ is a constant, we prove it requires no more than $m = O(d^2 \log d)$ samples and runtime $O(md^2)$. Moreover, we show that a broad class of algorithms require $\Omega(d\log d)$ samples, even under higher rank scenarios. In contrast, existing upper bounds on the sample complexity are at least as large as $d^{1.5} \mu^{\Omega(1)} \log^{\Omega(1)} d$, where $\mu$ can be $\Theta(d)$ in the worst case. Prior work obtained these looser guarantees in higher rank versions of our problem, and tend to involve more complicated algorithms.
URL: https://openreview.net/forum?id=ggAphfUt1J
---
Title: Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning
Abstract: Initializing with pre-trained models when learning on downstream tasks is becoming standard practice in machine learning. Several recent works explore the benefits of pre-trained initialization in a federated learning (FL) setting, where the downstream training is performed at the edge clients with heterogeneous data distribution. These works show that starting from a pre-trained model can substantially reduce the adverse impact of data heterogeneity on the test performance of a model trained in a federated setting, with no changes to the standard FedAvg training algorithm. In this work, we provide a deeper theoretical understanding of this phenomenon. To do so, we study the class of two-layer convolutional neural networks (CNNs) and provide bounds on the training error convergence and test error of such a network trained with FedAvg. We introduce the notion of aligned and misaligned filters at initialization and show that the data heterogeneity only affects learning on misaligned filters. Starting with a pre-trained model typically results in fewer misaligned filters at initialization, thus producing a lower test error even when the model is trained in a federated setting with data heterogeneity. Experiments in synthetic settings and practical FL training on CNNs verify our theoretical findings.
URL: https://openreview.net/forum?id=wW4Cvhkxcx
---
Title: Memory Meets (Multi-Modal) Large Language Models: A Comprehensive Survey
Abstract: Memory plays a foundational role in augmenting the reasoning, adaptability, and contextual fidelity of modern Large Language Models (LLMs) and Multi-Modal LLMs (MLLMs). As these models transition from static predictors to interactive systems capable of continual learning and personalized inference, the incorporation of memory mechanisms has emerged as a central theme in their architectural and functional evolution. This survey presents a comprehensive and structured synthesis of memory in LLMs and MLLMs, organizing the literature into a cohesive taxonomy comprising implicit, explicit, and agentic memory paradigms. Specifically, the survey delineates three primary memory frameworks. \textit{Implicit memory} refers to the knowledge embedded within the internal parameters of pre-trained transformers, encompassing their capacity for memorization, associative retrieval, and contextual reasoning. Recent work has explored methods to interpret, manipulate, and reconfigure this latent memory. \textit{Explicit memory} involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations—such as textual corpora, dense vectors, and graph-based structures—thereby enabling scalable and updatable interaction with information sources. \textit{Agentic memory} introduces persistent, temporally extended memory structures within autonomous agents, facilitating long-term planning, self-consistency, and collaborative behavior in multi-agent systems, with relevance to embodied and interactive AI. Extending beyond text, the survey examines the integration of memory within multi-modal settings, where coherence across vision, language, audio, and action modalities is essential. Key architectural advances, benchmark tasks, and open challenges are discussed, including issues related to memory capacity, alignment, factual consistency, and cross-system interoperability. By charting the current landscape and identifying critical research directions, this survey aims to inform the development of memory-augmented (M)LLMs that are more flexible, context-sensitive, and aligned with the requirements of real-world intelligent systems.
URL: https://openreview.net/forum?id=Sk7pwmLuAY
---
Title: Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agents
Abstract: Data science aims to extract insights from data to support decision-making processes. Recently, Large Language Models (LLMs) are increasingly used as assistants for data science, by suggesting ideas, techniques and small code snippets, or for the interpretation of results and reporting. Proper automation of some data-science activities is now promised by the rise of LLM agents, i.e., AI systems powered by an LLM equipped with additional affordances--such as code execution and knowledge bases--that can perform self-directed actions and interact with digital environments. In this paper, we survey the evaluation of LLM assistants and agents for data science. We find (1) a dominant focus on a small subset of goal-oriented activities, largely ignoring data management and exploratory activities; (2) a concentration on pure assistance or fully autonomous agents, without considering intermediate levels of human-AI collaboration; and (3) an emphasis on human substitution, therefore neglecting the possibility of higher levels of automation thanks to task transformation.
URL: https://openreview.net/forum?id=MB0TCLfLn1
---
Title: Data-Driven Discovery of PDEs via the Adjoint Method
Abstract: In this work, we present an adjoint-based method for discovering the underlying governing partial differential equations (PDEs) given data. The idea is to consider a parameterized PDE in a general form and formulate a PDE-constrained optimization problem aimed at minimizing the error of the PDE solution from data. Using variational calculus, we obtain an evolution equation for the Lagrange multipliers (adjoint equations), allowing us to compute the gradient of the objective function with respect to the parameters of PDEs given data in a straightforward manner. In particular, we consider a family of temporal parameterized PDEs that encompass linear, nonlinear, and spatial derivative candidate terms, and elegantly derive the corresponding adjoint equations. We show the efficacy of the proposed approach in identifying the form of the PDE up to machine accuracy, enabling the accurate discovery of PDEs from data. We also compare its performance with the famous PDE Functional Identification of Nonlinear Dynamics method known as PDE-FIND \cite{rudy2017data} among others, on both smooth and noisy data sets. Even though the proposed adjoint method relies on forward/backward solvers, it outperforms PDE-FIND in the limit of large data sets thanks to the analytic expressions for gradients of the cost function with respect to each PDE parameter.
URL: https://openreview.net/forum?id=Az3mJ4d1eT
---
Title: ViTime: Foundation Model for Time Series Forecasting Powered by Vision Intelligence
Abstract: Time series forecasting (TSF) possesses great practical values in various fields, including power and energy, transportation, etc. TSF methods have been studied based on knowledge from classical statistics to modern deep learning. Yet, all of them were developed based on one fundamental concept, the numerical data fitting. Thus, the models developed have been long known for being problem-specific and lacking application generalizability. Practitioners expect a TSF foundation model that serves TSF tasks in different applications. The central question is then how to develop such a TSF foundation model. This paper offers one pioneering study in the TSF foundation model development method and proposes a vision intelligence-powered framework, ViTime, for the first time. ViTime fundamentally shifts TSF from numerical fitting to operations based on a binary image-based time series metric space. We also provide rigorous theoretical analyses of ViTime, including quantization-induced system error bounds and principled strategies for optimal parameter selection. Furthermore, we propose RealTS, an innovative synthesis algorithm generating diverse and realistic training samples, effectively enriching the training data and significantly enhancing model generalizability. Extensive experiments demonstrate ViTime's SOTA performance. In zero-shot scenarios, ViTime outperforms TimesFM by 9-15\%. With just 10\% fine-tuning data, ViTime surpasses both leading foundation models and fully-supervised benchmarks, a gap that widens with 100\% fine-tuning. ViTime also exhibits exceptional robustness, effectively handling missing data and outperforming TimesFM by 20-30\% under various data perturbations, validating the power of its visual space data operation paradigm.
URL: https://openreview.net/forum?id=XInsJDBIkp
---
Title: TFAR: A Training-Free Framework for Autonomous Reliable Reasoning in Visual Question Answering
Abstract: Recent approaches introduce chain-of-thought (CoT) reasoning to mitigate the challenges, such as hallucination and reasoning deficit in multimodal large language models (MLLMs) and enhance performance. However, existing CoT-based methods often rely on extensive data annotation and training. To overcome these limitations, we propose a training-free framework for autonomous and reliable reasoning (TFAR), which only uses common lightweight vision tools to improve the reasoning ability of MLLMs. TFAR enables an MLLM to autonomously and accurately identify relevant regions of interest (RoIs) and support CoT reasoning, without requiring additional training or annotations, and with low computational overhead during inference. However, the use of external tools will introduce noise and uncertainty. To mitigate the uncertainty introduced by external tools and select the optimal pathway, we propose a conformal prediction-based uncertainty quantification method that calibrates the outputs from external tools and dynamically selects the most appropriate tool based on the MLLM’s output uncertainty. Experiments across five datasets demonstrate that TFAR improves performance over the base MLLM by an average of 4.6$\%$, in some cases even outperforming fine-tuned baselines, while maintaining low inference cost. These results offer new insights into training-free CoT guidance for MLLMs and underscore the value of reliable visual tools.
URL: https://openreview.net/forum?id=cBAKeZN3jy
---
Title: LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation
Abstract: Scaling language models to handle longer contexts introduces substantial memory challenges due to the growing cost of key-value (KV) caches.
Motivated by the efficiency gains of hybrid models and the broad availability of pretrained large transformer backbones, we explore transitioning transformer models into hybrid architectures for a more efficient generation.
In this work, we propose \textsc{LightTransfer},
a lightweight method that transforms models such as LLaMA into hybrid variants.
Our approach identifies \textit{lazy} layers---those focusing on recent or initial tokens---and replaces their full attention with streaming attention.
This transformation can be performed without any training for long-context understanding tasks or with minimal fine-tuning for o1-like long reasoning generation tasks that require stronger reasoning capabilities.
Experiments across diverse benchmarks and models (e.g., LLaMA, Mistral, QwQ-STILL) demonstrate that,
even when half of the layers are identified as \textit{lazy},
\textsc{LightTransfer} achieves up to 2.17$\times$ throughput improvement with minimal performance loss ($<1.5\%$ on LongBench) and achieves 53.3\% on math benchmark AIME24 of advanced o1-like long reasoning model QwQ-STILL.
URL: https://openreview.net/forum?id=kne4vWICr0
---
Title: Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation
Abstract: Recent progress in text-to-music generation has enabled models to synthesize high-quality musical segments, full compositions, and even respond to fine-grained control signals, e.g. chord progressions. State-of-the-art (SOTA) systems differ significantly across many dimensions, such as training datasets, modeling paradigms, and architectural choices. This diversity complicates efforts to evaluate models fairly and pinpoint which design choices most influence performance. While factors like data and architecture are important, in this study we focus exclusively on the modeling paradigm. We conduct a systematic empirical analysis to isolate its effects, offering insights into associated trade-offs and emergent behaviors that can guide future text-to-music generation systems. Specifically, we compare the two arguably most common modeling paradigms: Auto-Regressive decoding and Conditional Flow-Matching. We conduct a controlled comparison by training all models from scratch using identical datasets, training configurations, and similar backbone architectures. Performance is evaluated across multiple axes, including generation quality, robustness to inference configurations, scalability, adherence to both textual and temporally aligned conditioning, and editing capabilities in the form of audio inpainting. This comparative study sheds light on distinct strengths and limitations of each paradigm, providing actionable insights that can inform future architectural and training decisions in the evolving landscape of text-to-music generation. Audio sampled examples are available at: https://huggingface.co/spaces/Unk-Uname/ARvsFM
URL: https://openreview.net/forum?id=xXc5DeaBYw
---
Title: Differentially Private Clustered Federated Learning
Abstract: Federated learning (FL), which is a decentralized machine learning (ML) approach, often incorporates differential privacy (DP) to provide rigorous data privacy guarantees to clients. Previous works attempted to address high structured data heterogeneity in vanilla FL settings through clustering clients (a.k.a clustered FL), but these methods remain sensitive and prone to errors, further exacerbated by the DP noise. This vulnerability makes the previous methods inappropriate for differentially private FL (DPFL) under structured data heterogeneity. To address this gap, we propose an algorithm for differentially private clustered FL, which is robust to the DP noise in the system and identifies the underlying clients’ clusters correctly. To this end, we propose to cluster clients based on both their model updates and training loss values. Furthermore, for clustering clients’ model updates at the end of the first round, our proposed approach addresses the server’s uncertainties by employing large batch sizes as well as Gaussian Mixture Models (GMM) to reduce the impact of DP and stochastic noise and avoid potential clustering errors. We provide theoretical analysis to justify our approach and evaluate it across diverse data distributions and privacy budgets. Our experimental results show the approach’s effectiveness in addressing high structured data heterogeneity in DPFL.
URL: https://openreview.net/forum?id=JSsko0a4yr
---
Title: IndicFake Meets SAFARI-LLM: Unifying Semantic and Acoustic Intelligence for Multilingual Deepfake Detection
Abstract: Audio deepfakes pose a growing threat, particularly in linguistically diverse and low-resource settings where existing detection methods often struggle. This work introduces two transformative contributions to address these challenges. First, we present \textbf{IndicFake}, a pioneering audio deepfake dataset with over 4.2 million samples (7,350 hours) spanning English and 17 Indian languages across Indo-European, Dravidian, and Sino-Tibetan families. With minimal overlap (Jaccard similarity: 0.00--0.06) with existing datasets, IndicFake offers an unparalleled benchmark for multilingual deepfake detection. Second, we propose \textbf{SAFARI-LLM} (Semantic Acoustic Feature Adaptive Router with Integrated LLM), a novel framework that integrates Whisper’s semantic embeddings and m-HuBERT’s acoustic features through an adaptive Audio Feature Unification Module (AFUM). Enhanced by LoRA-fine-tuned LLaMA-7B, SAFARI-LLM achieves unmatched cross-lingual and cross-family generalization. Evaluations across IndicFake, DECRO, and WaveFake datasets demonstrate its superiority, outperforming 14 state-of-the-art models with standout accuracies of 94.21\% (English-to-Japanese transfer on WaveFake) and 84.48\% (English-to-Chinese transfer on DECRO), alongside robust performance across diverse linguistic contexts. These advancements establish a new standard for reliable, scalable audio deepfake detection. Code and resources are publicly available at: \href{https://anonymousillusion.github.io/indicfake/}{\textcolor{blue}{URL}}.
URL: https://openreview.net/forum?id=s8pPYRVVTU
---
Title: Mamba State-Space Models Are Lyapunov-Stable Learners
Abstract: Compute-efficient methods–e.g., mixed-precision fine-tuning (MPFT) and parameter-efficient fine-tuning (PEFT)–have become standard tools for Transformer-based large language models (LLMs). While near-ubiquitously adapted, we empirically show that, under different combinations of MPFT and PEFT, Transformer LLMs may drastically diverge from their respective full-precision counterparts. In stark contrast, we show that recent Mamba LLMs based on state-space models (SSMs) are significantly more stable to changes introduced by combinations of MPFT and PEFT. This robustness is due to the recurrent dynamics of Mamba SSMs, which we prove are guaranteed to be stable using dynamical systems theory (in particular, Lyapunov exponents). Additionally, we demonstrate how targeting different Mamba parameters for low-rank adaptation provides regularization and impacts PEFT generalization. We conclude by using MPFT and PEFT to novelly study Mamba LLMs’ in-context learning (ICL) abilities on natural language tasks, thus supplementing other recent work.
URL: https://openreview.net/forum?id=wzsYQYs3dO
---
Title: PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization
Abstract: Accurate 3D shape representation is essential in engineering applications such as design, optimization, and simulation. In practice, engineering workflows require structured, part-based representations, as objects are inherently designed as assemblies of distinct components. However, most existing methods either model shapes holistically or decompose them without predefined part structures, limiting their applicability in real-world design tasks. We propose PartSDF, a supervised implicit representation framework that explicitly models composite shapes with independent, controllable parts while maintaining shape consistency. Thanks to its simple but innovative architecture, PartSDF outperforms both supervised and unsupervised baselines in reconstruction and generation tasks. We further demonstrate its effectiveness as a structured shape prior for engineering applications, enabling precise control over individual components while preserving overall coherence.
URL: https://openreview.net/forum?id=zl43C1yBKv
---
Title: Efficient Ensembling Improves Training Data Attribution
Abstract: Training data attribution (TDA) methods aim to quantify the influence of individual training data points on the model predictions, with broad applications in data-centric AI, such as mislabel detection, data selection, and copyright compensation. However, existing methods in this field, which can be categorized as retraining-based and gradient-based, have struggled with the trade-off between computational efficiency and attribution efficacy. Retraining-based methods can accurately attribute complex non-convex models but are computationally prohibitive, while gradient-based methods are efficient but often fail for non-convex models. Recent research has shown that augmenting gradient-based methods with ensembles of multiple independently trained models can achieve significantly better attribution efficacy. However, this approach remains impractical for very large-scale applications.
In this work, we discover that expensive, fully independent training is unnecessary for ensembling the gradient-based methods, and we propose two efficient ensemble strategies, DROPOUT ENSEMBLE and LORA ENSEMBLE, alternative to naive independent ensemble. These strategies significantly reduce training time (up to 80%), serving time (up to 60%), and space cost (up to 80%) while maintaining similar attribution efficacy to the naive independent ensemble. Our extensive experimental results demonstrate that the proposed strategies are effective across multiple TDA methods on diverse datasets and models, including generative settings, significantly advancing the Pareto frontier of TDA methods with better computational efficiency and attribution efficacy. We conduct a theoretical analysis that provides insights into the success of our empirical findings.
URL: https://openreview.net/forum?id=PjldrbYqeu
---
Title: Let Your Light Shine: Foreground Portrait Matting via Deep Flash Priors
Abstract: In this paper, we delve into a new perspective to solve image matting by revealing the foreground with flash priors. Previous Background Matting frameworks require a clean background as input, and although demonstrated powerfully, they are not practical to handle real-world scenarios with dynamic camera or background movement. We introduce the flash/no-flash image pair to portray the foreground object while eliminating the influence of dynamic background. The rationale behind this is that the foreground object is closer to the camera and thus received more light than the background. We propose a cascaded end-to-end network to integrate flash prior knowledge into the alpha matte estimation process. Particularly, a transformer-based Foreground Correlation Module is presented to connect foregrounds exposed in different lightings, which can effectively filter out the perturbation from the dynamic background and also robust to foreground motion. The initial prediction is concatenated with a Boundary Matting Network to polish the details of previous predictions. To supplement the training and evaluation of our flash/no-flash framework, we construct the first flash/no-flash portrait image matting dataset with 3,025 well-annotated alpha mattes. Experimental evaluations show that our proposed model significantly outperforms existing trimap-free matting methods on scenes with dynamic backgrounds. Moreover, we detailedly discuss and analyze the effects of different prior knowledge on static and dynamic backgrounds. In contrast to the restricted scenarios of Background Matting, we demonstrate a flexible and reliable solution in real-world cases with the camera or background movements.
URL: https://openreview.net/forum?id=vxUiVJp2eM
---
Title: Global Optimization Algorithm through High-Resolution Sampling
Abstract: We present an optimization algorithm that can identify a global minimum of a potentially nonconvex smooth function with high probability, assuming the Gibbs measure of the potential satisfies a logarithmic Sobolev inequality. Our contribution is twofold: on the one hand we propose said global optimization method, which is built on an oracle sampling algorithm producing arbitrarily accurate samples from a given Gibbs measure. On the other hand, we propose a new sampling algorithm, drawing inspiration from both overdamped and underdamped Langevin dynamics, as well as from the high-resolution differential equation known for its acceleration in deterministic settings. While the focus of the paper is primarily theoretical, we demonstrate the effectiveness of our algorithms on the Rastrigin function, where it outperforms recent approaches.
URL: https://openreview.net/forum?id=r3VEA1AWY5
---
Title: A Language Anchor-Guided Method for Robust Noisy Domain Generalization
Abstract: Real-world machine learning applications are often hindered by two critical challenges: distribution shift and label noise. Networks inherently tend to overfit to redundant, uninformative features present in the training distribution, which undermines their ability to generalize effectively to the target domain's distribution. The presence of noisy data further exacerbates this issue by inducing additional overfitting to noise, causing existing domain generalization methods to fail in effectively distinguishing invariant features from spurious ones. To address these challenges, we propose Anchor Alignment and Adaptive Weighting (A3W), a novel algorithm based on sample reweighting guided by natural language processing (NLP) anchors that seeks to extract representative features. In particular, A3W leverages semantic representations derived from natural language models to serve as a source of domain-invariant prior knowledge. We also introduce a weighted loss function that dynamically adjusts the contribution of each sample based on its distance to the corresponding NLP anchor, thereby improving the model’s resilience to noisy labels. Extensive experiments on benchmark datasets demonstrate that A3W outperforms state-of-the-art domain generalization methods, yielding significant improvements in both accuracy and robustness across various datasets and noise levels.
URL: https://openreview.net/forum?id=XsPk2n706g
---
Title: Robust Multimodal Learning via Cross-Modal Proxy Tokens
Abstract: Multimodal models often experience a significant performance drop when one or more modalities are missing during inference. To address this challenge, we propose a simple yet effective approach that enhances robustness to missing modalities while maintaining strong performance when all modalities are available. Our method introduces cross-modal proxy tokens (CMPTs), which approximate the class token of a missing modality by attending only to the tokens of the available modality without requiring explicit modality generation or auxiliary networks. To efficiently learn these approximations with minimal computational overhead, we employ low-rank adapters in frozen unimodal encoders and jointly optimize an alignment loss with a task-specific loss. Extensive experiments on five multimodal datasets show that our method outperforms state-of-the-art baselines across various missing rates while achieving competitive results in complete-modality settings. Overall, our method offers a flexible and efficient solution for robust multimodal learning. The code and pretrained models will be released on GitHub.
URL: https://openreview.net/forum?id=Wtc6wvcYJ0
---
Title: Spatial Uncertainty in Wildfire Forecasting Using Multi-Modal Earth Observation
Abstract: Accurate wildfire forecasting from remote sensing data is essential for climate resilience and emergency planning. Beyond predictive performance, understanding where and why uncertainty arises is critical for operational trust. We analyze the spatial structure of predictive uncertainty in wildfire spread forecasts using multimodal Earth observation (EO) inputs, including Sentinel-2 vegetation indices and VIIRS thermal reflectance. Using Monte Carlo dropout and deep ensembles, we show that predictive entropy maps exhibit coherent spatial patterns aligned with fire boundaries, unlike randomized baselines. We introduce a novel and interpretable centroid-oriented distance metric that reveals high-uncertainty regions consistently form 20–60 meter buffer zones around predicted firelines. Feature attribution using integrated gradients highlights vegetation condition and recent fire activity as primary drivers of model confidence. Deep ensembles further confirm that these uncertainty estimates are probabilistically well-calibrated across multiple folds. Together, these results suggest that spatial uncertainty in EO-based wildfire forecasting is structured, interpretable, and operationally actionable. The code for all experiments is available on GitHub.\footnote{\url{https://github.com/roloccark/wildf-UQ}}
URL: https://openreview.net/forum?id=txWN7MiWAI
---
Title: Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles
Abstract: The application of rule-based reinforcement learning (RL) to multimodal large language models (MLLMs) introduces unique challenges and potential deviations from findings in text-only domains, particularly for perception-heavy tasks. This paper provides a comprehensive study of rule-based visual RL using jigsaw puzzles as a structured experimental framework, revealing several key findings. \textit{Firstly,} we find that MLLMs, initially performing near to random guessing on simple puzzles, achieve near-perfect accuracy and generalize to complex, unseen configurations through fine-tuning. \textit{Secondly,} training on jigsaw puzzles can induce generalization to other visual tasks, with effectiveness tied to specific task configurations. \textit{Thirdly,} MLLMs can learn and generalize with or without explicit reasoning, though open-source models often favor direct answering. Consequently, even when trained for step-by-step reasoning, they can ignore the thinking process in deriving the final answer. \textit{Fourthly,} we observe that complex reasoning patterns appear to be pre-existing rather than emergent, with their frequency increasing alongside training and task difficulty. \textit{Finally,} our results demonstrate that RL exhibits more effective generalization than Supervised Fine-Tuning (SFT), and an initial SFT cold start phase can hinder subsequent RL optimization. Although these observations are based on jigsaw puzzles and may vary across other visual tasks, this research contributes a valuable piece of jigsaw to the larger puzzle of collective understanding rule-based visual RL and its potential in multimodal learning.
URL: https://openreview.net/forum?id=XqQCsuyPve
---
Title: Crowdsourcing with Difficulty: A Bayesian Rating Model for Heterogeneous Items
Abstract: In applied statistics and machine learning, the ``gold standards'' used for training are often biased and almost always noisy. Dawid and Skene's justifiably popular crowdsourcing model adjusts for rater (coder, annotator) sensitivity and specificity, but fails to capture distributional properties of rating data gathered for training, which in turn biases training. In this study, we introduce a general purpose measurement-error model with which we can infer consensus categories by adding item-level effects for difficulty, discriminativeness, and guessability. We further show how to constrain the bimodal posterior of these models to avoid (or if necessary, allow) adversarial raters. We validate our model's goodness of fit with posterior predictive checks, the Bayesian analogue of $\chi^2$ tests, and assess its predictive accuracy using leave-one-out cross-validation. We illustrate our new model with two well-studied data sets, binary rating data for caries in dental X-rays and implication in natural language.
URL: https://openreview.net/forum?id=rcRoBVygzt
---
Title: Towards Efficient Training of Graph Neural Networks: A Multiscale Approach
Abstract: Graph Neural Networks (GNNs) have become powerful tools for learning from graph-structured data, finding applications across diverse domains. However, as graph sizes and connectivity increase, standard GNN training methods face significant computational and memory challenges, limiting their scalability and efficiency.
In this paper, we present a novel framework for efficient multiscale training of GNNs. Our approach leverages hierarchical graph representations and subgraphs, enabling the integration of information across multiple scales and resolutions. By utilizing coarser graph abstractions and subgraphs, each with fewer nodes and edges, we significantly reduce computational overhead during training. Building on this framework, we propose a suite of scalable training strategies, including coarse-to-fine learning, subgraph-to-full-graph transfer, and multiscale gradient computation.
We also provide some theoretical analysis of our methods and demonstrate their effectiveness across various datasets and learning tasks. Our results show that multiscale training can substantially accelerate GNN training for large scale problems while maintaining, or even improving, predictive performance.
URL: https://openreview.net/forum?id=2eZ8xkL2ZB
---
Title: Generalized Image and Video Quality Assessment using Prompt-Guided Latent Diffusion Models
Abstract: The design of image and video quality assessment (QA) algorithms is extremely important to benchmark and calibrate user experience in modern visual systems. A major drawback of the state-of-the-art QA methods is their limited ability to generalize across diverse image and video data with reasonable distribution shifts. In this work, we leverage the denoising process of diffusion models for generalized image QA (IQA) and video QA (VQA) by understanding the degree of alignment between learnable quality-aware text prompts and images or video frames. In particular, we learn cross-attention maps from intermediate layers of the denoiser of latent diffusion models (LDMs) to capture quality-aware representations of images or video frames. Since applying text-to-image LDMs for every video frame is computationally expensive for videos, we only estimate the quality of a frame-rate sub-sampled version of the original video. To compensate for the loss in motion information due to frame-rate subsampling, we propose a novel temporal quality modulator. Our extensive cross-database experiments across various user-generated, synthetic, low-light, frame-rate variation, ultra high definition, and streaming content-based databases show that our model can achieve superior generalization in both IQA and VQA.
URL: https://openreview.net/forum?id=FjhvVevAoQ
---
Title: Decomposed Direct Preference Optimization for Structure-Based Drug Design
Abstract: Diffusion models have achieved promising results for Structure-Based Drug Design (SBDD). Nevertheless, high-quality protein subpocket and ligand data are relatively scarce, which hinders the models’ generation capabilities. Recently, Direct Preference Optimization (DPO) has emerged as a pivotal tool for aligning generative models with human preferences. In this paper, we propose DecompDpo, a structure-based optimization method aligns diffusion models with pharmaceutical needs using multi-granularity preference pairs. DecompDpo introduces decomposition into the optimization objectives and obtains preference pairs at the molecule or decomposed substructure level based on each objective’s decomposability. Additionally, DecompDpo introduces a physics-informed energy term to ensure reasonable molecular conformations in the optimization results. Notably, DecompDpo can be effectively used for two main purposes: (1) fine-tuning pretrained diffusion models for molecule generation across various protein families, and (2) molecular optimization given a specific protein subpocket after generation. Extensive experiments on the CrossDocked2020 benchmark show that DecompDpo significantly improves model performance, achieving up to 98.5% Med. High Affinity and a 43.9% success rate for molecule generation, and 100% Med. High Affinity and a 52.1% success rate for targeted molecule optimization.
URL: https://openreview.net/forum?id=dwSpo5DRk8
---
Title: Bi-Mamba: Towards Accurate 1-Bit State Space Model
Abstract: The typical selective state-space model (SSM) of Mamba addresses several limitations of Transformers, such as quadratic computational complexity with sequence length and significant inference-time memory requirements due to the key-value cache. However, the growing size of Mamba models continues to pose training and deployment challenges and raises concerns due to considerable training and inference compute consumption. In this work, we introduce Bi-Mamba, a scalable and powerful 1-bit Mamba architecture designed for more efficient large language models with multiple sizes across 780M, 1.3B, and 2.7B. Bi-Mamba models are trained from scratch on data volume as regular LLM pertaining using an autoregressive distillation loss. Extensive experimental results on language modeling demonstrate that Bi-Mamba achieves performance comparable to its full-precision counterparts (e.g., FP16 or BF16) and much better accuracy than post-training-binarization (PTB) Mamba and binarization-aware training (BAT) Transformer baselines, while significantly reducing memory footprint and computational consumption compared to the original Mamba model. Our study pioneers a new linear computational complexity LLM framework under low-bit representation and facilitates future design of specialized hardware tailored for efficient 1-bit Mamba-based LLMs. Our code is provided in Supplementary Material and the pre-trained weights are available anonymously at https://drive.google.com/drive/folders/1jfk_TlDzFbER84ITvU2hOX2VyPC9H4MA?usp=sharing
URL: https://openreview.net/forum?id=CKQ4AgoRQm
---